Sentinel-1 (S-1) single-look complex (SLC) synthetic aperture radar (SAR) images allow for a very high geolocation accuracy down to centimetric levels, if one makes use of the mission’s precise orbit products and if the known perturbing effects are compensated for in post-processing. These perturbing effects involve the path delays of troposphere and ionosphere, the Earth’s tidal deformation signals, and residual systematic effects associated with the S-1 SAR data processor. All of these effects have been studied and validated extensively by the S-1 SAR Mission Performance Cluster (SAR-MPC) [1]. The SAR-MPC is an international consortium of SAR experts and is in charge of the continuous monitoring of the S-1 instruments status, as well as the monitoring of the quality of the L1 and L2 products. This monitoring also includes the geolocation quality of S-1 mission. Therein, non-critical but nevertheless noteworthy residual offsets of 0.1 to 0.3 meters are still present to various degrees in azimuth geolocation residuals of reference corner reflectors (CRs) measured with S-1A/B/C satellites [1]. They are considered to be at least partly related to SAR antenna characteristics of the particular spacecraft [2]. Investigation and possible mitigation of these effects is an ongoing effort at SAR-MPC. As part of SAR-MPC’s contribution to the S-1C and S-1D in-orbit commissioning activities, geolocation analyses have been performed over a multitude of calibration sites, including El Sosneado and Casleo in northern Argentina, which are operated by CONAE for the SACOM mission [3]. These two sites offer several unique features which make them very interesting for our S-1 data analysis: Each site is equipped with eight or more very large CRs of 3 m inner-leg size, which are differently oriented to support both the ascending and the descending passes. Even with the medium-resolution S-1 IW C-band data, they enable precise azimuth geolocation measurements of 0.1 m or better due to their large dimensions. Moreover, the sites are situated in the high-altitude terrain of the Andes, which is located some 30 degrees south of the geomagnetic equator, a region that this strongly affected by ionosphere dynamics. Finally, the sites happen to show up at burst edges or in the burst overlap area of the tightly maintained S-1 IW data footprints, where the SAR Doppler gets largest and thus any TOPS data focussing limitations or antenna effects become more evident. All these features make it challenging to perfectly accommodate the S-1 azimuth geolocation results of these CRs for the various available pass geometries, exposing any shortcomings in the S-1 TOPS data processing and the ionospheric corrections. Currently, we are investing three different effects that should allow us to even further enhance the azimuth geolocation accuracy of S-1 TOPS data and possibly resolve the dcm-level residual azimuth shifts among the sub-swaths: Firstly, there is the impact of the azimuth antenna pattern. The loss of isolated transmit-receive-modules (TRMs), which at some point occurred for each of the S-1 satellites, or of an entire antenna tile such as for S-1A in June 2016 [2], can alter the azimuth antenna phase pattern and therefore introduce sub-swath-dependent shifts if not considered during the processing. Secondly, hyperbolic modelling of the sensor-to-ground distance function, which is part of the SAR-IPFs focussing scheme, deviates from the true distance function. For S-1 TOPS data, the deviation towards the edge of the bursts becomes large enough to cause residual shifts of up to 0.1 m. Thirdly, there are the azimuth shifts introduced by the ionosphere if strong along-track ionospheric gradients are present in the scene. In principle, this effect is well understood but the coarse resolution of ionospheric total electron content (TEC) maps makes it difficult to accurately determine the effect. SAR-based interferometric methods such as split-spectrum analysis offer a mean to derive relative ionospheric azimuth shifts, but they operate on image pairs and require good coherence for stable results [4]. Again, the CONAE sites are favourable in that regard, as they are located in the arid regions of the Andes that provide high long-term-stable coherence in SAR data, which allows us to compare both methods. In summary, the large CRs of the two CONAE sites provide a mean to test the modelling of these three azimuth shift effects against ground truth. In our conference contribution, we plan to present them in more detail and report on the status of our analysis. Eventually, the outcomes will also contribute to further advancement of the S-1 Extended Timing Annotation Dataset (ETAD) product, which offers post-processing corrections for all the consolidated S-1 perturbation effects in a comprehensive product in-line with S-1 level-1 data [5]. [1] G. Hajduch et al.: S-1 Annual Performance Report for 2024. Technical note by SAR MPC, SAR-MPC-0715, issue 2.1, 02/04/2025. Available on SentiWiki: https://sentiwiki.copernicus.eu/web/document-library [2] MPC-S1-team: Sentinel-1A Tile #11 Failure. Technical note by SAR MPC, MPC-0324, issue 1.2, 13/10/2026. Available on SentiWiki: https://sentiwiki.copernicus.eu/web/document-library [3] M. Thibeault, CONAE Target Sites. Proceedings of CEOS SAR Cal/Val Workshop 2021. Available Online: https://calvalportal.ceos.org/sarcv [4] G. Gomba, F. Rodríguez González and F. De Zan: Ionospheric Phase Screen Compensation for the Sentinel-1 TOPS and ALOS-2 ScanSAR Modes, in IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 1, pp. 223-235, Jan. 2017, doi: 10.1109/TGRS.2016.2604461 [5] Gisinger, C., Libert, L., Marinkovic, P., Krieger, L., Larsen, Y., Valentino, A., Breit, H., Balss, U., Suchandt, S., Nagler, T., Eineder, M., Miranda, N.: The Extended Timing Annotation Dataset for Sentinel-1 - Product Description and First Evaluation Results. IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-22, 2022. doi: 10.1109/TGRS.2022.3194216 Acknowledgement The results presented here are outcome of the ESA contract Sentinel-1 / SAR Mission Performance Cluster Service 4000135998/21/I BG, funded by the EU and ESA. The views expressed herein can in no way be taken to reflect the official opinion of the European Space Agency or the European Union.

Synthetic aperture radar (SAR) data is now a well-known source of surface displacement data for earthquakes under or near land. The NASA-ISRO SAR (NISAR) mission is a new SAR satellite with two radar systems, the L-band (24 cm wavelength) SAR provided by NASA and the S-band (12 cm wavelength) SAR provided by ISRO and has a 12-day repeat cycle. Both are left-looking instrument. The L-band SAR images all land areas between 77.5°N and 87.5°S in 240 km wide swaths, while the S-band SAR is operated over South Asia and a few other locations worldwide. NISAR entered the science operations phase at the beginning of January 2026. NISAR is the first SAR satellite to provide standard products that include level 2 geocoded unwrapped interferograms (GUNW) for the adjacent-in-time pairs of NISAR scenes with 80-meter pixel spacing. The GUNW products include an estimate of the ionospheric correction (phase screen). The analysis-ready data (HDF5) files for the GUNW products also include geocoded wrapped interferograms with 20-meter pixel spacing. The NISAR L-band SAR data products will be available soon through the NASA EarthData system, easily searchable through the Alaska Satellite Facility Vertex search tool or through applications programming interfaces. The NISAR S-band products will be available through the ISRO data portal, Bhoonidhi. The standard product latency provides the processed L2_L_GUNW data within two days after acquisition by the satellite, when the most accurate satellite orbits are available. The NISAR mission has a special urgent response system that can give higher priority to the data downlink from the satellite and expedited L-band processing using preliminary orbits with a goal of products available, including GUNW, within 2-6 hours after data downlink from the satellite. The system is triggered automatically for earthquakes and volcanic eruption, or manually through requests from vetted U.S. agency end users. For earthquakes, it is triggered automatically from the USGS earthquake feed based on magnitude (near USA or India magnitude 7.0 with depth less than 50 km) or on the PAGER estimate (yellow/orange/red in USA or India and orange/red in rest of the world). The preliminary L-band diata products will be available immediately after processing.

Digital Elevation Models (DEMs) are static expressions of elevation. InSAR products provide estimates of elevation change over time. In highly dynamic and/or subsiding low-lying regions the combination of both parameters is required. We propose a Dynamic Digital Elevation Model, or D-DEM, describing elevation in a uniform geodetic datum as a function of time rather than as a static value. The D-DEM defines for individual geographic positions or areas a model of elevation as a function of time, and specifies alongside the estimated parameters their quality descriptions as well. We create a D-DEM by combining five geodetic techniques: leveling, GNSS, Airborne Laser Scanning (ALS), InSAR, and absolute gravimetry, over uniform grassland parcels, i.e., areas that were until recently considered to be incoherent for time series C-band InSAR applications. The D-DEM utilizes datasets ranging from the early 1950s (so-called surface leveling) up to recent Sentinel-1 SAR acquisitions. Instantaneous elevation data sets have been acquired at five epochs; one from surface leveling and four from ALS. We supplement these DEMs with four tracks of Sentinel-1 imagery from 2015 onwards. The subsequent DEMs exhibit biases due to reference frame differences and a shifting vertical reference frame (VRF). Vertical surface motion cannot directly be estimated reliably from subsequent DEMs, as the possible biases are sufficiently large to introduce major biases in the subsidence rate estimates. These biases are therefore estimated by using collocation of leveling benchmarks located in buildings of which the rooftop ridge line is visible in the ALS DEMs. Assuming internally rigid buildings, the vertical motion of the benchmark and the roof ridge line are assumed to be identical, allowing for estimation of the relative biases between the DEMs. The leveling benchmarks and DEMs are corrected for potential vertical motion of the VRF reference benchmarks estimated from time series of absolute gravimetry, resulting in five DEMs referenced to the same datum, yet this datum may also still be biased with respect to the Amsterdam Ordnance Datum we intend to use. The bias of the reference DEM with respect to ITRF is estimated using Integrated Geodetic Reference Stations (IGRS), collocating a GNSS module, an ALS reference plane, and InSAR corner reflectors in one instrument. For InSAR the IGRS allows us to eliminate the motion of the reference point, which is known from GNSS. From the five corrected DEMs the elevation per spatial unit at the reference epoch is estimated. A kinematic model based on the expected temporal behavior is estimated for all observations. We applied the methodology over highly dynamic peat soils in the ’Green Heart’ region in the Netherlands. Over the entire Green Heart we estimate an average mean subsidence rate of −4.1 mm/y between the 1960s and 2023. Validation with reference datasets in small regions of the Green Heart shows that our models are statistically the same as the reference datasets. Because of the combination of elevation and elevation change in the D-DEM, elevation at any epoch can be derived anywhere, even in such highly dynamic environments.

The Copernicus SAR mission Sentinel-1 is currently providing systematic global data acquisition, offering the scientific community both extensive historical archives and continuous new observations at an unprecedented volume. ROSE-L, an L-band SAR mission to be launched in the coming years, will further extend these capabilities complementing the characteristics of Sentinel-1. To ensure that data from both the operational Sentinel-1 mission and the future ROSE-L mission can be exploited efficiently, particularly for time series analysis, the definition of Analysis Ready Data (ARD) products and the development of corresponding processors are essential. The Committee on Earth Observation Satellites (CEOS) [1] is promoting the definition of SAR ARD with a set of data standards and guidelines aimed at improving quality, interoperability, and accessibility of EO data acquired from various satellite SAR missions. In this framework, ESA funded the project “Prototype Processor for ARD of Copernicus SAR Missions” to analyze candidate ARD products and to implement prototype processors suitable for both Copernicus SAR missions. The main objective of this project is to define advanced algorithms and implement and validate the corresponding processors and products for a new generation of Analysis-Ready Data (ARD). This abstract presents the development and utility of these products, which are engineered to provide end-users with easy-to-use data while maintaining state-of-the-art quality. By prioritizing an intuitive user experience, we have created solutions that are highly accessible yet fully capable of delivering professional-grade results. The project is organized in two phases. The first phase focused on analysis and on generating demonstration products derived from Sentinel-1 and from existing L-band SAR missions. These demonstrators were designed to represent a set of use cases aligned with ROSE-L’s primary scientific objectives. At the end of Phase 1, two ARD products were selected for prototype implementation: CSLC: Co‑registered Single Look Complex GMLP: Geocoded MultiLooked Phase The selected products are suitable for timeseries analysis and enable advanced interferometric processing either at full resolution in radar geometry (CSLC), preserving the complete information content, or in multi-looked and geocoded form (GMLP), which simplifies processing while still maintaining rigorous information quality. Similar, though not fully equivalent, products are foreseen in the NISAR mission, based on ISCE 3 software [2]. Products The CSLC product provides geometrically aligned SAR images on a common reference radar grid, compensating for geometric distortions, atmospheric effects (tropospheric and ionospheric), and long-wavelength artefacts such as tidal motion. It preserves the full fidelity of the original SAR measurements: amplitude is calibrated to Beta Nought, or to Sigma Nought [3] and the complex phase is maintained without distortion. DEM-based distorsion compensation [4] and state-of-the-art model-based [5] and data-driven corrections for geometric and atmospheric contributions, including Enhanced Spectral Diversity (ESD) [6] and split-spectrum methods [7], are integrated in the processing. As data are preserved in native radar geometry, the CSLC product is ideally suited for high-precision interferometric applications, including the Small BAseline Subset (SBAS), the Persistent Scatterer (PS), and concept of Distributed Scatterer (DS) appraoches, coherence analysis, and phase unwrapping [8-11]. The GMLP product is an innovative ARD product that inherits all CSLC corrections while simplifying interferometric processing for both single interferometric pairs and full time series [12-13]. It is geocoded to a standard cartographic coordinate system (UTM), enabling seamless integration into GIS software. The phases are referenced to a unique temporal reference, inherently enforcing phase closure and allowing immediate interferogram generation from any pair of acquisition dates. The processing workflow incorporates advanced bias correction strategies to minimize long-term phase drifts without requiring exploitation of the full covariance matrix, facilitating efficient updates as new acquisitions arrive. Auxiliary coherence layers accompany the product to support phase unwrapping and provide information on long-term phase stability. Prototype processor State-of-the-art algorithms are adopted throughout the processing chain. The prototype processors are implemented in Python, leveraging the scientific software stack Xarray, Dask, and Zarr to enable cloud-native, scalable, and parallel geospatial data processing. This architecture supports chunked and lazy computation, distributed processing on high-performance or cloud infrastructures, and the generation of analysis ready outputs in interoperable formats. The design emphasizes modularity, allowing integration of alternative correction modules or new sensors, and ensures that the processing can scale to the data volumes expected from Sentinel-1 and ROSE-L systematic acquisition strategy. ARD products and processors are validated using both real and simulated datasets to cover the full range of representative use cases. The validation methodology also anticipates the future availability of ROSE-L data and defines clear procedures to verify ARD product performance once the mission is operational. Validation approach The validation strategy for CSLC products focuses on assessing their suitability for advanced interferometric applications. A primary metric is co-registration accuracy, verified using artificial corner reflectors (CRs) to ensure compliance with stringent requirements for image alignment. The quality of the radar signal is further evaluated through the amplitude stability of these same CRs and the phase stability of persistent scatterer (PS) points extracted from the scene. These empirical measurements are complemented by a theoretical model comparison, where the product's statistical properties are checked against expected distributions. Finally, an external software comparison using other tools like ISCE [2] (Interferometric synthetic aperture radar Scientific Computing Environment) provides a benchmark by analyzing the quality of the coherence of generated interferograms, thereby confirming the product's consistency. For the GMLP, the validation approach is tailored to its multi-looked and geocoded nature, emphasizing its readiness for large-scale deformation studies. A direct comparison with an independent implementation like EMI [12] (Eigendecomposition based Maximum-likelihood-estimator of Interferometric phase) is performed, focusing on distributed targets through the analysis of short-term coherence. For point-like targets, the validation examines the long-term coherence and compares the measured phase with the average phase at PS locations. Importantly, this process validates the quality of this phase product without requiring the execution of a complete interferometric PS or SBAS chain. The analysis incorporates a theoretical coherence model and evaluates short-term noise characteristics using SLCs from end-to-end simulations. To ensure geophysical signal fidelity, long-term trend consistency is assessed by comparing phase-derived trends from the L-band GMLP product with independent measurements from C-band sensors at compatible spatial scales. Conclusions The analysis and prototype development carried out as part of this project demonstrate the feasibility and scientific value of CSLC and GMLP products as ARD products for current and future Copernicus SAR missions. The processors, based on cloud-native geospatial technologies, successfully address the challenges posed by large-scale systematic SAR acquisitions and provide a scalable basis for generating high-quality ARD products. The validation results confirm that ARD products meet the accuracy and stability requirements for advanced interferometric applications, supporting a wide range of scientific and operational use cases. These developments are the basis for the future integration of ROSE-L and Sentinel-1 data and will enable more efficient, robust, and interoperable SAR time series analysis across the Copernicus program. REFERENCES [1]    CEOS ARD Data Definition Team (2025). Combined CEOS-ARD for Synthetic Aperture Radar. Product Specification Document https://ceos.org/ard/files/PFS/SAR/v1.2/ CEOS-ARD_PFS_Synthetic_Aperture_Radar_v1.2.pdf. [2]    Fattahi, H., “ISCE3: InSAR Scientific Computing Environment Enhanced Edition”, vol. 2023, no. 488, Art. no. G23C-0488, 2023. [3]    Shiroma, G. H. X., Lavalle, M., and Buckley, S. M. (2022). An area-based projection algorithm for SAR radiometric terrain correction and geocoding. IEEE Transactions on Geoscience and Remote Sensing, 60:1–23. [4]    Sansosti, E., Berardino, P., Manunta, M., Serafino, F., and Fornaro, G. (2006). Geometrical SAR image registration, IEEE Transactions on Geoscience and Remote Sensing [5]    Sentinel-1 ETAD Product evolution: new processor version (v3.0) https://sentinels.copernicus.eu/web/sentinel/-/sentinel-1-etad-product-evolution-new-processor-version-v3.0 [6]    Yagüe-Martínez, N., Prats-Iraola, P., Rodríguez González, F., Brcic, R., Shau, R., Geudtner, D., Eineder, M., and Bamler, R. (2016). Interferometric processing of Sentinel-1 TOPS data. Geoscience and Remote Sensing, IEEE Transactions on, 54(4):2220–2234. [7]    Gomba, G., Parizzi, A., De Zan, F., Eineder, M., and Bamler, R. (2016). Toward operational compensation of ionospheric effects in SAR interferograms: The split-spectrum method. IEEE Transactions on Geoscience and Remote Sensing, 54(3):1446–1461. [8]    Berardino, P., Fornaro, G., Lanari, R., and Sansosti, E. (2002). A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Transactions on Geoscience and Remote Sensing, vol. 40, no. 11 [9]    Ferretti, A., Prati, C., and Rocca, F. (2000). Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. Geoscience and Remote Sensing, IEEE Transactions on, 38(5):2202–2212. [10] Ferretti, A., Fumagalli, A., Novali, F., Prati, C., Rocca, F., and Rucci, A. (2011). A new algorithm for processing interferometric data-stacks: Squeesar. Geoscience and Remote Sensing, IEEE Transactions on, 49(9):3460–3470. [11] Vecchioli, F., Costantini, M., Minati, F., and Zavagli, M. (2023). A Novel Algorithm for Point Coherence Estimation in SAR Interferometry. IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 7868–7871. [12] Ansari, H., De Zan, F., and Bamler, R. (2018). Efficient phase estimation for interferogram stacks. IEEE Transactions on Geoscience and Remote Sensing, 56(7):4109–4125. [13] De Zan, F. (2022). Recursive and robust InSAR phase estimation. In EUSAR 2022; 14th European Conference on Synthetic Aperture Radar, pages 1–5.

CEOS Analysis-Ready Data (CEOS-ARD) is a joint effort by the Committee on Earth Observation Satellites (CEOS) to streamline data flows and enable interoperable products between sensors and data providers, and, specifically, to broaden the Earth Observation user community by provision of data products that do not require expert knowledge to ingest and analyse. This last point is perhaps particularly relevant for Synthetic Aperture Radar (SAR), where the potential to contribute to today’s great environmental challenges with unique information is significant, but with the SAR user community remaining small and expert-oriented even after 30 years of operational SAR missions. CEOS-ARD is an opportunity to bridge that gap. In a coordinated effort by the CEOS Land Surface Imaging Virtual Constellation (LSI-VC) and the CEOS Working Group on Calibration and Validation (WGCV) SAR Subgroup, four SAR-specific specifications, included in a single unified “CEOS-ARD for SAR” Product Family Specification (PFS) have been developed: Normalised Radar Backscatter (NRB). Polarimetric Radar (POL). Ocean Radar Backscatter (ORB) Geocoded Single-Look Complex (GSLC). Interferometric Radar (INSAR). Multi-Source Backscatter (MSB) – under development The CEOS-ARD GSLC product describes the complex radar reflectivity on the surface with all propagational phases removed, so that the amplitude and phase values represent properties of the surface and not the instrument. GSLC data are presented in a common, often user-defined, ground-based coordinate system (e.g. UTM, geographical coordinates, etc.), rather than in radar slant range coordinates, to facilitate use by non-radar-specialists. The CEOS-ARD INSAR product specification covers a suite of three products generated by InSAR processing of (at least) two images captured of the same geographic area at different times: Wrapped interferogram: Image of differential phase signals between two SLC images Unwrapped interferogram: Image of differential phase signals where the wrapped fringes are summed (“unwrapped”) to give a continuous phase signal across the image Interferometric coherence: Image of phase coherence between the two images. The NRB, POL, ORB, GSLC and INSAR specifications have been endorsed by CEOS LSI-VC and can be accessed on the CEOS ARD website (ceos.org/ard). The MSB product specifications is being prepared for endorsement in April 2026. There has been a significant interest in CEOS-ARD by space agencies and public and private data providers. The first CEOS-ARD products for the InSAR community have been developed by NASA JPL and ISRO. The JPL OPERA project has had its Sentinel-1 based InSAR surface displacement products passed as compliant against the PFS, with both Sentinel-1 and NISAR GSLC products currently under development for assessment. Furthermore, ISRO is developing NISAR GSLC products. In this presentation we will show some examples of these new InSAR ARD products.

The exploitation of Sentinel‑1 Interferometric Synthetic Aperture Radar (InSAR) data has become increasingly important for Earth Observation (EO) applications, particularly for monitoring surface deformation and related natural hazards. However, the processing of Sentinel‑1 Single Look Complex (SLC) data, especially for multi-temporal techniques such as Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS), remains computationally demanding. These techniques require handling large volumes of SAR data and must rely on robust, scalable computing infrastructures, which often exceed the capabilities of many users and organizations. Cloud‑based processing environments offer a transformative alternative by providing elastic scalability, robust data management, and direct access to EO data archives. To address these challenges, we developed an open-source, fully cloud-native InSAR processing solution integrated into openEO within the Copernicus Data Space Ecosystem (CDSE). While openEO previously supported Sentinel‑1 GRD backscattering workflows, it lacked native capabilities for SLC‑based interferometric processing. We closed this gap by enabling the execution of complete InSAR workflows directly in the cloud, ensuring modularity, reproducibility, and user-driven customization. New openEO processes were developed to cover all key steps of the Sentinel‑1 SLC interferometric workflow, including burst selection, co‑registration, interferogram generation, coherence estimation, and phase unwrapping. All processes are fully open-source and implemented using the Common Workflow Language (CWL), providing a standardized and portable way to define complex, container-based processing chains. These CWL-based processes can be seamlessly combined with existing openEO building blocks to form standardized, reusable process graphs. This modular architecture allows users to adapt workflows, integrate additional datasets, and perform scalable analyses over large spatial and temporal domains. The processing system is based on burst-level operations, a design that significantly enhances computational efficiency in cloud environments. By exploiting the Sentinel‑1 TOPS acquisition structure, the workflow minimizes data transfers, optimizes resource allocation, and enables large‑scale parallelization. To validate the implementation, we applied the workflow to two real-world use cases: (1) multi-temporal coherence analysis for debris‑covered glacier detection, and (2) interferogram time series generation for permafrost‑related deformation monitoring. In detail, in the first use case, we produced long-term coherence time series for all the glaciers in South Tyrol, processing 25 bursts across 5 acquisition geometries. Persistent coherence patterns associated with constant‑moving debris enabled the automated delineation of debris-covered glacier areas. These results were validated against existing glacier inventories, derived from high‑resolution orthophotos and LiDAR acquisitions, demonstrating the reliability of cloud‑native coherence time series and their suitability for operational cryosphere applications. In the second use case we generated consistent interferogram time series over permafrost areas in alpine terrain. Special attention was given to ensuring compatibility with MintPy, a widely used tool for multi‑temporal InSAR analysis. The interferograms produced with openEO were successfully ingested into MintPy confirming that the outputs meet the necessary technical standards for downstream deformation analysis. This demonstrates the capability of the system to support hybrid cloud‑desktop workflows and strengthens interoperability with established InSAR processing ecosystems. Overall, the results confirm that integrating cloud-native InSAR processing into openEO and CDSE enables scalable, reproducible, and user-friendly workflows for advanced SAR applications. The developed solution expands openEO and CDSE into a fully interferometric processing environment, supporting both research and operational applications.

Operational InSAR services are often limited by two practical bottlenecks: reliable, automated access to large Sentinel-1 archives and reproducible, scalable processing that can be deployed beyond a single workstation. We present an end-to-end processing environment that couples “phidown” for programmatic discovery and downloading of Copernicus Sentinel data from the Copernicus Data Space Ecosystem with “sarpyx”, a Python SAR toolkit that orchestrates ESA SNAP Graph Processing Tool (GPT) workflows for InSAR. The system is designed around burst-level processing: users specify an area of interest, acquisition constraints, and a time range; the required Sentinel-1 TOPS bursts are retrieved, cached, and processed through configurable SNAP graphs. “sarpyx” manages batch execution and tiling to support large product volumes and produces geocoded outputs suitable for GIS and downstream analytics. We focus on the engineering choices required for operationalization: declarative pipeline configuration, capture of processing provenance (input product IDs, auxiliary orbit/DEM dependencies, and graph versions), deterministic product naming, and automated quality indicators (e.g., coherence statistics and coregistration diagnostics). The environment generates standard intermediate and final data products including coregistered SLCs, interferograms, coherence and amplitude mosaics, and geocoded rasters, packaged to integrate with catalogues and service endpoints. A demonstration on representative Sentinel-1 stacks illustrates how burst-centric acquisition and tiling reduce unnecessary I/O and accelerate iteration from research prototypes to repeatable processing services.

The launch of radar satellites like the European Copernicus Sentinel-1 mission (S1) paved the way for free of charge massive amounts of SAR data availability. This huge influx of spatial images requires adapting the way we work with the data and associated derived products. Indeed, new specific tools and services must be developed to access, store, manage and use them, including by non-expert users. To address this challenge in France, the FLATSIM (FormaTerre LArge-scale multi-Temporal Sentinel-1 InterferoMetry) service provides the French community with ground motion measurements through large-scale processing of Sentinel-1 data using multi-temporal Interferometric Synthetic Aperture Radar (InSAR). Developed within the framework of the national data hub Data Terra FormaTerre, FLATSIM is based on the New Small temporal and spatial Baseline (NSBAS) chain. This service emerged from a fruitful partnership between the French Space Agency (CNES, currently operating the service) and scientific teams from French laboratories involved in the development of NSBAS and InSAR tools, which also led to the creation of the on-demand InSAR processing platform (GDM-SAR-In). FLATSIM's primary focus is to support Earth science research projects covering regions larger than 250,000 km² using the Sentinel-1 data archive (available since October 2014). This service aims to address scientific research topics such as seismology, tectonics, volcanology, and the hydrological cycle, as proposed by the French scientific community. Three calls for proposals have selected 20 projects in 2020, 2022, and 2025, resulting in more than 800 To of InSAR-derived products. We are pleased to announce that the restrictions on the FLATSIM products from the first call of proposals (2020) will soon be lifted, making over 70k products available to the research community. This release covers 7 regions: Afar, Andes, Ozark, Balkans, Okavango, Tarim, and Türkiye from 2014 to 2021. The dataset includes wrapped and unwrapped interferograms, spatial and temporal coherence maps, displacement time series and mean velocity fields, together with their associated auxiliary files. Products are provided in radar and ground geometry, filtered or not and at multiple resolutions (from 20m to 160m). FLATSIM's processing approach offers several key advantages over alternative chains and services, including predictive atmospheric corrections applied before phase unwrapping to reduce artefacts and ease subsequent unwrapping. Then, unwrapping is performed iteratively, guided by the amplitude of the filtered signal, ensuring robust phase recovery over large areas. The approach also relies on long temporal interferometric baselines to reduce phase biases and improve noise integration in time series analysis, as well as the processing of a large number of bursts to provide a GNSS-independent long-wavelength solution essential for tectonic and geodynamic deformation studies. Building on this wealth of results, we will present the product architecture and coverage areas, showcase key analyses derived from these data as published in the literature, and provide updates on the next steps and future developments for the service.

The increasing availability of high-frequency satellite radar acquisitions has enabled the transition of Interferometric Synthetic Aperture Radar (InSAR) from retrospective deformation analysis to operational ground motion monitoring for industry applications. One such application is in the energy sector, where ground deformation directly reflects subsurface pressure and mass redistribution processes from practices such as fluid injection, hydrocarbon production, and underground gas storage. However, conventional InSAR products remain largely velocity-based, emphasizing long-term linear trends. Such representations are insufficient for operational environments where timely detection of deviations from expected behavior is critical. This work presents the integration of a Cumulative Sum (CUSUM) change-detection framework into an InSAR monitoring service designed for continuous surveillance of energy assets. The objective is not merely to quantify displacement rates, but to detect statistically significant changes in deformation trends at the earliest possible stage. CUSUM, widely used in statistical process control [1], accumulates sequential deviations from an expected model, thereby amplifying small but persistent departures that would otherwise remain masked by noise or gradual transitions. As such, it has been successfully used for detecting volcanic unrest [2]. Within the proposed processing environment, deformation time series derived from Persistent Scatterer and Small Baseline Subset (SBAS) workflows are continuously updated as new acquisitions become available. An expected deformation model, typically linear or linear and seasonal, is estimated over a reference interval. Residual deviations are subsequently evaluated using one-sided and two-sided CUSUM statistics, with thresholds calibrated to site-specific noise characteristics. The approach is computationally lightweight and scalable to millions of measurement points, enabling portfolio-level deployment across geographically distributed assets. The method has been operationally tested in multiple industrial settings. In California, for example, oil production fields such as the Belridge field produce from highly compressible diatomite reservoirs, where fluid injection is used to enhance recovery and maintain reservoir pressure. Because diatomite is mechanically weak and prone to compaction, subsurface pressure must be carefully managed. Both underpressure (leading to compaction and subsidence) and overpressure (increasing risks to well integrity and potential leakage pathways) require close monitoring. CUSUM statistics, derived from multi-year InSAR time series, clearly reflect large-scale changes in injection/extraction strategy, while remaining nearly imperceptible in the overall InSAR displacement time series. Thus, it provides previously unseen feedback on the response to subsurface pressure changes. Additionally, through parameter tuning, we can highlight short-term deviations as well, which is useful for detecting underground leaks, also known as blisters. From a service perspective, CUSUM enhances standard deformation products by introducing a temporal anomaly statistic that converts descriptive displacement information into actionable monitoring indicators. The algorithm allows for parameter tuning to highlight relevant displacement rate anomalies. This enables InSAR-based services to move beyond static velocity maps toward dynamic, process-aware monitoring frameworks that integrate smoothly with Subsurface pressure management and proactive risk mitigation. [1] Chang, T. C., & Gan, F. F. (1995). A Cumulative Sum Control Chart for Monitoring Process Variance. Journal of Quality Technology, 27(2), 109–119. [2] Albino, F., Biggs, J., Yu, C., & Li, Z. (2020). Automated methods for detecting volcanic deformation using Sentinel‐1 InSAR time series illustrated by the 2017–2018 unrest at Agung, Indonesia. Journal of Geophysical Research: Solid Earth, 125(2), e2019JB017908.

The growing volume and diversity of SAR acquisitions require processing chains capable of producing InSAR time series at scale. Operational ground deformation monitoring, in particular, demands workflows that handle large numbers of interferometric pairs from multiple sensors and orbital geometries, run incrementally as new acquisitions become available, and operate with minimal manual intervention. AMSTer (SAR & InSAR Automated Mass processing Software for Multidimensional Time series) is an open-source toolbox that provides a complete, automated workflow from SAR data download to multidimensional deformation time series and web-based dissemination of results. AMSTer integrates three main components. The AMSTer Engine is a command-line InSAR processor derived from the CSL InSAR Suite (Centre Spatial de Liege). It performs coregistration, interferogram computation, filtering, phase unwrapping, and geocoding for a wide range of SAR sensors: ERS-1/2, EnviSAT, ALOS/ALOS-2/ALOS-4 (upcoming), RadarSAT, CosmoSkyMed, TerraSAR-X, TanDEM-X, Sentinel-1 A/B/C/D, Kompsat-5, PAZ, SAOCOM, ICEYE, and NISAR. The Engine also includes split-band interferometry capabilities for ionospheric correction and absolute phase unwrapping, multiple unwrapping algorithms (SNAPHU, a branch-cut method, and the exploratory pre-unwrapping tool DetPhun), adaptive filtering, and a multi-level masking strategy. The MSBAS (Multidimensional Small Baseline Subset) module performs time series inversion by combining deformation maps from multiple satellites and acquisition geometries. In its standard configuration, it retrieves displacement components in two dimensions (east-west and vertical). A three-dimensional inversion mode is available either when sufficient viewing geometry diversity exists (e.g. combining right-looking and left-looking acquisitions) or when displacement is assumed to occur along the steepest slope, using slope gradients as an additional constraint. The AMSTer Toolbox consists of bash and python scripts that orchestrate the full processing chain, from data download through interferometric processing, MSBAS inversion, and web-based product dissemination. Beyond mass processing for deformation time series, AMSTer can also be used for individual differential interferogram computation (for deformation measurement or DEM generation), as well as for producing coregistered time series of coherence or amplitude maps suitable for pixel tracking, for example. Several pair selection strategies are available to build the interferometric network: baseline criteria with dual temporal and spatial thresholds (to accommodate changes in the orbital tube, e.g. after the loss of Sentinel-1B), Delaunay triangulation, k-shortest connections, and an optimization procedure based on a coherence proxy. Sentinel-1 ETAD (Extended Timing Annotation Dataset) data can be integrated to apply tropospheric, ionospheric, and geodetic corrections to the interferometric products and to improve the geometric accuracy of the geocoding to the centimetric level. Recent developments include support for NISAR data processing, a diagnostic toolbox for quality control, and tools for exporting products as Cloud Optimized GeoTIFFs. AMSTer is structured for fully automated operational monitoring. The processing chain is organized in sequential steps scheduled through cron jobs: data download, image reading and coregistration on a Global Primary image, interferometric mass processing, and MSBAS inversion with time series extraction. Each step is incremental, processing only newly available data without reprocessing the existing archive. Built-in verification procedures check image integrity, detect conflicts between concurrent processing runs, quarantine problematic acquisitions, and identify duplicate products. At the European Center for Geodynamics and Seismology (ECGS), AMSTer is operationally deployed for the continuous monitoring of multiple targets worldwide, including South Kivu (Democratic Republic of the Congo), Piton de la Fournaise (La Reunion, France), Domuyo and Laguna del Maule (Argentina), Galeras (Colombia), the Comoros archipelago, Luxembourg, the Himalayan Mountains (Nepal), and so on. For each target, the toolbox processes several thousands of interferograms across ascending and descending geometries. Results are disseminated through automatically updated web pages, which can be password-protected when needed, displaying baseline plots, velocity maps, amplitude time series, and (double-difference) deformation time series at predefined points of interest. Differential deformation maps and additional time series can be generated on demand. This web interface allows both project partners and monitoring agencies to access regularly updated deformation products without requiring InSAR expertise. AMSTer is distributed on GitHub under the GNU AGPL v3 license, together with an installer script, a user manual, installation guides for Linux and macOS, processing flowcharts, and training course materials. The software has been under continuous development since the early 2010s and is actively maintained to keep up with new SAR missions and evolving data access infrastructure. On a best effort basis, the European Center for Geodynamics and Seismology (ECGS) can provide assistance for installation and training.

The Philippines is one of the most vulnerable countries in the world, due to its geological situation, human activities and the impact of climate change. Earthquakes, volcanic eruptions, landslides, subsidence, floods, typhoons, and sea level rise in its coastal regions are examples of resultant geohazard and environmental challenges. In order to strengthen the Philippines’ response capability and resilience to such natural and man-made hazards through the strategic use of space-based data, and help reduce vulnerabilities of the people of the Philippines to climate hazards, support climate adaptation, and environmental protection, the European Commission signed a contribution agreement with the European Space Agency (ESA) for the implementation of a national Copernicus data centre in the Philippines in 2023 (https://copphil.philsa.gov.ph). This agreement initiated the ESA-funded CopPhil project: Earth Observation Service Development & Transfer, which officially commenced in January 2024. One of the thematic objectives of CopPhil project is to promote the uptake of Copernicus data in the Philippines through the development of a Ground Motion Monitoring Service (GMS). The GMS enables the local stakeholders and partners to monitor, analyze, interpret and further predict ground motion related to those hazards, through a nationwide, automated, user-friendly, standardized workflow, supported by cloud computing. The CopPhil-GMS is based on time series InSAR (Interferometric Synthetic Aperture Radar) techniques, including two approaches: SBAS (Small BAseline Subset) [1] and PSI (Persistent Scatterer Interferometry) [2], and is built entirely upon open-source software and tools. Over the past two years, we have developed a generic GMS processing workflow using the improved GMTSAR software for interferogram generation and SBAS processing [3] and STAMPS processing tool [4] for PSI processing. This workflow is implemented within a Docker environment in which all necessary software and dependencies are automatically pre-installed, and can 1) automatically access Sentinel-1 SAR data through Creodias (https://creodias.eu) without local downloading by mounting the data directly into the processing environment, and seamlessly provide DEMs and orbital data using conda-installed packages within the Docker container; 2) perform end-to-end tooling by completing the whole SBAS or PSI processing executed with a single command, while also exposing key processing parameters (such as region of interest, temporal baseline, multilooking factor in range and azimuth direction, master acquisition date, amplitude dispersion index and atmospheric phase removal method) for end-user customization; 3) generate three types of InSAR products, taking EGMS (European Ground Motion Service, https://egms.land.copernicus.eu) as a reference: P1.1 - Basic: Line of sight velocity maps and deformation time series with annotated quality measures per measurement point. P1.2 - Calibrated: Line of sight velocity maps and deformation time series in ascending and descending orbits referenced to a model derived from GNSS (Global Navigation Satellite System) time-series data. P1.3 - Ortho: components of motion (horizontal East-West and vertical Up-Down) retrieved by combining the ascending and descending products [5, 6]; 4) offer the products in CSV, GeoPackage and KMZ (Keyhole Markup Language Zipped), which are codefined with local stakeholders.  The latest version of our open-source GMS tool, named GMTSAR+, is will be publicly available on Github. For details, refer to [7]. As a GMS demonstration, we processed three years of Sentinel-1 SAR images that cover ~50% of the Philippines’ territory, focusing on Luzon and Mindanao, using the Geoville cloud computing platform. This results in the generation of P1.1 and P1.3 GMS products –SBAS deformation time series at a 100 m spatial resolution. The P1.2 GMS product is still in the pipeline, pending the collection of sufficient GNSS observations from local stakeholders, while the corresponding processing workflow has already been fully integrated and tested within the GMS framework. References: Berardino, P., Fornaro, G., Lanari, R., Sansosti, E., 2003. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Transactions on geoscience and remote sensing 40, 2375–2383. doi:10.1109/TGRS.2002.803792. Ferretti, A., Prati, C., and Rocca, F., 2001. Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(1):8–20. Sandwell, D., Mellors, R., Tong, X., Wei, M., Wessel, P., 2011. Open radar interferometry software for mapping surface deformation. doi:10.1029/2011EO280002. Hooper, A., Zebker, H., Segall, P., Kampes, B., 2004. A new method for measuring deformation on volcanoes and other natural terrains using InSAR persistent scatterers. Geophysical research letters 31. doi:10.1029/2004GL021737. Hanssen, R.F., 2001. Radar interferometry: Data interpretation and error analysis. Springer. doi:10.1007/0-306-47633-9_4. van Leijen, F.J., 2014. Persistent Scatterer Interferometry based on geodetic estimation theory. 86, Netherlands Geodetic Commission. Li, B., Feng, W., Chang, L., 2026. GMTSAR+: An extended GMTSAR workflow streamlining SBAS products with standardized geospatial outputs. Submitted to Environmental Modelling & Software.

AMSTer (SAR & InSAR Automated Mass processing Software for Multidimensional Time series) is an open-source toolbox that provides a complete, automated workflow from SAR data download to multidimensional deformation time series and web-based dissemination of results. The AMSTer is a continuous development of three scientific components: • The AMSTerEngine: A command line InSAR processor allowing to perform all required SAR interferometric processing. The AMSTerEngine is a set of command line routines written in C. • The AMSTer toolbox scripts: A set of bash and python scripts meant to automate the AMSTerEn- gine and manage time series. • The MSBAS (Multidimensional Small Baseline Subset) software AMSTer aims at processing a large number of interferometric pairs to feed and run the MSBAS processor to obtain the desired 2D or 3D deformation maps and time series products. We present here new developments integrating Extended Time Annotation Dataset (ETAD) management and fine coherence tracking approach within the AMSTerEngine, and so, within the AMSTer toolbox. ETAD aims at correcting Sentinel1 SLC data range and azimuth times for inaccuracies in Synthetic Aperture Radar (SAR) focusing and for geophysical effects using external models such as the operational Integrated Forecasting System (IFS) tropospheric models of ECMWF, Digital Elevation Models (DEM) or land tides models. These corrections being different from an image to another, time inaccuracies differences are still present in interferometric processing (InSAR) leading to sub-pixels local misregistration with respect to an ideal co-registration corresponding to a perfectly stationary situation. Therefore ETAD management allows improving the georeferencing and geoprojection of InSAR products and mitigating non-turbulent atmospheric phase component. On it side, fine coherence tracking performs a series of co-registrations, shifted a fraction of a pixel away from the stationary solution. This sequence of co-registrations allows computing a stack of interferograms and coherence images across which the range and azimuth shifts leading to the optimum coherence can be found on a pixel-by-pixel basis. This approach leads to a pixel-wise 2D mapping of coherence with respect to imposed shifts. It is shown that subtle local misregistration of pixels can be measured with precision through 2D curve fitting. This coherence tracking method being purely data-driven, it is suited to perform a cross validation with differential ETAD shifts that are mainly model-based. In addition to providing local range and azimuth shifts, side products of the methodology are the optimised coherence itself and the tracked optimised interferogram that can in turn be used in time series processing. (AMSTer Toolbox). AMSTer software is freely available under the terms of the GNU Affero General Public (AGPL) License.

The continuous production and injection of hydrocarbons induce changes in reservoir pore pressure that modify the effective stress within the rock framework, resulting in compaction or expansion that manifests at the surface as subsidence and uplift. The Karamay oilfield in China provides a clear example of such deformation driven by evolving reservoir dynamics. In this study, we assess the ground deformation across the Karamay oilfield using Sentinel-1 InSAR observations from 2017 to 2025 combined with geotechnical modeling. The InSAR time series reveals pronounced uplift and subsidence during the 2017–2018 period, with peak rates reaching approximately 118 mm/yr and –38 mm/yr, respectively. After this initial phase, deformation magnitudes declined substantially, with an approximately 80% reduction in peak uplift and ~50% reduction in peak subsidence by 2025, indicating progressive reservoir pressure equilibration. Three-dimensional surface motion was reconstructed by integrating ascending and descending line-of-sight velocities, while the north–south component was estimated using a tilt-based approach derived from spatial gradients of the vertical deformation field observed during 2017–2018. The resulting mean deformation pattern is characterized by dominant vertical uplift accompanied by secondary horizontal divergence, consistent with reservoir inflation. Geomechanical modeling was conducted using ascending LOS deformation during the 2017–2018 uplift phase to constrain reservoir geometry. These structural parameters were then held fixed to evaluate temporal pressure evolution in subsequent periods. The modeling results indicate strong reservoir pressurization during 2017–2018, reduced pressurization between 2019 and 2020, and a transition to pressure depletion and elastic compaction from 2020 to 2025, consistent with the observed LOS. This study provides a detailed assessment of surface deformation in the Karamay Oilfield using multi-temporal Sentinel-1 SBAS InSAR observations. The deformation time series indicates a progressive transition from pronounced uplift to peak and spatially complex deformation, followed by persistent subsidence. These stages correspond to shifts in reservoir conditions from pressurization to stabilization and subsequent depletion, offering insight into the long-term geomechanical behavior of the reservoir. The agreement between the observed line-of-sight deformation patterns and the modeled pressure variations indicates that surface displacement is primarily governed by subsurface pressure evolution. These findings improve understanding of the long-term mechanical response of the Karamay reservoir and demonstrate the value of combining multi-geometry InSAR observations with tilt-based 3D reconstruction and geomechanical modeling for monitoring deformation associated with hydrocarbon production.

The European Ground Motion Service (EGMS), managed by the European Environment Agency, is part of the Copernicus Land Monitoring Service portfolio. It provides freely accessible, continental-scale land deformation data across Europe based on Sentinel-1 SAR imagery. The objective of EGMS is to deliver a consistent and long-term monitoring tool to support the understanding and management of land dynamics, including those driven by climate change. Given the large spatial coverage of EGMS and the complexity of InSAR data for non-expert users, a systematic and continuous validation framework is essential. In parallel with each EGMS release, we have developed the first continental-scale validation procedure, enabling both quantitative and qualitative evaluation of successive product updates. Validation activities carried out across Europe assess the agreement between EGMS products and independent reference datasets (in-situ and Earth Observation), based on two main criteria: (1) Consistency & Accuracy and (2) Applicability & Usability. Consistency and Accuracy are evaluated through comparisons of measurement point (MP) velocities and time series with GNSS and in-situ observations, together with assessments of geolocation accuracy and temporal behaviour using corner reflectors. Applicability and Usability are analysed by examining the coherence of EGMS outputs with other Ground Motion Services, geospatial datasets, and inventories, supported by the identification of Active Deformation Areas (ADAs) derived from EGMS MPs. This work is carried out by a multidisciplinary consortium spanning six European countries, bringing together geological surveys, a research institute, and an industry partner, reflecting the diversity of expertise required for continental-scale validation. In this study, we present the validation framework, methodologies, and results for the first three EGMS releases: 2015–2021, 2018–2022, and 2019–2023 developed within the contract No 3506/R0-COPERNCA/EEA.59565 valid between 2021 and 2025.

Petroleum Development Oman (PDO) has developed an integrated geospatial and geomechanical monitoring framework combining nationwide Interferometric Synthetic Aperture Radar (InSAR), continuous GPS stations, microseismic networks, production data, and stratigraphic analysis to support both Carbon Capture, Utilization, and Storage (CCUS) site selection and sustained hydrocarbon production. Leveraging Sentinel-1 SAR imagery from 2016 to May 2022, PDO with its business partner SkyGeo generated the first millimetre-precision deformation map of Oman, revealing that over 90% of the country exhibits minimal vertical displacement (

Synthetic Aperture Radar Interferometry (InSAR) enables precise observation of surface deformation. Wetland InSAR provides a unique capability to map water-level fluctuations across vegetated wetlands at high spatial resolution by leveraging double-bounce scattering between the water surface and emergent herbaceous vegetation. The Ciénaga Grande de Santa Marta (CGSM) in northern Colombia is a vast deltaic wetland system (~4,931 km2). Designated as a Ramsar site and a UNESCO Biosphere Reserve, it functions as an important carbon sink. The CGSM is experiencing a severe environmental crisis, in which extensive dikes and road infrastructure have disrupted hydrological connectivity, resulting in hypersalinization and widespread mangrove degradation across tens of thousands of hectares. Climate-driven variability, including El Niño-Southern Oscillation effects on precipitation and freshwater inflow, further compounds salinity and hydraulic stress. Although periodic monitoring is essential, in situ gauge observations are sparse and inherently point-based, and the limited CGSM monitoring network makes a remote-sensing-based approach indispensable. This study uses ALOS-4 PALSAR-3 to retrieve high-resolution water-level changes in the CGSM and addresses two practical limitations of wetland InSAR: spatial discontinuities and temporal gaps. Spatial discontinuities frequently arise near wetland boundaries where scattering mechanisms transition abruptly, making mosaicking across neighboring tracks and acquisition dates challenging and reducing spatial continuity. Leveraging ALOS-4’s high-resolution, wide-area observations, we establish continuous large-area coverage by mosaicking nine Stripmap acquisitions collected over adjacent tracks and evaluate the feasibility of generating a spatially continuous, high-resolution interferogram. We further employ a cross-mode InSAR strategy that integrates Stripmap and ScanSAR data to compensate for temporal gaps arising from acquisition constraints and to assess performance at longer temporal baselines. Despite the enhanced observation capability enabled by ALOS-4’s digital beamforming, the current reliance on fixed pulse repetition frequency (PRF) operation introduces blind areas (~10–20% of the scene) and may limit acquisition opportunities, potentially increasing temporal baselines. Such constraints are particularly critical in wetlands, where rapid water-level variability and vegetation-driven changes in scattering characteristics accelerate decorrelation, underscoring the need for strategies that preserve both spatial and temporal monitoring capability. We generated interferograms from mosaicked Stripmap acquisitions on 17 June and 26 August 2025 and produced a cross-mode interferogram by incorporating a ScanSAR acquisition on 2 December 2025. Processing was performed using GAMMA: oversampling was applied to harmonize azimuth pixel spacing across acquisitions, multilooking with a 4 × 12 (range × azimuth) was used, and the topographic phase was removed using the Copernicus 30 m DEM. Adaptive filtering was then applied to suppress phase noise. The mosaicked Stripmap interferogram achieved a high mean coherence of 0.76 and revealed clear fringe patterns consistent with water-level changes in the CGSM. Importantly, fringes were continuous across mosaic boundaries, indicating that the proposed mosaicking strategy can produce spatially consistent interferograms over large wetland areas. The Stripmap-ScanSAR interferogram yielded a mean coherence of ~0.40, demonstrating the feasibility of cross-mode interferogram generation and suggesting that mode integration may help mitigate decorrelation as temporal baselines increase. Overall, these results support large-scale wetland monitoring with ALOS-4 and provide a foundation for developing precise time-series datasets for the CGSM.

Vertical land motion (VLM), defined as the upward or downward movement of the Earth's surface over time, is a critical component of coastal change assessments. VLM can vary substantially across local to regional scales and over decadal to multi-year timescales due to differences in local geology, natural processes, and anthropogenic influences. These spatial and temporal variations introduce significant complexity into relative sea-level rise (RSLR) assessments. Therefore, accurate characterization of both components is essential for reliable RSLR projections, particularly along low-lying coastlines. Coastal Massachusetts (MA) is a low-lying coastal area encompassing geologically diverse environments from densely urbanized shorelines in Boston Harbor to environmentally sensitive wetlands along Cape Cod. In this study, we characterize near-decadal (2016–2025) spatial and temporal variability in VLM across coastal MA by integrating continuous Global Navigation Satellite System (GNSS) time series with high-resolution (~90 m) Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) observations. GNSS analysis reveals significant temporal variability in VLM, including accelerating subsidence across the Cape Cod Peninsula, decelerating trends along the South Coast, and near-stable conditions in Massachusetts Bay. Building on the GNSS-based insights into temporal variability in VLM, we examine the spatial variability of present-day deformation across coastal MA by referencing InSAR observations to temporally representative GNSS-derived VLM rates. The resulting deformation map reveals a north–south gradient in present-day vertical motion, ranging from near-stable conditions along the North Shore and Massachusetts Bay to subsidence rates exceeding ~2–3 mm yr⁻¹ toward the Cape Cod Peninsula and Outer Islands. We further assessed implications for coastal flooding by incorporating InSAR-derived VLM rates into mid-century (2050) RSLR projections under the SSP2-4.5 scenario at tide stations and other coastal locations. These locally resolved VLM estimates yield spatially variable RSLR projections ranging from approximately 0.48 m at Cape Ann to over 0.59 m in the Outer Islands, differing from those based on regionally averaged Intergovernmental Panel on Climate Change (IPCC) VLM estimates. This study demonstrates that integrating temporally representative GNSS-derived rates reduces inconsistencies arising from time-dependent VLM and improves characterization of present-day coastal deformation. With increasing availability of Synthetic Aperture Radar (SAR) observations and longer GNSS records, integrating both datasets will be valuable for tracking the evolution of deformation patterns and updating coastal vulnerability assessments. Overall, this study contributes essential insight into the complexity of VLM in coastal MA and provides a critical foundation for adaptive coastal management strategies in an era of accelerating sea-level rise (SLR).

There is a clear link between global warming and the increase in glacier melting, leading to the expansion of glacial lakes, often dammed by fragile moraines. Triggers such as heavy rainfall, earthquakes, landslides, avalanches, glacier breakoffs, or thawing permafrost can cause glacial lake outburst floods (GLOFs). These events result in moraine breaches, releasing flood waves of mud and debris that can cause significant damage and endanger populations. On August 16, 2024, a GLOF from the Thyanbo glacial lakes affected the village Thame, Nepal. This flood caused destruction of the local infrastructure, buildings and agricultural land, and displaced over 135 inhabitants. According to first investigations, it seems that an initial trigger originated from the upper glacial lake and overtopped its terminal moraine. This flood wave further ran into the lower glacial lake, which overtopped the terminal moraine and caused it to breach. All mentioned cascading incidents triggered the GLOF running downstream. As no in situ data is available, we used high-resolution optical as well as Synthetic Aperture Radar (SAR) remote sensing data to map the lakes dynamics and measured the ground deformations at the terminal moraines. To date, such analyses have been applied only to glacial lakes and terminal moraines without documented GLOF events, but not to systems affected by a previously occurred GLOF.High-resolution PlanetScope multispectral images from 2019 to 2024 showed an expansion of the upper lake by 213.3 % before the event, followed by a loss of 25.9 %, while the lower lake just increased slightly by 2.8 % over the timeseries, but lost over 74.3 % of its area during the GLOF. The analysis showed that the upper terminal moraine has not eroded at all or only very slightly, whereas the lower moraine has largely eroded. Consequently, while the lower lake no longer represents a future hazard, the upper lake continues to pose a high risk.SAR Sentinel-1 images from 2020 to 2024 were used to perform a Persistent Scatterer Interferometry (PSI) with the Stanford Method of Persistent Scatterers (StaMPS). By combining ascending and descending orbits, the vertical and horizontal movements of the resulting scatterers were deconstructed. As the GLOF was likely triggered by an external factor, no abrupt movements were detected in advance by the PSI. Nevertheless, significantly stronger vertical and horizontal subsidence was observed at the lower terminal moraine, reflecting its greater exposure to the GLOF relative to the upper moraine. The analysis demonstrated that, with certain improvements, remote-sensing data combined with PSI can be used to assess the overall stability of terminal moraines and enable meaningful comparisons between them. Nevertheless, numerous limitations and restrictions have arisen along the workflow, and improvements identified should be taken into account and incorporated in future studies.Based on this case study, the methodology will be transferred to a regional approach in the Himalayas in a future study to contribute to a more comprehensive inventory of potentially dangerous glacial lakes by adding the parameter of terminal moraine stability, which has not yet been considered in depth.

Introduction Monitoring infrastructure has become a key application of InSAR time-series analysis since the introduction of Persistent Scatterer (PS) processing [1], later extended to include less reflective Distributed Scatterers (DS) [2]. As traditional in situ monitoring methods for primary road infrastructure have grown increasingly costly for Rijkswaterstaat, InSAR has become an attractive alternative for deformation monitoring of road infrastructure. In some cases, applying a PS and DS processing chain without incorporating contextual information produces useful results. However, in many situations the processing approach must be adapted to obtain reliable InSAR deformation time series. Here, we present a collaboration between Rijkswaterstaat and SkyGeo demonstrating how the integration of object-specific information can substantially improve the quality of the final deformation product, using the sunken access roads of three tunnels in the Netherlands as a case study. These sunken roads are currently being monitored for uplift, after a U-shaped concrete road element of a similar sunken access road was forced upward by groundwater pressure, following the failure of its ground anchors [3]. To avoid prolonged road closures caused by potential uplift of road elements at other locations, a monitoring system based on X-band InSAR time-series data was implemented. However, achieving the required vertical displacement accuracy of 1 mm proved impossible without substantial modifications to the standard PS and DS processing chain. Unwrapping network An initial analysis revealed that phase unwrapping errors are a common issue in these types of structures. This is caused by annual horizontal displacements of several centimetres resulting from thermal ratcheting of the entire construction. Along the sunken access road, adjacent U-shaped concrete road elements exert longitudinal forces on each other due to thermal expansion, leading to cumulative horizontal movement along the length of the road. The smallest displacements occur near the tunnel entrance, whereas the largest are observed at the beginning of the access road. Incorporating this structural behaviour into the unwrapping network resolves the ambiguity issues, as the differential movement between adjacent elements is much smaller than their absolute displacement. By densifying the unwrapping network accordingly, the phase unwrapping errors can therefore be mitigated. Similar challenges may arise in other segmented structures, such as bridges, viaducts, and flyovers [4], which are a key element of nationwide infrastructure monitoring programs [5]. Road Maintenance Another common challenge is the decorrelation of time series caused by road maintenance activities. Such interventions often divide a continuous InSAR time series into two or more independent coherent time series. Although several processing strategies exist to automatically detect coherence loss and split time series accordingly [6], these approaches may incorrectly identify the timing of maintenance, or leave it undetected, resulting in phase jumps in the final time series. Moreover, once a time series is split, the separate segments must still be reconnected to reconstruct a consistent deformation history. In our approach, we use precise information on the timing of maintenance activities to segment the time series. Because no significant modifications are made to the physical structure of the construction during maintenance, we can assume that the radar reflections originate from the same physical location, meaning the scatterer coordinates remain unchanged. Therefore, we incorporate a step function at the precise dates of the maintenance into the temporal displacement model. This allows us to use the entire time series to estimate the model parameters, including the phase jumps during maintenance. This circumvents the need to split and stitch time series and therefore minimizes the loss in precision due to maintenance. Assignment of DS to road elements Finally, the correct assignment of individual PS and DS points to specific road elements is essential for a reliable decomposition in horizontal and vertical time series. In standard DS processing, the search for “brotherhood” pixels is performed within a predefined neighbourhood in the radar grid [7, 8]. In this case, however, such an approach would allow DS pixels from different tunnel elements to be grouped together, thereby mixing the deformation signals of adjacent elements and contaminating the results. This issue can be avoided by restricting the selection of brotherhood pixels to those located within the same road element. To achieve this, the pixel locations must be known prior to the brotherhood selection step, whereas in conventional workflows this assignment is typically performed afterwards based on the phase data. Because the exact geometry of the structure and its surroundings is available, we can pre-assign pixels to individual tunnel elements using a high-resolution 0.5-m DEM. This enables a processing approach that does not mix radar reflections of different road elements in the final deformation product. Conclusion Combining the presented methods, we could improve the quality of the final InSAR time series in such a way that they now meet the quality requirements needed to monitor the tunnel access ramps. This would not have been possible without incorporating contextual information about the structure, its maintenance history, and its expected deformation behaviour. These results demonstrate that integrating technical knowledge of the monitored object can substantially enhance both the development of InSAR processing strategies and the reliability and usability of the resulting deformation products. References: [1] A. Ferretti, C. Prati, and F. Rocca, “Permanent Scatters in SAR Interferometry,” IEEE Transactions on Geoscience and Remote Sensing, vol. 39,no. 1, pp. 8–20, 2001. [2] S. Samiei Esfahany, “Exploitation of distributed scatterers in synthetic aperture radar interferometry,” Ph.D. dissertation, Delft University of Technology, Delft, 2017. [Online]. Available: https://doi.org/10.4233/uuid:22d46f1e-9061-46b0-9726-760c41404b6f [3] M. Harbers, https://zoek.officielebekendmakingen.nl/kst-29296-50.odt, pp. 1–4, 9 2023. [4] X. Song, Z. Lei, and Z. Lu, “Retrieval of Discontinious Deformation Induced by Thermal Expansion and Contraction of Bridges with Adaptive MTInSAR,” in IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024, pp. 11 825–11 828. [5] N. Dore, V. Belloni, A. Mazzoni, and M. Crespi, “Safe Bridge: Geomatic Monitoring Services for Safe Bridges,” in IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024, pp. 1607–1610. [6] F. Lattari, A. Rucci, and M. Matteucci, “A Deep Learning Approach for Change Points Detection in InSAR Time Series,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, 2022. [7] K. Spaans and A. Hooper, “InSAR processing for volcano monitoring and other near-real time applications,” Journal of Geophysical Research: Solid Earth, vol. 121, no. 4, pp. 2947–2960, 4 2016. [8] A. Parizzi and R. Brcic, “Adaptive InSAR stack multilooking exploiting amplitude statistics: A comparison between different techniques and practical results,” IEEE Geoscience and Remote Sensing Letters, vol. 8, no. 3, pp. 441–445, 5 2011.

Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) is a consolidated approach for ground deformation investigations. However, the potentialities of the technique for structural health monitoring, especially by integrated methods based on multi-resolution and multi-frequency approaches, are still scarcely explored. In this framework, the proposed approach employs the MT-InSAR technique to detect ground deformation phenomena and to monitor displacements and deformations across an asset of infrastructures. In particular, an integrated methodology combining MT-InSAR products, conventional monitoring techniques and Building Information Modeling (BIM), is presented. This integrated approach represents a pivotal component in the assessment of infrastructure safety, in the generation and definition of the digital twins of the built environment and, more in general, in the digital transition in civil engineering. The methodology adopts a dual-scale analysis and employs multi-frequency satellite radar data from the European Space Agency's (ESA) Copernicus project and the Italian Space Agency (ASI). First, wide-area screening is performed using European Ground Motion Service (EGMS) calibrated products, processed from ESA Sentinel data, with a spatial resolution of 20 m x 5 m; this provides the ground deformations across the entire study area (the municipality of Modena, Italy). Second, high resolution (3 m x 3 m) X-band data from the ASI COSMO-SkyMed constellation (1st and 2nd generation) are processed via MT-InSAR to characterize displacements at the scale of single structures. The novelty of this work lies in the procedure adopted to ensemble displacement data from MT-InSAR with BIM and structural analysis. This integration simplifies the classification and management of risk, as exemplified by a specific case study related to a bridge. This predictive maintenance paradigm allows for early intervention before significant damage occurs paving the way to the effective long-term monitoring of infrastructures with assessment of structural safety. In the near future, the presented approach will benefit from the availability of recent and future SAR constellations, particularly those operating in L-band, such as the Argentinian SAOCOM (Satellite Argentino di Osservazione COn Microonde), NISAR (NASA-ISRO Synthetic Aperture Radar) and ROSE-L (Radar Observing System for Europe in L-band) by ESA. The availability of SAR data at different frequencies could enhance the structural analysis of infrastructures, facilitating the disentangling of ground deformation phenomena from structural behavior and supporting the safety assessment. This work was supported by the Università di Modena e Reggio Emilia – Fondazione di Modena Project “Ensembling SATellite monitoring and BIM methods in the SAFety assEssment of road infrastructure (SAT‐SAFE)”, FAR 2024 - Bando per il finanziamento di progetti di ricerca interdisciplinari.

ePISAV (Enhanced Permanent Interferometric Scatter Analysis & Visualization) is an advanced tool for integrated analysis of PS-InSAR displacement data. The tool is designed for operational monitoring tasks, including slope stability assessment, subsidence evolution, and long-term ground-motion analysis. Developed on the ENVI geospatial platform, the system consolidates data import, visualization and modelling into a single workflow, enabling the use of PS-InSAR measurements and complementary reference datasets such as GPS data. The tool combines different functions for PS (Permanent Scatters) analysis, in particular: advanced time-series analysis, frequency analysis by FFT (Fast Fourier Transformation) transformation, statistical analysis, supervised and unsupervised point classification, data transformation from point clouds to raster surfaces and data calibration between InSAR and GPS data. These functions allow rapid comparison of displacement behaviors in different areas and in different periods, highlighting the anomalies and characterizing the deformations with higher interpretability. ePISAV enhances the interpretability of PS data through a set of modules: i.-visualization module the color-coded intervals can be assigned to any PS attribute, enabling fast mapping of sectors with similar velocities or accelerations and revealing coherent deformation domains in the same area; ii-3D module converts PS distributions into continuous raster or raster-series surfaces, making spatial gradients and evolving deformation fronts immediately visible through 3D visualization and temporal animation; iii-calibration module aligns PS-derived displacement with GPS trends, reducing systematic offsets and improving reliability in multi-sensor comparison scenarios; iv-classification module supervised and unsupervised classification algorithms (Minimum Distance, SAM, Binary Encoding, SID) group PS based on the similarity of their displacement signals, delineating areas with distinct kinematic behaviors useful to better understand landslides, subsidence and calibrate geomechanical modelling for structural monitoring. The aim of ePISAV is to simplify InSAR analysis into a unified environment that reduces processing time and promotes consistent methodologies across diverse case studies, from local monitoring tasks to regional-scale ground-motion analyses. By integrating all analytical steps, the tool provides an operational framework that can be directly integrated with external thematic datasets, such as geological information, land-use maps, hydrological layers and geomechanical models, enabling efficient transition from data to insights, ensuring consistent, interpretable and robust analysis capabilities for a wide range of deformation-monitoring applications.

Underground natural gas storage systems constitute a fundamental component of modern energy infrastructure, ensuring supply security and market stability in response to seasonal demand fluctuations. In many countries, natural gas consumption increases significantly during winter months due to residential heating and industrial needs, leading to substantial withdrawal from storage facilities. Conversely, during warmer periods, excess supply is injected back into underground reservoirs to balance the system. These cyclic injection–withdrawal operations induce pressure variations within the storage formation, potentially altering the mechanical equilibrium of the reservoir and overlying strata.Such pressure-driven changes may result in measurable surface deformation, particularly in geological settings where storage occurs within porous or cavernous formations. Although these deformations are often subtle, their cumulative or cyclic nature can have implications for infrastructure integrity, environmental safety, and long-term reservoir performance. Therefore, monitoring surface responses to underground gas storage operations is critical for distinguishing between natural hydrogeological trends and storage-induced mechanical effects, and for supporting sustainable energy management practices.This study was conducted at the Tuz Gölü Underground Natural Gas Storage Facility in Türkiye and is based on a seven-year InSAR time series analysis covering the period from 2018 to 2025. Sentinel-1 SAR datasets were processed using the Small Baseline Subset (SBAS) approach. Atmospheric phase delays were corrected using ERA5 reanalysis data and ascending-descending orbit data were jointly analyzed to decompose surface displacements into vertical and horizontal components.The results reveal that vertical surface deformation approaching approximately 6 cm per year occurs in the vicinity of the storage facility. Time series analyses demonstrate a clear temporal consistency between observed surface deformation patterns and gas injection–withdrawal cycles. Noticeable deformation changes correspond to increased gas withdrawal during winter months, while different deformation behavior is observed during reinjection periods in summer. These findings indicate that reservoir pressure variations associated with storage operations exert a direct influence on surface deformation patterns.In addition to storage data, groundwater level variations in the region were evaluated to assess their potential contribution to surface movements. Long-term groundwater observations were statistically compared with deformation time series. Although initial correlation analyses suggested some degree of association, detrended analyses-performed to eliminate the influence of long-term trends-demonstrated that no strong or direct causal relationship exists between groundwater level changes and the observed surface deformation.Overall, the integration of multi-temporal InSAR analysis, atmospheric corrections, component decomposition, and statistical evaluation indicates that the detected surface deformations are predominantly linked to natural gas storage operations. This study provides a robust methodological framework for monitoring surface-reservoir interactions in underground gas storage fields and contributes to the sustainable management of critical energy infrastructure.

Land subsidence is a widespread geohazard threatening infrastructure and water resources sustainability in many alluvial plains worldwide. Accurate characterization of its spatiotemporal dynamics requires integrating multiple observation techniques due to limitations inherent to individual sensors. This study proposes a multi-sensor data assimilation framework that combines Interferometric Synthetic Aperture Radar (InSAR) time series, Global Navigation Satellite System (GNSS) measurements and hydro-meteorological data to analyze and predict land subsidence in the Choushui River Alluvial Fan, Taiwan. InSAR-derived deformation fields were calibrated using GNSS observations to generate a high-resolution deformation dataset. A Long Short-Term Memory (LSTM) neural network was then employed to model nonlinear temporal relationships between ground deformation and hydrological drivers, including groundwater level fluctuations and precipitation. Results demonstrate that the proposed framework improves deformation accuracy and captures subsidence dynamics effectively, enabling short-term forecasting of cumulative subsidence and the generation of susceptibility maps. The methodology provides a transferable approach for large-scale subsidence monitoring and hazard mitigation in sedimentary basins worldwide.

InSAR without context is useful only for users with a high tolerance for errors. There’s probably no better demonstration of this statement than configuring InSAR displacement monitoring of manmade offshore structures, such as artificial islands or fixed oil & gas platforms. Many processing challenges await the InSAR practitioner here: offshore sites are often far beyond the phase decorrelation distance from land, meaning reliable spatial reference points are absent. Moreover, continuous structural changes on offshore sites often invalidate traditional coherent scatterer pre-selection techniques, and the common assumption of long-term scatterer coherence no longer holds. Rapid, non-linear settlement, often exceeding the phase ambiguity, makes reliable phase unwrapping extremely challenging. Under these conditions, InSAR can only deliver accurate and actionable results when supported by strong contextual constraints. With SkyGeo’s contextual InSAR approach, we show that we can still derive decision-critical insights for our customers even under these highly incoherent offshore conditions. We present an ongoing project monitoring settlement during the construction of an artificial offshore island comprising 28 individual caissons deployed over two years. Continuous monitoring was configured using both ascending and descending TerraSAR-X orbits. On-site context strictly drives the coherent point selection and phase unwrapping, as caissons are deployed with a multi-day lag, initially undergoing primary settlement of >10 cm within days, followed by secondary settlement due to installation of neighbouring caissons. To utilise acquisitions as soon as individual caissons become coherent, we apply the Temporary Coherent Scatterers approach [1]. The primary settlement is constrained using in-situ tachymetry and GNSS observations, while InSAR is used to estimate the secondary and long-term settlement. The InSAR time series functional model for unwrapping is guided by predicted caisson settlements from the structural simulation model. Based on the structural design norms, each caisson has a strictly defined monitoring segment. Therefore, we use precise, sub-pixel geolocation of scatterers and careful aggregation on the individual monitoring segments. Because structural engineers work in 3D, not satellite line-of-sight or 2D projection, we estimate full 3D displacement vectors using strap-down decomposition [2], resolving seaside-landside tilt and vertical settlement. Our contextual InSAR monitoring approach satisfies the settlement monitoring standards required by the structural engineering team. For most caissons, estimated values match the predicted settlements. During the winter construction pause, we also detected a significant (> 5cm) horizontal tilt of the southern wave-break wall, likely caused by temporary exposure of the inner caisson wall, not designed to bear the load of the predominant ocean currents. This finding provided valuable input for potential remedial actions. Remote sensing always helps in boots-on-the-ground costs - but here we show how it is crucial for continuous monitoring of this island, as the construction site remains inaccessible for large portions of the year. [1] Hu, F., Wu, J., Chang, L., & Hanssen, R. (2019). Incorporating Temporary Coherent Scatterers in Multi-Temporal InSAR Using Adaptive Temporal Subsets. IEEE Transactions on Geoscience and Remote Sensing, 57(10), 7658-7670. Article 8756305. https://doi.org/10.1109/TGRS.2019.2915658 [2] Brouwer, W. S., & Hanssen, R. F. (2024). Estimating three-dimensional displacements with InSAR: The strapdown approach. Journal of Geodesy, 98(12), Article 110. https://doi.org/10.1007/s00190-024-01918-2

Within the Italian Government's guidelines on space and aerospace matters, "Telecommunications, Earth Observation and Navigation" satellite services and applications (so-called "downstream") will be exploited by citizens and valorized by Institutions under an integrated application perspective. The Italian Space Agency (ASI) is committed to contribute to the downstream development, in order to support national policies related to many global challenges, including mitigation of weather-climatic events and the effects of global warming. Following the ASI’s roadmap for scientific downstream applications (Tapete & Coletta, 2022), ASI runs the “Innovation for Downstream Preparation for Science” (I4DP_SCIENCE) program devoted to the Scientific User Community, i.e. Italian Universities and Public Research Bodies. I4DP_SCIENCE is composed of joint projects with ASI demonstrating the usefulness of either novel or consolidated methods and algorithms to support applications of users’ interest falling within topics of national relevance, e.g. defined by the National Copernicus User Forum, and/or falling within international agendas, e.g. the UN Sustainable Development Goals (SDGs). All the demonstrations are carried out jointly with the reference users who are actively engaged since the initial user requirement consolidation and, throughout the project, via capacity building and training activities towards the user uptake. Interferometric Synthetic Aperture Radar (InSAR) techniques are nowadays very well established approaches for hazard assessment and have achieved a level of operational maturity that, in Italy, they are already exploited to support institutions and public administrations, e.g. for civil protection and structural monitoring purposes. This outcome has also been facilitated by the availability of long-term regular SAR image collections over the national territory not only via Sentinel-1 constellation, but also via the COSMO-SkyMed MapItaly Plan since 2011 and the systematic L-band SAOCOM acquisition plan, both coordinated by ASI. The combination of these observation capabilities provides an extraordinary wealth of multi-sensor SAR datasets over Italy that can enable a multitude of operational services and downstream applications. However, further efforts are required in order to expand the portfolio of institutional users that can benefit from InSAR-based services, for example to better inform urban planning and land management. The present paper will showcase examples from the demonstration projects undertaken in the framework of ASI’s I4DP_SCIENCE programme. An example is the recently completed GEORES project (Agreement ASI – UNIBA n. 2023-42- HH.0 – CUP F93C23000240005), funded by the Italian Space Agency (ASI) with University of Bari and the Institute for Electromagnetic Sensing of the Environment (IREA) of the National Research Council of Italy (CNR) (Lafortezza et al., 2024). Multi-mission InSAR deformation data have been used to address the Land Displacement module inputting into a multi-risk assessment framework to identify “hot-spots” of urban and peri-urban territory in Apulia region, southern Italy, at high risk from the point of view of land degradation caused by phenomena of hydrogeological instability, sediment flow or vegetation fires. The paper will also discuss how visualisation and analytical tools such as WebGIS platforms are effective ways to disseminate InSAR data and associated thematic products, and enable final users to utilise these geospatial layers to support the decision making process. References Lafortezza R. et al. (2024) The GEORES Project: Geospatial Application in Support of Environmental Sustainability and Resilience to Climate Changes in Urban Areas. IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, pp. 1384-1387, https://doi.org/10.1109/IGARSS53475.2024.10642728 Tapete, D. and Coletta, A. (2022) ASI’s roadmap towards scientific downstream applications of satellite data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5643, https://doi.org/10.5194/egusphere-egu22-5643

Accurate three-dimensional (3D) displacement estimation is crucial for interpreting surface changes driven by natural and anthropogenic processes and for improving hazard assessment in areas affected by complex deformation. Interferometric Synthetic Aperture Radar (InSAR) is widely used for displacement monitoring due to its high spatial resolution and precision. However, InSAR measurements are limited to the satellite line-of-sight (LOS) direction. The decomposition of LOS signals into complete 3D displacement: north–south (dN), east–west (dE), and vertical (dU) remains challenging, particularly for the NS component, which usually exhibits the lowest accuracy. To address this limitation, this study proposes an integration framework that combines LOS displacement measurements with independent 3D displacement data derived from laser scanning techniques, including airborne laser scanning (ALS), UAV-borne laser scanning (ULS), or UAV photogrammetry. For both laser scanning and photogrammetric datasets, 3D displacements were estimated from multi-temporal Digital Surface Models (DSMs) using Least Squares Matching (LSM) method implemented in the OPALS software. The joint decomposition is solved using a Weighted Least Squares (WLS) approach, enabling robust decomposition and improved estimation of all displacement components. The approach was tested in a challenging mining area affected by underground exploitation, located near the Marcel Mine in the Upper Silesian Coal Basin (southern Poland). This type of environment is particularly demanding for InSAR due to decorrelation over rural areas and the potential for advanced time-series approaches to underestimating rapid or large deformations when their temporal models cannot capture the deformation dynamics. Therefore, we used a classical DInSAR workflow to generate cumulative LOS displacement maps. Sentinel-1 data from three viewing geometries (one ascending and two descending orbits) were used to maximize LOS diversity. ALS data were obtained from the national geodetic repository (geoportal.gov.pl), while ULS and UAV photogrammetry campaigns were carried out during one of our projects. For validation, a network of stabilized control points was measured using GNSS-RTN for horizontal displacements and precise leveling for vertical displacements, providing independent reference observations for accuracy assessment. We evaluated several weighting strategies in WLS, including variants based on accuracy estimated from in situ reference data, quality indicators such as InSAR coherence, DSM roughness and their combinations, as well as equal weights. Integration with ULS achieved RMSE values of 0.040 m (dN), 0.031 m (dE), and 0.031 m (dU), while ALS integration yielded 0.102 m, 0.075 m, and 0.066 m, respectively. Photogrammetry-based integration provided RMSE values of 0.054 m (dN), 0.022 m (dE), and 0.016 m (dU). The most pronounced improvement was obtained for dN, with an accuracy gain of up to 90%. The best performance was achieved when observation weights were derived from reference-based accuracy estimates. Nevertheless, the equal-weight solution remained a practical alternative when reference data were unavailable. The results confirm that multi-sensor integration within a WLS framework substantially enhances LOS decomposition and enables reliable retrieval of all 3D displacement components in demanding deformation settings. The approach is intentionally generic, as it is not restricted to a particular InSAR processing product (DInSAR or multi-temporal methods), and can incorporate additional observations whenever they are available and trustworthy. Likewise, the surface-based displacement input can be derived from different point-cloud sources, including laser scanning and photogrammetric dense image matching, making the methodology broadly applicable to monitoring tasks beyond mining subsidence such as landslides, open-pit mining, infrastructure deformation, and other high-risk environments.

This study assesses the structural vulnerability of built cultural heritage in the Historic Centre of Mexico City (CH-CDMX), a UNESCO World Heritage Site affected by long-term land subsidence. The research integrates satellite radar interferometry (InSAR) and geospatial data science to quantify, at multiple spatial and temporal scales, the impact of ground deformation on individual heritage structures. The main objective is to develop an integrated methodological framework for subsidence monitoring based on structure-specific parameters. The workflow comprises: (1) integration and validation of heterogeneous geospatial datasets to generate a unified geodatabase of heritage assets (building footprints, addresses, and architectural plans); (2) application of high-resolution SAR interferometry to characterize regional subsidence dynamics across the CH-CDMX; (3) building-scale parameterization of four deformation patterns—differential settlement, apparent subsidence/emergence, and directional tilting; (4) implementation of a multi-criteria exposure index to identify the most affected structures; and (5) benchmarking the capability of openly available SAR data for cost-effective, continuous monitoring. A total of 34 X-band SAR scenes acquired between 2011 and 2013 by TerraSAR-X in StripMap mode (3 × 3 m spatial resolution) were processed over multi-temporal windows up to two years, generating more than 430,000 line-of-sight deformation velocity measurements. Unlike conventional regional-scale assessments, deformation statistics were computed individually for each heritage building, enabling structure-specific evaluation of displacement gradients and tilt vectors. Operational thresholds were defined to classify structural behavior, identifying 46 buildings exhibiting critical deformation patterns. Building–environment interaction analysis revealed spatially coherent tilting trends predominantly oriented toward the southeast and northeast, as well as significant intra-structural differential gradients in monumental constructions. Additionally, C-band SAR data from Sentinel-1 (~5 × 20 m spatial resolution) were processed to evaluate the performance of moderate-resolution, openly accessible imagery for monitoring structural deformation in dense historic urban environments. The comparative analysis demonstrates the trade-offs between spatial resolution and monitoring scalability, highlighting the potential of Sentinel-1 for sustained, low-cost surveillance of subsidence-prone heritage districts. The five buildings exhibiting the highest structural vulnerability indices were Palacio Nacional, Antiguo Convento de la Encarnación, Palacio de Bellas Artes, Museo Nacional de Arte, and Museo Franz Mayer. This research delivers the first integrated InSAR-based framework for structural vulnerability assessment of heritage buildings in CH-CDMX. The proposed methodology supports preventive conservation prioritization, demonstrates transferability to other subsiding megacities, and advances the application of SAR interferometry as an operational tool for cultural heritage risk monitoring.

Coal mining in South Limburg (the Netherlands), active from the late 19th century until the 1970s, has left a lasting imprint on a now densely populated mining region of ~234 km². Decades after mine closure, subsurface processes continue to induce surface deformation and sinkhole hazards, posing risks to infrastructure and the built environment. Here, we present a multidisciplinary approach that combines satellite geodesy (InSAR), hydrogeological observations, geological data, and historical mining records through geomechanical models to better understand and manage post-mining hazards. From the previously identified potential after-effects we focus on sinkholes and other surface deformation due to their strong potential for damaging infrastructure and buildings. Central to this framework is the analysis of ~28 years of InSAR data acquired from five satellite missions (ERS, Envisat, Radarsat, TerraSAR-X, and Sentinel), enabling the characterisation of long-term surface deformation patterns. These data reveal a persistent regional uplift signal (~5 mm/yr), first detected in early ESA missions and still on-going and confirmed by more recent observations. By integrating piezometer data, we relate this uplift to mine water rebound and aim to predict coupled groundwater and surface displacement dynamics. At the local scale, we combine deformation data with geological, mining information and geomechanical models from a known sinkhole location to identify areas with similar subsurface conditions, significantly reducing the search space for potential sinkhole occurrence. These zones are further linked to exposure datasets (buildings and infrastructure) to assess vulnerability. The results have first been integrated into an Atlas as a building block for supporting probabilistic hazard assessment. Here, we present the status of ongoing development of a framework to assess vulnerability, current results, and prospects to accommodate for annual updates of InSAR data. This study highlights the value of long-term InSAR observations as a key component in a holistic framework for post-mining aftercare.

Taiwan, situated at a convergent plate boundary, is characterized by steep terrain and fractured geology. Under the influence of extreme climate events, frequent typhoons and heavy rainfall often induce large-scale landslides and debris flows. During severe weather, conventional optical remote sensing is frequently limited by extensive cloud cover, creating a critical information gap that hinders immediate disaster response and spatial situational awareness. To address this challenge, this study establishes a wide-area rapid screening mechanism based on Synthetic Aperture Radar (SAR) imagery, leveraging its all-weather, cloud-penetrating capabilities to identify and assess large-scale landslides and landslide-dammed lakes (barrier lakes). We utilized Sentinel-1 and ALOS-2 satellite imagery, employing the Log Ratio Method to calculate differences in backscatter intensity before and after disaster events. By analyzing the statistical distribution of pixel values and applying 95th or 99th percentile thresholds, we effectively extracted hotspots of surface change. To enhance interpretation accuracy, a noise filtering algorithm was implemented to exclude artifacts caused by riverbed sediment transport, flat terrain, and geometric distortions, retaining only significant deformation areas larger than 1 to 2 hectares for subsequent hazard assessment. Following major meteorological events in 2025, specifically Typhoons Danas (July 4–9) and Wipha (July 17–21), we conducted a wide-area rapid assessment. Using multi-temporal Sentinel-1 ascending and descending orbit imagery, we successfully identified critical disaster hotspots, including dammed lakes in the Qingshui River (Central Taiwan) and the upstream Mataian River (Eastern Taiwan). The Mataian landslide-dammed lake, validated via Planet optical imagery with an area of approximately 146,383 m², served as a primary case study. By integrating digital terrain models (DTM) and the dimensionless blockage index (DBI), we estimated the short-term stability of the dam. These results were promptly disseminated to relevant disaster prevention authorities, demonstrating the rapid response capability of radar satellites in monitoring remote mountainous areas under cloud cover. Furthermore, following the heavy rainfall event from July 28 to August 1, 2025, an island-wide screening using integrated Sentinel-1 and ALOS-2 imagery revealed surface change hotspots concentrated in the Gaoping River basin (Southern Taiwan) and the Beinan, Hualien, and Xiuguluan River basins (Eastern Taiwan). Notably, two large-scale landslides (totaling ~14 hectares) were identified at a tributary confluence in the middle reaches of the Baolai River, posing a high risk of river blockage. ALOS-2 imagery also indicated potential instability in the Lakusi River basin, where six landslide areas (>0.3 ha) were detected. To validate the timeliness and accuracy of the radar-based detection and to investigate the landslide mechanisms, we incorporated data from the Broadband Seismic Network. Through seismic signal inversion, we pinpointed the collapse times of the Baolai River landslides to 06:26:10 and 06:26:40 on August 1, 2025, with estimated volumes of 1.75 and 2.14 million m³, respectively. These high-precision temporal and volumetric parameters not only corroborated the spatial changes detected by SAR but also provided critical physical constraints for slope stability analysis, compensating for the temporal resolution limitations of satellite imagery. Finally, the study conducted a detailed disaster simulation for the high-risk Mataian dammed lake. Using DTM-based spatial intersection analysis, we determined the dam geometry and reservoir capacity. Hydraulic dam-break simulations were performed total dam failure and partial failure scenarios with breach durations of 0.5, 1.0, and 3.0 hours. Numerical results indicate that in the worst-case scenario (total dam failure, 0.5-hour duration), the peak discharge at the downstream Mataian Bridge would reach 10,546 CMS with a water level of 8 meters. Even under a partial breach scenario, the peak discharge (8,424 CMS) significantly exceeds the 100-year return period design flow (4,043 CMS). These findings highlight the catastrophic potential of a rapid breach and provide a scientific basis for emergency evacuation planning. In conclusion, this study presents a comprehensive disaster assessment framework integrating wide-area SAR screening (36,000 km²), seismic signal validation, and hydraulic simulation. Proven by the 2025 typhoon and rainfall events, this workflow effectively overcomes weather limitations to rapidly identify high-risk zones, filling the information void during early disaster stages and significantly enhancing monitoring and early warning capabilities for compound disasters in extreme environments.

Mértola is a town in the south of Portugal, near the border with Spain, whose earliest evidence of human occupation dates back to the Iron Age. Over the centuries, Mértola has been witness to several civilisations, such as Roman and Islamic, all of them leaving marks of their cultural identities. One of the most distinctive features of Mértola is that many of the ancient structures have reached the present days in a good conservation state and functionality, providing invaluable information about the past occupation of the territory. The most distinctive structures are, nowadays, the castle and the wall that surrounds the ancient area of the town. However, the town is often subjected to severe meteorological events, such as the recent floods that inundated the port area and almost reached the town's defensive wall. The structural safety of the wall poses a special concern, as many infrastructures – municipal buildings, roads, residential properties, markets, museums – are located in its vicinity and will be highly affected in case of failure. In the current context of climate change, monitoring of Mértola defensive wall is of the utmost importance to ensure the safety of people and property. ARTEMIS project – Applying Reactive Twins to Enhance Monument Information Systems – funded by Horizon Europe program, envisages to develop a reactive digital twin of Mértola wall. This twin will integrate data from several sensors to enable continuous, timely, and remote monitoring of the structure, capable of issuing warnings if any signs of anomalous behaviour are detected. In this study, the potential of InSAR data to inform the reactive digital twin is evaluated. Data from the European Ground Motion Service (EGMS) was collected and used to evaluate displacements in Mértola historic centre, including the wall. Data from different satellite orbits were combined to achieve vertical and east-west components of displacements, and spatiotemporal patterns were analysed. Dissimilarities between displacement time series were evaluated and used to form clusters of measurement points with similar behaviour. There were no spatial constraints imposed on the clusters, as they were formed based on the similarity of the displacement time series alone. The identification of spatial clusters of points exhibiting anomalies in their displacement time series may indicate potential structural instability. In the case of Mértola historic centre, a method highly sensitive to the presence of outliers was employed to compare the displacement time series. This strategy enabled the automatic aggregation of measurement points according to their displacement magnitude, movement direction and the presence of trends or discontinuities in their displacement time series. Recent structural occurrences suggest some degree of deterioration of the wall, which should be interpreted considering the trend of the measured displacements. Additionally, while providing insights into the evolution over the past few years, the InSAR analysis results also supported decision-making regarding the high-risk areas for the installation of the monitoring system.     The results show that InSAR is a promising method for inclusion in digital twins of cultural heritage sites, as it is a non-destructive monitoring method and each measurement point can be used as an individual sensor contributing to the model. This preliminary study used EGMS data from 2019 to 2023, but the findings indicate that it is worth investing in a near-real-time InSAR monitoring system and applying systematically the proposed method.

Rwanda is a densely populated, mountainous country located next to the East African Rift System, where steep topography, intense seasonal rainfall, land‑use pressure, and long‑term deforestation contribute to a high susceptibility to landslides and other ground‑instability hazards. Past rainfall triggered landslides have resulted in significant loss of life and displacement, particularly in the northern and western regions of the country. Despite the societal impact of these hazards, information on landslide activity and ground deformation in Rwanda remains fragmented across disparate studies and reports, limiting its operational use for hazard assessment and early warning. This feasibility study outlines the conceptual framework for a Rwanda Ground Motion Service based on satellite synthetic aperture radar (SAR) interferometry, developed within the PanAfGeo+ Country Window Rwanda Project. The proposed service aims to systematically map and monitor ground deformation associated with active landslides, mining and post‑mining areas, and tectonically influenced zones. Owing to Rwanda’s favorable geographic position near the African Rift Valley System, the country benefits from dense temporal and spatial coverage of Sentinel‑1 SAR data, including both ascending and descending acquisition geometries. A single Sentinel‑1 acquisition covers nearly the entire national territory, significantly simplifying large‑scale InSAR processing and national level product generation. The Ground Motion Service will employ multi‑temporal SAR interferometric techniques, including Small Baseline Subset (SBAS) and Persistent Scatterer Interferometry (PSI), to derive ground motion maps and displacement time series. The availability of dual‑orbit Sentinel‑1 data enables improved characterization of deformation patterns and enhances the robustness of detected signals. Climatic conditions, however, impose important constraints on SAR interferometry in Rwanda. The climate is characterized by two main rainy seasons (September–December and March–May), a short less‑rainy period (January–February), and a dry season (June–August), all of which influence surface coherence and must be explicitly considered during InSAR processing and interpretation. By integrating Sentinel‑1 SAR interferometry with a coordinated institutional framework for monitoring and early warning, the proposed Ground Motion Service represents a scalable and cost‑effective approach to hazard assessment in Rwanda.

The closure of deep underground mines induces a gradual rise in groundwater levels, triggering a series of geomechanical processes observable at the surface. These processes commonly manifest as continuous deformations, including ground uplift, and discontinuous deformations, notably sinkholes, which typically develop in areas of historical shallow mining. Discontinuous deformations present a considerable risk to infrastructure and to the safety of communities residing in post-mining areas. In recent years, particularly across Europe, the rate of underground mine closures has increased, highlighting the need for effective surface deformation monitoring, improved understanding of post-mining dynamics, and informed strategies for preventive planning and sustainable land management. This study examines the Olkusz–Pomorzany zinc and lead mining district in southern Poland, where extraction ceased in 2021. Since mine closure, numerous sinkholes, as well as zones of surface uplift and localized subsidence, have been documented, impacting forests, agricultural lands, and infrastructure. The site presents a particularly complex case due to its long mining history, dating back to the 13th century, encompassing diverse extraction methods, including shallow and partially undocumented workings. Further complexity arises from the geological framework, comprising fractured and locally karstified carbonate rocks overlain by unconsolidated Quaternary deposits of highly variable thickness. The study aimed to identify potential precursors of sinkhole formation using satellite-based observations. In the first stage of research, variations in the C-band backscatter coefficient, the Moisture Index (MI), and the Normalized Difference Vegetation Index (NDVI) were analyzed. Breakpoint analysis of a 26-month pre-event time series revealed a common structural change in mid-2021, approximately six months prior to the first recorded sinkhole, coinciding with a rapid rise in groundwater levels. Chow tests confirmed statistically significant differences in regression coefficients across the identified breakpoint. In the second stage of research, satellite radar interferometry (InSAR) was employed to analyze a one-year observation period in 2024. This analysis delineated zones of potential displacement indicative of emerging deformation fields associated with sinkhole initiation. Comparison of these zones with discontinuous deformations observed in 2025 demonstrated the high efficacy of InSAR in detecting areas prone to sinkhole-related surface movements. This study enhances our understanding of aquifer system deformation mechanisms in post-mining areas. The results also allow to identify potential precursors associated with sinkhole formation.

The highly developed Dinaric karst systems are emblematic of a particularly well-expressed karst morphology. They feature complex hydrological networks in which water flows primarily underground through fractures towards springs (e.g. Milanović, 2015). Understanding these systems is crucial for hydroelectric engineering, on which the countries of the Adriatic coast strongly depend, but also for water supply to cities and agriculture in these heterogeneously populated regions that must accommodate high seasonal tourist demand. Water transfer through karst depressions and fractures towards discharge areas may induce measurable deformation of the Earth’s surface, as suggested by e.g. Silveri et al. (2019) or Lesparre et al. (2016). During periods of substantial recharge, on the one hand, overpressure within fractures, porous or clay-rich formations generates uplift and outward horizontal deformation, while, on the other hand, water loading induces subsidence and horizontal motion towards the added load. The relative contribution of these two water-driven deformation mechanisms depends on the local hydrological setting and mechanical properties of the subsurface. In this context, the development of hydrogeodetic approaches based on InSAR, offering dense spatial coverage, spatial continuity and multi-year monitoring capability (e.g. Chaussard et al., 2017), provides a powerful tool to study karst hydrogeology. Though, it remains challenging for InSAR to measure low amplitude deformations related to hydrology, whose spatio-temporal patterns remain poorly constrained, particularly in regions with high topographic gradients and where ground-based independent data for validation and comparison are scarce. In this study, we use Sentinel-1 InSAR time series processed using the CNES-FormaTerre FLATSIM service (Thollard et al., 2021) over the eastern Adriatic (Croatia, Bosnia, Montenegro, part of Albania, Slovenia, and Serbia), over the 2014-2021 period, from 2 descending and 2 ascending tracks. We perform a parametric decomposition of each time series in the Line of Sight (LOS), separating linear trends, coseismic signals (displacement steps for the largest magnitude earthquakes), and seasonal variations (sum of a sine and a cosine terms). Focusing on the seasonal component, we first reference the sine and cosine amplitude to a null-seasonal-deformation polynomial surface. This enables to extract, in a regionally and from track to track consistent way, areas where seasonal transients are significant. We then analyse the characteristics of the amplitude and timing (phase) of these seasonal displacements, and investigate potential hydrology-driven controlling mechanisms. The seasonal signal reveals spatial correlations with known hydrological features. Seasonal amplitudes reach several centimeters, with maxima mainly located in karstic depressions (“poljes”) filled with Tertiary sediments. Phase analysis reveals a bimodal behavior: one group exhibits maximum motion towards the satellite at the end of the winter (coeval with peak groundwater level), in anti-phase with the other group. Decomposition into vertical and horizontal components reveals specific spatial patterns of horizontal extension in several poljes, associated with significant uplift, suggesting that the horizontal deformation provides complementary and potentially discriminating constraints on the main driving mechanisms of seasonal deformation measured by InSAR. We further compare the observed seasonal displacement with forward models of poroelastic strain and elastic loading (Larochelle et al. 2022) at two representative sites : the Nevesinjsko polje in Bosnia and Herzegovina and the Skadar lake between Montenegro and Albania. The comparison suggests that overpressure-related mechanisms (from poroelastic deformation or fracture opening) dominate at Nevesinjsko polje, whereas elastic loading prevails in the Lake Skadar catchment area. The bimodality of the phase observed at the large regional scale reflects these two end-member mechanisms of deformation. Although local complexities in subsurface properties and hydro-climatic forcings, and anthropogenic water usage may limit the applicability of both simple parametrization and simple models, our results demonstrate the significant potential of multi-track Sentinel-1 time series to constrain seasonal hydro-mechanical processes in complex karst environments using horizontal and vertical deformation patterns. References : Chaussard et al. (2017). doi : 10.1002/2017JB014676 Larochelle et al. (2022) doi : 10.1029/2021JB023097 Lesparre et al. (2016). doi : 10.1093/gji/ggw446 Milanović, Petar. (2015). doi : 10.1007/s12665-014-3923-0 Silverii et al. (2019). doi : 10.1016/j.epsl.2018.10.019 Thollard et al. (2021) doi : 10.3390/rs13183734 FLATSIM Data Products. CNES. (Dataset). doi: https://doi.org/10.24400/253171/FLATSIM2020

The recent availability of high-resolution, open-access MT-InSAR data, alongside free tools for data interpretation such as ADAtools, has enabled the development of wide-area, value-added geospatial products. This study demonstrates the integration of data delivered by the European Ground Motion Service with open-access ancillary datasets, ADAtools and machine learning tools to efficiently identify, map, and classify ground deformation phenomena across the countries covered by EGMS between 2015 and 2021. The first step of our work consists of identifying clusters of measurement points into polygons of Active Deformation Areas (ADAs), thus reducing data complexity and simplifying the interpretation by focusing the analysis and classification on significant deformation areas. Hence, two alternative techniques to classify the ADAs into different classes of ground deformation processes have been developed and compared. The first technique, ADAclassifier, part of ADAtools, is based on a scoring system and multiple parallel decision trees to assess each deformation category. The algorithm provides default values of thresholds and scores, although expert users can customize them to their specific needs. ADAclassifier classifies ADAs into five different deformation classes, i.e. landslides, subsidence, uplift, construction settlement and sinkholes. The second technique, ground motion classifier (GMC), is based on a supervised classification of ADAs into four deformation classes, landslide, deep-seated gravitational slope deformation subsidence and uplift, using machine learning. The training dataset for this classifier is obtained by matching the European ADA map with ground truth/labelling data, including the Italian National Landslide Inventory, the subsidence map of Emilia-Romagna region (Italy), and clusters of known uplift areas across Europe, e.g. the dewatering areas in the United Kingdom. The supervised ground motion classifier is implemented through the Extreme Gradient Boosting (XGB) technique. XGB belongs to the ensemble learning family and is used in various applications due to its good performance, versatility, and capability to cope with missing values. In this work, the Catboost implementation of XGB was chosen due to its better performance. The XGB classifier employs spatio-temporal features extracted, for each ADA polygon, from different data sources, i.e. the EGMS-PSInSAR data (e.g. mean velocity, acceleration, seasonality, temporal coherence of the measurement point displacement values), Corine Land Cover map, Digital Elevation Model (DEM) and its derived terrain attributes (local slope and aspect). Being based on the availability of specialized datasets, such as landslides or sinkhole inventories, the ADAclassifier was implemented in the territory of Spain, whereas its expansion over the whole European territory is an ongoing work. On the other hand, GMC is based on pan-European datasets, prioritizing scalability, and providing a dataset of classified ADAs over the whole European territory, however with a reduced number of classes with respect to ADAclassifier. A central component of this study is the validation of the ADA classification results, which is performed over the ADAs contained in the Spanish territory. Expert user validation addressed discrepancies and ensured the accuracy of the classifications, particularly in complex scenarios. In addition, the ADAclassifier and GMC output were compared, showing a high degree of consistency, which reinforces their reliability. The classification results highlighted subsidence and landslides as prevalent phenomena, aligning with known geohazard distributions. While the ADAclassifier effectively identified subsidence, landslides, and uplifts, it faced challenges in distinguishing construction settlement and sinkholes, indicating a need for further refinement. Future work should focus on refining decision-tree methodologies, integrating time-series data, and enhancing classification accuracy for overlapping deformation types. The proposed methodologies are a first step towards systematic geohazard monitoring, supporting risk assessment and the development of targeted mitigation strategies. References Barra, A., Solari, L., Béjar-Pizarro, M., Monserrat, O., Bianchini, S., Herrera, G., Crosetto, M., Sarro, R., González-Alonso, E., Mateos, R. M., Ligüerzana, S., López, C., Moretti, S., 2017. A Methodology to Detect and Update Active Deformation Areas Based on Sentinel-1 SAR Images. Remote Sensing, 9(10), 1002. https://doi.org/10.3390/rs9101002 Crosetto, M., Crippa, B., Mróz, M., Cuevas-González, M., Shahbazi, S., 2025. Applications based on EGMS products: A review. Remote [42] Sensing Applications: Society and Environment, 37. https://doi.org/10.1016/j.rsase.2025.101452. Palamà, R., Barra, A., Cuevas-González, M., Monserrat, O. Crosetto, M., 2024. Ground Motion Classification from European Ground Motion Service Data Using Extreme Gradient Boosting. IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. https://doi.org/10736-10739. 10.1109/IGARSS53475.2024.10640768. Cuevas-González, M., Barra, A., Palamà, R., Ezquerro, P., Monserrat, O., Crosetto, M., Navarro, J. A., Rivera-Rivera, J., Béjar-Pizarro, M., García-Davalillo, J. C., Galve, J. P., Beyond the European Ground Motion Service: identification and classification of unstable areas at the national level, submitted to IEEE Journal of Selected Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Continuous Global Navigation Satellite System (GNSS) position time series represent a fundamental geodetic observable for the quantitative characterisation of long-term crustal deformation processes. Their millimetric accuracy in three-dimensional positioning enables the estimation of velocity fields associated with regional tectonics, postglacial rebound, and large-scale geodynamic processes. In this study, a new European long-term surface displacement model derived from the analysis of 5,980 continuous GNSS stations distributed across the European continent and surrounding regions, is presented. The dataset was retrieved from the archive of the Nevada Geodetic Laboratory (UNR-NGL) and referred to the IGS14 reference frame, aligned with ITRF2014 and consistent with the Eurasian plate-fixed realization. A fully automated and reproducible processing chain was developed to isolate the low-spatial-frequency tectonic component from the high-spatial-frequency one mainly due to localized displacement phenomena. The methodological framework is based on a robust trend estimation of the GNSS stations and on a spatial correlation analysis of the velocity components, with the primary objective of excluding possible perturbations in the long-term signal (e.g., antenna modifications, co-seismic offsets, anthropogenic disturbances, undocumented modifications). In particular, the initial stage of the workflow consists of a rigorous screening of GNSS time series, in order to ensure the reliability of velocity components estimation. The GNSS time-series that in the corresponding metadata exhibit discontinuities or gaps are subject to automatic modifications to exclude possible bias in the long-term velocity estimation. Moreover, after the screening procedure only the GNSS stations that provide a minimum temporal coverage of two years, will be considered in the analysis. This step reduces the initial dataset of GNSS to a subset characterised by temporal stability and linear long-term trends. Subsequently, a spatial similarity analysis is performed to discriminate GNSS stations influenced by local-scale deformation phenomena. For each GNSS station, a similarity index is computed separately for the East–West, North–South, and vertical mean velocity components, within a 1° × 1° spatial window. The similarity metric is a quantifiable measure of the coherence between the velocity vector of a target GNSS station and those of neighbouring GNSS stations. It is noteworthy that only GNSS stations that satisfy a high coherence for all the three components are considered reliable for the model reconstruction. After this procedure a subset of 4,545 GNSS stations was considered representative of regional tectonic signals. The implemented procedure continues by applying an inverse distance weighting (IDW) interpolation on GNSS velocity components, with the aim of creating a denser grid of points with a spacing of 0.5° in both latitude and longitude, to ensuring uniform spatial sampling and enhancing the stability of subsequent interpolation procedure. More precisely, the final continuous displacement field was obtained through the utilisation of a moving-window kriging interpolation. The final grid resolution was set to 10 arc-seconds, enabling high-resolution representation of low-spatial-frequency deformation patterns. It is important to highlight that no a priori fault geometry or kinematic constraints were imposed during interpolation. The resulting European long-term surface displacement model is provided as 1° × 1° GeoTIFF tiles referenced to WGS84 and consistent with the naming convention adopted for SRTM products. The model well fits the principal geodynamic domains of Europe, encompassing the south-westward motion of the Aegean–Anatolian region (horizontal velocity of up to 2 cm/yr), the uplift of the Central Alps (vertical velocity of less than 0.2 cm/yr), the radial pattern of Fennoscandian postglacial rebound (vertical velocity of up to 1 cm/yr), and the spreading regime of Iceland along the Mid-Atlantic Ridge (horizontal velocity of up to 2 cm/yr). A validation process comparing both the exploited screened GNSS velocity dataset and the EGMS-GNSS long-term model, was performed. Such a validation indicates that discrepancies are predominantly below 0.3 cm/yr for horizontal components. However, larger residuals in the vertical component are observed, which are indicative of a higher sensitivity to localized vertical deformation. Furthermore, discrepancies are primarily concentrated along major active faults, thereby highlighting the effect of not using a fault model in the implemented procedure. In conclusion, the principal enhancements in the usability of GNSS velocity data are twofold. First, the data are now provided as spatially continuous distributed in a tiled structure and made accessible in GeoTIFF format. This significantly improves data handling, visualization, and interoperability with standard GIS and remote sensing software. Second, the spatial resolution has been substantially increased, from the 50 kilometers grid spacing of the EGMS-GNSS product to a resolution of 10 arc-seconds (approximately 300 meters) in our datasets.

The Bangkok Metropolitan Region (BMR), Thailand’s economic and administrative centre, has undergone significant land subsidence over the past several decades. Subsidence is primarily attributed to excessive groundwater extraction and the consolidation of thick, highly compressible Quaternary Bangkok clay deposits underlying this low-lying deltaic plain. The BMR includes Bangkok and the surrounding provinces of Samut Sakhon, Samut Prakan, Nakhon Pathom, Nonthaburi, and Pathum Thani, forming one of Southeast Asia’s most densely urbanised and industrialised regions. Ongoing vertical ground deformation threatens critical infrastructure, increases flood susceptibility, and amplifies the impacts of relative sea-level rise in a region already vulnerable to climate-driven hydrological extremes. This study quantifies the spatial and temporal evolution of land subsidence across the BMR using Sentinel-1 Synthetic Aperture Radar (SAR) time-series interferometry. We generated interferometric products using the UK Centre for Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET)-LiCSAR automated processing system, and displacement time-series analysis was performed with LiCSBAS. A total of 368 Sentinel-1 acquisitions spanning December 2014 to December 2024 were processed to derive line-of-sight (LOS) velocity fields and cumulative displacement time series. The applied workflow enables systematic, large-area deformation monitoring with millimetre-scale precision and temporal consistency. Results reveal spatially heterogeneous yet regionally persistent subsidence patterns. Maximum LOS subsidence rates reach approximately 30 mm/year in Samut Sakhon, with substantial ongoing deformation also identified in parts of Samut Prakan, Pathum Thani, and western Bangkok. High subsidence rates correlate spatially with zones of intensive groundwater extraction and areas underlain by thick compressible clay sequences. Temporal analysis indicates sustained deformation in industrial estates and peri-urban districts, whereas selected sectors exhibit attenuated subsidence trends, potentially reflecting the long-term effects of groundwater regulation policies. These findings demonstrate the robustness of the LiCSAR–LiCSBAS Sentinel-1 framework for regional-scale geodetic monitoring in complex megacity environments. The derived deformation fields provide quantitative constraints for hydrogeological assessment, subsidence hazard evaluation, and integrated urban resilience planning. Continuous satellite-based InSAR monitoring is essential for resolving evolving deformation dynamics and mitigating compounded risks associated with anthropogenic subsidence and accelerating relative sea-level rise in the Bangkok Metropolitan Region. Keywords: Land subsidence; Sentinel-1 InSAR; LiCSAR; LiCSBAS; Time-series deformation; Bangkok, Thailand

Vegetated, deep‑seated landslides in the Polish Carpathians pose a significant challenge for satellite synthetic aperture radar (SAR) monitoring because dense vegetation and limited infrastructure provide few persistent natural radar scatterers. As a result, the engineering applicability of conventional Interferometric SAR (InSAR) techniques is often constrained by low coherence, temporal decorrelation, and unfavorable viewing geometry. To overcome these limitations, we installed six artificial corner reflectors (CRs) designed to act as stable, high backscatter radar targets. The network consists of two off‑slide reference reflectors and four reflectors within the active Kłodne landslide, including two sites colocated with piezometer/inclinometer installations to enable cross‑validation with in‑situ deformation measurements. We combined Sentinel‑1 SAR data acquired in both ascending and descending orbits with multi‑temporal InSAR processing to construct a long, high quality line‑of‑sight (LOS) displacement time series covering the period 2018–2024. The dual geometry SAR configuration is critical in this setting, as it allows partial separation of vertical (Up) and horizontal (East) displacement components, which cannot be resolved from a single viewing direction. To isolate geophysically meaningful deformation from SAR‑specific artefacts, the LOS time series was decomposed using discrete wavelet analysis into long‑term trends, seasonal or thermally driven components, and short‑term anomalies. Epochs affected by snow cover,identified as periods with degraded radar backscatter and phase stability, were explicitly masked to avoid bias related to snow‑induced decorrelation and phase delays. The combined ascending and descending SAR observations indicate consistent eastward motion of approximately 2–4 mm yr⁻¹ at few reflector locations. Seasonal signals are most pronounced in the Up component, highlighting the sensitivity of SAR measurements to vertical motion and thermally or hydrologically driven surface processes. By integrating SAR‑derived displacement with rainfall and groundwater observations, we observe modest but physically consistent hydro kinematic relationships. In particular, rainfall to groundwater response lags of approximately 4–5 days are evident, while rainfall to displacement lags vary spatially from 0 to 86 days, reflecting heterogeneity in subsurface structure and landslide kinematics. Validation against nearby inclinometer records confirms that the eastward displacement inferred from SAR and corner reflector measurements is consistent with independently observed subsurface deformation trends. A comprehensive error budget is presented, accounting for SAR measurement noise, geometry‑related uncertainties, and time‑series decomposition effects. Based on this analysis, deformation detection thresholds of approximately ≥ 0.5–1.0 mm yr⁻¹ (95% confidence interval) are achieved, demonstrating that SAR monitoring supported by artificial reflectors can resolve slow landslide motion at engineering relevant scales.

The increasing frequency and severity of natural hazards pose significant challenges for disaster managers, emergency responders, and scientists studying long-term Earth surface change. Such hazards often affect large and/or remote regions, complicating ground-based delineation of affected areas and populations and delaying the deployment of personnel and equipment. Spaceborne synthetic aperture radar (SAR) and optical imagery provides timely, broad-scale, and repeatable observations that enable rapid and comprehensive characterization of disaster-impacted regions. SAR-based products provide all-weather, day-night observations of surface deformation, flooding, and land cover change, supporting rapid hazard assessment preceding, during, and following disasters. Complementary optical products enable mapping of surface water extent, vegetation impacts, burn severity, and geomorphic change where cloud-free observations are available. Integration of these data facilitates rapid emergency response while the repeat acquisition of observations enables investigation of the longer-term impacts of natural hazards. However, SAR and optical satellite imagery is often not immediately interpretable for practical applications (e.g., mapping flood, wildfire, or landslide extents), creating a clear need for standardized, analysis-ready products that can be readily accessed and used by both scientists and emergency responders. The Observational Products for End-Users from Remote Sensing Analysis (OPERA) project at NASA’s Jet Propulsion Laboratory (JPL), in response to the needs identified by the Satellite Needs Working Group (SNWG), is delivering a suite of open-source and analysis-ready products derived from SAR and optical satellite imagery to address a wide range of scientific disciplines and disaster response needs. These products are free and available for download from NASA's Distributed Active Archive Centers (DAACs; see links below). By leveraging systematic observations provided by the Copernicus Sentinel 1/2 and NASA/USGS Landsat 8/9 constellations, as well as data from the recently-launched NISAR mission, OPERA produces standardized, validated datasets designed to lower barriers to use across scientific and applied communities. In this presentation we will highlight examples of disaster applications enabled by OPERA’s SAR- and optically-derived science-level products. Example applications include: (1) hurricane-induced coastal flood inundation mapping with the OPERA Dynamic Surface Water Extent from Sentinel-1 and Harmonized Landsat and Sentinel-2 (DSWx-S1/DSWx-HLS) products; (2) wildfire delineation and burn progression tracking with the OPERA Land Surface Disturbance from Sentinel-1 and Harmonized Landsat and Sentinel-2 (DIST-S1/DIST-HLS) products; (3) landslide detection with the OPERA Radiometrically Corrected (RTC) radar backscatter product; and (4) synergistic applications using multiple OPERA products. Additionally, we will discuss a collection of in-development tools (see link below) designed to automate notification, discovery, processing, and visualization of OPERA products over disaster-impacted areas and demonstrate how these open-source tools and products can be integrated into operational and research workflows to more rapidly quantify hazard impacts across large regions and support more comprehensive situational awareness for emergency responders. We seek and welcome feedback from those working across the academic, disaster management, and emergency response community who have applied OPERA products in their own work, as well as those looking to integrate these data in their own operational and/or research workflows. -------------------------------------------------------------------------------------------------------------- More information about OPERA’s mission, funding, and the Satellite Needs Working Group (SNWG): https://science.nasa.gov/science-research/earth-science/opera-addressing-societal-needs-with-satellite-data/; https://www.earthdata.nasa.gov/data/projects/nsite More information about the OPERA project and data: https://www.jpl.nasa.gov/go/opera/; https://www.jpl.nasa.gov/go/opera/products/ More information about OPERA data access: https://www.earthdata.nasa.gov/data/projects/opera More information about open-source OPERA disaster response tools: https://github.com/OPERA-Cal-Val/disasters

Satellite-based interferometric synthetic aperture radar (InSAR) is attracting increasing attention as a tool for monitoring deformation in road and railway infrastructure. Despite this growing interest, its practical application is still limited. To estimate the usability and applicability for a routine monitoring, the vertical settlements along a 30 km railway line were estimated using Sentinel-1 data and compared with existing in-situ surveys. The suitability of pre-processed data from the European Ground Motion Service (EGMS) and custom-processed Sentinel-1 data was evaluated. The comparison was made between Sentinel-1-derived settlements and conventional railway measurements for five selected bridges, including the adjacent free-track sections. Additionally, deformation trends were assessed along the entire corridor. Persistent scatterer (PS) points were aggregated along the line using spatial clustering, which reduces sensitivity to unreliable individual scatterers and accounts for geolocation uncertainty at the metre level. A median-based statistic is then applied within the clusters and the line of sight (LOS) deformation is converted into vertical and horizontal settlement components, enabling comparison with geodetic results. There was very good agreement between the InSAR-derived settlements and the local measurement data for the investigated section. Unlike previous experience, custom processing did not substantially improve the accuracy of the results compared to those obtained using EGMS products. However, the recently constructed section of the corridor must be analysed, as it is not yet included in the EGMS database. The evaluation highlights two key challenges in interpretation. Firstly, rapid surface changes due to track ballast tampering can occur within a revisit interval of six days, exceeding the reliable deformation increment of approximately one quarter of the radar wavelength (1.4 cm). This increases the risk of phase unwrapping ambiguities. Therefore, integrating tampering records is essential, as this enables sudden surface changes to be successfully interpreted that would otherwise be poorly represented in the InSAR time series. A simplified method of accounting for this has been developed for demonstration purposes only; a more adequate method will follow. Secondly, transitions between areas with different settlement behaviour (e.g. bridge structures versus the surrounding ground) cannot always be resolved precisely due to limitations in spatial sampling. In practice, an effective along-track spacing of 20 metres can only be achieved under optimal conditions; 50 metres is a more robust alternative. To strengthen future monitoring, corner reflectors were introduced as stable reference targets. Three reflectors have been installed and their detectability was verified to provide deformation measurements representative of the behaviour of the bridge rather than the track alone. Overall, the study shows that Sentinel-1 EGMS products can support netwide settlement assessment, and that local interpretation benefits greatly from additional maintenance data and dedicated reference targets.

Port infrastructure monitoring requires distinguishing between benign consolidation and critical structural anomalies from InSAR displacement time series. This study presents a Multiple Hypothesis Testing (MHT) framework that tests competing physical models to detect regime shifts while controlling false alarm rates. The approach employs Baarda's B-method for dimension-fair model selection, comparing linear velocity, thermal expansion, Heaviside step functions, and wave-climate interaction models against observed deformation patterns.         To bridge the gap between point-level measurements and operational decision-making, we define Vulnerable Areas of Interest (VAIs) as standardized spatial units representing distinct structural assets. Within each VAI, we derive four monitoring metrics: State (cumulative displacement), Rate (instantaneous velocity), Rate of Change (velocity increments at breakpoints), and Offset (modeled discontinuities). Spatial coherence is assessed via Moran's Index to distinguish systematic structural responses from scatterer noise.         The framework is applied to three Spanish ports using five years of EGMS Sentinel-1 data (2018--2023). At Castellón, the analysis reveals step-function offsets temporally aligned with Storm Gloria (January 2020) and spatially concentrated on the outer East Breakwater face, suggesting localized structural response to extreme wave loading (H_s > 6 m). The Closure Breakwater exhibits gradual differential settlement in mid-reach sectors, indicating ongoing consolidation processes. At Huelva, the Muelle Ingeniero Juan Gonzalo shows expected consolidation curves with seasonal thermal components, while the Paseo de la Ría displays differential settlement patterns correlating with documented remediation efforts. Dique Juan Carlos I reveals offset detections in early 2018 coinciding with energetic wave events from SIMAR records. At Algeciras, multi-geometry analysis of the detached breakwater shows divergent ascending and descending track trends in the northern sector, suggesting horizontal displacement components that single-geometry analysis would not resolve.

SAR interferometry (InSAR) is currently the primary source of information for surface motion measurements across a wide range of spatial scales, particularly when historical deformation analysis is required. The Copernicus Sentinel-1 mission represents a game changer, as its systematic acquisition strategy and open data policy have democratized access to satellite observations, allowing both the reconstruction of displacement histories over areas of interest worldwide and the development of operational surface motion monitoring chains. The emergence of cloud-based platform solutions is further accelerating the uptake of satellite data for mapping and monitoring surface motion by ensuring seamless access to data archives, large storage capacity, computational resources, and the execution of advanced processing chains in a straightforward manner. This significantly lowers the technical requirements for thematic domain professionals seeking to utilize these measurements within their areas of expertise. In this context, the mining sector can greatly benefit from satellite-based surface motion monitoring for assessing the stability of mine walls, pit slopes, tailings facilities, and above-ground infrastructure within operational mining sites. However, one of the intrinsic characteristics of open-pit mines is the continuously changing surface topography, which introduces challenges for most multi-temporal SAR processors. Rapid surface changes may disrupt the continuity of phase stability, which is a fundamental requirement for Persistent Scatterer Interferometry (PSI), leading to reduced spatial coverage of PS targets, especially when long observation periods are considered. To mitigate this limitation, we propose a sequential processing strategy based on the division of the entire observation period into distinct automatic or user-defined sub-periods. Independent interferometric processing is performed for each sub-period, followed by a concatenation procedure to reconstruct a continuous displacement time series. The linkage between independent time series accounts for potential offsets arising from different starting dates, the need to extrapolate over temporal gaps where linear behavior can be reasonably assumed, and trend adjustments across overlapping intervals. Such an approach enhances measurement density while allowing the ingestion of updated Digital Elevation Models (DEMs) representing contemporary topography. This is particularly critical in mining environments where significant topographic changes occur due to excavation activities. Since updated DEMs are often derived from drone-based surveys and may be spatially limited to areas of active elevation change, a dedicated assimilation strategy is implemented to integrate these high-resolution local datasets with global height products. Assumptions still remain, primarily concerning the exact spatial correspondence of measurement points between independent processing intervals. In the current implementation, this is addressed through sampling individual datasets over a common grid prior to the concatenation process, effectively transitioning from distributed point targets to regularly spaced grid points. This ensures spatial consistency and allows the generation of continuous time series over the full observation period. The developed workflow has been implemented as an upgrade to the existing SNAPPING PSI service of the Geohazards Exploitation Platform (GEP), offering to mining operators the benefit of utilizing an automated online service. In addition to the standard service capabilities, users can define temporal breakpoints and optionally upload updated external height datasets for each processing interval. Results from the analysis of multiple mining sites demonstrate a significant enhancement in measurement density and spatial coverage, allowing comprehensive surface motion monitoring without restricting the analysis to shorter time spans. Acknowledgements This work was carried out within the framework of the MASTERMINE project (Grant Agreement No. 101091895), funded by the European Union’s Horizon Europe research and innovation programme.

InSAR is becoming more widely recognised as a valuable tool for monitoring ground and structural movement at various scales. At one end of the scale, Sentinel-1 can provide a cost-effective way of monitoring movement at the scale of regions, countries or even continents. SatSense have processed Sentinel-1 data to produce nationwide maps of the United Kingdom and New Zealand as well as large areas affected by extractive industries such as the Permian Basin in the USA. These large scale InSAR datasets can be kept up to date as new Sentinel-1 images are acquired and the data are made available through a dedicated online visualisation platform. At the other end of the scale, high-resolution InSAR data from satellites such as TerraSAR-X (TSX) and COSMO-SkyMed (CSK) can be used for detailed analysis of individual structures and to provide a more granular picture of movement across smaller regions. Here, we will show examples of how InSAR data have been used across multiple scales, industries and locations. In the UK, we will show how nationwide Sentinel-1 data can be used to monitor entire infrastructure networks for customers such as Network Rail and others. InSAR offers several valuable opportunities to infrastructure owners: Remote monitoring can reduce the number of in-person inspections, reducing risk to personnel, costs and carbon footprint InSAR can provide a consistent, network-wide picture of movement risk Areas of concern can be flagged for further inspection, enabling prioritisation of resources We will also show examples of how nationwide InSAR can also be used to monitor the ongoing risk from historic mining across large areas of the UK. These data can be used by regulatory authorities, insurance companies and geotechnical engineers to understand not just the risk of ground movement in a given area, but whether the ground has actually moved in the last 10 years. In addition to our UK-wide InSAR dataset, SatSense have partnered with Earth Sciences New Zealand (previously GNS Science) to provide a nationwide Sentinel-1 InSAR dataset for New Zealand. New Zealand is exposed to a range of geohazards and InSAR provides valuable insight about how these geohazards are evolving at scale. We will show examples of how this InSAR data is used by academics, government agencies and industries throughout New Zealand to monitor landslides, geothermal fields, fault movements and more. In the Permian Basin, USA, we will show examples of InSAR imaging complex movements associated with oil and gas extraction, and accompanying wastewater pumping. These movements can vary in space and time as fluids move beneath the surface and are well imaged using wide-area InSAR. InSAR offers cost-effective, wide-area context and opportunities to monitor ongoing, dynamic movements which can be indicative of subsurface operations. Throughout the world, some applications require higher resolution InSAR data. TSX and CSK data can be used to provide measurements at the property level or for high-value assets such as tunnels, bridges, dams, airports and more.  The higher spatial resolution and improved height estimation from their baseline variation allows for more precise positioning of the radar reflectors in 3D. These precise 3D positions, coupled with higher sensitivity to small movements due to a shorter X-band wavelength make these satellites well suited to monitoring how distinct parts of structures move over time. We will show examples of how these higher resolution satellite datasets can be used to monitor a range of properties and structures for different kinds of movement. The combination of wide-area Sentinel-1 data and focused high-resolution InSAR datasets means InSAR is becoming more widely recognised as valuable for a range of sectors. The recent launch of NISAR and the expected launch of further InSAR capable satellites over the coming years will lead to even more potential for InSAR monitoring.

Triggered by the systematic availability of SAR imagery acquired by the ESA ERS satellites, InSAR time-series methodologies began to be developed in the late 1990s. One example is the Delft Implementation of Persistent Scatterer Interferometry (DePSI) (van Leijen, 2014), the extension to TU Delft’s DORIS InSAR package (Kampes et al, 2003) . DePSI is characterized by the use of geodetic estimation and hypothesis-testing techniques to rigorously assess parameter estimates and identify and remove incorrectly estimated parameters. By implementing DePSI in MATLAB—the primary programming language used in the educational program at Delft University of Technology—students were able to work with and further develop the code base. Since then, the paradigm has shifted toward open-source software solutions, and students are now primarily trained in Python. At the same time, the rapidly increasing volume and resolution of SAR acquisitions from modern satellite missions pose significant challenges in terms of scalability and extensibility. To ensure the future development of DePSI, we have reimplemented the software in Python. The resulting open-source package is designed to efficiently handle large InSAR datasets while adhering to modern Python software engineering standards. It builds on the original MATLAB implementation (van Leijen, 2014) and extends it with a scalable, modular, and community-oriented architecture. To address the challenges of intensive InSAR processing, DePSI is built on Xarray and Dask, enabling efficient manipulation of multi-dimensional datasets and seamless scalability from local laptops to High-Performance Computing (HPC) environments. This design allows DePSI to process large SAR stacks while maintaining memory efficiency and parallel performance. DePSI adopts a functional programming–oriented design, facilitating the integration of new PSI algorithms alongside existing conventional methods. Comprehensive user and developer documentation, including example Jupyter notebooks, is provided to lower the barrier for adoption and extension. Modern software quality practices—such as unit testing, continuous integration, and version control—are fully implemented, ensuring robustness and long-term maintainability and fostering community-driven development. DePSI aims to provide a scalable, extensible, and high-quality open-source platform for next-generation PS-InSAR research and applications. In our contribution we will present the software design and use, together with example use cases. The DePSI repository can be accessed via https://github.com/TUDelftGeodesy/DePSI. Kampes, B. M., Hanssen, R. F., and Perski, Z., 2003. Radar interferometry with public domain tools. In Third International Workshop on ERS SAR Interferometry, ‘FRINGE03’, Frascati, Italy. van Leijen, F., 2014. Persistent Scatterer Interferometry Based on Geodetic Estimation Theory, Ph.D. dissertation, Delft University of Technology, Delft, The Netherlands.

Land subsidence, predominantly driven by excessive groundwater extraction, poses significant risks to urban infrastructure and public safety in Iran. Previous national-scale studies have highlighted Iran as one of the countries most affected by subsidence, with accelerating trends over recent decades. However, detailed, high-resolution assessments of subsidence hazards within the country’s largest urban centers remain limited. This study leverages ten years of Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) data to conduct a comprehensive analysis of land subsidence across the most populous megacities of Iran with populations exceeding 1.5 million, including Tehran, Mashhad, Shiraz, Tabriz, Karaj, and Isfahan. Our investigation focuses on multiple facets of subsidence dynamics, emphasizing both vertical and horizontal ground deformations and their implications for urban systems. First, we provide an updated overview of general subsidence patterns in these cities, identifying hotspots of accelerated deformation and regions where subsidence trends are intensifying over time. Beyond this general assessment, the study presents a precise evaluation of differential subsidence hazards, with particular attention to densely built urban areas and critical infrastructures, including transportation networks, utilities, and historic sites. A key contribution of this work is the spatio-temporal characterization of subsidence within each city. High-resolution InSAR time-series analysis enables the detection of localized deformation variations, revealing how subsidence evolves differently across urban neighborhoods and industrial zones. Additionally, we implement a novel methodology to decompose line-of-sight (LOS) deformation into three-dimensional displacement components, allowing for the first detailed mapping of horizontal ground strains induced by subsidence. This approach highlights areas where horizontal deformation may compromise structural integrity, complementing differential vertical deformation assessments and providing a more complete hazard evaluation framework. Results indicate that all six megacities exhibit significant subsidence, with pronounced spatio-temporal variability and heterogeneous strain distributions. Cities such as Tehran and Isfahan show persistent high subsidence rates correlated with intensive groundwater withdrawal and urban expansion. The integration of vertical and horizontal deformation patterns provides actionable insights for urban planners, policymakers, and disaster risk managers, enabling targeted interventions to mitigate infrastructure damage and enhance urban resilience. This study underscores the critical need for continuous, high-resolution monitoring of subsidence in rapidly growing urban centers and demonstrates the utility of Sentinel-1 InSAR data for multi-dimensional hazard assessment in megacities prone to anthropogenic land deformation.

Miami’s barrier islands have experienced a rapid increase in high-rise development over the past decade, with construction ongoing along much of the coastline. In addition, the tragic collapse of the Champlain Towers South in 2021 raises the possibility that some deformations may be related to construction activities and subsurface conditions. This study uses Interferometric Synthetic Aperture Radar (InSAR) time-series analysis, processed with the open-source SARvey software framework, which builds on Mintpy and MiaplPy, to analyze subsidence occurring along Miami’s barrier islands between 2017 and 2025. Using Sentinel-1 and TerraSAR-X data, the study documents radar line-of-sight (LOS) displacements up to 15 cm affecting highrises and their surroundings in multiple sub-regions (Sunny Isles, Surfside, North Beach, and South Beach). In Sunny Isles, which appears to have experienced more building development than other areas over the past decade, we documented deformation with an average of 10 mm/yr LOS displacement velocities monitored in nearly one third of the buildings over 100 meters in height. The detected subsidences are presumed to be construction related, considering the construction activities taking place nearby. We also correlate the measured displacements with geological cross-sections obtained from geotechnical reports, and we consider the possibility that these subsidences may have been caused by the dynamic settlement and creep of sandy layers within the limestone. This study examines that construction of highrise buildings can lead to creep deformation in the Miami limestone area, and highlights the capability of InSAR technology to observe settlement and integrity of structures.

The European Ground Motion Service (EGMS), as part of the Copernicus Land Monitoring Service, represents a paradigm shift in regional and continental geohazard monitoring, providing standardized InSAR displacement maps across Europe. However, the operational exploitation of these massive datasets remains a significant challenge for many end-users. The primary obstacles include the management of extremely large files in Text File Document (.txt) format, which often leads to software instability in standard Geographic Information Systems (GIS), and the high computational demand required for spatial subsetting and format conversion on local workstations. To address these bottlenecks, we present EGMStream, an innovative, high-performance web ecosystem designed to streamline the downstream processing of EGMS products. The technical core of EGMStream has evolved significantly from its original R-based implementation into a robust, server-side architecture. The current web-based version is built within a Python environment and encapsulated using Docker containers on a dedicated server infrastructure at the University of Florence. This containerized approach ensures maximum portability, eliminates library dependency conflicts, and guarantees consistent performance regardless of the host operating system. The most significant innovation is the implementation of a parallelized conversion engine. By distributing the processing load across multiple CPU cores, EGMStream can handle the ingestion and transformation of millions of persistent scatterers (PS) in a fraction of the time required by traditional single-threaded desktop tools, making the process virtually independent of the user's local hardware limitations. The user interface is meticulously organized to facilitate complex data management through a streamlined, two-panel interactive layout. The left panel serves as the primary control hub where users input EGMS download hyperlinks and define advanced processing parameters. A key feature is the ability to handle InSAR time series optionally, allowing users to reduce file size when only average velocity is required. The spatial management has been revolutionized with the introduction of an advanced cropping module. Users can define their Area of Interest (AoI) by uploading custom vector files in Shapefile, .KML, or .KMZ formats. This allows for precise data extraction tailored to specific administrative or geological boundaries. A fundamental pillar of EGMStream is its asynchronous operational logic. Recognizing that converting continental-scale datasets can be time-consuming, the application leverages a server-side queuing system. Once the process is initiated, the user is free to close the web browser or shut down their device, as the server handles the entire task autonomously. Upon completion, an automated routine generates a secure download link and notifies the user via email. Supporting multiple output formats such as GeoPackage, GeoJSON, and Shapefile, EGMStream ensures seamless interoperability with modern geospatial software. Accessible at https://egmstream.unifi.it/, this tool represents a critical contribution to the Open Science movement, democratizing access to European Ground Motion Service data and empowering researchers and practitioners to focus on geohazard interpretation rather than data pre-processing.

Since their publication in mid-2020, the European Ground Motion Service (EGMS) products have been seen as a breakthrough in InSAR applications for the analysis and monitoring of natural and man-made hazards on Earth’s surface. The EGMS measurements reliable InSAR measurements of ground deformations and include three types of products: (i) Basic (Level 2A) which provides line-of-sight velocity maps in ascending and descending orbits referenced to a local point; (ii) Calibrated (Level 2B) provides GNSS-calibrated full-resolution velocity and displacement time series for the ascending and descending orbits; (iii) Ortho (Level 3) calculated displacement vectors in the vertical and E-W directions, resampled to a 100 x 100 m grid. These datasets are available for five-year periods, except for the 2015-2020 period, which constitutes the EGMS baseline, followed by the 2018-2022 and 2019-2023 periods, and will continue to be updated until 2027, according to the official reports, providing continuity to existing datasets. Their versatility makes them useful for a wide range of analyses and investigations of many geological and geomorphological processes, including slope-related processes, land subsidence, sinkholes, volcanic activity, and more. Also, their availability enables the assessment and monitoring of structural and infrastructure displacements, aiming to mitigate potential hazards that could affect society. Although the products clearly increased the use of InSAR across many studies, their full potential remains to be unlocked. Their underuse is mainly limited by the interpretability of the ascending and descending orbit measurements, which are not always straightforward, whereas the Ortho products average deformation velocities, making them unfit for local-scale investigations such as landslide and building monitoring. In this work, we aim to minimise this shortcoming by developing a command-line workflow in the R environment that computes displacement vectors in the vertical and east-west directions at the measurement-point level. Basically, every single calibrated ascending or descending InSAR measurement point (MP) is considered for the computation of displacement components by searching for its nearest neighbour from the other orbit. For the identified pair of points, the middle point is computed, and for that point, the mean velocity and displacement time series for the vertical and East-West components are calculated. The resulting synthetic points, along with time series and mean velocity for the vertical and east-west directions, will significantly improve understanding of local deformations. We tested our approach on sinkholes, slope-related deformations, and mining activity in different environments, delivering results that would have provided important information prior to the event.

The “Romanian Ground Motion Pilot Service for Sustainable Infrastructure” (RO-GMS) addresses the Earth Observation (EO) market in Romania with the primary objective of facilitating the operational uptake of Persistent Scatterer Interferometry (PSInSAR) by public authorities. By leveraging the Copernicus European Ground Motion Service (EGMS), the project delivers tailored, actionable monitoring services for critical local infrastructure. A central component of this initiative is a customized, user-friendly web platform (https://pstool.terrasigna.com/) engineered to lower the barrier to entry for users with limited remote sensing expertise. The environment hosts specialized analytical tools, including an interactive transect tool that enables PSInSAR results analysis and animations across target areas. Additionally, an on-the-fly analysis module provides immediate stability assessments at the scale of individual infrastructure elements uploaded by the user. To enhance data accuracy, the project introduces advanced methodological improvements. Firstly, the standard PSInSAR processing algorithm was refined to successfully detect and map quick, highly non-linear ground dynamics. Secondly, we integrated a novel thermal layer, utilizing thermal sensitivity models calculated from EGMS data to separate temperature-correlated displacements from actual structural motion. The efficacy of these advancements is demonstrated through two distinct case studies. The first case study highlights a bridge subjected to large-scale dynamic shifts related to thermal motion. The thermal models estimated from long term EGMS data and local weather information can be used to improve phase unwrapping in short time monitoring projects on areas with large thermal motion. The second case study examines a water dam that underwent rapid subsidence triggered by a significant drop in the reservoir's water level. The Sentinel-1 derived PSInSAR results for the water dam were rigorously validated against in-situ terrestrial measurements spanning from 2017 to 2024. This ground-truth validation relied on a dedicated geodetic monitoring system, comprising a crest subnetwork and markers installed to measure quarterly the behavior of the surrounding rocks, using high-precision total stations and leveling instruments. The strong correlation between these geodetic field campaigns and the satellite observations underscores the reliability of the RO-GMS platform as a robust, operational tool for sustainable infrastructure management. Part of the presented results were obtained within the ESA project “Verification of innovative applications integrating national InSAR capabilities and the European Ground Motion Service” (2025 – 2026).

Soil moisture refers to the quantity of water present within the unsaturated zone of the soil. It is a key indicator of many Earth’s surface processes, governing plant water uptake, crop yields, groundwater recharge, and the exchange of heat and carbon between the land and atmosphere. Accurate and timely knowledge of its spatial and temporal distribution is therefore indispensable for many applications such as precision agriculture, drought early‑warning systems, and flood forecasting. Remote sensing sensors represent a unique possibility for measuring soil moisture in a frequent and non-invasive manner from large up to global scale. Well-known microwave radiometer missions such as NASA’s Soil Moisture Active Passive (SMAP) and ESA’s Soil Moisture and Ocean Salinity (SMOS) have provided daily global acquisitions at rough resolutions from some kilometers. Current Synthetic Aperture Radar (SAR) systems such as the Sentinel-1 constellation, which is capable of acquiring data at a resolution of 10 m, have attracted the attention of the scientific community to improve the quality and the resolution of soil moisture products. In our study, we investigate a novel deep learning-based (DL) solution for accurate and time-tagged soil moisture retrieval by combining, for the first time, backscatter and repeat-pass interferometric information derived from Sentinel-1 multi-temporal data. To overcome the challenge posed by the scarcity of high-quality reference data required for fully-supervised training, we propose a two-step method: a weakly-supervised pre-training of the model on a larger amount of data with lower accuracy, followed by a fully-supervised fine-tuning, from on-ground high-reliable measurements of soil moisture from 0 to 5 cm depth. The selected initial DL model architecture is based on a state-of-the-art fully convolutional neural network (CNN), chosen for its proven ability to extract multi-scale spatial features from radar data for regression tasks (Carcereri et al. 2023). It follows a modular structure consisting of an Input Block, five Hidden Blocks, and an Output Block. The architecture comprises approximately 760,000 trainable hyperparameters, ensuring sufficient model capacity to learn complex nonlinear relationships between radar observables and soil moisture dynamics while maintaining computational efficiency. The input feature set includes SAR backscatter intensity, InSAR coherence at different temporal baselines, and interferometric phase triplets, i.e. residual phase closure components computed from three consecutively acquired interferograms (De Zan et al. 2014).. All these input features provide together sensitivity to both surface scattering properties and structural changes related to soil moisture dynamics. By processing 5 Sentinel-1 acquisitions, 5 input SAR channels are obtained and 10 InSAR channels for each InSAR feature are derived. Overall, considering interferometric coherence and phase triplets, up to 20 input InSAR channels are used in the deep learning investigations. The target spatial resolution for the first implementation is set at 1 km, ensuring consistency with existing products. However, the framework is designed with scalability in mind, with the potential to refine the spatial resolution down to 100–200 m, leveraging the full information content of Sentinel-1 observations and improved calibration from high-resolution reference datasets. The experimental setup is based on five distinct Regions of Interest (ROIs) selected to represent diverse environmental and climatic conditions across the study area in central Europe (Belgium, Netherlands, Central Germany, East Germany, and Denmark). For each ROI, a three-year time span (2018–2020) of Sentinel-1 acquisitions is considered, providing a consistent temporal coverage for soil moisture retrieval and model evaluation. Each ROI comprises approximately 180 time-series samples, capturing seasonal variations and a wide range of soil and vegetation states. This multi-regional, multi-temporal configuration ensures that the model is trained and validated under heterogeneous conditions, thereby enhancing its robustness and generalization capability across different land cover types and climatic regimes. A geographic subdivision of the ROIs is performed for splitting the dataset into training, validation, and testing regions. This avoids data leakage and ensures independency among the different subsets. To avoid the estimation of soil moisture over forested areas, the ESA WorldCover 2020 is used to generate a land-cover land-use (LCLU) mask. Tree cover, built-up areas (mainly cities), water bodies, and permanent snow, are filtered out. Moreover, the generation of a LCLU mask allows us to investigate the soil moisture retrieval over different land cover types. For the first investigations on weakly-supervised learning of the proposed DL model, we define a common setup, which consists on dividing the defined ROIs in training (Denmark, Central Germany, and East Germany), validation (Netherlands) and testing (Belgium). The separation of the ROIs attends to have a similar distribution of the used data set in all cases. As reference map we use the Copernicus Global Land Operations - Surface Soil Moisture (CGLS-SSM) product, which provides daily observations at 1 km spatial resolution of soil moisture saturation level in percentage. This dataset is generated from Sentinel-1 backscatter using a radiative transfer model developed by TU Wien (Bauer-Marschallinger et al. 2019). As input channels for the DL model, we limited the set of Sentinel-1 input features to: SAR backscatter, InSAR phase triplets, and local incidence angle (LIA), to account for the acquisition’s geometry. As first results on the testing dataset, we have obtained that the combination of Backscatter and LIA achieves a Mean Error (ME) of 6.63%, a Mean Absolute Error (MAE) of 10.01%, a Root Mean Squared Error (RMSE) of 13.20% and a correlation coefficient (R2) of 0.83. These results improve to ME=2.63%, MAE=8.71%, RMSE=11.37%, and R2=0.87, when adding the phase triplets. On the other hand, the combination of phase triplets and LIA alone achieves poor results, with a MAE and a RMSE around 20% and a R2=0.4%. Looking at the influence of the land cover type, we obtain in general better results for cropland than when considering grassland/schrubland/bareland. As example, when considering all three input channels: ME=3.19%, MAE=7.94%, RMSE=10.36%, and R2=0.90, are obtained for cropland, while for the other considered land cover types, ME=1.20%, MAE=10.72%, RMSE=13.64% and R2=0.79. First analysis on the temporal generalization capability of the model shows a good agreement with the reference data. For the months between January and May, there is a quite uniform distribution of the reference data with values between 0% and 95%, and the results of the proposed DL model on the testing dataset achieves R2 between 0.8 and 0.9, depending on the considered land cover class. During summer (June – August), the reference soil moisture data values are concentrated between 30% and 50%. The obtained R2 during these months is 0.5 – 0.6. The effects of crops, with significant variations in vegetation growth during such a period, affects the generalization capacity of the DL model investigated. These preliminary results show in general a promising performance for the pretext, weakly-supervised task. Only few combinations of input channels have been investigated up to now, but they already reveal the potential of the Sentinel-1 short time series and DL for soil moisture retrieval. It appears that the combination of backscatter and LIA shows the most informative content, which is expected, since the reference map has been generated by considering Sentinel-1 backscatter data. When using the InSAR feature alone (Phase triplets and LIA), the results are poor. This might be caused by either an inconsistency between the reference data and the information provided by the phase triplets or by the high level of noise that might affect the phase triplets in presence of complex terrain characterizing the selected ROIs in central Europe. Moreover, the phase triplets show anyway to help the DL model to better understand some cases, since the results for the combination of all three Sentinel-1 input features obtain the best results. (1) D. Carcereri, P. Rizzoli, D. Ienco and L. Bruzzone, "A Deep Learning Framework for the Estimation of Forest Height from Bistatic TanDEM-X Data," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 8334-8352, 2023. (2) F. De Zan, A. Parizzi, P. Prats-Iraola and P. López-Dekker, "A SAR Interferometric Model for Soil Moisture," in IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 1, pp. 418-425, Jan. 2014. (3) B. Bauer-Marschallinger et al., "Toward Global Soil Moisture Monitoring with Sentinel-1: Harnessing Assets and Overcoming Obstacles," in IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 1, pp. 520-539, Jan. 2019.

Interferometric Synthetic Aperture Radar (InSAR), which measures surface displacement from repeat-pass phase differences, provides millimeter-level, wide-area deformation measurements and, with expanding satellite archives, multi-temporal InSAR has gradually evolved into a routine monitoring tool [1], [2]. In urban environments, the presence of abundant man-made stable scatterers and the need to estimate residual height errors make Persistent Scatterer InSAR (PS-InSAR) a particularly suitable framework: it jointly estimates deformation, residual height error, and atmospheric delay over pixels that remain coherent over time, effectively exploiting stable reflectors associated with built structures [3], [4]. Nevertheless, robust and reliable phase-quality estimation remains challenging. Some methods suppress phase noise and facilitate phase-quality estimation through spatial averaging, but they are not well suited to urban scenes with strong height variations [5], [6]. PSP-based approaches provide a simple arc-wise phase quality measure using double-differenced observations, yet their high computational cost often forces restrictive arc selection in practice, compromising the full exploitation of available information in exchange for efficiency [7]. To address these challenges, we propose Exhaustive Pairwise InSAR (EP-InSAR), an arc-based PS framework aimed at densely recovering pixels with reliable phase histories in complex urban areas exhibiting localized and non-linear deformation. By replacing the conventional single-master network with a daisy-chain baseline linkage, where interferograms are formed only between adjacent acquisitions, EP-InSAR emphasizes short-interval phase evolution, tolerates more unmodeled phase variations, and strengthens the ability to capture non-linear deformation. Because deformation phase accumulates only weakly over such short intervals, the temporal deformation term in the double-differenced phase series has minimal impact on arc assessment, allowing each arc to be solved primarily for height difference and reducing the demanding two-dimensional parameter search to a one-dimensional problem. Leveraging this simplification, EP-InSAR provides a fully vectorized implementation that efficiently batch-processes millions of arcs, enabling substantially denser arc exploration. Building on the efficient arc-evaluation capability, this study further proposes a flexible arc search framework that aims to approximate exhaustive exploitation of high-quality arcs while controlling computation. Instead of relying on a single conservative point-selection criterion, we distinguish anchor points and usable points using separate thresholds defined by the quality, number, and spatial extent of their associated arcs. Starting from an initial anchor set, we iteratively expand the network via triangulation-based k-ring expansion by linking each candidate point to nearby anchors, promoting new anchors, and thereby discovering additional usable points. This iterative expansion reduces reliance on conventional phase-quality priors and empirically approaches near-exhaustive coverage in urban windows while examining only a small fraction of all possible pixel pairs. From the resulting set of high-quality arcs, we provide a complete deformation time-series workflow for deformation field reconstruction. Arc-wise observations of differential residual height error and thermal expansion coefficient are first integrated into global parameter fields using Huber-type IRLS to down-weight outliers, with initial weights determined by arc coherence. The integrated fields are then used to compensate interferograms, effectively reducing phase spatial frequency and easing phase unwrapping. At this stage, baseline combinations are no longer restricted to the daisy-chain configuration. Based on the compensated interferograms, we apply spatiotemporal filtering to derive the final deformation time series. Specifically, atmospheric delays are estimated by applying spatial low-pass filtering to the high-frequency temporal components, yielding localized long-term deformation after removing correlated artifacts. A large-scale experiment over eastern Shanghai using TerraSAR-X validates the framework and demonstrates its ability to reveal localized deformation signals. The dataset comprises 54 descending scenes acquired between 2020 and 2024, covering the Yangtze River estuary and the surrounding islands. Due to Python’s inefficient inter-process data sharing in multiprocessing, the scene is divided into dozens of patches, processed independently, and then mosaicked to produce an overview deformation map. As reported in many previous studies, pronounced subsidence is observed along northeastern boundary embankments associated with large-scale land reclamation. Beyond these broad patterns, the proposed method also captures a large number of highly localized yet reliable signals, including deformation around road junctions and individual buildings. By comparison with historical satellite imagery, we interpret representative patterns consistent with typical urban geotechnical processes. For a concise reliability check, we further compare results over Pudong International Airport using different sensors and algorithms. Applying the proposed method to a descending Sentinel-1 stack yields deformation zones broadly consistent with the TerraSAR-X results, whereas StaMPS, run on a TerraSAR-X subset with a standard SBAS network, struggles to recover localized deformation. The discussion notes that EP-InSAR differs from existing approaches in its spatial differencing pattern, baseline combination strategy, and relaxed prior assumptions. By allowing unmodeled components to retain signal content or exhibit abrupt transitions, the framework seeks to exploit more of the available phase information rather than treating it as noise. However, in the presence of phase wrapping, different baseline combinations cannot be made statistically equivalent simply by reweighting, and the conventional single-master strategy can retain practical advantages in stability. This motivates the use of baseline-dependent statistical thresholds. In addition, we evaluate the arc expansion strategy against a brute-force reference in a 500×500 pixel urban window. As the k-ring degree increases from 0 to 5, only a small fraction of arcs needs to be examined, while the recovered point set quickly approaches the brute-force upper bound. Degree 0 recovers 62% of all points with 4.53M arcs, and degree 5 reaches 83% (21,960 points) with 44.7M arcs. By contrast, a 150 m radius-dense scheme examines 1.20B arcs (about 27× more) yet yields slightly fewer points (21,908). The remaining gap is mainly confined to window corners where a fully enclosed observation network cannot form. In summary, EP-InSAR presents an arc-based PS InSAR framework aimed at dense recovery of reliable phase histories in complex urban environments. By combining daisy-chain interferogram formation, batched one-dimensional periodogram search, near-exhaustive arc exploration, robust global integration, and spatiotemporal filtering, the method improves spatial coverage and reveals more spatially and temporally compact deformation signals with stable and interpretable time series, supporting large-scale urban deformation monitoring. Several limitations remain. The daisy-chain linkage reduces computational burden and is more tolerant to unmodeled phase variations, but it can increase arc-level phase noise and blur arc-quality thresholds. Omitting explicit estimation of linear deformation velocity may also cause points with extreme linear deformation to be missed. Finally, relaxing model constraints can introduce artifacts, motivating careful verification and more robust baseline-dependent reweighting. This work was supported by the National Key Research and Development Program of China (Grant No. 2025YFC3215200 and 2025YFC3215200-01). Keywords: EP-InSAR; urban deformation monitoring; time-series InSAR; scientific computing; parameter estimation [1]   D. Perissin and T. Wang, “Time-Series InSAR Applications Over Urban Areas in China,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 4, no. 1, pp. 92–100, Mar. 2011, doi: 10.1109/JSTARS.2010.2046883. [2]   P. Ma et al., “Toward Fine Surveillance: A review of multitemporal interferometric synthetic aperture radar for infrastructure health monitoring,” IEEE Geosci. Remote Sens. Mag., vol. 10, no. 1, pp. 207–230, Mar. 2022, doi: 10.1109/MGRS.2021.3098182. [3]   A. Ferretti, C. Prati, and F. Rocca, “Permanent scatterers in SAR interferometry,” IEEE Trans. Geosci. Remote Sensing, vol. 39, no. 1, pp. 8–20, Jan. 2001, doi: 10.1109/36.898661. [4]   A. Ferretti, C. Prati, and F. Rocca, “Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry,” IEEE Trans. Geosci. Remote Sensing, vol. 38, no. 5, pp. 2202–2212, Sep. 2000, doi: 10.1109/36.868878. [5]   A. Hooper, P. Segall, and H. Zebker, “Persistent scatterer interferometric synthetic aperture radar for crustal deformation analysis, with application to Volcán Alcedo, Galápagos,” J. Geophys. Res., vol. 112, no. B7, p. B07407, Jul. 2007, doi: 10.1029/2006JB004763. [6]   A. Hooper, D. Bekaert, K. Spaans, and M. Arıkan, “Recent advances in SAR interferometry time series analysis for measuring crustal deformation,” Tectonophysics, vol. 514–517, pp. 1–13, Jan. 2012, doi: 10.1016/j.tecto.2011.10.013. [7]   M. Costantini, S. Falco, F. Malvarosa, F. Minati, F. Trillo, and F. Vecchioli, “Persistent Scatterer Pair Interferometry: Approach and Application to COSMO-SkyMed SAR Data,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 7, pp. 2869–2879, Jul. 2014, doi: 10.1109/JSTARS.2014.2343915.

Sinkhole hazards remain a major concern in former mining areas due to the long-term instability of abandoned underground workings. In many European regions, these former mining areas are now densely urbanized, increasing the need for reliable and spatially extensive ground stability monitoring (e.g. Strozik et al., 2015; Delnauy et al., 2025). For instance, in France, monitoring mainly relies on localized in situ instrumentation deployed in identified high-risk areas, resulting in limited spatial coverage. In this context, Interferometric Synthetic Aperture Radar (InSAR) has recently emerged as a complementary approach, providing spatially continuous measurements of millimetric ground deformation over large, urbanized mining areas (e.g. Raucoules et al., 2007; Radutu and Vlad Sandru, 2023). In this study, in the frame of the EU funded SIRIMA project (Sinkhole hazard and risk management in post-mining areas), we performed advanced InSAR time-series analyses to monitor two French post-mining regions: Thil in the Lorraine basin and Saint-Étienne in the Massif Central. In Saint-Étienne, a sinkhole occurred in 2021 under an industrial building, whereas Thil former shallow galleries are under in-situ monitoring for sinkhole risk, following the recent underground collapses of 2018-2020. To monitor those areas with InSAR, the entire Sentinel-1 archive (2015–2025) was processed using IPTA (Interferometric Point Target Analysis), i.e., the Persistent Scatterer InSAR (PS-InSAR) techniques implemented in the GAMMA processing toolbox (Wegmüller & Werner, 1998). Velocity maps obtained over both mining sites reveal several localized deformation zones, reaching up to 8 mm/yr, primarily associated with former mining activities. In Thil, the InSAR time series over some high-risk areas that were only recently placed under supervision show the onset of ground deformation, providing complementary information to the field instrumentation. At the 2020 Saint-Étienne sinkhole site, time-series analysis over the damaged building highlights an acceleration of subsidence during the three months preceding the collapse, with a total of ~9 mm of cumulative movement. However, the medium spatial resolution of Sentinel-1 limits the direct characterization of small features, such as the ~10 m diameter sinkhole, emphasizing both the strengths and limitations of medium-resolution SAR data, such as Sentinel-1, for operational monitoring of sinkhole hazards in urban post-mining environments. Acknowledgment This work is conducted in the frame of the SIRIMA project (Sinkhole hazard and risk management in post-mining areas), funded by the EU RFCS 2013, Grant agreement 101157400. References Delaunay, T, Lefebvre, O, Vuidart, I & Bigarré, P 2025, 'Sinkhole post-mining risks: the French methodology', in S Knutsson, AB Fourie & M Tibbett (eds), Mine Closure 2025: Proceedings of the 18th International Conference on Mine Closure, Australian Centre for Geomechanics, Perth, https://doi.org/10.36487/ACG_repo/2515_47 Radutu, A. and Vlad Sandru, M.I. (2023). Review on the Use of Satellite-Based Radar Interferometry for Monitoring Mining Subsidence in Urban Areas and Demographic Indicators Assessment. Mining Revue / Revista Minelor. 29. 42-62. 10.2478/minrv-2023-0004. Raucoules D., Colesanti C. and Carnec C, (2007), Use of SAR interferometry for detecting and assessing ground subsidence, Compte Rendus Geosciences, vol 339, n°5, p 289 Strozik G, Jendruś R, Manowska A, Popczyk M. Mine Subsidence as a Post-Mining Effect in the Upper Silesia Coal Basin. Polish Journal of Environmental Studies. 2016;25(2):777–785. doi:10.15244/pjoes/61117. Wegmüller, U., & Werner, C. L. (1998). SAR processing, interferometry, differential interferometry and geocoding software. InEuropean conference on Synthetic Aperture Radar, EUSAR98, Friedrichshafen, Germany, 25-27 May 1998.

As an island setting, Taiwan is highly vulnerable to coastal environmental change. The sandy coasts of western Taiwan, particularly from Taichung to Pingtung, have experienced persistent land subsidence and coastal inundation over the past five decades. Accelerated subsidence has significantly amplified flood hazards, posing complex challenges to coastal resilience. Although terrestrial geodetic measurements and InSAR analyses have been applied to selected local areas, comprehensive large-scale assessments integrating land subsidence with sea-level rise scenarios remain limited. In this study, we quantify decadal-scale coastal subsidence using multitemporal InSAR observations and evaluate associated flood risks under combined effects of land subsidence, sea-level rise, and extreme weather events. SAR datasets acquired from ERS-1/2, Envisat, and Sentinel-1 spanning three distinct time periods were processed using ISCE and MintPy to generate line-of-sight deformation time series. Constrained by horizontal GNSS velocities, we derived two vertical land motion models assuming uniform and nonlinear subsidence rates across western Taiwan. These models were further used to project coastal elevation changes over the coming decade. By integrating projected land subsidence with sea-level rise scenarios under global warming and extreme weather conditions, our results indicate that approximately 4–5% of Taiwan’s land area could be exposed to coastal inundation. Furthermore, hydrological simulations incorporating climate change–driven extreme precipitation were conducted for the Choushui River basin, the largest river system in Taiwan. The simulations suggest that the combined effects of subsidence, rising sea levels, and intensified rainfall could lead to widespread flooding and substantially elevate risks along major river corridors. These findings provide critical quantitative constraints for future coastal resilience planning and digital twin–based hazard assessment frameworks.

Multi-temporal (MT) Differential SAR Interferometry (DInSAR) techniques are widely used in Earth Observation applications, due to their capability to detect and monitor ground displacements associated with natural and anthropogenic hazard scenarios with sub-centimetric accuracy [1-5]. Among several MT-DInSAR techniques, the Parallel Small BAseline Subset (P-SBAS) approach [6-7] represents a consolidated and computationally optimized implementation of the original SBAS algorithm [8], able to retrieve spatially and, whenever possible, temporally dense deformation time series and the corresponding mean deformation velocity maps of an area of interest. In particular, the P-SBAS processing chain leverages both High-Performance Computing (HPC) architectures and multi-node/multi-thread parallel computing strategies to efficiently and automatically manage massive multi-look SAR interferometric datasets, thus enabling the extraction of medium-resolution LOS-projected displacement measurements, for regional, national- and continental-scale surface deformation analyses. Recent advancements have led to the Full-Resolution P-SBAS (FR P-SBAS) approach [9], which further enhances the detection and mapping of a wide range of displacements by enabling the estimation of displacement time series at the native spatial resolution of the exploited SAR data. This evolution is highly supported by advanced HPC environments and Graphics Processing Unit (GPU)-based parallelization strategies, which allow us to process large DInSAR datasets in significantly short time frames. The FR P-SBAS approach allows the monitoring of a wide range of deformation phenomena: indeed, it is particularly suited for investigating localized displacements associated with extended built-up environments, as those affecting critical infrastructures and individual buildings, while maintaining the capability to effectively perform advanced DInSAR analyses across multiple spatial resolution scales (for both regional and local scale investigations). With this respect, the exploitation of SAR images characterized by high spatial resolution represents a valuable solution to maximize the MT-DInSAR mapping capabilities and retrieve accurate deformation signals, which are essential for assessing building and infrastructure vulnerabilities and supporting risk mitigation strategies. In this context, the SAR sensors onboard the Italian COSMO-SkyMed constellation of first (CSK) and second (CSG) generation of the Italian Space Agency (ASI), provide a unique source of X-band (~3 cm wavelength) SAR data, characterized by high spatial resolution (less than 3 m in Stripmap acquisition mode), relatively short revisit intervals (on the order of a few weeks), and long-term temporal continuity since 2009. Owing to these characteristics, the integrated CSK/CSG datasets are particularly well suited for FR P-SBAS-based deformation monitoring applications related to bridges, dams, transport infrastructures, pipelines, and urban buildings, where it may be crucial to identify localized deformation signals, differential displacements at the scale of single buildings or specific structural elements of extended built-up environments, thus requiring high spatial resolution and consistent temporal coverage for reliable detection. In this study, we present a nationwide MT-DInSAR assessment analysis based on the FR P-SBAS approach, exploiting more than a decade of Stripmap SLC data collected since 2011 by the SAR sensors of the CSK and CSG constellation, as part of the MapItaly program [10]. This huge SAR dataset is suitable for investigating spatial and temporal variations of very localized displacements associated with anthropogenic hazard scenarios, as well as for assessing the structural conditions of critical infrastructure assets across the Italian built-up environment. The presented results, achieved by performing a FR P-SBAS analysis over selected Italian cities, including Roma, Napoli, and Bologna, highlight the potential of the combined FR P-SBAS and CSK/CSG framework to support infrastructure resilience and risk mitigation strategies through DInSAR-based mapping. A key aspect of the FR P-SBAS approach is the capability to perform multi-scale DInSAR analyses [11] by means of the decomposition of displacement time series into spatially low-pass and high-pass components. This feature simplifies the identification of localized differential displacements through the discrimination between soil-driven regional deformation (low-pass component) and localized structural responses (high-pass component), which is crucial in areas characterized by dominant regional-scale deformation phenomena that can mask localized displacements affecting single buildings and/or infrastructures. To enhance robustness and spatial completeness of our displacement retrieval assessment, we also present the FR P-SBAS results obtained by processing L-band SAR data (~10 × 5 m spatial resolution in the Stripmap mode) acquired through the twin satellites of the Argentinian SAOCOM-1 constellation, operated by CONAE. Owing to the longer wavelength of L-band data, these acquisitions are less affected by temporal decorrelation and phase unwrapping errors. These intrinsic properties enhance their mapping capability and increase the density of coherent points, providing a valuable complement to the X-band analysis [12]. References [1]     Ferretti, A., C. Prati, and F. Rocca, “Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry,” IEEE Trans. Geosci. Remote Sens., vol. 38, no. 5 I, 2000, doi: 10.1109/36.868878. [2]     Werner, C., U. Wegmüller, T. Strozzi, and A. Wiesmann, “Interferometric Point Target Analysis for Deformation Mapping,” in International Geoscience and Remote Sensing Symposium (IGARSS), 2003. doi: 10.1109/igarss.2003.1295516. [3]     Mora, O., J. J. Mallorquí, and A. Broquetas, “Linear and nonlinear terrain deformation maps from a reduced set of interferometric SAR images”, IEEE Trans. Geosci. Remote Sens., vol. 41, no. 10, pp. 2243–2253, 2003, doi: 10.1109/TGRS.2003.814657 [4]     Lanari, R., Mora, O., Manunta, M., Mallorquí, J.J., Berardino, P., and Sansosti, E., ”A small baseline approach for investigating deformations on full resolution differential SAR interferograms”. IEEE Trans. Geosci. Remote Sens., 42, 1377-1386, 2004. [5]     Hooper A. J., “A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches,” Geophys Res Lett, vol. 35, no. 16, 2008, doi: 10.1029/2008GL034654. [6]     F. Casu et al., “SBAS-DInSAR parallel processing for deformation time-series computation,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 7, no. 8, 2014. [7]     M. Manunta et al., “The Parallel SBAS Approach for Sentinel-1 Interferometric Wide Swath Deformation Time-Series Generation: Algorithm Description and Products Quality Assessment,” IEEE Trans. Geosci. Remote Sens., vol. 57, no. 9, pp. 6259–6281, 2019. [8]     Berardino, P., Fornaro, G., Lanari, R., and Sansosti, E., “A new Algorithm for Surface Deformation Monitoring based on Small Baseline Differential SAR Interferograms”. IEEE Trans. Geosci. Remote Sens, 40, pp.2375-2383, 2002. [9]     M. Bonano et al., "New Advances of the P-SBAS Approach for an Efficient Parallel Processing of Large Volumes of Full-Resolution Multitemporal DInSAR Interferograms," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 18, pp. 2317-2341, 2025, doi: 10.1109/JSTARS.2024.3507542. [10] ASI, Italian Space Agency Upgrades Access To MAPITALY Data, Accessed: Dec. 1, 2023. [Online]. Available: https://www.asi.it/en/2023/12/asi-italian-space-agency-upgrades-access-to-mapitaly-data [11] Manunta M., M. Marsella, G. Zeni, M. Sciotti, S. Atzori, and R. Lanari, “Two‐scale surface deformation analysis using the SBAS‐DInSAR technique: a case study of the city of Rome, Italy,” Int. J. Remote Sens., vol. 29, no. 6, pp. 1665–1684, Mar. 2008. [12] De Luca, C, et al. "SAOCOM-1 L-band DInSAR Time Series generation through the P-SBAS approach: algorithm extension and products analysis." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 18 (2025): 2680-2703.

Satellite-based Synthetic Aperture Radar Interferometry (InSAR) deformation monitoring has reached a high level of scientific maturity; however, its systematic integration into disaster risk reduction (DRR) and emergency management frameworks remains limited. This contribution demonstrates how operational InSAR products can be embedded within national and local disaster management processes to support evidence-based decision making. Long-term SAR observations acquired from Sentinel-1 and complementary high-resolution missions were processed to generate deformation maps and time-series products for earthquake, landslide and subsidence hazards across selected regions of Türkiye, including the Konya Closed Basin, East Marmara (İzmit Bay) region and western Anatolian landslide-prone zones. In these areas, vertical deformation rates ranging between –5 mm/yr and –35 mm/yr were detected, with localized subsidence exceeding 40 mm cumulative displacement over multi-year observation periods (2014–2023). Co-seismic and post-seismic deformation patterns associated with recent moderate-to-strong earthquakes were resolved with sub-centimetric accuracy. The derived deformation products were directly integrated into Local Disaster Risk Reduction Plans (İRAP) and the Turkey Disaster Response Plan (TAMP) workflows, enabling spatial prioritization of high-risk zones, identification of vulnerable infrastructure corridors and support for preparedness planning. Institutional challenges such as data latency, product interpretability and inter-agency coordination are discussed, and practical solutions including standardized deformation indicators, GIS-based dissemination tools and targeted capacity-building mechanisms are proposed. The results demonstrate that InSAR-based deformation monitoring significantly enhances situational awareness, supports preventive planning and strengthens preparedness strategies when aligned with existing disaster governance structures. This contribution highlights the critical role of Earth Observation in bridging the science–policy gap and strengthening disaster resilience at regional and national scales.

Regions of underground mining activities impose significant challenges for satellite-based terrain monitoring. Source modelling of mining-induced is particularly complex due to the co-appearance of multiple triggering mechanisms acting simultaneously. On one hand, shallow mining activities generate small-range displacement patches forming subsidence bowls with highly dynamic spatial and temporal evolution. Their appearance is strongly related with the depth and thickness of the exploited seams, the extraction sequence, and the applied mining technology. In addition, regulated underground explosions with higher power (often triggering tremors of up to M4) can influence the displacement pattern. Accurate delineation of the deformation source geometry has to properly consider the atmospheric phase screen affecting to the displacement signal. Moreover, temporal decorrelation typical of rural areas can influence the quality of the unwrapping process. Besides that, soil moisture variability should be properly assessed for avoiding a bias in the interpretation of the surface displacement. In this study, an eight-year InSAR monitoring analysis over an intensively exploited coal mining area in Southern Poland, covering the Upper Silesian Coal Basin (USCB) is presented. The Enhanced Small BAseline Subset (E-SBAS) method, implemented in SARscape software, is applied to multi-temporal dataset of Sentinel-1 imagery. The observed surface displacements rates range between 0.5 and 1.5 m/year and exhibit non-linear spatial and temporal behaviour. The E-SBAS approach integrates Permanent Scatterers (PS) and Distributed Scatterers (DS) pixels, increasing spatial density and improving robustness in areas affected by decorrelation. The displacement time series are classified into regions of behaviour based on temporal evolution patterns, supporting the delineation of deformation zones and associated risk levels. Intensity time-series analysis is incorporated to assess correlations with soil moisture variations and crop condition changes. This multi-parameter evaluation supports discrimination between deformation-driven and moisture-driven signal components. Multi-temporal forward modelling is applied to delineate the multi-source deformation events. Monthly stacked displacement fields are modelled using the Okada elastic half-space. Model parameter optimisation is conducted through iterative fitting between modelled and observed displacement fields, allowing the assessment of model residuals and uncertainty ranges. The proposed integrated strategy aims to complement conventional modelling and prediction procedures in mining operations, which are typically based on in situ levelling measurements, typically acquired at monthly to six-month intervals. By providing higher temporal resolution and spatial coverage, the methodology improves the understanding of complex mining-induced deformation processes and supports risk management in intensively exploited coal mining regions.

The closure phase, constructed by a circular summation of three interferometric phases, each obtained from multilooking a SAR interferogram, consists of a geophysical component and phase noise, often exhibits non-zero values. These non-conservative closure phases challenge the validity of the implicit phase consistency assumption in SAR interferometry. This assumption relies on a geometric interpretation of the interferometric phase, where the expected values of the three interferometric phases are redundant, given that their sum, the closure phase, is equal to zero. While non-zero closure phases have been studied at local scales, a comprehensive and systematic statistical characterization of closure phases across broader spatial and temporal scales remains lacking. This study aims to fill this gap by systematically analyzing closure phases and exploring their statistical properties, with emphasis on assessing the extent to which the observed closure phases can be explained by current geophysical understanding. Beyond phase noise, the existing literature identifies three contributors to geophysical closure phases: (i) changes in the dielectric properties of the medium, including soil moisture variations, wet biomass accumulation, and snow metamorphism; (ii) volume scattering in combination with perpendicular baselines; and (iii) differential skewed motions [1, 2, 3]. Analytical models have been developed for two mechanisms: one involves the variation in propagation properties of dielectric media due to soil moisture changes, and the other relates to volume scattering in the presence of perpendicular baselines [1, 4]. Large systematic closure phases of geophysical origin often appear in low-coherence scenarios with high phase noise levels. Estimating closure phases under these conditions requires maintaining a certain level of coherence or applying extensive spatial averaging (multilooking) to reduce noise. To minimize coherence loss due to temporal decorrelation, we use continuous 6-day revisit Sentinel-1 acquisitions to construct closure phases. For multilooking, we employ an adaptive strategy incorporating a statistical test on amplitudes to select suitable samples for averaging, ensuring they are realizations of the same random distribution. In addition, since we are interested in covering a wide area, we studied closure phases with a kilometer resolution across the extensive Iberian Peninsula, which encompasses various land cover types [5] and spans multiple climate zones [6]. Using the multilooked products, we analyze the statistics of closure phases for sub-regions categorized by various land cover and climate conditions. We start with estimating the standard deviation of the closure phases using a null hypothesis that they arise from phase noise, and then assess the statistical significance of the geophysical closure phases. Next, we evaluate the mean values and percentiles of closure phases and discuss their spatiotemporal patterns. Subsequently, we apply existing interferometric models for soil moisture variation and volume scattering mechanisms [1, 4, 7] to quantify the contribution of each component to the closure phases. In this study, the coefficient of determination ($R^{2}$) is used to estimate the proportion of explainable variance attributable to the known mechanisms. For cases with high unexplained variance, we visualize and discuss time-series examples to investigate unexplained signals that are not yet captured by current models. Our results confirm the widespread presence of geophysical closure phases, characterize closure phase signatures in areas with varying geophysical conditions, highlight distinct attributes that reveal the mechanisms underlying the geophysical components, suggest new opportunities for using closure phases to detect Earth surface variations, and emphasize the importance of understanding the origin of non-zero closure phases in accurate deformation estimation. References[1] Francesco De Zan, Mariantonietta Zonno, and Paco Lopez-Dekker. Phase inconsistencies and multiple scattering in sar interferometry. IEEE Transactions on Geoscience and Remote Sensing, 53(12):6608–6616, 2015.[2] Simon Zwieback, Xingyu Liu, Sofia Antonova, Birgit Heim, Annett Bartsch, Julia Boike, and Irena Hajnsek. A statistical test of phase closure to detect influences on DInSAR deformation estimates besides displacements and decorrelation noise: Two case studies in high-latitude regions. IEEE Transactions on Geoscience and Remote Sensing, 54(9):5588–5601, 2016.[3] Francesco De Zan, Paloma Saporta, and Giorgio Gomba. Spatiotemporal analysis of C-band interferometric phase anomalies over Sicily. In EUSAR 2022; 14th European Conference on Synthetic Aperture Radar, pages 1–4. VDE, 2022.[4] Francesco De Zan, Alessandro Parizzi, Pau Prats-Iraola, and Paco López-Dekker. A sar interferometric model for soil moisture. IEEE Transactions on Geoscience and Remote Sensing, 52(1):418–425, 2013.[5] Marcel Buchhorn, Bruno Smets, Luc Bertels, Bert De Roo, Myroslava Lesiv, Nandin-Erdene Tsendbazar, Linlin Li, and AJ Tarko. Copernicus global land service: Land cover 100m: Version 3 globe 2015-2019: Product user manual. 2020. [6] Murray C Peel, Brian L Finlayson, and Thomas A McMahon. Updated world map of the köppen-geiger climate classification. Hydrology and earth system sciences, 11(5):1633–1644, 2007. [7] Francesco De Zan and Giorgio Gomba. Vegetation and soil moisture inversion from sar closure phases: First experiments and results. Remote sensing of environment, 217:562–572, 2018.

The progressive expansion of spaceborne Synthetic Aperture Radar (SAR) missions, and in particular the operational continuity of the Sentinel-1 constellation within the Copernicus Programme, has enabled the generation of national-scale Differential SAR Interferometry (DInSAR) products. These datasets, derived through advanced multi-temporal DInSAR algorithms such as the Parallel-Small BAseline Subset (P-SBAS) approach, consist of millions of Measurement Points (MPs) characterized by displacement time series spanning several years and the retrieved mean annual velocity. While such massive datasets represent an unprecedented opportunity for large-scale ground deformation monitoring, they also present a critical methodological challenge: the necessity for scalable, automated, and statistically robust tools capable of exploiting both the temporal and spatial domains of DInSAR information. In this work, an integrated analysis framework specifically designed for national-scale applications, the TS-DInSAR tool, is presented. The tool is developed and tested over the entire Italian territory using Sentinel-1 SAR data acquired between June 2016 and November 2023 and processed via the P-SBAS algorithm implemented by the Institute for Electromagnetic Sensing of the Environment (IREA). The processing chain generates more than 64 million MPs, geocoded onto a standard 1 arc-second SRTM grid, providing both vertical and horizontal displacement components derived from ascending and descending Line-of-Sight (LOS) measurements. The TS-DInSAR workflow is structured into four main analytical steps. The initial step pertains to the reduction of data dimensionality. Given the original dataset size of approximately 250 GB, the MPs were filtered by applying a velocity threshold of ±1 cm/year (representing 3σ) in at least one displacement component. This threshold reduces the dataset to approximately 600,000 MPs characterized by significant kinematic behaviour, while preserving the full paired time series (horizontal and vertical) for each selected MP. The subsequent step involves the unsupervised temporal characterisation of deformation patterns. Principal Component Analysis (PCA) is applied independently to both horizontal and vertical time series datasets after standardisation, enabling dimensionality reduction while maximizing retained variance. The extracted Principal Components (PCs) represent dominant temporal behaviours within the dataset. Subsequently, a K-means clustering algorithm is implemented with the number of clusters automatically determined according to the optimal number of retained PCs. This procedure yields a categorical label for each MP in both components (EWcluster and UPcluster), summarizing the temporal pattern of the DInSAR time series. The third step involves the integration of geomorphological information through the incorporation of a high-resolution Digital Elevation Model (DEM), such as the terrain slope. Additionally, a kinematic ratio parameter (KVH), defined as the absolute ratio between vertical (VV) and horizontal (VH) mean velocities, is introduced to quantify the relative predominance of displacement components. The fourth step of the process is the implementation of a supervised classification, that integrates the parameters defined in the preceding steps into a decision-tree framework. The model assigns each MP to one of seven deformation-triggering categories: (i) landslide, (ii) potential landslide, (iii) subsidence, (iv) uplift, (v) uplift related to volcanic/tectonic activity, (vi) soil erosion, and (vii) undefined. It is noteworthy that the classification procedure has been designed to operate without the use of pre-existing thematic inventories (e.g. national landslide inventories), thereby ensuring methodological reproducibility across different geographic contexts. The application of TS-DInSAR at the national scale revealed that slope-related instabilities (i.e. landslide and potential landslide combined) represent the most frequent deformation class (~53% of selected MPs), followed by subsidence (~18%), soil erosion (~15%), and volcanic/tectonic uplift (~11%). A number of case studies have been selected for illustrative purposes, including the Berceto deep-seated landslide, subsidence at the airport of Fiumicino (Rome), gully erosion in the Atri Natural Reserve, and bradyseismic uplift in the Campi Flegrei caldera. These case studies demonstrate the capability of the TS-DInSAR tool to capture distinct kinematic signatures and correctly associate them with geomorphological and geodynamic processes. In conclusion, the TS-DInSAR tool constitutes an automated, scalable, and statistically robust framework that integrates both temporal and spatial domains of DInSAR analysis within a unified methodological procedure. The pivotal enhancement introduced by the tool lies in its capability to systematically manage and interpret massive DInSAR datasets at the national scale. A key methodological strength of the proposed approach is its independence from pre-existing thematic inventories, such as landslide inventories. This inventory-independent architecture enables the implementation of the tool in regions where ancillary datasets are incomplete, outdated, or unavailable, while preserving an objective, data classification of deformation-triggering phenomena.

We present a masking methodology based on interferogram phase that exploits non-zero phase closure residuals as a proxy for decorrelation noise. InSAR time series products are limited by decorrelation-driven phase noise, which propagates through filtering and phase unwrapping and is typically mitigated by masking “low quality” pixels. The most common quality metric, spatial coherence, is estimated over a window and may therefore mix spatially heterogeneous scattering behaviours: isolated high-quality pixels can be rejected if surrounded by decorrelated neighbours, while low-quality pixels can be retained inside generally coherent areas. This motivates a need for alternative quality indicators that operate closer to pixel scale. For a closed loop of three acquisitions A, B, C, with interferograms AB, BC and CA, the wrapped phase closure should cancel to zero. In practice, non-linear interferogram operations such as multilooking and spatial filtering, which are applied independently to each interferogram, break strict phase consistency and generate a non-zero closure residual. These residuals are commonly attributed to volume scattering effects often associated with spatial and temporal variations in vegetation and soil moisture. We compute the per-pixel wrapped closure residual, and use its absolute value as an indicator of pixel quality. Intuitively, pixels dominated by a single strong scatterer (PS-like behaviour) or by a homogeneous distributed field (DS-like behaviour) tend to yield small residuals after complex averaging. Heterogeneous or rapidly changing scattering within the multilook window or filter produces larger residuals. Individual closure residual maps are noisy due to stochastic variations, but in general agree well with the level of decorrelation noise within the constituent interferograms. We construct an interferogram network and extract linearly independent closures to calculate an average closure residual map calculated over a temporal window. This yields a stable, high-resolution pixel quality layer derived purely from phase information, without requiring amplitude statistics, deformation smoothness assumptions, or a particular time-series model. We demonstrate the method on a large Sentinel‑1 time series across an active mine, where spatial and temporal variations in decorrelation behaviour pose a challenge for conventional coherence-based masking. A phase closure residual can be combined with per-interferogram coherence constraints to handle short-term temporal variability, and long-timespan pairs where decorrelation is systematically higher. The resulting average residual map preserves fine spatial texture while clearly separating stable from decorrelated areas. Compared to coherence-only masking, closure-based selection increases the number of unwrapped pixels at similar uncertainty levels (assessed via the standard deviation of linear fit to displacement time series). Beyond masking, we discuss how the spatial distribution and temporal evolution of closure residuals can further inform filtering operations and change detection, offering a lightweight diagnostic tool for adaptive processing in large-scale InSAR pipelines.

Landslides can occur on any hillslope, making them a far-reaching and impactful hazard. Various phenomena, such as heavy precipitation, debuttressing, and earthquake loading, can lead to hillslope acceleration and possible failure. In the last few decades, interferometric synthetic aperture radar (InSAR) satellite data have been used to monitor slow-moving landslides and conduct research into the physical processes that govern the triggering conditions of slope failure. The ubiquitous nature and widespread potential hazard of landslides motivate monitoring hillslope movement on a regional or national scale, and the near-global availability of SAR imagery makes it an ideal monitoring tool. There are several challenges to address with InSAR-based landslide monitoring, however. Snow cover, rapid ground displacement, vegetation, soil moisture, and steep topography can all impact the interpretability of an interferogram. Fast-moving landslides can become incoherent. Interferograms with short temporal baselines maintain higher spatial coherence, but time series derived from such a network can have phase bias, leading to inaccurate displacement measurements. Some of these issues can be addressed using persistent scatterer techniques, but persistent scatterers are often rare on natural hillslopes. Here we investigate optimal InSAR network design to capture displacement measurements across an entire landslide for diverse environments and compare processed InSAR data to in-situ displacement observations. We analyze C- and L-band InSAR from Sentinel-1, ALOS-2 and UAVSAR, and in-situ data from GNSS (Global Navigation Satellite Systems) stations, extensometers, and ground-based SAR in a variety of landslide environments in the USA, including the Hooskanaden earthflow in southwestern Oregon, the Slumgullion landslide in Colorado, and the Barry Arm landslide in the Prince William Sound region of southcentral Alaska. These landslides were chosen for the various problems they pose for InSAR processing, including vegetation cover, rapid landslide movement, and significant snow cover, in addition to the availability of in-situ data. We process multi-year networks of interferograms using the InSAR Scientific Computing Environment (ISCE) and vary their spatial resolution (100 m, 30 m, and 20 m) and network connectivity (connect-1, connect-3, connect-5, all connections, where more connections increase redundancy). We use MintPy to process the time series and velocities and compare them to available in-situ data. We incorporate data-driven network modification using multiple thresholds of spatial coherence to evaluate if there is an improvement in displacement measurement accuracy. Increasing network connectivity without data-driven network modification resulted in poor results in all cases. When landslide movement remained coherent in interferograms with longer temporal baselines, higher network connectivity outperformed the connect-1 scenarios. In some cases, the lower spatial resolution (100 m) results more accurately captured landslide movement compared to higher resolution (20 m), likely due to better noise suppression in highly vegetated areas. Smaller landslides, however, could be missed at 100 m spatial resolution. Finally, we compare the new NASA product DISP-S1 from the OPERA team to the in-situ data and evaluate how successful it is. DISP-S1 combines persistent scatterer and distributed scatterer techniques to map land surface displacement using Sentinel-1 data over large regions of North America, including our study locations. Preliminary results indicate that the DISP-S1 product performs well in regions with high spatial coherence, but more work is needed to understand its accuracy in noisier regions. Our results in these differing environments will inform future work towards landslide monitoring on a regional scale.

Glacial mélange, which is commonly observed in the pro-glacial region of marine-terminating glaciers, is a complex granular material characterised as a mixture of sea ice, distintegrated calved ice, and large tabular icebergs. This material has been observed to interact with the terminus region of glaciers by exerting a backstress or “buttressing” varying in strength with the mélange thickness, which affects calving behaviour and flow velocities at the ice front. Intensity offset tracking produces an estimate of the magnitude of ice speed through a normalised cross correlation peak-fitting of Synthetic Aperture Radar (SAR) Single Look Complex (SLC) image pairs acquired on 6- and 12- day repeat passes of Sentinel-1. The application of offset tracking to glacial mélange is informative because it facilitates the interpretation of transient ice dynamics in the fjord over short (sub-weekly to weekly) timescales, and at critical transitional stages such as mélange formation and break-up. The Northeast Greenland Ice Stream presents a compelling case study for the investigation of the impact of mélange on ice frontal dynamics, as coupling between terminus and mélange speeds can be evaluated at high spatial and temporal resolution over two neighbouring fjords. Time series at Zachariae Isstrøm and Nioghalvfjerdsfjorden (79N) Glacier over the Sentinel-1 observational record show the temporal evolution of ice speed across both glaciers and their associated mélange, and speed trend maps indicate areas of localised acceleration and deceleration; these datasets are used here to evaluate the interconnectedness of extreme mélange conditions and glacier dynamics in this region within a decadal timeframe (2015-2025). Through a classification of ice velocity data obtained with the intensity offset tracking method, a proxy record for seasonal mélange cover is additionally generated within both fjords. The existing velocity processing chain for land ice is adapted to improve the accuracy of the tracking retrieval over mélange, and these results are compared with the terminus region of Zachariae Isstrøm for a period of a) exceptionally slow speeds at the grounding line during summer 2018 and b) a large observed deceleration at the grounding line at the end of the annual melt season in 2023. Paired with a novel sea ice thickness algorithm optimised for mélange, this methodology examines observational evidence for mélange-glacier connectivity both through time and concurrent with sub-seasonal ice speed extremes.

Azimuth phase perturbations caused by high-latitude ionospheric variability, particularly scintillations, introduce along-track image shifts in spaceborne SAR data. These shifts directly propagate into ice velocity products, generated measuring mutual shifts between images, producing errors of several meters and significantly degrading measurements accuracy. This effect is especially critical for precise glacier flow monitoring in Greenland. C-band data, such as those acquired by Sentinel-1, are moderately affected, and yearly averaging of all available shift estimates reduces the overall effect in the annual velocity measurements. However, short-term velocity changes remain difficult to monitor due to the scintillation-induced variability. Moreover, radars operating at lower frequencies, such as L- or P-band, are more sensitive to ionospheric disturbances, with the effect scaling by approximately one to two orders of magnitude, making precise displacement measurements not possible. High-latitude ionospheric variability exhibits a spatial correlation structure that can be described using anisotropic Matérn covariance functions or equivalently by power-law spectral density models. Climatological models, such as WBMOD, provide parameters for these correlation descriptions, including the integrated turbulence strength CkL. These parameters can be used to statistically evaluate expected azimuth shift errors or to generate realistic simulations of ionospheric phase screens. In addition to modeling approaches, SAR observations themselves provide an empirical source of information on ionospheric variability. We present a multi-year statistical analysis (2019–2025) of azimuth shift variability derived from hundreds of ice velocity products over Greenland generated from Sentinel-1 data. For each scene, azimuth-direction profiles were extracted and the yearly averaged glacier flow signal was removed. The residual along-track displacements were interpreted as ionospheric azimuth shift errors. The standard deviation of these residuals provides a measure of the scintillation variability impacting velocity retrieval. The resulting time series reveals a clear solar cycle dependence as well as a pronounced seasonal modulation, with peak scintillation-induced errors during winter months under enhanced auroral activity. These observations provide quantitative constraints on expected azimuth accuracy degradation at C-band and allow extrapolation toward L-band systems, including ROSE-L and NISAR. In addition to benefiting from the presented statistics, upcoming L-band missions may themselves serve as valuable data sources for extending such analyses. The derived empirical error distributions support performance prediction for SAR-based ice velocity retrieval, validation of ionospheric phase screen simulations driven by climatological parameters (e.g., CkL), and assessment of mitigation algorithm robustness under realistic high-latitude disturbance conditions. This work provides an observation-based benchmark for quantifying ionospheric impacts on azimuth geolocation and ice velocity accuracy in present and future SAR missions.

Interferometric synthetic aperture radar (InSAR)–derived ice velocities are commonly interpretedas measurements of surface glacier flow. However, radar wavelength fundamentally controls thedepth over which ice motion is sampled through penetration into snow and firn. In this study,I investigate wavelength-dependent velocity sensing at Pine Island Glacier, West Antarctica,by comparing Sentinel-1 C-band ice velocity products with multi-sensor MEaSUREs InSAR velocitiesthat incorporate longer-wavelength observations, including L-band SAR. After spatialharmonization and physically motivated masking, we analyze velocity differences and longitudinalprofiles along the glacier trunk. Systematic velocity offsets are observed near the groundingzone and along shear margins, where C-band velocities exceed longer-wavelength estimates byseveral hundred meters per year. These differences persist across seasons and between independentSentinel-1 monthly products, indicating a robust physical signal rather than seasonalor processing artefacts. I interpret the observed offsets as a consequence of depth-weighted velocity sampling, where longer radar wavelengths integrate motion over a greater fraction of thedeforming ice column. Extrapolating this framework suggests that future low-frequency SARmissions operating at P-band may provide enhanced sensitivity to internal glacier deformation.My results highlight the importance of penetration-aware interpretation of InSAR velocities forice-dynamical studies and future mission design.

Tenerife is the largest island of the Canary archipelago. Its complex volcanic landscape is the result of overlapping shield volcanoes, Las Cañadas caldera growth and collapse cycles, Teide-Pico Viejo evolved central stratovolcanoes and persistent rift activity. While eruptions are historically scarce, the island remains volcanically active, a fact proven by the historical records of the 1704-1705 Fasnia-Arafo-Siete Fuentes, 1706 Garachico, 1798 Chahorra and 1909 Chinyero eruptions and the seismic-volcanic crisis during 2004-2005. Following over ten years of relative stability, the volcanic monitoring system of the Instituto Geográfico Nacional (IGN, Spain) identified the onset of persistent seismic and geochemical unrest in 2016. This was followed by the detection of sustained, gradual ground deformation in 2023, a trend that remains ongoing. Synthetic aperture radar interferometry (InSAR) is considered one of the most relevant techniques for surface deformation monitoring. Implemented as part of the IGN volcanic monitoring operations, this method uses C-band Sentinel-1 and X-band PAZ radar data. The workflow follows a two-fold approach: producing displacement maps via traditional two-pass interferometry and generating long-term time series and velocities.   In this study, we present time series and velocity maps of Tenerife, derived from Sentinel-1 and PAZ sensors from 2017 to the present. These datasets were generated using Gamma software through a SBAS approach suited to Tenerife’s specific characteristics (oceanic island, prominent topography and a strong atmospheric component). The results from both datasets exhibit similar surface deformation patterns starting in 2023. An east-west trend is observed on both sides of Teide stratovolcano, located in the center of Tenerife island. This pattern is compatible with the radial extension detected by the permanent cGNSS station network of the IGN volcanic monitoring system, with velocities of a few milimetres per year. On the other hand, the vertical component exhibits a more challenging behavior, as no clear uplift is observed with either cGNSS or InSAR, except for a small area located on the northern flank of Teide edifice near the Icod valley, which also shows low velocities in the range of several milimetres per year. In this work, the obtained results will be modeled and analyzed, with the aim of identifying a source-model able to explain the origin of the deformations, ensuring compatibility with the rest of seismic and geochemical observables recorded by the IGN volcanic monitoring system.

Large-scale urban monitoring calls for fine-grained, high-density measurements to capture the spatially heterogeneous evolution of cities and to support infrastructure risk management. In this context, dense and reliable retrieval of both 3-D structural parameters (e.g., facade/roof heights) and surface deformation time series is essential for safety assessment and sustainable city management. Owing to the side-looking SAR imaging geometry, dense built-up areas are severely affected by layover, where multiple scatterers with identical slant-range positions are superimposed within a single resolution cell. SAR tomography-based PSInSAR (Tomo-PSInSAR) alleviates this limitation by extending time-series InSAR to the elevation dimension, enabling the separation of layovered scatterers and the joint estimation of their elevation and deformation parameters. Despite its potential, practical Tomo-PSInSAR performance for metropolitan-scale monitoring remains constrained by the finite tomographic elevation resolution imposed by the baseline aperture. In particular, two closely spaced scatterers may not be reliably separated, leading to missed detections and consequently limiting the achievable monitoring-point density in layover-dominated areas. Although super-resolution strategies have been introduced to mitigate this limitation, greedy compressive sensing solvers (e.g., OMP and CoSaMP) can still degrade when the tomographic dictionary exhibits strong atom correlation. In such cases, two nearby scatterers may be merged into a single reconstructed component or represented by atoms from the same neighborhood, weakening effective separation. To address this issue, we propose an enhanced two-tier Tomo-PSInSAR framework for fine-grained urban monitoring. The first tier constructs a PSC-based spatial network and refines its topology using an elevation Rayleigh-resolution constraint, thereby pruning unreliable arcs and improving network reliability. The second tier integrates our Adaptive Correlation Filtered Orthogonal Matching Pursuit (ACF-OMP) for arc-wise parameter estimation, which enhances the resolvability of closely spaced double scatterers. Specifically, ACF-OMP adaptively suppresses highly correlated atoms by evaluating the similarity between the inner products of selected atoms and the residual, thereby reducing duplicated false detections and improving the separability of closely spaced scatterers. This capability substantially increases the density of reliable monitoring points, which represents the primary improvement of our approach, while also enhancing the accuracy of height and deformation retrieval. Experiments on TerraSAR-X time series over Changsha demonstrate that, compared with a conventional Tomo-PSInSAR baseline using beamforming for arc-wise parameter estimation, the proposed method increases the number of effective monitoring points by more than 80% and reduces the deformation RMSE by approximately 22%, particularly in dense building blocks with severe layover. Keywords: urban monitoring, Tomo-PSInSAR, layover separation, deformation time series, height estimation

Over the past two decades, Differential SAR Interferometry (DInSAR) and Persistent Scatterer Interferometry (PSI) techniques have undergone major methodological and processing advances, supported by the continuous growth in spaceborne SAR data acquisition capabilities. The Sentinel-1 constellation, under the European Copernicus Programme, enables systematic monitoring of ground deformation at continental scale, combining short revisit times, high spatial resolution, and an open data policy. In parallel, the increasing availability of SAR missions with different acquisition geometries, revisit times, and spatial resolutions has created a multi-resolution Earth Observation landscape, requiring flexible methodologies capable of adapting to heterogeneous inputs. A major milestone in operational ground motion monitoring is the European Ground Motion Service (EGMS), which provides harmonized and standardized displacement information across Europe, covering both natural and anthropogenic processes. Updated annually and offering millimetric precision, EGMS products include mean annual velocities, displacement time series from 2015 onwards, ascending and descending line-of-sight measurements, and derived vertical and horizontal components. While EGMS is adopted in this work as the primary data source due to its continental coverage and consistency, the proposed methodology is sensor-agnostic and can be applied to InSAR products derived from different missions and processing chains, allowing adaptation to local resolution requirements and data availability. Despite the availability of wide-area displacement datasets, their direct operational exploitation remains challenging due to volume, density, and interpretation complexity. This work presents a methodology designed to systematically exploit displacement maps in an automated and scalable manner, with the objective of identifying buildings and urban structures potentially exposed to damage. The framework focuses on spatial gradients of displacement (differential deformation), a key indicator in built environments where structural damage is often associated with high deformation gradients rather than absolute displacement values. Two complementary analysis scales are implemented. The first approach is based on the automatic extraction of Active Deformation Areas (ADA), enabling area-based assessment using the full deformation information within each cluster. This provides systematic coverage over all deforming zones, delivering a low-to-medium level screening of structural susceptibility. The second approach operates at single-building scale, analysing only the displacement measurements directly associated with each asset. Although this method is applicable only where measurement density is sufficient, it provides more detailed, asset-specific outputs. Differential deformation values are used as intensity indicators to classify potential damage levels at both ADA and building scales. The methodology is currently being advanced within the RASTOOL-DoS project as a candidate service for integration into the Copernicus Emergency Management Service (CEMS) Risk and Recovery Mapping portfolio. Methodological details, application examples, and preliminary evaluation results will be presented.

Interferometric Synthetic Aperture Radar (InSAR) has become a central tool in geoscience for detecting ground deformation with high spatial detail across broad regions. Its value has been repeatedly demonstrated for monitoring landslides, subsidence, volcanic processes, and other surface changes. However, despite the method’s scientific maturity, many potential users still face substantial challenges in carrying out complete time-series analyses. The processing chain typically involves numerous software dependencies, command‑line operations, data‑handling steps, and parameter choices that can discourage newcomers and slow down non‑specialist workflows. As a result, there remains a demand for accessible, end‑to‑end environments that simplify routine tasks while still allowing scientists to make informed methodological decisions. To address this gap, we introduce a new open‑source graphical interface built in Python that guides users through the full workflow for generating Sentinel‑1 deformation time series. The platform wraps established processing components from GMTSAR and organizes them into a structured, step‑by‑step framework that reduces the need for command‑line scripting. Instead of replacing expert judgement with automated defaults, the environment emphasizes transparency and user interaction by exposing intermediate outputs and critical parameters at each processing stage. The interface covers the complete processing sequence, beginning with software preparation and project initialization. It supports automatic searching, downloading, and preparing of Sentinel‑1 scenes, as well as the retrieval of orbit files needed for accurate co‑registration. Baseline calculations, interferogram network design, and subsequent interferogram generation are handled within guided modules that summarize relevant information in an intuitive layout. Users can inspect spatial baselines, refine the connectivity of the interferometric network, and evaluate alternative choices before proceeding. Further utilities guide users through alignment and interferograms generation for time‑series analysis. A flexible and optional masking module allows users to define exclusion zones based on correlation thresholds, manually drawn polygons, or both, helping to remove unreliable areas for unwrapping, thereby enhancing the robustness, quality and efficiency of the process. Optional atmospheric corrections are supported through direct interaction with the Generic Atmospheric Correction Online Service, allowing users to incorporate external models before the inversion process. The environment also includes interactive visualization capabilities, enabling users to explore deformation patterns, assess temporal behaviour, and export results for further analysis. These tools are intended to help users interpret processing choices and understand the consequences of different parameter configurations throughout the workflow.

Interferometric Synthetic Aperture Radar (InSAR) reanalysis studies often mis-represent the true capabilities of InSAR for Near Real-Time (NRT) monitoring. While post-event retrospective analysis, benefiting from complete datasets and hindsight, reliably identifies precursory displacement, this approach fails to address the critical question for operational utility: when would a real-time decision-maker identify a threat and be able to take action? Retrospective analysis usually presents InSAR timeseries processed for all the available data up to the point of failure. However, the sources of error are rarely consistent in time and the lack of bounding data results in greater uncertainties at the start and end of the timeseries. Thus the data available to decision makers at any point is not simply the truncated version of the full timeseries that would tend to over-represent the power of InSAR to resolve early signs of e.g. acceleration. This paper investigates this divergence through comparative analyses of a prominent failure event where definite precursory displacement was observed. We generate the successive results that would be available to the end-user at each stage and compare them to the eventual retrospective-analysis product. At each stage we assess what other zones within the dataset exceed similar thresholds. Our case study demonstrates that while significant precursory displacement exists, its utility for raising an alarm in NRT is dependent on the inherent spatial and temporal variability of InSAR sensitivity (e.g., residual atmospheric phase, coherence drop). To further generalize, we extend the case-study results with simulated displacement patterns injected into multiple real SAR datasets complete with the various operational constraints (e.g. typical acquisition schedules, atmosphere, coherence degradation). We argue that the primary challenge for NRT monitoring is not just estimating displacement, but establishing reliable measures of significance across the entire monitoring area. To achieve this, quantitative assessment of the sources of error (variance and covariance) is essential. We advocate for a shift in priority from localized displacement accuracy to the ability to detect regions of displacement and/or acceleration with characterization of false-positive and false-negative rates. Ultimately, for InSAR to transition from a retrospective analysis tool to an effective NRT monitoring data stream, a framework for quantifying significance is mandatory for managing the high-consequence risks associated with catastrophic events.

Surface deformation observed by multi-temporal InSAR contains information on subsurface hydrological processes through poroelastic coupling; however, conventional groundwater inference typically relies on load and poroelastic Green’s function approaches, sometimes constrained by gravity variations at coarse scales, and the resulting inversion remains nonlinear, ill-conditioned, and dependent on strong assumptions about deformation sources. We present a physics-aware Swin3D U-Net framework that learns the spatiotemporal mapping between InSAR-derived deformation and groundwater storage anomalies, allowing high-resolution InSAR data to be exploited for aquifer-level groundwater inference rather than being constrained by coarse-scale analytical inversion schemes. The model ingests multi-temporal InSAR deformation stacks, optionally combined with auxiliary hydrological variables, and represents them as 3D spatiotemporal tokens processed through hierarchical shifted-window self-attention. This architecture captures local and long-range dependencies across space and time while preserving multi-scale structure. A U-Net–style encoder–decoder with skip connections reconstructs full-resolution groundwater storage fields. Physical consistency is encouraged through constraints linked to poroelastic response and regional water-mass balance. Training and evaluation are performed on aligned deformation and hydrological model datasets covering the period 2015–2025 over the Emilia-Romagna region, Italy, an intensively exploited aquifer basin exhibiting pronounced subsidence. The network reproduces coherent groundwater storage dynamics from surface motion signals, resolving basin-scale variability while retaining sensitivity to aquifer-level spatial patterns. From a computational perspective, GPU implementation provides a 7.5× increase in inference speed relative to convolutional baselines, enabling regional-scale monitoring. Keywords: Groundwater Storage Retrieval, Swin3D-UNet, Spatiotemporal Inversion, Poroelastic Lag, Multimodal Learning

Accurate deformation monitoring using Interferometric Synthetic Aperture Radar (InSAR) remains challenging in densely vegetated and geomorphically active environments where temporal decorrelation significantly limits the availability of persistent radar scatterers. This limitation is particularly critical along transportation corridors in the southeastern United States, where highway infrastructure frequently traverses forested terrain prone to slow-moving landslides and slope instabilities. Conventional InSAR time-series approaches, including Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique, often struggle to maintain signal coherence in such environments, resulting in sparse measurement coverage and increased uncertainty in deformation estimates. This study investigates the deployment of artificial trihedral corner reflectors (CRs) as stable radar targets to enhance InSAR-based deformation monitoring along highway corridors. A network of three CRs was installed in January 2026 along the Littleville corridor (U.S. Highway 43) in Alabama, a site identified through multi-year satellite observations as exhibiting active slope deformation. Historical observations from the Sentinel‑1 satellite mission indicate cumulative line-of-sight displacements exceeding approximately 60 mm between 2017 and 2025. However, the dense forest canopy in this region significantly limits the presence of natural coherent scatterers that are typically required for reliable InSAR time-series analysis. The deployed CR network therefore provides controlled, high-coherence radar targets that can support both calibration of satellite-derived displacement fields and validation of deformation measurements in areas where conventional approaches are challenged. Each reflector was carefully oriented to match the acquisition geometry of Sentinel-1 ascending passes, ensuring optimal radar return and consistent detectability across acquisitions. Prior to installation at the Littleville site, a calibration reflector was deployed at the National Center for Asphalt Technology test facility to verify installation procedures and evaluate satellite detectability under known conditions. The reflectors were successfully detected in the first Sentinel-1 acquisition following installation, demonstrating strong coherent radar backscatter responses and confirming appropriate geometric alignment with the satellite look direction. A key objective of this work is to evaluate how the presence of CR targets improves the reliability and calibration of different InSAR time-series processing methodologies. At the reflector locations, deformation time series will be derived using both the Persistent Scatterer Interferometric Synthetic Aperture Radar approach implemented within the Generic Mapping Tools Synthetic Aperture Radar processing framework (GMTSAR) and the New Small Baseline Subset time-series approach implemented through the LiCSBAS processing framework, which applies small-baseline interferometric time-series analysis to Sentinel-1 interferometric stacks. The CR targets provide well-defined, phase-stable reference points that enable direct comparison between PS-InSAR and small-baseline time-series displacement estimates, allowing evaluation of consistency, noise characteristics, and potential biases between the two processing strategies. The reflector network is further integrated with ground-based monitoring data to support validation of satellite-derived deformation measurements. In previous investigations conducted along other Alabama highway corridors, subsurface inclinometer measurements installed by the Alabama Department of Transportation were used to evaluate the performance of InSAR-derived deformation estimates in densely vegetated terrain. These comparisons demonstrated that while satellite-based InSAR techniques can successfully detect long-term deformation trends, the absence of stable radar scatterers often limits spatial coverage and introduces uncertainty in reference selection. The deployment of dedicated CR targets addresses this limitation by providing stable phase anchors that improve the robustness of time-series analysis and facilitate direct comparison with in-situ measurements. Beyond calibration and validation, the CR network provides an experimental testbed for evaluating the performance of multiple satellite missions in vegetated landslide environments. As additional Sentinel-1 acquisitions accumulate, the reflectors will enable millimeter-scale assessment of deformation time series derived from both persistent scatterer and small-baseline processing workflows. Furthermore, the established reflector infrastructure is well positioned to support future cross-mission comparisons with the upcoming NASA‑ISRO Synthetic Aperture Radar (NISAR) mission. The longer L-band radar wavelength of NISAR is expected to improve coherence in forested environments, providing an opportunity to evaluate the complementary capabilities of C-band and L-band radar observations for monitoring landslides affecting critical transportation infrastructure. The results demonstrate that strategically deployed corner reflectors can substantially improve InSAR observability in densely vegetated terrain by introducing bright, phase-stable artificial scatterers at controlled locations. This approach enables improved calibration of satellite-derived deformation measurements and supports rigorous validation of multiple InSAR time-series techniques, ultimately advancing the use of satellite remote sensing for operational monitoring of landslides along highway infrastructure corridors.

With rapid global urbanization, Interferometric Synthetic Aperture Radar (InSAR) has become a crucial tool for urban infrastructure safety monitoring due to its all-weather capability, wide-area coverage, and millimeter-level sensitivity to deformation (Wu et al., 2023). However, in dense urban high-rise environments, the side-looking SAR imaging geometry produces severe geometric distortions (e.g., layover and shadow), which strongly affect the visibility of individual buildings and the reliability of deformation measurements, introducing a “pixel-to-building” gap in urban InSAR practice. Consequently, before reliable deformation analysis can be conducted, two prerequisite challenges should be addressed. At the pixel level, scatterer attribution is critical to reliable deformation interpretation. Establishing an accurate link between SAR scatterers and real-world objects remains a key challenge (Yang et al., 2019). At the building level, owing to the impact of geometric distortions, it is equally essential to determine whether a building is suitable for InSAR monitoring under a given acquisition geometry. To enhance the engineering practicality of urban building InSAR monitoring, this study proposes an integrated supporting framework for urban building InSAR applications. The framework provides systematic solutions to two key prerequisite problems, including pixel-level 3D structure recovery and building-level monitoring-target selection. First, to establish an accurate link between scatterers and real-world objects, 3D InSAR point cloud reconstruction provides an effective solution by projecting SAR imagery into the 3D spatial domain. However, existing methods exhibit obvious limitations. SAR Tomography (TomoSAR) (Zhao et al., 2025; Zhu et al., 2016) demands dense spatial baselines and large data stacks, which limits its applicability over large regions. Persistent Scatterer Interferometry (PSI) (Ferretti et al., 2001) relies solely on highly coherent scatterers, leading to incomplete InSAR point cloud structures. Traditional Look-Up-Table (LUT)-based backward geocoding also suffers from positional ambiguity in layover areas, making it difficult to reconstruct the vertical structure of buildings accurately. To address these issues, we propose a Digital Surface Model (DSM)-assisted 3D InSAR point cloud reconstruction method, incorporating an Approximate Iso-Doppler (AID) forward geocoding approach and a dominant scatterer identification strategy. The AID method constructs an approximate iso-Doppler plane that intersects the DSM to derive terrain profiles. By searching for intersections using tangent lines to iso-range contours, it retrieves the 3D positions of potential scatterers within each SAR pixel while simultaneously detecting shadow and layover. To further mitigate layover-induced location ambiguity, dominant scatterers are identified via local fringe-frequency estimation from a single interferogram (Rossi and Eineder, 2015), enabling each layover pixel to be assigned to its most likely scattering source. The proposed InSAR point cloud reconstruction method was validated over the Wuhan University campus and surrounding area (~3 km²), using two TerraSAR-X Stripmap-mode images together with a 1 m LiDAR-derived DSM. Experimental results show that the AID geocoding method achieves a residual accuracy better than 10-3 pixels relative to the analytical solution of the rigorous Range-Doppler (R-D) model and is also over six times faster than traditional backward geocoding. The proposed approach can also identify typical slope clusters corresponding to roofs/ground and façades, providing a more complete 3D InSAR point-cloud foundation for subsequent deformation monitoring. Second, after obtaining 3D InSAR point clouds, another key question in engineering practice must be answered. In dense urban environments, which buildings are suitable for InSAR monitoring? Currently, there is a lack of applicability assessment methods specifically targeted at the urban building level. Previous studies have mostly focused solely on visibility analysis (Del Soldato et al., 2021), overlooking the completeness of the deformation information and the interpretability of the results. To address this, we propose a new building-oriented applicability assessment method within the upper module of the supporting framework. This framework introduces three complementary quantitative evaluation indices: (1) the Visibility Index (VID), which evaluates whether a building is observable under a given SAR viewing geometry; (2) the Deformation Completeness Index (DCI), which evaluates whether the visible building structures can provide sufficiently complete and unbiased deformation information , thereby avoiding biases dominated by a single component (such as the roof); and (3) the Deformation Interpretability Index (DII), which evaluates whether the extracted deformation signal can be clearly attributed to a specific building, thereby suppressing signal confusion caused by layover from neighboring buildings. All the aforementioned indices are calculated based on 3D InSAR point clouds and building footprint data. The proposed framework is validated over the main urban area of Shenzhen using high-resolution TerraSAR-X data, an open-source DSM (Zhu et al., 2025), and building footprint data. By combining the geometric characteristics of the 3D InSAR point cloud with the applicability assessment results, the buildings in the study area are classified into four categories: invisible (33.26%), visible but with incomplete deformation information (4.59%), visible but with deformation that is difficult to interpret (5.11%), and well-suited for monitoring (57.04%). The results indicate that more than half of the buildings are suitable for InSAR monitoring under the current viewing geometry, while layover and shadow remain the main limiting factors in dense high-rise areas. In conclusion, by integrating pixel-level 3D structural recovery with building-level applicability evaluation, the proposed supporting framework mitigates the inherent interpretation ambiguities caused by geometric distortions in dense urban areas. It provides a key theoretical and practical basis for future large-scale, standardized, and automated urban InSAR monitoring systems. References Del Soldato, M., Solari, L., Novellino, A., Monserrat, O., Raspini, F., 2021. A New Set of Tools for the Generation of InSAR Visibility Maps over Wide Areas. Geosciences 11, 229. https://doi.org/10.3390/geosciences11060229 Ferretti, A., Prati, C., Rocca, F., 2001. Permanent scatterers in SAR interferometry. IEEE Trans. Geosci. Remote Sensing 39, 8–20. https://doi.org/10.1109/36.898661 Rossi, C., Eineder, M., 2015. High-Resolution InSAR Building Layovers Detection and Exploitation. IEEE Trans. Geosci. Remote Sensing 53, 6457–6468. https://doi.org/10.1109/TGRS.2015.2440913 Wu, S., Zhang, B., Ding, X., Zhang, L., Zhang, Zhijie, Zhang, Zeyu, 2023. Radar Interferometry for Urban Infrastructure Stability Monitoring: From Techniques to Applications. Sustainability 15, 14654. https://doi.org/10.3390/su151914654 Yang, M., López-Dekker, P., Dheenathayalan, P., Liao, M., Hanssen, R.F., 2019. On the value of corner reflectors and surface models in InSAR precise point positioning. ISPRS Journal of Photogrammetry and Remote Sensing 158, 113–122. https://doi.org/10.1016/j.isprsjprs.2019.10.006 Zhao, X., Dong, J., Yu, Y., Liao, M., Zhang, L., Gong, J., 2025. A review of SAR tomography. Geo-spatial Information Science 1–44. https://doi.org/10.1080/10095020.2025.2510365 Zhu, X.X., Chen, S., Zhang, F., Shi, Y., Wang, Y., 2025. GlobalBuildingAtlas: an open global and complete dataset of building polygons, heights and LoD1 3D models. Earth Syst. Sci. Data 17, 6647–6668. https://doi.org/10.5194/essd-17-6647-2025 Zhu, X.X., Montazeri, S., Gisinger, C., Hanssen, R.F., Bamler, R., 2016. Geodetic SAR Tomography. IEEE Trans. Geosci. Remote Sensing 54, 18–35. https://doi.org/10.1109/tgrs.2015.2448686

Coastal environments worldwide are facing compounded threats from climate-driven Sea Level Rise (SLR) and Vertical Land Motion (VLM) (Shizaei et al., 2021; Nicholls et al., 2021). Accurate quantification of Relative Sea Level Rise (RSLR), particularly by resolving the spatial heterogeneity of VLM, over large spatiotemporal scales is critical for effective risk management (Tay et al., 2022). However, this remains technically challenging due to the complexities of generating seamless wide-area InSAR deformation fields and integrating them into dynamic hydrological models. As detailed in our recently published works (Gong et al., 2025; Gong et al., 2026), we present a comprehensive framework that bridges advanced multi-frame InSAR processing with hydrodynamic inundation modeling, using the Bohai Rim, a densely populated and geologically complex coastal zone in China, as a case study. In this framework, the ESA Sentinel-1 mission plays an indispensable role. Its systematic observation strategy, vast spatial coverage, and highly reliable C-band acquisitions provide the critical data foundation required for continuous, continent-scale deformation monitoring. First, to address the limitations of conventional InSAR merging, specifically inter-frame inconsistencies and systematic biases arising from varying incidence angles and atmospheric artifacts, we developed a novel adaptive gridded adjustment model. This method employs a quadtree-based decomposition algorithm that dynamically optimizes grid sizes based on deformation gradients, integrated with sparse GNSS constraints to remove long-wavelength orbital and atmospheric errors. Applied to 2,078 Sentinel-1 images from 2018 to 2022 across six frames, this approach reduced inter-frame discrepancies by 38%, and achieved a root-mean-square error (RMSE) of 0.4 mm/yr against GNSS validation. The resulting seamless deformation map reveals significant spatial heterogeneity in VLM, ranging from -141 mm/yr to +40 mm/yr, driven by diverse factors including groundwater depletion, hydrocarbon extraction, and sediment compaction. Building on this high-precision geodetic baseline, we demonstrate the critical role of VLM in RSLR assessment through dynamic inundation simulations under multiple climate scenarios. we assessed future inundation risks under IPCC AR6 scenarios (SSP1-2.6, SSP3-7.0, and SSP5-8.5) by 2100 using a "Flow-tub" dynamic inundation model. This approach advances beyond static "bathtub" methods by incorporating hydrological connectivity and path-based water level attenuation. Crucially, our results identify VLM as the dominant driver of local RSLR variability. Under the high-emission scenario (RSLR-SSP5-8.5), incorporating of InSAR-derived VLM expands the projected inundation extent to 17,756 km2, representing a 33-50% increase compared to SLR-only projections (13,314 km²), and potentially exposing 10.4 million residents to flood risks. In conclusion, this study combines advanced land deformation monitoring with flood modeling. We demonstrate that ignoring VLM significantly underestimates coastal risks. The proposed InSAR method offers an effective solution for large-scale monitoring. Furthermore, our findings provide practical guidance for coastal protection, emphasizing the urgent need to focus on areas with severe subsidence. Building upon the success of this regional framework, we are currently expanding this integrated approach to the entire coastline of mainland China. This ongoing scale-up initiative aims to bridge existing research gaps in national scale VLM quantification and deliver a comprehensive, high-resolution coastal hazard assessment to support resilient coastal planning. Keywords: Vertical Land Motion (VLM); Multi-frame InSAR; Adaptive Gridded Adjustment; Relative Sea Level Rise (RSLR); Dynamic inundation Modeling; References: [1] Gong, Z., Liao, M., Dong, J., Lan, Q., Wang, R., Lai, S. (2025). Wide-area coastal deformation extraction using multi-path/frame InSAR: A case study of the Bohai Rim. Remote Sensing of Environment, 114988. [2] Gong, Z., Wu, J., Dong, J., Lan, Q., Lai, S., Lin, J., Liao, M. (2026). Relative sea-level rise and inundation risks in the Bohai Rim: Dominant role of vertical land motion. International Journal of Applied Earth Observation and Geoinformation, 105115. [3] Shirzaei, M., Freymueller, J., Törnqvist, T. E., Galloway, D.L., Dura, T. (2021). Measuring, modelling and projecting coastal land subsidence. Nature Reviews Earth & Environment, 2(1), 40-58. [4] Nicholls, R. J., Lincke, D., Hinkel, J., Brown, S., Vafeidis, A.T., Meyssignac, B., Hanson, S.E., Merkens, J.-L., Fang, J. (2021). A global analysis of subsidence, relative sea-level change and coastal flood exposure. Nature Climate Change, 11(4), 338–342. [5] Tay, C., Lindsey, E.O., Chin, S.T., McCaughey, J.W., Bekaert, D., Nguyen, M., Hua, H., Manipon, G., Karim, M., Horton, B.P., Li, T., Hill, E.M. (2022). Sea-level rise from land subsidence in major coastal cities. Nat. Sustain. 5, 1049–1057.

Subsidence due to pressure decline from extensive groundwater extraction is one of the most common surface deformations detected by PSInSAR. This is exactly what is occurring at our case study site in Mezőtárkány, Hungary, where groundwater extraction driven by increased demand for drinking water, as well as industrial use has caused significant subsidence in the settlement's southeastern part. We processed both ascending and descending Sentinel-1A data from 2014 to 2024 using the IPTA method and a dual master approach. To achieve this, we combined results from two independent 5-year single-reference processing runs, selecting two reference epochs in 2017 and 2021. This ensured high-quality interferograms for displacement determination despite the long study period, thereby determining displacements in a total of 274 epochs in the descending orbit and 260 epochs in the ascending orbit. Time series confirm that the detected anomaly persists across the entire ~10-year study interval. The surface has subsided at a nearly uniform rate, exceeding 1 cm/year in the central area at the location of the well, resulting in total subsidence of over 10 cm during the period. In contrast, the western–southwestern side of the settlement, farther from the well, experienced less than 0.5 cm of displacement over the entire decade. These data indicate that the subsidence is truly local and most likely linked to the groundwater extraction. Surface deformation reflects subsurface hydrological processes, where substantial fluid withdrawal causes pressure decline in the rock matrix, leading to its compaction. We investigated these subsurface processes using groundwater flow modeling based on geological and production data provided by the water utility, using the MODFLOW modeling package. We compared the temporal patterns of production and modeled compaction, including the effects of delayed-response layers, the spatial shape and extent of the subsidence bowl, and the impacts of varying model and layer parameters on these features. Ultimately, we developed a model that best fits the satellite measurement data, assuming homogeneous layers. This model reveals the impacts of groundwater extraction, predicts future subsidence from continued overproduction, and identifies unaffected areas. The latter indicates aquifers uninvolved in overexploitation, whose location may be ideal for establishing new production wells in the future.

Ground deformation in urban environments can pose risks to high‑value infrastructure, yet dedicated structural monitoring is often limited or short‑lived. Interferometric Synthetic Aperture Radar (InSAR) provides a long‑term, independent record of displacement, but interpreting InSAR measurements on tall buildings requires approaches that account for complex motion and structure geometry. This work presents an InSAR‑based methodology developed by 3vGeomatics to reconstruct multi‑year displacement histories of high‑rise buildings, demonstrated using the Millennium Tower in San Francisco as well as several additional nearby skyscrapers. For Millennium Tower—a 58‑story (197 m) residential tower reported in 2016 to be experiencing settlement and tilt—we processed 225 descending and 107 ascending TerraSAR‑X StripMap scenes collected between 2009 and 2018 at 3 m resolution. From these data, we generated more than 2000 persistent scatterer measurement points distributed across all four façades and the roof. By modeling the building as a rigid body capable of vertical motion and tilting around a pivot near the base, we reduce thousands of InSAR measurement points into a small number of interpretable parameters: vertical displacement, tilt magnitude, and tilt direction. The approach is designed to work even with a single satellite viewing geometry. This flexibility is especially important in dense city centers, where certain viewing geometries may be partially obstructed by neighboring buildings. Analyzing ascending and descending data independently provides two self‑consistent solutions, while a combined fit yields the most precise displacement history. Across the nine‑year record, Millennium Tower was found to have undergone approximately 20 cm of vertical settlement and 45 cm of lateral movement at the rooftop, with both the rate and direction of tilt evolving over time. To quantify parameter uncertainties, the same modeling procedure was applied to multiple nearby skyscrapers with suitable data. Assuming these comparison buildings to be stable, the variance in their fitted parameters provided empirical uncertainty estimates: 2 cm for vertical displacement and 5 cm for tilt (95% confidence). Extending this analysis to other high‑rise structures in the surrounding district, we demonstrate the broader applicability of this method for detecting subtle, long‑term building deformation. These results highlight how multi‑year InSAR archives, combined with appropriate structural modeling, can support urban risk assessment, infrastructure management, and engineering investigations for large construction projects.

Abstract Large-scale land reclamation over thick, compressible marine deposits presents significant geotechnical challenges, particularly in understanding post-construction consolidation behavior. This study presents a comprehensive framework for four-dimensional (space and time) mapping of soil consolidation at the Hong Kong International Airport’s (HKIA) three-runway system expansion, where extensive reclamation employed two key ground improvement techniques: Prefabricated Vertical Drains (PVD) and Deep Cement Mixing (DCM)—the latter being first applied in Hong Kong for this project. We applied multitemporal Synthetic Aperture Radar Interferometry (MT-InSAR) using multiple SAR data stacks to investigate the spatiotemporal characteristics of land deformation following runway pavement completion. A combined persistent scatterer (PS) and distributed scatterer (DS) strategy was implemented to address low radar coherence at the site. Our observations reveal varying degrees of land subsidence, with maximum sinking rates reaching ~150 mm/year during September 2021–October 2023 and subsequently evolving to ~80 mm/year by September 2025. Three-dimensional deformation fields further detected apparent horizontal displacement in the taxiway southwest of the runway. To disentangle the complex consolidation mechanisms, we employed Independent Component Analysis (ICA) to identify underlying sources contributing to the measured deformation. Three distinct signals were unveiled: (1) an exponential decay signal representing rapid compaction of surficial materials, (2) a linear signal indicating continuous subsidence from marine deposits—predominantly located in areas with prefabricated vertical drains, and (3) a periodic signal associated with thermal effects on structures. Integrating InSAR observations with Terzaghi consolidation theory and 3D finite element modelling enabled us to extend surface displacement monitoring to subsurface characterization. This approach allowed retrieval of critical geotechnical parameters, including ultimate primary settlement, compression index (Cc), the coefficient of consolidation (Cv) and the degree of consolidation at unprecedented spatial detail. Mapping these derived parameters revealed distinct spatial heterogeneities that strongly correlated with the different treated foundations. This coupled approach captured the time-dependent consolidation behavior and quantified the distinct stability performance of PVD and DCM zones. Our quantitative analysis demonstrates that DCM achieves geological stability more rapidly than PVD, with a time advantage of approximately 0.08–1.39 years, while effectively controlling primary settlement to 29%–83% of that observed with PVD. This integrated framework advances traditional InSAR applications by transforming surface deformation measurements into actionable insights on underground consolidation processes. The findings provide a crucial quantitative basis for evaluating residual settlement, planning targeted reinforcement measures, and informing future reclamation practices—particularly those employing innovative DCM techniques in similar geological settings. Acknowledgements: National Natural Science Foundation of China (Grant No. 42304052), the Research Grants Council of Hong Kong (Grant No. 15229523, No. 25202125) and the Otto Poon Research Institute for Climate-Resilient Infrastructure (Grant No. P0055919).

Interferometric Synthetic Aperture Radar (InSAR) has emerged as a powerful geodetic tool for monitoring land deformation with high spatial resolution and temporal continuity, providing critical insights into deformation processes in rapidly urbanizing regions. This study presents updated spatiotemporal deformation data from October 2014 to December 2021 across the Greater Manila Area, derived from Sentinel-1 InSAR time-series analysis using UK COMET’s LiCSAR products and validated against global navigation satellite system (GNSS) measurements. The InSAR-derived deformation fields reveal spatially coherent subsiding zones in the Pampanga Delta near Manila Bay (with the highest rate observed at -95.2 mm/yr, sustained for more than seven years), western Rizal, northern Metro Manila, and portions of Cavite and Laguna. Horizontal compressive motion detected by InSAR further indicates lateral convergence toward areas of maximum subsidence, forming subsidence or settlement funnels. Correlation with lithological and hydrogeological datasets shows that deformation is most pronounced in areas underlain by Quaternary Alluvium and clay-rich Oligocene to Pliocene-Quaternary formations, which exhibit low permeability and high compressibility. Thick clay layers contribute significantly to subsidence in these zones. In contrast, regions underlain by Pleistocene tuffaceous formations with robust aquifer systems display relative stability. Similarly, sandy unconfined aquifers exhibit reduced deformation, while active recharge zones in western Metro Manila appear to mitigate subsidence. InSAR time-series analysis also reveals vertical velocities ranging from -36.7 mm/yr to 0.7 mm/yr, averaging -4.3 mm/yr within the Marikina Valley, a graben bounded by segments of the dominantly dextral Valley Fault System (VFS). Maximum subsidence occurs in its southernmost portion, where human-induced factors likely contributed to the larger displacement. Further south, near Biñan (Laguna) and Carmona (Cavite), InSAR deformation maps highlight elongated northeast–southwest subsidence zones that align with left-stepping en echelon fault structures. These features mark a releasing stepover segment of the VFS, where pre-existing extensional fractures and subsidiary splays accommodate differential settlement intensified by groundwater overextraction. This work underscores the effectiveness of InSAR as a monitoring framework for urban deformation, enabling the detection of subtle, spatially variable patterns that traditional ground-based methods may overlook. The findings emphasize the need to integrate InSAR-derived geospatial data with geological and hydrological information to support evidence-based land-use planning, hazard assessment, and subsidence mitigation strategies in the Greater Manila Area and similar urban environments worldwide.

Water cycle infrastructures are critical for adapting society to current and future climate scenarios. Among them, dams play a key role in water supply and regulation, while their safety frameworks must evolve to address emerging climatic and operational challenges. At the same time, infrastructure monitoring is rapidly advancing due to progress in remote sensing, modelling and machine learning. Dam managers increasingly face a context with multiple monitoring data sources—such as traditional instrumentation, remote sensing observations and environmental variables—that are often not fully integrated, which can hinder the operational adoption of new technologies. Recent advances in Synthetic Aperture Radar interferometry (InSAR) enable quasi-real-time monitoring of dams and surrounding infrastructures with high spatial coverage and millimetric sensitivity. The integration of multi-temporal InSAR (MT-InSAR) with classical instrumentation and contextual information provides new opportunities to improve dam safety assessment. Standardized databases and integrated analysis frameworks facilitate consistent data management and enable joint analysis of deformation measurements and external drivers. In this context, this work proposes a modelling framework based on model that integrates interpretable linear trend models, breakpoint detection and the estimation of linear dependence on external factors such as temperature and reservoir level. The approach enables assessing whether dam deformation follows the expected behaviour or evolves anomalously, while breakpoint detection supports a posteriori analysis of potential links between deformation changes and extreme events. By modelling deformation behaviour as a function of external variables that are easily measurable or routinely available, the proposed framework contributes to improving the interpretability and operational value of satellite-based monitoring. Such models support early detection of anomalous behaviour and strengthen the integration of remote sensing into dam safety practices, ultimately enhancing the safety and resilience of water infrastructures in the context of ongoing global change.

Differential interferometric synthetic aperture radar (DInSAR) has become a powerful technique for estimating precise surface deformation on Earth's dynamic surface. With the increasing availability of spaceborne synthetic aperture radar (SAR) observations, multi-temporal InSAR (MT-InSAR) enables the retrieval of long-term displacement time series. However, time-series displacement signals are often contaminated by various components, including coupled deformation, atmospheric delays, noise, and seasonal effects, despite numerous approaches to mitigate these artifacts. Such components can lead to misinterpretation of actual deformation patterns or their driving mechanisms. To isolate displacement-related signals from mixed time-series data, several statistical methods, such as seasonal trend decomposition using loess (STL), principal component analysis (PCA), and independent component analysis (ICA), have been widely applied to identify different deformation trends or reduce residual atmospheric delays and noise. Among these methods, ICA offers a distinct advantage by effectively separating statistically independent source signals from large, mixed datasets without requiring prior assumptions. Unlike PCA, which maximizes variance rather than physical independence, ICA is particularly suitable for separating deformation processes driven by different physical mechanisms. In this study, we applied ICA to extract spatially coherent deformation patterns that are statistically independent of MT-InSAR time-series displacements derived using the small baseline subset (SBAS) algorithm in Gimhae City, South Korea. The study area is characterized by soft ground conditions and thick sedimentary layers, which increase its susceptibility to persistent surface deformation. Ongoing urban development for residential and industrial purposes further underscores the importance of continuous surface-displacement monitoring for mitigating urban geohazards. We analyzed two descending-orbit SAR datasets: COSMO-SkyMed acquisitions from January 2013 to April 2019 and Sentinel-1 acquisitions from January 2016 to October 2021. MT-InSAR time-series displacements were generated for each dataset, and ICA was subsequently applied to the results. By combining these datasets, we reconstructed surface deformation from 2013 to 2021 and identified three independent components (ICs). These components correspond to linear, quadratic (acceleration-related), and seasonal trends, and their associated spatiotemporal IC score maps were used to identify clusters of similar deformation behavior. To evaluate the representativeness of the ICs, correlation analyses were conducted between the ICs and time-series parameters, including velocity and acceleration. In the COSMO-SkyMed results, IC1 and IC3 show strong correlations with velocity and acceleration, with coefficients of determination (R2) of 0.76 and 0.90, respectively. In the Sentinel-1 results, IC1 and IC2 are correlated with velocity and acceleration, with R2 values of 0.88 and 0.60, respectively. The remaining components, IC2 for COSMO-SkyMed and IC3 for Sentinel-1, are interpreted as representing seasonal effects with additional noise contributions. To reveal the deformation associated with each component and to identify deformation patterns in the hotspots, ICA was applied to the time-series displacements. Spatial analysis of the ICs reveals a clear uplift in the central urban area, with cumulative displacements of approximately 5.8 cm for COSMO-SkyMed and 2.3 cm for Sentinel-1. Acceleration-related ICs exhibit nonlinear temporal patterns, suggesting gradual stabilization of deformation, while seasonal effects are relatively minor. Comparison with groundwater-level observations reported in our previous study indicates that the groundwater recharge over the past 10 years is the primary driver of the observed uplift. In contrast, subsidence hotspots identified in the eastern part of the study area exhibit cumulative displacements of approximately -4.2 cm for COSMO-SkyMed and -7.4 cm for Sentinel-1. The COSMO-SkyMed IC3 time-series indicates accelerated deformation, whereas the Sentinel-1 IC2 time-series reflects deformation deceleration during the later observation period. Overall, this study demonstrates that applying ICA to MT-InSAR-derived deformation time series effectively separates deformation signals from seasonal effects and noise, enabling robust identification of uplift and subsidence hotspots and estimation of deformation velocity and acceleration. The proposed framework provides valuable insights into urban deformation processes and offers a practical tool for assessing infrastructure vulnerability in rapidly developing urban environments.

The Permian basin is one of the most prolific and developed onshore oil & gas basins in the world, and one of the most dynamically deforming regions driven by anthropogenic activity. Sustained saltwater injection associated with unconventional hydrocarbon production has been linked to induced seismicity and to the development of two distinct, superimposed deformation patterns. At the regional scale (∼1,000 km²), subsidence and uplift signals are driven by variations in the stress regime and differences in injection practices across distinct state regulations. Superimposed on the regional background, localized (∼1 km²) blisters represent rapidly developing, near-catastrophic uplift or subsidence events triggered by pressure changes within compartmentalized shallow formations (e.g., the San Andres Formation in the Midland Basin and the Delaware Mountain Group in the Delaware Basin), where saltwater disposal occurs. This study demonstrates: (1) how to overcome the challenges associated with near-real-time Interferometric Synthetic Aperture Radar (InSAR) monitoring in one of the most dynamic, human-impacted regions globally, the Permian Basin (∼90,900 km²); and (2) how rapidly evolving small-scale (∼1 km²) blisters can be systematically detected and quantitatively characterized. To monitor these surface changes, a Small Baseline Subset (SBAS) workflow was developed using two ascending and one descending Sentinel-1 regional-scale stacks. One of the main challenges of near-real time InSAR monitoring at a regional scale is atmospheric signal delay, which can impose meters of apparent displacement that must be corrected to identify the relevant mm-cm scale displacement patterns. We solve this challenge by modeling the atmospheric signal delay for each new acquisition based on the expected spatiotemporal patterns derived from over ten years of Sentinel-1 acquisitions. Temporal filtering enables separation of displacement signals from atmospheric phase delays, which is particularly useful to monitor slowly-evolving displacement; however, unfiltered time series must be examined to identify rapidly evolving, kilometer-scale blisters that may otherwise be attenuated by filtering. By clustering, the distinct temporal behaviors observed in the SBAS time series across the Permian Basin (i.e., blisters) can be isolated from the regional background signal. Recurrent comparison of the detected blisters, combined with expert interpretation, enables characterization of their temporal evolution and qualitative assessment of their behavior. Effective separation of atmospheric signal delay from true displacement, combined with the comparison between temporally filtered and unfiltered time series, is essential for reliably detecting blisters. This workflow demonstrates that rapidly evolving kilometer-scale deformation anomalies can be resolved at near-real-time frequency across a basin-wide region. It provides a critical tool for understanding saltwater disposal behavior in the subsurface, supporting geohazard mitigation, and informing regulatory decision-making in one of the most intensively developed hydrocarbon basins worldwide.

Deformation of solar evaporation pond perimeter dikes needs to be effectively monitored and managed to prevent overflow and stability issues that can lead to failure and major damage. This case study describes how Interferometric Synthetic Aperture Radar (InSAR), complemented by terrestrial survey methods, identified a new settlement anomaly in 2023 along a perimeter dike that presented immediate operational risk and raised concerns about a horizontal displacement component driven by one-sided hydraulic loading. Close collaboration between InSAR analysts and on-site teams turned the deformation measurements into actionable results framed around operational needs. We processed TerraSAR-X (descending) and Sentinel-1A (ascending and descending) data spanning January 2023 to November 2025 using the Small Baseline Subset (SBAS) approach. Since a stable reference area is difficult to guarantee in this environment, we first reference the results to the most stable and reliable scatterers within the area of interest and then correct the bias/rates based on ground control point time series. The TerraSAR-X and Sentinel-1A descending datasets show good agreement, strengthening confidence in the detected acceleration from July 2023 onward and in its subsequent evolution. Differences between ascending and descending Line-of-Sight (LOS) time series during the anomaly indicate that deformation is not purely vertical. Standard decomposition of dual-orbit InSAR datasets are often used to separate displacement into vertical and east–west motion. However, the orientation of the dike structures implies that the expected horizontal displacement may occur in a different direction. We employ the strapdown decomposition approach [1] and assume that horizontal displacement is predominantly perpendicular to the dike axis (transversal) and that the longitudinal (along-dike) component is negligible. We explicitly calculate and communicate the increase in uncertainty for dike segments where the tangent approached the north–south axis. We validate the InSAR results with existing in-situ survey monitoring. Crest points, spaced approximately 250 m apart, are monitored using static Global Navigational Satellite System (GNSS) receivers and closed-loop digital leveling. Comparisons at representative chainages confirm the July 2023 acceleration and show good agreement where the local dike orientation provides sufficient sensitivity with respect to the satellite LOS. The observations are consistent with the broader geohydrological setting, including sinkhole activity and brine discharge features observed near the shoreline. These findings support mitigation planning and prioritization, including accelerated crest raising and strengthening measures in the most critical sectors. [1] Brouwer, W. S., & Hanssen, R. F. (2024). Estimating three-dimensional displacements with InSAR: The strapdown approach. Journal of Geodesy, 98(12), Article 110. https://doi.org/10.1007/s00190-024-01918-2

Linear infrastructure monitoring with MTInSAR benefits from the wide swath of Sentinel-1 images as very long segments of the linear infrastructures are depicted in one frame. However, it comes with computationally expensive processing when using conventional MTInSAR for large areas, because the majority of the processed pixels are not directly relevant to the linear infrastructure, rather, they are required to aid phase unwrapping and are used to remove the atmospheric phase contribution. To address this limitation, we propose a new method that estimates the spatial displacement gradient solely along the linear infrastructure, thereby direclty providing the differential displacement and being computationally light as the displacement time series is not derived. While estimating local displacement signals at linear infrastructures from Sentinel-1 images is challenging due to the medium spatial resolution of the sensor, estimating displacement signals on a regional scale is reasonable from Sentinel-1 mission. The presented method is not intended to replace MTInSAR, but rather to provide a rapid screening of displacement signals at linear infrastructures on a large-scale, thus highlighting hazardous areas for which detailed MTInSAR processing is recommended.The proposed method takes a co-registered stack of SLC images and selects coherent pixels in single-look interferograms based on the location of the linear infrastructure which is known beforehand from GIS data, e.g. from OpenStreetMap. Coherent pixels within a buffer around the linear infrastructure are selected and grouped into grid cells of regular size. For each grid cell, the interferometric phases are averaged per interferogram. Then, the differential displacement velocity is estimated from the wrapped phases of two neighbouring grid cells by maximing the temporal coherence as it is commonly applied during temporal unwrapping. The result of the method comprises the differential displacement velocity between neighbouring grid cells which is used to assess the hazard related to the displacement. The proposed approach directly estimates the spatial displacement gradient and has the advantage that error propagation from pixels outside the linear infrastructure is avoided.We conducted an experiment with a study area in the Rhenish Coalfields in North-Rhine Westphelia in Germany exhibiting subsidence of up to 7.5cm/year. The area includes multiple railway tracks and highways for which the displacement gradients are assessed with our proposed method. We coregistered 58 Sentinel-1 images from descending track with GAMMA for the period 04-01-2021 to 30-12-2021. To validate the results of our method, we compare the displacement results with results from 29 high spatial resolution TerraSAR-X Stripmap images from descending track for the same study area and period. Moreover, we use the state-of-the-art MTInSAR method implemented in the open-source research software SARvey to estimate the displacement velocities and time series from the two datasets for comparison. The results indicate good agreement between the displacement velocities estimated from the TerraSAR-X and Sentinel-1 datasets for both the proposed gradient approach and the conventional MTInSAR method. The RMSE between the spatial displacement gradients from the proposed approach and gradients derived from the MTInSAR results are decreasing with increasing grid size from 0.35cm/year down to 0.05cm/year for a grid size of 50m up to 500m, respectively. The results show that the chosen grid size has a low-pass filter effect which preserves the main regional displacement signal along the linear infrastructure. Retrieving displacement signals with a higher spatial frequency is more likely with a smaller grid cell size, but depends on the availability of coherent pixels and comes at the cost of introducing higher sensitivity to single decorrelated pixels. The proposed method enables a fast assessment of displacement gradients for monitoring of linear infrastructure by exploiting the potential of the Sentinel-1 mission due to its wide area coverage and regular acquisition plan.

This study explores advanced InSAR post-processing techniques aimed at enhancing structural health monitoring for dam safety, focusing on two critical challenges: the mitigation of complex residual periodic signals and the interpretation of displacements in non-ideally oriented structures. 1. Mitigation of Residual Periodicity Standard InSAR software typically compensates for primary environmental factors, such as thermal expansion—a common practice in bridge or high-rise monitoring. However, dam monitoring often requires multi-factor compensation, including water level-induced (hydrostatic) displacements. Our analysis revealed that on an Earth-filled dam, even after accounting for thermal and hydrostatic effects, significant residual periodicity remained, particularly near the spillways. Initial visual inspection suggested a correlation with water temperature (approximate one-year cycle with minimum around March and maximum around November); however, a detailed parametric estimation of frequency, amplitude, and phase showed a near-uniform distribution of phase of the residual periods across the year. This contradicts the assumption of a single driving parameter, suggesting a more complex superposition of inseparable periodic signals. Furthermore, the presence of seasonal vegetation on the downstream face introduces noise and temporal decorrelation. To maximize the density of Persistent Scatterer (PS) points, our processing chain prioritizes point retention even at the cost of temporal discontinuities in the time series. We present a refined post-processing approach where thermal effects are iteratively re-estimated and subtracted. Following this procedure, the phase of the residual periods exhibits a bimodal distribution, confirming the presence of two superimposed signals that must be accounted for to accurately separate reversible movements from potentially risky permanent deformations. 2. Displacement Decomposition for East-West Oriented Dams Interpreting InSAR data for dams oriented in the East-West direction poses a significant geometrical challenge, as North-South displacements are often neglected in standard vertical and East-West decompositions. In such cases, Northward displacement typically inflates the vertical subsidence component by approximately 20%. To address this, we implemented a "lateral decomposition" targeting the vertical and dam-perpendicular directions. Despite the inherent noise and higher standard deviations (in the order of mm/y), the resulting displacement maps showed high spatial smoothness, likely due to high temporal coherence on the non-vegetated sections. Unfortunately, this “lateral decomposition” is highly unreliable in case there are real displacements in the longitudinal, i.e. East-West direction: such possible displacements biases the lateral and vertical component by 300% and 60% of the real longitudinal displacement, respectively. Displacement in the longitudinal direction, on the other hand, can be easily and reliably estimated. Conclusion By refining the compensation of periodic residuals and adapting decomposition geometries to the dam’s orientation, we provide a more reliable assessment of structural integrity, enabling a clearer distinction between cyclical environmental responses and critical geohazards.

Distributed crustal deformation across the Philippines, given its plate boundary setting, indicates fast fault slip rates that translate to high seismic hazard coming from several possible earthquake sources. Further, ongoing rapid urbanization introduces anthropogenic ground deformation that exacerbates impacts of existing hazards. Advancement of knowledge on the behavior of individual earthquake generators and regional tectonics has been hampered, however, by limited observations from ground instrumentation and a tropical setting that can be challenging for InSAR analysis. Recognizing the gap, the Philippine national government funded the Leveraging InSAR for Observation and modeling of Earthquake generators (LInOG; lee-nog) Project to move towards a systematic ground deformation mapping system with InSAR for the Philippines. In this work, we use available L-band data to identify the seismogenic potential of major branches of the Philippine Fault in Luzon and reveal other sources of ground deformation. We process ALOS PALSAR Fine Beam resolution L1.1 (SLC) data from 2007–2011 using ISCE2 (from coregistration to interferogram formation) and use MintPy for time-series analysis (assuming linear velocity function). Initially covering ~20,000 sq. km of the island of Luzon, we find evidence of significant localized subsidence in developing urban centers, with more than ~5 cm/yr of subsidence. An east-west long-wavelength ramp can be seen in the InSAR velocity field that may correspond to regional shortening due to compression of the Philippine Mobile Belt between the Philippine Sea Plate and Eurasia; however, the gradient exceeds the expected ~1.4 cm/yr E-W shortening measured from GNSS. This indicates that careful ramp removal should be considered in InSAR data processing in a rapidly deforming tectonic environment. Better coherence is found compared to C-band InSAR results, though thickly vegetated mountainous areas do exhibit low temporal coherence that should be masked. Distinguishable ionospheric signals are also present in specific dates of the time-series. We assess alignment of the InSAR velocity data with the local reference frame, as well as the effectiveness of higher-resolution digital elevation models for topography-based corrections. The insights from this work will inform upcoming efforts that aim to incorporate newer L-band SAR datasets (NISAR and ALOS-4) that could potentially offer higher temporal resolution, alongside with local tropospheric and ionospheric datasets. Further work is also expected to be done to translate the results to be useful for localized understanding and operational use.

The Świnna Poręba (Mucharskie) Reservoir in southern Poland, constructed between 1986 and 2017 on the Skawa River, is one of the longest-running hydrotechnical investments in Europe. Geologically, the reservoir is located within the Silesian and Magura nappes of the Outer Carpathian flysch belt, characterized by steep relief, low-permeability lithologies, complex tectonics, and interbedded sandstones and shales. These conditions, combined with shoreline undercutting, reservoir level oscillations, and wave erosion, predispose the area to slope instability and recurrent mass movements. Previous investigations under the Polish Landslide Counteracting System (SOPO) have primarily emphasized geological and hydrogeological controls, while anthropogenic drivers remain comparatively underexplored. This study proposes a multiproxy framework to disentangle natural and anthropogenic factors influencing slope stability around the reservoir. The research integrates: Multitemporal InSAR time-series analysis (Sentinel-1 interferometry data cubes) to detect millimetric ground deformation trends, Correlation of displacement signals with reservoir water level fluctuations and hydro-meteorological records, Optical and active remote sensing for land-use, water-level fluctuations and vegetation change detection, UAV-based photogrammetric models (high-resolution DEMs and digital outcrop models) for selected landslides and rockfall sites, Geological mapping and structural analysis, Geophysical investigations (ERT and conductivity profiling), Analysis of archival geotechnical documentation and landslides documentation. The core objective is to determine whether slope activation is primarily controlled by litho-structural conditions and hydrological forcing, or whether anthropogenic factors, such as reservoir-induced water level oscillations, historical excavation, shoreline modifications, and land-use changes - play a dominant role in reactivation dynamics. The use of multitemporal InSAR (also transformed to data cubes) enables continuous deformation monitoring over multi-annual timescales and allows synchronization of displacement acceleration phases with rapid drawdown events, seasonal hydrological variability and extreme precipitation. This approach supports quantitative assessment of reservoir-induced slope response mechanisms, including delayed pore-pressure dissipation and cyclic weakening. All the collected data and results may one day serve as a foundation for a Digital Twin of the reservoir slope system, integrating deformation monitoring with hydrogeological model and geological structure. Such a digital framework could support hazard assessment, spatial planning decisions, and infrastructure management along the reservoir shoreline. The proposed methodology aligns with ESA’s Earth Observation objectives by demonstrating how Copernicus radar time-series data, integrated with multidisciplinary datasets, can enhance understanding of complex landslide processes in flysch environments affected by anthropopressure and large hydrotechnical infrastructure.

Interferometric Synthetic Aperture Radar (InSAR) is a powerful tool for mapping surface movements, but tropospheric delays complicate deformation interpretation. Tropospheric errors are influenced by various spatiotemporal factors, including water vapor, temperature and pressure and all these factors are related to satellite orbit configurations. This means that although tropospheric errors are independent of signal wavelengths, different satellites may encounter completely different tropospheric effects. However, while previous studies focus on physical properties of the troposphere, orbit-specific tropospheric features remain underexplored. In this paper, we investigate the spatiotemporal characteristics of tropospheric effects using nine years of image pairs globally derived from Sentinel-1A/B’s orbit constellation configuration (acquisition intervals, dates and time of day) and the Generic Atmospheric Correction Online Service for InSAR (GACOS). Our findings quantify pronounced spatial heterogeneity and temporal variability in tropospheric errors, with globally variable linearity, seasonality and randomness in image pair time series. Linear constrained time series inversions (e.g., image pair stacking) demonstrate the effectiveness of long-temporal-baseline image pairs in enhancing accuracy, but such improvement is not continuously growing, highlighting the need to balance the number of image pairs with achievable accuracy. Obtaining seasonal deformation faces greater challenges due to dominant tropospheric seasonality, especially in cases with delayed seasonal responses driven by processes like groundwater extraction or water erosion. These findings offer a framework for understanding tropospheric effects and practical recommendations for improving deformation inversion accuracy, providing valuable insights that can serve as indicators for orbit parameter design and optimization of future SAR missions.

The accessibility of Synthetic Aperture Radar (SAR) data, particularly from missions such as Sentinel-1, has transformed remote sensing and fueled rapid growth in both scientific research and operational applications. However, converting raw interferometric phase measurements into reliable, actionable displacement products remains technically complex and poses a significant barrier for non-specialists. To bridge this gap, the Observational Products for End-Users from Remote Sensing Analysis (OPERA) project spearheaded by the NASA Jet Propulsion Laboratory, California Institute of Technology, leverages cloud-based processing to deliver standardized, continental-scale InSAR Analysis-Ready Data (ARD). This presentation focuses on the OPERA Surface Displacement (DISP) product suite, which systematically generates displacement time series across North America. Notably, DISP is the first-ever CEOS-ARD compliant InSAR product, setting a new international standard for interoperable InSAR time series data. OPERA is currently processing the Sentinel-1 archive from 2016, providing a continuous record of ground motion through the present. We will also highlight the NASA-ISRO SAR (NISAR) mission and the plan to integrate NISAR to the OPERA DISP suite (DISP-NI). This will complement the DISP-S1 products by significantly expanding spatial coverage, increasing temporal revisit times, and improving coherence in vegetated areas over North America. The DISP suite enables investigation of natural and anthropogenic processes, including but are not limited to volcanic unrest, tectonic processes, landslides, and land subsidence caused by hydrocarbon or groundwater extraction, by providing standardized displacement time series that reduces the computational burden of large-scale InSAR time series processing. These products also serve as the foundation for the forthcoming Vertical Land Motion (VLM) suite, a higher-level derivative that integrates InSAR displacement time series with geodetic reference frames and GNSS observations. The VLM suite is designed to provide consistent vertical displacement time series, and where feasible, horizontal components, enabling more robust and direct assessments of long-term subsidence, uplift, and broader crustal deformation processes. Beyond ground motion, OPERA also provides a comprehensive portfolio of Earth observation Level-3 products derived from SAR (Sentinel-1, NISAR) and optical sensors (Sentinel-2, and Landsat 8/9) on a near-global scope. These include the Dynamic Surface Water Extent (DSWx) and Surface Disturbance (DIST) suites for monitoring hydrological variations and land disturbance change. In this presentation, we will also showcase OPERA’s intermediate products: Radiometric Terrain Corrected (RTC) backscatter and Coregistered Single-Look Complex (CSLC) products. While these datasets serve as the primary inputs for the Level-3 suites, they also enable advanced users to generate customized products and perform specialized geophysical analyses. All OPERA products are freely available through the NASA Distributed Active Archive Centers and Earthdata Search, supporting easy access and rapid use in both research and operational contexts.

Instantaneous State InSAR (IS-InSAR) was recently introduced as an alternative to traditional history-based parameterizations for deformation monitoring, enabling recursive near-real-time estimation of displacement through a state-space formulation. Central to this framework is the smoothness doublet, which governs the stochastic behavior of the instantaneous velocity via an Ornstein–Uhlenbeck process. The smoothness doublet consists of two physically interpretable parameters: (1) the variance of the instantaneous velocity and (2) its correlation time. Together, these parameters control the degree to which temporal variations in motion are permitted, which influences both the predicted state evolution and the relative weighting of new observations in the recursive update. In this contribution, we present a systematic analysis of the smoothness doublet—which needs to be chosen based on external contextual information—and its impact on IS-InSAR performance. We investigate how variations in the velocity variance determine the amplitude of allowable short-term deviations from the mean motion, while changes in the correlation time regulate the temporal persistence of these deviations. Adjusting these parameters modifies the estimated instantaneous position and velocity components of the state vector, and propagates into the stochastic model underlying the phase observations. As a consequence, the estimation and validation of integer phase ambiguities—an essential component of high-precision InSAR—are significantly affected. We demonstrate that small velocity variances and long correlation times approximate the traditional assumption of near-infinite smoothness, leading to conservative updates and reduced responsiveness to behavioral change. Conversely, larger variances and shorter correlation times increase adaptivity but may amplify noise sensitivity and ambiguity instability. The smoothness doublet therefore acts as a critical tuning mechanism that balances stability and responsiveness in near-real-time deformation monitoring. To quantify these effects, we introduce a set of evaluation metrics that assess (i) state prediction consistency, (ii) innovation statistics, i.e., the difference between observations and predictions, (iii) ambiguity resolution reliability, and (iv) temporal responsiveness to behavioral change. Through simulated and experimental scenarios, we illustrate how different configurations of the smoothness doublet influence displacement reconstruction quality and ambiguity robustness. Our results provide practical guidance for selecting and tuning smoothness parameters in application-aware (triple-A) IS-InSAR projects. By explicitly analyzing the stochastic structure of the instantaneous velocity process, this study establishes a rigorous framework for optimizing recursive deformation monitoring under varying dynamic conditions, thereby enhancing the reliability and interpretability of near-real-time InSAR products.

Reliable characterization of uncertainty in Interferometric Synthetic Aperture Radar (InSAR) deformation time series is essential for the correct interpretation of geophysical signals. Although substantial progress has been made in modelling individual noise sources in InSAR data (such as decorrelation effects, atmospheric delays), most studies focus on the statistical properties of noise at the interferogram level time-series. However, InSAR deformation products are derived through complex time-series processing chains that include spatial and temporal filtering, atmospheric phase screen mitigation, and inversion procedures. These processing steps fundamentally modify the statistical characteristics of the noise and introduce correlations across both space and time, which are rarely accounted for in existing uncertainty descriptions. Although error propagation through the InSAR processing chain has been investigated, most existing approaches rely on the assumption that the stochastic properties of interferometric observations are known a priori. In practice, however, these properties are rarely known with sufficient accuracy, as they depend on factors such as acquisition geometry, surface characteristics, and noise contributions that may vary across scenes and acquisitions. Consequently, uncertainty modelling based solely on predefined stochastic assumptions may not adequately represent the true noise characteristics of InSAR observations, limiting the reliability of the resulting deformation estimates. Moreover, the processing parameters and filtering strategies vary significantly between studies, making it difficult to define a generic stochastic model for InSAR deformation products. In this study, we present an integrated framework for numerical assessment of the spatio-temporal noise structure in InSAR deformation time series by combining rigorous error propagation with Variance Component Estimation (VCE). The proposed method propagates uncertainty from interferometric observations to the final deformation estimates while simultaneously estimating the variance components associated with dominant noise sources. This integrated strategy removes the need for prior assumptions about the exact noise structure of the interferograms and allows the stochastic model to be inferred directly from the data within the processing framework. Particular emphasis is placed on the computational efficiency and scalability of the approach to enable its application to large InSAR datasets. An iterative estimation strategy is adopted, in which the noise structure is first characterised in the temporal domain and subsequently refined in the spatial domain. The framework focuses primarily on two dominant noise components in InSAR time series: atmospheric disturbances and decorrelation noise. In addition, the method accounts for model-induced noise arising from deviations between the true deformation signal and the functional models implicitly or explicitly assumed during spatio-temporal filtering and inversion. For error propagation, a flexible and general framework is employed, capable of accommodating different processing settings and spatio-temporal assumptions within the applied processing chain. The methodology is demonstrated using Sentinel-1 data over several subsidence regions in Iran, illustrating its applicability for large-scale InSAR time-series analyses. The results show that InSAR deformation time series commonly exhibit strongly correlated noise patterns in both spatial and temporal domains. The proposed framework quantifies how these correlations depend on the initial measurement uncertainties as well as on the applied processing settings. Such correlated noise can occasionally mimic real deformation signals, leading to potential misinterpretations. By explicitly estimating and propagating the noise structure through the processing chain, the proposed method enables a more reliable assessment of deformation signals.

High-resolution wide-swath (HRWS) SAR missions often use staggered acquisitions, varying the pulse repetition frequency (PRF) along azimuth to enlarge the swath while maintaining high resolution. A key side effect is spatially variant azimuth ambiguities that appear in multiple regions and degrade image quality. Existing countermeasures (e.g., Doppler shifting and coherence-based detection) can leave residual artifacts, while subaperture averaging reduces azimuth resolution. We propose an azimuth-ambiguity removal framework based on partially-missing-band synthetic-aperture processing. By repeatedly forming images while selectively excluding narrow azimuth-frequency (Doppler) bands, we isolate sidelobe-dominated components and preserve mainlobe returns. Contaminated bands are detected using a per-iteration mean-centered azimuth-time distance metric, which suppresses band-dependent biases and highlights sidelobe outliers, and are then suppressed while maintaining the effective imaging bandwidth required for high azimuth resolution. Experiments on ALOS-4 PALSAR-3 Level-1.2 variable-PRF data show reduced ambiguity over both ocean and land scenes and improved interferometric phase quality, indicating benefits for InSAR applications. Because the method targets ambiguity behavior induced by synthetic-aperture processing rather than sensor-specific tuning, it is expected to generalize to other variable-PRF missions such as NISAR and ROSE-L.

Ground-based frequency-modulated continuous-wave (FMCW) radar systems provide a versatile platform for high-resolution imaging of moving targets under well-controlled observational geometries. Compared with airborne or spaceborne systems, ground-based configurations enable repeated measurements, flexible parameter selection, and detailed experimental analysis of imaging characteristics. In this study, full-polarimetric inverse synthetic aperture radar (ISAR) imaging of a ship target is experimentally characterized using the GPRI-II ground-based Ku-band radar system. The objective is to evaluate polarization-dependent ISAR image formation and to examine the practical implementation of coherent full-polarimetric processing in a maritime observation scenario. The GPRI-II radar operates with FMCW waveforms and supports fully polarimetric data acquisition. The experiment was conducted in a coastal environment, where the radar antenna remained stationary in a staring configuration while the ship's motion generated the synthetic aperture required for cross-range resolution. In this geometry, the target motion induces Doppler modulation over the coherent processing interval, enabling two-dimensional ISAR image formation without physical antenna movement. Fully polarimetric measurements were recorded in the HH, HV, VH, and VV channels, allowing comprehensive characterization of scattering mechanisms across polarization states. Experiments were conducted using multiple chirp durations (0.5, 2, 4, and 16 ms) to investigate the influence of chirp duration on ISAR image formation. For the 2 ms acquisition, corresponding to a pulse repetition frequency of approximately 125 Hz, the effective coherent interval contributing to azimuth focusing is approximately 19 s for the ship moving at a nominal speed of 16.7 km/h. Under these conditions, with the ship observed at a slant range of about 430 m and predominantly cross-track motion, the available slow-time aperture provides sufficient Doppler bandwidth for high-resolution azimuth focusing, yielding cross-range resolution in the centimeter range under stable motion conditions. Varying the chirp duration modifies the PRF and consequently, the Doppler sampling characteristics, directly influencing focusing behavior in the azimuth direction and the achievable cross-range resolution. A unified signal-processing workflow was applied consistently across all channels to ensure comparability. The processing sequence included FMCW range compression through matched filtering, selection of a region of interest containing the ship target, and slow-time phase error estimation using a phase-gradient autofocus algorithm applied to the dominant co-polarized channel. To preserve inter-channel phase coherence, the phase correction estimated from the reference co-polarized channel was applied identically to the remaining channels prior to the Fourier transform in the azimuth direction. This strategy enables the coherent reconstruction of full-polarimetric ISAR images while minimizing relative phase inconsistencies arising from independent channel processing. Radiometric normalization and dynamic range adjustment were further applied to facilitate visual and quantitative comparison among polarization channels. The reconstructed ISAR images exhibit pronounced polarization-dependent scattering. The co-polarized channels (HH and VV) exhibit strong, spatially concentrated scattering centers corresponding to dominant structural components of the ship, including the hull, deck boundaries, masts, and superstructure elements. Specular reflections from metallic surfaces and double-bounce interactions between vertical structures and the water surface are clearly observed in these channels. In contrast, the cross-polarized channels (HV and VH) present comparatively lower overall intensity but reveal complementary structural details. Cross-polarized returns emphasize depolarization mechanisms associated with geometrically complex features, tilted surfaces, and multiple-scattering interactions. These differences highlight the sensitivity of polarimetric ISAR imaging to target geometry and orientation. To further interpret scattering behavior, Pauli-based polarimetric combinations were generated to visualize the relative contributions of different scattering mechanisms. Such representations enhance the discrimination between dominant structural scatterers and more diffuse or anisotropic responses. The spatial distribution of polarimetric signatures provides additional insight into structural heterogeneity across the ship body. Comparative analysis across channels confirms that full-polarimetric acquisition significantly improves interpretability relative to single-polarization imaging, particularly in distinguishing coherent structural reflections from depolarized components. The experimental results demonstrate the practical feasibility of full-polarimetric ISAR imaging using a ground-based FMCW radar platform and validate coherent multi-channel processing in a realistic maritime environment. The study provides a systematic assessment of polarization-dependent ISAR image characteristics and establishes an experimental foundation for further investigations into polarimetric target characterization and classification. These findings support the broader application of ground-based full-polarimetric ISAR systems in maritime monitoring, structural analysis, and radar-based target interpretation.

Phase bias in multilooked short-baseline interferograms introduces systematic distortions in InSAR time series, particularly in regions of low long-term coherence. While phase linking (PL) techniques can mitigate these effects, their applicability remains limited in densely vegetated or seasonally dynamic environments. We previously developed a mitigation strategy based on short-term loop-closure analysis and bias inversion from wrapped interferograms [1, 2]. The approach estimates bias terms from closure residuals and stabilises the solution using temporal regularisation. Validation over selected test sites demonstrated significant reduction of spurious deformation signals in vegetated regions. Here, we extend this framework to a nationwide-scale implementation over the Italian peninsula using seven ascending Sentinel-1 COMET-LiCSAR frames covering the entire region. The dataset spans multiple climatic zones and land cover classes, providing a comprehensive test bed for large-scale assessment. All interferograms required for closure analysis were generated using LiCSAR processing, and the inversion was performed under optimised regularisation weights derived from misfit–smoothness trade-off analysis. To further reduce unstable bias estimates, we implemented a two-stage quality-control framework. First, bias unknowns insufficiently constrained by loop-closure observations were identified and nulled based on per-unknown support metrics. Second, a temporal consistency criterion was applied to the estimated bias time series using a circular coherence measure within a moving temporal window to suppress oscillatory or poorly constrained solutions. This strategy significantly improved inversion stability in low-coherence and seasonally decorrelated regions. The large-scale processing enables systematic evaluation of phase bias behaviour across forests, agricultural areas, mountainous terrain, and urban regions. We quantify residual loop closures before and after correction and analyse the seasonal variability of bias amplitude. Results show that phase bias is strongly correlated with vegetation dynamics and moisture conditions, leading to false subsidence or uplift patterns in uncorrected velocity fields. The proposed correction significantly reduces closure residuals and achieves strong agreement with PL-derived velocities in coherent areas, while preserving meaningful deformation signals in decorrelated regions where PL fails. This work demonstrates the scalability and operational robustness of the proposed phase bias correction framework and provides new insights into the large-scale spatial and temporal characteristics of InSAR phase bias. [1]          Y. Maghsoudi, A. J. Hooper, T. J. Wright, M. Lazecky, and M. Pinheiro, "Advances in mitigating InSAR non-closure phase bias: A refined processing approach," Science of Remote Sensing, vol. 12, p. 100304, 2025/12/01/ 2025. [2]          Y. Maghsoudi, A. J. Hooper, T. J. Wright, H. Ansari, and M. Lazecky, "Characterizing and Correcting Phase Biases in Short-Term, Multilooked Interferograms," Remote Sensing of Enironment (in review), preprint available at EarthArXiv, 2021.

Interferometric Performance Evaluation of Sentinel-1C and Sentinel-1D: Results from the Independent Calibration Campaign of the Commissioning Phase Matteo Nannini1, Andrea Pulella1, Pau Prats-Iraola1, Patrick Klenk1, Dirk Geudtner2 1 Microwaves and Radar Institute, German Aerospace Center (DLR), Germany Münchener Str. 20, 82234 Weßling, Germany Email: matteo.nannini@dlr.de 2European Space Agency (ESA-ESTEC) This contribution summarizes the interferometric investigations carried out during the in-orbit commissioning phase of both Sentinel-1C and Sentinel-1D satellites. The presented investigations were conducted by the DLR-HR Institute during the commissioning phase of the two spacecrafts as part of the independent calibration campaign for the Sentinel-1C and Sentinel-1D SAR systems on behalf of ESA [1]. The analyses were performed on Level-1 data processed by the IPF processor of ESA. Sentinel-1C was launched on 5 December 2024 to replace Sentinel-1B (decommissioned after suffering non-recoverable on-board failures at the end of 2021). Sentinel-1D was launched on 4 November 2025 to eventually replace Sentinel-1A. This contribution focuses on reporting the Sentinel-1 InSAR-related performance in terms of the main interferometric parameters: perpendicular baseline, Doppler centroid, common Doppler bandwidth, and burst mis-synchronization. These figures of merit are particularly critical for the TOPS acquisition mode [2] adopted by the Copernicus Sentinel-1 constellation as default mode for acquisitions over land [3], as this mode imposes stringent interferometric requirements [4]. For example, assuming most of the above-mentioned parameters as nominal, the synchronization among bursts must remain within ±5ms to enable high-quality interferometry. These parameters have been evaluated through a statistical analysis of hundreds of InSAR data pairs acquired by the sensors during their respective commissioning phases, hence providing reliable statistics to assess the suitability of the system for InSAR applications. The annotation data, which accompany the SAR data, were the primary source of information for this analysis. It is worth noting, that after the retirement of Sentinel-1B, Sentinel-1A has operated concurrently with Sentinel-1C to ensure observational continuity. Consequently, some of the results presented in this work were obtained involving Sentinel-1A data, still demonstrating the usefulness of Sentinel-1A for InSAR. The analyses presented in this contribution cover both the co-sensor case, which involves data acquired by the same spacecraft (e.g., Sentinel-1C), to assess the individual sensor performance, and the cross-sensor case, which involves generating cross-interferograms between the different sensors. This latter analysis is crucial due to the synergistic design of the Copernicus constellation, since the cross-sensor configuration enables the possibility of halving the repeat-pass time. In addition to that, the commissioning phase provided a unique opportunity to begin analyzing data before the final operational orbit configuration with a 180° orbit phasing (i.e., the nominal six-day repeat) was implemented. Specifically, the first repeat cycles after the launch of a new Sentinel-1 sensor (C or D) were operated in a 30° orbit phasing, allowing for a one-day exact repeat with respect to the corresponding predecessor. This unique orbit phasing for the initial cycles enabled the generation of one-day repeat-pass cross-interferograms. To additionally evaluate the stability of the system in terms of the aforementioned parameters, long data take analyses were performed, demonstrating exceptional performance of both sensors and ensuring high-quality interferometric products. In addition to the quantitative assessment of the Sentinel-1C/D spacecrafts performance through interferometric statistical analysis, this contribution also presents a set of interferometric results to qualitatively demonstrate the impressive achievable interferometric performance. Stationary scenarios, such as the Atacama Desert [4], monitoring of ground deformation in Mexico City, and non-stationary scenarios, including the Mt. Fentale, Zachariæ Isstrøm Glacier, and the 2025 Myanmar earthquake [5], are exemplary outcomes that were obtained during the commissioning phase of Sentinel-1C. Furthermore, the performance for the different SAR acquisition modes, such as the Interferometric Wide (IW), Extra Wide (EW), and Stripmap modes will be reported. These results highlight the system's inherent operational readiness. The contribution provides insights into some of the analyses concerning interferometry that were conducted during the commissioning phase, underlying the importance of this phase in performing final system tuning before providing data to the end users. This comprehensive interferometric analysis confirms the readiness of the Sentinel-1C and Sentinel-1D sensors for operational use, ensuring high-quality interferometric products for a wide range of applications, from environmental monitoring to disaster response. Literature [1] Klenk, P., Giez, J., Schmidt, K., Nannini, M., & Schwerdt, M. "Independent calibration of the Sentinel-1C SAR system." EUSAR 2024; 15th European Conference on Synthetic Aperture Radar. VDE, 2024. [2] F. De Zan and A. Monti Guarnieri, TOPSAR: Terrain Observation by Progressive Scans, IEEE Transactions on Geoscience and Remote Sensing, Vol. 44, No. 9, September 2006, pp 2352-2360. [3]          R. Torres, R., et al., “GMES Sentinel-1 Mission”, Special Issue of Journal of Remote Sensing of Environment “The Sentinel Missions – New Opportunities for Science”, Vol. 120, pp. 9-24, May 2012. [4] N. Yague-Martinez, Prats, P., Gonzalez, F, R., Brcic, R., Shau, R., Eineder, M., Geudtner, D. and Bamler, R., “Interferometric Processing of Sentinel-1 TOPS Data”, IEEE Trans. Geoscience and Remote Sensing, Vol. 54, No. 4, pp. 2220-2234, 2016. [5] ESA - Sentinel-1C demonstrates power to map land deformation [6] ESA - Sentinel-1 captures ground shift from Myanmar earthquake

Differential SAR interferometry (DInSAR) and Multi-Temporal DInSAR (MTInSAR) are largely exploited for measuring slope stabilities. Several datasets are currently available at different wavelengths, spatial resolutions, and revisit times, spanning national or continental areas, and collectively covering long time periods (even more than 20 years). A reliable monitoring of ground instabilities and related early warning signals requires a detailed analysis of both spatial patterns and time series of displacement measurements derived from DInSAR/MTInSAR for assessing their availability, reliability, and significance with respect to the application on hand. End users, indeed, should be able to check accurately the availability of DInSAR/MTInSAR-based information over the area of interest, to model the sensitivity of interferometric measurements to the ground displacements, and to recognize different signal components and possible artifacts affecting the MTInSAR products, such as, for instance, those related to atmospheric artifacts or phase unwrapping errors. Consequently, end users need specialized skills and, possibly, tools, which may support a reliable exploitation of DInSAR/MTInSAR products covering wide areas and long time periods and consisting of a huge number of coherent targets (up to millions) (Bovenga, 2024). This is particularly pertinent when dealing with applications, such as slope instabilities, occurring in critical environmental settings that negatively impact DInSAR/MTInSAR products (Wasowski and Bovenga, 2014). First, the steep topography may lead to unfavourable illuminating conditions in terms of either unfeasible detection over layover and shadow areas or low sensitivity to the ground displacement. Second, the presence of dense vegetation and changeable cover conditions causes DInSAR signal decorrelation and a low density of MTInSAR coherent targets (CTs). Third, displacement kinematics are characterised by nonlinear components and high displacement rates, leading to measurements corrupted by aliasing. All these critical issues negatively impact the applicability and interpretation of this well-established technology. We developed a QGIS plugin based on the PyQGIS library (Bovenga and Piccolino, 2025), which, starting from standard DInSAR/MTInSAR products and a few ancillary layers, derives additional products useful for supporting the end-user interpretation and ground instability assessment over the area under investigation. First, the tool estimates the visibility of the area of interest (AOI) with respect to the satellite line of sight (LOS). It combines the satellite acquisition geometry and the ground geomorphic information to derive an index of visibility, which allows end-users to check the applicability of DInSAR analysis over the AOI just based on geometrical factors and before performing DInSAR processing. Moreover, the reliability of DInSAR products may also depend on the orientation of the local slope within the AOI. For instance, when dealing with landslides, for slopes facing north or south, the downslope movement is basically perpendicular to the LOS direction, thus leading to unfeasible DInSAR-based estimation of displacements. Hence, the tool estimates the percentage of downslope movement captured from the DInSAR geometry along the LOS and, for each CT, computes the downslope mean displacement rate corresponding to the LOS component measured by MTInSAR. These outputs of the tool may be combined with other layers such as NDVI, DInSAR coherence, and landslide inventory for performing a feasibility analysis before DInSAR/MTInSAR processing for both checking the reliability and supporting the interpretation of DInSAR/MTInSAR products for ground instabilities. Moreover, the tool automatically computes the percentage of the AOI surface covered by coherent targets (CTs). This allows end users to estimate how significant the information derivable from MTInSAR within the AOI is and to decide whether more complementary information is needed for assessing the instability of the area or not (Bovenga et al., 2023). Finally, the tool investigates the spatial homogeneity of the CTs' distribution within the AOI. Indeed, a relatively high percentage of surface covered by CTs does not necessarily imply a uniform coverage of the entire AOI, which may include regions lacking CTs. Hence, the tool, by using the inhomogeneous Ripley’s K-function (Dixon, 2002), detects the presence of voids in the CT spatial distribution, resulting in a lack of kinematic information needed for a reliable assessment of the ground instability. Since the tool was designed to deal with generic DInSAR/MTInSAR products, we tried to reduce the parameters required in the inputs associated with SAR/DInSAR processing, which may be inaccessible to the end user. To this aim, we developed an approximated formula for calculating the local heading angle, defined as the angle between the satellite ground track and a meridian for an arbitrary point on the ground track, by using just the pixel latitude, incident angle, the orbit elevation, and the number of satellite revolutions per day. Moreover, we developed an approximated relationship between look angle and incident angle (without the need for precise geocoding computation), in case the incident angle is not available. We assessed the errors associated with both the local heading angle and the look angle computed through the approximate formulas by comparing them with the values computed through the correct geometrical formulation involving orbital state vectors, carried out through a standard interferometric processing chain. We performed this analysis by exploiting four Sentinel-1 IW products acquired from both ascending and descending passes over two areas with large height variations located in both the Northern and Southern hemispheres and involving the three IW sub-swaths. The local heading angle error is below 0.7° with a mean of 0.53° and a standard deviation below 0.01°. Finally, the tool performs a displacement time series analysis based on automated procedures recently developed for identifying CTs with nonlinear signals and based on fuzzy entropy and Fisher statistics (Bovenga et al., 2022). This allows end users to focus their investigations on a smaller set of CTs affected by nonlinear displacements (including warning signals) and potentially deserving sophisticated geophysical or geotechnical analysis. The work introduces the methodologies and functionalities of the tool and provides examples of its application on DInSAR/MTInSAR products derived by processing Sentinel-1 data over mountainous areas, where slow mass movements with diverse mechanisms and with different deformation rates and patterns occur. References Bovenga, F., Argentiero, I., Refice, A., Nutricato, R., Nitti, D.O., Pasquariello, G., Spilotro, G., 2022. Assessing the Potential of Long, Multi-Temporal SAR Interferometry Time Series for Slope Instability Monitoring: Two Case Studies in Southern Italy. Remote Sensing, 2022, 14(7): 1677. DOI: 10.3390/rs14071677. Bovenga, F., Argentiero, I.. Belmonte, A., Refice, A., Cuozzo, G., Heredia, M. S., Callegari, M., Notarnicola, C., Nutricato, R., Nitti, D.O., 2023. Assessing Rock Glacier Activity In Val Senales By Exploiting Multiband SAR Data Through Differential SAR Interferometry And Offset Tracking in 12th International Workshop on "Advances in the Science and Applications of SAR Interferometry and Sentinel-1 InSAR" - FRINGE 2023, University of Leeds, Leeds, UK, 11-15/09/2023 Bovenga F., 2024. Cloud-based and On-premises Tools for Earth Observation Data Processing in Disaster Management Activities. 2024 IEEE International Humanitarian Technologies Conference (IHTC), Bari, Italy, 2024: 1–7. DOI: 10.1109/IHTC61819.2024.10855063. Bovenga, F., Piccolino, F., 2025. InSAR Product Analysis (IPA): a QGIS tool for slope instability assessment based on SAR interferometry. EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17224. DOI: 10.5194/egusphere-egu25-17224. Dixon, P. M., 2002. Ripley’s K-function. In Encyclopedia of Environmetrics, 1796–1803. Wiley 2024, 1-7. DOI: 10.1109/IHTC61819.2024.10855063. Wasowski, J., Bovenga, F., 2014. Investigating landslides and unstable slopes with satellite Multi Temporal Interferometry: Current issues and future perspectives. Engineering Geology 174: 103–138. DOI: 10.1016/j.enggeo.2014.03.003. Acknowledgment This work was supported in part by Regione Puglia (Italy) under project “Utilizzo di intelligenza artificiale e dati satellitari per il monitoraggio dell’instabilità del territorio”, POC PUGLIA FESR-FSE 2014 / 2020 - Programma Regionale RIPARTI, grant agreement 01975b92; and in part by the European Union - Next Generation EU, Mission 4, Component 2, CUP H53D23001660006 (PRIN22 Project "MIRAGE: Mass movement Investigation and prediction through geomorphology, Remote sensing and Artificial intelligence").

Accurate measurement of north-south surface deformation at sub-centimetre scale is essential for monitoring tectonic processes such as interseismic strain accumulation and postseismic relaxation along strike-slip fault systems. However, conventional Interferometric Synthetic Aperture Radar (InSAR) observations are primarily sensitive to vertical and east-west motion due to the side-looking radar acquisition geometry, leaving the north-south component poorly constrained. Future SAR missions aim to address these limitations by providing additional viewing geometries and improved azimuth displacement measurements, including the NASA-ISRO Synthetic Aperture Radar (NISAR) mission, ESA’s ROSE-L mission planned for launch in 2028, and ESA’s Harmony mission expected in 2030, which will operate alongside Sentinel-1 with companion satellites to improve line-of-sight diversity and enable more complete three-dimensional deformation measurements. Here we address this limitation by integrating Subswath and Burst Overlap Interferometry (SBOI) derived from Sentinel-1 TOPS acquisitions within the COMET-LiCS processing infrastructure. The objective is to generate operational along-track interferometric measurements that complement conventional line-of-sight InSAR products and improve the characterization of north-south deformation using the already more than decade-long Sentinel-1 observation record since end of 2014. The developed workflow extends the COMET LiCSAR processing system (Lazecký et al., 2020) to automatically generate SBOI interferograms from Sentinel-1 data. To improve the reliability of the along-track signal, several correction terms are implemented, including mitigation of ionospheric gradient effects, and removal of non-tectonic contributions such as solid Earth tides and plate motion projected into the along-track direction. The resulting datasets are analysed using COMET LiCSBAS time-series processing (Morishita et al., 2020) and evaluated across several north-south-oriented strike-slip fault systems worldwide, including the Dead Sea Fault, Chaman Fault, and East Anatolian Fault. The SBOI interferograms are generated from the same Sentinel-1 acquisitions as the conventional line-of-sight interferograms, ensuring temporal consistency between datasets while providing an additional measurement geometry. After applying the correction framework, the resulting time series reveal localized north-south tectonic deformation with millimetre-level sensitivity along major strike-slip fault systems. The integration of SBOI processing into the COMET-LiCS portal provides open and accessible along-track interferometric products for the geodetic community. When combined with conventional InSAR and GNSS observations (Elliott et al., 2025), these datasets improve the capability to resolve three-dimensional tectonic deformation and prepare the community for future SAR missions with enhanced viewing geometries. Elliott, J. R., Fang, J., Lazecký, M., Maghsoudi, Y., Ou, Q., Payne, J. A., Rollins, C., Wang, D., & Hooper, A. (2025). Deformation, Strains and Velocities for the Alpine Himalayan Belt from trans-continental Sentinel-1 InSAR & GNSS. Lazecký, M., Spaans, K., González, P. J., Maghsoudi, Y., Morishita, Y., Albino, F., Elliott, J., Greenall, N., Hatton, E., Hooper, A., Juncu, D., McDougall, A., Walters, R. J., Watson, C. S., Weiss, J. R., & Wright, T. J. (2020). LiCSAR: An Automatic InSAR Tool for Measuring and Monitoring Tectonic and Volcanic Activity. Remote Sensing, 12(15), 2430. https://doi.org/10.3390/rs12152430 Morishita, Y., Lazecky, M., Wright, T. J., Weiss, J. R., Elliott, J. R., & Hooper, A. (2020). LiCSBAS: An Open-Source InSAR Time Series Analysis Package Integrated with the LiCSAR Automated Sentinel-1 InSAR Processor. Remote Sensing, 12(3), 424. https://doi.org/10.3390/rs12030424

The proliferation of massive InSAR processing services, such as the European Ground Motion Service (EGMS), has provided an unprecedented volume of data regarding Earth’s surface dynamics. However, translating these complex displacement time-series into actionable insights remains a significant challenge for non-expert end-users in critical sectors. This communication presents the MoMo (Motion Modeling) service, an advanced post-processing framework designed to bridge the gap between Interferometric SAR outputs and decision-making in civil engineering and urban management. At the core of MoMo is a robust statistical strategy based on Multiple Hypothesis Testing (MHT). Following the Occam’s razor principle and the B-method for statistical testing, the algorithm automatically identifies the most parsimonious model that explains the displacement behavior of each measurement point. This approach enables the precise detection of break-points and seasonally driven deformation patterns through the integration of exogenous variables (e.g., temperature, groundwater levels, or precipitation), allowing the underlying drivers to be characterized with millimetric accuracy. The scientific value of the MHT strategy lies in its ability to filter noise and provide a reliable physical interpretation of the data. However, its true impact is demonstrated in its application to large-scale infrastructure management. We present case studies showcasing the monitoring of terrestrial transport networks, large-scale port facilities, and dam stability. Special emphasis is placed on the detection of "hotspots" in entire cities, where the tool transforms large volumes of EGMS-like data points into prioritized risk maps. The core innovation of MoMo lies in the democratization of advanced analytics for non-InSAR expert domains such as infrastructure management and urban planning. The service transforms millimeter-level displacement time series into actionable causality for asset managers, enabling them to move beyond “dots on a map” toward a clear understanding of deformation drivers. By systematically integrating climatic and environmental variables (such as temperature and precipitation), MoMo allows users to distinguish between seasonal structural responses and genuine geotechnical hazards. Furthermore, its intuitive detection of break-points and trend shifts provides infrastructure managers with precise temporal markers to assess the impact of extreme events (e.g., major storms or flooding episodes) on long-term structural behavior. By operationalizing global datasets such as EGMS within a user-friendly analytical framework, MoMo represents a concrete step toward data-driven infrastructure resilience within a digital twin paradigm. By automating the interpretation of InSAR time-series, MoMo facilitates the dissemination and adoption of radar interferometry in sectors that traditionally struggle with data complexity. This tool empowers stakeholders to move from reactive maintenance to proactive risk mitigation strategies. The service is available on the DestinE Platform. Destination Earth (DestinE) is a flagship initiative of the European Commission aimed at developing a high-precision digital representation of the Earth system. By combining large volumes of environmental data with advanced modelling capabilities, DestinE supports the monitoring and simulation of natural and human-driven processes, helping policymakers, researchers, and stakeholders make more informed decisions for climate adaptation and disaster risk management. Destination Earth (DestinE) is a European Union funded initiative, with the aim to build a digital replica of the Earth system. The initiative is being jointly implemented by three entrusted entities: the European Space Agency (ESA), responsible for building the ‘Core Service Platform’, the European Centre for Medium-Range Weather Forecasts (ECMWF), responsible for the creation of the first two ‘digital twins’ and the ‘Digital Twin Engine’, and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), responsible for the creation of the ‘Data Lake’. Access Destination Earth at destine.platform.eu.

Towards a national-scale high-resolution InSAR Ground Motion Service for the Netherlands Suresh Krishnan Palanisamy Vadivel1, Manon Verberne1, Ece Ozer2, Thibault Candela1, Kay Koster1, Megan Wouters1 1TNO Geological Survey of the Netherlands 2 TNO, Department of Reliable Structures Abstract Ground motion in the Netherlands, including subsidence, is often human induced, resulting from salt and hydrocarbon extraction, groundwater management, anthropogenic loading, and post-mining effects. Existing services like the European Ground Motion Service provides continental-scale products, however, higher-resolution InSAR data are required for the Netherlands-specific applications. This project develops a national, high-resolution ground motion service using RADARSAT-2 XF SAR data. TNO Geological Survey of the Netherlands implements an operational workflow combining persistent and distributed scatterer time-series InSAR to generate Level 2a line-of-sight (LOS) displacements. Products are calibrated with the GNSS-based A-EPND model to produce Level 2b data referenced to ETRF2014, ensuring geodetic consistency. A rigorous validation framework assesses the accuracy of deformation estimates, geolocation, and height estimates against in-situ observations. An interactive viewer integrates subsurface geological information to support interpretation. Initial results for South Limburg demonstrate the capability to provide insights into post-mining deformation and support the development of a nationwide service. 1. Introduction Subsidence in the Netherlands is mainly driven by human-induced processes, such as progressive lowering of groundwater levels leading to peat oxidation and soil consolidation, as well as the extraction of salt and hydrocarbon, resulting in cavern and reservoir compaction [1]. Reliable deformation monitoring is essential for infrastructure management, hazard assessment, and spatial planning. Satellite-based InSAR enables millimetre-scale detection of surface displacement over large areas. When integrated with GNSS observations, it provides a robust framework for geodetic referencing and validation. Although the European Ground Motion Service (EGMS) [2] delivers continental-scale products, Dutch stakeholders often require higher resolution products, especially when monitoring relatively small assets such as roads, railways, or dikes. To overcome this, this project aims to establish a national-scale, high-resolution ground motion service using RADARSAT-2 XF SAR data, delivering calibrated, validated, and publicly accessible deformation products integrated with subsurface information. 2. Objectives and Services Concept The primary objective of our national-scale InSAR ground motion service is to deliver publicly accessible, high-resolution ground deformation data using RADARSAT-2 XF SAR images. The scope includes nationwide coverage of the whole Netherlands and aims to significantly increase the density of persistent and distributed scatterers compared to medium-resolution Sentinel-1 data, enabling asset-level deformation assessment. TNO Geological Survey of the Netherlands (GDN) will implement an optimum strategy that integrate targeted services such as national-scale InSAR products and subsurface knowledge into a single platform. 3. Data and Processing Methodology 3.1. Data and Methods Netherlands ground motion products are generated from C-band SAR data acquired by RADARSAT-2 XF mode, selected for its long-term mission continuity from 2015 to 2025, and high-spatial resolution of 5m x 5m. Both ascending and descending tracks are processed to allow decomposition of line-of-sight (LOS) deformation into vertical and east-west components. Figure 1 illustrates the ascending and descending track geometries providing full national coverage. The processing chain presented in Figure 2 follows a time-series Persistent Scatterer (PS) and Distributed Scatterer (DS) InSAR methodology optimized for the Netherlands terrain conditions, where low deformation gradients and rural landscapes require high sensitivity and point density. Pre-processing includes precise orbit correction, radiometric calibration, sub-pixel co-registration, interferogram generation using a small-baseline network, and removal of topographic phase contributions using a high-resolution LiDAR-based national scale digital elevation model (AHN). Candidate scatterers are identified using amplitude dispersion and temporal coherence indices for persistent targets [3] and statistical homogeneity criteria for distributed targets estimated through phase-linking using coherence matrix generated from SHPs (Statistically Homogeneous Pixels) [4-6], maximizing coverage in both urban and agricultural areas. Atmospheric phase screen effects are estimated and mitigated through spatial-temporal filtering, and 2D phase unwrapping is performed prior to least-squares inversion for time-series estimation. The resulting deformation histories include both linear velocities and non-linear displacement components. Products are referenced to stable areas and integrated with GNSS observations to ensure consistency with the Earth-centred reference frame. 3.2. Products The Netherlands ground motion service delivers a hierarchical product structure designed to serve both expert and policy-oriented users. Level 2a (L2a) products consist of point-based deformation measurements in radar geometry, including LOS velocities, full displacement time series, coherence values and residual statistics for ascending and descending tracks separately. These products represent minimally interpreted geophysical observations and are primarily intended for advanced analysis and research. Level 2b (L2b) products are geocoded into the national coordinate system and include decomposed vertical and east-west velocity components derived from combined ascending and descending measurements, along with propagated uncertainty estimates. L2b products are optimized for GIS integration and practical application in infrastructure monitoring, urban planning, and water management. These products translate technical InSAR measurements into actionable information for policymakers and asset managers. 3.2. Validation Given the small magnitude of typical deformation signals in the Netherlands, the Netherlands ground motion services places strong emphasis on quality assurance and uncertainty quantification. Each measurement point is assigned quality indicators including temporal coherence, velocity standard deviation, residual phase statistics, geolocation and height estimates. Internal consistency checks are complemented by external validation against GNSS stations, corner reflectors (CRs), levelling benchmarks, EGMS datasets, and geological models. Level 2a and 2b products are evaluated at selected test sites including South Limburg mainly for deformation estimates, height errors and geo-localisations. This multi-layered quality control framework provides confidence in millimetre-scale velocity estimates and supports responsible interpretation in engineering and policy contexts. 3.3. Data Dissemination and viewer The Netherlands ground motion service promotes open data access and societal awareness through a publicly accessible web-based viewer designed for both expert and non-specialist users. The viewer allows visualization of velocity maps, inspection of individual deformation time series, and comparison of ascending and descending tracks. Users can query point attributes and download datasets. By providing intuitive visualization alongside transparent uncertainty information, the viewer enhances accessibility and fosters trust in satellite-based deformation monitoring. Figure 3 shows the interactive InSAR viewer displaying Level 2a LOS displacement results for the test site. 4. Case Study: South Limburg The South Limburg area, located in the southeasternmost part of the Netherlands, is characterized by structurally complex geology, past underground coal mining, and heterogeneous urban and rural land use. The region experienced historical subsidence related to past mining activities and deformation linked to anthropogenic loading and post-mining effects such as natural recovery of groundwater levels. A total of 120 RADARSAT-2 XF single-look complex (SLC) images covering the period 2015–2024 were collected for the South Limburg area. The dataset comprises ascending acquisitions from 11 June 2015 to 24 April 2024 and descending acquisitions from 4 December 2016 to 14 April 2024. The PSDS InSAR processing algorithm mentioned in Section 3.1 was applied to the test site to evaluate the performance, spatial consistency and sensitivity of the Level 2a products at regional scale. Within this test site, Level 2a LOS velocities derived from the processing workflow were analysed to assess spatial and temporal deformation patterns. External validation was performed through systematic comparison with continuous GNSS observations available within the South Limburg area. The InSAR-GNSS comparison was conducted using a double-difference approach to reduce reference frame inconsistencies [7]. GNSS data for the stations SEL2, VOER, MSTR, and AACH were obtained from Nevada geodetic Laboratory [8]. GNSS observations were projected from ENU to LOS to assess agreement in InSAR LOS displacements. Figure 5 presents the agreement between InSAR and GNSS LOS displacement time-series. 5. Outlook and National Implementation Future work focuses on the national implementation of high-resolution InSAR data production and dissemination using RADARSAT-2 XF data for operational ground motion services in the Netherlands. The next steps include the developments on nationwide processing, integration with the GNSS reference frame, and automated quality control to ensure millimetre-scale reliability. This service framework is designed for seamless dissemination of high-resolution InSAR data from 2015-2025 to public and stakeholders with annual updates until 2028. References Fokker, P. A. et al. (2025). Subsidence. In: Ten Veen, J.H., Vis, G.-J., De Jager, J. & Wong, Th.E. (eds): Geology of the Netherlands, second edition. Amsterdam University Press (Amsterdam), pp. 825-847. DOI: 10.5117/9789463728362_ch23 . Costantini, M., Minati, F., Trillo, F., Ferretti, A., Novali, F., Passera, E., ... & Andersen, H. S. (2021). European ground motion service (EGMS). 2021 IEEE international geoscience and remote sensing symposium IGARSS, IEEE, 3293-3296. DOI: 10.1109/igarss47720.2021.9553562. Ferretti, C. Prati and F. Rocca, "Permanent scatterers in SAR interferometry," in IEEE Transactions on Geoscience and Remote Sensing, vol. 39, no. 1, pp. 8-20, Jan. 2001, https://doi:10.1109/36.898661. Shirzaei, M. (2012). A wavelet-based multitemporal DInSAR algorithm for monitoring ground surface motion. IEEE Geoscience and Remote Sensing Letters, 10(3), 456-460, DOI: 10.1109/LGRS.2012.2208935. Ansari, H., De Zan, F., & Bamler, R. (2018). Efficient phase estimation for interferogram stacks. IEEE Transactions on Geoscience and Remote Sensing, 56(7), 4109-4125, https://doi:10.1109/TGRS.2018.2826045. Wang, C. et al., (2022). A new likelihood function for consistent phase series estimation in distributed scatterer interferometry. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-14, https://doi:10.1109/TGRS.2022.3170567. Hanssen, R. F. (2001). Radar interferometry: data interpretation and error analysis. Dordrecht: Springer Netherlands. Blewitt, G., Hammond, W., & Kreemer, C. (2018). Harnessing the GPS data explosion for interdisciplinary science. Eos, 99(2), https://doi.org/10.1029/2018EO104623.

GDM-SAR-In (Ground Deformation Monitoring from SAR data using InSAR) is one of the on-demand services offered by the French research infrastructure Data Terra, developed and operated by its solid Earth hub, FormaTerre, in collaboration with ISTerre / OSUG , IPGP, CNES and the National Observation Service ISDeform of INSU / CNRS. This service has been opened to French users in June 2024 and is dedicated for processing InSAR products from Sentinel-1 radar imagery. A wider opening of the service with a quota dedicated to EPOS users is planned by the end of 2026. The service web page https://en.poleterresolide.fr/gdm-sar-in-service/ gives information about the access and the use of the services. The service is deployed on the CNES computing center. Based on the NSBAS processing chain using a small baseline approach, GDM-SAR-In allows an automated computation of single interferogram or a network of interferograms with its associated unwrapped phase time series giving access to measurement of ground deformations worldwide and with a revisit time down up to 6 days. This service allows non-expert users to run processing with simple option choices without having to worry about setting up and maintaining a complex processing chain (including downloading Sentinel-1 images, precise orbit data, digital terrain model and atmospheric model data, and the demanding TOPSAR mode processing of Sentinel-1 acquisitions) on a computing cluster. It also offers expert users a simple and fast way to explore a new area or a specific phenomenon such as a volcanic or seismic crisis, while keeping a certain flexibility in the choice of processing parameters. The availability of intermediate products and processing information allow expert users to reprocess, by their own, parts of the processing if necessary. Users access the service through a web interface specifically designed for radar interferometry usage. The interface allows the user to interactively choose the study area and the Sentinel-1 data suitable for InSAR processing and to follow the progress of the processing. The generated products are available for download for a limited period of time (a few weeks). A preview of the products is possible directly on the interface. Most of the products are provided in both radar and ground geometry (in geotiff format), interferograms are available in different versions (wrapped/unwrapped, filtered/unfiltered, with/without atmospheric correction from global model) allowing for user-customized post-processing. A time series of the unwrapped phase can also be generated as well as many other auxiliary products allowing advanced analysis of ground displacements by the user. Products are compatible with the catalog and data formats of FormaTerre and of the Thematic Core Service Satellite Data of the European research infrastructure EPOS. Examples of applications and products are available from the service website covering multidisciplinary applications (e.g. volcanic eruptions, earthquakes, landslides, forest fire, ...). These examples are showing how the service can be used as a function of the scientific goal, taking into account the potential limits of an automatic InSAR processing, and they aim to encourage the use of the service in a broad range of applications.

The systematic monitoring of surface deformation, infrastructure stability, cryospheric dynamics, and tectonic activity increasingly relies on large-scale interferometric processing of Synthetic Aperture Radar (SAR) data. The Copernicus Sentinel-1 mission provides a global, high-revisit C-band archive that has become the backbone of operational InSAR services. However, transforming this continuously growing archive into actionable information requires more than algorithmic expertise. It demands scalable cloud infrastructure, efficient data formats, interoperable metadata standards, and AI-ready data representations. This contribution presents the capabilities of CREODIAS as an EO-cloud environment designed to support large-scale InSAR processing and advanced SAR analytics. The platform integrates co-located object storage and elastic compute resources, enabling direct access to the full Sentinel-1 archive without the need for local data replication. This architecture significantly reduces data transfer overhead and supports parallelized interferometric processing chains at regional to continental scales. A key component of scalable InSAR processing is burst-level handling of Sentinel-1 TOPS acquisitions. The platform supports extraction and indexing of individual bursts, enabling precise stack construction and optimized interferogram generation. This burst-oriented strategy reduces computational redundancy and facilitates localized deformation monitoring with improved geometric consistency. Containerized processing environments allow integration of standard and custom InSAR toolchains, ensuring reproducibility and flexibility across scientific and operational applications. Beyond raw and interferometric products, the generation of Analysis-Ready Data (ARD), including Radiometrically Terrain Corrected (RTC) backscatter and burst-aligned stacks, plays a central role in enabling downstream analytics. To support high-performance time-series analysis, datasets can be transformed into cloud-optimized formats such as chunked Zarr-based data cubes. These multidimensional structures allow efficient parallel access to spatio-temporal subsets, making them particularly suitable for distributed computing environments and machine learning workflows. Interoperability and discoverability are ensured through implementation of the SpatioTemporal Asset Catalog (STAC) standard. STAC-based indexing provides structured metadata for SAR-specific attributes such as polarization, orbit direction, acquisition geometry, and burst identifiers. Importantly, the catalog does not only reference raw Sentinel-1 products but also derived assets, including interferograms, deformation maps, and machine learning outputs. This approach guarantees compliance with FAIR principles and facilitates integration with widely used geospatial libraries and APIs. The platform further supports AI-ready data representations through large-scale feature extraction and embedding generation from SAR backscatter and time-series products. Self-supervised learning approaches enable the derivation of compact numerical embeddings that capture spatial and temporal radar signal characteristics without reliance on extensive labeled datasets. These representations can be used for similarity search, clustering, and anomaly detection in deformation fields or land surface dynamics. Embedding-based analytics complement classical InSAR pipelines by enabling data-driven identification of unusual spatio-temporal patterns, thereby enhancing early warning and monitoring capabilities. The integration of scalable compute resources, cloud-native storage, standardized metadata, and machine learning workflows demonstrates that modern InSAR services are fundamentally infrastructure-driven. Processing at global volume requires not only efficient algorithms but also carefully designed data architectures that ensure accessibility, interoperability, and reproducibility. In summary, CREODIAS provides a comprehensive EO-cloud ecosystem for burst-level InSAR processing, SAR data cube generation, STAC-compliant cataloging, and AI-enhanced analytics. Such integrated environments represent a critical step toward operational, scalable, and intelligent Earth Observation services capable of supporting scientific research, environmental monitoring, and decision-making processes at continental and global scales.

In recent years, the number of Synthetic Aperture Radar (SAR) satellites in low Earth orbit has increased significantly, largely due to the reduction in launch costs for commercial and scientific payloads. In particular, commercial X-Band imagery acquired by ICEYE, Capella space and Umbra space provide very high resolution (VHR) and relatively short revisit times, thereby offering unique opportunities when compared to traditional open-access missions. To achieve such revisit over an area, the satellites illuminate the target from highly varying geometries (ascending and descending orbits, with right or left look) and incidence angles. Here we present a DEM reconstruction pipeline applied to pairs of VHR SAR based on an existing open-source stereo reconstruction tool developed for optical satellite imagery, i.e., the Ames Stereo Pipeline developed by NASA AMES [1]. The Rational Polynomial Coefficients (RPCs) are used to model the sensor geometry. Two images are then map-projected, after tie points are detected and matched. Epipolar rectification is performed using the RPC model, and dense stereo matching is applied to generate a disparity map. The disparity field is then converted into a 3D point cloud and interpolated to produce a DEM. We tested our approach on challenging alpine terrain in the Aletsch Glacier region (Switzerland), characterised by strong relief with elevation ranging from ~700 to over 4’000 m a.s.l. We considered ICEYE Extended Spotlight datasets: one acquired between August and September 2022 (32 acquisitions), and a second acquired between 22 and 25 March 2024 (7 acquisitions). The most challenging task for the AMES tool has been finding robust image matching across acquisitions with differing viewing geometries. The best results were obtained using opposite-orbit, same side-looking acquisitions (e.g. ascending-left combined with descending-right acquisitions). These geometries entail long perpendicular baselines (up to ~100 km), which lead to high convergence angle (over 20°), without compromising the matching capabilities. Resulting DEM co-registration and error propagation analyses were performed using xDEM, following established methodologies [3]. Elevation differences were evaluated over stable, non-glacier terrain against the Swiss national lidar-derived DEM, swissALTI3D. For one pair (descending-right acquisition from 15 August 2022 and ascending-left acquisition from 5 September 2022), we obtained a mean vertical error of 1.72 m over stable terrain. Ongoing refinements aim to better adapt the pipeline to SAR image characteristics. This will open the opportunity of creating multiple DEMs over the same target area for surface change monitoring, as well as averaging multiple DEMs to further improve elevation accuracy. [1] Shean, D. E. et al. An automated, open-source pipeline for mass production of digital elevation models (DEMs) from very-high-resolution commercial stereo satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing 116, 101–117 (2016).[2] xDEM contributors. (2024). xDEM (v0.1.0). Zenodo. https://doi.org/10.5281/zenodo.11492983[3] Hugonnet, R. et al. Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15, 6456–6472 (2022).

Insarviz is a tool designed to visualize and interactively explore the spatiotemporal datacubes derived from InSAR data processing chains.For several reasons (data availability, non-intuitive radar image geometry, complexity of the processing, etc.), InSAR has long remained a niche technology and few free open-source tools have been dedicated to it compared to the widely-used, multi-purpose optical imagery. Most existing tools are focused on data processing. Generic remote-sensing or Geographic Information System (GIS) softwares are limited when used to visualize InSAR data, due to their unusual geometry and formats. A few visualization tools with dedicated InSAR functionalities exist, that were designed to visualize a single radar image or interferogram.However, recent spatial missions, like the Sentinel-1 mission of the European program COPERNICUS, with a systematic background acquisition strategy and an open data policy, provide unprecedented access to massive SAR datasets. From these new datasets, a network of thousands of interferograms can be generated over a single area. The consecutive step is a time-series analysis which produces a spatiotemporal data cube: a layer of this data cube is a 2D map that contains the displacement of each pixel of an image relative to the same pixel in the reference date image. A typical data cube size is 4000x6000x200, where 4000x6000 are the spatial dimensions (pixels) and 200 is a typical number of images taken since the beginning of the spatial mission.The aforementioned tools are not suited to manage such large and multifaceted datasets. In particular, fluid and interactive data visualization of large, multidimensional datasets is non-trivial. If data cube visualization is a more generic problem and an active research topic in EO and beyond, some specifics of InSAR (radar geometry, wrapped phase, relative measurement in space and in time, multiple types of products needed for interpretation…) call for a new, dedicated visualization tool.To that end, InsarViz aims to provide a user-friendly interface for visualization and analysis of InSAR products, with in the current version a particular focus on large data cubes visualisation. It boasts an array of useful features, such as : handling of most usual data formats (Tiff, R4, HDF, depl_cumul, ...) thanks to the ever-useful GDAL library handling of large multi-band data sets (> 10G) with ease easily combining multiple layers onto a data cube (e.g. a mean-velocity layer, or a DEM) precisely managing each layer's look through interactive histograms and a variety of color maps loading data in radar or ground coordinates, showing background maps when possible loading a LUT (lookup table) to visualize projections of radar data onto the ground interactively defining features (points and profiles) on the map, and plotting spatial and temporal measurements from those features real-time configurable model fitting for temporal plots, handling seasonal and semi-seasonal deformation, and post-seismic (logarithmic) relaxation Online documentation : https://deformvis.gricad-pages.univ-grenoble-alpes.fr/insarviz/latest/index.htmlQuick demo (video) : https://formater.osug.fr/demos/insarViz/insarViz-demo.mp4We will present the latest InsarViz version, and perform a live demo on a large data-cube over the region of Mexico. Afterwards, we will discuss further advancements into InSAR visualization, notably featuring interactive visualisation of interferogram networks, and radar-geo correspondence.

Monitoring ground deformation in urban environments is essential for infrastructure stability, hazard mitigation, and long-term urban planning (Kumar et al., 2021; Ramirez et al., 2020). The continuous acquisition of Sentinel-1 SAR imagery enables large-scale deformation monitoring using time-series interferometric techniques, with Persistent Scatterer Interferometry (PSI) widely applied to retrieve long-term displacement signals from stable radar targets (Crosetto et al., 2016). The StaMPS (Stanford Method for Persistent Scatterers) framework provides a well-established methodology for PSI time-series analysis and is widely adopted within the InSAR community (Hooper, 2008; Hooper et al., 2010). However, the original StaMPS implementation relies on a MATLAB-based environment and a multi-stage workflow that requires several external dependencies and manual configuration steps, which can limit accessibility and integration within modern data-processing pipelines. To address these limitations, this work presents PyStamps, a Python-based open-source implementation of the StaMPS PSI workflow developed within the UrbanSAR framework (Girohi et al., 2025), enabling flexible and scalable PSI processing. PyStamps preserves the core methodological structure of StaMPS, including temporal coherence estimation, persistent scatterer selection, phase unwrapping, and spatially correlated look-angle (SCLA) correction (Hooper et al., 2010), while adapting the workflow to a fully open-source Python architecture. The implementation leverages widely used scientific computing libraries to efficiently perform interferometric phase analysis and deformation estimation (Harris et al., 2020; Virtanen et al., 2020). The framework incorporates scalable data management and optimized computational strategies to support large interferometric stacks and improve computational efficiency. These developments maintain compatibility with the established StaMPS methodology while enabling greater flexibility for integration into automated and large-scale analysis systems. The performance and reliability of PyStamps were evaluated using Sentinel-1 InSAR time-series data over the Houston metropolitan region in the United States, an area characterized by well-documented subsidence associated with groundwater extraction and urban development. Two regions of interest were defined to assess scalability, including a compact study region and a larger regional extent within the same geographic setting. Results indicate that PyStamps produces deformation estimates consistent with those obtained using the original MATLAB-based StaMPS workflow. Velocity distributions from both implementations exhibit nearly identical behavior, with persistent scatterer velocities agreeing at sub-millimeter precision and more than 99.99% spatial overlap in the compact test area. Performance comparison further indicates that PyStamps operates with lower computational overhead, demonstrating reduced CPU usage while maintaining stable processing performance across different regions of interest and spatial extents. Overall, PyStamps provides a robust and permissively licensed implementation of the StaMPS workflow, enabling scalable PSI processing within a modern open-source InSAR framework. The approach supports efficient large-scale deformation monitoring and facilitates integration of PSI analysis into contemporary scientific computing and operational geospatial environments.

Abstract Due to their flexible acquisition geometry and revisit times, airborne platforms allow for very high-resolution imaging and timely monitoring of areas of interest by means of SAR imaging and repeat-pass SAR interferometry. A potential error source which might compromise the quality of the acquired SAR data (and thus the quality of derived geo/biophysical parameters) are errors in the sensor positioning. Such positioning errors are due to the limited accuracy of the employed GNSS-aided inertial navigation systems (INS) and may lead to not optimally focused SAR images as well as phase undulations in the single-pass and repeat-pass interferograms [1-2]. Previous work showed that residual positioning errors of agile platforms can be reduced by placing an ad-hoc GNSS reference station in the vicinity of the SAR sensor’s flight path, compared to the case in which only remote GNSS reference stations of a network of permanent GNSS receivers are employed [3]. In the present work, the impact of residual positioning errors in repeat-pass L and S-band InSAR data acquired from UAV platforms [4,5] is analyzed. The data were acquired using Gamma L-band and S-band SAR sensors, while the navigation setup consisted of a Honeywell Guide n580/n500 GNSS-aided INS, with a GNSS ad-hoc reference station located nearby. To assess the impact of potential residual errors in the repeat-pass interferograms, a subaperture-based approach was employed. Specifically, the original full aperture of the acquisitions composing each interferometric pair was split into multiple (possibly overlapping) subapertures of given length. Each subaperture was focused independently for each of the acquisitions. Image focusing was performed by means of a time-domain backprojection algorithm [6], which can accurately process data acquired even from significantly non-linear trajectories, as can happen in the case of UAV platforms. For each subaperture, interferograms were computed from the pair of SAR images thus obtained. Finally, the presence of residual unknown positioning error was assessed by checking for phase trends in the differential interferograms between interferograms of different subapertures. First results obtained from the analysis of a UAV-borne L-band dataset do not show significant phase undulations as a result of potential residual positioning. This is proven also by the high quality of the full-aperture interferogram, which has does not show any substantial phase variations, also due to the very short temporal baseline. The lack of significant residual positioning errors can be attributed to several factors, first of all the high accuracy of the navigation data (in the presence of a GNSS station nearby), combined with the relatively long wavelength of the L-band acquisitions. In our contribution, we will expand our analysis to further UAV-borne SAR data sets including different wavelengths. Bibliography [1] G. Fornaro, G. Franceschetti and S. Perna, "Motion compensation errors: effects on the accuracy of airborne SAR images," in IEEE Transactions on Aerospace and Electronic Systems, vol. 41, no. 4, pp. 1338-1352, Oct. 2005, doi: 10.1109/TAES.2005.1561879. [2] N. Cao et al., "Estimation of Residual Motion Errors in Airborne SAR Interferometry Based on Time-Domain Backprojection and Multi-squint Techniques," in IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 4, pp. 2397-2407, April 2018, doi: 10.1109/TGRS.2017.2779852. [3] R. Coscione, I. Hajnsek, C. Werner and O. Frey, "Assessing the Impact of Positioning Errors in Car-Borne Repeat-Pass SAR Interferometry With a Controlled Rail-Based Experiment," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 8402-8415, 2022, doi: 10.1109/JSTARS.2022.3193053. [4] O. Frey, C. L. Werner and R. Coscione, "Car-borne and UAV-borne mobile mapping of surface displacements with a compact repeat-pass interferometric SAR system at L-band," Proc. IEEE Int. Geosci. Remote Sens. Symp., Yokohama, Japan, 2019, pp. 274-277, doi: 10.1109/IGARSS.2019.8897827. [5] O. Frey, C. Werner, S. Leinss, T. Batt, R. Caduff, T. Dixon, T. Sadeghi Chorsi, R. Van Alphen, M. Schmitt, M. Eitel, F. Sica, E.J. Deeb, A.L. LeWinter, D.L. Filiano, C.J. Wagner, Z. Hoppinen, "Multicopter-UAV- and car-borne repeat-pass SAR interferometry and SAR tomography with the compact Gamma SAR systems: first examples and use cases at S- and L-band", In Proc. IEEE Int. Geosci. Remote Sens. Symp., Brisbane, Australia, Aug. 2025, pp. 1374-1377. IEEE. doi:10.1109/IGARSS55030.2025.11243477. [6] O. Frey, C. Magnard, M. Ruegg and E. Meier, "Focusing of Airborne Synthetic Aperture Radar Data From Highly Nonlinear Flight Tracks," in IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 6, pp. 1844-1858, June 2009, doi: 10.1109/TGRS.2008.2007591.

The safety and resilience of urban infrastructure are of great concern in the context of rapid urbanization. Human activities, such as construction, excavation, and groundwater extraction, may cause ground subsidence and threaten infrastructure, including buildings and roads. Interferometric Synthetic Aperture Radar (InSAR) is an advanced SAR technique used to identify and measure surface deformation with sub-centimeter precision. Recent advances in InSAR techniques, the availability of long time series of SAR data, and cloud-based processing facilitate long-term monitoring of surface deformation within urban environments. However, the use of InSAR in infrastructure safety assessment presents challenges and requires careful data processing, analysis, and interpretation due to heterogeneity of urban areas. For example, tall buildings may cause geometric distortion and variable coherence over time, which can complicate time series inversion and interpretation. Within the project Actionable Data Space for Urban Climate Adaptation and related socio-ecological, local Transformation (ADUCAT), we aim to develop an automated pipeline tailored to urban applications to enable stakeholders on demand InSAR processing using freely available Sentinel-1 time series data. The development of the methodology is twofold: (1) automatization of surface deformation measurements, including (semi-)automated SAR data query and download, interferogram stack generation, surface deformation and velocity estimation in vertical and horizontal directions, and (2) localization of surface deformation based on infrastructure types, such as buildings and roads, by including coherence information and high-resolution digital elevation model data. Precise in-situ measurements and user feedback will be used to assess the accuracy and applicability of the results and the proposed pipeline. The proposed pipeline supports on-demand InSAR analysis using existing SAR data in an open-source environment. Tailored for urban areas, the pipeline facilitates systematic deformation monitoring and facilitates early warning by detecting and mapping areas exhibiting surface displacement, which may indicate areas susceptible to structural damage and infrastructure instability.

Deep learning has established a new paradigm in Synthetic Aperture Radar (SAR) image despeckling, shifting the focus from traditional spatial filters to sophisticated neural architectures. Among these, the MERLIN (coMplex sElf-supervised despeckLINg) framework has emerged as a state-of-the-art solution by exploiting the statistical independence of the real and imaginary components of Single-Look Complex (SLC) images for self-supervised training. However, ensuring the necessary orthogonality of these components is technically demanding in practice. It requires mandatory, specialized preprocessing—ranging from spectral recentering in Stripmap data to complex deramping for Sentinel-1 TOPSAR—to correct for asymmetrical spectra and Doppler offsets that would otherwise introduce significant artifacts. These preprocessing requirements and the strict necessity for full complex SLC data create a significant operational bottleneck for large-scale industrial Interferometric SAR (InSAR) and monitoring production chains. Conversely, an amplitude-only input would bypass these SLC-dependent constraints, facilitating the seamless integration of advanced deep-learning despeckling into legacy processing pipelines and low-latency services. Previous attempts to adapt the MERLIN framework for amplitude-only inference, as noted in the original literature, have proven suboptimal due to the spatial characteristics of speckle. Generating a synthetic phase field for intensity-only images introduces high-frequency texture artifacts and residual noise stemming from the correlation mismatch between the actual intensity and the random phase. While these effects can be partially mitigated through spatial subsampling to whiten the speckle, such preprocessing unavoidably degrades high-frequency structural details, such as thin lines. Consequently, the operative understanding has been that full, correctly preprocessed SLC data remains a non-negotiable prerequisite for high-fidelity self-supervised despeckling. In this paper, we present an operational breakthrough that bridges the architectural gap between complex-domain self-supervision and amplitude-domain inference. We propose a Teacher-Student Knowledge Distillation (TSKD) framework that decouples the learning of speckle statistics from the constraints of the inference data format. Our approach distills the "collective intelligence" of multiple sensor-specific "Teacher" models—each optimized for X-, C-, and L-band SLC data—into a single, robust "Student" network. By training the Student to regress the Teachers' denoised outputs using only amplitude inputs, the model effectively internalizes diverse spectral shapes and spatial correlation patterns. This allows the Student to produce high-fidelity, speckle-free imagery without requiring the complex-domain preprocessing (deramping or demodulation) typically needed to model these statistics. Experimental results across X-, C-, and L-band sensors demonstrate that the Student model replicates the Teachers’ performance with near-lossless fidelity, achieving an average PSNR of 37.81 dB and an SSIM of 0.978 on the test dataset. The primary advantage of this framework is the total elimination of complex-domain preprocessing during the inference phase, enabling truly sensor-agnostic deployment. While the original MERLIN framework requires networks tailored to specific sensors and acquisition modes, our approach yields a single, universal model. By operating strictly on amplitude data, it bypasses metadata-dependent steps, providing high-quality products across varying systems. Furthermore, by processing a single amplitude channel instead of dual complex components, the model achieves a significant increase in inference speed. This efficiency is crucial for wide-area-processing services, transforming deep-learning despecklers into scalable, on-demand solutions for global SAR monitoring.

Interferometric Synthetic Aperture Radar (InSAR) measures phase differences between multiple SAR images and allows observation of a variety of signals, including surface deformation from seismicity, volcanic unrest , and anthropogenic activities (e.g., wastewater injection or groundwater withdrawal). The precision of InSAR-derived vertical displacement rates can approach a millimeter per year over spatial scales of a few kilometers or less, particularly when a large number of observations are available spanning multiple years. However, the quality of individual interferograms may be affected by a variety of noise sources, including noise introduced by water vapor. Characterization of noise sources is an essential part of the development of high-quality constraints on subjects of interest (e.g., ground deformation or glacial dynamics). As microwaves pass through the troposphere, they are refracted, introducing errors of up to tens of centimeters or more. Many InSAR studies attempt to reduce the impact of atmospheric noise by using many images, under the assumption that these effects will average to zero. Since these “stacking” approaches rely on multiple acquisitions, small transient deformation signals spanning only a few acquisitions may still be completely overwhelmed by tropospheric noise. Therefore, other methods dedicated to the correction of tropospheric delays have been devised. This includes empirical models, where the relationship between water vapor delays and elevation is utilized to estimate tropospheric effects. Other approaches for mitigating the effects of the troposphere on InSAR observations use humidity, pressure, and temperature derived from weather model products (e.g., ERA-5) to predict and remove models of tropospheric delay. These weather model-based corrections tend to have spatial resolutions >10 km and temporal resolutions on the order of hours. In most cases, the spatiotemporal resolutions of these models are far too coarse to resolve the fine structures of fast-moving storm systems, where cumulative phase delays vary horizontally within 100s of meters. This has major implications for monitoring ground deformation in study regions such as ours in Oklahoma, where the presence of storm systems in SAR images is commonplace. Using a novel high-pass phase-based approach for filtering and pixel selection, we demonstrate the limitations of current InSAR tropospheric models when attempting to correct for storm signatures. Furthermore, we propose an empirical correction technique based on independent ground-based weather radar. We validate our model through numerical weather simulations in the Weather Research and Forecasting Model. Finally, we quantify the errors that storms may have on geophysical parameter estimation.

Using the InSAR-related NSBAS chain processing (Doin et. al 2011, Thollard et. al. 2021) and the 2016-2025 Sentinel-1 radar data, we aim at obtaining a velocity map over the European Alps with a target uncertainty of a mm/yr at the scale of the massif. Mountainous areas present inherent challenges for InSAR study such as geometric distortions, decorrelation due to vegetation or snow cover changes, and tropospheric delays. To achieve millimeter-per-year precision, particular care must be taken when mitigating the atmosphere contribution in interferometric processing. Tropospheric contributions have spatial and temporal variations that do not cancel when forming interferograms (Massonnet and Feigl, 1994) due to pressure, temperature and relative humidity changes. Tropospheric delays are still a major factor limiting the accuracy of InSAR measurements in slowly deforming areas, as a change of 20% of relative humidity between two acquisitions is responsible for a 10 cm error in deformation interpretation (Zebker and Rosen, 1997). Additionally, tropospheric delays are not easily separable from the rest of the phase contributions and can be misinterpreted. Therefore, its impact has been widely studied and several methods have been proposed to mitigate tropospheric noise (Bekaert et. al. 2015). Among them, we focus on two main approaches: mitigation by trying to separate stochastic noise from ground motion signal (for example, exploiting empirical relationship between elevation and troposphere) and by the usage of external data. For the latter, two main sources of data are available. We can benefit from data from global atmosphere models (GAM) (Doin et al., 2009; Jolivet et. al. 2011) (particularly the ERA5 reanalysis of the ECMWF) or from zenithal troposphere delays (ZTD) obtained during the GNSS processing (e.g. Onn and Zebker, 2006; Albino et. al., 2025). In mountainous areas such as the Alps, the ability of the these three methods to correct tropospheric delays is crucial but limited by the complexity of orogenic features and associated atmospheric flows (e.g. Baines, 1998 or Sandu et. al., 2019). Empirical models, when their complexity is increased to adjust to the lateral variations in stratified delays in separate valleys, may remove elevation-dependent deformation. GNSS sparse network forces interpolation. GAM is limited by its spatial resolution (30x30 km for ERA5), especially considering that delay correction in deep valleys are obtained from an extrapolation of temperature, humidity and pressure below the surface of the model. For example, a previous InSAR processing in the Alps (Mathey et. al. 2020) has shown that atmospheric-related contribution remained in deep Alpine valleys because of the coarse resolution of ERA5. We explore here the usage of GNSS Alps data processed using the GipsyX software at EPOS-UGA to mitigate tropospheric delays in interferograms, but also of the new CERRA reanalysis which has a finer spatial resolution (5x5 km). As we aim to reach the mm/yr accuracy in the measurement of the Alps uplift, we must carefully examine potential trends in these data sets over the period 2015-2025. We thus first compare dispersion, bias and bias trend in ZTD time series of the EPOS-UGA to other solutions namely the SPOTGINS GNSS solution from the CNES and the CERRA on common locations. Then, to compute atmospheric phase screens from GNSS ZTD, we take into account the vertical dependent term as well as the lateral variations due to atmospheric turbulence using Iterative Tropospheric Decomposition (ITD) (Yu et. al. 2018). As the Alps GNSS stations are sparse, we also test a combination of a GAM with GNSS results. We present the method, the results obtained and a comparison against the widely used ERA5 model. Finally, we assess methods’ performance using the correlation of phase delay residuals with elevation because atmosphere stratified component is the predominant source of troposphere errors. Moreover, since atmospheric noise is relatively high compared with deformation signal, we study the standard deviation reduction in unwrapped interferograms when we apply or not our corrections. We focus our conclusion by the analysis of the deep Alpine valleys and foothills where tropospheric delays are the harder to mitigate. Albino, F. et al. (2025) ‘Benefits of GNSS Local Observations Compared to Global Weather-Based Models for InSAR Tropospheric Corrections Over Tropical Volcanoes: Case Studies of Piton De La Fournaise and Merapi’, Journal of Geophysical Research: Solid Earth, 130(4), p. e2024JB028898. Baines, P. G. (1998) ‘Topographic Effects in Stratified Flows’, Cambridge University Press. Bekaert, D.P.S. et al. (2015) ‘Statistical comparison of InSAR tropospheric correction techniques’, Remote Sensing of Environment, 170, pp. 40–47. Doin, M.-P. et al. (2009) ‘Corrections of stratified tropospheric delays in SAR interferometry: Validation with global atmospheric models’, Journal of Applied Geophysics, 69(1), pp. 35–50. Doin, M.-P. et al. (2011) ‘Presentation of the small baseline NSBAS processing chain on a case example: the Etna deformation monitoring from 2003 to 2010 using ENVISAT data’. In Proc. of the Fringe 2011 Workshop “Advances in the Science and Applications of SAR Interferometry” (Vol. ESA SP-697). Frascati, Italy : ESA. Jolivet, R. et al. (2011) ‘Systematic InSAR tropospheric phase delay corrections from global meteorological reanalysis data’, Geophysical Research Letters, 38(17). Massonnet, D. and Feigl, K.L. (1998) ‘Radar interferometry and its application to changes in the Earth’s surface’, Reviews of Geophysics, 36(4), pp. 441–500. Mathey, M. et al. (2022) ‘Spatial Heterogeneity of Uplift Pattern in the Western European Alps Revealed by InSAR Time‐Series Analysis’, Geophysical Research Letters, 49(1), p. e2021GL095744. Onn, F. and Zebker, H.A. (2006) ‘Correction for interferometric synthetic aperture radar atmospheric phase artifacts using time series of zenith wet delay observations from a GPS network’, Journal of Geophysical Research: Solid Earth, 111(B9). Sandu, I. et al. (2019) ‘Impacts of orography on large-scale atmospheric circulation’, npj Climate and Atmospheric Science, 2(1), p. 10. Thollard, F., et al. (2021). Flatsim: The form@ ter large‐scale multi‐temporal sentinel‐1 interferometry service. Remote Sensing, 13(18), 3734. Yu, C., Li, Z. and Penna, N.T. (2018) ‘Interferometric synthetic aperture radar atmospheric correction using a GPS-based iterative tropospheric decomposition model’, Remote Sensing of Environment 204, pp. 109–121. Zebker, H.A., Rosen, P.A. and Hensley, S. (1997) ‘Atmospheric effects in interferometric synthetic aperture radar surface deformation and topographic maps’, Journal of Geophysical Research: Solid Earth, 102(B4), pp. 7547–7563.

Interferometric synthetic aperture radar (InSAR) phase estimations are typically interpreted using physically motivated forward models. However, the estimated phase is a superposition of multiple contributions, the separation of which is often model dependent. In this study, we present a unified approach for identifying and extracting theoretically expected phase components using independent component analysis (ICA), without imposing theoretical models during the separation stage. The key idea is to first perform model-free source estimation and separation, and then validate the extracted sources using theory-consistent tests based on correlation structures and expected coefficient dependence. We present two complementary applications: (1) estimation of ionospheric delay components from a single interferogram using split-spectrum processing and (2) estimation of residual DEM error-related components from time-series InSAR results. In the case of ionospheric delay, we start with a single interferogram and use split-spectrum processing to generate multiple band-limited interferograms. ICA is then performed on these derived images to separate independent sources embedded in the interferometric phase, aiming to recover a component attributable to ionospheric delay. Because the estimation is driven by statistical independence rather than a prescribed ionospheric model, this approach provides a data-driven test of whether an ionospheric-like component is detectable. Candidate components are evaluated using correlation analyses between spatial patterns and frequency-dependent coefficients, and by comparing ICA-estimated coefficients with the dependence expected from standard split-spectrum theory. In the case of DEM error, ICA is applied to the cumulative phase time series to separate statistically independent sources. Candidate DEM error-related components are evaluated using quantitative consistency tests that focus on whether their temporal coefficients exhibit acquisition-geometry dependence expected from DEM error propagation. We compute correlation coefficients in both spatial and temporal domains, and compare ICA-derived mixing coefficients and spatial patterns with values predicted by the conventional geometry-based model equation. This provides an objective assessment of whether a DEM error contribution is present in the time series and whether ICA can recover it in a physically interpretable form, despite not assuming a DEM error model in the decomposition process. Across both applications, the main contribution is a consistent validation framework that links model-free ICA decomposition to theory-consistent interpretation. By combining ICA with correlation-based comparisons in space–frequency (for ionospheric delay) and space–time (for DEM error), we demonstrate how components described by standard InSAR theory can be identified and extracted without incorporating model assumptions into the separation process itself. This approach provides an assumption-light pathway for identifying nuisance contributions such as ionospheric delay and residual DEM error, with direct implications for improving the interpretability and reliability of deformation estimates from InSAR data.

Tropospheric delay remains one of the dominant sources of error affecting the accuracy of Interferometric Synthetic Aperture Radar (InSAR) time-series analysis. Atmospheric stratification produces phase signals that are strongly correlated with topography, particularly in mountainous regions where large elevation gradients interact with complex atmospheric circulation. These effects can degrade the quality of deformation measurements derived from InSAR and complicate the interpretation of centimeter-scale surface motion. Improving the mitigation of atmospheric artifacts is therefore essential for reliable deformation monitoring in high-relief environments. The Eastern Cordillera of the Southern Central Andes in northwestern Argentina provides an ideal natural laboratory to investigate atmospheric correction strategies under complex environmental conditions. The study region is characterized by steep climatic and topographic gradients, where low-elevation eastern foreland areas with dense vegetation transition to semi-arid and arid high-elevation terrain with sparse vegetation cover. Elevation differences exceeding several kilometers occur over relatively short horizontal distances, and the region is influenced by strong seasonal climatic variability associated with the South American Summer Monsoon. Moisture transport across the orogen generates pronounced spatial variability in atmospheric water vapor, producing strong elevation-correlated tropospheric delay signals that significantly influence interferometric phase observations and complicate deformation measurements. In this study we analyze multi-year Sentinel-1 C-band SAR observations acquired between 2014 and 2019 over the Eastern Cordillera of northwestern Argentina. The interferometric time series is generated using a small-baseline interferometric network, enabling consistent estimation of surface deformation over multi-year observation periods. Our InSAR time-series analysis reveals numerous slow-moving landslides across the region, which typically deform at rates of approximately 5–10 cm yr⁻¹. We present a comprehensive comparison of stratified tropospheric delay correction approaches based on atmospheric reanalysis products and spatial statistical methods. Tropospheric delays are estimated through ray-tracing calculations using atmospheric profiles derived from global weather reanalysis datasets including ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis version 5), ERA5T (the near-real-time extension of ERA5), and MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2). These datasets provide vertically resolved atmospheric parameters such as temperature, pressure, and water vapor, allowing the propagation of radar signals through the troposphere to be modeled along the radar line of sight. Because ray-tracing corrections depend on assumptions about the vertical extent of the atmospheric column used in the delay calculation, we investigate the sensitivity of tropospheric delay estimates to variations in the maximum atmospheric integration height used during the ray-tracing process. This parameter determines the vertical portion of the atmosphere included in the modeled delay and may influence how effectively elevation-dependent atmospheric signals are captured in regions characterized by large topographic relief. To further address atmospheric variability that may not be fully captured by large-scale atmospheric models, we introduce a robust sliding-window atmospheric correction approach that estimates localized elevation–phase relationships within moving spatial windows. This spatially localized framework allows stratified atmospheric delay to be modeled at regional scales while reducing the influence of deformation signals and outliers. By capturing spatially varying atmospheric gradients across steep terrain, the method provides an adaptive strategy for mitigating residual elevation-correlated atmospheric artifacts. The performance of the proposed approach is evaluated using both real InSAR observations and synthetic simulations in which stratified atmospheric delays and deformation signals representative of slow-moving landslides are generated. These simulations allow the capability of the sliding-window method to separate atmospheric artifacts from deformation signals to be assessed under controlled conditions. Our results indicate that the selected maximum atmospheric integration height influences the magnitude and spatial distribution of modeled tropospheric delay in steep mountainous terrain. When the integration height is insufficient, stratified atmospheric delay may be underestimated, leaving residual elevation-dependent phase signals in corrected interferograms. Increasing the integration height improves atmospheric delay estimation up to a threshold, beyond which excessively large integration heights may introduce artifacts in the modeled delay field. Both synthetic experiments and real InSAR time-series analysis demonstrate that the proposed robust sliding-window atmospheric correction effectively reduces elevation-correlated phase artifacts and improves the stability of deformation estimates in areas affected by complex atmospheric variability. These findings highlight the importance of appropriate parameterization of atmospheric corrections and demonstrate the potential of localized atmospheric modeling approaches for improving InSAR time-series analysis in mountainous environments.

Radar interferometry is a technique capable of measuring precise surface displacement by analyzing the interferometric phase between two images acquired at different times. Spaceborne SAR is effective for large-scale studies but remains limited by long revisit cycles. Furthermore, geometric distortions such as layover, foreshortening, and shadowing frequently result in significant blind spots in the field of view. To overcome these limitations, ground-based radar (GBR) systems have been increasingly deployed, offering high temporal resolution and flexible imaging geometries tailored to specific monitoring objectives. In contrast, GBR offers flexible control over acquisition time, location, and antenna geometry to specific monitoring objectives. GBR is particularly effective for observing rapid and localized movements in slopes, landslides, and critical infrastructure. Despite these advantages, ground-based radar is inherently sensitive to atmospheric phase delays caused by temporal and spatial variations in temperature, humidity, and barometric pressure. Temporal and spatial variations in temperature, humidity, and pressure alter the atmospheric refractivity. This fluctuation modifies atmospheric refractivity, altering radar wave propagation paths and inducing phase components unrelated to actual surface displacement, with the effect becoming more pronounced at higher frequencies such as the Ku-band. This study aims to quantitatively evaluate the impact of atmospheric interference on GBR persistent scatterer interferometry (PSI) and proposes an atmospheric correction methodology using synchronous meteorological data. The experiment was conducted using the Gamma Portable Radar Interferometer-II (GPRI-II) system, which operates in the Ku-band (17.1–17.3 GHz). A 33-hour continuous dataset comprising 397 Single-Look Complex (SLC) images was collected at a levee site in South Korea at a 5-minute acquisition interval. Synchronous temperature and humidity data were recorded on-site using hygrometers near the corner reflectors (CRs). Barometric pressure data were obtained from a meteorological station located 6 km away to refine calibration model accuracy. The dataset was partitioned into eleven 3-hour segments for detailed correlation analysis. This analysis revealed a strong relationship between the calculated atmospheric refractivity and the interferometric phase, yielding a maximum coefficient of determination (R2) of 0.9 between 20:00 and 23:00. An initial atmospheric phase model for calibration was developed using the calculated refractivity. This model was then refined by analyzing the interferometric phase observed at the corner reflectors. Finally, 396 atmospheric-phase images generated from the refined model were applied to the SLC stack to compensate. The efficacy of the proposed correction was validated using two installed corner reflectors, assuming zero actual displacement. Experimental results demonstrated a substantial improvement in phase stability. The standard deviation of the interferometric phase decreased from 0.26 to 0.12 radians for the first CR and from 0.36 to 0.14 radians for the second CR. Furthermore, the estimated line-of-sight displacement rate error was significantly mitigated, dropping from 0.5–0.7 cm/hour to 0.0–0.1 cm/hour after correction. This study concludes that integrating meteorological-based atmospheric modeling effectively enhances the precision of GBR PSI, providing a reliable framework for monitoring rapid geotechnical and structural deformations.

The vast amount of Sentinel-1 data over the last decade and the high accuracy of related data such as precise orbit ephemerides (3-D RMSE below 10 mm) allows for the exploration of measurements of the solid Earth surface dynamics in a global reference frame. This is applicable in regions lacking geodetic instrumentation, and particularly useful at tectonic transform boundaries where plate motion models may not be accurate. On the other hand, model residuals allow us to evaluate non-tectonic signals, including due to the atmosphere or orbit inaccuracies. We updated our previously published methodology (2023) investigating precise along-track (azimuth) coregistration offsets from spectral diversity of burst overlaps, averaged over Sentinel-1 frames consisting of ~13 bursts per swath w.r.t. satellite position. This was leading to decomposed absolute velocities at coarse resolution (~250x250 km^2 pixel size) estimated from 6-year time series over the Alpine-Himalayan Belt. Median 2-sigma errors were ~4 mm/year northwards and ~20 mm/year eastwards. We have further extended our dataset, partly reprocessed using updated orbits in a modified processing chain, e.g. avoiding intensity cross-correlation in azimuth to prevent ionosphere-related errors. We improved ionospheric correction by scaling JPL high resolution global ionosphere maps (GIM) vertical total electron content (VTEC) using geometry estimated from IRI2020 ionospheric profiles. Finally, we added time series of range pixel offsets and corrected them for solid Earth tides, ionosphere and tropospheric delay. We observe and further investigate and report on unmapped consistent signal over humid/highly vegetated regions, particularly in range, and constant offsets between Sentinel-1A and Sentinel-1B in both azimuth and range directions. By decomposing overlapping ascending and descending data from both azimuth and range in the same framework covering 2016-2022 (avoiding peaking solar cycles) to absolute velocities, we estimate median 2-sigma accuracy of [3.5, 6.7, 5.2] mm/year for [eastwards, northwards, vertical] directions, respectively. Furthermore, we fully remove eastward bias w.r.t. GNSS data from the median 9 mm/year reported in the original work. Velocities estimated from the overall range and azimuth coregistration offsets can be directly combined with the standard mean-centered line-of-sight and burst-overlap InSAR velocities, respectively, in order to leverage them into the global reference frame measurements. Due to the existing noise that is higher in humid regions and during increased solar activity, we evaluate the best-case scenario over the arid/semi-arid region of the Dead Sea transform zone, comparing to velocities from GNSS and reporting on achievable accuracy.

Interferometric synthetic aperture radar (InSAR) is a common remote sensing method used to measure ground deformation for a variety of applications. For a given stack of SAR images, InSAR produces 1D displacement estimates along the satellite line-of-sight (LOS). While 3D decomposition is theoretically possible using three or more LOS observations with different viewing geometries, the near-polar orbits of current-generation SAR satellites mean that angular diversity is limited, and the 3D inverse problem is ill-posed. Capella Space’s constellation of high-resolution X-band SAR satellites, occupying both sun-synchronous and inclined orbits, have the potential to enable native 3D InSAR measurements. Capella Space is working towards an operational interferometric capability, and the data used here were collected during experimental testing in June-August 2024.In this study, we present high-resolution 3D InSAR displacement time series estimates using a combination of Capella-14 imagery (45° orbital inclination, 2.95 day revisit) and TerraSAR-X/PAZ data (97° orbital inclination, alternating 4 and 7 day revisit). LOS time series were analysed using a small baseline subset approach, and the 3D solution was calculated using a generalized least squares formulation. The test site used is an active open-pit mine, with fast displacement and varied motion geometries.We compare the 3D results to a conventional 2D (East-West/Up-Down decomposition) scenario using only TSX/PAZ data, and use combinations of three to five datasets to examine the impact of different observation geometries on the uncertainty budget of the 3D solution. Results indicate that the 3D solution provides sensitivity to previously underestimated North-South (NS) displacement, and captures the expected motion direction well. Overall uncertainty increases when solving in 3D due to the additional degree of freedom and the less well constrained NS component. Including additional viewing geometries, however, leads to substantial reductions in both NS and total uncertainty. We also show that the 3D solution removes biases that arise in the EW/UD 2D approach due to unmodeled NS displacement.These results highlight the significant benefits of SAR satellites in mid-inclination orbits for InSAR. High-resolution 3D InSAR would allow observations of slopes and displacement orientations that current satellites have limited sensitivity to, enabling end users to extract more complete information and develop more robust interpretations.

The revisit cycle of a single synthetic aperture radar (SAR) satellite system directly determines the temporal resolution of interferometric SAR (InSAR) observation, which is a critical factor for accurate surface deformation monitoring. Although both ALOS-4 PALSAR-3 and ALOS-2 PALSAR-2 provide a nominal 14-day revisit cycle, actual acquisition opportunities are often limited by their global observation scenarios, constraining the achievable temporal sampling density. To overcome these limitations, this study investigates the feasibility of cross-sensor InSAR between ALOS-4 PALSAR-3 and ALOS-2 PALSAR-2 under specific acquisition conditions. In general, InSAR processing using different satellite systems is feasible when the sensors operate at the same center frequency, share nearly identical orbital geometry, and have similar incidence angle. ALOS-4 PALSAR-3 and ALOS-2 PALSAR-2 satisfy these conditions, as both operate at L-band and follow the same orbital path with a 14-day revisit cycle. Therefore, their combined use has the potential to reduce temporal gaps and enhance effective temporal resolution. In this study, cross-sensor interferometry was evaluated in the Stripmap (SM) and ScanSAR wide-swath (WD) modes to assess phase continuity across different sensor–mode combinations. To assess feasibility across varying land-surface cover, datasets were collected in Jinju (South Korea), Hokkaido (Japan), and Chittagong (Bangladesh). This study reports the results from Bangladesh, where the temporal baselines between acquisitions were shortest, minimizing temporal decorrelation effects and allowing a clearer evaluation of coherence behavior. The acquisition dates were: ALOS-4 SM (25 March 2025), ALOS-4 WD (8 April 2025), ALOS-2 SM (14 April 2025), and ALOS-2 WD (28 April 2025). All single look complex (SLC) images were resampled to match the pixel spacing of the ALOS-4 SM reference by compensating for differences in range and azimuth sampling caused by analog-to-digital converter rate and pulse repetition frequency variations, followed by precise co-registration. Differential InSAR processing was applied to remove flat-earth and topographic phase components and generate interferograms for all possible pairs. Common-band and adaptive filtering were used to reduce phase noise. Residual phase ramps were subtracted using a quadratic model of unwrapped interferometric phases. The highest mean coherence (~0.93) was observed for the 2SM-4SM pair, followed by 2SM-4WD (~0.92), 4SM-4WD (~0.86), 2SM-2WD (~0.71), and 4SM-2WD (~0.55). Interestingly, the 2WD-4WD pair, which is generally considered incompatible for interferometric processing due to significant spectral and acquisition differences, exhibited low mean coherence (~0.40). However, locally coherent interferometric phase signals were still observed in portions of the interferogram, indicating that interferometric phase formation is not entirely precluded even in this challenging configuration. Similar coherence trends were observed in Jinju and Hokkaido, suggesting that the cross-sensor and cross-mode interferometry using ALOS-4 PALSAR-3 and ALOS-2 PALSAR-2 were achievable. To further interpret variations in coherence, spectral analysis was conducted in both the range and azimuth dimensions. Spectral overlap was more diverse in the azimuth direction than in the range direction. The pairs showing the highest and lowest coherence (2SM–4SM and 2WD–4WD) corresponded to the maximum and minimum azimuth spectral overlap. The other pairs did not consistently exhibit coherence levels proportional to their spectral overlap, suggesting that additional geometric factors beyond spectral overlap influence coherence. Overall, despite variations in coherence values across interferometric pairs, interferogram generation was generally achievable under compatible acquisition conditions. These findings demonstrate the technical feasibility of cross-sensor interferometry between ALOS-2 and ALOS-4 when orbital geometry and incidence angles are sufficiently compatible. The combined use of both satellites can enhance acquisition density and improve temporal resolution for long-term InSAR time-series analysis, thereby strengthening deformation-monitoring capabilities.

As a well-established method for high-precision ground motion monitoring, SAR Interferometry (InSAR) enables the detection of slow deformations – typically due to subsidence, landslides, earthquakes, and volcanic phenomena, also affecting buildings or infrastructures – with millimetric precision and sub-metric spatial detail [1]-[3]. The cornerstone of this technique lies in the identification of points that exhibit consistent interferometric phase coherence over a sequence of acquisitions: over time, several approaches have been brought to an operational level, including the Small BAseline Subset (SBAS), the Persistent Scatterer (PS), and concept of Distributed Scatterer (DS) appraoches [4]-[19]. All the mentioned techniques identify the coherent points, that typically are in correspondence of man-made structures or natural terrains scarcely vegetated and non-cultivated, through the usage of different statistics based from the image amplitudes (e.g. amplitude dispersion, signal-to-clutter ratio) and/or phases (e.g. temporal coherence). In this context, we recently proposed an effective, efficient, and elegant algorithm for the selection of the measurement points, called Point Coherence Estimation (PCE) [20]. The main novelty and relevance of the PCE approach is that it is based only on a commonly accepted assumption about the statistical independence between the SAR signals in neighboring pixels. Based on this, we derive an algorithm capable to determine the coherence of all points in the interferometric image stack without critical approximations and at full resolution (without any pixel averaging operation). In this work, we provide a rigorous mathematical formulation of the PCE algorithm. Furthermore, we present a comprehensive quantitative validation of our processing chain across multiple radar wavelenghts (using COSMO-SkyMed, Sentinel-1 and SAOCOM data for X, C, L bands, respectively), analyzing a wide range of deformations (including landslides, subsidences, volcanic activities, bradyseism) in diverse geomorphological scenarios (urban areas, flat or mountain regions, coastal areas, and in general all types of areas with coherent point-like or distributed scatterers). The PCE methodology begins by considering the phase differences between neighbouring points (typically within a few hundred meters). In these differences, spatially correlated components, such as atmospheric delays, orbital artifacts, and large-scale motions, cancel out. The remaining signal allows for the estimation of the differences in elevation and velocity by maximizing the temporal coherence of these arcs. The temporal coherence of each pair-of-points depends primarily on the localized phase noise (thermal, temporal, and geometric decorrelations, for instance) of the two constituent points. Assuming that the phase noise is statistically independent between the neighbouring points of each pair, the expected value of the temporal coherence for an arc is equal to the product of the expected coherence values of the two individual points. By taking the logarithm of these relationships, we derive an overdetermined system of linear equations linking the arc coherences with the individual point ones. This system can be solved with good computational efficiently using solvers minimizing residuals via L1 or L2 norms. This yields a reliable, consistent estimate of the temporal coherence for every single point, facilitating the final identification of measurement points. Crucially, this method requires no prior assumptions regarding the probability distribution of the phase noise. However, under a Gaussian hypothesis, the system elegantly simplifies to the principle that the noise variance of a phase difference is the sum of the individual noise variances. By operating as a selection filter that avoids spatial "smearing," the PCE ensures that the original phase information remains unaltered, providing a clean, high-integrity input for the subsequent InSAR processing steps, including phase unwrapping. To assess the performance of the PCE-based workflow, results are benchmarked against current state-of-the-art PS and DS methodologies, and existent monitoring services like the European Ground Motion Service (EGMS) [3]. Metrics such as measurement density and coverage are considered. In addition, the cleanliness (noise and outliers) of the measurements signal is evaluated by statistical spatio-temporal analyses. Results show that the PCE method makes it possible to obtain more measurement points with respect to standard PS techniques, both in terms of density and of coverage of areas. Among the other things, it is worth noting that the PCE method makes it possible to obtain a significant number of interferometric measurements not only corresponding to strong scatterers but also in areas characterized by low and distributed backscattering (such as bare soil and scarcely vegetated non-cultivated terrains), where standard PS techniques are not able to detect signals. Regarding DS techniques, they are very effective in obtaining interferometric measurements in these types of areas, but effects due to changes in the dielectric constant (e.g., soil moisture and atmospheric humidity) can accumulate over time in the phases obtained by DS techniques, leading to signals that can be erroneously interpreted as ground deformations [21]. On the contrary, the PCE method exploits direct measurements at large spatial and temporal baselines and is not affected by these problems; moreover, the PCE technique preserves the signal at full resolution, without the need of performing phase averages and adaptive multilooking. Tests against current DS approaches show that PCE method can provide a good trade-off between point coverage and preservation of original phase values. However, it is worth noting that the PCE algorithm can be applied to any type of SAR images stack: to maximize the points coverage, a DS-filtered images stack could be set as input for the algorithm. The quantitative assessment performed in this study highlights the PCE algorithm, part of our innovative processing chain, as a robust and efficient alternative to traditional methods for the measurement point selection in InSAR. The validation campaign shows a superior balance in terms of accuracy, density, and coverage of ground deformations measurements, compared to current state-of-the-art InSAR methods. REFERENCES [1] M. Crosetto, O. Monserrat, M. Cuevas-González, N. Devanthéry, B. Crippa, "Persistent Scatterer Interferometry: A review," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 115, pp. 78-89, 2016. [2] D. HO TONG MINH, R. Hanssen, F. Rocca, “Radar Interferometry: 20 Years of Development in Time Series Techniques and Future Perspectives,” Remote Sens., vol. 12, pp. 1364, 2020, https://doi.org/10.3390/rs12091364 [3] M. Costantini et al., "EGMS: Europe-Wide Ground Motion Monitoring based on Full Resolution InSAR Processing of All Sentinel-1 Acquisitions," IEEE International Geoscience and Remote Sensing Symposium - IGARSS, Kuala Lumpur, Malaysia, pp. 5093-5096, 2022, doi: 10.1109/IGARSS46834.2022.9884966. [4] P. Berardino, G. Fornaro, R. Lanari, and E. 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Monells, “The coherent pixels technique (CPT): An advanced DInSAR technique for nonlinear deformation monitoring,” Pure and Applied Geophysics, vol. 165, no. 6, pp. 1167–1193, 2008. [13] F. Zhao and J. J. Mallorqui, "A Temporal Phase Coherence Estimation Algorithm and Its Application on DInSAR Pixel Selection," IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 11, pp. 8350-8361, Nov. 2019. [14] M. Costantini, S. Falco, F. Malvarosa, F. Minati, F. Trillo, and F. Vecchioli, “Persistent Scatterer Pair Interferometry: Approach and Application to COSMO-SkyMed SAR Data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, manuscript ID JSTARS-2014-00117. [15] A. Ferretti, A. Fumagalli, F. Novali, C. Prati, F. Rocca and A. Rucci, “A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR,” IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 9, pp. 3460-3470, Sept. 2011. [16] E.A. Hetland, P. Musé, M. Simons, Y.N. 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The Sentinel User Preparation- Synthetic Aperture Radar (SUPSAR) programme of the European Space Agency (ESA) aims to prepare the scientific community for the synergistic exploitation of the next-generation SAR missions, such as Sentinel-1 Next Generation (NG) and ROSE-L, as part of a broader System-of-Systems. These future missions will offer complementary C- and L-band capabilities, increase temporal density and enable advanced retrieval of forest parameters and dynamics. Yet temporal gaps and irregular sampling in SAR time series will remain limiting factors, particularly over forested regions where temporal decorrelation and acquisition constraints can compromise interferometric analyses. To fully exploit multi-mission synergies and identify optimal baseline configurations for multi-frequency SAR integration, reliable data simulation methodologies are required to reconstruct or interpolate missing SAR observations and ensure consistent temporal coverage for both scientific and operational applications. In this work, we propose a data-driven simulation framework designed to predict missing single-look complex (SLC) or coherence images in multi-temporal SAR datasets. The approach is developed from perspective that each SAR acquisition represents a unique spatial–temporal “view” of a scene. Given a stack of L available SAR acquisitions over a targeted region, a network is designed and trained to reconstruct one missing observation using the remaining L–1 images as input. The task is formulated as an interpolation problem, where the model learns spatial and temporal dependencies directly from the data. Our current investigation explores the combination of Convolutional Neural Networks (CNNs) and Transformer architectures to jointly capture the spatial structure and temporal evolution of SAR coherence. The CNN component encodes local spatial relationships, while the transformer module models long-range temporal correlations between acquisitions. This hybrid design aims to improve the model’s capacity to predict realistic SAR image or coherence for a given spatial (bₙ) and temporal (tₙ) baseline pair. By controlling the interferometric baselines within the simulation framework, the methodology can be adapted to both near-zero baseline configurations, such as those of ROSE-L or Sentinel-1, and non-zero baseline configurations, such as BIOMASS data. The results obtained from representative applications under each scenario, for example forest tomography in the case of non-zero baselines and vegetation temporal indices in near-zero baseline conditions, will be reported and analyzed to evaluate model performance. Ultimately, this work contributes to the SUPSAR objective of developing innovative approaches that maximize the scientific return of upcoming Copernicus Sentinel Expansion missions and enhance readiness for multi-frequency SAR synergy and data-driven simulation in Earth observation.

In this work, we study some of the building blocks of the processing chain that underpins deformation monitoring using PSInSAR (Persistent Scatterer Interferometric Synthetic Aperture Radar). While a complete understanding of the entire chain is necessary to grasp the technique, an exhaustive treatment would be extremely time-consuming. As a consequence, we focus on two of the main techniques often used in this chain: phase unwrapping and Kriging interpolation. We re-examine the methods in depth and conduct experiments to test their performance. We also provide an open-source implementation and online demo for phase unwrapping. ***Introduction*** In both methods, we study maps recorded at different dates containing a sparse scatter plot of points at which a signal of interest remains coherent over the time series. We shall call these points PSs or Persistent Scatterers [5]. In particular, the scatter plot is not a regular grid, so phase unwrapping has to be adapted for this situation, and Kriging interpolation can be used to obtain an estimate of atmospheric signals on the regular grid. We shall begin with phase unwrapping, which deals with the removal of the ambiguity mod 2π of the phase differences observed between two dates. Doing so, we follow the steps of Costantini, who first proposed a formulation of the problem as a minimum-cost flow (MCF) optimization over a grid [1], and as a more general linear programming (LP) problem in the case of nonplanar graphs, for instance, those exhibiting a degree of edge redundancy [2]. In both cases, the author seeks to minimize the L1 norm of the correction to be added to the wrapped, observed gradient, ensuring the consistency of the solution across all the cycles of the graph. The solutions are guaranteed to be multiples of 2π, making the methods both elegant and efficient. -We examined and implemented Costantini’s work from a grid to a general planar graph along with a way to find a minimum cycle basis on which to carry out the MCF algorithm. In particular, we focused on Delaunay triangulations, which are planar graphs like the grid. -In the case of LP, we tested Costantini’s idea on Delaunay graphs with redundant edges, rendering them nonplanar. We noted that adding redundant edges is rarely useful for unwrapping unless the phase signal is accompanied by severe atmospheric noise. Also, since LP is less efficient than MCF and redundancy adds many edges, redundancy might not only introduce errors but also make the work very computationally intensive. -We published in the IPOL journal a detailed article with an online demonstration open to the public and an open source implementation [6]. Next, we have work in progress regarding Kriging for atmospheric signal interpolation, mainly based on the literature [3], [4]. In the literature, Kriging has been used for atmospheric delay interpolation [5]. We tested Kriging both on real and simulated data. -We found that the performance of Kriging interpolation for atmospheric signals is relatively dependent on input parameters, to such an extent that it might be outmatched by parameterless methods such as Natural-neighbor interpolation when the input strays too far from the ground truth. For real atmospheric data, good-quality Kriging typically implies fitting a function such as a covariance or a variogram very precisely. These input parameters (covariance, variogram…) are well-documented and modeled in the literature [3], but we also witnessed significant deviations from the models when using real data, which might be problematic for Kriging with a high level of precision. -While we also noticed that Kriging is relatively slow, we noted that Kriging is approximately a local weighted nearest neighbors for atmospheric signals, that is, the interpolation at a point depends essentially on the known values of the first few closest sample points. This means Kriging can be achieved and greatly accelerated in a typical divide-and-conquer setting using overlapping patchwork methods without significant loss in precision. The individually Kriged patches are then sewn back together on the overlapping areas using a continuous weighting scheme, yielding a seamless rendering. -As in the case of phase unwrapping, we are planning to publish an online demo showcasing Kriging interpolation along with an associated, detailed article. ***Method and experiments*** Phase unwrapping relies on the smoothness of the signal to recover its gradient on a graph and, through integration, to recover the full signal up to a global constant. To do so, we seek to remove the residues of the wrapped observed gradient on the graph cycles. Under the smoothness hypothesis, we expect the wrapped observed gradient to be an exact estimate very often; otherwise unwrapping becomes intractable. If errors are made, the estimate is almost surely not a global gradient, i.e., it will display nonzero residues. To recover the signal through integration, we must fix this estimated gradient by adding the least correction in L1 norm to render it conservative by cancelling the residues. When the graph is planar, we can exploit the dual graph to formulate an MCF problem, yielding a solution that cancels out the residues. When the graph is not planar, we switch to the more general but slower LP method, cancelling the residues by writing down the necessary equations over a subset of cycles of minimal cardinality called a cycle basis. We carried out experiments on both simulated and real terrain profiles [6], and in almost all the cases, we noted a sharp transition from a perfect unwrapping to a very flawed, unexploitable one at a threshold in percentage of wrongly estimated gradients. This was done by gradually contracting or dilating the terrain by manipulating the unit distance, so the wrongly estimated gradients were not distributed uniformly. However, we saw that the transitions were induced by local errors in unwrapping salient features becoming too steep. For instance, dilating a peak too much induces a ring of wrongly estimated gradients around it, ultimately resulting in a local error in a whole region (the interior of the ring, i.e., the peak itself), whose estimation was either bumped up or down by a multiple of 2π. Because those regions represent a fraction of the whole image, this translates into an error curve which is almost zero below the threshold, and then exhibits a sudden, very steep increase, looking like a staircase. Each step or jump corresponds to a spurious region, whose size depends on the area and the magnitude of the error. The only case in which such a sharp transition did not happen was the case of images containing no salient features, e.g., only noise on a flat terrain or a gentle ramp. Also, in almost all the cases, lower redundancy allowed to unwrap steeper terrains. Higher redundancy was shown to be marginally helpful only in the case of severe noise. Kriging is an interpolation method which can be interpreted as a weakened form of the conditional expectation. More specifically, it is the conditional expectation restricted to linear functions. This restriction makes the computations tractable, relying only on first and second-order moments (for Simple Kriging), or even only on second-order moments (for Ordinary Kriging). It is required to be unbiased, making it the best unbiased linear estimator. It is extensively discussed in the literature [4] and can be used in the context of PSInSAR [5]. Given the values of some sample points (the PS) and a covariance function or a variogram, it is possible to estimate the value at an unknown point by minimizing the square error made when assuming it is a linear combination of the values at the known sample points. If the mean of the signal is unknown, unbiasedness is achieved by adding a condition over the coefficients, which must sum up to one and adds a Lagrange multiplier to the square error to minimize. It then suffices to solve for the coefficients and infer from them the estimated value from the sample points. If N is the number of points and the number of PS is O(N), then Kriging has time complexity O(N^3). Nevertheless, experiments show that good approximations can be obtained using only the nearest neighbors. Therefore, we propose a method that divides the Kriging process into overlapping patches, that are then sewn back together using a continuous weighting scheme. The result is obtained very quickly with almost no loss in precision. The only downside is that Kriging is heavily dependent on input parameters like the covariance function or the variogram. If a fit can be achieved with enough precision, Kriging works quite well, otherwise, standard parameterless techniques such as Natural nearest neighbors weighting can outperform it. The problem is that having to fit a variogram can make reliable unsupervised Kriging challenging. In practice, it is often observed that the power spectral density (PSD) of the atmospheric signal in frequency space follows an inverse power law of exponent alpha, i.e. PSD(k) ~ 1/k^alpha [3]. Assuming that the signal is second-order stationary, that is, its first and second-order moments exist and are invariant under translation, we can use the Wiener-Khinchin theorem to extract the autocorrelation or covariance function of the signal by computing the inverse Fourier transform of the PSD, when possible. However, the PSD might not be integrable as the exponent alpha can be greater than two, leading to a divergence at zero. In this case, the signal isn’t second-order stationary anymore, the Wiener-Khinchin theorem does not hold and the covariance function does not exist. Nevertheless, it can be shown that when 2 < alpha < 4, the variogram gamma behaves in the space domain as gamma(h) ~ h^(alpha-2) . We experimented Kriging interpolation on both simulated and real data. For simulated data, we used an inverse power-law PSD, typically with exponent 8/3, and we scattered a percentage of PS points uniformly across the image. We show that Kriging works best using a power-law variogram with an exponent close to alpha-2, otherwise significant deviation leads to errors of a magnitude such that even a parameterless method like Natural interpolation might provide better results. Empirically, for real data, we show that PSD(k) ~ 1/k^(8/3) and gamma(h) ~ h^(2/3) on average, but that some deviation from this model also occurs naturally (~10% in relative value) and in this case the exponent alpha needs to be fitted properly to avoid Kriging errors. As discussed in the previous paragraph, accounting for this phenomenon can introduce significant errors in Kriging. Therefore, good quality Kriging depends on the precision of the fit of the variogram which has to be made for each image. ***Conclusion*** We have described two of the main methods intervening in the building blocks of the PS chain, along with the experimental results that they yielded. It remains to interlock these methods in the right order to remove unwanted components in the original, raw signal and retrieve only the signal related to deformation, which is the goal of this present work. ***Bibliography*** [1] Costantini, M. (1998). A novel phase unwrapping method based on network programming. IEEE Transactions on Geoscience and Remote Sensing, 36(3), 813–821. https://doi.org/10.1109/36.673674 [2] Costantini, M., Malvarosa, F., & Minati, F. (2012). A general formulation for redundant integration of finite differences and phase unwrapping on a sparse multidimensional domain. IEEE Transactions on Geoscience and Remote Sensing, 50(3), 758–768. https://doi.org/10.1109/TGRS.2011.2162630 [3] Hanssen, R. (2002). Radar Interferometry, Data Interpretation and Error Analysis. Kluwer Academic Publishers.https://doi.org/10.1007/0-306-47633-9 [4] Wackernagel, H. (2003). Multivariate Geostatistics (3rd edition). https://doi.org/10.1007/978-3-662-05294-5 [5] Ferretti, A., Prati, C., & Rocca, F. (2001). Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(1), 8–20. https://doi.org/https://doi.org/10.1109/36.898661 [6] Alexandre Achard-de Lustrac, Roland Akiki, Axel Davy, and Jean-Michel Morel, L1-Norm Redundant Delaunay Phase Unwrapping and Gradient Correction, Image Processing On Line, 15 (2025), pp. 108–162. https://doi.org/10.5201/ipol.2025.583

Homogeneous pixel selection (HPS) is a key step in time-series distributed scatterer InSAR (DS-InSAR) processing, and its quality directly affects the stability and accuracy of local covariance estimation, phase linking, and subsequent deformation time-series inversion. Existing HPS methods can be broadly categorized into nonparametric test-driven approaches and parametric assumption-driven approaches. The former typically determine homogeneity by applying distribution-consistency tests to amplitude (or intensity) time series, such as the Kolmogorov–Smirnov (KS) and Anderson–Darling (AD) tests. Although these methods are statistically interpretable and computationally simple, they often suffer from limited statistical power when the number of acquisitions is small, and are sensitive to distribution shifts under complex scattering mechanisms or pronounced nonstationarity, which leads to insufficient detection of homogeneous pixels, threshold sensitivity, and unstable neighborhood sizes. The latter aim at fast screening by introducing simplified amplitude/intensity statistical models and deriving decision rules based on confidence intervals, with representative methods such as FaSHPS. While being efficient and interpretable, their effectiveness relies on specific modeling assumptions and parameter estimation; model mismatch in real scenes can cause over-selection or under-selection, and the resulting uncertainty may further propagate to covariance estimation and phase linking, ultimately increasing phase residuals and degrading inversion stability. In recent years, deep learning has been introduced into HPS to learn local scattering structures and spatial patterns, but existing strategies typically depend on manually labeled samples or use statistical-test outputs as pixel-wise pseudo-labels for all pixels within a window. Such designs are vulnerable to pseudo-label noise and severe class imbalance, causing conservative predictions, an insufficient number of homogeneous pixels, and unstable spatial patterns. To address these issues, we propose a prior-constrained and consistency-learning DS-InSAR homogeneous pixel selection method, termed DLHPS. DLHPS constructs a statistical prior by fusing voting results from KS, AD, and ttest with respect to the window-center reference pixel, and further extracts high confidence homogeneous and high confidence non-homogeneous sample sets. By replacing dense hard supervision over all window pixels with sparse high-confidence constraints, DLHPS alleviates imbalance-induced degradation and reduces the adverse impact of pseudo-label noise. In addition, DLHPS incorporates amplitude-perturbation-based data augmentation with a dual-view consistency constraint, together with a lightweight spatial coherence regularization, to improve robustness and spatial continuity. Experimental results demonstrate that DLHPS achieves a 90.55% increase in mean coherence and a 71.89% reduction in phase residuals, providing more reliable homogeneous neighborhoods for subsequent DS-InSAR phase linking.

With the high quality, regularly acquired SAR time series that are now available from constellations such as Sentinel-1, ALOS, and others, routine processing of InSAR displacement histories is becoming much more common, with these products reaching a steadily growing community. In recent years, it has also been widely recognized that contributions to the interferometric phase from changes in surface characteristics (e.g., soil moisture and vegetation water content) and shallow surface change (e.g., small-scale deformation at the pixel-to-pixel scale) can introduce biases to interferogram time series beyond simply the bias associated with the process itself. Spatial averaging/ filtering of the complex-valued phase introduces a bias when the phase values within the averaging window have a non-zero skewness that has some correlation over time - this is true even when the "unwrapped", real-valued phase values within that window have a zero-mean. Such biases can significantly degrade the utility of InSAR time series products, particularly for those aimed at relatively new members of the community. Here we examine methods for assessing whether this bias is "correctable": We explore techniques including the widely-used approach of incorporating redundant interferograms (i.e., not only the nearest-neighbor pairs), as well as an empirical model for the sensitivity of the phase of a given pixel to variations in soil moisture. We have previously used this model to show that the phase changes associated with a large tropical cyclone that impacted the southern Arabian peninsula can be reduced through corrections derived from phase changes associated with two unrelated storms. Each full-resolution pixel exhibits a similar phase change relative to its neighbors for every event. Some pixels are associated with a larger effect than others, to a degree that roughly follows an exponential distribution.   A major limitation of this model is that it works best when there are few other sources of temporal decorrelation, such as that introduced by vegetation change. It also implicitly assumes that phase changes are due to soil moisture variability rather than to actual ground displacement. We will show examples from the western United States where these assumptions break down, and explore methods for incorporating independent metrics for ground displacement and soil moisture into our workflow. Examples include the use of co- and cross-polarized data (rather than just co-polarized), and the use of SAR-based soil moisture models (e.g., from the new NASA-ISRO SAR platform, NISAR), or the direct use of the SAR backscatter values that inform such models.  

Synthetic Aperture Radar (SAR) enables high-resolution Earth observation, which is less sensitive to weather conditions and day-night cycles, and is therefore widely used for urban infrastructure monitoring and disaster response. In dense urban environments, however, high-rise buildings, bridges, and complex man-made structures frequently cause layover, in which scatterers at different elevations are superimposed within the same resolution cell. This superposition increases ambiguity in interpretation because phase contributions from multiple scattering mechanisms are mixed within a single resolution cell. Because conventional line-of-sight SAR imaging is inherently two-dimensional, it is fundamentally difficult to geometrically separate mixed scatterers. Consequently, 3-D reconstruction with explicit elevation resolution is required to determine the vertical distribution of urban scattering components. SAR tomography (TomoSAR) is a 3-D imaging technique that exploits a multi-baseline SAR stack acquired over the same area with varying baselines to estimate the reflectivity distribution along the elevation dimension, in addition to the slant-range and azimuth dimensions. In multi-baseline observations, baseline-dependent phase variations can be interpreted as sampling of the elevation spatial frequency, enabling the separation of multiple scatterers within a single resolution cell. TomoSAR can unmix superimposed returns from building facades, roofs, and the ground, and reconstruct the 3-D scattering distribution in urban scenes where layover is prevalent. In this study, we use an X-band COSMO-SkyMed multi-baseline dataset acquired over urban Pohang, Republic of Korea, to (i) quantitatively detect layover-affected areas and (ii) reconstruct 3-D scattering distributions of man-made structures via elevation-wise scatterer separation. To mitigate sidelobe leakage and false multi-peak responses in the elevation spectrum, we perform precise coregistration and apply phase calibration steps to reduce residual orbital errors and long-wavelength phase components. For tomographic inversion, we evaluate three representative spectral reconstruction approaches under consistent conditions: conventional beamforming, Capon-based high-resolution spectral estimation, and compressed sensing (CS)-based reconstruction. Beamforming is computationally efficient and straightforward, but may exhibit limited sidelobe suppression. Capon methods employ adaptive weighting to improve effective resolution and sidelobe control. CS-based reconstruction can provide super-resolution under the assumption of elevation sparsity, but it is computationally demanding and sensitive to regularization and model parameters. We compare these methods in terms of effective elevation resolution relative to the theoretical Rayleigh resolution, peak sidelobe ratio, multi-scatterer separability, and computational cost. Layover detection is formulated as a hypothesis-testing problem using a generalized likelihood ratio test (GLRT). We consider the single-scatterer versus double-scatterer hypotheses within each resolution cell. Based on the tomographic reconstruction, we estimate the maximum likelihood under each hypothesis and compute the GLRT statistic using the likelihood ratio. Pixels for which the double scatterer hypothesis is significantly favored under a false-alarm-controlled threshold are labeled as layover candidates. This approach is intended to reduce false alarms relative to decisions based solely on the presence of multiple peaks, and to produce layover masks and 3-D scatterer point candidates that are directly usable for structural interpretation. Finally, we extract elevation profiles over representative targets in urban areas (e.g., high-rise apartment complexes, bridges, and major infrastructures) and assess the consistency and error bounds of reconstructed elevations in areas where reference height information is available, such as public elevation datasets (e.g., DSM/topographic products) or optical/visual height cues. By integrating GLRT-based quantitative layover detection with a comparative analysis of tomographic spectral estimation methods, this work aims to provide an optimized framework for urban 3-D reconstruction and quality control, ultimately supporting more reliable deformation analysis and structure-level monitoring.

1 in 6 properties in England are at risk of flooding from rivers, sea and surface water. Recent UK-wide flood events following the succession of Storms Claudia, Bram, Goretti and Chandra from November 2025 to January 2026 highlight the current threat from flooding. Flood risk is only set to increase in the future as a result of climate change driven sea-level rise and increases in the frequency and magnitude of rainfall events. Lack of information about current and historical floods was highlighted as a major limitation to improving flood resilience and evaluating warnings by flood responders and forecasters at a workshop organised by the Environment Agency (EA) in March 2023. Traditional methods of flood mapping, such as aerial surveillance and visual, in-situ observations, are often cost and time-intensive, unacceptably dangerous and may not provide a consistent record of past floods. Earth observation satellites offer an alternative method of monitoring floods and are uniquely capable of systematically observing large geographical areas at a resolution of 20 m or less. The Copernicus Global Flood Monitoring (GFM) program was developed in 2021 and provides a near real-time service by automatically processing new Sentinel-1 acquisitions. However, the approach has been generalised to meet the demands of global monitoring and provides inconsistent analysis products over the UK. Meanwhile, products using commercial satellites are only capable of observing small regions on a case-by-case basis and would require considerable funding to provide an ongoing service across the UK. Through a collaboration with the Met Office and the University of Leeds, SatSense has designed an algorithm (FloodMap) to meet the needs of a cost-effective UK-wide flood monitoring service. FloodMap combines open-access Sentinel-1 data with UK-wide EA LiDAR data in a probabilistic time-series approach. The result is a product that accounts for uncertainties inherent to SAR data while significantly reducing the number of misclassifications from such noise through the application of physically realistic constraints on the flood surface. We demonstrate this capability by producing flood maps for three different case studies of recent large-scale UK flooding. We also perform a validation against independent observations from the Sentinel-2 optical satellite and compare this to GFM to demonstrate the improved performance of FloodMap.

Persistent Scatterer Interferometry (PSI) is a remote sensing technique well suited for regularly monitoring ground surface deformation. The launch of the Sentinel‑1 (S1) satellites has provided a continuous stream of SAR images, prompting a shift in recent PSI developments from analyzing fixed time periods to continuously updating deformation datasets as new data become available. However, frequently processing an ever‑growing dataset raises questions regarding persistent scatterer (PS) density, processing efficiency, and the comparability of results across successive updates. In previous studies, we evaluated three strategies for processing a steady stream of SAR images. The first strategy involved processing all available SAR images at each update, resulting in a continuously expanding dataset. The second strategy processed the incoming SAR images in consecutive, non‑overlapping subsets. The third strategy used overlapping consecutive subsets, similar to a sliding window. Comparing the results of these three strategies—specifically in terms of PS density, processing efficiency, and the consistency of results across updates—showed that the third strategy is best suited for long‑term monitoring of specific objects or areas of interest. To fully exploit the potential for reusing intermediate results within this third strategy, we designed and implemented the PSISlider processing chain. Its main deviation from a conventional PSI workflow is the use of two different reference images: one for co‑registration and another for interferogram formation. All secondary images are co‑registered to the same reference image, regardless of which image is used as the local reference for interferogram formation. The reference image used for co‑registration is termed the co‑registration reference image, while the reference image used for interferogram formation is termed the local reference image. Using two different reference images has several implications: For new subsets, only the newly acquired SAR images require interferometric pre‑processing. Spatial baselines between secondary images and the local reference image must be calculated. Differential interferograms between secondary images and the local reference image must be generated. However, employing a consistent co-registration reference image across all subsets enables the reuse of several intermediate products from previous processing steps, including: The amplitude dispersion index for selecting PS candidates (PSC). The probability of a PSC being a PS, for PS selection. The spatially uncorrelated look‑angle error. In this presentation, we outline the PSISlider processing chain and demonstrate its application using a case study from the coastal city of Patras, Greece.

Mining-induced ground subsidence is one of the most severe long-term geohazards in Poland, posing a direct and growing threat to public safety, critical infrastructure, and sustainable land use. In regions affected by intensive underground mining, annual subsidence rates locally exceed one meter, resulting in progressive deformation of the ground surface. Such rapid and large-magnitude ground movements lead to damage and failure of linear infrastructure such as roads, railways, pipelines, and power lines, as well as to structural degradation of residential, industrial, and public buildings. The cumulative effects of long-term mining activity significantly increase the vulnerability of densely populated and industrialized areas, making reliable subsidence monitoring a key component of risk management and spatial planning. This study presents an integrated approach combining satellite-based Interferometric Synthetic Aperture Radar (InSAR) measurements from the European Ground Motion Service (EGMS) with differential digital terrain models derived from airborne LiDAR data to improve the assessment of mining-related deformation hazards. The research focuses on three Polish regions experiencing the most significant and persistent subsidence: the Legnica–Głogów Copper District (LGCD), the Upper Silesian Coal Basin (USCB), and the Lublin Coal Basin (LCB). These regions represent different geological settings and mining histories but share a common challenge of intense and long-lasting surface deformation. In areas characterized by extremely high deformation rates, EGMS InSAR observations primarily delineate the outer boundaries of mining influence zones, where ground motion remains within the physical limits of the interferometric method. The most hazardous deformations, typically located in the central parts of subsidence basins, are often not detected due to phase decorrelation and velocity saturation. As a result, the zones with the highest rates of surface lowering—frequently exceeding one meter per year and posing the greatest risk to infrastructure and buildings—are insufficiently represented in satellite-based deformation products. To overcome these limitations and to fully exploit freely available geospatial data, the Polish Geological Survey applies differential airborne LiDAR-derived terrain models to quantify large-magnitude surface subsidence. By comparing high-resolution digital terrain models acquired at different time intervals, it is possible to directly measure rapid and high-amplitude vertical displacements that are invisible to InSAR techniques. These LiDAR-based differential models provide detailed information on the geometry and depth of subsidence troughs and allow for accurate identification of zones of maximum deformation. The work presents a methodology for integrating EGMS InSAR measurements with LiDAR-derived differential models to reconstruct complete and continuous deformation fields. The combined dataset captures both small-magnitude, spatially extensive ground motion detectable by InSAR and extreme subsidence occurring in the centers of mining-induced depressions. This integrated approach significantly improves the reliability and completeness of deformation mapping and provides a more realistic representation of mining-related geohazards. The results demonstrate that data fusion is essential for effective geohazard identification, risk assessment, and land-use planning in mining-affected regions. The research is conducted as part of statutory activities of the Polish Geological Survey and supports national geohazard monitoring, crisis management, and decision-making processes at local and regional administrative levels, with the ultimate goal of enhancing public safety in areas impacted by long-term mining activity.

Multi-track Interferometric Synthetic Aperture Radar (InSAR) methods hold significant potential for three-dimensional (3-D) deformation monitoring. However, both right-looking-only and joint right–left-looking combinations remain susceptible to noise amplification, particularly in the weakly constrained North-South (N–S) component.While regularization is an effective tool to suppress noise, traditional empirically tuned or single-parameter schemes often struggle to balance bias and variance across all three components simultaneously.To address this, this study proposes a spectrum-block-based regularization parameter-selection method. The proposed method analyzes the spectrum of the 3-D normal equations to identify the dominant noise-amplification mode for a given combination of observations. It then applies spectrum-block-based regularization with separate strengths for the strong subspace and the weak component, stabilizing the weak component while limiting bias in the others. Mogi simulations show that, compare with least squares (LS) and the single-parameter L-curve method, the proposed method substantially reduces component-wise RMSE under ill-conditioned observation combinations. Furthermore, for the eruption of the Hayli Gubbi volcano in November 2025, the joint results of the 3-D decomposition of NISAR - Sentinel-1 in the right-left look keep the components E-W and U consistent with LS while effectively suppressing high-frequency noise in the N–S component. Thereby highlighting that the proposed method improves the robustness of 3-D deformation reconstruction under ill-conditioned geometries and weak-constraint scenarios.

Infrastructure monitoring is essential for assessing structural performance, detecting and mitigating potential damage before it evolves into hazardous conditions, and supporting early response strategies. Interferometric Synthetic Aperture Radar (InSAR) has become a widely applied tool for this purpose due to its spatial coverage, medium temporal sampling rate, and cost-effective operational capabilities. However, accurate interpretation of structural deformation requires appropriate physical modeling of phase contributions. In this study, we investigate the Shezi Bridge in Taipei, Taiwan, an asymmetric cable-stayed structure connecting Shezi Island with the Beitou district across the Keelung River. We analyze ascending and descending Sentinel-1 acquisitions spanning 2014–2025, complemented by 60 high-resolution TerraSAR-X descending images covering 2017–2021. Time series analysis was conducted using the single-look InSAR methodology implemented in SARvey. The standard phase model in SARvey retrieves DEM error and linear velocity from arc-based phase observations. We extend this model by incorporating a thermal expansion component, which is expected to be significant given the region’s high temperature variability and humidity.Results reveal substantial differences between the original and extended phase models. The conventional model struggles to retrieve coherent points along the bridge deck, frequently classifying them as low coherent. In contrast, the extended model successfully captures thermally induced deformation patterns across the bridge structure. Using the extended model, we have found an annual seasonal signal with amplitudes of around 10 mm. Results from ascending and descending orbits show similar amplitudes, however this seasonal signal features different phase shifts resulting in a signal with peaks that match summer time in the descending orbit and peaks matching winter time in ascending orbit. Descending orbit from high resolution TerraSAR-X dataset features the same patterns as Sentinel-1 descending orbit.The discrepancy between ascending and descending orbits seems to be anchored by the bridge pylon and suggest a longitudinal deformation with respect to it. These findings demonstrate that incorporating thermal expansion into InSAR phase modeling significantly improves deformation retrieval in large bridge infrastructures. Moreover, multi-geometry time-series analysis enables the resolution of subtle, temperature-induced structural responses with high sensitivity and reliability, highlighting the potential of advanced InSAR modeling for structural health monitoring applications.

Land subsidence has become a major environmental concern in Golestan Province, northern Iran, where intensive agricultural development and long-term groundwater extraction have altered subsurface hydrological conditions. The Gorgan Plain, dominated by cultivated land, has experienced progressive ground deformation over the past decade. However, assessment of long-term deformation dynamics is limited by sparse historical SAR acquisitions before 2014 and low coherence over agricultural surfaces. In this study, we investigate the spatial pattern and temporal evolution of land subsidence in Golestan from 2014 to 2024 using Sentinel-1 time series InSAR and examine its relationship with hydro climatic variability and available groundwater observations. By combining ascending and descending Sentinel-1 observations, we derived vertical subsidence time series for the study area. The results reveal a broad, elliptical subsidence bowl centered within the Gorgan Plain. Maximum vertical subsidence rates reach approximately 15 cm/year, indicating substantial and sustained ground deformation during the observation period. The spatial continuity and persistence of deformation suggest a basin scale response consistent with regional groundwater withdrawal rather than localized structural instability. To analysis temporal variability, the mean vertical displacement time series over the main subsiding area was decomposed using Seasonal Trend decomposition based on Loess (STL). This approach separates the signal into trend, seasonal, and residual components, allowing the long-term deformation tendency to be isolated from seasonal fluctuations. The temporal derivative of the extracted trend component was then computed to characterize changes in subsidence rate over time. This derivative based metric was compared with the Standardized Precipitation-Evapotranspiration Index (SPEI), which reflects hydro climatic conditions. The analysis yields a maximum correlation coefficient of approximately 0.6, indicating a meaningful relationship between climate variability and temporal modulation of subsidence rates. These results suggest that while long-term subsidence is primarily driven by sustained groundwater extraction, short-term to mid-term acceleration or deceleration of deformation is partially influenced by climate related recharge variability. Groundwater level records from available monitoring wells were also examined. Although the number of wells is limited and some time series contain data gaps. Some wells show declining trends that are consistent with the subsidence patterns derived from InSAR. In contrast, several wells show relatively stable or locally rising water levels. Such heterogeneity may be related to differences in well depth, aquifer properties, or localized groundwater management practices. These observations indicate spatial variability in aquifer response across the plain. By integrating decadal Sentinel-1 deformation time series with STL based temporal analysis, hydro climatic indicators, and in situ groundwater observations, this study provides a consistent interpretation of subsidence dynamics in a data limited agricultural basin. The results demonstrate the capability of C-band time series InSAR to quantify large magnitude subsidence and to resolve its temporal variability in semi-arid environments, supporting improved groundwater management and hazard assessment in the Golestan Province.

The identification and monitoring of surface deformation over hydrocarbon fields are essential for ensuring that the benefits of hydrocarbon exploration and production are achieved in harmony with environmental sustainability. In hydrocarbon-producing regions, ground deformation - particularly land subsidence - can pose significant risks to infrastructure, ecosystems, and local communities. Therefore, understanding the magnitude, spatial extent, and temporal evolution of surface deformation within production zones is crucial for effective resource management, environmental protection, and risk mitigation. In this study, surface deformations around the hydrocarbon fields of the Niger Delta, Nigeria, are analyzed using a combination of Interferometric Synthetic Aperture Radar (InSAR) and Geographic Information System (GIS) techniques. The methodological framework consists of three main components. First, regional-scale deformation detection is carried out to generate a comprehensive deformation map across the Niger Delta oil-producing region. This step enables a detailed assessment of deformation patterns and rates across a broad spatial extent. Second, active deformation areas (ADAs) are identified to pinpoint zones most susceptible to subsidence and ground instability. Third, inventory integration and localized analysis are performed by combining a spatial database of known hydrocarbon fields with the derived deformation map within a GIS environment. This integration facilitates the spatial correlation of deformation patterns with specific production sites and operational activities. To ensure the robustness and reliability of the results, the methodology will be validated by applying the same processing workflow to descending orbit geometry data. The comparison between ascending and descending geometries will allow for cross-verification of deformation signals and improve confidence in the interpretation of vertical and horizontal displacement components. This validation step enhances the methodological rigor and supports the consistency of the findings. The results are expected to provide critical insights into the relationship between subsidence and hydrocarbon production activities in the Niger Delta. Such insights are vital for sustainable resource management, infrastructure planning, and the mitigation of environmental impacts in hydrocarbon-rich regions. Overall, the proposed methodology offers a reliable and transferable framework for policy-makers, regulators, environmental authorities, and industry stakeholders seeking to ensure that oil and gas exploration and production proceed in a manner that minimizes environmental and societal risks.

Interferometric Synthetic Aperture Radar (InSAR) is a remote sensing technique that enables precise large-scale displacement monitoring with accuracy comparable to terrestrial geodetic methods. However, the relative character of InSAR displacement measurements in the Line-of-Sight (LOS) direction poses challenges for achieving absolute geodetic accuracy. To address this limitation, collocation with complementary geodetic techniques, such as Global Navigation Satellite Systems (GNSS), is essential to tie InSAR observations to a globally recognized terrestrial reference frame. The Slovak InSAR Corner Reflector Network (SKICRN) is an innovative geodetic infrastructure developed to integrate InSAR and GNSS measurements and to serve as an experimental framework. The SKICRN includes strategically deployed corner reflectors co-located with GNSS stations (part of the Slovak real-time positioning service (SKPOS) network) across Slovakia (21 sites), enabling detailed analysis of radar backscattering properties and spatial alignment. The experimental setup also involved precise determination of the spatial vectors between GNSS and InSAR reference points, achieved through high-precision geodetic surveying techniques, including static GNSS observations and total station measurements. This collocation strategy not only enhances the reliability of displacement monitoring but also establishes a scalable framework for integrating InSAR with existing geodetic networks. All stations were analyzed for their radar characteristics (SCR, RCS) and InSAR displacement time series. Selected stations were compared with the GNSS displacement time series. Processing of the GNSS data from the SKPOS network was performed using Bernese 5.4 software, following the latest Guidelines for EPN Analysis Centres. The SKPOS network solution is aligned with the EPN reference frame solution using a minimum-constraints condition at 12 EPN reference stations of class C0–C2. The results (cleaned daily XYZ coordinates) are estimated in IGS20/IGb20 and subsequently transformed to ETRS89 (ETRF2000) at the mid-epoch of the day. A geodetic approach implemented in the GECORIS software package was used to process Sentinel-1 data. For each station, all available tracks (covering the period from CR installation to April 2026) were processed within the immediate vicinity (10 × 10 km) using a free-network solution, including tropospheric effects. All stations demonstrated robust backscatter properties, with SCR values exceeding the threshold required for reliable InSAR processing. The results indicate that the SKICRN improves the geodetic precision of InSAR measurements by transforming relative LOS displacements into a terrestrial reference frame. This research demonstrates the viability of the SKICRN as a foundation for geodetic innovation, highlighting its potential for regional- and national-scale implementation in Slovakia and beyond.

During the last decade, wide area monitoring of infrastructure with InSAR has seen tremendous progress. A large number of case studies, proposals for regional monitoring systems for diverse tasks and first operational monitoring services have appeared. In particular, the monitoring of linear infrastructure (roads and train tracks) is of high relevance. With regard to the coverage of roads with InSAR measurements, the joint use of PS and DS has been proven to be significantly superior to only using PS. Here, we will show that in X-band and to a lesser degree in C-band there is room for distinct qualitative improvements in estimating DS signals on asphalt surfaces by using Temporary Coherent DS (TCDS). Complementary to this submission, a second submission to Fringe 2026 has been made (A study on Temporary Coherent Distributed Scatterers on roads and runways on the level of single scatterers, A. Fingerle, M. Even, A. Seidel, H. Kutterer), that studies Temporary Coherent Distributed Scatterers on roads, runways and train tracks on the scatterer level. It contrasts DSC appearances on asphalt and train tracks regarding e.g. bandwidth, grouping criterion and surface properties and discusses the impact of these factors on grouping and several quality numbers. Our approach for TCDS is based on a new closure phase-based matrix and the phase triangulation matrix. Beside adding another type of information, the closure phase-based matrix improves the discernability between acquisitions for which the DS in question has low-quality signal and acquisitions with high quality. The phase triangulation matrix measures how well the estimated DS phase history is able to reproduce the phases of the coherence matrix. Specifically in the case of asphalt surfaces, a simple approach for discerning quality of acquisitions is successful. It is based on the observation, that asphalt surfaces in X-band posses low to medium but persistent coherence values. Only for certain acquisitions, coherences and values of are close to zero, which presumably is caused by altered backscattering properties because of water or snow. In order to detect such acquisitions, we derive two types of quality numbers from the columns of above matrices. Simple thresholding on these two numbers allows to discard acquisitions (specific for each DS candidate) with low quality signal. In order to assess our approach, beside the quality numbers phase triangulation coherence and a measure of phase closure consistency, the number of discarded acquisitions and the temporal coherences of point pairs are investigated. We are confident that this approach can be refined for application in cases, where has a block-structure (often the case on train tracks).

During the last decade, wide area monitoring of infrastructure with InSAR has seen tremendous progress. A large number of case studies, proposals for regional monitoring systems for diverse tasks and first operational monitoring services have appeared. In particular, the monitoring of linear infrastructure (roads and train tracks) is of high relevance. With regard to the coverage of roads with InSAR measurements, the joint use of PS and DS has been proven to be significantly superior to only using PS. Here, we will present preparatory investigations to identify Temporary Coherent DS (TCDS) in X-band and in C-band on asphalt surfaces. Based on our findings, we will show in a second submission to Fringe 2026 (Even et al. Temporary Coherent Distributed Scatterers on roads and runways) that TCDS in X-band and to a lesser degree in C-band allow for distinct qualitative improvements for displacement analysis on asphalt surfaces. In order to develop an approach for TCDS, we studied the coherence matrix, a new closure phase-based matrix, the residual phase angle matrix and the phase triangulation matrix. Beside adding another type of information, the new closure phase-based matrix improves the discernability between acquisitions for which the DS in question has low-quality signal and acquisitions with high quality. The residual phase angle matrix and the phase triangulation matrix measure how well the estimated DS phase history is able to reproduce the phases of the coherence matrix. Specifically in the case of asphalt surfaces, a simple approach for discerning quality of acquisitions is successful. It is based on the observation, that asphalt surfaces in X-band posses low to medium but persistent coherence values. Only for certain acquisitions, coherences and values of entries of the closure phase-based matrix are close to zero, which presumably is caused by altered backscattering properties because of water or snow. In order to detect such acquisitions, we derive quality numbers for each acquisition from the columns of the named matrices. The simplest approach to identify acquisitions (specific for each DS candidate) with low quality signal is thresholding on these numbers. We investigate their suitability for this purpose for many examples of asphalt surfaces of different width and variability in mean amplitude with X- and C-band. Setting thresholds based on e.g. closure phase consistencies proves to be easier than using coherence because they do not exhibit the pronounced dependence on magnitude of coherence and neighborhood size. In addition, we study the influence of the grouping method on the selection of the statistically homogenous pixels and on the quality numbers. Our findings show that the proposed approach works well on asphalt surfaces. Beyond that, we are confident that this method can be refined for application in cases, where the new closure phase-based matrix has a block-structure (often the case on train tracks).

Limited attention is typically paid to the cause-effect relationship between land subsidence due to aquifers overexploitation in expanding metropolises and urban growth models and patterns. This paper implements an integrated urban and satellite Interferometric Synthetic Aperture Radar (InSAR) approach to investigate subsidence, multi-decadal urban growth and peopling trends in the Metropolitan Area of Morelia (ZMM) in the Mexican state of Michoacán. Stacking of JRC’s Global Human Settlement Layer, DLR’s World Settlement Footprint and INEGI’s National Geostatistical Framework datasets revealed a predominant edge-expansion growth model, with urban densification in 1975–2020 and some sprawling in 1990–2000. Population of the ZMM doubled in the last 30 years, reaching over 1 million inhabitants. The ENVISAT and Sentinel-1 InSAR analysis confirms that subsidence is structurally-controlled by the main normal faults within the Cuitzeo half-graben. Differential sinking and ground discontinuities are aligned with buried tectonic faults and contrasting compressible sediment thickness. Non-linearly deforming subsidence bowls develop at extraction wells in both old and newly urbanized sectors of the ZMM. Maximum vertical displacement velocities increased from –2.5 (2003–2010) to –9.0 (2014–2021) cm/year, with subsidence migrating towards recently urbanized zones. More than 250 new groundwater wells were added to the public registry since 2000, many of which within new urban sectors. Time-lapse InSAR reveals a 4 km2 rapidly subsiding bowl that formed at the largest social housing neighbourhood of Villas del Pedregal, as building lots were progressively completed and sold, and new wells registered. With angular distortions due to the differential subsidence reaching 0.12% in 2014–2021, new buildings and roads are exposed to fracturing and surface faulting risk of comparable level as the city historic building blocks located along the main faults. By providing useful insights into the relationship between urban growth and land subsidence in the ZMM, the approach proves valuable for application to other metropolises worldwide. Full paper: Cigna F., Tapete D. 2022. Urban growth and land subsidence: Multi-decadal investigation using human settlement data and satellite InSAR in Morelia, Mexico. Science of The Total Environment, 811, id.152211, doi:10.1016/j.scitotenv.2021.152211

Tropical peatlands are major global carbon stores, yet widespread drainage and land conversion have transformed many into persistent carbon sources through peat oxidation. In Indonesia, Sumatran peatlands are estimated to contribute to two-thirds of the country’s peat carbon emissions, but emissions remain poorly constrained due to limited observations. Peat subsidence provides a measurable indicator of peat oxidation and carbon loss, but large-scale, spatially consistent monitoring has been hindered by dense vegetation, cloud cover, and sparse field measurements. Here, we present an eight-year (2016 to 2024) L-band Interferometric Synthetic Aperture Radar (InSAR) analysis of vertical land motion across 6.81 million hectares of peatlands in Sumatra. The L-band wavelength allows detection past dense tropical vegetation. By integrating ALOS-2 InSAR with Global Navigation Satellite System (GNSS) data and isolating tectonic signals, we derive spatially continuous peat motions at 180 m resolution, enabling regional-scale interpretation of peat dynamics independent of tectonic deformation. We find that 93% of Sumatran peatlands are subsiding, with median rates of -2.0 cm yr-1 (5th / 95th percentiles: -4.3 / 0.3 cm yr-1). Subsidence is widespread across all provinces and not confined to intensively drained land uses. Acacia plantations – the most intensely drained land use – show the fastest mean subsidence of -3.3 ± 1.2 cm yr-1 as expected. However, peat swamp forests – areas classified as pristine and without drainage – also exhibit subsidence, with a mean rate of -1.5 ± 1.0 cm yr-1, indicating ongoing carbon loss, and challenging assumptions that forests typically function as carbon sinks. Some degraded peatlands including Acacia plantations, oil palm plantations, and smallholder agriculture areas also subside more slowly than previously reported. Variability of peat motions within the same land use and drainage classes is high, demonstrating that categorical proxies alone cannot reliably represent peat carbon loss. The high variability in InSAR-derived peat motions within the same land use classes indicates that Intergovernmental Panel on Climate Change (IPCC) Tier 1 accounting, which assigns fixed emission factors to each land use class, can introduce substantial bias. To address this, we convert pixel-level subsidence rates into carbon emissions to implement a Tier 3-equivalent approach, which entails site-specific, locally informed estimations at fine spatial resolution. This yields total peat carbon emissions of approximately 61 million t CO2-C yr-1 across Sumatra. By capturing spatial heterogeneity that Tier 1 emission factors cannot resolve, the InSAR-based method provides an observation-driven framework to refine greenhouse gas inventories and support more accurate climate mitigation strategies.

Over recent decades, glacier retreat in the European Alps has accelerated dramatically due to climate change, exposing extensive glacier forefields that are undergoing rapid geomorphological transformation. These newly deglaciated areas are characterised by unstable sediments, degrading dead-ice bodies, reworked moraines, and increased slope activity. As a result, the spatial distribution and intensity of geomorphological processes are changing, posing emerging risks to alpine infrastructure such as mountain huts, hiking trails, and access routes. Continuous and spatially comprehensive monitoring of these dynamic environments is therefore essential, particularly under accelerating climate warming and increasing geomorphic instability worldwide today. Synthetic aperture radar (SAR), and particularly interferometric SAR (InSAR), offers unique advantages for monitoring high-mountain terrain, as it enables weather-independent, large-scale, and repeatable measurements of surface displacement. In this contribution, we present a radar-based investigation of the glacier forefield of Taschachferner in the Ötztal Alps (Austria), integrating multi-temporal Sentinel-1 InSAR time series analysis with detailed geomorphological mapping. Surface deformation rates are derived from Sentinel-1 C-band data using multi-temporal interferometric processing. The resulting velocity rates are analysed in relation to geomorphological units identified from optical imagery and field-based interpretation, including lateral and terminal moraines, debris cones, rock glaciers, and debris-covered glacier remnants. Attention is paid to spatial variations in deformation across different geomorphological features. The InSAR results reveal heterogeneous deformation patterns within the glacier forefield, including zones of enhanced subsidence likely related to dead-ice melt, as well as localised displacement signals associated with sediment redistribution and slope processes. Stable sectors, in contrast, show minimal displacement and persistent coherence. By comparing radar-derived kinematic information with geomorphological interpretation, we demonstrate how InSAR can refine the delineation of debris-covered dead-ice areas and identify sectors with anomalous deformation rates that may require intensified hazard monitoring. The study highlights the added value of combining geomorphological expertise with radar-based surface motion analysis to better understand landscape dynamics in rapidly evolving alpine terrain. Beyond scientific insights, our approach provides a transferable framework for monitoring of recently deglaciated areas. Sentinel-1 time series analysis thus represents a powerful tool for supporting hazard assessment and adaptive management strategies in high-mountain environments increasingly affected by climate-driven change.

Ensuring the structural integrity of dams is a critical challenge in civil engineering, particularly as these infrastructures age and face increasing stress from environmental and anthropogenic factors. While traditional monitoring methodologies, such as in-situ sensors and geodetic surveys, offer 8high sensitivity, they are often limited by high costs, sparse spatial coverage, and labor-intensive operations, which struggle to capture large-scale, complex deformation patterns. To address these limitations, the DARTWIN project proposes a transformative approach by integrating satellite-based remote sensing, physics-based numerical modeling, and artificial intelligence (AI) within a Hybrid Digital Twin (HDT) framework. The core of the DARTWIN methodology relies on the refinement of Multi-Temporal InSAR (MT-InSAR) techniques to provide continuous, millimeter-scale deformation monitoring. A significant challenge in dam surveillance is the detection of rapid or non-linear deformations, such as those associated with primary consolidation, rapid reservoir drawdown/filling, or episodic slope instabilities. Current regional services, such as the European Ground Motion Service (EGMS), often struggle to capture these fast-moving or complex behaviors due to constraints in temporal resolution and observation windows. DARTWIN overcomes this by leveraging a synergistic use of multi-mission SAR data, combining high-resolution X-band data from the PAZ and TerraSAR-X missions with extensive C-band archives from Copernicus Sentinel-1. This multi-frequency approach, processed through advanced tools, allows for more reliable monitoring in diverse environments, including data-scarce scenarios. A key innovation of the project is the development of an AI-driven layer designed to automatically detect and classify anomalous movements within MT-InSAR time series. By employing machine learning algorithms such as Long Short-Term Memory (LSTM) networks and Random Forests, the system can distinguish between expected seasonal oscillations and accelerating structural anomalies, providing a predictive rather than reactive monitoring strategy. Furthermore, to bridge the gap between high-fidelity simulations and real-time monitoring, DARTWIN implements Reduced-Order Models (ROMs) derived from Finite Element Method (FEM) simulations. While traditional hydro-mechanical FEM models are computationally intensive, ROMs allow for simplified yet accurate simulations that can be updated in near real-time using observational data. These models are integrated into the HDT, creating a continuously updating digital replica of the dam's behavior. Finally, all results are delivered through a geospatial viewer, an interactive platform that facilitates data visualization and decision support for dam operators and Water Authorities, ensuring the long-term resilience of critical water cycle infrastructures.

Groundwater has been over-pumped and excessive use during the past decades due to the lack of sufficient surface water caused by rapid economic developments and growing population in the central Taiwan. The alluvial fan of the Cho-Shui River in Western Taiwan suffers the most serious land subsidence hazard with a maximum subsidence rate in excess of 3 cm/yr which affect the transportation infrastructures across the land subsidence area. The long-term spatial land subsidence variation from 1995 to 2024 reveals that the center of land subsidence area changed significantly with time from the coastal area to inland area. The decreasing of land subsidence could obviously be detected by the velocity profile along the Taiwan High Speed Rail during different time periods from geodetic measurements. Not only the vertical displacement increases the risk on potential damage on transportation infrastructures across the land subsidence bowls but also the induced additional horizontal displacement owing to the vertical subsidence could result in the unexpected risk for the infrastructure. In this study, we used the multi-temporal InSAR to calculate the vertical deformation and the east-west deformation combined with the velocity field of the ascending and descending orbits. Three large-scale subsidence bowls are detected and accompanied by maximum additional horizontal deformation of ~8 mm/yr than that predicted by tectonic movement outside of the subsidence bowl. This additional E-W displacement is the major risk concerns of the N-S trending Taiwan High Speed Rail. In addition, we also use hydro-geological data from several monitoring wells to construct numerical modeling to discuss the deformation patterns in different aquifer and aquitard system for the land subsidence induced horizontal deformation.

Hydrotechnical infrastructure, including flood protection dams, plays a huge role within critical infrastructure systems, climate change adaptation strategies, and the management of extreme precipitation events. The long-term operation of these structures, cyclical hydrological loads, and increasing flood frequency require systematic, precise monitoring of displacements. Satellite radar interferometry enables continuous monitoring by acquiring spatially information on deformation. It is an ideal complement to in-situ measurements, which provide more precise point-based monitoring. Importantly, these methods are non-invasive and do not interfere with the structural integrity of hydrotechnical facilities. However, monitoring earthen dams and reservoir embankmentsremains challenging due to signal decorrelation caused by vegetation cover and temporal variability in soil moisture conditions. This study presents a Persistent Scatterer InSAR (PSInSAR) analysis of selected retention reservoirs in Poland located in the Odra River basin (an area affected by the 2024 flood event)—including reservoirs: Racibórz Dolny, Otmuchów, Nysa, and Kozielno, Stronie Śląskie as well as Świnna Poręba in the Upper Vistula basin. The analysis was conducted using C-band SAR data from the Sentinel-1 mission covering the period 2022–2025. An integrated processing scheme combining Differential InSAR (DInSAR) and PSInSAR was applied to enhance deformation detectability across heterogeneous structural components. Particular attention was given to coherence threshold optimization and to the separation of seasonal signals in partially vegetated areas. A three-year InSAR time series enabled the estimation of linear displacement velocities and the identification of seasonal components. For the majority of the analyzed structures, no statistically significant long-term deformation trends were observed. The estimated displacement amplitudes were approximately 5 mm, which is consistent with the expected measurement uncertainty of the C-band PSInSAR approach. In contrast, for the Racibórz Dolny Reservoir, localized clusters of persistent scatterers exhibiting cumulative vertical displacements of up to –30 mm (approximately –9 mm/year) were identified within the northern sector of the embankment.Time-series decomposition indicates predominantly linear behaviour with minor seasonal modulation related to hydrological loading cycles rather than structural instability. The results are intended for integration into the National Satellite Information System (NSIS – https://nsisplatforma.polsa.gov.pl/?lang=en), operated by the Polish Space Agency (POLSA), enabling the operational dissemination of InSAR products to stakeholders responsible for hydrotechnical infrastructure monitoring and safety. The results confirm that Sentinel-1–based PSInSAR enables millimeter-level deformation detection in large hydrotechnical systems, supporting scalable satellite-based early warning frameworks and enhancing infrastructure resilience under changing climatic conditions. The product was developed on behalf of the Polish Space Agency (POLSA) by the Institute of Geodesy and Geoinformatics at the Wrocław University of Environmental and Life Sciences (UPWr).

As climate change, urban expansion, aging infrastructure, and resource development intensify global geotechnical challenges, the demand for scalable and cost-effective monitoring solutions continues to grow. Interferometric Synthetic Aperture Radar (InSAR) offers a non-invasive, globally applicable approach with significant potential for risk reduction and resilience enhancement. InSAR as a method, is used for at least two decades now for monitoring ground deformation and assessing infrastructure stability from space, using radar satellite data, and has applicability that can vary from regional to global scales. The millimeter-scale precision detection of surface displacements combined with the unparalleled spatial coverage, long-term historical analysis, and cost-effective monitoring compared to conventional ground-based techniques, make it an ideal solution for covering the deformation monitoring needs in several sectors. The rapid expansion of public and commercial SAR satellite missions has generated extensive multi-decadal archives, allowing both retrospective and near-real-time deformation analyses almost anywhere on Earth. The large spatial footprint of SAR imagery enables continuous monitoring over hundreds of square kilometers, offering a distinct advantage over localized ground instruments. This broad coverage supports the detection of both localized instabilities and regional deformation trends. Historical satellite archives allow further long-term structural integrity assessments of critical infrastructure. As satellite revisit frequencies increase, the potential for operational and near-real-time monitoring continues to grow. enable reliable deformation monitoring across diverse landscapes. InSAR achieves high-precision deformation measurements even in complex environments. In 2024, a dedicated and scalable commercial InSAR service, Inio, was launched as a result of the collaboration between Kongsberg Satellite Services (KSAT) and the Norwegian Geotechnical Institute (NGI). This partnership combines NGI’s geotechnical engineering expertise with KSAT’s global satellite data access and ground station infrastructure, delivering an integrated service that transforms satellite-derived deformation measurements into actionable engineering insight. Inio is designed to enhance ground stability assessments, mitigate operational risks, and support informed decision-making across multiple sectors worldwide. Inio applies these capabilities across transportation, water management, mining, energy, and urban development sectors. Bridges, roads, railways, tunnels, hydroelectric dams, and tailings storage facilities can be systematically monitored for subtle displacement that may indicate emerging instability. In urban environments, InSAR enables large-scale subsidence mapping, supporting municipal planning and infrastructure resilience. In mining and industrial contexts, deformation monitoring enhances risk management while reducing the need for continuous field-based instrumentation. Inio will be presenting examples of various cases worldwide, using both open source and commercial data, of different resolutions, to prove the InSAR’s effectiveness in environmental and geohazards monitoring, and assess instabilities and deformation, combined with geotechnical analysis. For cases where geotechnical and engineering monitoring to detect unstable ground was critical in construction and infrastructure, in order to manage and assess stability and identify hazardous movements early, subtle surface movements over large areas have been detected. Additionally, timely and actionable information was provided, supporting disaster preparedness. With multi-temporal InSAR analysis for mapping deformation patterns across the affected areas, we are able to identify active zones of displacement. The spatial continuity of satellite observations is providing comprehensive coverage beyond what ground-based measurements alone could achieve, demonstrating the technology’s value in remote and hazard-prone regions. Beyond traditional geotechnical applications, Inio supports emerging energy-sector needs, including Enhanced Oil Recovery (EOR) and Carbon Capture, Utilization, and Storage (CCUS). Fluid injection and extraction can induce measurable surface deformation due to subsurface pressure changes. Continuous InSAR monitoring allows operators to track subsidence or uplift, verify reservoir behavior, and detect anomalies that may signal geomechanical instability. Integrating deformation data with geological and reservoir models enhances environmental safety, regulatory compliance, and operational resilience. The defining strength of Inio lies in its multidisciplinary framework. Satellite-derived deformation data are interpreted alongside geological information, digital elevation models, engineering parameters, and historical records. This integration ensures that displacement measurements are contextualized within a robust geotechnical framework, reducing uncertainty and supporting practical mitigation strategies. Rather than delivering raw remote sensing outputs alone, Inio provides comprehensive assessments of ground stability and associated risks. The launch of Inio marks a significant step in operationalizing advanced InSAR methodologies within a commercial, engineering-oriented service model. InSAR has evolved into an indispensable global technology for ground stability assessment and geohazard mitigation. Through the combined expertise of KSAT and NGI, Inio maximizes the value of satellite radar data by integrating technical precision with engineering interpretation. This global service enhances safety, safeguards investments, and supports sustainable infrastructure and energy development in an increasingly complex and dynamic world.

Monitoring and understanding surface deformation associated with subsurface energy storage is becoming increasingly important as underground gas, CO₂, and hydrogen storage expand in scale and strategic relevance. Traditional monitoring methods these activities struggle to correlate surface deformations with deep subsurface changes, which are critical for ensuring safety, managing pressure changes, and preventing incidents in subsurface storage facilities Synthetic Aperture Radar Interferometry (InSAR) offers spatially dense measurements of surface displacement, but interpreting these observations in terms of subsurface processes requires physically based modelling. This project demonstrates a combined geomechanical - InSAR workflow using open data from the Yela Underground Gas Storage (UGS) facility in Spain as a test case, aiming to assess the value of integrated deformation monitoring for operational storage sites. We processed 190 Sentinel‑1 scenes (2020–2025) using Enhanced Persistent Scatterer (EPS) and Enhanced SBAS (ESBAS) workflows, comparing their performance in an area dominated by farmlands and sparse shrublands. The EPS workflow provided the most reliable results, generating stable deformation time series with low noise outside agricultural zones. Despite displacement magnitudes being small (within ±5 mm), the time series above the Yela reservoir revealed a clear seasonal signal corresponding to the annual gas injection and withdrawal cycle. A 2D axisymmetric geomechanical, numerical model was developed in Abaqus to simulate vertical and horizontal deformation associated with seasonal reservoir pressure changes. Sensitivity analyses showed that reservoir and underburden stiffness, together with pressure variation, exert the strongest control on surface deformation. Model results reproduced the observed annual uplift–subsidence pattern but overestimated the amplitude, reflecting uncertainties in geomechanical input data, reservoir geometry, and operational timing. This underscores the potential for InSAR to refine model calibration when accurate injection schedules and geomechanical information are available. By comparing modelled and observed deformation at varying distances from the facility, the study demonstrates how InSAR can be used not only for detection of seasonal deformation, but also for identifying anomalous behaviour, informing parameter calibration, and improving confidence in subsurface monitoring. The project highlights the need for integrated workflows that translate InSAR displacement information into mechanical Earth model (MEM) inputs—a critical missing step for future operational monitoring of CO₂ and H₂ storage. This work provides a foundation for developing automated, scalable monitoring systems that combine satellite observations, computational analytics, and geomechanics. Acknowledgement: This work has been funded by the Norwegian Space Agency under grant JOR25032.

RASTOOL-DoS focuses on the development of a prototype ground-motion analysis service designed to be integrated within the Copernicus Emergency Management Service (CEMS) On-Demand Mapping framework. The methodology builds upon previous European initiatives on geohazard monitoring and prevention, evolving from periodic deformation mapping toward semi-automated detection and interpretation of active ground motion. Central to this approach is the identification of Active Deformation Areas (ADAs), obtained by clustering Persistent Scatterer time-series derived from multitemporal InSAR datasets. These ADAs enable the extraction of deformation hotspots and the derivation of higher-level products, including preliminary impact indicators based on the spatial intersection with exposed infrastructure and population datasets. The proposed workflow emphasizes reproducibility and rapid deployment during emergency scenarios. Standardized processing chains and predefined analytical steps allow users with limited InSAR expertise to exploit ground-motion information within operational timelines. This represents a shift from purely scientific analysis toward service-oriented products aligned with the needs of emergency response actors. The applicability of this framework is illustrated through several recent emergency cases in Spain. Following the catastrophic 2024 Valencia DANA hydrometeorological event, ADA-derived information from EGMS data was integrated with cadastral datasets to identify buildings with pre-existing deformation patterns and to assess potential cascading effects associated with unstable slopes near reservoirs. Complementary SAR products, including high-resolution flood mapping datasets provided by commercial constellations, supported hydrodynamic modelling efforts and improved situational awareness. During the intense rainfall episodes in southern Spain (January–February 2026), EGMS time-series were analysed to identify pre-existing ground deformation in mountainous sectors. These areas experienced significant impacts, including the full evacuation of one village and the partial evacuation of another due to landslide hazards and concerns over possible karstic collapse. Exploratory comparisons between the temporal evolution of precipitation and deformation signals were conducted to investigate possible terrain responses to previous rainfall events. Additionally, Sentinel-1 interferograms were generated to evaluate localized post-event changes, revealing the limitations of regional-resolution InSAR products for small-scale instabilities and emphasizing the need for rapid access to high-resolution SAR acquisitions. These real-world applications highlight key challenges for operational InSAR services, including data availability constraints, the need for workflows prepared prior to emergencies, and the importance of translating deformation measurements into prioritized actions for field teams. The RASTOOL-DoS initiative contributes to addressing these gaps by advancing toward scalable ground-motion services capable of supporting both preparedness and response phases within the Copernicus ecosystem. Overall, this work demonstrates how EGMS-derived information, when integrated with automated ADA detection and standardized analytical pipelines, can support the transition from satellite-based deformation monitoring to operational ground-motion services tailored to emergency decision making.

Operators of critical rail infrastructure face a continuous challenge in monitoring their networks for ongoing maintenance, and preventing potentially catastrophic accidents. In recent years, Earth observation (EO) technology has greatly enhanced our ability to undertake remote, large-area monitoring, yet many challenges remain unresolved. The upcoming Copernicus Expansion Missions will provide new sources of data and opportunities to address these challenges. Working in synergy, the Radar Observing System for Europe in L-band (ROSE-L) and Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) missions will provide particularly useful data for rail infrastructure monitoring purposes, by bringing together routine, high precision L-band SAR and hyperspectral information for the first time. In this context, ESA has funded the Rail-Adjacent Vegetation monitoring with Copernicus Expansions (RAVE) project under the auspices of the Sentinel User Preparation initiative, which is currently being delivered by Airbus Defence & Space UK in collaboration with Champion Users Network Rail and the Société Nationale des Chemins de fer Français Réseau (SNCF Réseau). These entities are the primary rail operators in the UK and France, respectively. Here, we showcase the potential of the Copernicus Expansion Missions for a range of Champion User-defined critical infrastructure monitoring applications, with a particular emphasis on new insights that will be enabled by ROSE-L. Specifically, we present new backscatter- and InSAR-enabled workflows for the retrieval of rail-adjacent Tree Height, Tree Windthrow and Flood Extent monitoring, designed and tested using emulated ROSE-L imagery produced from existing L-band SAR data sources (ALOS, SAOCOM-1A/1B) over a selection of UK- and France-based test locations. As part of the wider SUP initiative, these emulated datasets will be made publicly available via ESA’s Project Results Repository and the Network of Resources to support other Copernicus Expansion Mission preparation activities in the future. Further information about the RAVE project, including links to early outputs, can be found on the project website (https://rave.apex.esa.int/en).   

Persistent Scatterer Interferometry (PSI) is widely used for long-term deformation monitoring with Sentinel-1 time-series data. In operational PSI processing, phase stability forms the basis of displacement estimation, whereas intensity images are primarily used for visualization and scatterer selection. Nevertheless, intensity products remain essential for interpreting structural geometry and contextualizing persistent scatterer distributions, particularly in infrastructure monitoring applications such as bridge analysis. This study presents a practical integration of conventional intensity-domain super-resolution into PSI workflows. The objective is to enhance the spatial interpretability of Sentinel-1 intensity images without modifying the interferometric processing chain. The proposed framework operates exclusively in the intensity domain and does not alter interferograms, coherence estimation, or displacement retrieval. Consequently, deformation analysis remains strictly unaffected, and the enhancement can be introduced as an optional module within existing PSI environments. The super-resolution method applies a conventional multi-frame super-resolution approach based on Iterative Back-Projection (IBP) to log-intensity images derived from Sentinel-1 IW-mode SLC amplitudes. Multiple co-registered acquisitions from the same relative orbit are used to exploit natural sub-pixel spatial diversity arising from slight orbital variations and sampling offsets. A key aspect of the integration is the use of sub-pixel co-registration parameters estimated during standard PSI interferometric processing. Rather than performing independent alignment, the IBP reconstruction relies on the same geometric solution used for interferogram generation. This shared co-registration strategy ensures strict geometric consistency between the enhanced intensity products and the interferometric stack while avoiding redundant processing and additional alignment uncertainties. Reconstruction follows a conventional IBP scheme assuming a Gaussian point spread function as an approximation of the Sentinel-1 IW-mode impulse response. Starting from an upsampled estimate, simulated low-resolution observations are generated and iteratively compared with measured intensity images, with residuals back-projected to refine the solution. Processing in the log-intensity domain converts multiplicative speckle into an additive component, thereby improving statistical stability. The procedure is computationally lightweight and fully model-based, requiring no training data or sensor-specific tuning beyond the assumed point spread function. Although recent SAR super-resolution studies increasingly employ data-driven or deep learning approaches, the present framework deliberately adopts a transparent and reproducible conventional method. For infrastructure monitoring within operational InSAR contexts, methodological interpretability and deterministic behavior remain essential, particularly when analysis results may support engineering decisions. The integration was evaluated using Sentinel-1 IW-mode SLC data acquired over coastal built environments containing bridge structures and adjacent water surfaces. These scenes are characterized by strong amplitude contrasts, double-bounce scattering, and sparse but dominant persistent scatterers aligned with structural elements. The IBP-enhanced intensity products demonstrate improved continuity of linear scatterers, clearer delineation of structural edges, and enhanced geometric readability compared to conventional multi-look intensity images. Importantly, interferograms and PSI-derived deformation time series remain unchanged, confirming that the enhancement preserves interferometric integrity. The method does not eliminate speckle and cannot exceed intrinsic system bandwidth limits; excessive iteration may amplify noise in low-signal regions. However, the objective is not to generate artificial detail but to exploit existing spatial diversity in a geometrically consistent manner. By maintaining strict alignment with the PSI co-registration framework, the enhancement supports improved interpretation of infrastructure geometry without altering deformation estimation. In conclusion, conventional IBP-based multi-frame super-resolution can be structurally integrated into PSI workflows through direct use of interferometric co-registration parameters. By operating solely in the intensity domain and preserving phase-based processing integrity, the approach provides a transparent and operationally compatible enhancement mechanism. The results indicate that physically interpretable super-resolution techniques can improve the spatial clarity of infrastructure-related scatterers in Sentinel-1 imagery while remaining fully consistent with PSI-based deformation monitoring.

Tectonic Subsidence and Coastal Erosion: A PSI-InSAR Study in the Southern Caribbean Coast José Francisco Valverde Calderón Affiliation: School of Surveying, Cadastre and Geodesy, National University, 86-3000, Heredia, Costa Rica e-mail: jose.valverde.calderon@una.cr Gustavo Barrantes Castillo Affiliation: School of Geographic Sciences, National University, 86-3000, Heredia, Costa Rica e-mail: gbarrantes@una.ac.cr Name: Diana Ninette Paniagua Jiménez Affiliation: School of Surveying, Cadastre and Geodesy, National University, 86-3000, Heredia, Costa Rica e-mail: diana.paniagua.jimenez@una.cr Name: Matías Andrés Poch Clavero Affiliation: Independent researcher, Santiago, Chile e-mail: pochclavero@gmail.com The Southern Caribbean coast of Costa Rica represents a complex and geodynamically active region. This area is characterized by the North Panama Deformed Belt (NPDB), a broad deformation zone featuring reverse faults and folds that extends westward into the Costa Rican territory. In recent years, the most notable seismic events occurred on April 22, 1991. The major shock had an intensity Mw 7.7, which caused substantial coseismic uplift along the coast. In the decades following this event, the interseismic behavior of the crust—specifically whether the land is rising or sinking—has been less understood due to a lack of continuous geodetic data available for the zone. Understanding Vertical Land Motion (VLM) in this region is critical because any subsidence of the land, together the effects of sea-level rise, contributes directly accelerating coastal erosion and increases vulnerability of coastal communities. Recent observations indicate that sectors such as Cieneguita and Cahuita are suffering from severe erosion, beach shortening, and vegetation loss, prompting the need for a comprehensive assessment of the variables causing these effects. To address the lack of spatial data on vertical ground movements, a study was done applying Persistent Scatterer Interferometry (PSI) to estimate VLM velocity across the Southern Caribbean of Costa Rica, covering key areas such as the city of Limón, Cahuita, Puerto Viejo, and Manzanillo. The research utilized Single Look Complex (SLC) images from the Sentinel-1 mission, acquiring data from both ascending and descending orbits to resolve the vertical component of the displacement. A total of 33 ascending images (spanning January 2019 to February 2021) and 32 descending images (spanning January 2019 to February 2021) were processed. The VV polarization was selected for processing due to its superior coherence stability over the region’s land cover, which includes urban areas, rock outcrops, and dense vegetation. The image processing begins with preprocessing in the SNAP software to coregister images, correct orbital errors using precise Copernicus orbits, formation of the interferograms and remove the topographical phase. Later, the Stanford Method for Persistent Scatterers (StaMPS) was employed to identify persistent scatterers (PS). Atmospheric phase contribution was corrected using a linear phase-based tropospheric model available in the TRAIN toolbox. Finally, LOS velocities from both geometries ascending and descending were combined to obtain vertical velocity. As results, average RMSE for the LOS velocities was ±1.67 mm/yr for the ascending orbit and ±1.21 mm/yr for the descending orbit, indicating a high level of precision in the interferometric results. The estimated vertical velocities for the study area averaged          -7.04 mm/yr, with values ranging from a minimum of -20.4 mm/yr to a maximum of -1.54 mm/yr. Specific analysis of the city of Limón showed vertical velocities between -3.5 mm/yr and -6.0 mm/yr, particularly in the eastern sector bordering the coast. The reliability of these satellite-derived measurements was validated by comparing them with data from the continuous GNSS station "LIMN," located in Limón. The InSAR vertical velocity of this station aligns well with the station's geodetic record showing a vertical velocity trend of -4.32 mm/yr. Furthermore, another nearby station, VRAI, reported a vertical velocity of -5.88 mm/yr, providing further independent corroboration of the subsidence magnitude and direction identified by the PSI analysis. These findings have implications for the tectonic study of the region. The observed subsidence is consistent with an interseismic phase of a subduction cycle. Following the coseismic uplift of the 1991 earthquake, the plates have likely re-coupled. In this locked state, the accumulation of stress drags the upper plate (the Panama Microplate) downward, resulting in the observed elastic subsidence. This geophysical behavior mirrors the well-documented cycle of the Nicoya Peninsula on Costa Rica's Pacific coast, where the land subsides during the interseismic period due to the strong coupling between the Cocos and Caribbean plates, only to rise suddenly during major earthquakes. Beyond tectonics, the study highlights the critical impact of Vertical Land Motion on coastal dynamics. The relative sea-level rise experienced by a coastal community is the sum of the absolute sea-level rise (climate-induced) and the vertical movement of the land. Previous analyses of the Limón tide gauge suggested an accelerated relative sea-level rise rate of over 8 mm/yr in recent years; however, when corrected for the tectonic subsidence identified in this study (approx. -4 to -7 mm/yr), the absolute sea-level rise aligns more closely with global averages, yet the relative threat remains critically high. This rapid relative rise provides a physical explanation for the erosive processes reported in recent years, such as the destruction of infrastructure and beach retreat in Cieneguita and Cahuita. Consequently, this research emphasizes that coastal risk management and adaptation strategies in the Southern Caribbean must account for this tectonic subsidence. Ignoring the vertical land motion component would lead to a underestimation of future sea-level impacts and coastal vulnerability. Finally, the study demonstrates the value of interferometric techniques as a complementary tool to GNSS networks, providing high-density spatial data essential for monitoring crustal deformation and informing decision-making in coastal zones.

Interferometric Synthetic Aperture Radar (InSAR) is a powerful remote sensing technique used for high-resolution topographic mapping and surface deformation monitoring. However, the quality of InSAR measurements is often degraded by interferometric decorrelation, which leads to gaps and loss of phase information in the resulting interferograms. Decorrelation can be caused by various factors, including dense vegetation, significant changes in land cover over time, and large deformation gradients, such as those associated with earthquakes. The presence of these decorrelated areas poses a significant challenge to the subsequent analysis of interferograms, as the continuity of the wrapped phase field is a fundamental prerequisite for downstream processing steps like phase unwrapping and inversion. Therefore, the ability to accurately reconstruct the InSAR phase in these decorrelated regions is of paramount importance for a wide range of geophysical applications, including the monitoring of ground subsidence, the analysis of seismic and volcanic deformation, and the generation of accurate digital elevation models. The paper introduces a novel two-stage adversarial joint learning model designed to address the challenge of reconstructing the InSAR phase in decorrelated areas. The proposed methodology is based on a Generative Adversarial Network (GAN) framework and is divided into two main stages: an Edge Mapping Stage (EMS) and a Phase Predicting Stage (PPS). The Edge Mapping Stage employs an Edge Connector Network (ECN), which is a gradient-based edge detector built upon the VGG19 model. The primary function of the ECN is to identify and reconstruct the fringe lines, which represent phase discontinuities, within the interferogram. The ECN is an encoder-decoder network that utilizes dilated convolutions and is trained using a combination of adversarial and feature-matching losses. The Phase Predicting Stage then utilizes a Phase Predictor Network (PPN) to predict the phase values within the decorrelated regions. The PPN, which shares the same generator and discriminator architecture as the ECN, uses the reconstructed fringe lines from the EMS as a guide for the phase prediction. This stage employs an image-to-image translation technique and adversarial self-supervised learning. The training of the PPN is performed using a composite edge map that combines the background region edges with the generated edges from the ECN. The training of the entire model is conducted in two phases: individual stage training followed by joint learning. The joint learning phase utilizes a patch discriminator with both pixel-space and feature-space losses to ensure a more robust and accurate reconstruction. The model was trained on a large dataset of 100,000 simulated interferograms, which were generated using the forward path of the Okada model and included various noise sources to mimic real-world conditions. The proposed two-stage adversarial joint learning model demonstrates a significant improvement in the reconstruction of InSAR phase in decorrelated areas. The model's performance was evaluated using a variety of metrics, including accuracy, precision, recall, Mean Absolute Error (MAE), Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR). In the Edge Mapping Stage, the VGG-based method significantly outperformed the traditional Canny edge detector, achieving improvements of 0.8%, 3.5%, and 3.4% in accuracy, precision, and recall, respectively. The joint learning of the ECN and PPN led to substantial improvements in the phase prediction, with MAE and MSE improving by as much as 700% and 852.1%, respectively, in noisy interferograms. The overall performance of the model in fringe reconstruction achieved an accuracy of 84% and an SSIM of 96%. The model's effectiveness was further validated on real-world data from two seismic events: the M 6.5 Tonopah, Nevada earthquake of May 15, 2020, and the M 6.3 Western Xizang earthquake of July 22, 2020. The model successfully reconstructed the co-seismic deformation interferograms in these challenging cases, demonstrating its robustness and practical applicability. The cross-correlation between the reconstructed and original interferograms in the Greater Bay Area (GBA) dataset ranged from 0.72 to 0.87, further highlighting the model's ability to produce high-quality reconstructions. In conclusion, the novel two-stage adversarial joint learning model presented in this paper offers a powerful and effective solution for reconstructing the InSAR phase in decorrelated areas. The model's superior performance, validated on both simulated and real-world data, underscores its potential to significantly enhance the utility of InSAR for a wide range of scientific and engineering applications.

The European Ground Motion Service (EGMS) provides continental-scale Persistent Scatterer Interferometry (PSI) time series derived from Sentinel-1 acquisitions, representing one of the most comprehensive operational ground-motion datasets currently available. EGMS distributes two Level 2 products: a basic product (L2a) and a calibrated product (L2b), which are referenced to a deformation model derived from GNSS data. However, the L2b correction exhibits step-like temporal discontinuities that, if left unaddressed, can propagate into downstream analyses. In addition, temporal variations in the dispersion of the time series may affect the stability of multi-geometry deformation reconstruction. This study presents a systematic GNSS-assisted pipeline for post-processing EGMS Level-2 products prior to geometry decomposition and evaluates the resulting solutions against EGMS ORTHO products at locations collocated with GNSS stations. The processing pipeline consists of three sequential calibration steps. In the first step, we extract the L2b correction by computing the pointwise difference between the L2b and L2a time series. This correction is computed step-wise, introducing small temporal discontinuities. We apply a smoothing procedure to reduce them and reconstruct from the L2a product a compatible L2b product. In the second step, we calculate a daily dispersion diagnostic for the deformation time series at each PSI measurement point (MP). We first detrend each MP's time series to isolate short-term variability, then compute a robust estimate of the dispersion across MPs at each epoch. The resulting daily robust dispersion estimates are normalized to obtain a scaling factor that serves as an empirical temporal uncertainty proxy, providing a day-dependent σ that captures the collective variability across MPs. In the third step, the calibrated time series from ascending and descending orbits are integrated with GNSS observations to reconstruct three-dimensional deformation. GNSS data are sourced from Nevada Geodetic Laboratory PPP solutions and independently corrected for network common-mode effects prior to integration. GNSS East, North, and Up displacements are projected into each PSI line-of-sight (LOS) direction with full three-dimensional error propagation, using its incidence and heading angles, ensuring geometry-consistent integration at the PSI level. Integration is performed through weighted least squares, combining ascending LOS, descending LOS, and GNSS constraints within a consistent uncertainty model. The integration framework explicitly accounts for InSAR’s intrinsic limited sensitivity to the North component, which is primarily informed by GNSS, and quantifies the information loss incurred when geometry-only decomposition is applied without geodetic constraints. Solutions are computed for ascending-only, descending-only, and combined ascending-descending configurations, with and without GNSS integration, enabling a controlled assessment of each data source’s contribution to component-reconstruction stability. Final ENU deformation solutions are compared with EGMS ORTHO products around GNSS-collocated locations. ORTHO products represent the officially distributed geometry-combined solution, providing East–West and vertical motion components with deformation velocities. Therefore, it serves as a natural benchmark for evaluating the structural consistency of the proposed pipeline. This comparison is designed to characterize how the temporal variability at the L2 stage relates to ORTHO behaviour, whether such variability propagates into geometry-combined solutions or is inherently absorbed during ORTHO generation. This provides operational users with a quantitative basis for deciding when explicit L2 calibration adds value over directly adopting the distributed combined product. The framework is demonstrated on a regional case study and is designed to be computationally lightweight, reproducible, and transferable across EGMS coverage areas. Looking ahead, the approach is intended to scale toward high-density GNSS environments, such as local monitoring networks equipped with cost-effective Galileo-enabled receivers, where multiple closely spaced geodetic constraints can further improve component reconstruction and support applications in structural monitoring, infrastructure assessment, and local geohazard characterization.

Accurate and scalable monitoring of ground surface displacement is fundamental to understanding geophysical processes and mitigating the risks associated with natural hazards such as landslides, earthquakes, volcanic activity, and subsidence. Spaceborne and airbone Synthetic Aperture Radar (SAR) has become one of the most powerful tools for this purpose due to its all-weather, day–night imaging capability and sensitivity to surface motion. However, conventional SAR-based techniques—particularly interferometric SAR (InSAR) and amplitude-based pixel offset tracking (POT)—face persistent challenges in complex environments. These include temporal decorrelation, heterogeneous land cover, steep terrain, and rapidly evolving surface conditions, all of which degrade measurement reliability and spatial resolution. As a result, some of the most hazardous and dynamic regions remain difficult to monitor effectively. This study introduces DeepOT, a deep learning–based offset tracking framework designed to estimate pixel-level ground surface displacement directly from SAR amplitude image pairs. The proposed approach complements traditional methods by leveraging data-driven learning to infer displacement fields without relying on explicit cross-correlation or phase-based measurements. A central innovation of DeepOT lies in its synthetic-to-real training strategy, which addresses one of the most significant bottlenecks in supervised learning for geophysical applications: the scarcity of reliable ground-truth displacement data. Instead of depending on observational labels or outputs from conventional algorithms—which may propagate biases and limitations—we embed geophysically plausible synthetic displacement fields directly into real SAR amplitude imagery. This process generates large-scale, pixel-accurate training datasets while preserving the statistical and radiometric properties of real SAR observations. The synthetic displacement generation framework is designed to capture the diversity and complexity of real-world deformation patterns. Multiple displacement components are probabilistically combined to produce composite fields that mimic natural geophysical behavior. These include anisotropic localized deformation representing landslides or subsidence zones; fractional Brownian motion fields that reproduce spatially correlated rough displacement patterns or atmospheric artifacts; constant displacement fields simulating rigid-body motion; topography-correlated displacement reflecting terrain-driven effects; and transitional or step-like displacement patterns analogous to fault slip or shear zones. Randomized parameter sampling ensures that the training dataset spans a broad range of spatial scales, magnitudes, and deformation styles, promoting robust generalization to unseen scenarios. DeepOT is implemented as a modular and extensible framework that supports multiple deep learning architectures, including FlowNet2, U-Net, U-Net++, and Siamese-network-based designs (CC-ResSiamNet), as well as a newly introduced deformable convolution-based model (DisplaceDCN) tailored for spatially heterogeneous displacement fields. This flexibility allows the framework to accommodate varying levels of model complexity and adapt to different application contexts. In addition, the workflow is designed with scalability in mind, incorporating efficient data management, high-throughput training pipelines, and compatibility with large-scale SAR datasets. We evaluate the performance of DeepOT using both synthetic validation experiments and real-world case studies. Two contrasting landslide environments are used for quantitative and qualitative assessment: the Slumgullion landslide in Colorado and the Barry Arm landslide in Alaska. At Slumgullion, independent extensometer measurements provide ground-based validation of displacement time series, enabling direct comparison with model predictions. Results demonstrate that DeepOT successfully reconstructs spatially coherent displacement fields and captures temporal evolution with strong agreement to in-situ observations. Notably, the framework maintains performance in areas where InSAR coherence is low and where conventional offset tracking struggles due to surface heterogeneity or noise. In the Barry Arm case study, characterized by complex terrain and rapid deformation, DeepOT produces continuous and detailed displacement maps that reveal fine-scale spatial patterns often obscured by traditional methods. The model effectively resolves localized deformation zones and sharp gradients without the smoothing artifacts commonly associated with window-based cross-correlation techniques. Furthermore, qualitative analyses of earthquake-induced deformation indicate that DeepOT generalizes beyond landslide scenarios, demonstrating its applicability to large-scale, high-gradient displacement fields. Compared to classical POT approaches, DeepOT offers several key advantages. First, it eliminates the need for predefined correlation windows, enabling higher spatial resolution and improved sensitivity to localized motion. Second, it significantly reduces computational costs during inference, allowing rapid generation of displacement maps over large areas. Third, by avoiding reliance on POT-derived labels, it mitigates the risk of inheriting systematic biases and artifacts from traditional methods. These improvements collectively enhance the reliability and scalability of SAR-based displacement monitoring. Despite its strengths, the proposed framework also highlights important considerations for future research. While synthetic training provides a powerful solution to data scarcity, the realism and diversity of simulated displacement fields remain critical factors influencing model performance. Continued refinement of synthetic generation strategies—potentially incorporating physics-based models or hybrid approaches—could further improve generalization. Additionally, integration with multi-sensor data, such as optical imagery or GNSS measurements, may enhance robustness and enable more comprehensive monitoring systems. In summary, DeepOT represents a significant step forward in the application of artificial intelligence to geophysical remote sensing. By combining synthetic training data with advanced deep learning architectures, the framework enables direct, pixel-level estimation of ground displacement from SAR amplitude imagery in challenging environments where traditional methods often fail. Its modular design, scalability, and demonstrated performance across diverse case studies position it as a promising tool for next-generation hazard monitoring and early warning systems. The approach also establishes a broader paradigm for leveraging synthetic data to overcome fundamental limitations in Earth observation, opening new avenues for data-driven analysis of complex geophysical processes. Reference: Kim, J.-W., Jung, H., Lu, Z., DeepOT: A Deep Learning Framework for Pixel-Level Ground Surface Displacement Estimation from SAR Amplitude Imagery. ESS Open Archive . January 16, 2026. DOI: 10.22541/essoar.176853814.47876330/v1

The continued decline in the Dead Sea water level in recent decades has resulted in sinkhole collapses along its western shores, posing a serious hazard on agricalture, industry, infrastructure, and daily life in the area. The sinkhole collapses are accompanied by gradual ground subsidence before, during, and after the sinkhole’s collapse. The Geological Survey of Israel (GSI) monitors the sinkhole-related land subsidence using InSAR measurements every 11 days from TerraSAR-X ascending and descending tracks, and annual LiDAR measurements, regularly since 2019. The current mapping of sinkhole-related subsidence relies on manual interpretation of wrapped phase data, a time-consuming and human-error-prone procedure. Deep Learning provides an opportunity to reduce processing time, increase precision and scalability, and support real-time decision-making. Encoder–decoder–based semantic segmentation models, such as UNet, Attention UNet, SAM, TransUNet, and SegFormer have shown effectiveness in learning geospatial deformation patterns in InSAR and related remote sensing data. We provide an evaluation of a Deep Learning UNet segmentation model applied to InSAR data for annotating land subsidence areas that occur as part of the sinkhole-formation process along the western shores of the Dead Sea. We use the manually delineated subsidence of the GSI’s operational sinkhole monitoring system between 2019 and 2023 as the ground truth in the supervised learning process. The wrapped phase data include atmospheric signals, compaction subsidence along the Dead Sea shores, decorrelation, and other signal and noise sources, as well as the sinkhole-related subsidence. This unique data poses challenges for annotation, learning, and interpretability, making the dataset both non-trivial and valuable for advancing research in applied remote sensing and its application in the Dead Sea. The model is trained across three partition schemes–random tiles partitioning, temporal partitioning, and geospatial partitioning–each representing a different type and level of generalization. We use object-level metrics to assess the subsidence-area detection ability while accommodating human-induced annotation variability and uncertainty. Our data include 400 fully and partly annotated interferograms with ~47,000 sinkhole-related subsidence polygons. We demonstrate the model’s ability to effectively identify and generalize subsidence areas in InSAR data across different setups and temporal conditions. The model shows promising potential for geographical generalization in previously unseen areas. Finally, we infer full interferogram subsidence areas by reconstructing smaller-scale patches and evaluate them for different confidence thresholds.

Urban building subsidence monitoring is essential for risk mitigation and asset management, particularly in Chinese megacities where numerous existing buildings and dense populations are exposed to long-term ground deformation driven by groundwater dynamics, urban loading, and intensive underground construction [1]. Interferometric Synthetic Aperture Radar (InSAR) time-series observations provide wide-area, cost-effective deformation measurements and therefore offer a practical basis for city-scale building monitoring [2-4]. Nevertheless, interpreting InSAR scatterer measurements to provide reliable building-level assessments remains challenging. First, the correspondence between InSAR scatterers and buildings is frequently ambiguous. Deterministic spatial assignment strategies based on simple geometric rules (e.g., nearest-footprint or footprint-intersection criteria) are sensitive to geocoding uncertainty, footprint boundary effects, radar layover/shadow, and environmental scatterers from roads and other urban objects. These factors can contaminate building-level deformation time series and make it difficult to quantify the reliability of building-level results. Second, urban building deformation is inherently complex, and different modes such as overall settlement, progressive tilting, differential settlement, and episodic accelerations may co-occur. As a result, a single indicator (e.g., mean velocity) can lead to both false alarms and missed detections. To address these issues, we propose an AI-assisted urban building subsidence monitoring framework using Sentinel-1 InSAR time-series data in Chinese megacities (e.g. Shanghai, Beijing, Shenzhen, and Guangzhou). We first develop a probabilistic scatterer-to-building attribution model that integrates geometric likelihood, radar observability constraints, and temporal consistency, producing posterior probabilities between scatterers and candidate buildings, together with building-level measurability and confidence indicators. These posteriors act as attention-like weights to suppress ambiguous or non-building scatterers and to yield more reliable building observations. Based on the attributed observations, we then derive three complementary building-level deformation sequences. They are (i) a settlement time series obtained by confidence-weighted aggregation of line-of-sight deformation, (ii) a tilting time series estimated via robust plane fitting, and (iii) a differential settlement time series quantified using robust within-building dispersion measures. Finally, we introduce a multivariate deep time-series anomaly detection model that learns joint patterns across settlement, tilt, and differential-settlement sequences. It identifies buildings with abnormal evolution and outputs anomaly evidence by localizing the most contributing sequences and time windows, together with a confidence score to support risk assessment. Preliminary experiments in Chinese megacities demonstrate the feasibility of the proposed pipeline. The probabilistic attribution step produces more coherent building-level time series than deterministic geometric assignment, particularly near footprint boundaries and in complex urban environments, and the resulting confidence indicators effectively flag buildings with insufficient measurability. Moreover, joint anomaly detection across the three building-level sequences highlights candidate buildings with consistent signatures of accelerated settlement and increasing differential deformation, providing interpretable cues for subsequent inspection. These preliminary results suggest that the proposed framework can serve as a generalizable and confidence-aware strategy for building-level InSAR monitoring and early risk identification in large metropolitan regions. References: [1] Drougkas A, Verstrynge E, Van Balen K, et al. 2021. Country-scale InSAR monitoring for settlement and uplift damage calculation in architectural heritage structures. Structural Health Monitoring, 20(5): 2317-2336. [2] Ferretti A, Prati C, Rocca F. 2000. Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 38(5): 2202-2212. [3] Ferretti A, Prati C, Rocca F. 2001. Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(1): 8-20. [4] Hooper A, Bekaert D, Spaans K, et al. 2012. Recent advances in SAR interferometry time series analysis for measuring crustal deformation. Tectonophysics, 514: 1-13.

Ground motion represents a persistent and often underestimated hazard for transport infrastructure, particularly in high-latitude and hydro-climatically sensitive environments. Landslides, subsidence, freeze–thaw dynamics, and underground construction can progressively weaken railways and roads, making early detection of displacement essential for risk mitigation. Interferometric Synthetic Aperture Radar (InSAR) offers a unique capability to monitor such processes with millimetre-scale precision over large spatial extents, yet the choice of InSAR time-series methodology strongly influences measurement density and interpretability along narrow infrastructure corridors. This study presents a systematic comparison of two widely used InSAR approaches for infrastructure monitoring in Sweden: (1) Persistent Scatterer Interferometry (PSI) as implemented in the European Ground Motion Service (EGMS, PS-only calibrated LOS product), and (2) a modern phase-linked Persistent- and Distributed-Scatterer (PS+DS) workflow. Five transport corridors were analysed, spanning railways, highways, and underground tunnels across diverse geomorphological and climatic settings, including landslide-prone terrain, freeze–thaw-affected regions, and zones influenced by large-scale excavation. Using identical quality thresholds for amplitude dispersion and temporal coherence, we evaluated point density, displacement time-series residuals (RMSE), short-range velocity variability, land-cover sensitivity, and the spatial relationship between measurement points and mapped road and railway centrelines. The PS+DS workflow consistently produced substantially higher point densities across all corridors, particularly in natural and mixed land-cover environments, while maintaining displacement RMSE values comparable to PSI, typically below 6 mm in the satellite line-of-sight direction. Although EGMS exhibits narrower RMSE distributions, reflecting its conservative PSI strategy, median error levels and velocity patterns are broadly similar between the two approaches. Both datasets capture coherent displacement signals along known unstable or climatically sensitive segments, and distance-to-centreline analyses reveal no systematic spatial bias favouring either method. Overall, the results demonstrate that phase-linked PS+DS processing significantly enhances spatial coverage for infrastructure-scale monitoring without compromising displacement accuracy. These findings have direct implications for national ground-motion services and operational risk assessment in Sweden, particularly where early warning and corridor-wide situational awareness are critical. The comparison also highlights practical trade-offs between conservative continental-scale products and more flexible, corridor-focused processing strategies for operational infrastructure surveillance. These differences are particularly relevant for agencies seeking scalable yet corridor-sensitive ground motion monitoring frameworks to support prioritised maintenance and early-warning decision workflows.

Santiago de Puriscal, Costa Rica, is a city located approximately 40 km southwest of the country's capital, San José, with a population of about 12,500. The urban area is characterized by the active presence of a landslide covering between 4–5 km2. According to the National Emergency Commission of Costa Rica, the Puriscal canton faces both hydrometeorological and geological hazards. The Santiago de Puriscal landslide falls into the latter category; it is the largest in the country and is situated directly beneath the most densely populated sector. The landslide's impact is intensified by periods of local seismic activity and the rainy season. Studies conducted in the 1990s showed displacement velocities between 5 and 15 cm/yr toward the northwest. Consequently, local infrastructure has been severely impacted: several buildings have been demolished, and others have sustained significant cracking. The most notable example is the old Catholic church, which was declared uninhabitable due to the risk of structural collapse. The present project used Single Look Complex (SLC) images from the Sentinel-1 mission, acquiring data from both ascending and descending orbits. A total of 92 ascending images (spanning January 2018 to December 2023) and 90 descending images (also spanning January 2018 to December 2023) were processed. The VV polarization was selected for processing. To minimize the possible effects of temporal decorrelation, the processing period was split into 2 segments: the first one from January 2018 to November 2020 and the second from December 2020 to December 2023. The image processing begins with preprocessing in the SNAP software to coregistered images, correct orbital errors using precise Copernicus orbits, formation of the interferograms and remove the topographical phase. Later, the Stanford Method for Persistent Scatterers (StaMPS) was employed to identify persistent scatterers (PS) and its velocities. Finally, LOS velocities from both geometries ascending and descending were combined to obtain vertical velocity. As results, for the first period of processing, the average LOS velocity is -10.18 mm/yr and +7.58 mm/yr for both ascending and descending orbit. RMSE for the LOS velocities is ±1.54 mm/yr for the ascending orbit and ±1.31 mm/yr for the descending orbit, indicating a high level of precision in the interferometric results. The estimated vertical velocities for the study area averaged -2.40 mm/yr, with values ranging from a minimum of -14.84 mm/yr to a maximum of +5.01 mm/yr. The projection of the horizontal velocity (vHald) has an average value of 13.41 mm/yr, with a maximum value of +24.32 mm/yr and a minimum value of -7.42 mm/yr. With respect to the second period of processing, the average LOS velocity is -6.02 mm/yr and +7.64 mm/yr for both ascending and descending orbit. RMSE for the LOS velocities is ±1.55 mm/yr for the ascending orbit and ±1.31 mm/yr for the descending orbit, indicating a high level of precision in the interferometric results. The estimated vertical velocities for the study area averaged +0.47 mm/yr, with values ranging from a minimum of -8.54 mm/yr to a maximum of +9.78 mm/yr. The projection of the horizontal velocity (vHald) has an average value of +10.51 mm/yr, with a maximum value of +28.95 mm/yr and a minimum value of -2.99 mm/yr. This study evaluated the potential of applying InSAR techniques in Costa Rica for landslide monitoring, specifically the PSI-InSAR method, using the Santiago de Puriscal landslide as a case study. This landslide has been active for several decades and has caused damage to public and private infrastructure. The results show that the landslide is still active, making it necessary to continue its ongoing monitoring. Finally, the study demonstrates the value of interferometric techniques as a complementary tool to GNSS networks or other geodetic techniques, providing high-density spatial data essential for monitoring active landslides.

The “Democratization of Earth Observation” (DEM-EO) project, funded by the Italian Space Agency (ASI), aims to reduce key barriers that still limit the operational uptake of Earth Observation for ground-motion monitoring - namely fragmented access to data and services, complex processing and interpretation workflows, and the scarcity of specialized expertise within end-user organizations - by developing an innovative, web-based platform that makes advanced EO analytics more accessible. Despite the growing availability of EO data and services, many potential users still face fragmented access to datasets, complex processing chains, and significant interpretation challenges, which often require highly specialized skills and lead to reliance on outsourcing. DEM-EO addresses this gap through an integrated environment that streamlines the path from data discovery and acquisition to the generation, visualization, and export of information products, with a specific focus on monitoring terrain instability and the behavior of critical infrastructures. The platform integrates complementary methodologies to support robust interpretation across a range of scenarios: Advanced Differential InSAR (A-DInSAR) for retrieving the temporal evolution of ground deformation from multi-temporal SAR interferometry, and PhotoMonitoring™ techniques to derive high-detail displacement and change information from optical imagery, including Digital Image Correlation (DIC) with sub-pixel sensitivity and Change Detection (CD) for mapping radiometric and geometric variations relevant to hazard evolution and asset integrity. A multi-mission approach (e.g., Sentinel-1, COSMO-SkyMed, SAOCOM) is considered to enhance spatial and temporal coverage and strengthen the understanding of deformation processes by combining different acquisition geometries, revisit times and wavelengths. A central element of DEM-EO is the emphasis on usability and operational transfer: the platform is intended to guide users through coherent, repeatable workflows, reducing manual steps and minimizing the need to master multiple disconnected tools. This is particularly relevant in organizational contexts where EO competences are scarce, where procurement cycles are long, and where the need for rapid, defensible information can arise both in routine monitoring and in emergency conditions. In addition to technology integration, DEM-EO explicitly embeds user enablement mechanisms—guidance, optional support and validation pathways, and a dedicated training plan—to foster uptake beyond expert communities and help organizations build internal capability for continuous monitoring and timely decision-making. The training activities are designed to transfer not only “button-level” platform skills, but also the conceptual background required to interpret deformation and change signals correctly, understand uncertainty sources, and recognize typical pitfalls in EO-based monitoring (e.g., the role of acquisition geometry, coherence loss, atmospheric artefacts, seasonal effects, or illumination differences in optical change analyses). In this way, DEM-EO supports a progressive pathway from assisted use to greater user autonomy, enabling stakeholders to integrate EO products into their internal procedures and reporting chains. Planned validation activities play a key role in ensuring that the platform’s outputs are credible and actionable. For ground-motion products, A-DInSAR results are expected to be compared - where available - with independent in-situ observations as well as with ancillary information that can support interpretation (e.g., known instability inventories, infrastructure maintenance records, or documented events). Complementary usability and performance assessments will verify that the platform remains effective for both expert and non-expert users, and that it can support different operational rhythms, from periodic surveillance to rapid screening after triggering events. The integration of SAR-based ground-motion analytics with optical change and displacement mapping is specifically intended to improve interpretability: deformation time series can be complemented by optical evidence of surface changes, damage proxies, or localized displacement patterns, supporting a more robust understanding of the processes at work and helping users prioritize field inspections and mitigation actions. Ultimately, DEM-EO seeks to accelerate the transition from EO data availability to operational use by pairing reliable processing capabilities with practical knowledge transfer and adoption support. By reducing barriers to access and interpretation, expanding the pool of EO-enabled stakeholders, and shortening decision latency through more autonomous use, the project aims to strengthen the EO downstream ecosystem for infrastructures and geo-environmental risk management. In this perspective, DEM-EO contributes to making EO-based ground-motion monitoring not only technically feasible, but also organizationally sustainable, supporting routine risk-informed management and more timely responses when critical conditions emerge.

Western Carpathians region is due to its complex lithology and humid climate is highly prone to landslides. Most of them are rather stable. Since catastrophic mega-landslide in Handlová in 1960 a lot of effort has been put into mapping, monitoring and remediation (cite) also of other locations than Handlová town. For decades the overall situation was really stable, only solitary locations were activated. Until it came “year of landslides”, 2010. Exceptionally heavy precipitation during May and June 2010 triggered 577 newly active landslides, and many previously stable sites were activated. Surveying was done mainly by the geologists from SGIDS. Probably the worst case was a night catastrophic landslide directly in the center of village Nižná Myšľa on 4. June 2010 when overnight 40 houses were damaged and as a consequence 144 inhabitants had to be evacuated. Landslide 1 500 x 500 m with slip surface about 14 m deep appeared from evening till morning. The reason was high water saturation in deep clay and tuffite sediments, Neogene in age.. Remediation work hand in hand with monitoring commenced immediately in the area. Horizontal and vertical boreholes were drilled, ground water was drained, water flow was monitored, network of devices like inclinometers and ground water level measuring indicators were installed. Since year 2018 the location has been observed also by PS InSAR technique with Sentinel-1 data. Natural scatterers as well as artificial corner reflectors are in use. In this study we combine long term Earth Observation data deriving precipitation, soil moisture, evapotranspiration and water saturation, with ground-based measurements about surface and undersurface displacement, and ground water regime. Mainly free accessible ESA Copernicus L1, L2 data serve as input, yet combined with other higher level products – e.g. TU Wien ACTIVE/PASSIVE soil moisture, ECMWF climate indicators. There is automatic climatological station in the settlement, thus also these data together with high resolution data from the meteorological radar are also compared. First we evaluate data quality and assess relationships among the datasets. Next step is to apply time series multivariate statistical methods in order to quantify dependency between PS InSAR displacements, underground slip surface deformations, and atmospheric and surface climatological circumstances. Using 20 years of historical data, different model cases are described by means of exact correlation coefficients. These shall be applied later on in raster-based spatio-temporal monitoring model yet on different places without in-situ ground-based measurements.

The Flemish Institute for Technological Research (VITO) provides centralized access to remote sensing datasets and scalable processing capabilities via Terrascope, with a strong focus on science-enabling and decision-ready data products. In collaboration with NASA’s JPL Advanced Rapid Imaging and Analysis (ARIA) team and the Alaska Satellite Facility (ASF) Distributed Active Archive Center (DAAC), the TerraScope portfolio has been expanded to re-host over Belgium Geocoded Unwrapped Interferograms (GUNW) and Radiometrically Terrain Corrected (RTC) products derived from Sentinel-1 and NISAR. These datasets are fully integrated within the TerraScope ecosystem, lowering barriers for local and regional user communities to engage in advanced SAR and InSAR analysis. This provides a straightforward entry point for multi-frequency investigations within a scalable and extensible framework that can readily expand to other regions. Example applications include change detection, disturbance mapping, surface water extent monitoring, and displacement analysis. The uniform accessibility of these SAR datasets along side optical and hyperspectral products further facilitates the development of advanced analytics and machine learning approaches built upon consistent, multi-frequency sensor data streams. We will present two features namely the TerraScope viewer and the openEO framework in the context of Sentinel-1 and NISAR data access and processing. The TerraScope interactive viewer enables discovery, query, and elementary analysis of optical, hyperspectral, and SAR observations across a broad portfolio of low- to higher-level Analysis Ready Data (ARD), spanning domains from ecosystems to solid Earth monitoring. Its backbone relies on a STAC-compliant architecture, allowing users to seamlessly access and integrate datasets for cross-domain and multi-sensor investigations. A new TerraScope QGIS plugin and Leafmap compatibility allow for streamlined discovery and visualization. The OpenEO framework enables scalable downstream processing, where users request processing through an awarded credit system and execute either their own workflows or existing ones (e.g., within the CDSE ecosystem). We introduce a new openEO workflow for the generation of customizable GUNW products from Sentinel-1, enabling users to process interferometric data over user-defined areas with tailored processing parameters.

Interferometric Synthetic Aperture Radar (InSAR) has become a key technique for millimetre-level monitoring of ground deformation over large spatial scales. While the method is inherently relative, many geodetic and infrastructure-monitoring applications require transformation of these relative measurements into a stable and traceable terrestrial reference frame. Achieving this objective demands well-characterised fiducial radar targets and robust integration with established national geodetic infrastructure. To address this need, Lantmäteriet (The Swedish mapping, cadastral and land registration authority) has established a national SAR fiducial network consisting of active electronic radar transponders and passive corner reflectors distributed across Sweden. Since 2020, three active transponders and twenty passive reflectors have been installed at carefully selected locations that ensure long-term stability and optimal radar visibility. The network has been designed with emphasis on geometric configuration, environmental stability, and compatibility with Copernicus Sentinel-1 acquisition geometry. All radar targets are co-located with permanent GNSS stations and, where available, absolute gravity benchmarks. These multi-technique reference sites strengthen the national geodetic infrastructure by providing observations from independent measurement techniques. The configuration supports reference frame maintenance, enhances traceability, and enables rigorous comparison between InSAR-derived deformation and other geodetic time series. The sites also provide opportunities for calibration and validation (Cal/Val) of SAR-derived products, including independent assessment of results from the European Ground Motion Service (EGMS). Furthermore, the infrastructure facilitates linking relative InSAR-derived ground motion to the national geodetic reference frame. This contribution presents the recent progress in the installation and characterisation of both active and passive radar targets is described. Performance analyses have been carried out for selected sites, with particular focus on radar cross section (RCS) stability and signal-to-clutter behaviour. Special attention is given to seasonal effects during snow-covered periods, which are of particular relevance in high-latitude environments. Initial characterisation results are presented and discussed in relation to their implications for long-term stability assessment and future Cal/Val applications. The establishment of this national SAR fiducial network represents an important step toward strengthening the geodetic integration of InSAR in Sweden and contributes to ongoing European efforts to ensure consistent and traceable ground motion products.

1                Objectives, Study Area, and Data This study aims to improve the monitoring and understanding of earthquake-induced landslides as well as associated ground deformations, thru the application of advanced satellite remote sensing techniques. The main objectives are as follows: • Detect and quantify surface displacements triggered by seismic activity, • Identify areas vulnerable to landslides in order to refine hazard assessment, • Implement Synthetic Aperture Radar (SAR) methodologies, particularly Differential InSAR (DInSAR), Coherence Change Detection (CCD), and InSAR time-series analysis via the LiCSBAS software, to comprehensively monitor the spatiotemporal evolution of ground movements, • Validate deformation measurements derived from SAR by integrating high-resolution optical images from Sentinel-2 satellites and complementary field inspections. The research focuses on the Mila region, located in northeastern Algeria, within the seismically active Mediterranean zone, characterized by the convergence between the African and Eurasian plates. On August 7, 2020, a moderate earthquake with a moment magnitude (Mw) of 5.0 occurred near Mila (epicenter coordinates: 36.550°N, 6.271°E, depth ~10 km), causing significant landslides and damage in the towns of Kherba and Grarem. Fig. 1. shows a location map of Mila, orbit footprints, and earthquake epicenters. 3D representations of Kherba and Grarem AoIs (QGIS, ESRI basemap), with boundary of change identified by InSAR. In order to capture a robust temporal and spatial representation of ground deformations before and after the seismic event, the study relies on a dataset covering the period from April 2015 to October 2020. This includes 35 Sentinel-1 C-band SAR images (ascending and descending orbits) used for interferometric analysis, allowing for the application of DInSAR and CCD for the assessment of displacements and coherence. Complementary Sentinel-2 multispectral optical images are used to validate the surface changes detected by SAR methods. This multi-sensor and multi-temporal approach allows for a detailed analysis of the dynamics of landslides triggered by the earthquake and contributes to the identification of potential precursor movements or persistent post-seismic deformation. 2                Methodology The study employs several remote sensing approaches focused on Interferometry Synthetic Aperture Radar (InSAR) to analyze earthquake-induced landslides and associated ground deformations. The synthetic aperture radar (SAR) is an active microwave imaging system capable of acquiring high-resolution data, regardless of weather conditions and solar illumination, thus offering robust spatial and temporal monitoring capabilities. Principles of Synthetic Aperture Radar Interferometry (InSAR) InSAR techniques exploit the phase difference between two or more SAR images acquired over the same area at different times, in order to detect subtle surface changes with millimeter-level precision. The interferometric phase includes contributions related to surface displacement, topographic variations, atmospheric effects, orbital errors, and noise. A rigorous data processing is therefore necessary to identify the displacement component relevant to geotechnical phenomena. Differential Interferometric Synthetic Aperture Radar (DInSAR) DInSAR specifically targets phase variations associated with surface deformation by subtracting topographic contributions and other static phase components. It allows for the effective measurement of coherent ground movements, such as subsidence or uplift, that have occurred between two successive SAR acquisitions. However, DInSAR has limitations in cases of rapid or large-scale landslides, where the temporal decorrelation of the radar signal disrupts phase coherence and makes displacement estimates unreliable. Coherence Change Detection (CCD) To overcome the limitations of phase-based methods in highly dynamic landslide areas, CCD uses the coherence metric, which quantifies the similarity of radar signals between image pairs, in order to identify areas of significant surface disturbance. A decrease in coherence indicates changes such as ground cracking, vegetation modifications, or soil disturbances. CCD thus serves as a complementary tool for effectively mapping landslides marked by decorrelation, expanding detection capabilities beyond coherent deformations measurable by DInSAR. LiCSBAS Time-Series Analysis Advancing beyond pairwise interferograms, time-series analysis, applied using the open-source software LiCSBAS, processes large sets of Sentinel-1 SAR images to generate displacement velocity maps and temporal deformation profiles. This approach improves noise reduction, mitigates atmospheric artifacts, and reveals slow or precursor ground movements over long periods. It proves essential for distinguishing trends before, during, and after seismic events, thereby facilitating a better understanding of the triggering and evolution of landslides. Processing optical images for validation In addition to SAR data, multispectral optical images from Sentinel-2 satellites are used to validate and contextualize the detected surface changes. Optical images offer an intuitive visualization of surface features, such as landslide cracks and vegetation changes, although their usefulness may be limited by cloud cover and lighting conditions. True-color compositions, processed via QGIS software, allow for spatial correlation with deformation maps derived from SAR, thereby enhancing the reliability of remote sensing interpretations and supporting field validation. 3                Results and Discussion The application of SAR interferometric techniques, complemented by optical imagery and field surveys, allowed for a comprehensive detection and characterization of landslides and ground deformations induced by the earthquake in the Mila region of Algeria. Two main areas of interest, Grarem and Kherba, exhibited significant deformation patterns related to the Mw 5 magnitude earthquake that occurred on August 7, 2020. Fig. 3. Ground cracks due to landslides in Kherba, Mila, 2:5m offset towards the north. (a) Drone aerial photo from LNHC (2021). (b, c) Lateral displacements (photos: courtesy M. Yacoub Ali, University of Setif, Algeria). Landslide and Ground Deformation Detection In the Grarem area, differential interferograms revealed small but distinct deformation fringe patterns, corresponding to ground displacement increments on the order of half a wavelength of the Sentinel-1 radar (approximately 2.77 cm per fringe). These coherent deformations were spatially confined to an area of approximately 3.94 km², validated by coherence losses at the fringe boundaries and by field observations highlighting surface cracks. The Time-series analysis further demonstrated that the displacements were co-seismic with the event, without any indication of precursor deformation in the preceding years. Fig. 4. Detected fringes in interferograms 3, 17, and 22, with images focused on the Grarem zone. Conversely, the Kherba area exhibited more complex ground behavior, including large landslides causing significant decorrelation and inconsistent radar returns, limiting the effectiveness of DInSAR phase analysis alone. In this context, coherence change detection (CCD) proved indispensable, allowing for the mapping of two distinct landslide toes and the detection of coherence reductions reaching 23%. The CCD time series showed that landslide activity persisted and evolved dynamically in the weeks following the earthquake, although data quality subsequently deteriorated later due to environmental and anthropogenic noise. Fig. 5. Coherence time series maps of the Kherba landslide, Sentinel-1. Fig. 6. Poor coherence in the acquisition on 3 August was due to unfavorable weather conditions, while other acquisition showed better coherence. Displacement Velocities and Time-Series Insights The long-term analysis conducted using LiCSBAS allowed for the establishment of velocity maps, revealing a significant post-seismic subsidence reaching approximately 110 mm yr⁻¹ at the back of the slope of the Kherba landslide, likely triggered by a mass redistribution following the earthquake. On the other hand, the displacements in the Grarem area remained relatively stable during the post-event monitoring period. These spatiotemporal displacement patterns highlight the critical value of extended time-series analyzes to distinguish co-seismic transient deformation from ongoing or pre-existing ground movements. Validation and Integration with Optical and Field Data The Sentinel-2 optical images proved to be an effective validation tool, confirming the boundaries of landslides as well as surface cracks identified by SAR methods. However, optical images did not detect certain subtle or initial features of landslides visible thru coherence changes in SAR data, emphasizing the superior sensitivity of radar monitoring in vegetated regions or those subject to frequently cloud cover. Field inspections confirmed the presence of fractures and slope instabilities in the mapped landslide areas, reinforcing the reliability of remote sensing observations. Fig. 7. Sentinel-2 optic images: (a) of 30 July 2020 and (b) dated 9 August 2020, co-event coherence ratio, Sentinel-1. The green box indicates RoI and red spots represent significant changes of coherence in the landslide region. Implications and Limitations The study demonstrates the complementary strengths of DInSAR, CCD, and LiCSBAS time-series analysis for multi-temporal and multi-scale monitoring of earthquake-induced landslides. While DInSAR proves effective for detecting coherent and relatively slow ground movements, CCD is essential for identifying areas affected by rapid or incoherent changes associated with large landslides. Time-series analysis enriches the depth of observation by revealing displacement trends and potential precursor activities, although no such signal was identified before this event. However, challenges remain in accurately quantifying the horizontal and vertical components of displacement, as well as in reducing noise related to atmospheric disturbances, vegetation changes, and weather conditions affecting coherence. Moreover, the limitations of phase unwrapping for high displacement gradients restrict the accuracy of deformation estimates in heavily damaged areas. 4                Conclusions This study demonstrates that InSAR techniques offer an effective and precise means of monitoring earthquake-induced landslides, enabling the detection and quantification of ground deformations across large and complex terrains. Three major landslides were successfully identified and characterized in the Mila region, with detailed measurements revealing substantial ground shifts, most notably the up to 2.5-meter displacement in the Kherba area. The complementary application of Differential InSAR (DInSAR) and Coherence Change Detection (CCD) highlights their respective strengths, DInSAR is well suited for detecting coherent, relatively slow ground movements, while CCD excels in capturing rapid, large-scale landslides marked by strong signal decorrelation. The findings underscore the importance of integrating satellite-based observations with in-situ monitoring tools such as GPS and inclinometers to enhance both spatial and temporal resolution. Additionally, Persistent Scatterer InSAR (PS-InSAR) is identified as a promising approach for future high-precision, long-term monitoring of slope dynamics and stability in critical zones.

In the context of displacement monitoring using satellite SAR data, measurable points are confined to persistent scatterers; however, PSInSAR analysis is highly effective because of its capacity for estimating high-precision displacement. Osaka Bay in Japan contains numerous reclaimed land areas, including Kansai International Airport and Kobe Airport, with certain locations experiencing subsidence. The annual average subsidence at Kansai International Airport was recorded as 6 cm for Phase 1 Island and 24 cm for Phase 2 Island, respectively. This study analyzed ground surface displacement around Osaka Bay using PSInSAR with C-band Sentinel-1 and L-band ALOS-2 SAR data acquired over approximately eight years, from January 2017 to March 2025. The Sentinel-1 data utilized for the analysis comprised 245 scenes at 12-day intervals, whereas the ALOS-2 SAR data comprised 33 scenes covering the northern area, including Kobe Airport, and 32 scenes covering the southern area, including Osaka International Airport. In particular, within reclaimed coastal areas where significant land subsidence has been observed, the influence of the SAR sensor type, characteristics of the observed structures and ground conditions, analysis period, number of SAR scenes, and amplitude dispersion index (ADI) threshold used for PS point extraction was quantitatively evaluated in terms of measurable displacement coverage and estimation accuracy. Furthermore, validation using ground-surveyed subsidence data from Kansai International Airport demonstrated that Sentinel-1 enabled millimeter-level accuracy in displacement estimation. The comprehensive PSInSAR findings are as follows: For PS points selected using Sentinel-1, the analysis results for Kobe Airport and Port Island over 1, 2, 4, and 8 years revealed that for buildings, roads, and runway lights, where surface changes over time were estimated to be minimal, the number of PS points decreased with longer analysis periods and more scenes, albeit only by approximately 20%. In contrast, for structures such as seawalls, where changes over time are estimated to be larger than those for buildings, the number of PS points decreased by approximately 40%. For runways, where changes over time are estimated to be even larger, the number of PS points decreased by approximately 10%. When comparing the number of PS points using ALOS-2 with Sentinel-1, both employing an amplitude dispersion index threshold of 0.4 for PS point selection, the number of PS points for buildings and similar features was approximately ten times higher for ALOS-2. Regarding the effect of the amplitude dispersion index threshold on the PS points, thresholds of 0.3 and 0.1 resulted in reductions of approximately 40% and 10%, respectively, compared with the threshold of 0.4. Areas with grass growth near the runways were more readily detected by ALOS-2 than by Sentinel-1, and PS points were obtained at some locations even during the eight-year analysis period. The accuracy of measuring PS points using Sentinel-1 SAR data, as determined from the analysis results for 8, 4, 2 years, and 1 year, indicated that for buildings and roads, the standard deviation of displacement in 50m×50m blocks decreases with longer analysis periods and an increased number of scenes. Specifically, for 8-, 4, and 2 years periods, it is approximately 1 mm/year or less, whereas for 1-year periods, it is approximately 4 mm/year. Notably, for periods of two years or longer, augmenting the number of scenes does not significantly enhance the measurement accuracy. At Osaka International Airport, the measurement accuracy of the PS points using Sentinel-1, adjusted by establishing reference points on each island, revealed an RMSE of approximately 3 and 4 mm for the first-phase island and approximately 7 and 9 mm for the second-phase island over the 2 years and 1 year analysis periods, respectively. Despite the large annual ground subsidence of up to 28 cm and the measurement values being in the centimeter range, it was confirmed that displacement measurements can be achieved with high accuracy in the millimeter range. The measurement accuracy of ALOS-2 was compared with the analysis results from Sentinel-1 for Kobe Airport and Port Island. The standard deviation of vertical displacement in 50m×50m grids at amplitude dispersion indices of 0.4, 0.3, and 0.1 was observed to be 2.0 mm/year, 1.6 mm/year, and 1.2 mm/year, respectively, for structures such as buildings. This illustrates the influence of the amplitude dispersion index on measurement accuracy.

Deformation time series derived from Synthetic Aperture Radar (SAR) interferometry are routinely generated through established processing chains and operational services. While the generation of these products is well established, structured environments for their systematic downstream analysis remain limited. Interpretation of spatial patterns, temporal evolution, and acquisition geometry is often implemented through study-specific analytical workflows rather than through structured and reusable frameworks. This makes it challenging to reproduce analyses consistently and to apply comparable methods across different datasets. We present ITAS (InSAR Time-Series Analysis), an open-source Python framework for structured downstream analysis of InSAR-derived deformation products. ITAS operates on deformation time series generated by external InSAR processing chains and integrates spatial context, temporal behaviour, and acquisition geometry in a modular framework. The framework is structured around a user-defined Area of Interest (AOI) and implements a reproducible project structure with transparent data handling and modular analytical components. ITAS is organized into three complementary analytical domains: Spatial Data Analysis (SDA), Temporal Data Analysis (TDA), and Spatio-temporal Data Analysis (STDA). SDA addresses the spatial characteristics of deformation fields, including geometric relationships to terrain and acquisition geometry, spatial calibration, directional projection, and component decomposition. This module establishes a consistent spatial reference framework for multi-geometry integration and supports geometry-aware transformation of line-of-sight measurements into interpretable deformation components. TDA focuses on the temporal behaviour of deformation time series at individual locations, including trends, variability, and time-dependent changes in deformation characteristics. The module also supports integration of complementary time series, such as meteorological observations, to facilitate exploration of potential relationships between external forcing and deformation response. STDA integrates spatial and temporal perspectives to examine how deformation patterns evolve coherently across space and time. Together, these domains provide a structured analytical framework for consistent interpretation of InSAR-derived deformation products across different study contexts. ITAS has been developed with slope instability research as a primary application domain, while remaining adaptable to other deformation contexts including subsidence, infrastructure monitoring, and cryospheric processes. By providing a modular and reproducible analytical environment, the framework supports consistent interpretation of InSAR-derived deformation products across different study areas and datasets. The framework is implemented in Python and designed for interactive and script-based workflows, facilitating transparent and transferable analytical practice.

The monitoring of ground deformations in mining areas is essential for ensuring urban safety. The study investigates land subsidence caused by underground hard coal mining at the former “Pokój” mine located in the urbanized center of Ruda Śląska, within the Upper Silesian Coal Basin (USCB), Poland. The objective is to determine the duration required for subsidence troughs to stabilize, assessed on an annual basis covering the years 2019 – 2025. The project compares radar images from before and after the closure of the mine, taking into account that the definitive cessation of the extraction took place in early 2021. The investigated area covers 11.2 km2 and includes significant urban infrastructure, such as key transport routes and various public buildings. The analysis employs a satellite radar interferometry (InSAR) approach to detect deformations using Sentinel-1 imagery. The dataset consists of C-band, SLC images acquired in interferometric wide (IW) swath mode. For each year, a pair of images from the first quarter were processed using the differential satellite interferometric synthetic aperture radar (DInSAR) method. The time interval between each image in a pair was 36 days. All these factors allowed to obtain satisfying coherence and identify deformations in further steps. The final results of the research present displacement maps with defined boundaries of the subsidence basins. The findings demonstrate the presence of active subsidence troughs in 2019 and 2020, which was the period of active extraction. They occurred in the Wirek and Bielszowice districts, with displacements reaching up to -8 cm within the analyzed 36-day windows. Comparing 2019 to 2020, spatial analysis reveals an increase in troughs extent and their migration following the advancing mining front. During the analyzed periods after the cessation of extraction, in 2021 – 2025, no new displacements were recorded. These results illustrate that the mine closure process does not necessarily lead to long-term subsidence lasting for several years post-decommissioning. The rock mass can return to a stable state shortly after the cessation of exploitation, depending on factors such as mining intensity and depth. In the case of the "Pokój" mine, the relatively shallow exploitation levels (320 m, 600 m, and 790 m) favored rapid terrain stabilization. Consequently, the “Pokój” mine represents an example of a mining area where the formation of subsidence troughs ceased almost immediately after the closure of the extraction and the terrain remained stable in the following years.

The rapid increase in the availability of Synthetic Aperture Radar (SAR) data has enabled the generation of large-scale deformation maps at unprecedented spatial and temporal resolutions. Services such as the European Ground Motion Service (EGMS) and numerous national initiatives now provide extensive time-series datasets covering continental and regional scales. However, despite this abundance of data, there remains a significant gap in the automated analysis and interpretation of deformation patterns. Existing approaches are often limited by closed-set assumptions, where all observations are forced into predefined classes, reducing their ability to capture the inherent complexity and variability of real-world geophysical processes. This paper proposes a novel framework for the classification and analysis of SAR-derived deformation time series, combining deep learning–based embeddings with open-set recognition. The approach leverages a time series foundation model to extract discriminative features and integrates supervised classification with a probabilistic open-set detection mechanism. The methodology follows a structured workflow. First, real-world deformation data are analyzed to define the target classes of interest. Second, a synthetic dataset is generated to simulate representative deformation behaviors, including linear, piece-wise linear, step-like, thermal (harmonic), and non-linear profiles. The non-linear class is treated as an unknown category to support open-set calibration. Controlled noise is introduced into the simulated data to mimic real-world measurement uncertainties and to increase classification difficulty. Third, the deep learning model is fine-tuned as a closed-set classifier on the simulated dataset. Fourth, the open-set recognition framework is calibrated using a combination of distance-based and probabilistic features. Finally, the trained model is applied to real-world deformation data from the Praid area in Romania, a region affected by salt mining activities and flooding-induced ground instability. Experimental results on the simulated dataset demonstrate strong overall performance, achieving an accuracy of approximately 0.81 and balanced F1-scores across most classes. High classification performance is observed for well-defined deformation patterns, while more complex or overlapping classes show slightly reduced accuracy. The detection of unknown patterns remains more challenging, reflecting the intrinsic difficulty of open-set recognition. Initial empirical analysis on real-world data indicates that the model is consistent with the trends observed in the simulated experiments.

Near geostationary Synthetic Aperture Radar (SAR) systems represent a promising solution for near-real-time Earth observation, thanks to its capability of ubiquitous and continuous monitoring, addressing stability and health of large infrastructures, urban mapping and planning and risk management in various areas subject to deformations, besides rapid disaster response. Such systems compensate the huge attenuation due to the distance by long integration time, of several minutes. However, performance remains strongly affected by the random and unknown fast fluctuation of the Atmospheric Phase Screen (APS), which introduces phase distortions mainly driven by tropospheric turbulence. This reduces the feasibility to C and X band systems, since lower frequencies are highly penalized by the variation of the background ionosphere, whereas higher frequencies are strongly impacted by the spatio-temporal variation of the ionosphere. Furthermore, the ITU regulations in the orbit occupation by geostationary satellite limit the achievable resolution. This study proposes a different concept, made by two satellites operating in Ku-band and placed in a close along-track formation. In this case it would be possible to achieve a reasonable fine resolution, with compact payload in terms of antenna size, and – at the same time - retrieve the atmosphere, thanks to short temporal baseline interferometry. The MIMO-2 architecture provides at one time three temporal baselines to enhance the retrieval of the APS, and an increase of a factor four in Signal-to-Noise Ratio (SNR). These along-track interferograms will benefit of a limited decorrelation due to the short time interval, but also the very small baselines that can be achieved in such formation. In addition, the AT interferometric configuration supports further applications, like ship detection or monitoring of fast changes. On the top of this, one-day revisit interferometric applications will be made possible over the stable targets. In this work, the feasibility is addressed by a quantitative and thoughtful analysis of the impact and compensation of ionosphere and troposphere. The performance of such system will be compared with a system with single geostationary satellite operating in X band. This study was carried out within the Space It Up! project and received funding from the ASI and the MUR –Contract n. 2024-5-E.0 -CUP n. I53D24000060005. This manuscript reflects the views and opinions of the authors only; the funding bodies cannot be held responsible for them.

A geostationary SAR mission such as Hydroterra+ offers unique capabilities for persistent observation of a wide area, enabling continuous monitoring of the same region with daily or even sub-daily interferometric revisit times. Its equatorial orbital position makes the system particularly well suited for observing sub-tropical to mid-latitude regions, for example from sub-Saharan Africa to the entire Mediterranean basin within our hemisphere. This would provide products like: Water-Vapor, Soil Moisture, Snow Water Equivalent, Deformation and change maps, frequently sampled in the day, to understand underlying physics and to be assimilated into forecasts models. Furthermore, it allows for a flexible acquisition planning where resolution can be traded with coverage, and revisit time, and pointing can be switched over a super-continental access area within minutes. In terms of revisit time and South–North line of sight, such systems are complementary to polar-orbiting LEO satellites such as Sentinel-1, which achieve frequent revisits at high latitudes and operate with an East–West line of sight. Clearly, the imaging performance obtained from an altitude of approximately 37,000 km and with today technologies in terms of power and antenna width, cannot match that of a LEO SAR operating at typical ranges of 800–1000 km. Nevertheless, a geostationary system benefits from reduced temporal decorrelation thanks to its short revisit intervals. This characteristic can significantly improve interferometric quality over distributed targets and enables interferometric observations of rapidly evolving phenomena that would otherwise be difficult or impossible to monitor, such as snow accumulation and melt cycles or lava flows. For both GEO and LEO SAR systems, such as Hydroterra+ and Sentinel-1, interferometric performance results from a trade-off among several factors: Noise Equivalent Sigma Nought (NESZ), that accounts for Radio Frequency Interferences, temporal decorrelation, and additional decorrelation sources such as atmospheric turbulence during the integration time. Interferometric quality also depends on the number of looks used for phase estimation, and therefore on the achievable spatial resolution. In this work, we present a comprehensive model for assessing the interferometric performance of a C-band geostationary SAR. The analysis is based on large-scale datasets, including the Global Seasonal Sentinel-1 Interferometric Coherence and Backscatter Dataset provided by ASF and the WRF-OL meteorological data from CIMA. We also provide a comparative assessment of the expected performance relative to Sentinel-1.

Differential Interferometric Synthetic Aperture Radar (DInSAR) is nowadays a key technique for monitoring surface deformation, achieving centimeter/millimeter-level accuracy. Since the 1990s, large-scale SAR data archives have fostered advances in multi-temporal DInSAR methods, enabling the tracking of natural and anthropogenic deformation phenomena by generating displacement time series. Expanding the SAR sensor availability across different bands (e.g., X, C, and L) further enhances spatial and temporal resolution. However, traditional large satellite missions are expensive to develop and launch. Consequently, a shift is occurring towards modular, flexible solutions based on smaller SAR satellites, driven by ease of design, serial production capabilities, and the ability to deploy entire constellations with a single launch, drastically reducing mission costs. Companies like ICEYE, Capella Space, and Synspective, along with the forthcoming Italian constellation IRIDE (managed by ESA with ASI support), exemplify this trend. In particular, the IRIDE constellation, expected to be completed in 2027, will integrate X-band SAR platforms and optical sensors to deliver advanced monitoring services for the national territory and bolster Civil Protection activities. Simultaneously, the DInSAR user community increasingly demands more sophisticated and technically demanding interferometric products. Meeting these needs requires shorter revisit times, improved spatial resolution, and diverse acquisition geometries to capture three-dimensional surface deformation. Therefore, exploring alternative orbital configurations beyond the conventional sun-synchronous orbits (SSO) is crucial. SSOs provide global coverage and simplify energy management through consistent illumination, but they can limit DInSAR coverage performance in mid-latitude regions and poorly capture north-south deformation. Recent advancements in satellite miniaturization and launch logistics now effectively enable consideration of mid-inclination orbits (MIOs), particularly when the Area of Interest is located at medium latitudes and global monitoring is not required. Indeed MIOs offer higher revisit frequencies over target areas and significantly improved sensitivity to north-south ground displacement, enabling more accurate retrieval of 3D deformation. The present study investigates the orbital configuration and interferometric strategy for the NIMBUS mission of the Italian IRIDE program, consisting of two batches of six X-band small SAR satellites each. These satellites can operate with different SAR acquisition modes, and we select the Stripmap mode as the baseline due to its balance between spatial resolution and coverage (the nominal swath width of each NIMBUS satellite is of about 27.5 km). The constellation design is guided by two main objectives: (i) ensuring systematic coverage of the Italian territory, and (ii) minimizing the interferometric revisit time. The methodology adopted in this study is based on repeat ground track (RGT) orbits, which are fundamental to interferometric applications. Since each NIMBUS batch consists of six satellites, we select a six-day RGT cycle. Each satellite is assigned a dedicated beam with an average swath width of 27.5 km, designed to fill the spatial gaps between consecutive ground tracks (GTs). The analysis considers both SSO and MIO options as potential orbital configurations for NIMBUS. We use the same RGT cycle (N=6) and beam assignment strategy for the cases herein reported. In particular, for the two batches, we select two MIO configurations based on the following reasons: a 49° right-looking MIO, because it is the minimum orbital inclination angle that guarantees full coverage of the Italian territory in a right-looking acquisition mode exploiting off-nadir angles higher than 20°; a 44° left-looking MIO, because it further enhances the angular diversity and DInSAR capabilities of the overall NIMBUS constellation. This work examines the validity of this design choice and highlights the advantages of MIOs for interferometric applications. In particular, we investigate the coverage capability of the DInSAR performance constellation over the Italian territory. Prior to the NIMBUS launch (first batch by 2026), we can anticipate its performance by analysing real data from commercial satellites operating in 45° MIO orbits. This study utilizes Capella Space satellite data acquired over the Campi Flegrei caldera, an active volcanic area in Southern Italy, processed using the Parallel Small Baseline Subset (P-SBAS) chain to assess MIO ability to recover north-south deformation component. The achieved results provide a valuable precursor study for NIMBUS capabilities and confirm the growing importance of small SAR constellations in advanced DInSAR monitoring.

Public SAR missions such as Sentinel-1 and NISAR have established the foundation for systematic, wide-area ground deformation monitoring. However, applications involving rapid or transient deformation, including fast-moving landslides, short-lived volcanic episodes, or infrastructure failures, demand finer spatial resolution, shorter revisit intervals, and, in some cases, sensitivity to the north-south component of motion that Sun-synchronous polar orbits cannot provide. Commercial New Space SAR constellations are now positioned to fill these observational gaps.We present approximately two years of repeat-pass InSAR results from Capella's X-band constellation. Starting in mid-2024, the Capella-14 satellite (and later the Capella-13 satellite), were placed in 45-degree (53-degree, resp) mid-inclination orbit with a 2.95-day repeat ground track cycle. Automated orbit maintenance has regularly kept the satellites within the presceibed orbital tube, yielding perpendicular baselines consistently below 500 meters. We characterize the interferometric performance of this system, including coherence as a function of spatial and temporal baseline, and phase stability for both persistent scatterer (PS) and distributed scatterer (DS) targets.We demonstrate the displacement monitoring capability through several case studies. A multi-year time series (over 120 acquisitions) over the Rosamond, California calibration site provides a controlled assessment of long-term phase stability and decorrelation behavior. We examine the ongoing eruptive activity at Kīlauea volcano (Hawai'i, USA), where episodes of rapid inflation and deflation, often occurring on sub-daily timescales separated by multi-day pauses, are captured by the 3-day revisit and sub-meter resolution. Changes to coherence map clearly show locations of new lava flows. We also demonstrate high-quality results for the Portuguese Bend landslide complex in Rancho Palos Verdes (California, USA), which accelerated to rates exceeding 30 cm/week in 2023. Here we are able to compare to the a longer time series using JPL’s OPERA Surface Displacement from Sentinel-1 (DISP-S1) 12-day revisit time series. We find that the coarser resolution combined with the slower revisit cycle was often insufficeint to resolve rapid movements; Capella's higher temporal sampling provides substantially improved displacement tracking over this site for the studied time period.Our InSAR processing uses the open-source ISCE3 framework for SLC coregistration and the Dolphin library for PS/DS phase estimation, the same algorithms underpinning NASA's OPERA Surface Displacement product suite. This methodological consistency enables direct, quantitative comparison between medium-resolution C-band (Sentinel-1) and high-resolution X-band (Capella) displacement results over the same regions, providing insight into the complementary roles of public and commercial InSAR systems.The mid-inclination orbit geometry additionally offers ascending-descending viewing configurations with substantially different look directions than polar-orbiting missions, improving sensitivity to the north-south deformation component and enabling more complete 3D displacement retrieval when combined with Sentinel-1 or NISAR observations. Our results demonstrate that New Space SAR constellations have reached the operational maturity required for sustained, science-quality InSAR time series, and we discuss the implications for multi-mission deformation monitoring strategies.

Interferometric synthetic aperture radar (InSAR) has been widely used to measure Earth’s surface deformation with a millimetre accuracy, providing routine monitoring of infrastructure, mining operations, natural disasters or environmental hazards. Most of currently available SAR satellites operate in the near-polar sun-synchronous orbits (SSOs) with side-looking geometry. SSO-only InSAR monitoring makes satellite’s line-of-sight (LOS) measurements highly sensitive to vertical and east-west displacement with almost no sensitivity to the north-south component of the actual three-dimensional (3D) displacement field. In many applications, either of the east-west or north-south displacements have been assumed negligible so that the LOS InSAR measurements can be projected to either one-dimensional (1D) component in vertical plane or two-dimensional (2D) components in the vertical and east-west planes. Both 1D and 2D analyses with this simplified assumption can however introduce substantial bias when the east-west and north-south components are not negligible. An unbiased solution for 3D time series displacement components can be obtained by at least three stacks of non-coplanar LOS measurements with different viewing geometry. MDA Space will soon be launching CHORUS, a next-generation dual-frequency C- and X-band SAR satellite constellation featuring a unique mid-inclination orbit. CHORUS supports both left- and right-looking imaging capability between incidence angles of 24.5° and 63.9° with a repeat cycle of 9.85 days. The resulting 700 km accessible swath and coverage between ±62.5° latitude (89% global access) can offer substantial wide-area monitoring capabilities. CHORUS’s mid-inclination orbit can provide improved sensitivity to the north-south component of surface displacement field over the wide area. MDA CHORUS combined with existing SSO sensors such as RADARSAT-2, will enable the precise 3D deformation monitoring with its diverse non-polar imaging geometry. Time series of 3D deformation components can be retrieved by mathematical inversion of InSAR LOS measurement stacks collected from different orbits and viewing geometries at different times. However, 3D deformation decomposition inherently becomes an ill-posed inversion problem due to a lack of angular diversity in the SAR datasets. Furthermore, extending 2D InSAR decomposition to 3D InSAR decomposition leads to a more ill-posed inversion problem due to an additional unknown parameter representing north-south displacement component, where any small errors in the measurements can cause a more significant deviation between the true surface displacement and the decomposed solution. Here we introduce the advanced 3D InSAR algorithm to retrieve more reliable time series of 3D displacement components using the combination of different SAR sensors. For this study, temporal stacks of CHORUS-C and RADARSAT-2 interferograms are simulated with seasonal atmospheric and decorrelation noise components assuming hypothetic surface deformation based on Mogi model. Regularization method with various constraints is applied to stabilize the inversion process, enabling recovery of the reliable surface displacement components from the noisy ill-posed linear system of simulated InSAR LOS measurement stacks. Weighted Network Inversion (WNI) can be optionally applied to the stacks of InSAR LOS measurements to mitigate the impact of noise and unwrapping errors, enabling more precise and robust analysis of 3D deformation time series. We demonstrate how the proposed 3D InSAR algorithm effectively improves the recovery of 3D deformation components from the combination of simulated CHORUS and RADARSAT-2 datasets.

Spaceborne SAR interferometry has traditionally relied on large satellites operating in carefully designed near-frozen repeating orbits to ensure stable interferometric baselines and predictable geometric evolution. Achieving comparable interferometric stability with agile microsatellite platforms presents additional challenges. Smaller satellites experience stronger sensitivity to atmospheric drag relative to mass, more rapid orbital element evolution, and tighter propulsion resource constraints, leading to increased baseline drift if not actively controlled. These effects, combined with the absence of classical frozen-orbit tuning, can result in greater orbital variability and more rapidly changing interferometric geometry. In this contribution, we present recent advances in operational interferometry using the ICEYE X-band microsatellite constellation, highlighting the transition from single repeat-track satellites with limited geographic coverage and wide orbital tubes to a proliferated Daily Ground Track Repeat (DGTR) architecture with significantly improved baseline stability and expanded global coverage. Earlier ICEYE repeat-track satellites enabled daily coherent acquisitions over selected regions; however, total normal baseline variation over extended periods could reach tens of kilometers, limiting systematic time-series analysis. The current constellation configuration includes four satellites operating in independent 24-hour repeating ground track orbits. These satellites operate in independent 24-hour repeating ground track orbits and are phased to expand aggregate geographic coverage while minimizing redundancy. The result is nearly two-thirds of global land areas being accessible with daily revisit at the equator, and higher revisit density toward polar latitudes. This architecture represents a substantial increase in coherent coverage compared to earlier isolated repeat-track implementations. Although these orbits are not yet fully frozen, active orbit control strategies constrain baseline evolution within a significantly narrower envelope than previous generations. During uninterrupted monthly acquisition campaigns, total normal baseline variation remains below approximately 850 m. Consecutive daily acquisitions exhibit perpendicular baselines well below 200 m and typically substantially smaller. This controlled baseline regime enables predictable interferometric geometry over extended time periods and supports systematic time-series processing despite operating outside classical frozen-orbit configurations. Ongoing orbital optimization efforts continue to further reduce baseline variability. The proliferated DGTR architecture enables rapid stack accumulation. With daily revisit, interferometric stacks exceeding 100 acquisitions can be formed in slightly more than three months, significantly accelerating the time required to reach robust Persistent Scatterer Interferometry (PSI) solutions. High temporal sampling density provides several advantages: improved separation of atmospheric phase screen components, enhanced temporal coherence estimation, reduced phase aliasing risk for rapidly deforming targets, and increased sensitivity to transient or accelerating deformation phenomena. This sampling density is particularly valuable in scenarios approaching critical failure conditions, such as infrastructure instability, mining-induced subsidence, or landslide acceleration, where deformation rates may evolve on timescales shorter than conventional satellite repeat intervals. In addition to repeat-track deformation monitoring, the constellation supports high-resolution spotlight imaging modes with ground resolutions down to approximately 25 cm. These coherent acquisitions enable fine-scale Coherent Change Detection (CCD), supporting detection of subtle anthropogenic or environmental changes that may not be discernible in wider-swath products. The combination of high spatial resolution and daily revisit enhances monitoring capability for rapidly evolving ground conditions. Representative results are presented including: Differential InSAR time-series over active volcanic regions Persistent Scatterer deformation monitoring in urban and mining environments High-resolution coherent change detection examples demonstrating fine-scale surface modifications To facilitate independent validation and algorithm development within the InSAR community, selected interferometric stacks from DGTR satellites are being released under a CC BY 4.0 open license as part of ICEYE’s Open Data initiative. By providing sustained daily-repeat stacks under controlled but non-frozen orbital conditions, this initiative supports benchmarking of time-series methodologies and encourages broader scientific engagement with microsatellite-based interferometry. These results demonstrate the maturation of agile microsatellite SAR from early repeat-track experimentation toward a managed, proliferated daily repeat architecture capable of delivering sustained, high-quality deformation and change-monitoring products at regional to near-global scale.

Synspective has launched and operated seven small Synthetic Aperture Radar (SAR) satellites, "StriX," since the launch of its first satellite in 2020, as of the end of 2025. The company is proceeding with the manufacturing and development of its satellites to construct a constellation of over 30 satellites starting in 2028, which will enable the observation of any point on Earth and the delivery of analysis-ready data within approximately one hour of acquisition. Key features of the small SAR satellite constellation are high resolution and high frequency. The best spatial resolution currently available from StriX is approximately 0.5m in ground range and 0.25m in azimuth. The shortest revisit time is one day, which enables daily ground displacement measurement, a capability impossible with conventional SAR satellites. StriX satellites utilize two types of orbits: Sun-Synchronous Orbit (SSO) and Mid-Inclination Orbit (MIO). While MIO precludes observation of high-latitude regions, it increases the observation frequency in mid-latitude areas. Furthermore, MIO offers sensitivity to the North-South component of displacement, which is not available with SSO InSAR, making it possible to reconstruct the 3D displacement field. Although StriX currently does not maintain strict orbit control and cannot continuously acquire data applicable to Interferometric SAR (InSAR), there are specific periods when the perpendicular baseline shortens. During these opportunities, we successfully acquired datasets suitable for continuous displacement measurement and InSAR-based topographic measurement. We introduce three case studies below. 1. Validation of mm-Level Measurement Accuracy using Time-Series InSAR Analysis with Consecutive 1-Day Repeat-Pass Data From December 13 to 23, 2025, when the baseline of StriX-4 (MIO) was short, we acquired consecutive interferometric data to demonstrate and validate the accuracy of daily InSAR time-series analysis. The observation conditions were Ascending, Left-looking, Incidence Angle 39°, and Azimuth Angle 69° (East-Northeast). The maximum perpendicular baseline was 1.2 km, which is sufficiently smaller than the critical baseline of 32 km. The observation mode was Staring Spotlight 4 (600 MHz bandwidth), with a spatial resolution of approximately 0.5 m in ground range and 0.25 m in azimuth. The Area of Interest (AOI) was Tsukuba City, Ibaraki Prefecture, Japan, where five Corner Reflectors (CRs) were installed. The height of two CRs was artificially changed by 1–2 mm seven times in total. CR displacement was measured by levelling. The maximum cumulative height change was approximately 1 cm. The results of the InSAR time-series analysis and levelling showed good agreement with the artificial displacement, with the difference in displacement converging within a standard deviation of 1 mm at all points. This confirmed that daily InSAR with StriX can measure displacement with 1 mm accuracy at a high frequency of one-day intervals. 2. High-Resolution Pixel Offset Time-Series Analysis The Pixel Offset (PO) method uses two SAR intensity images acquired under the same observation geometry to measure ground displacement that occurred between the two epochs through detailed image coregistration. Unlike InSAR, it does not require phase unwrapping, making it robust for measuring displacement with large spatial gradients, and its effectiveness has been demonstrated in capturing m-level displacement associated with large earthquakes. The achievable measurement accuracy is proportional to the spatial resolution of the SAR image, generally estimated to be about 1/10 of the resolution. Since conventional high-resolution SAR data typically had a spatial resolution of about 3 m, the measurement accuracy achieved by the PO method was limited to a few tens of centimetres, inferior to the mm-cm order accuracy achieved by InSAR. Furthermore, the need for a certain window size for detailed coregistration based on cross-correlation resulted in lower spatial resolution compared to InSAR, with measurable displacement scales generally considered to be 100 m or more. However, small SAR satellites are making higher-resolution data than conventional systems abundantly available. It is expected that the use of high-resolution data will enable cm-level displacement measurement even with the PO method. We analyzed the Port of Karachi in Pakistan. The port is equipped with floating-roof tanks whose roofs move up and down depending on the contents' volume. The tanks have a diameter of approximately 25–50 m. StriX-2 (SSO) acquired 10 days of consecutive data from February 13 to 22, 2025. The observation conditions were Descending, Left-looking, Incidence Angle 32°, and Sliding Spotlight 1 (1 m resolution). The maximum perpendicular baseline was about 7 km, and the critical baseline was about 13 km. In conventional PO analysis for wide-area ground displacement measurement, relatively large window sizes, such as 32x32, are often used to reduce noise. However, for targets with strong scattering intensity and characteristic structures, as in this case, a sufficiently high correlation was obtained even with a small window size of 8x8. By setting the sampling interval to half the window size, we calculated a high-resolution offset field of approximately 3.2 m. Even for the pair with a perpendicular baseline of about 7 km, a high correlation coefficient was obtained around the tanks, and clear displacement was captured. The theoretical standard error of the measured value depends on the correlation coefficient, but it was estimated to be smaller than 1/10 of the spatial resolution, at several centimetres, around the tanks. Since redundant displacement values from multiple pairs are obtained from all combinations of consecutive observation data, time-series analysis can be applied, similar to unwrapped InSAR images. The time-series analysis results yielded displacement time series believed to reflect the vertical movement of the roof for specific tanks. Although ground truth data was not available, the estimated error from the consistency between redundant multiple pairs in the time-series analysis was generally less than 10 cm, and around 1–2 cm in good areas. This result demonstrates the potential of PO analysis using high-resolution SAR data to estimate structural displacement with spatial resolution and measurement accuracy comparable to InSAR. 3. High-Frequency Monitoring and Topographic Measurement of Changes Associated with Volcanic Eruption Activity On June 22, 2025, Mt. Shinmoedake, located in the Kirishima mountain range on the border of Kagoshima and Miyazaki prefectures in Japan, erupted for the first time in seven years. The initial eruption occurred on the northeast side of the crater, followed by continued active eruptive activity. Eruptions were also confirmed on the southeast side of the crater around July 3, and the plume reached 5,500 m above the crater rim on August 28. The series of eruptions continued until September 8. The rapid assessment of the crater's location and shape is crucial for estimating the distribution direction of volcanic ejecta, the flow direction of pyroclastic flows, and the evaluation and prediction of eruptive activity. Traditionally, visual observation and optical images from helicopters or aircraft have been frequently used, but there are challenges: flight is impossible in bad weather, and the crater cannot be fully assessed at night or when the plume is active. SAR images, on the other hand, have the advantage of observing surface conditions regardless of weather or plume effects. For this eruptive activity, observations were conducted by ALOS-2/4, acquiring a total of 13 data sets between June 28 and September 25, 2025, capturing changes around the crater. However, SAR satellite observation opportunities are limited to when the satellite passes over the target area, making agile observation difficult. ALOS-2/4 is limited to two observation times per day, around 0:00 and 12:00, and the revisit time is 14 days, making continuous daily observation impossible. Furthermore, the maximum spatial resolution is about 3 m, which limits the detection of local topographic changes smaller than 10 m. For this eruption, Synspective conducted high-frequency, high-resolution observations using three StriX satellites (StriX-2, 3, and 4), capturing detailed surface changes associated with the eruptive activity. A total of 63 data sets were acquired between June 28 and October 1, 2025, with an average observation interval of approximately 1.5 days. The spatial resolution ranged from a lower resolution of 1 m to a higher resolution of 0.25 m, capturing local changes that were difficult to detect with ALOS-2/4 at high spatio-temporal resolution. StriX-2 and StriX-3 employ SSO, similar to conventional SAR satellites like ALOS-2/4, resulting mainly in observations from the East-West direction. StriX-4, however, employs MIO, enabling observations including the North-South component. The combination of ascending/descending and left/right-looking observations allows for observations from up to eight directions. Integrating data from multiple directions can mitigate the effects of layover and shadow in areas with steep topography. During the aforementioned observation period, data with baseline conditions suitable for InSAR were almost unavailable. However, in December 2025, after the eruptive activity subsided, StriX-4 reached orbital conditions suitable for InSAR, and one-day interval interferometric data of Mt. Shinmoedake were acquired. This data is applicable for DEM generation. The InSAR phase includes contributions from atmosphere, ground displacement, topography, and errors due to decorrelation. Single-pass InSAR using two simultaneous satellites does not suffer from atmospheric noise, ground displacement, or temporal decorrelation, thus containing only the topographic component, which allows for high-precision DEM estimation. Conversely, with repeat-pass InSAR, which has a time difference, components other than topography generally become sources of error, making high-precision DEM estimation difficult. However, under limited conditions, it is possible to extract high-precision topographic information from repeat-pass InSAR. First, to minimize the effects of ground displacement and temporal decorrelation, pairs with a sufficiently short time interval are selected. StriX's one-day interval pairs are suitable in this respect. Next, an appropriate perpendicular baseline pair is selected, which has sufficient sensitivity to topography and sufficiently small geometrical decorrelation effects. Since sensitivity to topography is inversely proportional to the wavelength, the X-band tends to have higher sensitivity than the C-band or L-band. A perpendicular baseline of about 500 m is optimal in this case, as the height ambiguity is about 15 m, providing sensitivity to heights of a few meters. Furthermore, to separate atmospheric error and the topographic component, we focus on the difference from an existing DEM. If the height change at the observation date from the existing DEM is local, and since atmospheric error has spatial correlation, applying a low-pass filter to the differential interferogram can isolate only the atmospheric error, allowing the extraction of the height difference component from the existing DEM. By adding this back to the original existing DEM, a high-precision refined DEM can be generated. The advantage is that the spatial resolution of the resulting height difference or refined DEM does not depend on the existing DEM's resolution but on the InSAR resolution, allowing for a high-resolution DEM even if the existing DEM's resolution is low. This method was applied to two independent one-day interval pairs: December 23–24 and December 27–28, 2025. The observation mode was Staring Spotlight 4, with an incidence angle of 45°. Both pairs had a perpendicular baseline of about 500 m and a Height ambiguity of about 14 m. As a result, topographic changes exceeding 20 m around the crater due to the eruption were revealed with a spatial resolution of less than 1 m. The difference between the two independent pairs was generally within a few meters. This case study demonstrates that high-frequency, high-resolution observation by small SAR satellite constellations is effective for assessing crater shape and topographic changes associated with eruptive activity, contributing to the advancement of future volcano monitoring methods. Acknowledgements The CR validation utilized deliverables from the FY2025 Demonstration Project Led by the Cabinet Office to Expand the Use of Small SAR Satellite Constellations. The installation and measurement of the CRs were carried out in cooperation with Pacific Consultants Co., Ltd.

Agriculture covers about 44% of the global habitable land and plays a particularly important role in global sustainability. Cropland makes up a third of agricultural areas and is crucial for food security (direct use and livestock feeding), raw materials, and biofuels. Livelihoods and economies around the world depend on agriculture and its close connection to the water and carbon cycle. About 100 million hectares of productive land per year are lost to land degradation, desertification, urbanization, or drought. Extreme weather such as droughts, floods, wind, and hail are magnified by climate change and endanger farmers around the world. Additionally, the uncertainties in agricultural carbon stocks and fluxes remain significant. The airborne AGRIROSE-L campaign coordinated and performed by the German Aerospace Center (DLR) was conducted in cooperation with the LMU, GFZ and CzechGlobe over an agricultural area in southern Germany called Puch. The campaign is an ESA supported campaign. The campaign's primary goal is to provide calibration and validation data to support future Earth observation missions, specifically ROSE-L and CHIME, with a focus on improving the monitoring of soil moisture and health, crop growth, and other agricultural parameters from space. The data collected is crucial for developing and testing algorithms for sustainable agriculture. ROSE-L mission will increase the understanding of soil moisture and agricultural systems with its high sensitivity to both vegetation and soil water. AgriROSE-L campaign aims to contribute to this understanding with the following goals: Imaging of a representative region of interest with varying crop types, crop stages, and soil moisture conditions every, at minimum, six days to ensure a high temporal resolution; Provision of an extensive time-series covering the full crop cycle; Simultaneous recording of C-Band and L-Band frequencies maximizing comparability; Gathering of tomographic, fully polarimetric, and interferometric information within a multi-baseline repeat-pass configuration. In addition, a number of headings has been flown; Acquisition in sub-meter azimuth and range resolution depending on the frequency; Acquisitions with three-day separation for higher temporal resolution and three flights per day for intra-daily information; Gathering of in-situ reference data on more than 10 fields in cooperation with the Ludwigs-Maximilians University, Munich; Acquiring data in the optical and hyperspectral domain for cross-comparison Providing the collected data as AI-ready datasets stemming from the multispectral as well as the SAR domain; Operation of the ground-based wideband scatterometer WBSCAT during the campaign Organization of acquisitions of relevant radar satellites to improve model transferability. For this the DLR’s F-SAR system recorded a globally unique dataset across four different frequency ranges (the X, C, S and L bands). In total, the radar team carried out 23 measurement flights between April and July covering the whole agricultural vegetation season [1]. On selected days, the flights took place in the morning, at midday and in the evening to record any daily changes in the soil and vegetation. The data was collected using innovative imaging techniques such as polarimetry, interferometry and tomography. Experienced DLR test pilots flew specified paths with metre-level precision, supported by the satellite-based navigation system integrated into the F-SAR. Parallel to each flight, a team from LMU collected ground measurements of soil and vegetation parameters, such as soil moisture, surface roughness, plant water content and plant biomass. In addition, four times also the DLR’s hyperspectral sensor HySpecs was flown over the same area and one-time CzechGlobes hyperspectral sensor acquired data. In this research work the campaign, its collected data and the first performance analysis are presented.    [1] Campaign Implementation Plan, submitted to ESA 2025

SAR instruments have a long history in planetary exploration, particularly with regards to Venus. Venera 15&16 and Magellan have been exceptional past examples, making available the first Venusian Digital Elevation Model (DEM). Following their legacy, NASA’s VERITAS mission is being planned. In this context, the limited downlink capacity has long been a major constraint for Synthetic Aperture Radar (SAR) and Interferometric Synthetic Aperture Radar (InSAR) observations, making the development of efficient on-board compression strategies a critical aspect for such missions. In this work, we propose a Deep Learning (DL)-based approach to jointly denoise and compress the interferometric phase (InSAR phase) on board. To this purpose, a Convolutional AutoEncoder (CAE) is trained end-to-end in a supervised manner and using a synthetic dataset, derived by exploiting experimental TanDEM-X data together with corresponding InSAR geometries and underlying topography. Such a network is naturally divided in two parts: an encoder and a decoder; the former, responsible for the compression of the acquired data, is supposed to be implemented on board the satellite, while the latter allows for decompressing and reconstructing the received data on ground. The encoder reduces the spatial dimensions of the input by applying a combination of convolutional and pooling operations, therefore achieving data compression as the dimensionality of the transmitted latent space is considerably lower than that of the original uncompressed data. On the other hand, the decoder reconstructs a denoised version of the original SAR interferogram from the received latent space on ground; however, as the original sampling is reconstructed through standard up-sampling techniques, the original high-resolution spatial details are partially lost. The proposed architecture is designed in a parametric way, thus allowing for hyper-parameter tuning on different settings, such as network depth and number of channels/features in the resulting latent space. A family of models is then trained targeting three different performance metrics: denoising capability, compression ratio, and preserved details. The performance of the proposed methodology is assessed according to the three metrics mentioned above. The standard deviation of the phase error between the reference noiseless and estimated phase and across the test scenes is used as indicator for the denoising performance. The achieved compression ratio is evaluated using the Bits Per Original Pixel (BPOP) metric, representing the number of bits used by the compressed latent space with respect to the original number of pixels of the input uncompressed data. Last, the level of detail preserved in the autoencoder output is assessed by progressively removing high-frequency content from the reference noise-free phase and comparing the result with the decoder reconstruction; the best match determines the percentage of high-resolution details retained. Results are then assessed with respect to a combination of boxcar filtering and JPEG 2000 compression, referred to as baseline method, which reflects one of the possible strategies reported in the literature and possibly to be exploited for currently-planned missions. As far as phase denoising is concerned, we employ a boxcar filter, consisting of an averaging operation over a sliding square window; more sophisticated filtering approaches, such as nonlocal filters, are not considered given their high computational complexity, which jeopardizes their use for extraplanetary missions. Note that denoising is achieved at the cost of spatial resolution, thus directly impacting the obtained compression rate as well. In particular, different windows are considered in order to investigate different trade-offs between denoising and compression performance. As expected, boxcar filtering achieves a better denoising at the cost of spatial resolution. Indeed, when increasing the window size, the phase error decreases, while, on the other hand, less resolution is preserved. Also, bigger windows lead to higher compression ratios. As far as the proposed methodology is concerned, we assess different groups of models, each tuned on a specific performance metric. It is worth mentioning that the proposed autoencoder allows for a higher overall flexibility compared to the baseline method, since the whole performance space can be explored by tuning specific hyper-parameters during training. For example, a better denoising is expected at the cost of data volume reduction; also, in these cases, the corresponding models preserve more original high-resolution details compared to other autoencoder solutions, except for those optimized for this specific task. On the other hand, the best spatial details preservation is achieved at the cost of phase denoising capability, similarly to, e.g., a [3 x 3] boxcar filter. Note that the proposed autoencoder allows for an overall good trade-off between the three performance measures, even if tuning the network for the compression task only. Moreover, we evaluate some additional models representing good trade-offs between all performance measures. Overall, these solutions demonstrate competitive performance with respect to the considered baseline approaches. As final remark, results look promising and have shown already an enhanced overall flexibility with respect to state-of-the-art baseline methods, in terms of performance scalability. Moreover, the proposed technique represents a valid alternative to most traditional approaches in terms of absolute performance as well, mainly targeting denoising capability and high-resolution details preservation.

During the past several years, we have designed, implemented, and operated compact, lightweight synthetic aperture radar (SAR) end-to-end systems at L- and S-band [1] for a broad range of applications. Our FMCW SAR systems are deployable on ground vehicles, UAVs, manned aircraft, and potentially future high-altitude pseudo-satellite (HAPS) platforms. They support SAR data acquisition and interferometric applications, including ground deformation monitoring, vegetation and forest parameter retrieval, and snow property estimation, etc. Our end-to-end system comprises: (1) a compact FMCW SAR sensor (L-band with up to 200 MHz bandwidth; S-band with up to 400 MHz bandwidth), (2) a compact GNSS-aided INS navigation unit (Honeywell HGuide n580/n500), (3) SAR focusing and processing software, and (4) interferometric processing and higher-level value-adding software tools. The FMCW-SAR architecture incorporates two alternating transmit channels and up to four simultaneous receive channels. Depending on mission requirements and platform constraints, the system supports single-polarization interferometry, polarimetric interferometry, and single-pass multi-baseline acquisitions. The system’s compact hardware form factor and a flexible SAR processing chain enable deployment on agile platforms (e.g., UAVs, vehicles) as well as platforms with stringent payload limitations (e.g., UAVs, HAPS). Consequently, a wide range of acquisition geometries and operational scenarios can be realized. Of particular relevance are use cases requiring short repeat-pass intervals and adaptable viewing geometries that complement spatiotemporal sampling schemes of current spaceborne SAR systems. Terrestrial, UAV-borne, airborne, or HAPS-based acquisitions enable quasi-geostationary repeat-pass observation schemes with short temporal baselines. Such configurations are well suited for monitoring rapidly evolving landslides, providing timely snow parameter updates, frequently mapping tropospheric water vapor distributions, and supporting emergency or disaster response scenarios. In our presentation, we include (1) results from repeat-pass interferometric campaigns conducted in 2024 and 2025 using L- and S-band Gamma SAR systems mounted on vehicles, including case studies of the Brinzauls (Switzerland) and Madesimo (Italy) landslides within the ESA MODULATE project, (2) results from subsequent UAV-based deployments using quadcopter and octocopter platforms, including interferometric analyses derived from repeated overflights. In addition, UAV-based bistatic SAR imaging and interferometry provide a attractive and flexible framework for experimentally investigating bistatic SAR concepts. At present, UAV-based bistatic SAR demonstrator platforms that are suitable for experimentally advancing bi-static and multi-static SAR mission concepts are only about to emerge. This applies to both key areas: (1) technology development—such as synchronization, precise positioning, and constellation design—and (2) UAV-based investigation of bistatic radar backscatter signatures across different bi-static geometries, including the development of new or improved bio- and geophysical parameter retrieval methods. In 2025, we performed bistatic SAR experiments using two Gamma L-band SAR systems, each deployed on a Harris Aerial HX8 UAV. In our presentation, we include bistatic SAR imagery and interferometric results derived from these most recent experimental campaigns. REFERENCES [1] Frey, O., Werner, C., Leinss, S., Batt, T., Caduff, R., Dixon, T., Sadeghi Chorsi, T., Van Alphen, R., Schmitt, M., Eitel, M., Sica, F., Deeb, E., LeWinter, A., Filiano, D.L., Wagner, C.J., Hoppinen, Z., 2025: Multicopter-UAV- and car-borne repeat-pass SAR interferometry and SAR tomography with the compact Gamma SAR systems: first examples and use cases at S- and L-band, Proc. IEEE Int. Geosci. Remote Sens. Symp., Brisbane, Australia, 1374-1377.

As the core SAR component of the Copernicus Program, Sentinel-1 (S1) has delivered exceptional performance across a broad spectrum of applications for over ten years. Not least, S1 has marked a step change in polar satellite Earth observation since 2014. A key strength of the mission is its systematic acquisition strategy over the polar regions, ensuring consistent and repeated coverage of the Greenland and Antarctic Ice Sheets, as well as other polar ice caps. This systematic acquisition approach has enabled to implement operational monitoring of key ice sheet parameters, including ice velocity and ice discharge—capabilities that were formerly limited to specific glaciers and targeted measurement campaigns. Current ice velocity products continue to face observational limitations, including data gaps in regions with highly variable surface conditions, rapidly flowing glaciers, and shear zones along glacier margins that lead to signal decorrelation. Moreover, the loss of S1-B in December 2021, which increased the mission’s repeat cycle to 12 days, created further significant challenges for ice velocity monitoring. This stimulated efforts to investigate synergies with other satellite systems to maximize the value of S1 data. This includes complementary observations from other missions, such as the L-band SAR constellation SAOCOM. The activities not only helped to mitigate immediate data gaps but also provided important preparation for new and upcoming L-band missions, including the Copernicus expansion mission ROSE-L and the joint NASA/ISRO NISAR mission. We will present an overview of key achievements and recent advances from activities carried out within the framework of the European Space Agency Climate Change Initiative (CCI) and Polar+ programs, as well as the Copernicus Climate Change Service (C3S) of the European Union, focusing on ice velocity and ice discharge monitoring using SAR Earth observation data. This includes the development of advanced ice velocity products through the integration of complementary techniques such as InSAR and offset tracking, and the combined use of multi-sensor datasets operating at different frequency bands, including C-band and L-band. The resulting velocity fields, together with ice thickness data, provide the foundation for calculating and analyzing changes in ice discharge, needed to estimate freshwater fluxes and overall ice sheet mass balance. We show that integrating L-band SAR with S1 C-band data allows the generation of detailed ice velocity maps from crossing-orbit InSAR, exceeding what can be achieved using S1 alone. The deeper penetration of L-band within the snowpack ensures high coherence between image pairs over longer time intervals when surface processes and snowfall, wind drift and melt cause changes on the surface. Moreover, L-band data exhibit lower fringe density within shear margins and over fast-flowing glaciers, which improves phase unwrapping reliability in heavily crevassed areas where C-band data frequently decorrelate. This enhanced performance facilitates more robust retrieval of ice velocities, particularly in rapidly moving regions. However, our results also point out the higher sensitivity of L-band to ionospheric effects that can occasionally reduce performance. In practice, combining L-band with S1 C-band is often the optimal strategy. Our findings demonstrate the complementarities and opportunities of S1 and other current and upcoming SAR missions ensuring a long-term monitoring of essential climate variables, providing critical data for understanding ongoing changes in the polar regions.

NISAR launched in july 2025 provides a comprehensive coverage of Antarctica in left looking mode. Sentinel-1 d launched in November 2025 has provided an extensive coverage of Antarctica at a one day repeat cycle for six months. iCEYE US and OY operate a constellation of Xband SARs at a one day repeat cycle. In this talk we will show early results of NISAR in grounding line and ice velocity mapping with phase only in areas not well covered in the past, new grounding lines with sentinel-1 and also fine details in key areas with ICEYE. We will show examples of seawater intrusions and close interactions with the subglacial water system.

Frequent and precise monitoring of Antarctic ice shelf calving front locations is essential for constraining ice shelf mass balance and understanding dynamic ice-ocean interactions. Beyond ice dynamic change, recent studies have shown that calving events also have an important role to play in triggering ocean mixing through the genesis of submarine tsunamis. Historically, calving front measurements have been made through manual delineation of satellite images, which is time consuming to produce and reduces the feasibility of making regular repeat measurements over large areas. Developments in the field of deep learning and Artificial intelligence (AI) provides an opportunity to overcome these methodological limitations, enabling automatic delineation of the calving front location. Here, in this study we use a decade of Sentinel-1 Synthetic Aperture Radar (SAR) data combined with AI methods to automatically delineate the location of ice shelf calving fronts in Antarctica. While Sentinel-1 SAR data provides the continuous, weather-independent imagery necessary for this task in the polar regions, automated delineation is frequently hindered by the ambiguous scattering surface at the ice-ocean boundary. Dense ice mélange (sea ice and broken iceberg mix) often mimics the backscatter properties of the crevassed ice shelf surface, making the boundary difficult to delineate independent of the method used. In contrast, most delineation methods perform well when identifying the calving front boundary where an ice shelf meets open ocean, as the boundary between these two features is clearly distinct. To overcome these complex physical variations, deep learning approaches require extensive, manually labelled training datasets, which creates a significant bottleneck when considering continent-scale monitoring. To overcome the training data bottleneck, we present a novel, weakly supervised workflow for calving front delineation. Rather than relying on manual delineation, our approach automatically generates pseudo-labels for training by using existing coastline datasets combined with an unsupervised clustering approach using Gaussian Mixture Models (GMMs) to differentiate distinct surface types (e.g., solid ice, open water, and ice mélange). We engineer a feature space to emphasise the textural differences between these surface types and improve the accuracy of the annotation. We train a U-Net segmentation architecture on these automatically generated labels, where the calving front boundary is initially masked out. Our results show that the model learns robust feature representations of the distinct ice and water classes, while ignoring the high-uncertainty boundary zones. This enables the model to accurately infer the boundary when exposed to the full, unmasked SAR scenes. Our results show that this weakly supervised framework successfully circumvents the need for manual annotation while effectively mapping complex ice-water margins. We investigated the performance of this architecture quantitatively by evaluating its boundary delineation accuracy against a manually annotated validation subset of Sentinel-1 imagery and a high-resolution dataset from ICEYE. We extend the intercomparison to also compare our satellite derived results to in-situ imagery of the calving front acquired during a field campaign, which enables us to more accurately time stamp the calving events to understand more about the changes in surface characteristics before and after the calving occurs. By automating both the training data generation and the inference pipeline, this scalable approach paves the way for continent-wide, high-temporal-resolution calving front tracking in Antarctica, providing vital boundary conditions for predictive ice sheet and ice shelf modelling work. Future studies should apply this method more widely to other regions in Antarctica, in order to deliver a continent-wide monitoring system for ice shelf calving front locations.

Ice cover provides an effective and economical base for seasonal roads. These roads facilitate valuable land transport links to e.g. communities and industries in isolated cold regions of Canada and other northern countries. Current methods to ascertain the trafficability of ice roads involve in situ measurements and are therefore time-consuming, hazardous and expensive. Climate change adds to a need for alternative methods because it diminishes the value of historical expertise. This paper focuses on a section of the Tibbitt-to-Contwoyto Winter Road (TCWR) in the Northwest Territories of Canada. The TCWR is dedicated to the transport of goods that are critical for the year-round operation of diamond and gold mining industries that contribute to Canada’s GDP in a major way. The suitability of an ice road to traffic depends on several variables. This includes ice cover characteristics as well as other variables such as weather conditions. Logically, the thickness of the ice is of great importance. However, the composition and integrity of the ice also play a role. For example, an ice cover comprised of columnar ice (aka black ice) has a larger loading capacity than an ice cover that includes snow ice (aka white ice)—assuming both ice covers are equally thick. Similarly, the presence of e.g. cracks can negatively affect an ice cover’s trafficability. Earlier studies have shown that conventional, high frequency, dual-polarization (or better) radar systems can provide valuable information regarding the distribution of ice types and the presence of hazardous features such as cracks and ridges. The application of SAR technology to estimate the total ice thickness remains under development. This paper will describe the initial results of a study that aims to assess and develop the utility of Pol-InSAR and SAR Tomography for the mapping of ice cover composition, i.e. its vertical structure due to the presence of different ice types and/or ice impurities such as gas bubbles. The present study reports preliminary results achieved by means of data that were acquired by the F-SAR system of the German Aerospace Center (DLR) in the context of the PermASAR19 Campaign. Two tomographic flight lines composed of 9 and 6 passes, with relative horizontal baselines ranging from 0 to 100 m and 55m respectively, and two other InSAR flight lines (e.g. two passes separated by a 15 m horizontal baseline). For each SAR flight pass, fully polarimetric acquisitions were taken at X, C and L bands, where incident angle ranging from 27o to 56o along SAR range. To support the airborne campaign, in situ field measurements were collected during the same week of the airborne acquisitions. 11 GPR (Ground Penetration Radar) transects, 54 ice cores (extraction and characterization i.e. thickness of each ice type (clear ice, snow ice, or slush ice) and snow cover thickness) were collected. Snow dielectric measurements were taken routinely and the local air temperature, at the time of the field visit, was recorded occasionally. PolInSAR coherence diagrams derived over the ice‑core sites revealed clear polarization‑dependent variations in both coherence amplitude and phase across all three radar wavelengths. Depending on the local incidence angle and perpendicular baseline, several local polarimetric coherence diagrams follow the Random Volume over Ground (RVoG) scattering model, where polarimetric interferometric phase diversity increases at shorter wavelengths and with greater ice‑layer thickness. However, X‑band coherence results become noticeably noisier when the perpendicular InSAR baseline exceeds ~40 m. A series of tomograms was produced using both the Capon and MUSIC algorithms for six polarimetric configurations: HH, VV, HV, RR, RL, and HH–VV. The MUSIC method consistently yields sharper vertical spectral peaks than Capon, enabling clearer identification of scattering‑center displacement across polarizations. While tomographic peak widths show partial correlation with ice thickness, no consistent relationship with ice type is observed. Nonetheless, spatially unstable tomographic profiles suggest the presence of vertically layered ice columns (e.g., snow ice, slush ice, and clear ice). As observed in the PolInSAR results, X‑band tomograms are more sensitive to large spatial baselines, which introduce noisier coherence terms into the tomographic covariance matrix. Reducing the range of spatial baselines helps refine the resulting vertical structure profiles. Overall, the analysis of PolInSAR and SAR tomographic data over lake ice remains challenging due to spatial and vertical heterogeneity, combined with the inherently low dielectric constant of pure ice and the relatively thin ice layers (50–158 cm). These factors reduce both top‑interface interactions and volumetric scattering contributions. More advanced quantitative analyses and scattering‑model evaluations will be presented at the conference.

Landfast sea ice is a critical component of Arctic coastal ecosystems, indigenous subsistence activities, maritime operations, tourism, and over‑ice logistics. Despite its importance, consistent seasonal and long‑term monitoring of fast‑ice extent and mechanical stability remains limited. We are developing a new operational InSAR‑based workflow funded by the ESA Arctic Phi‑lab, with additional support from the Svalbard Integrated Arctic Observing System (SIOS) and the Svalbard Environmental Protection Fund. The system provides fully automated, large‑scale mapping of landfast sea ice using Sentinel‑1 coherence estimates, enabling reliable detection of fast‑ice presence during the winter season. Multiple orbits are utilized to generate a Svalbard‑wide dataset with an effective 6‑ or 12‑day repeat cycle. Beyond ice‑edge detection, we derive fast‑ice deformation and mechanical response to environmental forcing to infer stability. This is achieved by translating phase gradients into localized strain estimates on the order of centimetres per kilometre. The result is a near‑operational system capable of routinely characterizing both the extent and dynamic behaviour of landfast sea ice. The aim is for the system to be used routinely by ice analysts at the Norwegian Sea Ice Operational Service by the end of the project to improve landfast ice mapping for maritime safety and to evaluate decadal‑scale changes in fast‑ice extent and stability. Validation efforts is planned to include comparison with airborne measurements of ice and snow thickness as well as ice roughness collected by the Alfred Wegener Institute (AWI), as well as evaluation against optical and SAR‑based mapping carried out by the Norwegian Sea Ice Operational Service. While InSAR techniques for fast‑ice mapping have been demonstrated previously, our focus is on developing a fully automated end‑to‑end processing chain designed for continuous, regional‑scale, operational monitoring, with potential for future expansion to other relevant Arctic coastal regions. With the processing chain now completed, early outputs reveal substantial spatio‑temporal variability in winter ice mobility, including localized regions experiencing stronger dynamic responses. Systematic, Svalbard‑wide archives are currently being generated, enabling long‑term analyses of fast‑ice regimes and their response to climate‑driven environmental forcing. This project demonstrates the feasibility and value of scaling Sentinel‑1 InSAR for continuous monitoring at an operational level and for generating new data products with widespread relevance to Arctic research and operations, spanning marine‑mammal ecology, biological and cryospheric systems, climate‑driven environmental change, maritime activities, coastal community resilience, and interdisciplinary scientific research.

The Arctic is undergoing rapid warming, with Svalbard experiencing temperature increases well above the global average. Permafrost degradation, active-layer thickening, and slope instability increasingly affect ground stability in both natural and built environments. These processes generate measurable surface deformation signals that require systematic spatial observation. Reliable, spatially extensive monitoring of ground displacement is critical for geohazard assessment, infrastructure planning, and understanding climate-driven landscape processes. While operational InSAR Ground Motion Services (GMS) exist for mainland Norway and continental Europe, Svalbard remains outside existing operational frameworks due to environmental and processing constraints. The InSAR Svalbard Ground Motion Service was developed to provide systematic deformation monitoring for High Arctic conditions. The service is based on Sentinel-1 C-band data and applies a Small Baseline Subset (SBAS) multi-temporal processing strategy tailored limited snow-free seasons and strong seasonal deformation signals. Processing strategies were further refined to account for non-linear thaw-freeze dynamics and spatially heterogeneous ground behavior characteristic of continuous permafrost terrain. To quantify ground displacement at multiple temporal scales in permafrost environments, two complementary processing schemes were implemented. Seasonal time series quantify cumulative line-of-sight (LOS) displacement within individual snow-free periods (2016–2024), associated with thaw subsidence, frost heave, and seasonal slope creep. From these series, metrics such as maximum seasonal displacement and the timing of peak displacement (Day of Year) are derived to characterize intra-seasonal behavior. In parallel, interannual time series quantify long-term LOS velocity trends between consecutive snow-free seasons (2018–2024), enabling detection of gradual subsidence, persistent slope movement, and long-term permafrost change. The current release covers five pilot areas in Western and Central Spitsbergen, including Longyearbyen, Ny-Ålesund, Svea, Hornsund, and Kapp Linné. These areas include main settlements, research stations, geomorphologically active slopes, and cultural heritage sites. Products are provided as open-access CSV datasets and are accessible through an interactive WebGIS platform developed in dialogue with end-users, including local authorities and research institutions in Svarlbard. Results demonstrate that the service can resolve both rapid seasonal deformation and subtle interannual trends across diverse permafrost terrains. Observed displacement patterns illustrate how systematic InSAR monitoring can contribute to the assessment of sediment characteristics and ground-ice conditions in permafrost terrain. The InSAR Svalbard Ground Motion Service represents the first operational InSAR-based deformation monitoring framework established in a continuous permafrost setting in the High Arctic. Ongoing development will expand spatial coverage, incorporate new satellite acquisitions, and further refine time-series analysis to support long-term hazard assessment and climate change monitoring in Svalbard.

As satellite Earth observation systems mature into long-term operational infrastructures, understanding their environmental limitations becomes as important as demonstrating their capabilities. In polar and subpolar regions, rapid climate-driven transformations and expanding human activity demand reliable, repeatable monitoring tools capable of functioning under highly variable surface conditions. Yet, extreme seasonal forcing, particularly in freeze-thaw dominated terrains, challenges the temporal stability assumptions that underlie interferometric analysis. Establishing quantitative performance bounds forradar coherence in such environments is therefore critical for defining when measurements can be trustedand when caution is required. Satellite radar interferometry (InSAR) is widely used for operational deformation monitoring, however, its reliability in high-latitude freeze-thaw environments remains strongly season-dependent due to rapid changes in surface dielectric properties, snow accumulation, meltwater infiltration, and permafrost dynamics. These processes introduce temporal decorrelation that may obscure true surface displacement signals. A structured, quantitative assessment of coherence stability under such conditions is therefore required to define the operational limits of C-band InSAR in Arctic environments. This study evaluates season-dependent Sentinel-1 coherence reliability over Longyearbyen, Svalbard, a compact Arctic testbed characterized by exposed bedrock, glaciers, thaw-sensitive permafrost, and critical infrastructure. A total of 334 Sentinel-1 SLC bursts acquired between 2019 and 2024 were processed using a single relative orbit to ensure geometric consistency. All acquisitions were IW-mode, in HH-HV polarization, reflecting Sentinel-1’s high latitude acquisition configuration over Svalbard, Longyearbyen during the study period. Short baseline (12-day) interferograms were generated to isolate seasonal decorrelation effects across multiple freeze-thaw cycles. Interferometric processing was conducted in ESA SNAP using the Sentinel-1 TOPS workflow, includingprecise orbit correction, burst splitting, back-geocoding with enhanced spectral diversity, interferogramformation, topographic phase removal using the Copernicus 30 m DEM, multilooking, and terrain correction. Coherence statistics were evaluated seasonally to quantify temporal stability across surfaceTypes. Based on seasonal coherence distributions, a three-tier operational usability classification is proposed:Reliable (coherence > 0.5), Usable with Caution (0.3-0.5), and Not Reliable (< 0.3). This frameworktranslates coherence variability into practical decision-support guidance for Arctic monitoring applications.Results indicate pronounced seasonal variability. Mean summer coherence during early years (2019-2021) decreased to approximately 0.31 over snow and melt-affected surfaces, while winter coherencein later years increased to values approaching 0.56 over exposed and infrastructure-dominated areas.These findings confirm that freeze-thaw transitions represent a dominant limitation on phase stability,with winter acquisitions providing substantially more reliable interferometric performance. By quantitatively linking environmental forcing to C-band coherence stability, this study defines operational reliability thresholds for Sentinel-1 IW, HH-HV acquisitions in freeze-thaw dominated terrain and provides a transferable evaluation methodology for other high-latitude monitoring scenarios. Keywords: Sentinel-1, InSAR, coherence, Arctic monitoring, freeze-thaw, Svalbard, TOPS, SNAP

Accurate and spatially comprehensive monitoring of seasonal snow depth (SD) is crucial for understanding and predicting hydrological processes, energy balance, and ecological dynamics in mountainous regions. In-situ measurements, while precise, are spatially limited and logistically challenging. Alternatively, LiDAR sensors can provide highly accurate measurements but are constrained by limited spatial and temporal coverage due to sparse sampling and low revisit frequencies. In this context, interferometric synthetic aperture radar (InSAR) systems offer a valuable alternative for assessing SD by providing high-resolution data over extended areas, independent of weather conditions or illumination. In this research, we address key associated challenges for deriving accurate SD from Digital Surface Models (DSMs) generated from Synthetic Aperture Radar Interferometry (InSAR), specifically focusing on mitigating errors introduced by radar wave penetration into the snowpack and ensuring precise DSM calibration. Current approaches rely on simplified assumptions regarding the compensation for the penetration depth, often averaging DSMs acquired under varying conditions, which introduces substantial uncertainty, particularly in complex alpine environments. Similarly, accurate DSM require a precise calibration due to the presence of residual offsets and tilts, which is expensive and impractical for large-area monitoring. This paper outlines a novel, end-to-end framework for robust SD estimation from InSAR data. The core strategy involves differencing precisely corrected DSMs, one representing snow-free conditions and the other the snow surface. A critical innovation lies in the correction through a three-stage methodology designed to tackle the limitations of existing techniques. First, we develop an automated and reference-free approach for precise mutual calibration of InSAR-derived DSMs utilizing Persistent Scatterer Candidates (PSC) from Sentinel-1 data to establish natural calibration tie-points. This method eliminates the dependency on costly and potentially unavailable GPS or LiDAR ground truth data, while also compensating for residual offsets and tilts. Exploiting Sentinel-1’s global repeat-pass acquisition capability in conjunction with high-resolution TanDEM-X data ensures scalability and quality. Second, we implement a data-driven, deep learning (DL)-based approach to accurately estimate and correct for the radar penetration bias in snow-covered areas. Preliminary results on the Greenland Ice Sheet achieve state-of-the-art performance (RMSE = 0.65m, R² = 0.90) for X-band data, demonstrating the potential of this approach. Finally, after compensating the DSMs for both the calibration offset and tilts and the radar penetration, these corrected DSMs are used to perform DSM differencing to retrieve SD. The estimation of the radar wave penetration into the snowpack can be adapted to multiple frequencies (from X- to C- and L-band) and potentially used in future InSAR missions such as Harmony and ROSE-L. For transferring the framework to C-band, we are considering the usage of historical STRM and Sentinel-1 data. For L-band, available airborne F-SAR data can be used, in view of extending the DL model with upcoming NISAR/ALOS-4 data. The proposed framework enables reliable, large- scale SD estimation from InSAR DSM differencing, improving the understanding of cryospheric processes and leveraging the capabilities of current and future InSAR missions.

The recently launched NASA–ISRO Synthetic Aperture Radar (NISAR) mission will provide global L-band and regional S-band time-series measurements of polarimetric radar backscatter and InSAR-derived ground displacements. While the mission focuses on the operational generation of Level-1 and Level-2 data products, these products must also satisfy Level-3 science requirements defined by the Solid Earth, Ecosystems, and Cryospheric Science communities. As part of the Solid Earth requirements, NISAR must enable semi-monthly measurements of surface deformation in permafrost-affected regions at 100 m spatial resolution during snow-free months. The mission is required to measure these displacements with an accuracy of 4(1 + L^1/2) mm or better, over length scales of 0.1 km < L < 50 km, across 80% of selected regions, and within any 90-day interval. To validate the NISAR permafrost displacement requirement, we implemented a two-pronged approach. In Approach 1, we compare InSAR-derived surface displacements derived from NISAR Level-2 products with field observations collected at four permafrost validation sites in Alaska. At each site, we acquire dense in-situ measurements of seasonal permafrost displacement using repeated leveling and kinematic phase-based GNSS surveys. Observations are collected at 150 locations per site—50 along each of three transects within a single 100 m NISAR product pixel. This spatial sampling density is necessary to evaluate the NISAR permafrost displacement requirement with the necessary accuracy. In Approach 2, we analyze NISAR InSAR data over regions with negligible ground displacement. Although stable terrain is uncommon in permafrost regions, we identified exposed bedrock sites on the Canadian Shield and the North Slope of Alaska for this purpose. Tropospheric and ionospheric correction layers are applied to raw phase time series, and spatial structure functions are computed to quantify InSAR noise as a function of length scale. These structure functions are compared to the scale-dependent NISAR permafrost displacement requirement to assess compliance at the 80th percentile. This paper will present the selected validation sites and summarize the field protocols and analytical methodologies implemented for both approaches. Initial results from the NISAR permafrost requirement assessment will be presented, including comparisons between in-situ measurements and InSAR-derived surface displacements, as well as structure-function analyses of phase noise, both using Sentinel-1 data as a proxy alongside early NISAR observations. The paper will also describe planned field activities for the upcoming thaw season and outline the timeline for completing the overall validation effort.

Spaceborne Synthetic Aperture RADAR data are a key asset in the generation of geoinformation products for ice sheets in Antarctica and Greenland. SAR interferometry in particular proves crucial to generate ice velocity and grounding line information. In Antarctica, observations started in the early 1990’s and early campaigns were regionally limited. While a backscatter map of the continent was achieved with a single satellite in 1997 (RADARSAT Antarctic Mapping Project - RAMP), full interferometric coverage would take another decade and a coordinated effort of three missions in support of the International Polar Year (IPY 2007-09). The launch of the Sentinel-1 constellation fundamentally changed data availability, because ESA committed to ongoing acquisitions in coastal Antarctica thus generating an archive more than a decade deep and going strong, with satellites 3 and 4 commissioned and a second generation of satellites in development. This coastal coverage continues to be augmented by acquisitions from other missions increasing area coverage, or providing acquisitions better suited for the analysis of fast glaciers. Together, the available missions form a virtual constellation that exceeds the information content of any single mission and provides a long term data record. Acquisition plan coordination, where available, allows targeted acquisitions to each mission strengths and responsible use of space assets. This virtual constellation has been serving the science community for many years now with shifting capabilities and new opportunities as missions undergo generational changes and new missions come online. In recent years, the commercial sector has started to contribute to the constellation and added the capability to collect fast (1-day) repeat InSAR data. Dense time series in fast changing environments opens the door for new research, the data are therefore particularly suited for fast glaciers. The launch of NISAR represents another leap in data availability, as this left looking science mission will cover most of the continent with ascending and descending acquisitions. Here we provide a summary of our group's long-standing effort to provide Earth System Data Records for Antarctica based on the virtual SAR constellation. We highlight contributions and strength of the various missions and show examples of multi-mission based products like our most recently published 33 year grounding line record. Our results show that 77% of the grounding line has remained stable for the observation period. Grounding line retreat is concentrated in some key regions where retreat ranges from 10 to more than 40km. Based on our findings, we make recommendations for future acquisitions of missions that do not already have ongoing acquisition plans in place. This work is funded by NASA.

In the framework of interferometric applications devoted to ground instabilities, artificial reflectors, both passive corner reflectors and active transponders, are usually designed and exploited to calibrate the interferometric measurements as well as to provide displacement measurements where natural coherent targets are missing within the area of interest. Passive devices have drawbacks related to their large size (specifically for long SAR wavelengths) and weight that make deployment problematic in difficult areas, as well as to possible loss of phase signal coherence due to geometric deformations and material degradation. Compact active transponders have been proposed as alternatives to corner reflectors, showing promising results but also some issues related to their relatively high cost and signal stability. The aim of this study is to assess the reliability and limits of corner reflectors and active transponders for supporting multi-temporal SAR interferometry (MTInSAR) in displacement measurements, with a specific focus on urban areas. An experimental calibration site consisting of two corner reflectors and three active transponders was established on the roof of the Physics Department of the Polytechnic University of Bari (PhyBA) (Southern Italy). The site is operated by Geophysical Applications Processing (GAP), in collaboration with two research institutions (CNR and PoliBA). The corner reflectors s are trihedral structures with internal edge lengths of 69.5 cm (CR0) and 1.05 m (CR1). The active reflectors include a C-band Electronic Corner Reflector (ECR-C) developed by MetaSensing and two Active Radar Calibrators, a C-band unit (ARC-C) and an X-band unit (ARC-X), both developed by the Remote Sensing Group of PhyBA. The ARC devices, designed in the early 1990s for NASA/JPL airborne campaigns and the SIR-C/X-SAR mission, use military-grade Gallium Arsenide Field-Effect Transistor amplifiers and horn antennas. In this setup, ECR-C and ARC-C are tuned for the ESA Sentinel-1 SAR mission, while ARC-X is used for the ASI COSMO-SkyMED constellation. ECR-C can switch electronically between ascending and descending passes, whereas ARC-C and ARC-X require mechanical reorientation. This calibration site provides a controlled environment to test passive and active reflectors under real urban conditions and by simulating displacements at different rates and directions. Data acquired up to 2026 from Sentinel-1 and COSMO-SkyMED along both ascending and descending passes were processed through the SPINUA MTInSAR algorithm to derive time series of both SAR amplitude and displacement values. These estimations were then analysed for assessing the quality of backscattering and interferometric phase with respect to reflector size, geometry, polarisation, and distance from other reflectors and structures. Results show that measured backscatter is consistent with theoretical expectations. Reflector size proved to be a key factor: CR0, being smaller, was noisier and more affected by nearby structures, while CR1 provided more stable responses. Active reflectors offered strong amplitude signals, though their phase stability was sometimes lower than expected. Performance was also influenced by interactions among artificial reflectors as well as with other structures present on the building roof. Specific experiments were designed and performed to investigate the possibility of monitoring subpixel differential displacements by exploiting both the polarisation diversity and the time diversity induced by a delay line. Since January 2024, ARC-C has been operated in VH polarisation, while CR0, which responds in VV polarisation, has been positioned at about 1 m from ARC-C and occasionally moved upward up to 1 cm. By processing both VH and VV Sentinel-1 datasets, it was possible to identify the two reflectors and correctly measure the line-of-sight component of the induced CR0 displacements. Similar results were obtained by setting ARC-C in VV polarisation and by delaying its response through a 30 m long coaxial cable calibrated for C-band. The delayed ARC-C signals were imaged a few pixels away from the CR0 in the VV Sentinel-1 data, thus allowing us to analyse separately the two targets and thus distinguish their behaviour in time. These configurations allowed us to test the measurement of the differential displacement between two point-like targets located within the same Sentinel-1 resolution cell, opening opportunities for monitoring closely spaced scatterers and for exploiting the VH polarisation, which has so far been underused in MTInSAR applications. Moreover, since in urban settings the precise geocoding of the structures becomes pivotal, we assessed the performance of geocoding of Sentinel-1 data in relation to the increase of the Sentinel-1A orbital tube from mid-April 2024, which improved the precision of target elevation estimation through MTInSAR. Further work focused on innovative reflector geometries and low-cost transponders. Specifically, a hexagon-periodic corner reflector with a side length of 46 cm was designed to achieve a more compact and less visually intrusive structure, suitable for architecturally sensitive environments. A prototype was built and tested after 3D electromagnetic simulations. The reflector showed good performance at C-band, but it exhibited a highly directive response. Despite this drawback, the design remains promising for applications where aesthetics is a primary concern. In addition, a prototypal low-cost transponder was designed and built up by PoliBA. Experimental tests have so far been performed using both Sentinel-1 C-band data and COSMO-SkyMED X-band data. There is, however, a growing interest in extending these investigations to the L-band, where transponders could provide a major advantage compared to passive corner reflectors in terms of physical size. Moreover, L-band is expected to ease the preservation of transponder phase stability due to the lower frequency with respect to C/X bands. In the future, the proposed calibration site may become an open experimental facility, providing data available for testing and validating interferometric techniques. Acknowledgment This work was supported in part by ESA under the project “Quantum computing for ground motion measurement.”, ESA/RFQ/3-17708/22/I-DT-lr. The authors also wish to acknowledge all researchers and professionals involved in research activities related to the Experimental SAR Calibration Site of Bari (Italy) under self-financed projects.

Europe’s bridge stock is aging, and many structures are approaching or exceeding their design life, which increases the need for continuous, objective evidence to support safety assurance, maintenance prioritization, and life-extension strategies. Periodic visual inspections alone can miss early-stage deterioration and are difficult to scale at the frequency required for risk-informed management. At the same time, Structural Health Monitoring solutions based on a single technology often provide only a partial view of structural behaviour; they can capture local changes but may fail to contextualize long-term trends or external drivers acting at the scale of the surrounding environment. ISABHEL (acronym of Integrated SAtellite and ground-based monitoring for Bridge HEalth Lifetime assessment) addresses this gap by demonstrating an integrated monitoring service that combines satellite A-DInSAR, contact sensors, Photomonitoring™, and Finite Element Modelling within a single decision-oriented workflow and a unified user-facing platform. The project is co-funded by ESA and implemented with the Municipality of Turin as pilot user. The service is deployed on two river bridges in Turin, Italy, namely the Amedeo VIII bridge and the Regina Margherita bridge. These case studies were selected because they represent different structural typologies and monitoring needs while sharing exposure to hydraulic hazards such as potential scouring. The Regina Margherita bridge also provides an opportunity to capture wider-area ground processes in the surrounding environment, including possible slope-related phenomena that can influence structural performance. The ISABHEL concept is built on complementarity. Satellite A-DInSAR techniques provide millimetric-scale deformation information over long time spans and wide areas. This supports historical screening and trend interpretation and can reveal subtle anomalies that are hard to detect with on-site instrumentation alone. In ISABHEL, high-resolution COSMO-SkyMed data are processed with a Persistent Scatterer Interferometry (PSI) approach; the availability of archived imagery since 2009 enables retrospective analysis and the reconstruction of long-term displacement histories. For the Turin demonstration, hundreds of SAR images in both ascending and descending geometries are exploited over the 2011–2024 period. Results are referenced to stable points and quality-controlled using standard deviation and temporal coherence indicators, then calibrated and validated against a GNSS station in the municipality area. Beyond standard line-of-sight deformation products, ISABHEL applies vector decomposition using both geometries to derive vertical and east–west components through Synthetic Measurement Points. The processing and exploration of results are supported by a dedicated toolchain integrated in QGIS, enabling infrastructure-oriented queries, time-series inspection, and the extraction of deformation indicators around specific elements. Ground-based monitoring complements EO by delivering high-frequency, high-accuracy measurements of structural response and enabling near-real-time tracking of key parameters. The monitoring strategy is differentiated across the two bridges. The Amedeo VIII bridge is primarily monitored statically, targeting rotations and displacements that can indicate progressive settlement or changes at key structural interfaces. The Regina Margherita bridge combines static and dynamic monitoring to detect potential scour-related effects and to assess deck behaviour and load-bearing capacity through vibration-based indicators. The sensor suite includes biaxial inclinometers, triaxial accelerometers, strain gauges, displacement transducers, and temperature probes. A resilient communications architecture supports operational continuity, using 5G for high-speed transmission and satellite communication as a backup channel. The test results confirmed reliable data acquisition and transmission, with sampling at 200 Hz for accelerometers and 1 Hz for static sensors, and stable data integrity and continuity. Automated processing routines transform raw measurements into standardized outputs and compute basic statistics on an hourly basis. Dynamic data are further processed through Automated Operational Modal Analysis to estimate modal parameters such as natural frequencies, damping ratios, and mode shapes, producing hourly results suitable for long-term trend tracking. Photomonitoring™ adds a non-contact layer based on image sequences acquired by site-installed cameras; it supports both displacement analysis and change detection, enabling the identification of surface modifications and localized anomalies that may be relevant for inspection planning. The service includes controlled image synchronization and a processing chain implemented through dedicated software. Image transfer can be completed in less than one minute, and processing times were verified at less than one minute per image pair in the tested configuration, with sub-pixel registration accuracy and quantitative acceptance criteria for change-detection overlap against reference polygons. A central differentiator of ISABHEL is the Digital Twin layer based on Finite Element Models developed for each bridge. The modelling strategy progresses from simplified representations to more detailed formulations as needed to capture deck behaviour. The models are informed by technical documentation, historical A-DInSAR trends, which in some cases enable the observation of the seasonal thermal behaviour of the structures, and on-site inspections and are calibrated using load-test data and progressively refined as monitoring data streams become available. This physics-based layer supports interpretation of measured responses and reduces false alarms by accounting for expected structural behaviour under operational loads and environmental forcing. It also enables the definition of model-driven thresholds tied to structural mechanisms and limit states rather than relying only on statistical deviations. ISABHEL implements a multi-level thresholding framework: an initial level reflects operational conditions derived from preliminary model evaluations; then, a second level captures statistically significant deviations from normal behaviour using baselines built during the first monitoring period. Finally, a third level is linked to ultimate limit states derived from numerical simulations and represents conditions approaching failure. Temperature is treated as a key explanatory variable across the workflow because thermal effects can dominate both long-term and cyclic responses in many bridge typologies. Separating temperature-driven variability from anomalous behaviour is essential for robust alerting and for reliable interpretation of both EO and in-situ measurements. All information streams converge in a web-based platform designed for operational use by different stakeholder profiles. The platform aggregates satellite deformation products, Photomonitoring™ outputs, contact-sensor time series, and Digital Twin results. It provides a dashboard and geospatial views and issues alerts when thresholds are exceeded. The performed tests validated end-to-end functionality across modules, including SAR processing and geohazard layer integration, sensor acquisition and transmission with SatCom backup, Photomonitoring™ displacement and change detection, and FEM model generation and calibration activities. Early EO results already illustrate the value of integration. For the Regina Margherita bridge, COSMO-SkyMed analysis highlights localized deformation at midspan and indicates asymmetry between carriageways that can guide targeted inspections, sensor placement, and modelling priorities; for the Amedeo VIII bridge, deformation appears within stable ranges with time series dominated by seasonal cycles around a near-zero average trend, supporting a differentiated interpretation of behaviour and a tailored threshold definition. Overall, ISABHEL demonstrates a practical pathway from satellite-to-sensor monitoring to decision-ready bridge lifetime assessment. The integration of wide-area EO evidence, local high-frequency instrumentation, image-based diagnostics, and model-driven interpretation supports early detection of change and a more reliable classification of what constitutes normal behaviour. The Turin pilots provide a replicable blueprint for scaling the service to additional bridges and networks, enabling more proactive maintenance, improved intervention prioritization, and enhanced resilience of critical transport assets, with planning for critical transport corridors.

Differential land subsidence affects many world metropolises, impacting their public and private infrastructure, including housing, transport and utility networks, social, healthcare and education facilities and, in turn, causing socio-economic impacts. This work showcases an innovative workflow based on geospatial data for exposure-vulnerability rating, hazard quantification and risk assessment. The methodology integrates Interferometric Synthetic Aperture Radar (InSAR)-derived information on ground displacement from Copernicus European Ground Motion Service (EGMS), with land cover and settlement characteristics from freely and openly available global datasets including the Copernicus Global Human Settlement Layer (GHSL) and DLR’s World Settlement Footprint (WSF). Such an integrated approach represents a significant step forward from InSAR displacement velocity-based approaches that are nowadays common in the specialist literature, to actionable risk information that are still rare. Land subsidence-induced deformation and structural stress on urban assets are quantified within the 15 metropolitan cities of Italy, along with the distribution and amount of residential/non-residential infrastructure and population exposed. Deformation-induced risk is assessed via the implementation of a tailored risk matrix enabling the geospatial intersection of four hazard (H1 to H4) and four exposure-vulnerability (EV1 to EV4) classes into 16 combinations of likelihood and impact (or also, probability and severity), and the consequent classification of risk in three levels (R1 to R3). The analysis shows that a total of 1.44 out of 2665 km2 urbanised land within the 15 cities is at high risk (R3) due to significant angular distortions (and, sometimes, additive threat from horizontal strain) affecting very high exposure-vulnerability infrastructure. Moreover, it is estimated that, for more than 2700 buildings within the 15 cities, there is high likelihood of already occurred/incipient structural damage. The reference knowledge-base on present-day subsidence-induced risk can inform land and risk management at national scale, and provides a baseline for future assessments to build upon with a look to the next decades and sustainable urban development. This work is funded by the European Union – Next Generation EU, component M4C2; project SubRISK+ (https://www.subrisk.eu/), 2023–2026 (CUP B53D23033400001). Value-added risk mapping outputs and statistics are openly available via the SubRISK+ ‘Control Room’ web platform (https://controlroom.subrisk.eu/). Full details about the workflow and results are available in the full paper: Cigna, F., Paranunzio, R., Bonì, R., Teatini P. 2025. Present-day land subsidence risk in the metropolitan cities of Italy. Scientific Reports, 15, 34999 (https://doi.org/10.1038/s41598-025-18941-8).

Keywords (7): InSAR, TerraSAR-X, Bridges, Viaducts, Infrastructure, Monitoring, Segmentation Introduction: Monitoring Needs within the Dutch National Infrastructure Monitoring the stability of critical infrastructure is essential for ensuring safety and maintaining asset performance within the portfolio of the Ministry of Infrastructure and Watermanagement managed by Rijkswaterstaat. Responsible for a safe, sustainable and liveable environment, Rijkswaterstaat maintains thousands of kilometres of road, waterways and dikes, and hundreds of civil engineering structures on a national scale. As many of these assets were built after 1945, they are approaching the end of their designed service life. To facilitate renovation and replacement on a national scale, Rijkswaterstaat must be able to prioritise assets based on objective indicators of structural condition. Reliable deformation information is therefore essential for identifying structures that require attention urgently. Traditional geodetic measurement techniques such as levelling, GNSS, and total station surveying provide highly accurate deformation information, but they require on-site access, traffic management measures, and extensive safety precautions. These operational constraints make large-scale and frequent monitoring costly and logistically challenging. With a growing number of civil structures that require monitoring, Rijkswaterstaat must adopt methods that can provide broader coverage without increasing operational burden. Therefore, InSAR is being implemented as a scalable technique for stability assessment. Its wide spatial coverage, frequent revisit rates, and remote, non-intrusive data acquisition make it well suited for monitoring large and dispersed assets, alleviating some of the challenges inherent to traditional methods. While InSAR-based monitoring has been implemented successfully for relatively uniform assets such as roads and dikes, dynamic civil structures such as bridges and viaducts introduce a fundamentally different set of challenges. Features including expansion joints, liftable bridge decks, tall support pillars and steel trusses create strong spatial variability in deformation behaviour and complicate radar imaging through shadow, layover and double-bounce effects. These structures also exhibit operational dynamics, such as bridge openings or variable loading, that alter their appearance in the radar signal. Tailoring InSAR-processing chains accordingly is therefore necessary to accommodate these structural and imaging complexities and ensure that deformation patterns are captured and interpreted in a physically meaningful way. Study Area: Botlek Bridge and Adjacent Viaducts in Rotterdam, The Netherlands To investigate these challenges in practice, this study analyses the Botlek Bridge and its adjacent viaducts in Rotterdam, The Netherlands, as a representative example of a complex civil structure. The system comprises two large liftable bridge decks that can be raised vertically to accommodate waterway traffic and that carry both road and rail transport. Its spans are supported by concrete pillars and segmented by expansion joints at multiple locations, leading to structural elements that respond differently to temperature, loading conditions and operational states. The presence of steel trusses, barriers, rail infrastructure and other load-bearing elements introduces a heterogeneous set of radar reflectors, while the liftable nature of the bridge decks periodically changes the observable geometry. Together, these characteristics make the Botlek bridge an ideal case study for evaluating how InSAR products behave on complex, dynamic structures and for identifying the adjustments required to make InSAR operationally useful for this object type. Methodology: From Radar Imagery to Deformation Timeseries For this analysis, high-resolution TerraSAR-X StripMap images spanning a period from January 2022 until July 2025 are used. Both an ascending and descending track are processed (level 2), resulting in Line-of-Sight (LOS) point deformation time series with an interval of 11 to 22 days. Combining information from each track allows us to decompose LOS deformation into horizontal and vertical displacements elementwise (level 3) under the assumption that all elements only move vertically and longitudinally in the horizontal plane. Element boundaries are derived from structural properties such as expansion joints to delineate sections that are expected to move homogenously. Finally, model-fitting is done on level 2 and level 3 data to visualise the spatial distribution of the deformation profile. Additional sources including a high-resolution DEM (0.5 m), sensor-derived bridge opening times, high-resolution aerial imagery and a digital object registration bank are used to enhance the InSAR-processing and form an initial structure-aware segmentation. For this project, SatSense was responsible for the InSAR-processing under commission of Rijkswaterstaat, keeping the technical challenges in mind. Fruitful discussions and cooperation between SatSense and Rijkswaterstaat resulted in the findings discussed next. Key Findings Thermal deformation dominates and varies strongly across structural boundaries The Botlek Bridge exhibits pronounced longitudinal thermal expansion and contraction, with horizontal amplitudes up to 15 mm. These signals vary sharply across expansion joints and viaduct segments, confirming that bridges show strong internal deformation gradients that require element‑wise analysis rather than treating the structure as a uniform object. Horizontal movement is opposed around expansion joints, highlighting that network selection and element segmentation must follow structural boundaries to ensure that the deformation behaviour of each component is represented accurately. Internal expansion of viaduct spans is observable when segment length allows subdivision Viaduct spans show internal thermal expansion when their length permits subdivision into multiple structural elements. Opposing horizontal motion at the boundaries of these elements reveals span-specific thermal behaviour that would be obscured if the viaduct were analysed as a single unit. This demonstrates that detectable internal deformation is contingent on both segment length and appropriate element delineation, illustrating the value of structure-aware segmentation for capturing localised responses. Structural complexity creates characteristic radar artefacts that influence observable deformation Tall pillars, steel trusses, barriers and overhanging components generate radar shadow, layover, and double‑bounce reflections, leading to uneven spatial coverage, occasional mislocated scatterers and mixing of deformation signals. These artefacts are not noise but predictable consequences of structure‑specific geometry in relation to the satellite viewing angle, reinforcing the need for object‑aware interpretation and scatterer filtering. Radar shadow and layover maps can be used to partially assess these effects a priori, helping set expectations for the achievable spatial coverage. Operational behaviour influences the temporal coherence of InSAR observations When bridge openings coincide with SAR acquisitions, the lifted decks change their radar appearance, breaking temporal consistency in the deformation signal. For the Botlek Bridge, 8 out of 112 descending‑track acquisitions and 10 out of 59 ascending‑track acquisitions were removed after visual inspection confirmed that deck geometry was altered during acquisition. Accounting for such operational events is critical in workflows for dynamic structures to avoid introducing artefacts into the displacement time series, especially since the bridge sensor data did not always reliably coincide with the opening events visible in the radar imagery. Discussion and Conclusions The results highlight that InSAR can capture the complex deformation behaviour that aligns well with the expected behaviour. Both internal expansion and opposed deformation around expansion joints are captured as a result of structure-informed segmentation. Additionally, the detection of subtle long‑term trends, such as minor subsidence and horizontal divergence near expansion joints, confirms that multi‑year InSAR time series can reveal gradual, persistent changes that may be difficult to observe with traditional measurements alone. Overall, this study shows that while InSAR provides valuable insights into the deformation behaviour of bridges and viaducts, its effective use depends on incorporating structural knowledge, accounting for radar‑specific artefacts and applying object‑appropriate interpretation guidelines. The insights gained from the Botlek bridge contribute to the development of object‑type‑specific InSAR methodologies for Rijkswaterstaat. The lessons-learned serve as a basis for the upcoming European tender to develop object-type-specific InSAR methodologies, supporting more efficient monitoring of Rijkswaterstaat’s nationwide infrastructure portfolio.

Satellite-derived surface motion measurements have become routinely available through Interferometric Synthetic Aperture Radar (InSAR) techniques applied to data from multiple SAR missions. Even the complexity traditionally associated with InSAR processing has been significantly reduced through the emergence of platform-based solutions, which promise to minimise data-handling requirements while enabling straightforward execution of advanced processing chains for the generation of displacement measurements. However, the transition from surface motion to meaningful estimates for engineers and, ultimately, to actionable information that allows the characterization of structural stress, remains a critical challenge. The concept of differential settlement causing deformation affecting urban infrastructures, expressed through the calculation of angular distortion, provides an important step in this direction. In existing approaches, higher-level products of angular distortion are typically derived solely from surface motion measurements through gridding and the calculation of local gradients. While effective at lower spatial scales, these methods do not explicitly incorporate the geometry and orientation of the infrastructure undergoing deformation. The explicit inclusion of the infrastructure itself allows for more customized calculations and improved local estimates, considering the actual direction along which tilt or differential motion occurs. In this study, Copernicus Sentinel-1 data over the metropolitan area of Rome, Italy, for the period 01/2022 to 12/2024 (approx. 3 years) are processed using both ascending and descending tracks through the SNAPPING PSI service on the Geohazards Exploitation Platform (GEP). Line-of-Sight (LoS) measurements are then combined to retrieve vertical motion time series, subsequently used as input for differential settlement calculations. A parallel assessment is performed to investigate the effect of gridding during the 3D decomposition stage on the resulting settlement estimates. The proposed methodology introduces an infrastructure-aware framework for angular distortion estimation. Enhanced triangulation of Persistent Scatterer (PS) is considered to link neighboring observations, followed by linear interpolation of displacements over structural elements. OpenStreetMap (OSM) building footprints and road network data are thus integrated in the processing chain to guide spatial analysis. Angular distortion is subsequently calculated in a manner tailored to the geometry and orientation of these structures, including not only the average motion rates, but the entire displacement time series. This infrastructure-aware geospatial analysis allows for more robust and physically meaningful estimates compared to conventional grid-based approaches. An intercomparison between the proposed methodology and existing approaches for deriving angular distortion from spaceborne point-like InSAR measurements demonstrates its capability to highlight localized differential motion patterns while also capturing the directionality of the deformation effect. The results demonstrate that angular distortion derived from spaceborne surface motion measurements can provide meaningful indicators of structural stress when calculated in an infrastructure-constrained framework. Such metrics can support the identification of zones where differential settlement may pose a risk to buildings and transport networks, thereby contributing towards large-scale, remotely derived assessments of structural stability in urban environments.

Preliminary evidence indicates the presence of significant slope instabilities within multiple fjord systems across western and eastern Greenland. In some cases, these instabilities could generate tsunamis if large-scale failures were to occur. To identify potentially hazardous sites and to support the development of an early warning framework, our study evaluates the feasibility of monitoring such mass movements using satellite Differential Interferometric Synthetic Aperture Radar (DInSAR). Due to the limited understanding of the deformation processes operating at the target sites—including their driving mechanisms, spatial extent, and temporal characteristics—a multi-stage analytical approach is necessary. This progressive strategy allows us to first establish a regional overview before focusing on site-specific deformation patterns requiring more sophisticated techniques. Our primary aims are therefore to characterize the current activity levels of the slope instabilities and reconstruct the temporal evolution of slope displacements from 2015 onward. Greenland presents significant challenges for DInSAR applications. While 80-85% of the island is covered by a permanent ice sheet, the ice-free coastal zones experience extensive winter snow cover lasting several months. The terrain itself is predominantly mountainous and rocky, characterized by a narrow, rugged coastline incised by deep fjords and flanked by towering mountains and tidewater glaciers. As a consequence, there are considerable areas not visible to the satellites or with poor line-of-sight sensitivity. Additionally, the ionospheric effects are more severe in polar regions compared to mid-latitudes. These combined factors make Greenland a particularly challenging environment for DInSAR analysis. As an initial step toward detecting and potentially quantifying very slow slope movements, we therefore applied a Sentinel-1 Persistent Scatterer Interferometry (PSI) analysis to three small test areas - each several hundred square kilometers (e.g., ~20km x 20km) - in the Uummannaq and Nuuk Fjord areas. Our preliminary analyses demonstrate that the temporal evolution of movements in these areas between 2015 and 2025 can be reliably captured with Sentinel-1 PSI. We identified a well-defined snow-free period extending from approximately May/June through October/November, characterized by reduced out-of-season snow cover precipitation and consistent height coverage throughout. Therefore, a simple coherence threshold allows for the selection of snow-free acquisitions. Tropospheric effects are primarily limited to a height-dependent component, which can be estimated and removed using a phase-to-height relationship, while turbulence is notably restricted due to the dry, cold air. Ionospheric effects are generally small for Sentinel-1 data on a local scale and can be effectively removed using large-scale filters. Sentinel-1 acquisitions are available at regular intervals of 6 days (2017-2021 and 2025-onward) and 12 days (2015-2016 and 2022-2024). These favorable conditions enable robust processing and allowed us to extend our analysis to larger areas on the order of several tens of thousands of square kilometers in the Uummannaq and Nuuk fjord systems in western Greenland and the Tasiilaq Fjord in eastern Greenland using both ascending and descending data. In this contribution, we will first discuss the main processing challenges and steps. We then present selected results from our study of slope instabilities. The large-scale overview of ongoing movements helped to prioritise relevant locations for detailed analyses and field visits. In the next phase of our work, we will conduct more detailed investigations to refine our understanding of the observed deformation patterns. Specifically, fast motions on the order of several cm/year—which cannot be detected with persistent scatterer interferometry—will be analyzed using multi-temporal, multi-looked interferograms. Future work also aims to progressively expand the analyzed area to additional ice-free regions of Greenland, currently encompassing approximately 338,000 to 410,000 km², or about 15-20% of the island's total area.

Land subsidence along the shores of the Dead Sea occurs in two primary modes: above dissolution cavities in association with sinkholes, and a continuous belt that follows the retreating shoreline. While the sinkhole mechanism is well established and forms the basis of the Geological Survey of Israel’s early-warning system, the origin, spatial extent, and temporal behavior of shoreline subsidence remain insufficiently understood. Clarifying its governing processes is essential for hazard assessment and infrastructure planning along the rapidly retreating shoreline. Since 2018, systematic TerraSAR-X acquisitions have enabled the generation of 11-day repeat InSAR time series along the entire western shoreline of the northern Dead Sea basin. This dense spatial and temporal sampling allows, for the first time, separation between sinkhole-related deformation and shoreline-controlled subsidence and enables characterization of both seasonal and multi-year responses to water-level variations. We analyzed 30 shore-perpendicular deformation profiles along the ~50-km shoreline of the northern DS basin for the period January 2019 to December 2024. A narrow subsidence zone, 100-400 m wide, is consistently observed landward of the shoreline. Line-of-sight displacement is maximal adjacent to the waterline and decreases westward. Subsidence rates reach up to 3 cm/month during summer, coinciding with peak evaporation and rapid lake-level decline, and decrease to 0-0.5 cm/month in winter when the water-level drop slows or temporarily reverses. The deformation time series correlates closely with Dead Sea level variations, with a delay shorter than 10 days, within the temporal resolution of both SAR and level measurements. A flash flood in December 2025 produced a rapid rise followed by a drop in water level in a semi-detached southern basin and triggered measurable shoreline subsidence within a single 11-day acquisition cycle, indicating an almost instantaneous mechanical response to hydraulic forcing. Concurrently, the location of peak deformation migrates eastward together with shoreline retreat. Points initially located at the shoreline continue to subside for several years after exposure, following a quasi-exponential decay with a characteristic time scale of a few years. Previous studies based on C-band InSAR and GNSS measurements attributed shoreline subsidence to regional aquifer-system consolidation driven by groundwater-level decline and increased effective stress. However, our observations challenge this interpretation: (1) the deformation is confined to a narrow shore-parallel belt rather than a broad zone of groundwater decline, and (2) seasonal subsidence amplitudes show no systematic dependence on shallow lithology derived from nearby boreholes. We therefore propose a two-scale consolidation mechanism. At the shoreline, newly exposed sediments undergo rapid subaerial dewatering and consolidation, producing deformation that tracks short-term water-level fluctuations with minimal delay. Simultaneously, increased effective stress propagates downward through the sediment column over longer timescales, generating multi-year post-exposure subsidence decay that may depend on deeper lithological properties. This combined hydromechanical adjustment explains both the immediate response to seasonal and event-scale water-level changes and the prolonged subsidence observed after shoreline retreat. Our results provide a physical framework for interpreting InSAR observations along retreating shorelines and improve hazard assessment in rapidly declining terminal lakes worldwide.

Coastal regions across Java- particularly low‑elevation coastal zones (LECZs)- are experiencing rapid land subsidence driven by groundwater extraction, urbanisation, and underlying geological conditions, with rates in many locations far exceeding global sea-level rise. This produces a compounding multi‑hazard in which climate‑driven sea‑level rise (SLR) and vertical land motion (VLM) interact to accelerate relative sea‑level change (SLC). Java, home to more than half of Indonesia’s population and undergoing rapid demographic and economic expansion, is already highly exposed to coastal flooding. Yet most global and national assessments rely primarily on climate‑driven SLR projections, often neglecting subsidence and therefore underestimating local risk. Understanding the spatial variability of VLM and its drivers is essential for effective coastal planning, hazard mitigation, and long‑term adaptation. To address this gap and provide a more realistic estimate of population exposure, we integrate InSAR‑derived ground deformation with physical datasets describing land cover, geology, and elevation, alongside socioeconomic information on buildings and population density, and projections of climate‑driven sea‑level rise. We generate a new island‑wide, high-resolution map of ground deformation for Java using Sentinel‑1 InSAR data from nine satellite tracks (2016-2023). Interferograms were processed using the LiCSAR (Lazecky et al., 2020) and LiCSBAS (Morishita et al., 2020) SBAS workflow, with atmospheric corrections from GACOS (Yu et al., 2018) and coherence‑based multilooking to ~500 m resolution. To ensure consistency across tracks, we referenced all line-of-sight (LOS) velocities to GNSS stations maintained by Indonesia’s Geospatial Information Agency, following a plane‑fitting approach to align InSAR and GNSS frames. By combining ascending and descending geometries and constraining the north component with GNSS, we decomposed LOS velocities into vertical and east-west components. All ancillary datasets- including land cover, geology, elevation, and population projections- were resampled to the InSAR grid to enable integrated analysis across Java’s coastal zone. We define the low elevation coastal zone in this study as areas ≤50 m elevation. To assess future exposure, we combined present‑day subsidence rates with high‑emission SLR projections (RCP8.5; ~0.73 m SLR by 2100) and 1 km population projections from Shared Socioeconomic Pathways (Wang et al. 2022). We assume current subsidence patterns persist spatially and temporally, providing a reasonable worst‑case scenario for long‑term coastal risk. Our results reveal widespread and substantial subsidence across Java’s northern coastal plain where unconsolidated alluvium and clay‑rich deposits dominate, and in many locations, subsidence rates are up to ten times higher than SLR, underscoring the dominance of local geological and anthropogenic processes in shaping coastal risk. Specifically, 7 million people and 10% of urban areas are subsiding faster than 14 mm/yr, and 5% exceed 28 mm/yr, and several cities exhibit extreme rates- Pekalongan and Demak show median subsidence of 87 mm/yr and 43 mm/yr, respectively. When combined with projected SLR, subsidence dramatically increases future exposure- we estimate that by 2100 and under the worst-case socioeconomic pathway, if we do not consider VLM, half a million people will be below sea level. However, when considering the relative sea level change due to SLR and VLM, this number increases by six times, to over 3 million people. This study demonstrates that failing to incorporate VLM leads to systematic underestimation of populations and infrastructure at risk from rising seas. Our integrated geodetic-geological-socioeconomic framework highlights the urgent need for locally tailored adaptation strategies, including groundwater regulation, sustainable urban development, and targeted coastal protection. As coastal megacities continue to grow, multi‑hazard assessments that capture the interaction between climate‑driven and geological processes will be essential for safeguarding livelihoods, infrastructure, and cultural heritage across Indonesia and other subsiding LECZs worldwide.

Monitoring surface motion in agricultural landscapes remains crucial for understanding subsidence and groundwater-related processes that directly affect the persistence of these regions. Recent studies on peat pastures demonstrate that integrating long-term C-band InSAR data from Sentinel-1 with contextual information and knowledge of surface motion characteristics enables the modeling, parameterization, and estimation of displacements, as well as the estimation of integer phase ambiguities. Within this framework, our main objective is to estimate InSAR-derived displacement parameters using the coherent periods of InSAR time series to characterize ground surface motion, including both seasonal uplift and subsidence. In this study, we upscale these estimates across croplands, where land cover changes, particularly during the vegetation growth cycle, shorten the duration of coherent InSAR data and lead to discontinuous time series. We investigate the potential of integrating multi-frequency InSAR data, combining C-band (Sentinel-1) and L-band (SAOCOM), to improve coherence persistence and observation stability in agricultural regions. We use SAR data acquired from August 2022 to December 2025 over an agricultural area in eastern Groningen, the Netherlands. We select two test sites, each containing at least five parcels with various land cover types and a corner reflector on an integrated geodetic reference station (IGRS) serving as the local reference point. By analyzing multi-temporal InSAR data across selected agricultural and peatland parcels, we evaluate temporal coherence statistics as both a quality metric and a physical descriptor of land surface dynamics. Preliminary findings indicate that L-band data maintain substantially sufficient coherence over vegetated parcels, yielding longer coherent periods. In permanent grasslands, the C-band provides a coherent InSAR phase time series for 49% of the observation period. In contrast, L-band provides 97%, improving continuous temporal coverage in the time series and reducing the unknown 'loss-of-lock' offset between coherent periods. Similarly, within cropped parcels, we can use 90% of the L-band and 37% of the C-band time series data. Note that, along a single orbit track, Sentinel-1 acquires images 2.5 times more frequently than SAOCOM. When combined, this multi-frequency framework increases the temporal density of reliable measurements, potentially enabling the upscaling of ground motion parameters across more heterogeneous vegetated regions. Using observations from coherent periods in both C-band and L-band, we will estimate InSAR-based displacement parameters to describe surface motion in relation to precipitation and evaporation recorded at a nearby meteorological station. This parameterization is preferred over estimating the absolute phase because (i) it minimizes errors introduced by the unknown offset between coherent segments, and (ii) it allows the estimated parameters to model surface displacement during incoherent periods and outside acquisition times, assuming that soil components remain unchanged. The results highlight the capability of multi-frequency InSAR to overcome decorrelation limitations in vegetated landscapes and to advance regional subsidence mapping strategies. The integration of ongoing C-band Sentinel-1 data with L-band SAOCOM and NISAR will create new opportunities for characterizing surface motion processes in agricultural and peatland environments.

In this study, a modified version of the approach proposed in [1] was applied to measure the spatio-temporal ground deformation pattern of the Store Vildmose area, in northern Jutland, Denmark. This area, once the largest raised bog in Denmark, now includes grasslands, cultivated fields, a peat extraction area, as well as remains of the old raised bog. Several restoration projects have been carried out and are ongoing on this site, lead by the Danish Nature Agency, as part of a national initiative to reduce greenhouse gas emissions. Sentinel-1 Persistent Scatterer InSAR was applied to 5 corner reflectors deployed by Geopartner Inspections in December 2021, revealing seasonal deformation patterns, with amplitudes up to several centimeters. Furthermore, a Distributed Scatterer (DS) multitemporal DInSAR approach was carried out each year within the most coherent season, spanning autumn through winter, revealing a significant spatial variability of the ground deformation, which correlates very strongly with in situ peat thickness measurements.    In order to characterize, both the spatial and the temporal variability of the deformation, the approach proposed in [1] was applied, exploiting the slightly different periods in which neighbouring fields retain a sufficient level of InSAR coherence. Interferograms with a short temporal baseline were formed within each coherent time-window of a specified minimum duration, and disconnected deformation time-series were generated after 2D phase unwrapping and a weighted least-squares inversion. To increase the coverage of the measurements, the coherent time-window, and thus the interferogram network, were computed on a per-pixel basis. Precipitation and evapotranspiration data from the Danish Meterological Institute were then used as an input to the SPAMS model [2], which was in turn used to connect the disjoint time-series, using the approach proposed in [1]. The results were validated against the corner reflector PSInSAR timeseries, and demonstrate that DSInSAR, applied to temporally disconnected coherent windows, can provide valuable information on the spatial variability of the ground deformations, for areas which do not feature any PSs, and are thus not covered by the EGMS or by national PSInSAR-based deformation products. Recovery of the full deformation time-series for these areas, using a model-based approach, requires prior knowledge of the land cover, and a complementarity between the coherent time-windows of different portions of the area of interest. References: [1] P. Conroy, S. A. N. van Diepen, F. J. van Leijen and R. F. Hanssen, "Bridging Loss-of-Lock in InSAR Time Series of Distributed Scatterers," in IEEE Transactins on Geoscience and Remote Sensing, vol. 61, pp. 1-11, 2023, Art no. 5220911.    [2] P. Conroy, S.A.N. van Diepen, R.F. Hanssen, ”SPAMS: A new empirical model for soft soil surface displacement based on meteorological input data,” Vol. 440, 116699, 2023.

Synthetic Aperture Radar Interferometry (InSAR) has become a cornerstone technique for the precise measurement of Earth’s surface deformation, enabling millimetric monitoring of phenomena such as urban subsidence, volcanic activity, and landslides. By exploiting the phase differences between radar acquisitions performed at different times, InSAR provides highly sensitive deformation measurements over wide areas and under nearly all weather conditions. The advent of systematic satellite missions such as Sentinel-1 has dramatically increased data availability, with free and frequent acquisitions every 6 to 12 days, leading to the generation of dense and high-dimensional SAR time series. While this abundance of data offers unprecedented opportunities for detailed temporal analysis of ground deformation, it also raises major challenges in terms of computational cost, memory requirements, and processing flexibility. Traditional Multi-Temporal InSAR (MT-InSAR) approaches rely predominantly on so-called offline processing strategies, in which all SAR images in a time series are processed jointly. Although these methods are known for their statistical robustness and estimation accuracy, they suffer from several limitations. First, they exhibit high algorithmic complexity, often scaling cubically with the number of acquisitions. Second, they require significant memory resources to store and manipulate large covariance matrices. Third, they lack flexibility when new acquisitions become available, since integrating additional images typically requires reprocessing the entire time series. These constraints limit their applicability in operational contexts where near-real-time monitoring of continuously evolving deformation phenomena is required. This work addresses these limitations by introducing a coherent methodological framework for the sequential estimation of interferometric phases in MT-InSAR. The central objective is to design algorithms capable of progressively integrating new SAR acquisitions while preserving the robustness and accuracy of state-of-the-art offline methods, and simultaneously reducing computational complexity and storage requirements. The proposed framework relies on a rigorous statistical modeling of SAR data, temporal correlations, and possible non-Gaussian behavior. In particular, the covariance matrix of the SAR time series is modeled in a factorized form that explicitly separates phase contributions from coherence structure, thereby enabling efficient parameter estimation strategies. Three complementary sequential approaches are proposed.- The first contribution, termed Sequential Maximum Likelihood Phase Linking (S-MLE-PL), extends classical Phase Linking methods into a sequential setting. Unlike conventional approaches that assume Gaussian-distributed SAR data, S-MLE-PL adopts a more general Scaled Gaussian model, which is better suited to heterogeneous environments and high-resolution imagery, especially in urban areas where heavy-tailed statistics are frequently observed. In this framework, when a new acquisition becomes available, the covariance matrix is updated in a structured block form, allowing the negative log-likelihood (NLL) function to be incrementally updated rather than recomputed from scratch. The joint estimation of the new phase, correlation parameters, variance terms, and scale factors is formulated as a constrained optimization problem. A Block Coordinate Descent (BCD) algorithm is derived, providing closed-form updates for each parameter block. This structured optimization significantly reduces computational complexity, from cubic dependence on the full time series length to quadratic dependence on the number of previously processed images, while maintaining estimation accuracy comparable to that of offline maximum likelihood methods. - The second contribution, Sequential Covariance Fitting Phase Linking (S-COFI-PL), shifts the perspective from likelihood maximization to covariance matrix fitting. Instead of directly optimizing a statistical likelihood, this approach seeks to minimize a dissimilarity measure between the empirical extended covariance matrix (including new acquisitions) and a theoretical covariance model parameterized by the interferometric phase vector. This formulation naturally accommodates the integration of multiple new acquisitions simultaneously, making it particularly suitable for block-wise updates. The optimization is performed on the complex torus defined by unit-modulus phase vectors, under geometric constraints ensuring physical consistency. Depending on the structure of the chosen matrix distance, such as the Kullback–Leibler divergence or the Frobenius norm, the optimization is carried out using Majorization–Minimization algorithm. An important strength of this method lies in its flexibility: it can incorporate robust covariance estimators, including phase-only or regularized covariance matrices, thereby enhancing resilience to non-Gaussian disturbances and model mismatches. - While the two previous methods still require storing historical acquisitions to update the covariance structure, the third contribution introduces a more memory-efficient strategy tailored to very long SAR time series. The proposed Sliding Interferometric Phase Linking (Sl-IPL) method relies on a sliding temporal window of fixed size, moving along the time axis with a specified overlap. Within each window, phase estimation is formulated as a covariance fitting problem under a circular complex Gaussian assumption. To ensure temporal consistency between consecutive windows, a penalization term enforces coherence between overlapping phase estimates. The optimization remains constrained to the unit-modulus torus, and the Frobenius norm is favored for its numerical stability and computational efficiency. By limiting the size of the processed covariance matrices, Sl-IPL achieves substantial reductions in both computational load and memory usage, while preserving performance close to full sequential methods. The proposed approaches were validated on both simulated datasets and real SAR time series processed through a complete multi-temporal InSAR chain, including co-registration, interferogram formation, topographic phase correction, phase unwrapping, and line-of-sight displacement estimation. Two contrasted case studies were considered: an urban area affected by significant land subsidence in Mexico City, and a volcanic environment in Hawaii Island. The results demonstrate that the sequential methods achieve mean squared errors and deformation time series estimates comparable to those obtained with reference offline approaches. Furthermore, comparisons with independent GPS measurements, when available, confirm the geophysical consistency of the estimated displacements. From a computational standpoint, the benefits of the proposed sequential framework are substantial. Processing times and memory consumption are significantly reduced, enabling incremental updates of deformation time series as soon as new SAR acquisitions become available. In summary, this work provides a comprehensive and coherent set of sequential methodologies for interferometric phase estimation in multi-temporal SAR analysis. By combining statistical robustness, optimization on complex manifolds, and efficient incremental updates, the proposed approaches address the growing challenges posed by dense and long SAR time series. They bridge the gap between high-accuracy offline processing and the practical requirements of scalable, near-real-time Earth deformation monitoring.

Synthetic aperture radar interferometry (InSAR) enables precise monitoring of surface and infrastructure stability. The increasing availability of InSAR data provides valuable opportunities for near real-time stability monitoring. To facilitate the systematic ingestion of newly acquired observations and the subsequent estimation of relevant parameters, Instantaneous State (IS) InSAR has been proposed as a novel framework for parameterizing the motion of InSAR scatterers. The method implements sequential estimation of the instantaneous kinematic state, e.g., position, velocity, and optionally acceleration, explicitly accounting for dynamic evolution between acquisitions by imposing smoothness constraints. To enable near real-time displacement anomaly detection, we propose a strategy that preforms synchronously with data acquisition and state estimation within the IS-InSAR framework. The method is implemented on individual arcs between pairs of scatterers, without requiring full InSAR network construction, thereby allowing anomalous behavior to be detected promptly. A Chi-squared test is employed to assess the temporal stability for each updated observation. At each epoch, predicted phase residuals are evaluated for multiple subsequent epochs, and observations from these epochs are incrementally integrated into the detection framework, thereby enhancing redundancy and reducing the likelihood of missed detections. To evaluate the risk associated with detected anomalies, we introduce the risk depth, defined as the number of the most recent observations used to assess the risk of the current epoch. The risk at each epoch is categorized into different levels based on multiple preceding observations, e.g., a high-risk level is assigned when continuous anomalies are detected. By integrating multiple preceding observations, the risk depth enables a more robust assessment of the risk of anomalies and reduces the likelihood of false alarms compared to relying on a single observation. The proposed approach is applied and evaluated using both Sentinel-1 and TerraSAR-X data. The results demonstrate that IS-InSAR effectively captures this dynamic behavior without relying on hindsight information. In addition, the reliability of anomaly detection is improved through the use of single- and multiple-update epochs, thereby reducing the likelihood of false alarms and missed detections. The method identifies anomalies associated with behavioral changes, including both deviations from the original motion pattern and transitions back to regular behavior, which is particularly important from a monitoring perspective. Furthermore, the method effectively detects anomalies simultaneously with the state estimation without re-evaluation of past observations, enabling near real-time monitoring.

Multi-track InSAR line-of-sight (LoS) displacements can be decomposed into full three-dimensional East–North–Up (ENU) motion given that the underlying deformation field is spatially smooth and exhibits coherent iso-displacement contours. Under these conditions, the strapdown decomposition method treats each Point Scatterer (PS) as an independent sample of the continuous displacement field. Thus, for every PS, a local Transversal–Longitudinal–Normal (TLN) coordinate system is defined, with the longitudinal axis aligned along iso-displacement contours (i.e., the zero-velocity direction), enabling a PS-specific ENU-to-TLN coordinate transformation. However, this requires the multiple InSAR LoS datasets to be referenced to a common datum. Each LoS velocity field is defined relative to a track-specific datum, typically realized by, e.g., the mean displacement or by a Point Scatterer with minimum normalized amplitude dispersion (NAD). Even millimetric offsets between these datums will propagate into the 3D reconstruction, biasing both the magnitude and direction (sign flip) of the estimated ENU components. To overcome this issue, we incorporate datum alignment directly into the strapdown-based 3D decomposition framework, specifically adapted here to bowl-shaped subsidence. We propose a modification to the original strapdown approach for subsidence regimes with smooth, radially inward deformation orthogonal to iso-deformation lines, with a sign convention that is positive toward bowl centers, consistent with gravity-driven motion. The method searches the TLN solution space to homogenize the overall displacement field. A bounded grid search aligns two Sentinel-1 LoS velocity datasets up to sub-millimeter per year precision and retrieves a physically consistent 3D displacement velocity field, including its full variance–covariance information. Applied to Jakarta—one of the fastest subsiding megacities worldwide—we identify six subsidence bowls with vertical velocities reaching −7.9 cm/yr and significant northward horizontal motion up to 1.7 cm/yr. Beyond these localized bowls, the entire metropolitan area experiences slow regional subsidence, with a mean vertical velocity of −11.2 mm/yr across Jakarta. Nevertheless, the strongest subsidence occurs in northern and western neighborhoods, where access to municipal piped water is limited. Relying solely on InSAR data, the modified strapdown decomposition exploits the spatial coherence of smooth iso-deformation contours and strong radial gradients of bowl-shaped deformation to jointly address two key limitations in InSAR-based displacement analysis: (i) reduced sensitivity to north–south motion and (ii) multi-track datum inconsistencies in the absence of GNSS constraints.

Multi‑temporal InSAR is widely used for infrastructure monitoring as it provides dense spatial sampling and regular temporal coverage. In many applications, empirical parameterizations or simple kinematic descriptions, such as combining a linear trend with a seasonal term, are sufficient to characterize the observed signals. However, in more complex settings the surface displacement field reflects multiple processes that are superposed in space and time and that depend on local geological and operational conditions. Reliable interpretation and prediction then requires a physics‑based model that causally links the measured displacements to some source mechanism. Such models usually depend on unknown local parameters, that often can not be robustly inferred from the either temporally or spatially sparse geodetic measurements that are available from levelling and GNSS. Long time series of InSAR data, however can provide the spatial and temporal density needed to identify and constrain these local parameters and to separate the signals of interest from confounding processes. The storage cavern site Epe (North Rhine-Westphalia, Germany) displays spatiotemporally complex surface displacements. These displacements are primarily caused by 114 gas‑ and liquid‑filled caverns in a salt rock layer of different sizes and depths that are operated by different provider companies with distinct filling schedules. As the pressure inside a cavern is kept lower than the surrounding lithostatic pressure, the cavern converges over time due to viscoelastic creep. This convergence causes the surface above to subside. The magnitude and shape of these displacements vary spatially and temporally with cavern geometry, depth, local geological conditions, and operational practices. Each cavern produces an individual spatiotemporal displacement pattern, and these patterns superpose at the surface. In addition to cavern‑related deep‑source displacements, Epe also exhibits a strong shallow surface response to groundwater‑level variations in some areas of the cavern field, resulting in isolation of the cavern induced signals to be challenging. We present an integrated monitoring and modeling approach for Epe that combines multi‑temporal InSAR, GNSS, and levelling to derive a geophysical source model that explicitly links cavern filling levels to convergence and surface displacements, while accounting for local geological conditions. We process ten years (2015–2025) of Sentinel‑1 acquisitions from four tracks in both ascending and descending orbits. Persistent and Distributed scatterers are used jointly to maximize spatial coverage in predominantly rural terrain. To separate the signals, we apply Independent Component Analysis to the InSAR time series to identify cavern‑induced displacements and to separate them from other correlated displacements, in particular those associated with groundwater dynamics. The source model describes cavern convergence by accounting for transient (primary) and steady‑state (secondary) viscoelastic creep of rock salt. We combine a Kelvin–Voigt representation (similar to Even et al., 2022) with a cavern‑pressure‑dependent Norton creep law (as in Ślizowski et al., 2010). We then propagate cavern volume loss to the surface using a multi‑cavern variant of the Sroka–Schober model (Sroka et al., 2017), which relates the volume loss of each cavern at depth to their associated surface displacements. Model parameters that represent local geological conditions and salt‑rock rheology are determined through a global optimization of rheological and site parameters under a multi‑dataset misfit, using the InSAR time series as the primary constraint and GNSS and levelling as supporting datasets. In our model, the lithostatic pressure is estimated from available depth and lithological information and is used to determine the stress state, defined as the pressure difference between the internal gas pressure and the estimated lithostatic pressure. Because temperature variations inside the caverns are relatively small, this stress difference predominantly controls the convergence rate. Since direct pressure measurements are usually not released by the provider companies, we substitute cavern pressure with the publicly available mean filling level curves per provider and impose assumed minimum and maximum pressures to bound plausible operating conditions. To address uncertainties in cavern depth and lithology, we include a per‑cavern lithostatic‑stress adjustment parameter that permits limited variation in the estimated lithostatic pressure and, consequently, in the inferred stress difference. The final model enables daily 3D surface displacement predictions across the entire storage field. The modeled displacements show good agreement with the InSAR observations and with independent GNSS and levelling measurements. The estimated convergence is consistent with annual measurements of cavern convergence supplied by one of the provider companies. Remaining residuals are plausibly linked to the use of provider‑mean filling level curves instead of per‑cavern filling levels or exact pressure histories, and further reduction would be expected if individual cavern operational data was available. Our work shows that the combined use of InSAR time series analysis and geophysical source modeling enables high resolution and accurate monitoring of cavern convergence induced surface deformation through local parameter derivation at underground energy storage sites such as Epe.

The Italian Space Agency (ASI) and the Japan Aerospace Exploration Agency (JAXA) have partnered to advance the understanding of the Earth’s surface and atmosphere from space through the use of Synthetic Aperture Radar (SAR) satellites in the fields of Earth sciences and Earth observation applications. This long-standing cooperation started with the Memorandum of Understanding concerning the Feasibility Study and Joint Research Activities for the Anticipated Mutual Cooperation in the Satellite Disaster Monitoring signed on 18 September 2009, and fruitfully continued with the “Implementing Arrangement concerning Mutual Cooperation for Satellite Support to Disaster Risk Management” (IA) that was signed on 25 November 2016 and is still ongoing. Since ASI and JAXA have both developed and operated SAR missions – COSMO-SkyMed First and Second Generations (CSK/CSG) on one side, and ALOS-2 and ALOS-4 on the other –, both the agencies have recognized the value to share an important experience in the operational use of X-band and L-band SAR data, and intend to increase the benefits of synergies in the use of virtual SAR constellation by combining L and X-band space-borne assets. In addition, both the agencies are jointly collaborating on new and innovative research on disaster management and other climate change study area using space-borne SARs. The bilateral cooperation includes three main activities: 1. establishment of a COSMO-SkyMed archive over Japan and an ALOS-2 archive over Italy for Disaster Risk Management activities; 2. acquisition of SAR data by COSMO-SkyMed constellation and ALOS-2 in response to emergency requests made by the other Party; 3. joint SAR research activities related to Disaster Risk Management. The present paper provides an overview of the ASI – JAXA cooperation for disaster risk management. In particular, the focus is on the synergies and results that have been achieved in order to provide mutual institutional support during emergencies, as well as building X and L-band data collections over the respective territories and hotspots of scientific interest to promote innovative research. In this respect, further stimulus to strengthen the cooperation came from the full operations of the COSMO-SkyMed Second Generation satellites and ALOS-4.

Accurate characterization of land subsidence is increasingly important for hazard assessment and infrastructure risk management. Interferometric Synthetic Aperture Radar (InSAR) is widely used to measure surface deformation; however, transforming Line-Of-Sight (LOS) observations into vertical and horizontal components remains a fundamental challenge. Full three-dimensional (3D) decomposition using data from two satellite tracks typically requires additional assumptions regarding the direction of the horizontal deformation component. Although several approaches have been proposed in the literature, these assumptions are not always objectively justified and may introduce bias. Recently, a gradient-based directional constraint has been introduced, assuming that the direction of horizontal deformation aligns with the horizontal gradient of the vertical displacement field (i.e., the tilt direction). In this study, we focus specifically on subsidence processes and systematically investigate the validity of this assumption in such environments. Importantly, the proposed framework is not intended for general deformation mechanisms, although it may also be applicable to other contexts (e.g., volcanic deformation). Our primary objective is to determine under which physical and spatial conditions the gradient–direction constraint is valid in subsidence areas. We first provide an analytical examination of the underlying assumptions and then evaluate their validity using synthetic simulations designed to represent realistic subsidence scenarios. These analyses identify the spatial configurations, deformation patterns, and boundary conditions under which the horizontal deformation direction can be reliably inferred from the vertical deformation gradient. The results demonstrate that the assumption is conditionally valid and depends on the mechanical and geometric characteristics of the subsidence field. Building upon this assessment, we propose an iterative strategy for full 3D decomposition of dual-track InSAR LOS data under the gradient–direction constraint. The method jointly estimates vertical and horizontal deformation components while enforcing consistency between the horizontal motion direction and the vertical displacement gradient. Particular attention is given to computational stability, especially in cases where the inferred horizontal direction approaches the satellite heading direction, leading to ill-conditioning. Strategies to mitigate numerical instability are incorporated into the estimation procedure. The proposed approach is applied to several subsidence areas in Iran and validated using available Global Positioning System (GPS) observations. The results demonstrate improved consistency in separating vertical and horizontal components and provide quantitative evaluation of reconstruction accuracy. We further discuss potential sources of bias, including model assumptions, noise effects, and geometric limitations.

The Tibetan Plateau is characterized by an extensive periglacial landscape. In such environments, cyclical expansion and contraction of the ground surface have been observed in numerous field surveys and associated with seasonal soil freezing and thawing. On Tibetan slopes, beyond observing these movements perpendicular to the surface, recent InSAR studies have also revealed systematic, short-wavelength millimeter to centimeter annual downslope displacement (Daout et al., 2020; Lemrabet, 2022; Watine et al., 2025). These movements were interpreted as solifluction, a slow but widespread downslope mass-movement process driven by seasonal freeze-thaw cycles and associated with water phase changes (Matsuoka, 2001). However, previous studies focusing on these downslope movements are limited to regional permafrost areas in the northeastern Tibetan Plateau, leaving the large-scale spatio-temporal variability of solifluction across the Tibetan Plateau largely unknown, particularly in regions dominated by seasonally frozen ground (~53% of the Tibetan Plateau surface; Zou et al., 2017). Furthermore, as climate warming alters ground thermal regimes, solifluction dynamics may evolve, potentially destabilizing slopes and increasing the risk of rapid failures. Yet the extent and timing of these changes remain unconstrained. Currently, the massive availability of SAR data makes it possible to reconstruct continental-scale ground deformation over periods of up to a decade. This allows InSAR approaches to explore surface displacement across the entire Tibetan Plateau and identify any acceleration in deformation. However, to date, no large-scale study has systematically investigated surface deformation processes across the Tibetan Plateau. Such processing requires particularly careful phase unwrapping strategies, especially on rapidly deforming slopes, in order to prevent signal aliasing and ensure reliable deformation estimates. Once these methodological challenges are overcome, large-scale InSAR observations offer a unique opportunity to map and quantify solifluction processes, and to investigate their sensitivity to climatic, morphometric, and geological controls. Here, we present the first continental-scale mapping and characterization of systematic slope deformation across the western Tibetan Plateau using the FLATSIM (ForM@Ter LArge-Scale Multi-Temporal Sentinel-1 InterferoMetry) service, operated by ForM@Ter and the CNES (Thollard et al., 2021). Our analysis is based on the New Small Baseline Subset (NSBAS) processing chain (Doin et al., 2011, 2015; Grandin, 2015). We used approximately 13,153 interferograms acquired between October 2014 and May 2022 over 7 ascending and 10 descending Sentinel-1 tracks, covering ~600 000 km² in 8-looks. Reconstructing reliable InSAR time series over the Tibetan Plateau is challenging due to its periglacial nature, and automatic processing is not always suitable. Rapid frost heave can generate strong phase gradients that can lead to phase unwrapping errors when the unwrapping path crosses highly deforming areas, potentially introducing modulo 2π ambiguities in the unwrapped phase. In addition, rapid soil moisture variations introduce systematic phase contributions, particularly in short temporal baseline interferograms, which bias SAR time series and reduce the reliability of deformation estimates. To ensure reliable InSAR time series, we implement a dedicated processing strategy in order to minimize phase aliasing and biases in InSAR time series over periglacial terrains. Note that the interferograms used are already corrected for tropospheric delays using ERA-5 atmospheric models. First, a seasonal deformation model is subtracted prior to the critical phase unwrapping step, reducing phase variability and minimizing errors during the unwrapping procedure. The reduced interferograms are then unwrapped following the collinearity of the filtered phase, starting from areas of high temporal coherence across the interferometric network. After unwrapping, the seasonal signal is added back to reconstruct the full unwrapped signal. An initial time series inversion is performed without automatic correction of network misclosures to identify inconsistencies in the network and the associated interferograms. These interferograms are either removed or reprocessed, and the inversion is repeated iteratively until network consistency is achieved. A final inversion is then performed, weighting interferograms by their temporal baseline to reduce moisture-related biases. Pixels with large residual errors, low mean temporal baseline, and high mean interferogram misclosure weighted by the temporal baseline are masked out after inversion to limit the propagation of errors and biases in the following post-processing. After these steps, time series displacement maps are spatially high-pass filtered using a Gaussian filter to remove residual atmospheric and large-scale tectonic contributions. Line-of-sight time series are temporally decomposed into linear velocity, acceleration, seasonal, and its temporal evolution. To study slope processes and, more particularly, to go in detail in their mechanism, products of the temporal decomposition are spatially inverted into slope-parallel and slope-normal components using multiple viewing geometries. This approach enables the direct correlation of slope-parallel and slope-normal components with morphometric and environmental variables such as air temperature or slope angle, providing a framework for analyzing solifluction processes. Pixels with slopes < 2° or > 30°, slopes oriented within ±30° of the north–south direction, and high RMS after spatial inversion are excluded to ensure robust analysis. The resulting products consist of large-scale maps of slope-parallel and slope-normal velocities and accelerations, as well as maps of slope-normal seasonal amplitudes and their temporal increase. They reveal widespread active downslope deformation across the western Tibetan Plateau, with velocities on the order of millimeters to centimeters per year. The extensive presence of solifluction landforms, observed in both satellite imagery and on the field, supports the interpretation that these movements correspond mainly to active solifluction processes. At first order, these large-scale measurements reveal that the spatial distribution of these processes is controlled by mean annual air temperature (MAAT), with more than 90% of areas exhibiting velocities greater than 5 mm/yr located in permafrost regions. Maximum velocities occur around a MAAT of approximately −8 °C and decrease toward warmer conditions. Although solifluction can occur in regions with only seasonally frozen ground, very little movement is observed there. This difference is likely explained by the generally dry conditions of the Tibetan Plateau limiting the water availability for free-thaw processes in seasonally frozen ground. In contrast, areas under permafrost retain sufficient water to sustain solifluction processes. At second order, solifluction velocity co-varies with slope angle and the amplitude of movements normal to the slope (contraction/expansion cycle). This relationship provides insight into the mechanisms controlling downslope motion. The observed velocities exceed those expected from frost creep alone, one of the main solifluction mechanisms, which results from slope-normal frost heave followed by near-vertical thaw settlement (Washburn, 1979). This suggests that gelifluction, the slow gravitational shear deformation during seasonal thawing, likely dominates the downslope motion. The relative contribution of these mechanisms varies with lithology: frost creep contributes up to 30% of the downslope movements in unconsolidated sediments such as moraines. However, frost creep is more limited compared to gelifluction in bedrock hillslopes covered by colluvium. These variations can be explained by differences in grain size, which affect frost susceptibility, and by variations in bedrock depth beneath the colluvium. Finally, analysis of the slope-normal component reveals increasing seasonal amplitudes and widespread irreversible ground subsidence within permafrost areas, indicating ongoing degradation. This degradation evolves through time but with a high spatial heterogeneity. Some regions show stabilization, with subsidence rates decreasing over time, locally by up to 80% between 2014 and 2022, whereas others exhibit acceleration. These trends directly influence solifluction dynamics, leading respectively to reduced or enhanced downslope velocities. These results demonstrate that the spatial distribution of solifluction is primarily controlled by the permafrost distribution on the western Tibetan Plateau, and that its ongoing degradation directly modulates solifluction dynamics. Daout, et al. "Ice loss in the Northeastern Tibetan Plateau permafrost as seen by 16 yr of ESA SAR missions." Earth and Planetary Science Letters 545 (2020): 116404. Doin, et al. "Presentation of the small baselin NSBAS processing chain on a case example: The Etan deformation monitoring from 2003 to 2010 using Envisat data." Fringe symposium. 2011. Doin, et al. "InSAR measurement of the deformation around Siling Co Lake: Inferences on the lower crust viscosity in central Tibet." Journal of Geophysical Research: Solid Earth 120.7 (2015): 5290-5310. Grandin. "Interferometric processing of SLC Sentinel-1 TOPS data." FRINGE’15: Advances in the Science and Applications of SAR Interferometry and Sentinel-1 InSAR Workshop, Frascati, Italy, 23-27 March 2015. 2015. Lemrabet, et al. "Referencing of continental‐scale InSAR‐derived velocity fields: Case study of the eastern Tibetan Plateau." Journal of Geophysical Research: Solid Earth 128.7 (2023): e2022JB026251.Matsuoka. "Solifluction rates, processes and landforms: a global review." Earth-Science Reviews 55.1-2 (2001): 107-134. Thollard, et al. "Flatsim: The form@ter large-scale multi-temporal sentinel-1 interferometry service." Remote Sensing 13.18 (2021): 3734. Watine, et al. "Downslope solifluction movements and permafrost degradation in the northeastern Qinghai-Tibetan Plateau revealed by InSAR." Remote Sensing of Environment 329 (2025): 114926. Zou, et al. "A new map of permafrost distribution on the Tibetan Plateau." The Cryosphere 11.6 (2017): 2527-2542.

These days, InSAR is a key technology for monitoring land subsidence and assessing its associated hazards and risks. While InSAR displacement maps provide essential information, effective subsidence risk evaluation requires accurate estimation of differential subsidence, e.g., spatial gradients and curvature, which are more directly related to infrastructure damage and ground instability than absolute deformation. All methodologies for estimating spatial gradients from InSAR data—whether implicitly or explicitly—rely on some form of local spatial modelling/interpolation of the deformation field. Conventional approaches typically assume a spatially linear deformation model within a fixed moving window, which implies constant strain over that area. The accuracy and reliability of this strategy depend on two key factors: the choice of the basis function and the selected window size. For robust gradient estimation, the true deformation field must be adequately represented by the adopted model within each local window. However, many subsidence systems are inherently multi-scale. Regional deformation driven by groundwater withdrawal may extend over broad areas, yet its spatial extent can vary significantly depending on hydrogeological conditions and extraction patterns. At the same time, localized subsidence may develop at much smaller scales, such as individual wells, buildings, infrastructure loads. These processes often coexist and overlap, resulting in heterogeneous displacement fields characterized by spatially varying gradients and curvature. In such environments, a fixed window size combined with a linear deformation model may either oversmooth localized features or become overly sensitive to noise. This mismatch between deformation complexity and model assumptions can lead to systematic underestimation of differential subsidence, thereby affecting hazard interpretation and risk zoning. To address these limitations, we propose an adaptive multi-scale framework for InSAR-based differential subsidence estimation. The proposed approach integrates two key components. First, instead of relying solely on a linear model, a second-order polynomial basis function is used within each local window, increasing flexibility to represent curvature and spatial variability. Second, an iterative adaptive window selection strategy is implemented. The procedure begins with an initial window size; model adequacy is assessed using a statistical consistency criterion. If the deformation is not sufficiently represented, the window size is reduced, and the process is repeated until the model assumptions are satisfied. This ensures that the spatial scale of analysis is consistent with local deformation characteristics. The method is first evaluated using controlled synthetic deformation scenarios, allowing quantitative testing under known ground-truth conditions and varying noise levels. The framework is subsequently applied to several subsidence-affected regions in Iran using Sentinel-1 data, covering different deformation mechanisms and spatial scales. These include areas dominated by groundwater-related subsidence with both regional and localized components in urban areas. The results demonstrate improved representation of spatial gradients compared to conventional fixed-window linear approaches, particularly in areas with strong deformation variability. Importantly, the method provides an additional scale-diagnostic capability. The final selected window size represents the spatial extent over which the chosen basis function remains valid. The spatial distribution of optimized window sizes therefore provides insight into the intrinsic scale of deformation processes, helping to distinguish between large-extent regional subsidence and localized deformation sources. This byproduct enhances geophysical interpretation and supports more robust subsidence hazard assessment. In conclusion, the proposed adaptive multi-scale methodology improves differential subsidence estimation from InSAR data, reduces bias in heterogeneous deformation fields, and introduces a novel indicator of deformation scale. The approach strengthens the link between satellite-derived deformation measurements and hazard-oriented subsidence analysis.

The Cascadia Subduction Zone, which stretches from northern California to southern British Columbia, has the potential to produce large hazards like megathrust earthquakes and tsunamis. This area is therefore heavily monitored by the US Geological Survey (USGS) with onshore and offshore instruments including seismometers, Global Navigation Satellite Systems, strainmeters, tiltmeters, and more recently Distributed Acoustic Sensing cables. Interferometric Synthetic Aperture Radar (InSAR) techniques provide a complementary dataset with dense spatial coverage over the entire Cascadia region. InSAR data have been critical for understanding and monitoring hazards. For example there has been success in studying slow slip events in Cascadia using Sentinel-1 C-band data. These data, however, decorrelate on longer time scales due to vegetation growth throughout the Pacific Northwest. As a result, long term tectonic monitoring and constraining small signals related to different parts of the earthquake cycle is challenging. NASA-ISRO’s NISAR mission uses an L-band radar, which has a longer wavelength that better penetrates through densely vegetated regions and maintains coherence on longer temporal scales. Therefore, more interferograms can be formed with longer temporal baselines, which are necessary for deriving accurate long term time series. Furthermore, these data, in combination with other and future satellites, e.g., ALOS-2 and ROSE-L, provide for more imaging geometries enable better constraints on horizontal and vertical displacements, which can ultimately improve fault slip solutions in the region. We perform a coherence analysis and comparison with the most recent ALOS-2 ScanSAR and Sentinel-1 data. Preliminary results using ALOS-2 ScanSAR data (path 170, frame 2800) from March 17 – 31, 2025 show that the vegetated area along the coast maintains coherence, ranging from 0.2 – 0.5, and the wrapped and unwrapped phase is spatially coherent. For the 2-month interferogram from January 20 – March 17, 2025 there is a drop in coherence along the coast but that the phase is still coherent in the wrapped interferogram. Despite lower coherence in the 2-month interferogram, the phase measurements at L-band are still usable for time series analysis. We download and process all available NISAR data in the southern Cascadia region and compute phase statistics to determine the impact of residual noise in interferograms on future derived time series. We demonstrate that NISAR data enables long-term monitoring of vegetated regions and that L-band InSAR data provide critical surface deformation measurements that help us better understand the earthquake cycle and our preparedness for future hazards in Cascadia.

Understanding how continental deformation partitions between distributed ductile strain and localized fault slip remains a central challenge in active tectonics and geodynamics, and it is also a key uncertainty in translating geodetic observations into seismic hazard assessments. The Tibetan Plateau – Earth’s largest region of active continental deformation – provides an exceptional natural laboratory, but until recently the spatial resolution of geodetic velocity fields has limited our ability to diagnose where strain is truly localized and where it is broadly distributed. Here we present high‑resolution geodetic velocity and strain‑rate fields for the Tibetan Plateau derived primarily from Sentinel‑1 InSAR. The results show how a small number of major strike‑slip systems exert first‑order control on the regional deformation pattern. We processed a decade of Sentinel‑1 observations (2016–2024) as a large network of short‑baseline interferograms across 127 ascending and 114 descending frames using the COMET‑LiCSAR system and LiCSBAS time‑series methodology to estimate average line‑of‑sight surface velocities. To reduce bias from earthquake transients, we removed coseismic deformation signals for earthquakes larger than Mw 6 before calculating mean velocities. We then jointly inverted InSAR mosaics with a compilation of GNSS velocities and levelling data to solve for reference frame adjustments and a smooth 3‑D velocity field on a coarse mesh. We used the referenced ascending and descending InSAR mosaics to derive 1‑km resolution east–west and vertical velocities using the coarse model north-south component as a constraint. Our estimated velocity uncertainties are generally

Surface velocities and strain rates from satellite geodesy have become essential tools for understanding the distribution of tectonic deformation, faulting and seismic hazard. However, across large regions of distributed continental deformation, such as the Alpine-Himalayan Belt, data are only sparsely available. While previous studies have mainly used spatially sparse GNSS to measure deformation at such large scales, these approaches cannot characterize shorter wavelength features of deformation in many places. We use Sentinel-1 radar images acquired during 2016–2024 to provide trans-national average surface velocities and time series at 1 km spatial resolution stretching a distance of over 11,000 km from south-western Europe to eastern China, covering an area more than 20 million square kilometres. We produce the velocity field by combining data from over 222,000 Sentinel-1 SAR images with a new belt-wide compilation of GNSS velocities, all combined in a consistent Eurasian reference frame. Horizontal strain rates are derived from gradients of the velocity field, yielding near-continuous spatial deformation information over the entirety of the largest deforming region on the planet. The horizontal velocities and strains are dominated by tectonic deformation, which has a bimodal behaviour – focused on major faults but distributed elsewhere. Shorter-wavelength vertical velocities are dominated by non-tectonic processes, in particular the widespread over-exploitation of groundwater. Our new velocity and strain rates are foundational data sets that reveal the details of how the continents deform for the first time at trans-continental scale.

Stress perturbations generated by magmatic intrusions provide a natural laboratory for investigating how networks of shallow faults slip in response to transient loading. Interferometric Synthetic Aperture Radar (InSAR) is crucial to making detailed measurements of surface deformation to infer the patterns of fault slip and magmatic inflation. The 2024-2025 Fentale-Dofen dike intrusion episode in the Northern Main Ethiopian Rift offers a unique opportunity to investigate time-dependent stress changes and associated fault slip, as surface deformation was captured at unusually high spatial and temporal resolution by COSMO-SkyMed and Sentinel-1 satellite radar missions. We reconstructed the evolution of deformation in an area 15 km north of the tip of the dike, where phase signals from a sequence of 8 moderate-sized earthquakes of M 4.5-6.0 can be separated from those of the dike. To achieve this, we processed COSMO-SkyMed and Sentinel-1 InSAR data to generate a network of interferograms that are inverted for displacement time-series using the Small Baseline Subset (SBAS) algorithm. We also map pre-existing and newly formed faults that slip at the surface using high-resolution Digital Elevation Models (DEM) and InSAR phase-gradient maps.   Since the deformation field exhibits high phase-gradients and discontinuous fringe patterns, standard phase unwrapping was challenging, and led to errors that bias the time-series or force the removal of valuable pixels. By inspecting individual complex interferograms and identifying unwrapping errors using the loop closure method, we corrected the unwrapping errors by masking displacement lineaments recognised as phase discontinuities in the phase-gradient maps prior to unwrapping. Using these masked interferograms led to an average network improvement of 52% in the root-mean-square misfit of loop-closure triplets after unwrapping. We then used the improved time-series to track the deformation history of the area. By comparing a relocated catalogue of >150 moderate-sized earthquakes (M4.5-6.0) with each interferogram, we isolate time periods dominated by seismic and aseismic deformation by fitting the pixel-by-pixel time-series using a step function at the time of each significant earthquakes and a linear velocity term for interseismic periods. We used these reconstructed deformation fields to derive seismic and aseismic slip distributions and associated static stress changes on the shallow fault network. We then test whether observed slip is consistent with one or more of the following driving mechanisms: (a) a normal mainshock–aftershock sequence governed by rate-and-state friction, (b) static Coulomb stress perturbations from ongoing dike intrusions that reload fault patches, or (c) elevated pore-fluid pressure that transiently reduces effective normal stress on the faults.   Finally, we quantify the proportion of seismic and aseismic strain accommodated by pre-existing and newly formed faults during the magmatic episodes, providing new constraints on fault mechanics and their interaction with magmatic processes in active rifts.

The southern Caribbean-South America (CA-SA) transform plate boundary in northern Venezuela takes up ~22 mm/yr of dextral relative motion with strain partitioned on two sub-parallel fault systems: (1) the immediately (

In the months to years following large earthquakes, a significant amount of deformation is observed both on the faults that ruptured coseismically and in the surrounding crustal regions. Postseismic deformation can result from the relaxation of static stress changes induced by the mainshock and/or be triggered by dynamic stresses associated with the passing of seismic waves. Postseismic deformation patterns typically include seismic and aseismic slip on faults, viscous lower crust and/or upper mantle relaxation, and poroelastic deformation. Shallow postseismic creep has also been observed on secondary faults that did not rupture coseismically following the 2021 Mw 6.4 and Mw 7.1 Ridgecrest (California) earthquakes and the 2021 Mw 7.2 Nippes (Haiti) earthquake, but no evidence of similar creep events was found after other earthquakes. Here we study postseismic deformation following 3 large continental strike-slip earthquakes or earthquake sequences, including the Ridgecrest sequence, the 2021 Mw7.4 Maduo (China) earthquake and the 2023 Mw 7.8 and Mw 7.6 Kahramanmaraş (Turkey) earthquake doublet. We map the ground displacement field with InSAR time series in order to finely characterize postseismic deformation patterns. As expected for each earthquake, we observe a large-scale signal resulting from deep afterslip and viscous relaxation of the lower crust. We also observe shallow afterslip in some parts of the coseismic ruptures and on secondary faults that did not take part in the main rupture. Finally, several previously creeping landslides accelerated shortly after the Kahramanmaraş earthquakes before gradually returning to their initial creep rate over the course of months. The temporal evolution of postseismic slip both on the main fault and on secondary faults follows a logarithmic decay compatible with gradual relaxation of coseismic stress changes or previously accumulated stresses. We put our measurements of postseismic slip in perspective with interseismic slip rates and coseismic stress changes to obtain valuable insight about the rheology of these secondary faults and their role in regional tectonics. Our three case studies present variable levels of fault maturity and fault zone complexity, and comparing the extent of triggered creep on secondary faults can help better understand how strain distributes in time and space within complex fault systems.

Slow-slip events (SSEs) represent a unique form of fault displacement, occurring over days to months rather than the seconds typical of earthquakes. While most commonly observed near tectonic plate boundaries, these events also present hazards in intraplate regions, particularly where underground infrastructure, such as production and saltwater disposal (SWD) wells, intersect fault zones. Unlike seismic events, SSEs are typically aseismic and can only be detected through geodetic measurements, making them a silent but significant risk to subsurface and surface operations. Between March 2022 and January 2023, a slow-slip event was detected in Howard County, in the Midland Basin, using Interferometric Synthetic Aperture Radar (InSAR) data processed through the Small Baseline Subset (SBAS) technique. This geodetic monitoring revealed surface deformation patterns indicative of fault activity that would otherwise have remained undetected. The region is known for extensive saltwater disposal operations, integral to oil and gas production, making accurate fault characterization crucial for risk management. The deformation velocity maps generated during this period were used to estimate fault parameters, including location, geometry, and slip kinematics, through Bayesian source inversion. This method provided probabilistic insights into the fault’s behavior, enhancing the reliability of the interpretations. The inversion results indicated a shallow, low-angle normal fault striking perpendicular to the geographically widespread Grenville thrust front that has released the equivalent energy to an earthquake with a moment magnitude of ~5.3. The SSE fault orientation and geometry align closely with secondary orthogonal seismogenic zones identified in 2D regional reflection seismic lines, reinforcing the validity of the geodetic analysis and seismic correlation. Interestingly, the slow-slip event was temporally preceded by a seismic swarm located at the northern edge of the identified fault plane. This correlation suggests a potential interaction between seismic and aseismic faulting processes. The seismic swarm may have induced stress changes that triggered the subsequent slow-slip event, offering new perspectives on fault dynamics in intraplate settings. These observations have practical implications for regional risk management. Saltwater disposal wells, essential for handling wastewater from oil and gas extraction, greatly increase pore pressure and affect areas that are particularly vulnerable to fault slip, which can compromise well integrity. Critically stressed faults might slip even under minor perturbations as might be imposed by saltwater disposal. By incorporating surface deformation patterns indicative of fault activity, operators can implement proactive measures, such as adjusting injection rates and relocating SWD wells, to mitigate the risk of damage to health, safety and the environment. This study underscores the critical role of geodetic monitoring, particularly InSAR, in detecting and analyzing slow-slip events in active oil-producing basins. The integration of advanced techniques like Bayesian fault inversion allows for more precise fault characterization, improving our understanding of fault systems and their potential impacts. As oil and gas activities expand in regions like the Permian Basin, incorporating geodetic surveillance into routine monitoring will be essential for minimizing geohazard risks and ensuring the safety of underground infrastructure and operations. The Howard County SSE case demonstrates the value of combining wide-area InSAR data with subsurface data to enhance hazard assessment and infrastructure protection, setting a precedent for future monitoring efforts in oil-producing regions.

Our understanding of the kinematics, dynamics, and seismic potential of tectonically active continental interiors remains a long-standing challenge, particularly in structurally complex and earthquake-prone regions such as the Tianshan orogen. Large-scale, high-resolution mapping of surface velocities and strain rates from satellite geodesy offers a powerful perspective for addressing these issues. Here, we utilize approximately 10 years (2014–2024) of Sentinel-1 SAR imagery together with data from ~1,000 GNSS stations to derive average surface velocities and horizontal strain rates across ~2 million square kilometers in and around the Tianshan at 500 m spatial resolution. The results reveal pronounced lateral variations in present-day Tianshan deformation. Strain and deformation are strongly concentrated in the southwestern segment, particularly within the Pamir–Tianshan convergence zone, along the Talas–Fergana Fault, and across the Kashi and Keping fold-and-thrust belts, whereas other regions are characterized predominantly by more distributed deformation. Overall, the Tianshan accommodates ~20 mm/yr of north–south shortening and ~5 mm/yr of east–west extension. Based on the derived surface velocities and strain rates, we first assess the seismic potential of the Tianshan. Using geodetically constrained surface strain rates within an elastic half-space framework, we model 97 major faults (439 segments) to invert for slip-deficit rates—an indicator of the rate at which elastic strain energy is accumulating on these faults. By integrating historical seismicity, we further quantify the regional seismic moment budget and identify fault segments capable of generating Mw ≥ 7.0 earthquakes. We further investigate the deeper lithospheric dynamics underlying the observed deformation. Constrained by surface velocities, we approximate the Tianshan and surrounding regions using a 2D faulted viscous continuum model that allows for lateral variations in lithospheric strength as well as displacement along major faults. The results highlight strong contrasts in lithospheric strength between the Tianshan–Pamir region and adjacent blocks, including the Tarim Basin, Junggar Basin, and Kazakh Platform, as well as moderate lateral variations in effective lithospheric viscosity within the Tianshan itself. Our modeling suggests that far-field boundary stresses associated with the India–Eurasia collision dominate the present-day deformation of the Tianshan, outweighing internal buoyancy forces related to gravitational potential energy gradients. Together, these results demonstrate that high-resolution geodetic velocity and strain rate fields provides critical insights into continental deformation processes and associated seismic hazards.

The 29th July, 2025 M8.8 Kamchatka earthquake was one of the ten largest earthquakes in instrumental history. It occurred along a portion of the southern shore of the Kamchatka peninsula where pre-event GNSS velocities had indicated a high likelihood of frictional locking of the Kamchatka megathrust. The estimated source location overlaps in space with the source region of the 1952 M9.0 great Kamchatka earthquake, however there were significant differences between the two events – notably in the sizes of the tsunamis, with the 1952 tsunami having significantly greater far-field runup (up to 6 m) across most of the Pacific basin than that of the 2025 event (up to 2.5 m).. We use InSAR data from multiple tracks (five ascending, three descending) of the Sentinel-1 mission to investigate the deformation of the 2025 earthquake. Using the ISCE2 software to process the data, we employ ionospheric corrections and GACOS troposphere corrections to reduce noise, and obtain good correlation across the peninsula despite the forested terrain. We resolve up to ~1.2 m of line-of-sight displacement, peaking along the SE peninsula shore. In addition, we identify a change in the orientation of the fringes on the northeastern Kuril Islands that constrain the likely westward terminus of rupture. Superimposed on this earthquake deformation field are multiple volcanic deformation signals across the peninsula and islands, apparently stimulated by the earthquake. The largest of these is a dyke intrusion under Krasheninnikov Volcano, around ~230 km north of the mainshock hypocenter, starting two days after the mainshock, that is precursory to that volcano's eruption on 2nd August. We use quadtree decomposition to downsample our InSAR data, and invert them for coseismic slip on a triangulated mesh of the megathrust, using our models of interseismic locking as a constraint on rupture area, and the plate motion direction as a constraint on rake. Models of uniform slip on our estimated locked area, or of slip limited to the ~5.8 m of slip deficit that should have accumulated since 1952, do not fit the data well. Instead, we find that we can reproduce our InSAR displacements with up to 12 m of slip, divided into two asperities, and modest (< 4 m) trenchward slip, consistent with the modest tsunami for the event. Our estimated moment-magnitude of 8.82 agrees well with seismic estimates. We note that: 1) tsunami inversions of the 1952 event require greater shallow slip than our model shows, suggesting that the 2025 event was not a repeat of 1952; and 2) the amount of peak slip is over double the slip deficit that likely accrued since 1952, given the 80 mm/yr convergence accommodated by the megathrust, implying that some of the slip deficit that accumulated prior to 1952 was instead released in 2025. Thus, despite the Kamchatka megathrust hosting two of the ten largest earthquakes in the instrumental record, we argue that both of these events were partial ruptures – i.e. neither of them is likely to have been the "big one" for the region. Rather, an event in 1737, with reported run up of over 15 m in the Aleutian Islands, is likely the last full rupture of the Kamchatka megathrust.

InSAR has been one of the principal tools for monitoring Earth’s surface deformation for more than three decades, supporting geodetic and geophysical investigations ranging from tectonic processes and earthquake mechanics to glacier dynamics and landslides. However, due to its near-polar orbit and side-looking acquisition geometry, conventional InSAR measurements are primarily sensitive to the east–west and vertical components of motion and have limited sensitivity to the north–south component ​(Wright et al., 2004)​. Consequently, accurate three-dimensional (3D) deformation retrieval typically relies on well-distributed GNSS observations or assumes that the north–south component is negligible when deformation is dominated by east–west or vertical motion. The burst overlap regions inherent to Sentinel-1 TOPS acquisitions provide an opportunity to extract azimuth displacement information and partially resolve the north–south deformation component from spaceborne SAR data. Exploiting burst overlaps within a swath (Burst Overlap Interferometry, or BOI) has been demonstrated as an effective method to improve sensitivity to along-track motion and to outperform conventional offset-tracking approaches in terms of precision​ (Grandin et al., 2016; Prats-Iraola et al., 2012)​. This can be extended by exploiting subswath overlap too (SBOI,​ Nergizci et al. 2025) but standard Sentinel-1SAR focussing still restricts SBOI coverage to approximately 10% of the full Sentinel-1 frame, limiting its spatial continuity and applicability to large rupture zones. IIn this study, we implement an Extended Subswath and Burst Overlap Interferogram (ESBOI) approach that expands the usable overlap regions by extending the Doppler spectrum used when focussing each burst. This strategy enlarges the effective coverage, albeit with increased phase noise. By increasing the spatial extent of overlap-derived interferograms, ESBOI greatly enhances coverage of north–south displacement measurements to more than 50% of a frame, and improves integration with line-of-sight and offset-derived products for 3D deformation reconstruction. We apply the ESBOI methodology to two major strike-slip earthquake sequences characterized by exceptionally large north–south displacements: the 2023 Kahramanmaraş doublet (Mw 7.8 and Mw 7.5) and the 2025 Myanmar earthquake (Mw 7.7). These events produced some of the largest north–south surface offsets observed in the geodetic era, locally exceeding 4 meters. These earthquakes provide ideal test cases to evaluate the ability of ESBOI to resolve large along-track deformation gradients in near-field regions, where conventional InSAR coherence is degraded, and to assess its contribution to improving our understanding of where fault slip occurred. ​​Grandin, R., Klein, E., Métois, M., & Vigny, C. (2016). Three-dimensional displacement field of the 2015 Mw8.3 Illapel earthquake (Chile) from across- and along-track Sentinel-1 TOPS interferometry. Geophysical Research Letters, 43(6), 2552–2561. https://doi.org/10.1002/2016GL067954 ​Nergizci, M., Lazecky, M., Wright, T. J., Hooper, A., Ou, Q., Magnard, C., & Çakir, Z. (2025). Refining 3D Displacement Fields and Coseismic Slip Models of the 2023 Kahramanmaraş Earthquakes Using Subswath and Burst Overlap Interferometry (SBOI). Journal of Geophysical Research. ​Prats-Iraola, P., Scheiber, R., Marotti, L., Wollstadt, S., & Reigber, A. (2012). TOPS Interferometry With TerraSAR-X. IEEE Transactions on Geoscience and Remote Sensing, 50(8), 3179–3188. https://doi.org/10.1109/TGRS.2011.2178247 ​Wright, T. J., Parsons, B. E., & Lu, Z. (2004). Toward mapping surface deformation in three dimensions using InSAR. Geophysical Research Letters, 31(1). https://doi.org/10.1029/2003GL018827 ​ 

Large earthquakes are commonly followed by postseismic stress relaxation in the surrounding lithosphere, including viscoelastic flow in the lower crust and upper mantle. These processes provide a valuable opportunity to constrain lithospheric rheological properties such as viscosity. On 6 February 2023, two major strike-slip earthquakes (Mw 7.8 and Mw 7.6) ruptured the East Anatolian Fault (EAF), the plate boundary between the Arabian and Anatolian plates. The coseismic rupture extended over ~500 km with peak slip exceeding 10 m, suggesting substantial postseismic deformation. To characterize the postseismic deformation, we processed Sentinel-1 SAR data spanning two years after the earthquakes. Time-series InSAR was used to retrieve line-of-sight (LOS) displacements, while burst overlap interferometry (BOI) was applied to derive azimuthal displacements. We analyzed three ascending and three descending tracks, each consisting of 4–5 standard frames, enabling reconstruction of the full four-dimensional surface deformation field (temporal evolution and east-west, north-south, and vertical components). Our results reveal pronounced spatial and temporal asymmetry in horizontal postseismic deformation across the EAF. Deformation on the Arabian plate decays more slowly than on the Anatolian side, indicating a mechanically stiffer lithosphere to the southeast. While spatial asymmetry alone could result from either lateral viscosity variations or elastic afterslip within a heterogeneous crust, the observed asymmetric temporal decay is consistent only with contrasting viscosities across the fault. Elastic afterslip would produce nearly synchronous surface motion on both sides of the fault and thus a symmetric temporal decay pattern. By independently modeling surface displacements on each side of the EAF, we infer a strong interplate viscosity contrast, with upper-mantle viscoelastic flow dominating the postseismic deformation relative to afterslip. In addition to horizontal deformation, significant vertical postseismic signals are observed. These are well explained by poroelastic rebound, requiring a Poisson’s ratio change of approximately 0.10–0.13 between undrained (coseismic) and drained (postseismic) conditions, indicating substantial earthquake-induced modification of crustal material properties. This study presents the first unambiguous observation of asymmetric temporal decay in postseismic deformation resolved by satellite InSAR. For large strike-slip earthquakes along plate boundaries, our comprehensive three-dimensional time-series deformation analysis provides critical constraints on dominant postseismic processes and the underlying rheological structure of the lithosphere.

Understanding how continental deformation is distributed in space and evolves through time is a central question in active tectonics, with important implications for lithospheric rheology and seismic hazard. While the upper crust accommodates strain through brittle failure during earthquakes, the lower crust and mantle relax stress through viscous flow. The rates and mechanisms of this relaxation vary both spatially and temporally, complicating interpretation of present-day strain rates and their relevance to long-term earthquake risk. Decadal geodetic observations provide a valuable window into the temporal evolution of crustal deformation. Yet regional strain rate fields, including those measured by Sentinel-1 InSAR across the Tibetan Plateau (Wright et al., 2026) and the broader Alpine-Himalayan Belt (Elliott et al., 2026), may be contaminated by long-lasting postseismic transients. Moreover, previously inferred lithospheric strengths span several orders of magnitude, arising from differences in constitutive assumptions, as well as the spatial coverage and time span of the available observations. Here we present a comprehensive geodetic study of long-lived deformation following the 2001 Mw 7.8 Kokoxili earthquake on the Kunlun fault, Tibet. We integrate GNSS observations (2001-2025) with multi-mission InSAR time series from ENVISAT (2003-2010) and Sentinel-1 (2016-2024). Pre-earthquake measurements from ERS-1/2 (1992-2001) constrain an interseismic slip rate of

The East Anatolian Fault Zone (EAFZ) is a major left-lateral plate boundary accommodating motion between the Anatolian and Arabian plates. Its slip rate decreases south-westward from ~10 mm/yr along the central EAF to ~4 mm/yr near the Amanos segment, close to the Arabia-Africa-Anatolia triple junction. On 6 February 2023, two large earthquakes (Mw 7.8 and Mw 7.6) ruptured ~350 km of the EAFZ and an additional ~150 km along the Çardak-Doğanşehir fault system, providing a unique opportunity to investigate post-seismic deformation across a mechanically complex plate boundary. Here we quantify the post-seismic deformation using standard line-of-sight (LOS) InSAR and Subswath and Burst Overlap Interferometry (SBOI) to derive a three-dimensional (3D) displacement field. LOS InSAR constrains primarily vertical and east-west motion, while SBOI is sensitive to along-track (approximately north-south) displacements, which are critical in this tectonic setting. We integrate cumulative LOS and SBOI time series with 100 continuous GNSS stations spanning two years following the earthquake sequence. Sentinel-1 data from COMET-LiCS frames are processed using LiCSBAS time-series analysis. To isolate tectonic signals, we apply corrections for atmospheric delays (GACOS), ionospheric effects derived from JPL-HR total electron content products, solid Earth tides, plate motion, and interseismic loading. After correction, LOS and SBOI cumulative time series are jointly aligned to GNSS observations at common 12-day epochs using a VELMAP-based referencing strategy, ensuring a consistent reference frame and minimizing long-wavelength residuals. The resulting 3D time series reveals postseismic deformation extending up to ~300 km from the rupture zone. Deformation is coherently resolved in the east, north, and vertical components and is dominated by left-lateral motion along both the East Anatolian Fault and the Çardak-Sürgü Fault system. Cumulative horizontal displacements reach 100-150 mm near the fault zone. Approximately 30% of the total postseismic deformation occurred within the first 120 days, followed by ~20% during the subsequent 120 days, indicating a decaying transient signal. This integrated 3D geodetic dataset provides one of the most detailed observations of postseismic deformation following the 2023 Kahramanmaraş earthquakes. The spatial and temporal evolution of deformation highlights complex interactions between fault slip and lithospheric processes, offering new constraints on the mechanical response of the Anatolian plate. The observed decay patterns raise important questions regarding the relative contributions of afterslip, viscoelastic relaxation, and poroelastic rebound to the postseismic deformation field. To investigate these mechanisms, we employ a cuboid-based volumetric strain modelling framework (Barbot et al., 2017; Qiu et al., 2018) to represent distributed lower-crustal deformation within an elastic half-space. This approach enables us to quantify depth-dependent anelastic strain and evaluate its contribution to the postseismic signal alongside conventional fault-based afterslip models. Barbot, S., Moore, J. D. P., & Lambert, V. (2017). Displacement and stress associated with distributed anelastic deformation in a half-space. Bulletin of the Seismological Society of America, 107(2), 821–855. https://doi.org/10.1785/0120160237 Qiu, Q., Moore, J. D. P., Barbot, S., Feng, L., & Hill, E. M. (2018). Transient rheology of the Sumatran mantle wedge revealed by a decade of great earthquakes. Nature Communications, 9(1), 995. https://doi.org/10.1038/s41467-018-03298-6

Large, shallow earthquakes occurring on continental faults are often the most destructive, causing thousands of casualties worldwide each year. Strikingly, these earthquakes are also among the most informative for researchers seeking to advance our understanding of earthquake processes. Here we present three new lessons learned from InSAR data and modeling of recent devastating earthquakes in Myanmar and Türkiye. The first lesson challenges conventional views of seismic gaps, which are commonly used in earthquake hazard assessments. Here we investigate what fault rupture conditions allowed the 2025 magnitude 7.7 Mandalay earthquake in Myanmar to grow far beyond its anticipated size. The earthquake ruptured, at super-shear velocity, a known ~250 km long seismic gap on the Sagaing fault in Myanmar and then went beyond it to produce a much longer rupture of about 460 km. We used SAR data to map the fault rupture and constrain the fault slip distribution and dynamic rupture modeling to show that the location where the rupture process initiated, rather than the super-shear rupture speed, was critical for producing the long rupture. The latter two lessons focus on the Kahramanmaras earthquakes that occurred in early 2023 on and near the East Anatolian Fault in southern Türkiye. Here we used high-quality 3D coseismic displacement derivations from InSAR to quantify the amount and extent of “missing” elastic deformation near the fault ruptures, which we interpret as permanent off-fault damage. We show that this damage is larger and more extensive than previously recognized around earthquake ruptures, with more damage near geometrical complexities than along straight fault sections. This indicates that the long-term slip focused on the fault at surface is lower than the tectonic plate motion, meaning that geologic fault-slip determinations around the world might systematically underestimate earthquake hazard. Finally, we show that the post-seismic deformation after the Kahramanmaras earthquakes is both spatially and temporally asymmetric, providing a unique opportunity in distinguishing between possible post-seismic mechanisms. We show that while the spatial asymmetry can be explained by both afterslip and viscoelastic relaxation, the temporal asymmetry eliminates afterslip and shows that viscoelastic relaxation in the upper mantle is the key postseismic deformation mechanism following the earthquakes. This result will help to calibrate earthquake cycle models in the region and in similar tectonic environments around the world for better earthquake hazard assessments.

As one of the most seismic regions in Europe, the Balkan Peninsula experiences frequent destructive earthquakes, as recalled by a series of recent Mw ≥6 events causing substantial societal impacts. These earthquakes highlight the complex geodynamic framework shaped by the interplay between the Hellenic subduction, the Alpine compression, and the North Anatolian Fault. The Balkans remain significantly under-instrumented compared to other parts of Europe. Previous GNSS studies (e.g., Piña Valdés et al., 2022) provide a first-order analysis of the large-scale kinematics, revealing low strain rates (few mm/yr) across most of the region. However, the sparse and uneven station coverage fails to resolve deformation localized on individual active structures, limiting our understanding of the geodynamic processes at play and their associated hazards. To overcome this spatial resolution gap, we take advantage of a new, extensive InSAR dataset over the western Balkans extending over 360, 000 km², processed by the FLATSIM service (Thollard et al., 2021) based on Sentinel-1 data. Leveraging the high spatial resolution (240 m) and frequent revisit times (6-12 days) of these 2014-2021 time series, we address two key methodological challenges: (i) accurately extracting the linear trend from InSAR LOS displacement time series, and (ii) referencing the derived mean velocity maps to a known reference frame. By adapting the approach from Lemrabet et al. (2023), we present the first consistent, large-scale InSAR average velocity field for the Balkan Peninsula, robustly referenced in ITRF14 (Altamimi et al., 2016) with minimal use of GNSS data. This referenced velocity field is in strong agreement with GNSS data (

Underground mining induces seismicity and surface displacement. In Poland, in the Legnica Glogow Copper District near Wroclaw, induced earthquakes are particularly frequent with earthquakes of Mw3 and larger occurring many times a year. These earthquakes have shallow hypocenters of often less than 1 km and mostly above the mined copper layer. The area around the mines also experiences a fast continuous surface subsidence of several millimeters per year caused by rock as well as groundwater extraction. This surface motion is observed through geodetic measurements on the ground and from space. Across wide areas above the active mining the spatially very heterogeneous surface motion even exceeds 10 mm/yr. Also, sudden coseismic acceleration of surface motion is observed at the time of the larger earthquakes with moment magnitudes of Mw 3.5 and larger through space-borne InSAR based on ESA’s Sentinel-1 mission. In these cases, we often observe motion of several centimeters within a few days and with spatial extensions reaching a few kilometers. In some areas of high seismicity within the Copper district, the InSAR time series products of the European Ground Motion Service do not show ground motion measurements. Because there is man-made infrastructure, which usually provides persistent scatterers, this suggests that the ground motion there is far from linear in time. Despite safety measures, the occurrence of some, also larger earthquakes is unexpected in space and time, which poses a particular threat to workers in the mines and also to the subsurface mine structures as well as generally to the people, settlements and infrastructure above ground. Our study investigates a number of larger events of the recent years by analyzing the locally recorded seismic waveforms jointly with measurements of the surface displacements based on InSAR and partly GNSS measurements. We aim to precisely locate and describe the source processes of larger induced earthquakes by characterizing the interplay between shear-failure and collapse using full moment tensor models in a fully Bayesian and joint-data inference framework. Potentially we can relate collapse and shear failure induced by the mining activities in the context of other influences and improve our understanding of these unwanted events for mitigation measures. The observations are best explained by often large negative isotropic components accompanied by significant shear failure mechanisms. Another finding is that our moment estimates systematically exceed the local catalog values. Challenges to be discussed are the impact of our short-duration point source model for possibly an accumulation of multiple events, possibly involving a larger volume and a longer duration, and the potential bias introduced by a simplified velocity model.

Constructing a fault slip (deficit) model is helpful for understanding earthquake mechanism. The Nankai subduction zone, where the Philippine Sea plate subducts beneath the Eurasian (or Amur) plate in southwest Japan, has the potential to cause earthquakes greater than M8. Many geodetic researches have estimated slip deficit distribution on the Nankai subduction zone (e.g. Ochi 2015; Yokota et al. 2016; Nishimura et al. 2018). Even though Japan has dense GNSS observation network (GEONET), its spatial spacing is limited to about 20 km. InSAR can observe surface displacements with high spatial resolution, and some researches revealed InSAR improved the resolution of slip deficit distribution even at deeper area of subduction zones (Maubant et al. 2023; Kinoshita and Furuta 2024). In this study, we focused on the impact of InSAR for slip deficit estimation on the Nankai subduction zone. We conducted GNSS and InSAR time-series analyses to detect interseismic displacements. In GNSS analysis, we first corrected postseismic displacements of the 2011 Tohoku-oki earthquake based on Tobita (2016), then we fitted a function composed of linear, trigonometric, and offset terms to GEONET and GPS-A coordinates (Takamatsu et al. 2023; Yokota et al. 2016) from September 2014 to March 2025. In InSAR processing, we first made interferograms for all possible pairs using ALOS-2 Stripmap mode SAR data in 7 frames within Shikoku island. All 7 frames were observed from descending tracks. We corrected ionospheric and neutral atmospheric noises (Wegmüller et al. 2018; Kinoshita 2022), solid earth tide effect (Yunjun et al. 2022), and postseismic effect of the 2011 Tohoku-oki earthquake (Tobita 2016) from each interferogram. We then obtained LOS velocity fields through NSBAS-based time-series analysis (Ló pez-Quiroz et al. 2009). Root mean square errors (RMSEs) between InSAR velocities and GNSS velocities projected to LOS directions were between 1.3 mm/year and 2.1 mm/year, which suggested that InSAR velocity fields were consistent with those of GNSS. All LOS velocity fields showed long-wavelength phase variations reflecting interseismic deformation at the Nankai subduction zone. InSAR velocities showed a maximum of 27 mm/year at Cape Muroto and gradually decreased with increasing distance from the Nankai Trough. Obvious velocity gradient changes around inland faults such as the Median Tectonic Line were not recognized, which was consistent with a previous study (Shimotsuma et al. 2026). Therefore, we did not consider inland fault effects in the following fault slip deficit estimation. Next, we estimated slip deficit rates on the Nankai subduction zone through fault slip inversion analysis. We used 115 GEONET stations mainly located in the Shikoku or Chugoku districts, 9 GPS-A stations in the Nankai offshore area, and InSAR velocities in Shikoku. Before the inversion, we reduced the number of InSAR pixels by performing a uniform downsampling procedure to approximately 4 × 4 km pixel interval. The subducted plate interface was divided into 25 × 25 km subfaults based on the plate geometry model (Iwasaki et al. 2015). Displacements due to subfaults’ slip were calculated using Okada dislocation model (Okada 1992) assuming a homogeneous elastic half-space. We imposed the smoothing constraint for slip deficit distribution as prior information, whose strength was determined based on Akaike Bayesian Information Criterion (ABIC) (Yabuki and Matsu'ura 1992). We iterated this inversion analysis by changing the relative data weight ratio between GNSS and InSAR, and we adopted the optimal ratio which minimized the weighted residual sum of squares (Maubant et al. 2023). Compared with a slip deficit rate distribution estimated from only GNSS, the distribution from GNSS and InSAR showed weaker smoothing. Considering the ABIC definition, this was attributed to the increasing amount of data, which reduced the penalty for violating the smooth slip. By adding InSAR data, slip deficit rates at the depth of about 40 km showed higher slip deficit rates, and slip deficit rates around the central Shikoku showed lower values.

Interferometric Synthetic Aperture Radar (InSAR) time-series analysis is a recognized remote sensing method for measuring surface deformation associated with volcanic activity and landslides. While the Small Baseline Subset (SBAS) algorithm (Usai et al. 2001) has become a standard technique for estimating millimetric surface motion, its reliance on consistent viewing geometries restricts analysis to single-sensor datasets. Consequently, observation frequency is strictly constrained by the satellite's revisit cycle. High temporal sampling is particularly critical for disaster monitoring, where sparse sampling can lead to the aliasing or complete omission of rapidly evolving precursors such as volcanic inflation or landslide acceleration. While the recent rise of small satellite constellations aims to address this temporal gap by reducing revisit times, these systems predominantly operate in X-band. Although effective for urban infrastructure, the short wavelength of X-band radar interacts primarily with the canopy surface, limiting coherence preservation in vegetated environments due to wind-induced motion or dielectric changes. Sentinel-1 (C-band), the current global standard, generally maintains better coherence than X-band. However, it still exhibits instability in the disaster-prone regions of Japan and Southeast Asia. Here, dense vegetation and seasonal snow cover frequently cause severe temporal decorrelation, resulting in data deficit exactly where monitoring is most critical. On the other hand, L-band missions (e.g., ALOS-4/NISAR) utilize a longer wavelength that penetrates vegetation to interact with stable ground scatterers, demonstrating superior coherence preservation. Yet, L-band systems are currently limited by a smaller number of operational satellites and generally lower observation frequencies, making it difficult to construct dense InSAR time series independently. These complementary characteristics motivate a framework that can combine temporally frequent yet spatially fragmented C-band observations with a more coherence-robust L-band backbone in a principled time-series inversion. The integration of such diverse datasets is formalized by the Multidimensional SBAS (MSBAS) framework (Samsonov & d’Oreye. 2012, Samsonov. 2024), which fuses observations onto a unified chronological timeline by estimating deformation velocities via singular value decomposition (SVD). However, conventional MSBAS processing typically relies on a common interferogram network designed for the full scene. This global selection creates a limitation in heterogeneous environments: locally valid measurements are often discarded if the overall scene coherence is low. Consequently, the inversion is forced to rely heavily on mathematical regularization (interpolation) rather than actual observational data to bridge gaps between accepted scenes. Independently, within the domain of single-sensor InSAR time-series analysis, Pixel-by-Pixel (PBP) approaches emerged to address similar coverage limitations. For instance, the Intermittent SBAS (ISBAS) method (Sowter et al., 2013) mitigates this by relaxing strict persistence requirements to exploit observations that are only intermittently coherent. By evaluating coherence on a layer-by-layer basis rather than enforcing a stack-wide threshold, ISBAS unwraps valid pixels individually and estimates velocities for points exceeding a minimum number of coherent layers. Similarly, Ishitsuka et al. 2016 introduced a pixel-based interferometric pair selection algorithm to tailor network connectivity based on local coherence statistics, further demonstrating the efficacy of spatially adaptive processing. In this study, we present Multidimensional-Intermittent SBAS (MI-SBAS), a framework that integrates the multi-sensor data fusion capabilities of MSBAS with the pixel-wise network selection strategy. Unlike standard techniques that enforce a globally fixed network, MI-SBAS dynamically constructs a unique set of valid interferograms for every pixel based on local coherence thresholds. This design aims to maximize the density of actual observations integrated into the inversion. Specifically, it allows for: (i) the retention of locally valid acquisitions rejected by global criteria; and (ii) the minimization of temporal gaps that require regularization, even when multi-sensor observations are sparse and misaligned. The inversion employs an SVD-based solver to robustly handle rank deficiencies, ensuring that the retrieved time series is constrained by physical measurements rather than interpolation wherever possible. We applied MI-SBAS to two Japanese volcanoes: Sakurajima (2015–2016) and Mt. Fuji (2021–2022). All interferograms were generated using the standard GMTSAR pipeline at a resolution of 50m, with a first-order polynomial fit applied to remove long-wavelength atmospheric and ionospheric phase delays. The Sakurajima dataset consisted of 34 Sentinel-1 scenes (405 interferograms) and 23 ALOS-2 scenes (252 interferograms). For Mt. Fuji, we utilized 60 Sentinel-1 scenes (989 interferograms) and 16 ALOS-2 scenes (105 interferograms), covering diverse surface conditions. GNSS observation data for validation were obtained from GNSS earth observation network system (GEONET) and Japan volcanological data network (JVDN). All computational processing benchmarks were conducted using a single core on an Apple M4 Pro Mac mini (14-core CPU, 64GB RAM). The results for Sakurajima demonstrate that MI-SBAS enhanced the density of input interferograms by recovering pairs that were excluded by the global coherence criteria of the common-network baseline. This led to an increase in the number of acquisition dates integrated into the inversion by up to 20 days (1.6 days on average). Although computational runtime increased from 119 s to 158 s, the retrieval quality improved: the median time-series uncertainty (temporal standard deviation) was reduced from 1.82 mm/yr to 1.69 mm/yr, and spatial smoothness (spatial standard deviation) improved from 17.3 mm/yr to 10.9 mm/yr. Validation against four GNSS stations indicates that the pixel-wise selection maintains comparable accuracy overall; for example, RMSE at station 960720 improved (29.3 mm to 22.1 mm), whereas station J884 showed a degradation (12.6 mm to 12.9 mm). Similarly, for Mt. Fuji, the number of valid dates in the input network expanded by up to 39 days (10 days on average) due to the inclusion of locally coherent interferograms. While the runtime increased from 377 s to 912 s, the method achieved a reduction in the median time-series uncertainty (from 3.74 mm/yr to 3.45 mm/yr) and better spatial smoothness (from 86.1 mm/yr to 58.6 mm/yr). GNSS validation across five stations showed substantial improvements in challenging areas, despite some degradation elsewhere; for example, the maximum RMSE reduction was recorded at station 021100 (126.2 mm to 55.2 mm), whereas station 93070 exhibited the largest degradation (20.3 mm to 32.4 mm). These metrics indicate that while the pixel-wise selection effectively maximizes observation density and recovers signals in difficult environments, it presents a trade-off with increased noise sensitivity in some areas. In conclusion, our results highlight the advantage of MI-SBAS. Whereas conventional methods that rely on regularization for apparent time-series continuity, our framework maximizes the density of direct observations. By prioritizing local signal quality and avoiding interpolation, MI-SBAS ensures that the retrieved time series are derived from coherent phase information. However, this approach presents specific limitations: (1) the output time-series coverage may decrease in some pixels where gaps are not interpolated, and (2) the increased volume of input data used for velocity estimation can introduce noise, potentially degrading accuracy in certain scenarios. Despite these trade-offs, the framework offers significant advantages in yielding spatially smoother displacement fields and higher effective temporal sampling frequencies. Although the computational overhead increases, the total processing time remains well within practical limits for operational monitoring. Future work will focus on integrating robust noise-aware weighting schemes to maximize the spatiotemporal coverage of the retrieved time series while maintaining consistent geodetic accuracy.

Time-Series Interferometric Synthetic Aperture Radar (TS-InSAR) is a powerful remote sensing technique for monitoring Ground Deformation (GD), which poses threats to geological stability and civil infrastructure worldwide. However, many existing TS-InSAR processing software tools face limitations, such as restricted geographic applicability, commercial licensing, and a lack of full end-to-end processing workflow support, forcing users to combine multiple processing platform, resulting in format incompatibility and workflow inefficiencies.While GMTSAR avoids some of these challenges, it still relies heavily on manual user intervention, requires the execution of C-shell commands, lacks a user-friendly interface, and misses key processing steps such as interferogram network pruning and anchoring of unwrapped interferograms, all of which hinder both accessibility and accuracy. This paper introduces DefoEye (v1), an open-source Python-based software that wraps GMTSAR to facilitate the full TS-InSAR processing chain for Sentinel-1 data through a user-friendly interface.DefoEye provides an end-to-end workflow, and the main contributions can be summarized as:• A streamlined, end-to-end TS-InSAR workflow that wraps GMTSAR, from SAR data download to final GD map generation and export, implemented through a structured sequence of processing steps and a user-friendly interface.• Parallelized job processing of key computational steps, including image co-registration, interferogram generation and correction, sub-swath merging, and phase unwrapping, significantly accelerating TS-InSAR analysis.• Newly added features, including automatic directory setup, interferogram network pruning using triplet loop closure, flexible anchoring methods (including point/region referencing and average-phase referencing), self-checks at different steps to ensure correct execution, and an enhanced export module with automatic validation using GNSS.Its applicability and reliability were evaluated in four regions with differing geological settings, causes of GD, and atmospheric and climatic conditions over the 2020-2024 period. For three regions, Bologna (Italy), Gotland (Sweden), and Houston (USA), DefoEye results were compared against 10 available GNSS stations, demonstrating strong consistency. For Karaj (Iran), where no GNSS stations were available, the DefoEye results were compared with those obtained from a widely used Python-based software, MintPy, for TS-InSAR analysis. MintPy requires a stack of unwrapped interferograms, which in this study were generated using GAMMA software. The same zero-reference points, processing parameters, and interferogram networks were used in the GAMMA + MintPy analysis as in the DefoEye processing to ensure a fair comparison between the two platforms. Comparisons show a very strong similarity between DefoEye and GAMMA + MintPy outputs, with DefoEye conducting the process through a unified workflow without the need for additional external software.Overall, the findings show that DefoEye delivers reliable TS-InSAR results suitable for a variety of geological, hydrological, and environmental applications. DefoEye software and a detailed step-by-step tutorial are available at: https://github.com/ATDehkordi/DefoEye.

The use of Synthetic Aperture Radar (SAR) has proven valuable in the context of agricultural monitoring due to its generally consistent acquisition schedule, on account of not being affected by illumination conditions and the presence of clouds. Additionally, SAR measurements are sensitive to structural and dielectric characteristics of the surface (soil roughness, presence and structure of the vegetation, vegetation water content and soil moisture), offering complementary information to the one provided by optical data. In agricultural applications, SAR-based monitoring commonly relies on time series of backscatter intensity and, in some cases, on polarimetric indicators to characterise crop development and soil conditions. In contrast, repeat-pass interferometric coherence has been comparatively less exploited, as its processing is computationally expensive and its interpretation in this context less intuitive. One frequent limitation in remote sensing studies is the scarcity of detailed ground truth data. The availability of comprehensive ground data in this study provides a valuable opportunity to investigate the complementarity of different radar observables for crop monitoring and to better understand the mechanisms driving temporal decorrelation over cultivated surfaces. This study focuses on the use of multi-year time series of Sentinel-1 backscattering coefficient and repeat-pass coherence amplitude over agricultural fields. The test site for the study is a set of agricultural parcels in the Cherkasy Oblast, Ukraine, comprising more than 15000 hectares of crops. The available dataset includes field boundaries, crop type, cultivated area, planting and harvesting dates, yield, and ancillary information on agricultural activities and some anomalies. Meteorological observations (daily temperatures, precipitation and snowfall) from nearby weather stations are also considered. This work takes advantage of the behaviour of coherence amplitude over vegetated surfaces. As vegetation grows, temporal decorrelation increases and coherence rapidly decreases, giving SAR sensitivity to the emergence of plants. Moreover, coherence is sensitive to other sources of temporal decorrelation, such as precipitation, snowfall and farmer activities in the fields, such as ploughing, sowing or harvesting, which are manifested as anomalies in the time series. Finally, changes over time in soil moisture, vegetation water content and its distribution affect both backscatter and coherence, giving information about water stress and plant health. The results highlight the potential of repeat-pass interferometric coherence derived from Sentinel-1 for crop monitoring applications. Paired with backscatter, coherence provides complementary information on vegetation structure and temporal dynamics, and it can be a useful tool for both field-level management and food security decisions.

This study investigates the water-surface dynamics of three lakes in the Wkra Forest (NW Poland): Piaski (32 ha), Piaszynko (2 ha), and Karpino (35 ha). The research was initiated following the complete disappearance of Lake Piaski in 2020, caused by a severe regional precipitation deficit. A multitemporal analysis of water surface area changes throughout 2020 was performed using Sentinel-1 C-band SAR data. Images acquired at six-day intervals were retrieved from the Copernicus Data Space Ecosystem and processed with SNAP. Thresholding classification was applied within the lake basins to delineate water extent. Derived surface areas were correlated with meteorological data (precipitation, temperature) from the Szczecin station. Results indicate that the drying process of Lake Piaski commenced in April/May 2020, resulting in total water loss by late July, which persisted until November. A partial recovery of water resources was observed in December 2020. Concurrently, Lake Karpino and Lake Piaszynko experienced reductions in surface area of 17% (6 ha) and 20% (0.3 ha), respectively. The Climatic Water Balance (CWB) during the summer of 2020 dropped below -150 mm, reaching a minimum of < -200 mm in July. The study demonstrates the high potential of high-temporal-resolution SAR data for monitoring hydrological drought in small lake ecosystems.

Rapid and reliable damage mapping is essential for effective emergency response after natural disasters. With the situational awareness that damage maps provide, responders can make plans which use their resources efficiently, make decisions on transport logistics, and identify locations that require more specific targeted attention. In the medium to long term, rapidly-produced damage estimates can be used to identify priority areas for follow-up damage surveys and for making informed decisions about disaster recovery operations.Synthetic aperture radar (SAR) satellite remote sensing is a valuable tool for the task of damage mapping thanks to three key advantages: It has all-weather, day-and-night data collection capabilities, wide spatial coverage, and it is sensitive to changes in the structure or topography of the ground surface, enabling the detection of damage-related disturbance and deformation. Current semi-operational damage proxy mapping (DPM) algorithms primarily rely on detecting atypical decreases in SAR’s interferometric coherence. These algorithms perform well in high-coherence areas such as urban environments. However, their sensitivity drops sharply in regions with naturally low or variable pre-event coherence such as forests, agricultural land, or some mixed-use land cover.In this research we present a new damage proxy mapping algorithm that fuses interferometric coherence and SAR intensity similarity using a spatially adaptive weighting scheme. While intensity similarity is less sensitive than coherence to damage in built-up areas, it proves to be more robust across low-coherence land cover types. We evaluate the algorithm’s performance using labelled data from independent sources including ground surveys, aerial surveys, and very high-resolution optical satellite imagery. To make sure that the algorithm has good transferability, our suite of test cases contains six diverse disaster events, including landslides, liquefaction, earthquake, tsunami, and fires.Our results show that the new method consistently outperforms or matches the benchmark coherence-based algorithm, particularly in vegetated and non-urban regions. The new algorithm performs well on both C-band radar data from the Sentinel 1 satellites, and L-band data from ALOS 2. It also performs well relative to the benchmark under limited data conditions using only two pre-event scenes. This versatile approach improves damage mapping across heterogeneous landscapes, offering significant potential for operational disaster response.

The increasing frequency and intensity of natural hazards, exacerbated by climate change and progressive urbanization, have amplified the need for robust, scalable, and financially interpretable risk assessment frameworks. In Europe, economic losses due to weather- and climate-related extremes have grown significantly over the past decades, while in Italy alone seismic recovery costs have reached hundreds of billions of euros in the last sixty years. These figures underline the urgency of transitioning from reactive post-disaster reconstruction strategies to proactive, data-driven risk mitigation and financial planning approaches. Within this context, the VALUESAFE framework (Vulnerability of Assets and Losses in mUltirisk Evaluations SAtellite data for Financial Estimation) proposes an integrated, multi-hazard and multi-scale platform designed to support both public authorities and private stakeholders in urban and territorial risk assessment, damage estimation, and asset depreciation forecasting. Current state-of-the-art methodologies for disaster risk reduction present several structural limitations. Data fragmentation across heterogeneous repositories, inconsistencies in spatial resolution, and the frequent separation between qualitative resilience indicators and quantitative economic loss modeling reduce comparability and operational usability. Indicator-based resilience frameworks offer conceptual flexibility but often lack direct translation into monetary loss. Conversely, engineering-based vulnerability assessments, although physically grounded, are resource-intensive and difficult to scale across large building stocks. These gaps motivate the development of standardized, automated, and interoperable platforms capable of bridging territorial screening with asset-level diagnostics. VALUESAFE addresses these limitations through a Software-as-a-Service (SaaS) Web-GIS architecture structured across three progressive levels of analysis - Explore, Insight, and Detail - each corresponding to increasing spatial resolution, data granularity, and computational depth. The framework is designed to ensure methodological coherence while maintaining scalability and adaptability to different stakeholder needs, ranging from preliminary territorial prioritization to facility-specific financial diagnostics. At the Explore level, the system performs automated large-area screening based primarily on existing global and national hazard inventories, census data, open geospatial building databases, and cadastral or market-based real estate quotations. Outputs are generated on a grid-based territorial scale and provide integrated risk classifications derived from the additive combination of hazard, vulnerability, and exposure indicators. Although spatially generalized, this tier enables rapid identification of macro-scale criticalities, particularly useful for regional planning authorities, portfolio managers, and preliminary investment screening. The Insight level refines the analysis at building or urban-cluster scale by integrating dynamic satellite-derived measurements and AI-driven image processing techniques. In particular, A-DInSAR time-series derived from Sentinel-1 data are incorporated to detect ground displacement patterns with millimetric precision, improving hazard characterization for landslide, subsidence, and ground instability phenomena. AI-based image segmentation algorithms extract building footprints, façade opening ratios, geometric attributes, and approximate construction age when not available from official datasets. This enrichment allows the calculation of hazard-specific vulnerability indices incorporating parameters such as building height, number of floors, structural typology, material class, and window-to-wall ratio. For seismic risk, fragility curves and EMS-98 classifications are used to estimate expected damage levels and condemnation probabilities. Flood vulnerability assessments integrate building height and hydraulic hazard layers to approximate inundation exposure. The resulting outputs include quantitative risk scores, expected damage scenarios, and estimated economic depreciation expressed in absolute and percentage terms, enabling operational decision-making for insurance providers, municipalities, and asset managers. The Detail level focuses on facility-specific diagnostics, leveraging high-resolution satellite data such as COSMO-SkyMed imagery to detect differential deformation at sub-building scale. When available, on-site surveys and user-provided structural information are integrated to refine vulnerability modeling, including foundation typology and structural system characteristics. Scenario-based analyses are performed under alternative hazard intensities (e.g., different peak ground accelerations for seismic events), producing detailed damage probability distributions and associated financial loss estimates. For flooding scenarios, local topographic profiles and cross-sectional analyses are combined with hydraulic hazard maps to simulate water depth variations and asset-specific exposure. Subsidence assessments incorporate time-series deformation trends and satellite-based stability indicators to evaluate potential structural impacts due to differential settlement. This tier delivers hazard-focused synthesis sheets that directly connect physical performance to financial consequences, offering actionable insight for high-value or critical infrastructure assets. The computational backbone of VALUESAFE is based on an additive risk formulation in which Hazard (IH), Vulnerability (IV), and Exposure (IE) indices are computed separately and combined to derive a global Risk index. Each index is structured upon a multi-tier severity scale and tailored to the hazard type and service level. Hazard indices integrate both static inventories and dynamic satellite-based measurements. Vulnerability indices evolve from multifactorial empirical formulations at Explore level to parameter-rich engineering-informed models at Insight and Detail levels. Exposure indices consider built volume, resident population, territorial extent, and asset economic value, with customizable weighting factors to prioritize human safety or financial assets depending on stakeholder objectives. A distinguishing feature of the framework is the explicit translation of risk indicators into financial loss and depreciation estimates. Economic exposure is derived from official real estate quotations and cadastral data, while reconstruction cost references and regulatory compensation frameworks are used to calibrate damage-to-loss conversion. This ensures that projected losses align with realistic post-disaster recovery mechanisms. The resulting depreciation indices quantify expected reduction in asset value under specified hazard scenarios, bridging engineering risk modeling and financial analytics. The methodology has been tested on case studies in central Italy, including large-scale applications in Rome municipality (Explore level) and multi-tier analyses in the Mugello area (Insight and Detail levels). In the Rome test case, grid-based territorial screening successfully captured macro-scale flood and landslide patterns near major river basins, demonstrating the effectiveness of automated large-area classification. In Mugello, a region affected by recent seismic events, building-scale risk assessments integrated fragility-based damage modeling with satellite-derived deformation velocities. Outputs included detailed numerical indicators of hazard, vulnerability, exposure, expected damage classes, condemnation probabilities, and associated depreciation values. High-resolution analyses at Detail level further identified localized deformation trends and differential structural behavior within single facilities. Preliminary validation against previous ad hoc engineering studies conducted in the same area showed substantial agreement in risk classification and damage estimation, while achieving significant reductions in computational time and resource expenditure. Overall, VALUESAFE demonstrates that the integration of Satellite Earth Observation (SatEO), AI-based building characterization, and engineering-based vulnerability modeling within a unified Web-GIS environment can overcome critical methodological gaps in multi-hazard assessment. The three-tier architecture ensures continuity between territorial screening and asset-level diagnostics, preserving consistency of indicators while adapting to increasing data resolution. By embedding financial estimation directly into the computational workflow, the framework supports not only disaster risk reduction strategies but also investment planning, insurance underwriting, and portfolio risk management. From a scientific perspective, the contribution of VALUESAFE lies in three main advancements: (i) the operational integration of dynamic InSAR-derived ground motion data into multi-hazard risk indices across different territorial scales; (ii) the extraction of vulnerability-related building parameters at urban scale and their processing to mitigate data fragmentation; and (iii) the systematic coupling of risk modeling with regulatory-aligned economic depreciation estimation. These elements collectively enable a scalable, standardized, and decision-oriented approach to urban resilience analytics. Future developments will focus on extending validation across diverse geographical contexts, incorporating probabilistic multi-scenario modeling, and enhancing dynamic monitoring capabilities through near-real-time satellite data streams. Additional research will also address uncertainty quantification within each index component and explore machine learning approaches for adaptive calibration of fragility and loss functions. By continuously refining its methodological robustness and operational usability, VALUESAFE aims to contribute to international resilience objectives and to provide a transferable model for integrated multi-risk assessment in climate-vulnerable territories. The research and operational developments presented in the contribution have been carried out within the framework of the European Space Agency (ESA) InCubed programme, fostering the transition from research-driven methodologies for innovative Earth Observation–based solutions to market-ready multi-risk assessment services.

Landfilling is widely recognised as one of the most environmentally harmful waste disposal methods. However, in many developing countries it is still commonly used because it is relatively low-cost. The key challenge is therefore how to identify acceptable landfill locations while taking socio-economic conditions into account.In our case study area, waste management is under increasing pressure. Rapid economic growth and urbanisation have led to a continuous rise in both the volume and complexity of solid waste. At the same time, existing landfill infrastructure is approaching its environmental capacity. Under these conditions, the identification of new landfill sites becomes essential for sustainable urban development and public health protection.This study applies a hybrid approach that combines remote sensing (including SAR data) with GIS-based Multi-Criteria Decision Analysis (GIS-MCDA) to assess landfill suitability. SAR information was used to characterise surface conditions and terrain constraints relevant to environmental risk assessment. Economic, environmental, topographic and social factors are evaluated together to support spatial decision-making. The results indicate that only 45% of the study area can be classified as highly or very highly suitable. Nevertheless, 19.78% of these areas still present notable environmental and human health risks. The proposed framework can also be adapted for other low- and middle-income countries facing similar municipal solid waste management challenges.

Forests are essential for global climate regulation, carbon storage, and biodiversity preservation, making rapid disturbance monitoring a critical priority. However, traditional optical satellite sensors are often limited by cloud and smoke cover during active disturbance events. This project addresses this gap by leveraging Interferometric SAR (InSAR) coherence to provide a robust, weather-independent monitoring framework. The study specifically focuses on the 2022 forest fire in the Bohemian-Saxon Switzerland region, which represents the largest fire event in modern Czech history. This event devastated approximately 1,300 hectares of forest between July and August 2022, highlighting the urgent need for advanced remote sensing tools that can penetrate atmospheric obstacles to map biomass loss in real-time. At its scientific core, InSAR coherence serves as a measure of the geometric and dielectric stability of the Earth's surface over time. In the context of forest monitoring, healthy and structurally complex forests typically maintain permanently low coherence values. This is primarily due to volume scattering, where microwave radiation penetrates the canopy and interacts with thousands of randomly oriented scatters such as leaves, needles, and small branches. These interactions cause a random summation of phases, leading to phase decorrelation. However, when a fire removes the forest canopy, the scattering process changes significantly, often resulting in increased coherence values as the remaining stable structures or the ground surface become the dominant reflectors. The methodology utilized in this research involved the analysis of an extensive time series from 2019 to 2025, consisting of 206 interferometric pairs from the Sentinel-1A satellite with a 12-day temporal baseline. The data processing was automated through a pipeline using SNAP GPT and PowerShell. The final output products achieved a spatial resolution of 30 meters per pixel. To isolate the specific signal caused by the fire from environmental noise, the study employed a Generalized Least Squares (GLS) statistical framework. This model was used for adjusting for various decorrelation sources, including the perpendicular baseline of satellite orbits and meteorological variables such as wind speed, precipitation, temperature, and snow cover. The results of the statistical modeling confirm that the 2022 fire had a significant impact on coherence levels within the affected areas. While the reference healthy forest showed no statistically significant change during the fire period, the burned areas exhibited a significant increase in coherence with an estimate of 0.045. This increase effectively marks the transition from a complex volume-scattering environment to a more stable, disturbed surface. Furthermore, the research identified that meteorological factors, particularly temperature and precipitation, were significant contributors to temporal decorrelation. In conclusion, the application of Sentinel-1 InSAR coherence provides an indicator for identifying biomass loss and forest disturbances. Its ability to provide consistent data regardless of smoke or cloud cover offers a distinct advantage over optical sensors during active forest fires. However, the study also notes that challenges remain, particularly regarding the influence of terrain topography and other geometric uncertainties in complex landscapes.

Forest biodiversity comprises both vertical structural heterogeneity and canopy compositional variability, which arise from distinct physical processes and are differentially observable across remote sensing domains. Long-wavelength P-band SAR is sensitive to volume scattering and canopy vertical organization, whereas optical hyperspectral and multispectral data primarily capture biochemical and mixture-dependent reflectance variability. This study investigates whether these domains provide interchangeable or complementary information for forest biodiversity characterization in the Taita Hills, encompassing montane cloud forests and lowland savanna woodlands.  Four hypotheses are tested: (H1) P-band PolSAR and PolInSAR observables are associated with independently measured vertical structural complexity; (H2) optical spectral diversity metrics are associated with in-situ compositional diversity; (H3) structural and spectral predictors explain partially independent variance in biodiversity metrics; and (H4) combined models outperform single-domain models.  Polarimetric (PolSAR) and interferometric (PolInSAR) metrics are derived from multiple acquisitions of the BIOMASS mission, including entropy, anisotropy, cross-polarized backscatter, and coherence-based vertical structure proxies. Airborne LiDAR point clouds provide independent structural reference metrics, while forestry inventory plots containing species composition, DBH, and tree height measurements provide compositional and structural diversity indices. Spectral diversity metrics are derived from PRISMA hyperspectral and Sentinel-2 multispectral surface reflectance data.  Random Forest regression with spatial block cross-validation is used to evaluate structural and compositional relationships and to quantify the complementarity of radar and optical predictors. The study clarifies the extent to which P-band interferometric observables represent vertical diversity and examines whether structural and spectral domains provide redundant or complementary information for biodiversity inference. 

Vegetation-Specific Correction for Improved Soil Moisture Estimation Using NavIC-IR Multipath Phase Analysis Integrated with CORS GNSS Network Framework Soil moisture is a fundamental variable in the Earth’s hydrological cycle and plays a decisive role in agricultural productivity, evapotranspiration, groundwater recharge, and land–atmosphere interactions. Accurate and continuous monitoring of soil moisture is essential for precision agriculture, drought assessment, irrigation planning, and climate modeling. Traditional in-situ methods provide high accuracy but limited spatial coverage, while satellite missions such as SMAP and SMOS offer broader coverage at relatively coarse spatial and temporal resolutions. Ground-based GNSS-Interferometric Reflectometry (GNSS-IR) provides a promising intermediate solution by enabling continuous, high-temporal-resolution monitoring using existing GNSS infrastructure. This study presents a novel vegetation-specific correction methodology for soil moisture estimation using multipath phase observations derived from Navigation with Indian Constellation (NavIC) signals. The approach leverages the sensitivity of multipath interference patterns to near-surface dielectric variations, which are directly related to volumetric soil moisture content (VMC). Unlike conventional GNSS-IR studies primarily focused on GPS, this work utilizes NavIC L-band signals, whose longer wavelength enhances penetration capability through vegetation and improves robustness in cropped agricultural fields. Theoretical Background GNSS-IR relies on the interference between direct satellite signals and signals reflected from the ground surface. When a geodetic right-hand circularly polarized (RHCP) antenna is mounted above the ground, the reflected signal experiences a path delay relative to the direct signal. This path difference produces a sinusoidal modulation in the Carrier-to-Noise ratio (C/No). The multipath phase is particularly sensitive to changes in surface dielectric properties, making it a strong indicator of soil moisture variations. However, vegetation growth modifies the reflective properties of the surface, introducing additional phase shifts and reducing estimation reliability if not properly compensated. NavIC Signal Characteristics NavIC satellites provide elevation angle coverage between approximately 15° and 30° in the usable GNSS-IR range. Within this low-elevation window, multipath effects are pronounced and suitable for soil moisture analysis. As elevation increases, direct signals dominate, diminishing multipath sensitivity. Therefore, this study restricts analysis to the optimal low-elevation regime. Vegetation-Specific Phase Compensation The novelty of this work lies in explicitly accounting for vegetation growth effects using NDVI (Normalized Difference Vegetation Index) as a vegetation proxy. The dataset was divided into two vegetation groups: Group 1: NDVI < 0.20 (early growth stage) Group 2: 0.20 ≤ NDVI < 0.35 (moderate vegetation stage) Analysis revealed systematic phase shifts between these groups. The generalized multipath phase was expressed as: where: = soil moisture contribution, = vegetation-induced phase component, = direct signal phase. To compensate for vegetation effects, normalization was applied: This normalization mitigates vegetation-induced bias while preserving soil moisture sensitivity. Machine Learning-Based Soil Moisture Retrieval A Random Forest Regressor was implemented to model nonlinear relationships between normalized multipath phase and measured soil moisture (VMC). The dataset was split 80:20 into training and testing subsets. Hyperparameter optimization using Grid Search Cross-Validation identified the optimal configuration: n_estimators = 150 max_depth = None min_samples_split = 5 min_samples_leaf = 1 The optimized model achieved a Mean Squared Error (MSE) of 1.63%, demonstrating high predictive capability and strong generalization performance across vegetation stages. Scatter plot analysis between predicted and true VMC values confirmed close alignment along the ideal 1:1 regression line, validating the robustness of vegetation-compensated multipath phase modeling. Integration with CORS GNSS Network A significant advancement of this work is its potential integration within a Continuously Operating Reference Station (CORS) GNSS network. CORS infrastructure, typically deployed for geodetic positioning and crustal deformation monitoring, can serve as a cost-effective environmental sensing network when multipath observables are systematically analyzed. In a CORS-based deployment: Existing geodetic-grade antennas can be used without additional instrumentation. Continuous multi-year datasets enable seasonal and inter-annual soil moisture monitoring. Large-scale spatial coverage can be achieved by integrating multiple CORS stations across agricultural regions. Real-time data streams can support near-real-time soil moisture monitoring. The National Centre for Geodesy (NCG) CORS network framework provides an ideal testbed for scaling this methodology. By embedding multipath phase processing algorithms into CORS data pipelines, soil moisture retrieval can become an additional value-added service of national geodetic infrastructure. Such integration transforms CORS networks from purely positioning infrastructure into multipurpose environmental observatories, supporting: Precision agriculture Drought early warning Irrigation optimization Land–atmosphere coupling studies Climate resilience modeling Furthermore, NavIC-based implementation strengthens national capability by leveraging indigenous satellite infrastructure for agricultural monitoring applications. Conclusion This study introduces a vegetation-aware multipath phase correction methodology for improved soil moisture estimation using NavIC-IR signals. By categorizing data according to vegetation growth stages and applying group-specific normalization, the model significantly enhances estimation reliability under dynamic crop conditions. The optimized Random Forest regression framework achieves high prediction accuracy (MSE = 1.63%), demonstrating that vegetation-compensated multipath phase is a robust proxy for soil moisture retrieval. Importantly, the approach can be operationalized using existing CORS GNSS networks, enabling scalable, continuous, and cost-effective soil moisture monitoring without additional sensor deployment. This integration establishes a powerful synergy between geodetic infrastructure and environmental remote sensing, contributing to sustainable agriculture and climate-adaptive resource management.

Every year, millions of metric tons of plastic waste enter the oceans from land-based sources, and projections suggest that annual inputs will continue to rise substantially (Borelle et al., 2020). Under the influence of converging currents, this debris accumulates into plastic islands floating on the surface of oceans, seas, and rivers. These accumulations represent a serious threat to both marine ecosystems and human health. They endanger aquatic organisms through entanglement and ingestion, disrupt marine habitats, and contaminate the food chain, ultimately affecting human consumers. In addition to ecological impacts, large floating plastic islands can also interfere with maritime activities and pose risks to vessels and navigation. This study presents preliminary results from two experiments that aim to evaluate the capability of Synthetic Aperture Radar (SAR) satellite data to identify plastic marine litter. To address this objective, a small artificial floating plastic island was constructed using various plastic items, including water bottles, detergent bottles, boxes, and plastic bags. These materials were arranged along multiple ropes to reproduce as closely as possible the configuration typically observed in natural floating plastic litter, which often organizes into long, narrow stripes. The assembled structure was deployed at two separate sites to test its detectability under different environmental conditions. The first experimental campaign was carried out in October 2024 at Lake Massaciuccoli, near Viareggio in the Tuscany region. The second campaign took place in April 2025 in a marine environment near Portovenere, within the Gulf of La Spezia in the Liguria region. During both experiments, high-resolution X-band SAR imagery was acquired by the Cosmo-SkyMed and ICEYE satellite missions, which feature varying viewing geometries, polarizations, and spatial resolutions, to assess their influence on the visibility of the floating plastic target. The results indicate that the artificial plastic island is visible in SAR scenes as a thin linear feature spanning only a few pixels, characterized by backscattering values slightly higher than those of the surrounding water surface. This behavior is consistent with known scattering mechanisms: floating debris introduces localized surface roughness, enhancing diffuse backscatter toward the sensor, whereas calm water surfaces act predominantly as specular reflectors, redirecting most of the incident radar energy away from the sensor. Furthermore, the comparison among different SAR acquisition configurations enabled an initial assessment of how key parameters—such as type of polarization and incidence angle—affect the visibility of floating plastics. The statistical analyses conducted on these preliminary datasets provide valuable indications for identifying the most suitable SAR configurations, serving as a preparatory step toward the generation of change detection maps specifically designed to monitor the evolution of floating plastic accumulations. While these findings provide encouraging evidence of the feasibility of SAR-based monitoring of plastic marine litter, additional investigations are required to better understand the role of each acquisition parameter and to distinguish the radar signature of plastic materials from other floating elements such as wood, algae, foam, and similar debris. This study was carried out within the Space It Up project funded by the Italian Space Agency, ASI, and the Ministry of University and Research, MUR, under contract n. 2024-5-E.0 - CUP n. I53D24000060005." REFERENCES Borelle, S.B.; Ringma, J.; Law, K.L.; Monnahan, C.C.; Lebreton, L. (2020). Predicted Growth in Plastic Waste Exceeds Efforts to Mitigate Plastic Pollution. Science 2020, 369, 1515–1518.

Paddy rice is essential for global food security, yet its accurate monitoring in tropical Asia remains challenging due to complex multi-season cultivation patterns and persistent cloud cover. Previous studies have achieved promising results on rice mapping using time series of Sentinel-1 data, but the multi-season cases have been rarely considered, and the evolution of different advanced SAR-derived features has not been fully investigated. In this study, we analyse the interferometric and polarimetric signals displayed by single- and double-season tropical rice fields, with the intention of finding useful information to indicate key phenological signals under tropical agronomy scenarios. The study area is located in Hainan Province, China, whose agricultural landscape exhibits typical characteristics of tropical Asia: small in size and irregular in shape. Time series of backscattering coefficients (σVH, σVV) and Radar Vegetation Index (RVI) from February to November 2025 were obtained from Google Earth Engine. In addition, during the periods from 8th February to 3rd May, and 7th August to 29th November, 6-day repeated observations were also provided by the newly operational Sentinel-1C alongside Sentinel-1A. The 12-day coherence over the full period and the 6-day coherence from August to November were extracted to evaluate the sensitivity of interferometric coherence and decorrelation to the phenological evolution of rice. Moreover, two dual-polarimetric decompositions (the dual-polarimetric H-alpha decomposition and a model-based dual-polarimetric decomposition) were applied to inspect the evolution of scattering mechanisms for single- and double-season tropical rice. Coherence is highly sensitive to ground variations and is usually low in paddy fields. Our preliminary experimental results indicate that the 12-day coherence of single-season rice in tropical Hainan displayed similar temporal trends to those of temperate climate zones. For both VH and VV polarizations, the 12-day coherence values were significantly higher in the field preparation and post-harvest stages than in the growth stage, and the coherence values of VH were lower compared to VV. The coherence of VV may exceed 0.5 during the start-of-season (SOS), then drops rapidly to a level around 0.2. This suggests that VV coherence may constitute a potential indicator for rice cultivation, especially under the circumstances where the flooding signals of early rice are not as significant as in temperate regions. For the double-season rice, in contrast, the 12-day coherence of VH was always low from the beginning of the season. For the VV polarization, some parcels exhibited slightly higher coherence in the SOS, but the difference compared to the growing stage was not as distinctive as in single-season rice. These results were reasonable because of a shorter land-preparation period for the rush of early-rice sowing, and thus a faster coherence loss. However, it was found that for some parcels, the coherence of VV rose in July or August, which could be attributed to the compound effects of grain drying, harvest, and straw residues of early-season rice. Furthermore, the 6-day coherence from August to November was inspected. Even though rapid biomass accumulation still leads to significant decorrelation, the 6-day coherence values were significantly higher than those of the 12-day baseline for both single- and double-season rice. The harvest signal of single-season rice was clearly reflected by the 6-day VV coherence, and for some parcels even in the VH polarization, where 6-day coherence exceeded 0.4 at the end-of-season (EOS). Regarding polarimetric features, since the dual-polarimetric covariance matrix does not provide a full picture of scattering mechanisms, these features did not provide superior performance as scattering mechanism indicators to backscattering intensities. Our results revealed that dual-polarimetric alpha (α), entropy (H), and the volume scattering component (Pv) from model-based decomposition all followed trajectories similar to and RVI. For single-season rice, these curves reached troughs during the flooding stage, then started to increase as crop biomass accumulated through the vegetative stage. During the maturation stage, mainly from late August to September, the curves declined at first, and then returned to a second peak rapidly, which can be easily confused with the actual harvest signal occurred subsequently in October. For the double-season parcels, the harvest timepoint of the first season rice was more difficult to identify because it was closely followed by the sowing of the second season. Even if the land still went through preparation, the scattering intensities might not decline significantly and could return rapidly to levels similar to July. Consequently, the harvest and re-sowing signals of rice were not clearly identified, and the backscattering intensity just fluctuated around a ‘stable’ level, which could lead to an overestimation of the growth period, and thus a misclassification of the double-rice as other crops. This research aims to supplement theoretical studies on multi-season rice monitoring in fragmented tropical agronomy landscapes. Our preliminary results indicate that polarimetric features reflected similar information to backscattering, whereas the timing and magnitude of coherence-loss might provide supplemental information for the identifications of the SOS or EOS. We assume that with a denser time-series, the synergy of intensity and interferometric information (for instance, the timing, degree, and rate of decorrelation) might improve the discrimination of early- and late-season rice. This requires a stable coherence pattern of rice, which should be attributed to real biophysical changes and reduce the influence of the environmental factors such as tropical precipitation, which will be considered in our future works.

Wetlands provide key ecosystem services to humans. However, most remain unmonitored, and the costs to monitor them are enormous. Monitoring is essential as unmonitored still waters are already facing accelerated Earth system change, driven by human activities and climate change, with unknown consequences. Interferometric Synthetic Aperture Radar (InSAR) is a promising technology for observing these resources from space. It employs the differences in the path length of two satellite acquisitions taken from the same orbit to generate maps of spatial and temporal changes of the water or land surfaces. Here we test two hypotheses concerning its application: First, besides determining water levels, InSAR can be used to track water surface extent in wetlands. Second, InSAR can be used to locate hydrological barriers, even those found below vegetation or not obvious from satellite imagery. To test both, we perform InSAR on ALOS PALSAR 1-2 imagery in several large wetlands worldwide and capply a convolutional neural network model to identify hydrological barriers in the wetlands. The model can successfully locate flow barriers by seeing abrupt patterns of differences in phase, enabling mapping of the hydrological barriers to flow in wetlands such as roads, ditches, or embankments. We further assess hydrological connectivity in other wetlands of the Ramsar Convention, identifying permanent and seasonal barriers and developing an indicator of connectivity. Regarding water surface extent, we combine InSAR with polarimetric analysis to detect flooded vegetation in a tropical wetland in Colombia. We find that InSAR detects water surfaces even in areas where conventional backscatter-based methods are limited by dense canopy and complex scattering. The method reveals extensive inundation beneath vegetation that is larger than that obtained from common SAR mapping approaches and allows to construct probabilistic flooded vegetation maps. In this time of rapid Earth system change and the availability of SAR sensors increasing worldwide, we show the unknown potentials of InSAR for the monitoring and hydrological assessment of the functioning of wetlands.

Mountain regions are highly sensitive to climatic change, and the effects of changing environmental conditions can destabilise rock and soil slopes. This can lead to increased hazards to people and infrastructure as it can result in accelerations of slow-moving landslides and reactivations of dormant landslides. As these changes occur over large geographic areas, regional-scale monitoring of slope dynamics is becoming essential for both hazard assessment and for understanding ongoing landscape change. Freely available ESA Sentinel-1 data enables wide-area surface displacement monitoring with frequent revisit times. This data is typically processed using PS-InSAR techniques, which focus on stable backscatterers. However, direct analysis of wrapped interferograms allows additional information to be extracted with substantially less processing. It also offers analysis ready data from single interferograms instead of requiring long time series, and avoids the need to blindly mask areas affected by phase aliasing. However, despite the abundance of available data, interpreting interferograms remains labour-intensive while requiring expert knowledge. Deep learning based semantic segmentation algorithms have recently emerged as a promising tool to enable scalable exploitation of these datasets. However, segmentation models are often trained and or validated on labels created using optical imagery, rarely account for task-specific uncertainties and inter-operator variability inherent to interferometric interpretation, and currently still focus on monitoring known mass movements rather than also performing detection. We address these gaps by comparing the performance of state-of-the-art semantic segmentation algorithms with the range of expert variability for mapping mass movement-related phase patterns. We processed Sentinel-1 SLC data over Valais, Switzerland (5600 km²), from tracks 088 (ascending) and 066 (descending) using 12 – 18 day temporal baselines during summer. We generated D-InSAR interferograms with Goldstein filtering and topography-based atmospheric correction. Expert delineations of ten selected case studies revealed low agreement (Intersection over Union (IoU): 0.21 – 0.41), highlighting the difficulty of consistently distinguishing coherent and incoherent phase patterns and separating deformation signals from noise. A custom training dataset (>1000 labels; velocity range ~15 – 210 cm/a) was created from four interferograms and used to evaluate different model configurations. The best-performing model, a U-Net++ with a ResNet-18 encoder, achieved IoU 0.531 ± 0.029 (Dice 0.693 ± 0.024) on positive-only samples and slightly lower performance (IoU 0.494 ± 0.045; Dice 0.661 ± 0.041) on mixed datasets. Comparison with expert mappings showed deep learning model performance within inter-expert variability (mean IoU 0.494 ± 0.045), with an IoU of 0.61 relative to training labels. Once trained, the model segments interferograms in seconds, whereas experts required more than three hours for the twelve selected case examples. These results show that deep learning can reduce manual mapping effort by guiding attention to relevant signals within the vast amounts of available spaceborne radar interferometry data, supporting expert-driven mass movement cataloguing, and facilitating future large-scale detection and monitoring applications.

Landslides represent some of the most destructive natural hazards encountered in unstable mountainous regions, such as the Western Ghats in India. The examination of landslides has garnered significant global attention due to their profound impacts on socio-economic activities. The utilization of remote sensing and geographic information systems has proven valuable for integrating the spatial factors that contribute to landslide occurrences. In this study, satellite imagery from Sentinel 1-C Band has been employed, and further interferometry techniques for detecting the landslide event in 2024 in the Wayanad district, Kerala. Leveraging Artificial Intelligence Techniques in RADAR remote sensing such as machine learning algorithms, particularly the Random Forest (RF) model, were utilized to classify the study area into affected and non-affected regions. The findings also indicate the affected land use and land cover in the given study area. In the end, it can be concluded that significant landslides on 30th July, 2024 in the Wayanad district were primarily precipitated by anthropogenic interventions, compounded by heavy precipitation and unstable topography. Activities such as stone quarrying and infrastructure development emerged as critical factors contributing to these landslides. This research provides valuable insights aimed at mitigating landslide hazards in the Wayanad district, thereby fostering sustainable development.

Interferometric Synthetic Aperture Radar (InSAR) has become a well-established technique for monitoring slow-moving landslides, particularly in mountainous regions where in-situ measurements are challenging due to limited accessibility and harsh terrain. The Sentinel-1 mission, with its 12-day repeat acquisition cycle and open-access data policy, provides a reliable and cost-effective resource for continuous surface deformation monitoring at regional scales. This study evaluates the applicability of Sentinel-1 data for landslide monitoring of the Mayoon landslide, located in Mayoon village within the Hunza-Nagar Valley, northern Pakistan. The landslide has evolved gradually over time but has shown signs of accelerated activity in recent years, raising significant concern. The area is highly vulnerable: approximately 120 families (∼1000 inhabitants) reside at the toe of the slope. Furthermore, the landslide is situated in close proximity to the Karakoram Highway, a strategic transportation corridor linked to the China–Pakistan Economic Corridor (CPEC). The geomorphological setting further amplifies the associated hazard. The landslide mass is adjacent to a river corridor, and a potential large-scale slope failure could obstruct the river flow, forming a temporary landslide dam. Such an event could trigger catastrophic upstream flooding and downstream outburst floods, posing severe risks to local communities and critical infrastructure. In this study, we investigated the Sentinel-1 InSAR time series using the Small Baseline Subset (SBAS) approach to retrieve surface displacement over the Mayoon landslide. The landslide exhibits an east-southeast facing slope, resulting in moderate geometric sensitivity with respect to the Sentinel-1 line-of-sight (LOS) configuration. Combined with generally good temporal mean coherence across large portions of the area of interest, these conditions make the site both challenging and suitable for evaluating the performance and limitations of Sentinel-1-based multi-temporal InSAR analysis in mountainous terrain. The dataset spans the period 2023–2024 and comprises 59 interferograms generated using the Alaska Satellite Facility (ASF) Hybrid Pluggable Processing Pipeline (HyP3) on-demand service. To enhance phase stability, multilooking with a factor of 20 × 4 was applied prior to time-series inversion. The HyP3-generated interferograms were subsequently processed using the MintPy framework to perform SBAS time-series inversion, from which line-of-sight (LOS) displacement time series and mean velocity fields were derived. The estimated mean LOS velocity of approximately −3.9 mm/year (negative values indicating motion away from the satellite) indicates ongoing active deformation within the area of interest. To further investigate the internal kinematics of the landslide body, transect-based time-series analyses were conducted by selecting three representative points across different sectors of the landslide. The results reveal spatially heterogeneous displacement behavior, with distinct sectors exhibiting varying displacement magnitudes and temporal evolution relative to the reference acquisition date, indicating non-uniform kinematic patterns across the slope. A key methodological challenge encountered in this study relates to reference point selection for time-series inversion. The valley floor is characterized by vegetation cover that reduces interferometric coherence, particularly after multilooking, while persistently coherent zones are primarily located along upper slope and ridge areas that may themselves be deforming. Reference selection within such regions can introduce bias in displacement estimates and ambiguity in inferred deformation rates.

The aim of the analyses is to assess the usefulness of satellite data from the European Ground Motion Service (EGMS), collected under the European Copernicus programme and based on Persistent Scatterer Interferometry (PSI), for analyzing manifestations of landslide activity in the Carpathian region. Observation and monitoring of landslides in this area constitute a significant organizational and financial challenge for local authorities.The study used EGMS Level L2B products as well as data from the SOPO (Counteracting Landslide System of Poland) database, including results from instrumental monitoring. As part of the work, Persistent Scatterer (PS) points were selected and subjected to geostatistical analyses. Subsequently, the ADAtools software was used to identify areas of active deformation.The results indicate that despite limitations related to forest cover and radar signal coherence loss, a considerable number of PS points from EGMS data are located within delineated landslides, enabling the analysis of deformation velocities and the preliminary inventory of landslide process activity.

The European Ground Motion Service (EGMS) provides ground displacement products across Europe through a standardized processing chain delivering Basic, Calibrated, and Ortho products derived from Sentinel-1 multi-temporal interferometry. This continental-scale framework ensures methodological consistency and comparability across regions; however, localized limitations may arise in rapidly evolving terrains, particularly within active and fast-moving landslide systems characterized by strong displacement gradients and temporal instability. In such environments, coherence degradation and high phase variability may significantly reduce point density, resulting in limited or absent coverage within the most actively displacing sectors. The Pissouri landslide in southern Cyprus represents a characteristic example of such conditions. The Pissouri landslide constitutes the most active and fastest-moving landslide on the island. Complex and spatially heterogeneous kinematics generate pronounced displacement gradients and variable phase stability across the sliding body, particularly within its central sector. These characteristics make it an ideal test site for evaluating targeted InSAR reprocessing strategies aimed at extending continental-scale displacement products in highly dynamic landslide environments. To overcome these limitations, a dedicated multi-temporal Sentinel-1 reprocessing for the 2022–2025 period was implemented, aligned with the EGMS Basic–Calibrated–Ortho product hierarchy while enhancing local validation and calibration capacity. The analysis was performed using an Interferometric Point Target Analysis (IPTA) approach. The workflow was initialized with a single-reference stack configuration to establish preliminary displacement rates and phase stability, followed by a multi-reference refinement to enhance spatial coverage, increase point density in high-gradient areas, and improve phase model consistency across the sliding body. The processing chain continued with the generation of Basic Line-of-Sight (LOS) displacement products for both ascending and descending geometries. Atmospheric phase contributions were mitigated through spatial–temporal filtering within the multi-temporal framework to reduce long-wavelength artefacts affecting velocity estimation. An independent performance assessment of the Basic products was conducted using dedicated Corner Reflectors installed at the Souni test site, part of the CyCLOPS Strategic Research Infrastructure Unit. The reflectors enabled amplitude stability evaluation, phase referencing verification, and LOS velocity validation under controlled backscatter conditions. Subsequently, Calibrated products were derived through integration with a localized GNSS velocity model developed within the CyCLOPS Strategic Research Infrastructure Unit. The CyCLOPS network comprises ten (10) continuously operating GNSS stations installed both inside and outside the active landslide area, providing stable geodetic reference conditions and independent velocity estimates. GNSS-based calibration was applied to align InSAR-derived LoS velocities with the local geodetic reference frame, correcting residual velocity offsets and improving georeferencing consistency across the sliding body. Following calibration, ascending and descending LoS datasets were combined to generate Ortho products through geometric decomposition, yielding East–West and vertical displacement components. These products were integrated into the Cyprus Ground Motion Service (CyGMS) as high-resolution displacement layers georeferenced to the ITRF2020 realization through GNSS-based frame alignment. The incorporation of locally calibrated Ortho components within CyGMS ensures geodetic consistency at national scale, complementing continental-scale EGMS outputs while enhancing spatial representation in dynamically active zones. The updated 2022–2025 analysis reveals sustained high-gradient displacement within the central sliding mass, with mean annual velocities approaching 80 mm/year and cumulative displacements exceeding 40 cm over the observation period. Relative to continental-scale EGMS outputs, the reprocessed dataset improves spatial density and displacement representation within the most dynamically active areas, demonstrating the capacity of locally enhanced processing to resolve displacement gradients that remain underrepresented in continental-scale products. The presented methodology demonstrates that localized, multi-sensor InSAR reprocessing supported by the CyCLOPS Strategic Research Infrastructure Unit can effectively extend continental-scale displacement services in rapidly evolving geohazard environments. Rather than replacing EGMS products, the approach complements them through high-resolution enhancement layers operationally integrated within CyGMS, illustrating a structured transition from research-grade processing to service-oriented national ground displacement monitoring. The same reprocessing framework is currently being applied to additional areas in Cyprus exhibiting similar displacement behaviour, demonstrating its reproducibility and scalability within the national ground motion service context. ACKNOWLEDGMENTS The authors would like to acknowledge the ‘CyCLOPS+’ (RIF/SMALL SCALE INFRASTRUCTURES/1222/0082) project, which is funded by the European Regional and Development Fund and the Republic of Cyprus through the Research and Innovation Foundation. The authors would like to acknowledge the ‘CyCLOPS’(RIF/INFRASTRUCTURES/1216/0050) project (www.cyclops.cy), which is funded by the European Regional and Development Fund and the Republic of Cyprus through the Research and Innovation Foundation in the framework of the RESTART 2016-2020 programme. The authors also acknowledge the ‘EXCELSIOR’: ERATOSTHENES: EΧcellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project (www.excelsior2020.eu). The ‘EXCELSIOR’ project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 857510, from the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development and the Cyprus University of Technology.

Landslides in mountainous regions represent a major hazard, and their early detection is essential for risk mitigation and infrastructure protection. Interferometric Synthetic Aperture Radar (InSAR) has become a key tool for monitoring slope dynamics due to its ability to measure ground deformation with millimetric precision, independent of weather or daylight conditions. The shorter wavelength, such as C-band, is more sensitive to decorrelation from vegetation and snow, which limits performance in densely vegetated or seasonally variable mountain environments. In contrast longer wavelength, such as L‑band, can penetrate vegetation more effectively and maintain coherence over longer time intervals, thereby enhancing the detection of slow-moving, deep-seated landslides. Therefore, by jointly analyzing C‑ and L‑band datasets, it becomes possible to exploit the complementary strengths of each frequency: high temporal resolution from Sentinel-1 C‑band and high coherence stability from NISAR L‑band satellite. This multi-wavelength approach expands monitoring capability across diverse land-cover conditions and increases the reliability of early-warning indicators in mountainous terrains. The results underscore the value of wavelength diversity for comprehensive landslide hazard assessment and long-term slope stability monitoring. This study highlights the benefits of integrating multi-wavelength InSAR observation, specifically C‑band and L‑band, for improved landslide detection in complex alpine terrain. Moreover, different approaches for non-deformation phase terms mitigation in complex height variation scenarios, such as turbulent and non-turbulent atmospheric effects, are assessed in order to obtain the accurate deformation phase term.

Although Poland is characterized by relatively stable geological conditions, it remains exposed to various natural hazards, including regularly occurring landslides. These phenomena constitute a significant environmental and economic problem, causing substantial material losses and posing a real threat to people and infrastructure. This situation highlights the need to implement effective monitoring tools to support efficient risk management.Remote sensing techniques, particularly synthetic aperture radar interferometry (InSAR), offer an efficient solution for large-scale and long-term monitoring of ground deformation. An important advancement of this approach is Persistent Scatterer Interferometry (PSI), which enables the detection of millimetre-scale surface movements using stable radar targets. This study demonstrates the potential of the European Ground Motion Service (EGMS) data, based on Sentinel 1 SAR imagery and PSI processing, for analyzing landslide activity and identifying unstable slopes. The paper presents two methodological approaches for the interpretation and mathematical processing of PSI data to identify the most effective way to use InSAR for determining landslide activity: calculation of slope velocities and decomposition of LOS velocities into vertical and east–west components.The results of one stack SAR images processing provide information on only one component of the true displacement vector, namely along the radar sensor’s line of sight (LOS), and the recorded deformation depends on the orbit geometry (ascending or descending). The relationship between slope geometry and the sensor’s viewing direction is therefore crucial when applying the PSI method in landslide analysis. Since most landslides move in the direction of the steepest slope, the LOS-measured deformation of PS points from the EGMS service was projected along the slope to estimate the actual ground displacement along the slopes.Since landslides have complex kinematics, it is recommended to decompose LOS displacement to retrieve the vertical and horizontal motion components. LOS-derived deformation can be decomposed into vertical and east–west components in varying grid cell resolutions, taking into account radar acquisition geometry and terrain morphology. In this study, the effect of grid cell size on the decomposition of LOS deformation into east–west and vertical components, and its influence on the number of available PS points within landslide areas, was assessed.The analyses were conducted in the Polish Carpathians, where the majority of landslides in the country are concentrated. This is due to the specific geological structure of the Carpathian massif and the dense network of tectonic discontinuities, such as faults and fractures, associated with the Alpine structure of the Carpathian orogen. The methodology was validated using landslides included in the SOPO (Landslide Counteracting System) database, based on the available data. The SOPO database is one of the largest landslide information inventories in Europe. Access to such an extensive and detailed dataset enabled high-accuracy analyses. Four landslides monitored within the SOPO project were selected for detailed investigation: Grybów, Zbyszyce, Szymbark Huciska, and Szymbark Zapadle, all located in the Carpathian region. The selection of these sites allowed for the assessment of the method’s applicability under diverse terrain conditions.

The Northern Apennines of Italy is a young, uplifting mountain chain where weak rocks are widely outcropping. Landsliding is a key geomorphological process actively shaping the landscape. Large complex landslides are widespread and characterized by long dormant phases interrupted by periodic catastrophic reactivation which pose a significant threat to human activities, due to their potential impact on houses, settlements and infrastructures. Catastrophic reactivations are invariably triggered by intense and prolonged rainfall events. However, they are often preceded by measurable deformations and are usually followed by a long deceleration phase leading to dormancy again. The Cà di Sotto landslide in San Benedetto Val di Sambro (BO), Italy is a well-documented large complex landslide (> 45 hectares) that in 1994 destroyed some buildings and dammed the river at the toe, requiring drainage works to mitigate the risk associated to a potential flood. This landslide originates in the upper part of the slope as a rockslide and progresses downslope evolving into an earthflow with total length of about 2 km. The affected material, belonging to the Monte Venere Formation, consists of tectonized calcareous-marly turbidites interbedded with arenaceous-pelitic strata. After 30 years of dormancy, in October 2024, following a heavy rainfall event, the landslide underwent a new catastrophic reactivation initially triggered by a rockslide in the upper slope. The deformation quickly progressed downslope, with peak velocities of several meters per day, disrupting previously established mitigation measures and generating a large water impoundment, upstream of the landslide toe. After about a week, the deceleration phase occurred gradually and was interrupted by brief episodes of acceleration linked to major rainfall events in December 2024 and February 2025. Still today, the landslide has not yet reached its dormant phase and heavy rainfalls occurred during the last winter season have caused local retrogression of the crown area and/or partial reactivation episodes along the landslide body. We use two-pass standard interferometry to investigate the pre- and post-failure behavior of the Cà di Sotto landslide. The two-pass technique retains all displacement information contained in interferograms and allows us to overcome some of the limitations associated with multi-temporal techniques, like sparse spatial coverage in highly deforming areas due to decorrelation and exceeding of measurable displacement rates. We take advantage of the short revisit time of Sentinel 1 satellites and process interferograms with temporal baselines of 6 to 24 days using gmtsar. Our analysis goes back to 2015 to investigate the dormant phase of the landslide and includes all phases of movement except the catastrophic reactivation phase (about 10 days) when displacement rates largely exceeded the upper detection limit of 6-days interferograms. The spatial extent of the catastrophic phase was instead resolved through amplitude change detection using pre- and post-event scenes. The results of our analysis are compared to available ground-based measures for comparison and validation. They include a GNSS-based monitoring system, comprising 31 evenly distributed periodic measuring points and three dual-frequency permanent GNSS stations. Few weeks later, also a robotic total station was installed as an early warning system for future possible reactivations. It provides hourly measurements with millimetric precision across 24 monitoring prisms regularly distributed along the landslide body. Our interferograms show variable degrees of decorrelation, depending on the perpendicular baseline, atmospheric disturbances and vegetation cover. Some of them do not allow to distinguish the deformation signal from the noise and were, therefore, discarded based on visual inspection. Despite being fully aware that the displacement signals retained in the remaining selected interferograms cannot be treated as rigorous measures, we interpret such signals with the aim of extracting information about the spatio-temporal evolution of the gravitational slope movement. Interferograms were unwrapped using snaphu and combined in interferometric stacks before and after the catastrophic reactivation. During the acceleration period, unwrapping was problematic due to high spatial fringe frequency and decorrelation yet wrapped interferograms can still be used together with the change detection maps to determine where high deformation occurred. Our results show that detectable displacements have been taking place in the upper part of the slope where the rockslide initially occurred, during the entire dormant phase, spanning from 2015 to 2024. During the same period, no clear displacement signals were detected along the landslide body nor were they detected on the ground and reported to authorities in charge of territorial management. During the catastrophic reactivation, peak displacement rates were several meters per day exceeding the detection capabilities of InSAR. However, low noise interferograms clearly delineate the spatial distribution of the active area with frequent phase jumps and decorrelation soon after the failure. During the post-failure stage, displacement rates progressively decreased and some interferograms have high enough coherence to map the deformation field. In early phases, the frequency distribution of displacement rates along the landslide approaches the theoretical upper limit of the technique (about 1 m/yr) and greatly exceed values typically measured by InSAR. When downslope-projected, displacement rates derived from our interferograms show a reasonably good agreement with ground-based data. Depending on the relative orientation between the line-of-sight and the direction of slope movement, the ascending and the descending geometry show variable capabilities to capture deformations associated to headscarp or lateral scarp retrogressions rather than the deformation of the earthflow body or toe. The comparison between InSAR data and on-site ground measures helped us to understand and interpret the remotely sensed information and highlights the potential and the limits of dual-pass interferometry to identify and monitor active landslides. Although retrieved information cannot be treated as measures due to residual noise associated with low target coherence, InSAR provides an unbeatable areal perspective that fast accessible which adds spatial information for the interpretation of geomorphological processes and their evolution in the different stages of a landslide.

This paper presents an initial analysis of landslide deformation in the district, located in the southeast of Turkey near the borders of Iraq and Syria. The study area is situated at an altitude of approximately 1,400 meters and has a mostly dry environment. The study uses the StaMPS approach of the Persistent Scatterer Interferometry (PSI) method with long-term interferometric Synthetic Aperture Radar (InSAR) data. Copernicus Sentinel-1A data of Single Look Complex (SLC) Interferometric Wide swath (IW) data are acquired from the NASA Alaska Satellite Facility and used for the extraction of landslide movement in the region. The dataset covers image acquisition of both ascending and descending tracks between October 2017 and August 2022. In total, 168 and 149 images of descending and ascending mode images were processed, respectively. SAR data was processed using SNAP v9.0 software, and interferograms were produced. The data in vertical transmitted and vertical backscattered polarization were analyzed using the StaMPS 4.1 beta version to produce time series and obtain velocity vectors of the landslide region. TRAIN software was also used to remove the atmospheric phase in the interferograms. According to the results obtained by the five-year time series analysis, the displacement varies between approximately -9 mm/year and 6 mm/year. In both cases, the standard deviation is less than 0.7 mm/year. The total maximum movement was determined to be approximately 45 mm. The time series analysis shows the dynamics of the slowly developing landslide in the long term. Most of the movement obtained was observed in the city center and over the human-made structures. In the velocity map, another small local movement is observed in the northeast of Sirnak city center, apart from the main landslide pattern. The study area has a continental climate, and the general natural vegetation is steppe since the season's precipitation is low. Therefore, slight displacements have been determined in regions other than man-made areas. Observation of opposite movements in the results obtained from descending and ascending images shows preliminary information that there is horizontal movement. By using these two datasets, the results obtained along the line-of-sight (LOS) direction can be converted into horizontal and vertical movements, and detailed information about the direction and size of the movement can be obtained. This is the first study that has been conducted for this region using the InSAR time series. Local movements are also observed in Turkey, except for the big cities, which are constantly monitored, and it is revealed that these areas should also be examined. Especially slowly developing landslides should be considered risky areas as they will affect both buildings and infrastructure for a long period, and precautions should be taken by examining the geological structure of the region. As a further study, small baseline methods can also be tested in order to increase the number of PS points in the region. The prediction approaches can be beneficial in risk reduction activities and sustainable urban management strategies.

Landslides result in numerous fatalities and significant infrastructure damage each year, leading to enormous individual and economic losses and severe damage to infrastructure worldwide. Due to climate change and its associated effects, the frequency and magnitude of landslides, rock falls, and mudflows increase globally. In March 2023, Tropical Cyclone Freddy brought extreme and prolonged rainfall to southern Malawi, triggering widespread landslides that affected settlements and critical infrastructure. Reliable information on the spatial extent of slope failures and their impact on infrastructure is essential for supporting humanitarian response and recovery planning. Synthetic aperture radar (SAR) provides important advantages in such emergency contexts, as it is independent of cloud cover and daylight conditions. In this study, we investigate the potential of Sentinel-1 SAR data for infrastructure-focused landslide assessment after Cyclone Freddy. First, we generate pre- and post-event coherence maps to detect surface changes associated with landslide occurrence and structural damage. Coherence loss patterns are analysed in relation to road networks, built-up areas, and critical facilities to evaluate potential disruptions and identify places where coherence reduction may indicate mass movement activity or structural impact. Second, we perform Interferometric SAR (InSAR) processing using a Small Baseline Subset (SBAS) approach to investigate surface deformation signals. Interferogram generation and co-registration are carried out using the InSAR Scientific Computing Environment (ISCE) processing framework. The resulting interferograms are filtered and phase-unwrapped before being imported into the Miami InSAR Time-series software in Python (MintPy) for time series analysis. Within MintPy, we create a deformation time series to estimate line-of-sight (LOS) displacement and velocity fields, enabling the detection of post-event ground displacement associated with landslide movement. The combined analysis of coherence change detection and deformation time series allows us to evaluate the strengths and limitations of radar-based approaches for landslide assessment under humanitarian constraints. We assess data availability, processing requirements and data quality in vegetated tropical terrain. By focusing on infrastructure impacts and practical use, this study links advanced radar methods to the needs of humanitarian organisations. The results demonstrate how radar-based techniques can contribute to targeted assessment of landslide impacts, thereby supporting decision-making during disaster response and recovery phases.

Civita di Bagnoregio, located in Central Italy at the boundary between the Vulsini volcanic district and the Tiber river valley, represents one of the most emblematic examples of Cultural Heritage threatened by geomorphological processes. The historic settlement is built on a pyroclastic plateau resting on thick Plio-Pleistocene clay formations, a geological asset that prompt slope instability driven by superficial erosion, tension crack development, toe erosion, and progressive retreat of the whole tuff cliff. The interaction between lithological contrasts (plastic bedrock over rigid outcrops), intense fracturing of the volcanic deposits, climatic forcing, and anthropogenic cavities has produced a highly dynamic landscape characterised by rockfalls, rotational slides, earth flows, and shallow debris flow. The rapid erosion of the surrounding “Valle dei Calanchi” makes Civita di Bagnoregio an exceptional natural laboratory for testing advanced Earth Observation approaches to Cultural Heritage monitoring. The present work shows the main results developed within the first Special National Plan for Monitoring and Conservation of Italian Cultural Heritage (NPMCCH), aimed at the monitoring, conservation, and proactive protection of cultural heritage, against the impacts of different hazards, both anthropogenic and natural, including climate-induced extreme events. Civita di Bagnoregio was selected as a priority case study to evaluate the potential of multi-mission InSAR approaches, using an A-DInSAR technique, and integrating remote sensing, GB radar monitoring and field surveys. A layered ground-motion analysis workflow was implemented. Regional-scale products, including Copernicus European Ground Motion Service (EGMS) Sentinel-1 time series, were first used to characterise the long-term deformation context and identify sectors requiring higher-resolution investigation. Although EGMS provides a consistent baseline, the Civita case highlights the limitations of radar C-band observations in mostly natural environments characterised by localised, non-linear, or rapidly evolving geomorphological processes. To overcome these constrains, high-resolution COSMO-SkyMed (X-band) time series covering the period 2022–2026 were processed using multi-temporal interferometric techniques. The analysis identified clusters of Persistent Scatterers showing millimetric deformation rates, particularly concentrated in two priority areas: the pedestrian bridge zone, connecting the village to the mainland and sectors of the historic centre, including structures facing the western side of the main square. Time-series analysis indicates deformation patterns consistent with structural stress redistribution and slope-controlled processes. Field inspections confirmed minor cracking and local structural distress corresponding to interferometric anomalies, supporting the reliability of the satellite-derived indicators. The study adopts a multi-frequency InSAR framework integrating C-band regional monitoring, X-band structural observations, and ongoing L-band processing using SAOCOM data. Wavelength complementarity enables the investigation of deformation across spatial scales, bridging building-level responses and landscape-scale slope dynamics while improving temporal continuity in challenging environments. L-band processing represents a central component of the study. Preliminary assessment confirms the expected improvement in temporal coherence over vegetated slopes and clay-rich terrains where shorter wavelengths experience decorrelation. L-band processing is currently ongoing and is expected to enhance temporal coherence and to enable the characterisation of slope deformations thanks to the multi-frequency framework. Preliminary results highlight that ground deformation in Civita is spatially heterogeneous and strongly controlled by the geological asset of the tuff-clay system and by climatic forcing, particularly precipitation-driven erosion and thermo-hydrological cycles. The combined satellite analysis and field investigations allowed the identification of priority monitoring targets and supported the design of an integrated low-impact monitoring strategy calibrating A-DInSAR observations with GNSS measurements and integrating them with in situ sensors, including crack meters, meteorological stations, and corner reflectors. Such hybrid architectures facilitate the transition from periodic assessment toward near-real-time early warning. The Civita di Bagnoregio case provides an experimental reference site to evaluate how multi band SAR observations can support conservation strategies in fragile cultural heritage environments. The proposed workflow positions EGMS as a screening layer, X-band as a structure deformation detector, and L-band as a sensor enabling the observation of spatially distributed geomorphological deformations in natural environments, contributing to the evolution of next generation ground motion services and future SAR mission exploitation.

The operational utility of Interferometric Synthetic Aperture Radar (InSAR) for regional geohazard and environmental monitoring is strongly influenced by the latency between data acquisition and delivery of analysis-ready time-series products. As Earth observation workflows evolve toward Digital Twin Earth concepts, there is increasing need for processing systems that combine scalability, reproducibility, and predictable response times. While individual algorithmic components of MT-InSAR processing have been accelerated, system-level characterization of end-to-end time-series production using containerized deployments, Slurm-based orchestration, and GPU-enabled HPC and cloud infrastructures remains limited. This study presents the development and benchmarking of a containerized, modular MT-InSAR processing pipeline designed for high-throughput execution across HPC clusters and cloud environments. The architecture employs CUDA acceleration for computationally intensive kernels and supports elastic scaling across GPU-enabled nodes. Performance evaluation is conducted on the AAU Strato system using a regional Sentinel-1 dataset over Emilia-Romagna, where the single-node wall time is reduced from 14.0 hours to 7.8 hours under GPU offloading. Strong-scaling experiments across 32 nodes achieve 68% parallel efficiency, reducing processing duration for large stacks from days to hours. To evaluate performance under application-driven load, a hydrology-oriented inversion surrogate is appended as a downstream workload. A 3D U-Net designed for groundwater storage retrieval is trained and tested using InSAR deformation data from Punjab, India, representing a region with intense groundwater depletion and complex deformation dynamics. This surrogate serves as a controlled proxy for emerging environmental InSAR applications and highlights the additional computational burden beyond deformation mapping alone. Benchmarking results show that end-to-end response time is frequently constrained more by data movement and storage behavior than by raw compute performance. Transitioning from shared parallel file systems to node-local NVMe staging reduces I/O share by approximately 10%, improving latency stability. The study provides a technical roadmap for deploying scalable, reproducible MT-InSAR processing systems across HPC and cloud GPU platforms and demonstrates how such infrastructures enable integration of computationally demanding application legs, including hydrological analysis. Key words: MT-InSAR; cloud computing; HPC; GPU acceleration; containerization; Deep Learning; operational latency; Sentinel-1

Linear infrastructure networks (highways, railways, pipelines and related structures) are increasingly exposed to interacting ground-related hazards—slow and rapid landslides, regional subsidence, and earthquake-induced liquefaction—whose impacts challenge both safety and long-term serviceability, and whose management is often hindered not by lack of information but by fragmented workflows that do not translate heterogeneous geospatial evidence into consistent, segment-level maintenance priorities. In this contribution, we present SGAM (Smart Geotechnical Asset Management) as a semi-automated, modular decision-support pipeline that couples Earth Observation (EO) InSAR ground deformation time series with thematic geodata (e.g., topographic derivatives, lithology, land cover, seismic parameters), inventories and engineering knowledge to deliver operational products for predictive maintenance and monitoring planning at network scale; beyond operations, SGAM can also support preliminary design by screening alternative corridors and highlighting geohazard-constrained sectors to help choose the least hazard prone option. SGAM is designed around hazard-specific modules—rather than a single monolithic model—so each process is represented with predictors, logic and outputs coherent with its physical drivers and data constraints, and results are then intersected with infrastructure geometries to produce standardized hazard/attention classes for assets or asset segments that can be directly ingested in asset-management workflows and GIS-based reporting. The approach has been tested on a ~110 km highway pilot corridor in Northern Italy, where multi-source datasets are fused to support both hazard characterization and interpretation of ongoing activity along the right-of-way. For landslides, SGAM combines machine learning-based susceptibility mapping (learned from inventories and terrain/environmental predictors such as slope, morphometry, lithology and land use) with systematic exploitation of Persistent Scatterers (PS) InSAR to infer activity state and refine attention attribution, using explicit class-matrix logic that links susceptibility levels to observed deformation ranges (e.g., velocity thresholds) so that slow-moving processes—where InSAR provides actionable kinematic evidence—are distinguished from rapid phenomena for which EO sampling may be intrinsically limited and thus treated conservatively. The outcome is a segment-ready Landslide Attention Index that supports targeted inspections, monitoring densification and prioritization of stabilization needs; for subsidence, SGAM leverages PS InSAR velocity patterns—interpreted within geological and geomorphological context—to identify and classify affected areas and to assign hazard levels along the corridor based on maximum vertical deformation rates and setting-specific constraints, providing an intuitive screening layer for diagnosing underlying drivers and planning follow-up investigations. For liquefaction, where persistent deformation signals are not the primary indicator, SGAM adopts a susceptibility framework grounded in seismic and geotechnical predictors and implements a segmentation-aligned hazard index with quantitative thresholds, allowing network-level comparison and harmonization with other hazard outputs while remaining updatable as new geophysical and seismic information becomes available. Beyond the hazard-specific results, SGAM introduces a summary geospatial synthesis layer that consolidates landslide, subsidence and liquefaction outcomes into a single, decision-ready product highlighting priority segments where hazards converge or where one mechanism reaches a severity threshold that warrants immediate action, implemented through transparent rule-based logic to preserve interpretability and traceability from portfolio-level priorities back to underlying drivers. SGAM also includes a guideline-oriented landslide extension that systematizes parameter calculation and attention-class assignment for linear infrastructure elements (including bridges) in alignment with established national practices, incorporating InSAR-derived kinematic evidence to infer activity state where inventories are incomplete and supporting infrastructure-focused analyses (e.g., buffer-based upslope assessment) for ranking and reporting. Overall, SGAM demonstrates a practical pathway from Copernicus-era EO deformation streams and multi-source geodata to actionable, segment-level prioritization for predictive maintenance, scalable to large networks and designed to evolve as continuous EO time series and complementary datasets (e.g., LiDAR, drones) enable periodic re-evaluation, and more proactive resilience planning for critical transport corridors.

In the framework of an ESA-PECS project (ID: 000114846/15/NL/Nde), the HUN-REN Institute of Earth Physics and Space Science in cooperation with the Budapest University of Technology and Economics and University of Leeds, a novel twin corner reflector design was developed to address the issue of limited coherent reflections in areas affected by landslide and other natural hazards, enabling accurate InSAR observations. This design was applied in an integrated approach combining InSAR and GNSS technologies to monitor landslides in Southern Hungary (Dunaszekcső settlement), where recurrent landslide has been active since 2007. Before installing the reflectors, there were practically no coherent reflections, making it difficult to process InSAR data. The reflectors, installed on a geodynamical benchmark, serve as coherent reflecting objects, allowing for accurate co-located InSAR and GNSS measurements. Our results show that the combined use of InSAR and GNSS datasets can provide accurate estimates of surface movements, with an accuracy in the range of 1-5 mm in the line-of-sight direction. The integration of InSAR and campaign GNSS measurements using Kalman-filtering enables the reconstruction of detailed displacement time series. Campaign GNSS measurements, carried out approximately every 6 months between 2016 and 2017, can be used to identify the pixels dominated by the integrated benchmarks, detect unwrapping errors and missing cycles, and also provide boundary values for Kalman-filtering. The latter is essential for estimating the north component of movements which cannot be determined by InSAR technologies alone. We have also installed low-cost GNSS receivers near the reflectors, which allowed us to collect multiple months of data, enabling a more comprehensive analysis of the landslide dynamics. During previous measurement campaigns it has become clear that landslide movement velocities often lead to phase unwrapping errors. Continuous GNSS measurements enable the detection of unwrapping errors, quality control and comparison with InSAR time series, providing a more complete understanding of landslide behavior. We will also compare the InSAR and GNSS derived time-series in combination and separately to assess the advantages and disadvantages of low-cost GNSS monitoring compared to the corner reflector solution. Based on the results a recommended strategy will also be presented for monitoring landslides and natural hazards using space-geodetic technologies.

The Subdirectorate General for for Monitoring, Warning and Geophysical Surveys, belonging to the National Geographic Institute of Spain (IGN) is responsible for planning and management of systems for observation, monitoring and communication to institutions of volcanic activity and determination of associated hazards within Spanish territory and around Spanish Antarctic bases, as well as conducting research in volcanology and the early detection of eruptive processes. Within this framework, the IGN operates a comprehensive suite of interdisciplinary observation systems encompassing geodesy, seismology, gravimetry, geochemistry, and geomagnetism. In order to monitor ground deformations, Spaceborne SAR interferometry (InSAR) is integrated with other techniques such as GNSS, tiltmeters or robotic total stations. In this context, InSAR is applied through several complementary methodologies. An automated processing workflow, in continuous operation for the past eight years, generates interferograms with each new satellite acquisition, supporting near‑real‑time detection of deformation signals. In addition, long‑term deformation time series are produced to characterize the temporal evolution of volcanic processes and to identify ground‑motion trends. Both processing strategies are implemented in the Canary Islands using data from the Sentinel‑1 and PAZ satellites. Due to the particular atmospheric and topographical characteristics of the Canary Islands, displacement and interferometric phase maps often exhibit a significant contribution from atmospheric artifacts. These effects are especially common on volcanic islands such as the Canaries, where strong vertical gradients in water‑vapor distribution and dominant moisture‑bearing winds from the ocean generate substantial atmospheric phase delays. In parallel, several processing workflows are in operation for the GNSS station network. In addition to providing precise ground‑deformation measurements, GNSS data supply zenith tropospheric delay (ZTD) estimates that can be used to correct atmospheric artifacts in the interferograms. In this work, we present the results obtained from applying several methodologies—such as GACOS atmospheric products, phase–topography correlation analysis, and GNSS‑derived ZTD corrections—to mitigate the impact of atmospheric variability on interferograms. To evaluate their performance, these approaches were tested on islands with contrasting atmospheric and topographic conditions, as well as different expected deformation patterns, in order to identify the most suitable correction strategy for each scenario. Based on these results, our aim is to incorporate atmospheric correction into the automatic processing workflow enabling the selection of the most appropriate correction method in each case.

The Copernicus Precise Orbit Determination (CPOD) Service has been a cornerstone of the Copernicus Earth Observation programme, routinely delivering high-accuracy orbit and auxiliary products for Sentinel-1, Sentinel-2, Sentinel-3 and Sentinel-6. Through sustained operational experience in GNSS-based precise orbit determination (POD) for Low Earth Orbit (LEO) satellites, CPOD has developed robust processing standards relying on multi-frequency GNSS measurements. Building on this knowledge and added value for the Copernicus missions, a proposal was submitted in response to the Biomass In-Orbit Commissioning (IOC) Call for Proposals to assess the feasibility of generating an independent precise orbit solution for ESA’s Biomass mission. ESA provided access to GNSS data acquired by the onboard LEORIX receiver. Biomass is equipped with a single-frequency receiver tracking GPS and Galileo satellites on L1, delivering C/A code and carrier phase observations. Unlike the dual-frequency configurations available on other Copernicus Sentinel missions, this single-frequency setup prevents the direct formation of ionosphere-free linear combinations. Consequently, first-order ionospheric effects cannot be eliminated using standard approaches, posing specific challenges for high-accuracy POD in a LEO environment. To overcome this limitation, a POD strategy based on the GRAPHIC (Group and Phase Ionospheric Correction) combination has been implemented. By combining code and phase measurements, the GRAPHIC approach mitigates first-order ionospheric effects while preserving the precision of carrier phase observations. The processing chain includes detailed force modelling (Earth gravity field, third-body perturbations, solar radiation pressure and atmospheric drag), observation modelling (antenna phase centre corrections, relativistic effects and Earth orientation parameters, and a dynamic orbit determination scheme based on batch least-squares estimation. Particular emphasis is placed on the challenges inherent to single-frequency GNSS processing, including reduced observability and sensitivity to ionospheric variability. Preliminary results demonstrate that a dynamically consistent precise orbit solution can be achieved. Orbit convergence behaviour and internal consistency metrics confirm the viability of the approach. However, several open issues remain, including unexpected features in code and phase residuals and sensitivities in the dynamic parameter estimation. These effects are analysed and discussed to identify modelling limitations and areas requiring further refinement. Finally, a Test Data Set (TDS) of Biomass POD products has been generated and will be made available to the community for independent assessment. Although the current solutions do not yet reach the maturity of established multi-frequency CPOD products, they represent a first step toward independent precise orbit products for Biomass and provide a foundation for continued improvement and collaboration.

Landslides represent a major geohazard in high mountainous regions of Asia, where extreme precipitation events such as cloudbursts frequently trigger large‑scale slope failures. Climate change is expected to further intensify the frequency and magnitude of these events, increasing risks to infrastructure, agriculture, and sustainable development. The Eastern Himalayas, particularly Arunachal Pradesh (India), are highly susceptible to rainfall‑induced landslides, which often remain undocumented yet cause significant downstream impacts, including artificial dam formation and secondary flooding in lowland areas. Mudslides, landslides, and severe convective storms occur frequently in Arunachal Pradesh. Although more than 70% of the region is forest‑covered and sparsely populated—limiting direct loss of life—the impacts on infrastructure and agriculture are substantial, resulting in considerable economic and environmental losses, especially during the monsoon season. The region’s geological setting is dominated by the Eastern Himalayan syntaxis, characterized by a complex assemblage of Precambrian to Tertiary lithologies organized into four major tectonic belts: the Sub‑Himalayan, Lesser Himalayan, Higher Himalayan, and Tethys Himalayan domains. While fragile lithology and active geodynamics provide preparatory conditions for slope instability, landslide occurrence is strongly concentrated during the monsoon months (June–September - JJAS), when intense rainfall acts as the primary triggering factor. Annual precipitation ranges from approximately 2,000 to 5,000 mm, with up to 60% falling during the JJAS period, frequently inducing shallow slope failures in steep, geologically young terrain. In densely vegetated mountainous environments, conventional pre‑failure deformation monitoring using Synthetic Aperture Radar (SAR) interferometry is hindered by low coherence, restricting analyses largely to post‑event conditions following vegetation removal and soil exposure. In this study, we assess the feasibility of integrating multitemporal Sentinel‑1 InSAR techniques, including Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) methods, with P‑band BIOMASS tomography data to characterize post‑failure landslide dynamics. While InSAR provides millimeter‑scale deformation measurements on exposed colluvial deposits, BIOMASS tomography offers complementary insight into the three‑dimensional structure of land cover and subsurface scattering mechanisms. Arunachal Pradesh was selected as a test site due to its high landslide incidence, extensive documentation in the literature, availability of Sentinel‑1 data, and the unique access to BIOMASS tomographic datasets as of early 2026. The proposed integrated approach demonstrates strong potential for improving post‑event landslide characterization in heavily vegetated mountainous regions.

Synthetic Aperture Radar (SAR) imagery is inherently corrupted by speckle, a multiplicative noise arising from coherent signal interaction. Speckle degrades radiometric accuracy, visual interpretability, and downstream tasks such as classification and parameter retrieval. It must be suppressed across all SAR modalities, including single-channel SAR, Polarimetric SAR (PolSAR), Interferometric SAR (InSAR), and Tomographic SAR (TomoSAR), while preserving both amplitude and phase information in complex-valued data. Local mean filters reduce speckle through spatial averaging but sacrifice spatial resolution and edge detail. Deep learning approaches achieve strong despeckling performance but require ground truth training data, which particularly lacks in case of multi-temporal data, thus limiting their generalizability to unseen sensor configurations and acquisition conditions. Non-Local Means (NLM) addresses these limitations by estimating each pixel as a weighted average of similar pixels within a broader search neighbourhood, where similarity is assessed at the patch level. NLM requires no training data and preserves structural detail, making it particularly attractive for SAR despeckling. Existing NLM implementations for different SAR modalities rely on different similarity metrics, such as Log-Euclidean, Wishart, and Kullback-Leibler distances. Each metric assumes a specific statistical model and data structure. Single-channel SAR filters differ from PolSAR or multi-channel (InSAR/TomoSAR) filters. Although some unified frameworks have been proposed, they mostly depend on explicit statistical modelling of each data type. This work introduces a new Non-Local Means framework for adaptive despeckling across different SAR modalities, from single-channel to multi-channel data. The approach is inspired by neuroscience, where biological neurons encode stimulus intensity or input features, such as polarimetric or interferometric phase, through spike timing. Two stimuli are considered similar when their spikes occur close in time. This coincidence-based similarity assessment is efficient and robust to noise. The proposed pipeline operates within a unified framework applicable to multiple SAR data modalities. The method has been evaluated on P-band coherency matrices from ESA’s BIOMASS mission over Mumbai, India, and initial results are competitive with established state of the art techniques such as MULOG. A more detailed quantitative analysis is ongoing, and the framework will next be assessed on interferometric, tomographic, and multi-temporal SAR datasets. As SAR data volumes continue to grow, computationally intensive model-based approaches face scalability challenges. In contrast, the proposed method is designed to remain computationally efficient and lightweight. The main contribution of this work is the introduction of a simple, model-free, and computationally efficient similarity metric for identifying similar pixels in SAR imagery. The metric is applicable across different SAR modalities while maintaining low computational complexity.

Polarimetric interferometric Synthetic Aperture Radar (PolInSAR) and Tomographic SAR (TomoSAR) are the advanced SAR techniques used to assess canopy height and forest vertical structure and canopy height. PolInSAR exploits the polarimetric dependance of interferometric coherence and phase to constrain parametric scattering models. In contracts, TomoSAR, reconstructs the vertical reflectivity profile using multi-baseline acquisitions, thereby separating the different scattering contribution at different elevations within the canopy. Comparative studies highlight the potential of P-band SAR for estimating vertical forest structure due to its strong canopy penetration and its ability to interact with canopy, ground, and canopy–ground scattering components. However, existing P-band studies have relied predominantly on airborne sensors, which are limited in spatial coverage and temporal consistency. The recently launched ESA BIOMASS mission, operating at P-band (~435 MHz) and launched in 2025, opens a new era for Earth observation–based retrieval of forest vertical structure over broader spatial and temporal scales. The main objective of this study is twofold: (1) to retrieve forest vertical structure parameters using using PolInSAR and TomoSAR frameworks; and (2) to quantitatively assess and compare the reliability and accuracy of the forest vertical structure information derived from each framework, using statistical performance indicators, including the coefficient of determination (R2), the Root Mean Square (RMSE), and completeness. Retrieved forest height and related vertical structure parameters are validated against reference measurements obtained from in-situ LiDAR data to evaluate estimation bias, dispersion, and overall retrieval performance. The study area is located in the Taita Hills, which is surrounded by semi-arid savanna and scrubland at 600-900 m a.s.l., creating strong climatic and structural contrasts over short distances. The focused study area was selected based on the overlapping spatial and temporal coverage of the available ESA BIOMASS, Level 1 Single-look Complex Slant Range (SCS), acquired in 2026 and in-situ LiDAR data with closely matched acquisition dates. In the framework of PolInSAR, forest height was estimated to use the Random Volume over Ground (RVoG) scattering model within three-stage inversion framework. First, the complex interferometric coherences were derived for multiple polarization channels, and the coherence region was constructed to characterize the volume-ground scattering mixture. Second, the ground phase was estimated by identifying the coherence point corresponding to the surface scattering contribution within the coherence region. Lastly, forest height and extinction parameters were retrieved through inversion of the RVoG model by fitting the observed polarimetric coherences to the theoretical volume-over-ground coherence formulation. The TomoSAR processing includes the implementation of single- and fully polarized CAPON, Multiple Signal Classification (MUSIC) and Expectation-Maximization (EM) followed by geometric-based volume delineation techniques to derive height cubes, and reflectivity maps. The forest vertical structure information derived from each technique then is validated against in-situ LiDAR data, and the best performance workflow is identified. The result of this study will improve the understanding of the applicability of forest vertical structure information retrieved from TomoSAR and PolInSAR techniques for forest characterization, with focus on Taita Hills. Furthermore, the proposed frameworks will provide an EO-based solution for continuous forest height and vertical structure characterization, supporting policy and monitoring initiatives, such as the European Union Deforestation Regulation (EUDR).

Measuring the Earth’s above-ground biomass is a mission-critical requirement for understanding the global carbon cycle. While high-frequency sensors often saturate in dense forests, the ESA BIOMASS mission’s use of P-band SAR offers a breakthrough by providing the canopy penetration necessary to interact with large woody structures. However, this perspective is clouded by speckle noise a physical corruption that threatens the integrity of the scattering matrix. This work represents a specialized evolution of a polarization-agnostic methodology, transitioning a general purpose framework into a mission specific tool for P-band PolSAR. By adapting a shared convolutional backbone with a dynamic masking data loader, we enable a unified approach to despeckling that maintains the physical and statistical authenticity of the radar return. Training on real multi-temporal data and utilizing a spatio-temporal MuLog-filtered supervision strategy, we demonstrate that this evolved architecture effectively preserves the sub-canopy textures essential for carbon density estimation. Introduction and Motivation The estimation of biomass is a critical variable in climate modeling, where the polarimetric signature serves as the primary signal for woody structures. Traditional filters often smooth forest textures, masking small-scale variations. Deep learning offers a solution, but adoption has been hindered by rigid architectures. Our approach builds upon an established polarization-agnostic framework, evolving its core logic to meet the rigorous physical demands of the BIOMASS mission. While BIOMASS data is primarily characterized by its Full-Pol (quad-polarization) nature, we retain the methodology’s underlying flexibility to ensure the network learns universal spatial manifolds that are not tethered to a single modality, thereby increasing the robustness of the feature extraction layers. Proposed Methodology The core innovation, inherited and refined for this application, is the ability to handle heterogeneous polarimetric inputs within a single unified architecture. Rather than using fixed-input dimensions, we implement a specialized masking-based data loader. In this approach, the network is designed to accept a maximum-capacity input tensor (e.g., a 9-channel stack representing the 3 x 3 covariance matrix C). During the data loading phase, a binary mask is applied to the input channels. For the BIOMASS mission's Full-Pol requirements, the mask acts as a gate that ensures all polarimetric relationships are preserved while the shared convolutional filters learn features that are invariant to channel presence. This masking strategy allows the model to leverage spatial textures found across the scattering matrix while remaining structurally flexible a direct evolution of previous work designed to process diverse mission modes without the need for costly retraining. Training on Real Data: The Multi-Temporal MuLog Strategy One of the primary critiques of DL-based despeckling is the reliance on synthetic Gaussian noise for training. To address this, we leverage multi-temporal stacks from real data: Spatio-Temporal Averaging: We take N co-registered images of the same forest track. By averaging them in the temporal domain, we significantly reduce speckle while maintaining the spatial resolution of the original sensor. MuLog Integration: The resulting images are processed using the Multi-channel Logarithmic framework. This transforms the multiplicative Wishart noise into an additive Gaussian-like space, allowing the CNN to optimize its weights more effectively. The resulting "pseudo-clean" labels represent the true statistical behavior of the scattering, providing a much more rigorous training signal than synthetic noise models. Physical Consistency and Loss Functions A significant portion of our technical development focused on engineering a loss function L that transcends simple pixel-wise comparisons. In the context of P-band PolSAR, a standard Mean Squared Error often leads to over-smoothed images that strip away the texture essential for distinguishing forest types. To avoid this, we utilize a multi-objective optimization strategy centered on the Frobenius loss and the Gradient loss. In PolSAR, the physical properties of the target are encapsulated in the matrix structure; therefore, the network must minimize the distance between the estimated matrix and the pseudo-clean reference across all elements simultaneously. While the Frobenius loss handles the "physics" of the signal, the gradient loss handles the "geography" of the scene. P-band images are characterized by significant texture variations caused by the forest canopy’s uneven structure. To prevent the convolutional layers from blurring these vital edges, the gradient loss penalizes the difference between the spatial gradients of the denoised image and the reference. Conclusion Ultimately, this framework provides the rigorous, scalable data processing needed for the BIOMASS mission ground segment. By evolving a polarization-agnostic model, we address the operational reality that while BIOMASS is a Full-Pol mission, the robust feature extraction granted by this methodology drastically reduces computational overhead and increases reliability. This approach demonstrates that we can clean the radar signal without destroying the delicate sub-canopy textures that signal carbon density. By bridging the gap between high-performance deep learning and the strict laws of radar physics, this evolved framework ensures that the BIOMASS mission provides the high-fidelity measurements needed to accurately track the pulse of our planet's forests.

Airbus is the market leader in the production and dissemination of global-scale, commercial off-the-shelf digital elevation datasets, derived predominantly from interferometric processing of imagery collected by the Airbus Radar Constellation (TerraSAR-X and TanDEM-X). Airbus’ flagship elevation product – WorldDEM Neo – provides customers with both a Digital Surface Model (DSM) and a Digital Terrain Model (DTM). The former is sensitive to both vegetation (including, most notably, tree cover) and man-made objects, whereas the latter is derived from the DSM via the removal of these objects. As the highest resolution, truly global elevation product on the market, WorldDEM Neo is provided with 5 m horizontal pixel spacing and has an absolute vertical accuracy of 1.4m at 90% confidence (LE90). In terms of digital terrain information, precise measurements are a critical requirement for Airbus’ customers given the foundational importance it plays across a wide range of downstream applications. Key use cases for terrain information (DTMs) include: Precision land use management (including agriculture and forestry);   Carbon crediting and environmental / climate change monitoring; Water resource management (e.g. for the design of water supply, flood control and hydrological structures); Civil Engineering (including critical infrastructure development and monitoring); Insurance, Environmental Impact Assessments and Disaster Mitigation Planning; Geological and Archaeological exploration; Defence and Security applications (e.g. for environmental domain awareness and strategic planning).       Despite the important use case applications listed above, little to no high accuracy terrain measurements exist across the world’s rainforest regions which, together with the planet’s other densely tree-covered regions, cover over 30% of Earth’s land area (FAO, 2025: https://www.fao.org/state-of-forests/en/). There, terrain information is typically interpolated from sparse field-based measurements or is ‘guesstimated’ from satellite-derived DSMs with the support of point-wise laser altimetry data, the latter of which can themselves be subject to significant measurement error. This error is due primarily to limitations in our ability to image the complex structure of the rainforests from space (let alone the terrain below), and the absolute vertical error associated with several market-leading DSM products can exceed 10 metres over selected areas of rainforest. Built by Airbus Defence and Space, ESA’s new BIOMASS P-band forest mission presents an exciting opportunity to peer through the rainforest canopy for the first time from space, and in doing so opens new doors for rainforest-scale DTM refinement. Here, we present ongoing progress towards the production of precision DTMs over the rainforests, with a view towards their integration into our Airbus WorldDEM Neo products and services. This progress exploits imagery acquired during the first (tomographic) phase of the BIOMASS mission, whose unique orbital configuration permits the mapping of pseudo-3D forest structure and underlying terrain in unprecedented detail. In light of the above, we aim to: Develop a sub-rainforest processor capable of creating pseudo-three dimensions maps of forest structure and the terrain below. In particular, we are developing a Tomographic SAR processor based on the first phase of the BIOMASS mission. To integrate the BIOMASS DTM into existing Airbus WorldDEM Neo workflows and elevation products (e.g. CO3D). A particular focus will be given to oversampling and interpolating the BIOMASS data in order to achieve the same spatial resolution of the WorldDEM Neo. To compare and benchmark the BIOMASS DSM against Copernicus DEM Global data. At conference time, the authors aim to show relevant examples of BIOMASS elevation data derived from the exploitation of BIOMASS Level-1C stacks over the rainforest areas (i.e. Amazonia).

1. Introduction In April 2025 ESA launched the BIOMASS satellite with the first orbital P-band SAR as the main instrument. The main objective of ESA’s BIOMASS mission is the mapping of forest biomass as a contribution to the understanding of the carbon cycle and climate system. In addition, the Biomass mission offers the opportunity to develop other applications of orbital P-band SAR data. Early results of the mission confirm the interferometric capability of the BIOMASS P-band SAR. But they also show that many of the interferograms are significantly affected by ionospheric effects. The main ionospheric effects are: (1) positional offsets in the azimuth direction caused by ionospheric path delay gradients along the synthetic aperture, (2) ionospheric path delay phase, and (3) Faraday rotation effects. The third effect affects the cross-polarization backscatter and polarimetric parameters. The Faraday rotation depends on the total free electron concentration along the imaging path. The first two effects depend on the spatial variation of the free electron concentration. In our contribution, we investigate the ionospheric effects on positional offsets and the interferometric phase. We first discuss methods to identify the presence of ionospheric effects in an interferometric pair. Then we present methods to mitigate ionosphere-related positional offsets and path delays and discuss the applicability of these methods. 2. Identification of ionospheric effects For an interferometric SLC pair, the presence of ionospheric effects can be identified based on the co-registration offset field or based on the sub-band double difference interferogram. Our co-registration offset-field based approach starts with the co-registration of the second SLC to the reference SLC geometry based on the orbital parameters and a digital elevation model. The related geometric model is refined with a single range and azimuth offset (not an offset field). Then, in a second step, matching techniques are used to determine an offset field between the reference SLC and the transformed second SLC. Spatially varying non-zero azimuth offsets indicate either ionospheric effects or ground-displacements. In the case of ionospheric effects, the corresponding range offset field does not show significant non-zero values. In the case of ground deformation, on the other hand, the range offset field also shows offsets for the instable areas. In addition, the shape of deformation patterns typically differs from the shape of patterns caused by ionospheric effects, and the displacement effects related to the topography (landslides, glaciers), natural processes (earthquakes, volcanoes, ice motion) or man-made activities (mining, oil, or water extraction). The split-beam double difference interferogram procedure includes the azimuth bandpass filtering of the two SLCs, the calculation of differential interferograms for the two azimuth sub-band SLC pairs, and the calculation of the double difference interferogram, which can be done in a combination of the two complex valued differential interferograms. It is relevant that the co-registration is not done with offset-field refinement, as this will remove the split-beam double difference interferogram. The resulting non-zero phase relates either to ionospheric effects – the signal of the two sub-bands propagates through different parts of the ionosphere to the same pixel on the surface – or to an along-track ground motion. Typically, motion effects and ionospheric effects can be discriminated since the prior ones relate to the surface characteristics or processes such as earthquakes or ice motion. 3. Mitigation of positional offsets Resampling the transformed second SLC with the determined offset-field permits achieving an accurate co-registration in the presence of ionospheric effects or ground-displacements. In the case of a very strong spatial variation of the ionospheric path delay, with azimuth offsets > 1 SLC pixel, the azimuth offset differs between different parts of the azimuth spectrum. In such cases it is possible to apply the procedure separately to smaller fractions of the azimuth spectrum This improves the co-registration in such cases, as confirmed by the higher coherence obtained, nevertheless we could not get perfect results in cases with very “wild” ionospheric effects as observed mainly in arctic regions. 4. Mitigation of ionospheric path delay phase effects For the estimation of the ionospheric path delay phase, we apply the split-spectrum method as described in [1,2]. The full bandwidth differential interferogram is generated and spatially unwrapped. The split-spectrum double difference interferogram is generated by combining the two complex valued sub-band differential interferograms. The resulting phase variations are typically smaller than half a phase cycle, permitting to directly extract the “unwrapped” phase values without spatial phase unwrapping step. In the BIOMASS case and using the lowest and highest third of the 6 MHz chirp bandwidth for the split-spectrum double difference interferogram, the factors used to calculate the ionospheric path delay phase based on the unwrapped full bandwidth differential interferogram phase and the split-spectrum double difference interferogram phase are 0.5 and -54.38. This ionospheric path delay phase can then be subtracted from the full bandwidth differential interferogram to get the “ionosphere corrected” differential interferogram. At P-band, the coherence of pairs with short intervals is typically very high, which facilitates the unwrapping step. In cases with low or intermediate level path delay variations the ionospheric path delay phase variations can typically be estimated and used to subtract this ionospheric path delay phase term from the BIOMASS interferograms. But there are cases with “too wild” ionospheric effects, observed in arctic regions, where the mitigation procedure used was not successful. 5. Discussion and conclusions The two approaches used to identify ionospheric effects are robust, reliable and can be automated. We typically use the co-registration refinement offset-field based approach as it also provides the refined co-registered SLCs. The co-registration quality is good enough to get coherence values interpretable with respect to the scatterer characteristics and phases that can be unwrapped – except for pairs affected by spatially very strongly varying ionospheric effects as observed sometimes in arctic areas. The mitigation of the ionospheric path delay using the described split-spectrum based approach usually works fine, again except for the cases with spatially very strongly varying ionospheric effects. Overall, the presence of ionospheric effects can be identified and, in most cases, corrected for. The cases with “wild distortions” can be identified. In these cases, fully correcting the data for ionospheric effects was not achieved. At the ESA BIOMASS workshop held in Ljubljana in early 2026 several presenters addressed the estimation of ionospheric phase screens, but nobody else seemed to use the split-spectrum method, which is typically used for this purpose with L-band and higher frequency data. Their reason for not trying it was the narrow 6 MHz chirp bandwidth of the BIOMASS data. Nevertheless, because of the lower frequency, the applicability is quite good, with scaling factors similar to L-band data with about 20 MHz bandwidth. Our results clearly confirm the applicability of the technique. 6. Acknowledgements ESA is acknowledged for providing us with access to the BIOMASS data used in our work. 7. References [1] Wegmüller, U., Werner, C., Frey, O., Magnard, C. and Strozzi, T.: Reformulating the Split-Spectrum Method to Facilitate the Estimation and Compensation of the Ionospheric Phase in SAR Interferograms. Procedia Computer Science, pp. 318–325, 2018. doi:10.1016/j.procs.2018.10.045 [2] Wegmüller, U., Werner, C., Frey, O., and Magnard, C., “Estimation and Compensation of the Ionospheric Path Delay Phase in PALSAR-3 and NISAR-L Interferograms,” Atmosphere, vol. 15, no. 6, p. 632, May 2024, doi: 10.3390/atmos15060632

During volcanic unrest, inverse modeling of InSAR surface displacements is often used to retrieve the parameters of the source characteristics of volcanoes. Such approach could be costly in terms of computing time, and therefore not always adapted to routine modeling. Here, we are interested to explore the potential and limitations of deep learning for the characterization of volcanic sources. We rely on our previous proof-of-concept work (Lopez Uroz et al., 2024), which has demonstrated the potential of neural networks in case of a point source model (Mogi 1958). This work will be used as a basis for expanding research into the benefits of deep learning in volcanic inverse modeling. In this study, we will address issues of image resolution, patch dimension, and SAR acquisition geometry in order to properly prepare the learning dataset and to further improve the prediction accuracy of neural networks. To do this, we continue with the Mogi model (Mogi, 1958) described by the depth of the volcanic source and its volume change. We propose multi-channel input, including multi-resolution and multi-geometry (i.e. descending and ascending) InSAR displacement fields in the case of previously deployed ResNet to estimate the depth and the volume change of the volcanic source. For results evaluation, besides the mean squared error of estimated parameters compared to the truth, we also consider structural similarity (SSIM) between the input displacement field and the reconstructed one as metric. Moreover, we perfome a sensitivity analysis in order to highlight the impact of resolution on each estimated parameter. The multi-channel ResNet model is trained and first evaluated by synthetic datasets where the ground truth allows for quantitative assessment of the learning performance. The trained model is also applied to Sentinel-1 InSAR datasets covering the Suswa volcano during the period of 2016-2020 (Albino & Biggs, 2021). The results are compared to those of the conventional inversion approach for validation. According to the obtained results, the input image resolution to the neural network has significant impact on its effectiveness. As shown by the sensitivity analysis, the depth of the volcanic source is more sensitive to near-field displacement information, while its volume change is more sensitive to far-field information. Given the fixed input image size, a multi-resolution input therefore seems optimal to ensure both near-field and far-field information. On the other hand, taking both ascending and descending geometries as input to the ResNet model is highly interesting for improving the inversion performance.

Volcanic activity can rapidly and completely reshape the Earth’s surface, from the emplacement of lava flows and ash to catastrophic explosive events that destroy volcanic edifices. Monitoring lava flow progression during eruptions is crucial for understanding volcanic behaviour and mitigating associated hazards, including the destruction of infrastructure, livelihoods, and loss of life. Satellite imaging has emerged as a powerful tool for monitoring volcanic eruptions, providing new insights where other remote sensing or ground-based observations may be limited due to inaccessibility, the hazardous environment or cloud coverage (e.g., meteorological or volcanic). However, tracking the progression and morphology of lava flows, especially during ongoing eruptions, remains a challenge. This study demonstrates the potential of SAR backscatter and coherence imagery for monitoring lava flow emplacement and morphology. Using high-resolution COSMO-SkyMed (CSK) data, we examine the 2017 eruption of Erta ‘Ale, Ethiopia and the 2016 lava flow at Kilauea, Hawai’i. SAR backscatter and coherence offer complementary strengths for detecting lava flow changes. Backscatter is sensitive to variations in the surface roughness, which highlights newly emplaced lava and provides insights into flow surface morphology. Coherence responds to temporal changes in the surface stability, with freshly emplaced lava producing high decorrelation. Individually, these datasets can identify volcanic activity, but combined they provide a more robust basis for detecting and characterising lava flows in various environments. We develop and evaluate an automated lava flow extraction framework based on pixel SAR timeseries. To improve detection accuracy, we assess the impact of spatial filtering through three scenarios: (1) no additional filtering, (2) bilateral filtering, which locally reduces speckle while preserving sharp flow edges, and (3) a combination of multi-looking and super-resolution processing, which improves the overall signal-to-noise ratio across the image while maintaining spatial detail. We apply a sequential change detection algorithm using the Cumulative Sum (CUSUM) method to automatically identify newly emplaced lava flows. CUSUM detects statistically significant deviations in pixel backscatter and coherence over time. It identifies abrupt shifts in the mean signal relative to a pre-determined background level that we correlate with lava flow emplacement. We evaluate detected changes against other datasets (e.g., field observations) at the pixel level or with an additional spatial aggregation of neighbouring pixels to better capture continuous flows and reduce isolated noise. This pixel-based temporal analysis on the SAR backscatter achieved 79% and 82% of pixels correctly classified compared to manually derived flow maps for Erta ‘Ale and Hawai’i respectively. Here, we observe rougher flow surfaces emplaced farthest from vent. Integrating backscatter and coherence provides a more comprehensive characterisation of lava flow extent and morphology than either dataset alone. In more complex environments, their combined strengths are expected to improve flow extraction and reduce misclassification, increasing confidence in mapping extents and morphological interpretation. To assess transferability, we test the approach on Sentinel-1 datasets at multiple volcanoes (e.g., Nyiragongo, Fuego, and El Reventador), which vary in environmental conditions, vegetation and seasonal dynamics. The comparison between CSK and Sentinel-1, allows us to explore the sensitivity of the method to radar polarisation (co- and dual-polarisation) and wavelength differences (C- and X-band), which influence backscatter sensitivity to surface roughness and vegetation, affecting accuracy of lava flow detection. We demonstrate that SAR timeseries analysis can provide a robust and transferable method for tracking lava flow progression and morphology, providing the frequent observations needed for volcano monitoring.

Volcanic eruptions occur when magma generated by partial melting of the mantle rises and is released on the Earth’s surface in the form of lava, pyroclastic material, and volcanic gases. Understanding eruption mechanisms and subsurface magma structures, including magma chambers and pressure sources, is essential for mitigating volcanic hazards. Mauna Loa, located in Hawaii, is the largest active volcano on Earth and has experienced frequent eruptions since 1843. One of the most significant eruptions began on November 27, 2022, providing an opportunity to investigate eruption-related surface deformation using remote sensing techniques. Synthetic Aperture Radar (SAR) is a remote-sensing technique that enables data acquisition regardless of weather conditions, making it particularly useful for monitoring inaccessible areas, such as volcanic regions. Differential interferometric SAR enables precise deformation measurements with centimeter- to millimeter-scale accuracy. These techniques are widely used to monitor surface displacements associated with volcanic activity, earthquakes, landslides, and subsidence. In this study, we monitored surface deformation associated with the 2022 Mauna Loa eruption using time-series Interferometric SAR (InSAR) and assessed how well a simple point-source model explains the observed spatiotemporal deformation complexity. Small Baseline Subset (SBAS) analysis was conducted using Sentinel-1 datasets acquired between June 2022 and December 2023. Since differential interferograms measure surface deformation along the satellite line-of-sight (LOS), observations from different viewing geometries are required to interpret the deformation components. In this study, both ascending and descending Sentinel-1 SAR tracks were used for SBAS time-series analysis and displacement decomposition. A total of 47 ascending and 47 descending SAR images were processed to generate cumulative LOS deformation maps. The time-series deformation results were validated using Global Navigation Satellite System observations from four stations near the summit region, which showed consistent spatial and temporal displacement patterns. Assuming negligible north-south displacement, east-west and up-down displacement components were estimated using three-dimensional deformation analysis before, during, and after the eruption. Although deformation in the north-south direction may exist in the Mauna Loa summit region, this assumption is commonly adopted because Sentinel-1 orbits nearly poleward and InSAR has limited sensitivity to the north-south component; thus, our three-dimensional results primarily constrain the east–west and vertical motions. The time-series results indicate deformation concentrated near the Mauna Loa summit caldera during the eruption, with LOS displacements ranging from approximately +0.32 m to –0.29 m over the study period. To distinguish the short-term eruptive signal from longer-term cumulative deformation, we also examined an event-focused differential interferogram spanning the eruption, which reached approximately 0.31 m and 0.34 m in the ascending and descending LOS geometries, respectively. The three-dimensional deformation analysis reveals complex displacement patterns with both horizontal and vertical components, consistent with previously reported deformation mechanisms of Mauna Loa associated with magma migration and the 2022 eruption. To further interpret the deformation source, magma-source modeling was performed using the Geodetic Bayesian Inversion Software based on the Mogi point-source model. The inversion was conducted separately for deformation fields derived from the ascending and descending SBAS time series displacement fields. The modeling results indicate a primary deformation point source beneath the summit of Mauna Loa, which may represent a pressure source associated with the subsurface magma system. The estimated source parameters show relatively stable behavior across inversions, suggesting that the best-fitting source is consistently centered beneath the summit area. However, a comparison of the modeled displacement field with the observed SBAS displacement revealed localized residual deformation near the caldera. While the point-source model can explain the first-order deformation pattern associated with the initial eruption, it does not fully capture the spatial complexity observed in the SBAS results. These residual signals are interpreted as deformation components that are not directly related to the initial magma-pressure source but may be associated with shallower subsurface deformation processes or surface displacement caused by lava-flow emplacement following the initial eruption. This result suggests that the 2022 Mauna Loa eruption involved multiple deformation mechanisms that cannot be fully represented by a single point source in a homogeneous elastic half-space, highlighting the limitations of single-source modeling and the importance of integrating time-series InSAR analysis with geodetic inversion. Keyword: Mauna Loa eruption, SBAS, Sentinel-1, Mogi point-source model

Geodetic models of volcanic deformation are commonly interpreted using simplified analytical sources (e.g., Mogi or sill geometries), implicitly assuming melt-dominated and spatially discrete magma storage. At Eyjafjallajökull, such approaches have supported interpretations of multiple shallow intrusions or stacked sills. Here, we revisit the 2010 eruption using integrated InSAR and GNSS time series, focusing on co-eruptive and post-eruptive deflation. We show that previously proposed sill-like geometries systematically underpredict the observed deformation amplitudes. In contrast, a single ellipsoidal source at ~6–7 km depth simultaneously reproduces the spatial and temporal evolution of subsidence. However, this geometry is inconsistent with a purely molten reservoir, as seismicity persists within the inferred source region. We reconcile these observations by interpreting the source as a mechanically heterogeneous, crystal-rich mush, in which deformation reflects bulk volume change while seismicity is accommodated within the rigid framework. Petrological and geochemical evidence further supports this interpretation, indicating magma mixing between newly intruded basalt and remnant silicic material stored in the system. Additionally, the discrepancy between erupted volume and geodetically inferred contraction suggests that magma compressibility and fracture compliance play a significant role in modulating the deformation signal. Together, these results challenge interpretations based on discrete melt bodies and instead support a vertically extensive, mush-dominated magmatic system beneath Eyjafjallajökull.

The magmatic unrest at Mount Agung preceding the November 2017 eruption was identified by previous InSAR studies in September 2017, but the precursory signal had begun much earlier, masked by a concurrent tectonic signal. Applying Variational Bayesian Independent Component Analysis to the full Interferometric Synthetic Aperture Radar (InSAR) time series, we separate the magmatic deformation from atmospheric noise and regional tectonic subsidence driven by interseismic loading on the Flores back-arc thrust, and reveal that magmatic inflation began in May 2017, six months before the eruption. The separated signals are independently verified by Global Navigation Satellite Systems displacement and seismicity patterns. Signal separation of tectonic and magmatic deformation, demonstrated retrospectively at Mount Agung, provides a framework for early precursor detection at tectonically complex arc volcanoes.

Terrestrial ecosystems play a critical role in biodiversity conservation and climate regulation through carbon storage and biogeochemical processes. Accordingly, land cover dynamics, including forests, vegetation, and surface water, serve as key indicators for sustainable ecosystem management and climate change studies, reflecting environmental responses to spatiotemporal variability. Remote sensing enables consistent large-scale monitoring of land cover structure and dynamics. At the same time, Synthetic Aperture Radar (SAR) is particularly indispensable due to its sensitivity to structural properties and its all-weather, day–night imaging capability. The synergy between SAR and optical remote sensing has become a standard methodology for vegetation monitoring, leveraging the complementary characteristics of active and passive sensors. While optical indices such as NDVI and EVI are reliable proxies for photosynthetic activity, their utility is often restricted by cloud persistence and atmospheric interference. Conversely, SAR observations provide moisture-sensitive and structural information independent of solar illumination or weather conditions, enabling continuous monitoring across heterogeneous and cloud-prone regions. In cloud-dominated areas, SAR-driven gap-filling of optical time series has significantly enhanced the operational performance of yield-prediction and vegetation-monitoring models. Nevertheless, the correlation between SAR-derived indices and optical metrics is known to vary with land-cover type and phenological status. The estimation of NDVI and EVI from SAR data has gained substantial traction with the evolution of machine learning and deep learning. Convolutional neural networks trained on paired SAR–optical datasets have demonstrated the feasibility of predicting NDVI directly from radar imagery, often outperforming conventional regression models. Similarly, temporal and cross-sensor modeling approaches exploiting SAR–optical time series have improved estimation performance by capturing non-linear relationships between scattering mechanisms and spectral vegetation signals. The study was conducted in the Gönen district of Balıkesir, northwestern Türkiye. The district features a Mediterranean climate with mild winters and long dry summers, and is centered on the Gönen Basin, where the Gönen Stream sustains irrigation across surrounding agricultural lands. Rice is the dominant crop in the region, covering approximately 8,450 hectares as of 2019 data from the Turkish Statistical Institute (TÜİK), and holds significant economic and ecological importance. This study investigates the cross-modal predictive capacity of S-band and C-band SAR observations for reconstructing optical vegetation indices derived from Sentinel-2 imagery. The target variables include NDVI, NDVI-red, and EVI. The primary objective is to assess whether multi-frequency SAR backscatter and radar-derived indices can reliably approximate optical vegetation metrics across contrasting land-cover conditions. To ensure statistical robustness and class balance, a stratified sampling strategy was implemented across three representative land-cover types: Rice, Water, and Forest, using 150 randomly distributed samples per class. Sentinel-1 was selected as the C-band source owing to its systematic global coverage and open-access data policy, while NovaSAR-1 was chosen as the S-band counterpart due to its ~10 cm wavelength sensitivity to vegetation structure and surface moisture. SAR acquisitions were temporally co-registered with Sentinel-2 optical imagery to minimize phenological discrepancy, with both datasets acquired on 11 July 2024. The predictor space consisted of six radar-derived variables. From Sentinel-1 (C-band), VV and VH backscattering coefficients were extracted, while NovaSAR-1 (S-band) provided HH and VV backscattering coefficients. In addition, the Radar Vegetation Index (RVI) was computed for each sensor independently. Given the intentionally low-dimensional structure of the feature set, no explicit feature selection or dimensionality reduction was performed. This methodological choice enabled a direct examination of intrinsic radar–optical relationships without introducing bias driven by optimization. Model development and validation were conducted using a 5-fold cross-validation framework to enhance generalizability and reduce the risk of overfitting. Six regression algorithms representing diverse statistical and ensemble learning paradigms were implemented: Random Forest, ExtraTrees, Gradient Boosting, Gaussian Process Regression, NGBoost, and CatBoost. Model performance was evaluated using mean coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Pearson’s correlation coefficient (r). Statistical significance of the radar–optical associations was assessed using p-values to determine whether observed relationships were distinguishable from random variability. The results reveal a pronounced dependence on land cover in SAR-to-optical translation performance. In the Rice and Water classes, regression models demonstrated strong predictive performance. In the Rice class, CatBoost achieved the strongest performance (R2=0.74), whereas ExtraTrees yielded the highest accuracy for Water surfaces (R2=0.76). Pearson correlation coefficients ranged between 0.85 and 0.88, with highly significant p-values (p

InSAR time series analysis of ground deformation in the Chiles-Cerro Negro region reveals a complex sequence of temporally and spatially linked magmatic and tectonic events. InSAR has detected several areas of uplift; preliminary interpretation supports the presence of shallow sill-type magmatic sources beneath and south of the volcanic complex (depth < 2km) and Potrerillos (depth < 5km). Mw5.8 and Mw5.6 earthquakes occurred in October 2014 and on 22 July 2022, respectively. Constant inflation of the volcanic complex and a 3 cm uplift event at the Potrerillos caldera a month prior the July 2022 earthquake appear to have promoted the earthquake. Furthermore, the noticeable increase in uplift following the July 2022 event (~8 cm in the caldera and south of Chiles) suggests a positive feedback not only between magma intrusion and earthquakes but also between the earthquake and the magmatic system. Stress changes due to magma intrusion promote the faulting while stress changes due to faulting promote further magma intrusion. To better understand these interactions, we conducted magmatic source inversions and Coulomb stress modeling to evaluate the spatial relationship between inferred pressure sources, fault geometries, and earthquake timing. The processes before and after the 2022 earthquake are somewhat similar to the October 2014 ones. Ebmeier et al. (2016) noted that the earthquake was a consequence of regional surface displacement around the complex.

Modern-day volcanic deformation in the Central Andes is anomalous, characterised by spatially extensive displacements, deformation sources that remain stationary over decades, no geomorphological evidence of net uplift, and little correlation between deformation and Holocene volcanic activity [1]. It has been proposed that uplift is caused by the ascent and then temporary accumulation of magmatic volatiles from the mid-crustal Altiplano-Puna and Southern Puna Magmatic Bodies; when volatiles are released, the ground subsides back to its initial position [1]. Here, we test this idea against 15 years of Sentinel-1 satellite radar observations of surface deformation in the Atacama. A ~50 km region within the Pastos Grandes Caldera, Bolivia, has been uplifting since early 2023 with a maximum rate of 70 mm/yr. This has started to decelerate to a rate of 40 mm/yr in late 2025, although this is still the fastest volcanic deformation that has been observed to date in the Central Andes. Preliminary analytical elastic half-space modelling suggests the pressurisation at Pastos Grandes is caused by a 1e8 m3 volume increase at about 13km below the surface. This is deeper than the 4-8 km depth of pre-eruptive plutons derived by thermo-barometry [2,3]. It is unclear if a 13 km depth is within the Altiplano-Puna magmatic body under Pastos Grandes. Pastos Grandes is a Pliocene caldera that last erupted 2.89 Ma, producing the 1500 km3 Pastos Grandes Ignimbrite [4]. There is still an active hydrothermal system with thermal springs surfacing in Laguna Pastos Grandes [5]. We are currently investigating the response of these thermal systems using median thermal anomalies derived from MODIS [6]. A thermal anomaly, with an onset in early 2023, is visible across Laguna Pastos Grandes and Laguna Q’ara. This suggests that degassing and hydrothermal activity increased at roughly at the same time as the inflation started. We also discuss new observations of small-scale (

High-resolution time-series InSAR provides the opportunity to resolve volcanic flank deformation beyond edifice-scale interpretations. We analyze 193 ascending and 208 descending TerraSAR-X Spotlight scenes (2012–2021) over Lastarria Volcano in the Central Andes to investigate the spatial organization and persistence of flank motion at meter-scale resolution. Line-of-sight velocities range from approximately –10 to +10 mm yr⁻¹ and reveal a distinctly non-radial deformation pattern. A polarity reversal across the summit ridge indicates opposing horizontal components on eastern and western flanks, consistent with downslope-directed motion rather than coherent radial spreading. On the eastern flank, a prehistoric debris-avalanche sector exhibits internally coherent but temporally variable creep. On the northwestern–western flank, deformation is strongly segmented by lithological and geomorphic contrasts. Four wedge-shaped mid-slope domains display sustained velocities of ~5–8 mm yr⁻¹, whereas coherent ignimbrite bodies and lava outcrops correspond to zones of minimal displacement. Deformation preferentially localizes at lithological transitions rather than on the steepest slopes and internally follows downslope-diverging channel networks, forming structured fan-like patterns. These spatial patterns persist throughout the available TerraSAR-X record and are detectable in earlier satellite observations, indicating multi-decadal gravitational creep organized into mechanically distinct compartments. Time-series processing was performed using an SBAS approach with quadratic ramp removal and geometry-dependent coherence thresholds; residual RMS statistics confirm that displacement signals exceed background noise levels. Across spatial scales, deformation is hierarchically organized: non-radial at the edifice scale, sector-confined at the flank scale, and internally structured within sub-kilometer domains. The results demonstrate that volcanic flanks may deform as mosaics of partially decoupled compartments rather than as coherent sliding bodies, highlighting the capability of high-resolution InSAR to resolve persistent, compartmentalized gravitational instability in mechanically heterogeneous volcanic systems.

On August 2, 2025 (16:38 UTC), Krasheninnikov volcano (Kamchatka, Russia) resumed eruptive activity after several centuries of quiescence. The first visual evidence was an ash plume rising 3–4 km above the summit (~5–6 km a.s.l.; KVERT), followed by effusive lava emplacement. The eruption occurred less than a week after the largest earthquake in Kamchatka in more than 70 years, a Mw 8.8 event on July 29, 2025, at 23:24 UTC. The August 2025 eruption occurred during an optimal observation period, allowing the use of Sentinel-1/2. Because seasonal snow cover typically masks the upper flanks from autumn through late spring, the summer 2025 eruption provided a rare opportunity to investigate syn-eruptive deformation using satellite radar observations. From October 2024 to late June 2025, heavy snow cover prevented detection of small-scale thermal or deformation signals from orbit. We analyze volcano deformation during the first two weeks of August 2025 using Sentinel-1A and 1C C-band SAR data from four ascending and descending tracks, incorporating pre- and post-eruptive acquisitions between June and October 2025 to constrain the temporal evolution of the deformation signal. More than 30 interferograms were generated using GMTSAR, SNAP, and ISCE, with and without tropospheric delay correction. We constrain the deformation to a short-lived episode that began after the Sentinel-1 acquisition on July 31, 2025 (~19:30 UTC). Independent interferometric pairs indicate that most line-of-sight (LOS) displacement occurred within ~24 hours and had ceased on or before August 5, 2025. The spatial pattern consistently reveals a localized LOS displacement centered on the summit, with no evidence of prolonged pre-eruptive inflation. Using published estimates of lava-flow extent between 2–12 August (~2.7 km²) and assuming plausible mean flow thicknesses of 2–5 m, we estimate an erupted bulk volume of ~5–14 Mm³. This volume is consistent with a short-lived, localized intrusion rather than a sustained magma supply episode. To explain the observed displacement field, we tested elastic source models using the four available satellite tracks, including spherical point sources (Mogi), sub-vertical dyke intrusions, and combined geometries to assess model robustness. The preferred solution is a shallow, sub-vertical dyke intrusion with a modeled volume change of ~20–40 Mm³ and an opening of 1–2 m, initially without surface breach. This source geometry is consistent with the observed eruptive behavior and its close temporal proximity to the July 2025 Kamchatka earthquake sequence, suggesting a short-lived intrusion occurring in close temporal association with the July 2025 earthquake sequence.

Mt. Baekdu is an active stratovolcano located along the border between the Korean Peninsula and northeastern China. Depending on meteorological conditions, an eruption could pose a significant hazard to the Korean Peninsula; therefore, continuous monitoring is essential to assess volcanic activity levels and prepare for potential eruptions. Satellite-based Synthetic Aperture Radar (SAR) enables day and night observation regardless of weather conditions and is particularly effective in detecting surface changes in geopolitically restricted environments such as Mt. Baekdu. This study presents a SAR-based volcanic monitoring system developed and operated by the Korea Meteorological Administration (KMA). The system consists of three core monitoring technologies: (1) quarterly average surface displacement analysis, (2) AI-based caldera lake monitoring, and (3) time-series InSAR-based magma chamber volumetric change modeling. First, quarterly average surface displacement monitoring is conducted using Sentinel-1 C-band SAR data through InSAR Stacking analysis. Line of sight (LOS) displacement measurements are utilized to estimate mean displacement values and evaluate anomalous deformation patterns on a quarterly basis. Monitoring results from 2017 to the present indicate that observed displacements remain within a few centimeters, falling within the historical range of variability with no clear evidence of sustained inflation or deflation trends. For Cheonji Lake monitoring, SAR datasets (Sentinel-1, ALOS-2, and TerraSAR-X) were utilized to design and train a U-Net-based segmentation model. A water body label dataset was constructed using SAR intensity images from these satellites to train the model. The trained model was then applied to multi temporal SAR images to segment water bodies and calculate the area of Cheonji Lake using the generated masks. Based on these results, long-term changes in the lake area were quantitatively analyzed, showing no statistically significant changes over the analyzed period. The final method utilized time-series surface displacement data obtained from Sentinel-1 InSAR to model the magma chamber using the Mogi model to interpret the magmatic activity process beneath Baekdu Mountain. To determine initial parameters for volumetric change estimation, InSAR stacking results were first used to estimate the depth and location of the subsurface pressure source by minimizing the residual difference between observations and simulated models through Monte Carlo simulation. With the estimated source location fixed, time-series volumetric changes were subsequently calculated. The results indicate an estimated magma chamber volume change of approximately 0.005 km3, suggesting negligible long-term volumetric variation. By integrating short-term quarterly deformation analysis, time-series magma chamber modeling, and caldera lake area monitoring, this framework enables a multi-indicator assessment of volcanic activity at Mt. Baekdu. This study demonstrates the effectiveness of satellite SAR-based multi-parameter remote sensing for volcano monitoring in environments with limited accessibility and its potential for the quantitative assessment of volcanic activity.

Volcano monitoring has advanced substantially in recent years, driven by the growing use of remote sensing techniques that overcome limitations inherent to ground-based instruments, such as restricted access, data-transmission constraints, and hazardous field conditions. Among these techniques, Interferometric Synthetic Aperture Radar (InSAR) has become particularly valuable for detecting volcanic deformation and improving our understanding of magmatic and subsurface processes. The development of the LiCSAR system [1], which automatically generates global deformation products at ~100 m resolution—now provides consistent resampled SLC (RSLC) image stacks for all volcanic regions worldwide from 2014 to 2026. In this study, we introduce a methodology to design a coherence-optimized interferogram network at ~30 m resolution for each volcano. The workflow compiles all RSLC images and generates coherence maps at 6–12 day intervals. Using Smithsonian volcano coordinates and a DEM, we determine summit elevation, delineate the volcanic edifice down to its base (defined as pixels below the 10th percentile of summit height), and compute summit-to-base distances to characterize edifice extent and set a stable analysis window. Mean coherence over this area yields a list of interferograms and an adaptive threshold to retain the most reliable pairs, producing full and filtered coherence matrices. We then select recurring acquisition dates and enforce temporal-density constraints to avoid gaps between acquisitions, generating long interferograms (6, 9, 12 months) as well as short-term interferograms between each date and its four consecutive acquisitions. Interferograms are processed with LiCSBAS [2,3] to produce deformation time series, with optional atmospheric corrections from GACOS and ERA5. We are currently applying this workflow globally, with particular emphasis on volcanoes active during 2025, while progressively extending the approach to the entire global volcanic catalog. We also compare the resulting InSAR time series with GPS observations from publicly available volcanic monitoring networks. The methodology enhances the efficiency of interferogram-network construction, reduces overall processing time, and introduces an incremental-update strategy that incorporates each new SAR acquisition without reprocessing the full archive. Together, these advances significantly improve the feasibility of near-real-time global volcano monitoring with InSAR. References: [1] Lazecký, M., K. Spaans, P. González, Y. Maghsoudi, Y. Morishita, F. Albino, J. Elliott, N. Greenall, E. Hatton, A. Hooper, D. Juncu, A. McDougall, R. Walters, C. Watson, J. Weiss, and T. Wright (2020b). “LiCSAR: An Automatic InSAR Tool for Measuring and Monitoring Tectonic and Volcanic Activity”. Remote Sensing 12(15), page 2430. doi: 10.3390/rs12152430 [2] Morishita, Y., M. Lazecky, T. Wright, J. Weiss, J. Elliott, and A. Hooper (2020). “LiCSBAS: An open-source InSAR time series analysis package integrated with the LiCSAR automated Sentinel-1 InSAR processor”. Remote Sensing 12(3), page 424. doi: 10.3390/rs12030424.330 [3] Lazecký, M., Q. Ou, L. Shen, J. McGrath, J. Payne, P. Espín, A. Hooper, and T. Wright (2024). “Strategies for improving and correcting unwrapped interferograms implemented in LiCSBAS”. Procedia Computer Science 239, pages 2408–2412. doi: 10.1016/j.procs.2024.03.258

Satellite-based Interferometric Synthetic Aperture Radar (InSAR), particularly using the Sentinel-1 constellation, has revolutionised global volcanic monitoring, providing an unprecedented volume of routinely acquired, open-access data. Automated systems, e.g., Looking into Continents from Space with Synthetic Aperture Radar (LiCSAR) and its timeseries processing system LiCSBAS, now continuously process interferograms and regularly update deformation timeseries, providing a valuable dataset for monitoring volcanoes globally. However, seasonal snow leads to coherence loss and subsequent unwrapping errors in interferograms. This results in network disconnections in the automated timeseries analysis, reducing deformation accuracy. We identified that 484 of 1183 (~41%) subaerial Holocene volcanoes globally exhibit seasonal snow cover, predominantly situated in high-latitude and high-altitude regions. Therefore, optimising InSAR processing of volcanoes with seasonal snow cover will substantially improve the monitoring of volcanic activity globally. In this study, we analyse whether an external optical remote sensing dataset (MODIS 8-Day Snow Products) can be used to predict which pixels will be coherent in the interferograms. Based on this, we develop an optimised InSAR timeseries processing workflow that combines InSAR data with the MODIS 8-Day Snow Products. Using the relationship between MODIS snow coverage map and Sentinel-1 InSAR coherence map, we adapted the standard LiCSBAS time-series processing strategy and optimised the network. We use a network selection algorithm that imposes dual constraints: 1) restricting the minimum and maximum connections of each SAR acquisition; and 2) ensuring every interferometric pair is embedded within a minimum of two closed loops. This algorithm guarantees the connectivity of the inversion network and ensures unwrapping errors can be detected. We test the workflow at Laguna del Maule, Chile, a deforming caldera volcano with seasonal snow cover (182 days in 2017). We process line-of-sight deformation using LiCSAR Frame 083D_12636_131313 and compare the LiCSBAS timeseries outputs between 10/2014 and 06/2023 with data from five continuous GNSS stations. The mask automatically generated by LiCSBAS default settings masked out signals near the deformation centre, primarily due to low average coherence, poor spatiotemporal consistency and a high prevalence of unwrapping errors causing unclosed loops. This results in the loss of critical deformation information, including that at three of the five operating GNSS stations. We calculate the RMS misfit between the unmasked InSAR timeseries and the GNSS timeseries. At the MAU2 station, which is located closest to the deformation centre, the misfit was ~167 mm. We then generated a manually-improved LiCSBAS timeseries, using over 2200 high-quality interferograms. The result shows that the crucial signals around the deformation centre are no longer masked, capturing ~1.04 m of cumulative deformation and the RMS misfit is reduced to ~15 mm at MAU2. Furthermore, we evaluate the ability of MODIS Snow Products to predict whether the interferometric coherence will exceed a customised threshold. For each interferometric pair, we identify snow-free pixels in both corresponding MODIS snow maps. Using a confusion matrix, we calculate a prediction accuracy of 87%. The network optimisation strategy reduced the required data by ~90% and the LiCSBAS processing time by ~80%. In the resulting timeseries, the LiCSBAS mask retains the critical pixels around the deformation centre and the values match GNSS observations well, with a maximum RMS misfit of ~16 mm at MAU2. Finally, we consider whether this approach would be applicable to other volcanoes with seasonal snow cover. Vegetation also causes loss of coherence and cloud cover can limit optical satellite observations. To evaluate the global applicability of this methodology, we calculate the Normalised Difference Vegetation Index (NDVI) and Cloud Cover Duration (CCD) for all subaerial Holocene volcanoes globally using MODIS products. We find that more than 50 of 484 global seasonally snow-covered volcanoes exhibit lower NDVI and CCD than LdM (NDVI=0.16; CCD=144 days), suggesting that seasonal snow is their dominant source of coherence loss and MODIS products are applicable. We then tested the applicability of our workflow to a diverse set of seasonally snow-covered volcanoes with different NDVI and CCD, such as the Ashikule volcanic field (China), Askja (Iceland), and Okmok (Alaska, USA). Where applicable, we process long-term deformation using Sentinel-1 data by LiCSBAS, and the results are compared with GNSS observations and the LiCSBAS default processing results. Looking ahead, the quantitative relationships established between MODIS snow products and interferometric coherence can be applied to optimise automated InSAR processing pipelines such as LiCSAR and LiCSBAS.

The growing demand for Critical Raw Materials (CRMs) driven by the energy and digital transition has led European countries to reassess their national mineral potential and resource recovery strategies. In Italy, the National Exploration Programme (PNE), developed in response to the Critical Raw Materials Act, aims to update national knowledge of the strategic and critical mineral potentiality through non-invasive exploration methodologies and the integration of previous datasets. In parallel, the URBan mining and Extractive waste information System (URBES) project the systematic mapping and characterisation of extractive waste deposits as potential secondary sources of SCRMs, in accordance with the regulatory framework governing mining residues. These parallel initiatives reflect a dual exploration strategy addressing both primary mineral systems and secondary resource reservoirs. Italy hosts a highly diverse geo-metallogenic framework shaped by the inheritance of Variscan basement domains, the development of Mesozoic passive margins, subsequent Alpine and Apennine tectonic evolution, widespread magmatism, and long-lived hydrothermal systems. These processes generated a broad spectrum of mineral occurrences including several materials currently classified as Critical Raw Materials, distributed across multiple metallogenic provinces. Extensive historical mining footprints have left thousands of abandoned sites and large volumes of extractive waste, particularly concentrated in districts such as Sardinia, Tuscany, the Alpine arc, parts of the Apennines and Sicily. These areas represent both environmental challenges and strategic opportunities for the identification of primary mineral systems and secondary resource reservoirs within contemporary exploration frameworks. The spatial diffusion of minerogenetic provinces, the presence of extensive leftover mining infrastructures, and the need for low impact exploration methodologies highlight the importance of large-scale Earth Observation (EO) approaches. Within this context, Synthetic Aperture Radar Interferometry (InSAR) can provide complementary information during early-stage mineral exploration and extractive waste assessment through non-invasive monitoring. Multi-temporal interferometric analyses enable the detection of millimetric surface deformation associated with processes relevant to mineral systems, such as subsidence linked to underground voids, slope instability in former mining districts, and ground responses controlled by structural discontinuities or fluid circulation. Rather than directly identifying mineral deposits, InSAR may help refine investigation areas by highlighting sectors characterised by active deformation or structurally controlled dynamics, thereby supporting geological and geophysical interpretation. In geothermal districts potentially hosting lithium-bearing fluids, for example, deformation signals may reflect ongoing tectonic or hydrothermal processes that are relevant for understanding the structural framework controlling fluid pathways. Similarly, in historical mining regions such as those of Sardinia or the antimony districts of Tuscany, deformation time series can assist in evaluating the stability of extractive waste deposits and in identifying geomorphological processes influencing material redistribution. Beyond phase-based measurements, SAR amplitude analysis provides additional insights into surface conditions. Variations in backscatter intensity may be related to surface disturbance, material texture, and moisture conditions, offering a means to characterise extractive waste deposits and their temporal evolution. Although still underexplored in the context of mineral exploration, amplitude-based approaches may contribute to the assessment of secondary resource potential and environmental risk. The integration of InSAR products within national geological databases and extractive waste inventories supports a multi-scale approach, from regional screening to site-specific analysis. In this perspective, satellite-derived deformation and surface characterisation are not presented as stand-alone exploration tools, but as elements that can complement traditional geological mapping, geophysical surveys, and field investigations within a responsible and low-impact exploration strategy.

Soil moisture (SM) is a fundamental variable in the evolution and functioning of agricultural and hydrological systems. Its spatial and temporal dynamics influence plant growth, irrigation scheduling, crop yield variability, and the onset and intensification of drought events. Accurate SM estimation is equally important for hydrological modelling, and sustainable water resource management. Conventional in-situ monitoring networks deliver high accuracy point measurements but are limited by their spatial sparsity and the cost of installation and maintenance. As a complement to ground based observations, Synthetic Aperture Radar (SAR) remote sensing offers a spatially continuous, and weather-independent means of mapping surface soil moisture over large areas. Microwave backscatter is sensitive to soil dielectric constant, which is strongly dependant on volumetric water content. This provides the physical basis for SM retrieval from SAR data. Over the years, retrieval strategies have evolved from physically-based scattering model inversion [1] to semi-empirical change detection approaches such as the TU Wien change detection algorithm [2], and more recently to data-driven machine and deep learning frameworks that exploit SAR time series [3] [4] [5]. Building on these developments, we investigate the capacity of Sentinel-1 dual-polarized SAR time series (VV and VH) to retrieve surface soil moisture during dry season conditions using a supervised neural network regression. In-situ SM measurements from the International Soil Moisture Network (ISMN) serve as the target variable. We conduct our analysis over the REMEDHUS soil moisture monitoring network in Spain, composed of 18 stations for our study year providing volumetric surface SM measurements, distributed across the provinces of Zamora and Salamanca [6]. The region has a Mediterranean climate with pronounced summer drought and agricultural land use. Importantly, the terrain exhibits topographic variability including slopes, piedmont zones, and drainage convergence areas, which induces lateral water redistribution. For this study, we selected six Sentinel-1 Interferometric Wide Swath (IW) Ground Range Detected (GRD) acquired during a dry season (July–September) time series; a period specifically chosen to limit the effect of vegetation on SAR backscatter. We carried out pre-processing using ESA SNAP software, including precise orbit correction, radiometric calibration to sigma-naught (σ⁰), speckle filtering, Range-Doppler terrain correction, and co-registration of all scenes to ensure temporal consistency across acquisitions. In addition to the SAR data, Local Incidence Angle and elevation were added, both derived from SNAP's terrain correction module. The pixels used for the database were extracted per station using a small spatial window centered on each station's coordinates. We define the regression feature vector as: X = [σ⁰_VH, σ⁰_VV, Local Incidence Angle, Elevation]. We deliberately restrict the feature space to SAR and geometric/topographic descriptors in order to isolate the dielectric sensitivity of C-band backscatter and evaluate the retrieval potential of Sentinel-1 dual polarization. We designed a feedforward neural network to model the relationship between the SAR observables and in-situ SM. Our architecture comprises an input layer, three hidden layers of 32 neurons each with Rectified Linear Unit (ReLU) activation, and a single output neuron representing predicted volumetric SM. We applied feature normalization prior to training, and performed model optimization by minimizing the Mean Squared Error (MSE) loss function via Adaptive Moment Estimation (Adam). Our model achieved R = 0.87, RMSE = 0.026 m³/m³, and R² = 0.76 (Fig. 1). The correlation reflects a strong relationship between predicted and observed SM; consistent with the expected sensitivity of C-band backscatter to soil dielectric contrasts under sparse vegetation cover. The RMSE is comparable to recently published SAR-based SM retrievals [4][5], and remains competitive with studies incorporating more features, including optical indices or surface roughness parameters [7][8]. These results are particularly encouraging given the limited number of acquisitions and features. We then applied our model to a full Sentinel-1 scene to generate a spatially continuous SM map shown in Fig.2. Given the limited geographic coverage of the in-situ network, pixel-wise quantitative validation across the entire scene was not feasible; the assessment then remains qualitative. Nevertheless, the resulting moisture distribution is well correlated with the expected hydrological behaviour of the terrain. Specifically, areas at relatively lower elevations tend to display higher SM values which coincide with slope outlets, where surface runoff accumulation and subsurface lateral flow convergence are expected to sustain higher moisture levels. This spatial correspondence between predicted SM and hydrological phenomena shows the reliability of the soil moisture retrieval. References [1] Dubois, P. C., van Zyl, J., & Engman, T. (1995). Measuring soil moisture with imaging radars. IEEE Transactions on Geoscience and Remote Sensing, 33(4), 915–926. [2] Wagner, W., Lemoine, G., & Rott, H. (1999). A method for estimating soil moisture from ERS scatterometer and soil data. Remote Sensing of Environment, 70(2), 191–207. [3] Zhu, L., Dai, J., Liu, Y., Yuan, S., Qin, T., & Walker, J. P. (2023). A cross-resolution transfer learning approach for soil moisture retrieval from Sentinel-1 using limited training samples. Remote Sensing of Environment, 301, 113944. [4] Lakra, D., Pipil, S., Srivastava, P. K., Singh, S. K., Gupta, M., & Prasad, R. (2025) Soil moisture retrieval over agricultural region through machine learning and sentinel 1 observations. Front. Remote Sens. 5:1513620. [5] Raut, D. B., Misal, V., Pangarkar, R., Raut, S., & Sayyad, S. (2025). Nonlinear soil moisture retrieval from Sentinel-1 SAR using ensemble machine learning. International Journal of Scientific Research in Science and Technology, 12(6), 616–626. [6] Sánchez, N., Martínez-Fernández, J., Scaini, A., & Pérez-Gutiérrez, C. (2012). Validation of the SMOS L2 soil moisture data in the REMEDHUS network (Spain). IEEE Transactions on Geoscience and Remote Sensing, 50(5), 1602–1611. [7] Celik, M. F., Isik, M. S., Yuzugullu, O., Fajraoui, N., & Erten, E. (2022). Soil Moisture Prediction from Remote Sensing Images Coupled with Climate, Soil Texture and Topography via Deep Learning. Remote Sensing, 14(21), 5584. [8] Liu, J., Xu, Y., Li, H., & Guo, J. (2021). Soil Moisture Retrieval in Farmland Areas with Sentinel Multi-Source Data Based on Regression Convolutional Neural Networks. Sensors, 21(3), 877.

Interferometric SAR (InSAR) has been widely used for monitoring ground deformation [1], which is crucial in disaster management through preparedness and mitigation. The European Ground Motion Service (EGMS), a pre-processed InSAR product provided by European Environmental Agency (EEA), has democratized the access to continental-scale Persistent Scatterers (PSs) for deformation monitoring [2]. However, interpreting this data often requires remote sensing expertise, limiting its usability for disaster managers. To address this gap, we introduce Visual Question Answering (VQA) [3], a system that generally accepts images and natural-language questions to produce answers. We aim to adapt VQA models to EGMS data and improve its usability in disaster management. EGMS data contains PS points with time series indicating land deformation. Its data format differs from traditional dense, natural images used in VQA, posing challenges in handling point modality and temporal context. We propose a novel VQA architecture specialized for this modality. First, we employ a temporal encoder Presto [4], a lightweight transformer architecture pre-trained on remote sensing data, to capture long-term dependencies in the time series at each point. Next, a spatial encoder Point-BERT [5] models spatial relationships among sparsely distributed points. The encoded spatio-temporal features are fed into an open-source Large Language Model (LLM) backbone (LLaVA-Vicuna [6]). Through instruction tuning with questions, the LLM learns to map specific latent deformation patterns to human-understandable concepts, and generate answers with high-level reasoning. A major bottleneck in developing VQA systems for EGMS is the lack of labeled instruction data. A semi-automated Knowledge Distillation pipeline is proposed to address this, which bypasses the need for expensive expert annotation. In the pipeline, GPT-4 [7] acts as a teacher. We feed structured physical attributes extracted from EGMS samples (e.g., velocity gradients, anomalies, spatial variability) into GPT-4 to generate high-quality, physically grounded question-answer pairs. This knowledge distillation process transfers the expert interpretation patterns simulated by GPT-4 into the resulting dataset, termed EGMS-Instruct. This dataset acts as a bridge, enabling our student model (Vicuna) to align EGMS-specific visual tokens with their corresponding linguistic semantics. The framework is currently being prototyped on strategic test sites across Europe. EGMS dataset has been collected, and its structural attributes have been defined. By the time of the conference, we will present preliminary experimental results demonstrating the framework’s effectiveness. Specifically, we will release the EGMS-Instruct dataset, a benchmark for EGMS-language tasks. We will also bring case studies on critical areas and highlight how the system enables disaster managers to rapidly interpret EGMS dataset and assess potential risks. In conclusion, this study proposes a paradigm shift in how we interact with EO data. By synergizingInSAR information with the reasoning power of LLMs, we aim to provide a user-friendly interface to improve the usability of EGMS dataset in disaster management. Keywords: InSAR, European Ground Motion Service (EGMS), Visual Question Answering (VQA), Large Language Models (LLMs), Disaster Management. References: [1] R. Bürgmann, P. A. Rosen, and E. J. Fielding, “Synthetic Aperture Radar Interferometry to Measure Earth’s Surface Topography and Its Deformation,” Annual Review of Earth and Planetary Sciences, vol. 28, no. Volume 28, 2000, pp. 169–209, May 2000, ISSN: 0084-6597, 1545-4495. DOI: 10.1146/annurev.earth.28.1.169. Accessed: Nov. 9, 2025. [2] M. Costantini et al., “European Ground Motion Service (EGMS),” in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Jul. 2021, pp. 3293–3296. DOI: 10.1109/IGARSS47720.2021.9553562. Accessed: Nov. 22, 2025. [3] S. Antol et al., “VQA: Visual Question Answering,” in 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile: IEEE, Dec. 2015, pp. 2425–2433, ISBN: 978-1-4673-8391-2. DOI: 10.1109/ICCV.2015.279. Accessed: Oct. 28, 2025. [4] G. Tseng, R. Cartuyvels, I. Zvonkov, M. Purohit, D. Rolnick, and H. Kerner, Lightweight, Pre-trained Transformers for Remote Sensing Timeseries, Feb. 2024. DOI: 10 . 48550 / arXiv . 2304 . 14065. arXiv: 2304.14065 [cs]. Accessed: Dec. 16, 2025. [5] X. Yu, L. Tang, Y. Rao, T. Huang, J. Zhou, and J. Lu, Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling, Jun. 2022. DOI: 10.48550/arXiv.2111.14819. arXiv: 2111.14819 [cs]. Accessed: Dec. 15, 2025. [6] H. Liu, C. Li, Q. Wu, and Y. J. Lee, Visual Instruction Tuning, Dec. 2023. DOI: 10.48550/arXiv.2304.08485. arXiv: 2304.08485 [cs]. Accessed: Oct. 29, 2025. [7] OpenAI et al., GPT-4 Technical Report, Mar. 2024. DOI: 10.48550/arXiv.2303.08774. arXiv: 2303.08774 [cs]. Accessed: Dec. 16, 2025.

Operational multi-temporal InSAR over geomorphologically complex scenes is often limited by (i) long runtimes, (ii) repeated manual parameter tuning, and (iii) hard-to-audit quality decisions. We present a modular, end-to-end SBAS DInSAR pipeline designed for horizontal scalability and transparent automation, targeting near-operational reprocessing and large parameter exploration. The workflow implements the full SBAS chain with multiple quality-control checks. These include coherence-driven acquisition screening, phase-consistency checks, statistical validation of intermediate products, and reliability metrics derived from inversion residuals. A multi-stage atmospheric correction cascade combines external reanalysis fields with scene-driven residual corrections to improve time-series interpretability in complex topography. To reduce turnaround time, the most expensive spatial stages are executed with a cloud-native, vendor-neutral distributed model. An orchestrator dispatches independent tasks through a centralized queue/status service and elastically provisions short-lived containerized workers, enabling high concurrency while preserving fault tolerance via retries, per-task timeouts and automatic resource cleanup. On three representative test sites covering distinct geomorphological settings, we process SAOCOM L-band stacks of 40-55 acquisitions generating up to 338 interferograms per viewing geometry. Cloud-distributed unwrapping of these networks completes in approximately one hour using up to 200 concurrent containerized workers, compared to multi-day sequential execution on a single node. Beyond cloud orchestration, the pipeline leverages Artificial Intelligence as an active decision-support system at every critical processing stage. Rather than relying on manual parameter tuning, traditionally one of the most time-consuming and expertise-dependent aspects of advanced InSAR workflows, the system employs Human-AI (HAI) interaction to dynamically manage algorithmic complexity. This AI integration operates across multiple dimensions of the processing chain. For adaptive parameter selection, embedded intelligence analyzes scene and terrain characteristics to suggest optimal processing thresholds, such as baseline constraints derived from geomorphological classification, or stable-point ensemble criteria for systematic bias estimation, ensuring that each dataset is processed with parameters calibrated to its specific conditions. For automated quality assessment, AI-driven routines continuously evaluate data reliability at each processing gate: screening acquisitions based on statistical coherence distributions, identifying unreliable pixels through phase-consistency analysis and verifying network integrity after each filtering step, all without requiring continuous manual intervention. For signal processing, intelligent estimation techniques guide the execution of computationally complex algorithms, from frequency-domain analysis for orbital artifact removal to multi-scale spatial interpolation for residual atmospheric signal correction, where AI feedback dynamically selects methods and parameters based on intermediate data characteristics. Critically, this level of automation is designed to maintain high-quality outputs. The system keeps the domain expert in the loop through transparent AI-suggested recommendations: the specialist can inspect, override, or validate any automated decision at each stage. Intelligent quality-control routines flag anomalies and low-confidence areas rather than silently discarding them, preserving traceability and enabling informed expert judgment. The result is a pipeline that scales operationally while maintaining the rigor expected of advanced InSAR analyses. The rapid realization of this complex, multi-disciplinary system was itself enabled by a novel AI-assisted development methodology. We adopted a "deep context" documentation approach: encoding scientific domain knowledge, algorithmic requirements and expert feedback into structured, persistent context files that make AI coding assistants fully domain-aware. This technique allowed rapid implement-test-refine cycles, enabling specialists and non-specialists alike to translate theoretical InSAR concepts into production-ready, parallelized code with continuous AI-driven feedback. The development impact was substantial: a processing environment of this complexity, encompassing cloud-distributed computing, multi-sensor support, multi-stage atmospheric correction and dozens of specialized tools, was built and iterated to operational readiness by a small multidisciplinary team within a timeframe that would be unrealistic under conventional development workflows. We propose this HAI development approach as a replicable model for multidisciplinary teams building complex Earth Observation processing environments, demonstrating that the combination of structured domain context and AI-assisted coding significantly accelerates the path from scientific literature to operational software.

InSAR-based landslide investigations remain constrained by manual parameter tuning. It limits their applicability at the sub-regional scale for susceptibility mapping and cataloguing of unstable terrain in mountainous regions. Open-access Sentinel-1 (S1) radar data offer extensive spatial and temporal coverage; however, their exploitation demands substantial time and computational resources across the full InSAR processing chain. Such requirements are often disproportionate to the comparatively smaller dimensions of the failure zones. Within the PEPR IRiMa program (a French government-funded Priority Research and Equipment Program) we developed an automated InSAR workflow tailored to gravitational movements. Our objective is to minimise manual interventions and deliver a rapid insight into the target area, while enabling the delineation of deformation extent (at a single burst level), which can be broader than in-situ observations. This initiative is willing to complement the existing PSI-based EGMS yearly-update products, whose reliance on long-term signal stability reduces their coverage in low-coherence natural terrain, especially on low-rate deformed slopes. To mitigate these limitations, we employ a standard stacking approach1 to detect spatially distributed hillslope motion and to derive (slow-to-moderate) mean-velocity maps, computed from a set of unwrapped differential interferometric phases. In this study, we also examine the phase gradient stacking approach2 in a landslide context. We implement the automated processing pipeline in the GAMMA software suite, using C-band S1 images acquired in the IW mode (Interferometric Wide swath), restricted to a reduced-swath segment (one to two bursts only). Additionally, we develop an in-house unwrapping procedure based on the phase gradient. We apply our methodology to the French-Alps region, leveraging existing landslide inventories3. In particular, we concentrate on two landslide case studies. Firstly, the Marie landslide, which is well known and instrumented on the ground. Secondly, the unknown -difficult to access La Pinée (Daluis) landslide whose kinematic behavior remains unknown today, yielding another unique opportunity to showcase the advantages of using spaceborne InSAR. We benchmark stacking-derived velocities against time-series methods to assess performance and identify biases. This evaluation provides a basis for refining our automated stacking strategy without adopting the heavy time-series processing. We further evaluate the results using ancillary datasets to support validation and interpretation. Keywords: landslide, InSAR, automated workflow, slope, mean-velocity estimation. References: 1)    Ciuffi, P., Bayer, B., Berti, M., Franceschini, S., Simoni, A.: InSAR stacking to detect active landslides and investigate their relation to rainfalls in the Northern Apennines of Italy, Geomorphology, Volume 457, 2024, 109242, ISSN 0169-555X, https://doi.org/10.1016/j.geomorph.2024.109242. 2)    Sandwell, D. T., & Price, E. J. (1998). Phase gradient approach to stacking interferograms. Journal of Geophysical Research, 103(B12), 30183–30204. https://doi.org/10.1029/1998JB900008 3)    Aslan, G., Foumelis, M., Raucoules, D., De Michele, M., Bernardie, S., Cakir, Z.: Landslide Mapping and Monitoring Using Persistent Scatterer Interferometry (PSI) Technique in the French Alps. Remote Sens. 2020, 12, 1305. https://doi.org/10.3390/rs12081305.

The Kandersteg region in the Swiss Alps presents a highly active paraglacial landscape characterized by deepseated gravitational slope deformations, complex mass wasting processes, and evolving rock glaciers. Continuous spatial monitoring is essential for understanding these slope failure mechanisms and assessing regional hazard potential. However, quantifying surface kinematics in such extreme high-relief environments poses significant challenges for spaceborne Synthetic Aperture Radar (SAR) techniques. Prolonged seasonal snow cover, highly dynamic tropospheric conditions, and steep terrain gradients frequently cause severe temporal decorrelation, layover, and shadow effects in C-band data. To capture the complete kinematic diversity across this complex terrain, we integrated InSAR time-series analysis with SAR amplitude offset tracking. Our dataset encompasses a five-year time series (2020–2025) of Sentinel-1 acquisitions from both ascending and descending orbit. Utilizing this dual-geometry configuration is critical for mitigating inherent geometric distortions in alpine valleys. It also allows for the robust decomposition of Line-Of-Sight (LOS) measurements into two-dimensional East-West and vertical displacement vectors, yielding a much more realistic representation of the actual slope mechanics. The data processing relies on a multi-platform pipeline tailored to address specific deformation regimes. For stable slopes and regions exhibiting slow to moderate creep, we generated unwrapped interferograms using the HyP3 cloud-processing architecture. Conversely, in the most active kinematic zones where rapid displacement gradients cause complete phase aliasing and decorrelation, we applied SAR offset tracking using the ISCE2 processing framework. By performing sub-pixel cross-correlation on the amplitude imagery, we successfully extracted large-scale displacements that would otherwise remain undetected by standard phase analysis. To seamlessly merge these distinct datasets, both the HyP3 unwrapped phase products and the ISCE2 offset tracking fields were ingested into MintPy for time-series inversion. This combined integration allowed us to generate continuous, multi-temporal deformation maps bridging the gap between millimeter-scale creep and meter-scale sliding. To ensure the reliability of the remote sensing observations, the derived SAR velocity fields were validated against kinematic measurements documented in existing regional literature. Following this independent validation, we systematically cross-correlated the extracted displacement time-series with localized environmental variables to assess climatic drivers. Specifically, Ground Surface Temperature (GST) records provided by the PERMOS (Permafrost Monitoring Switzerland) network were utilized to evaluate the thermal control on slope kinematics. This correlation isolates long-term gravitational deformation trends from transient, temperature-dependent accelerations. Ultimately, this framework provides critical physical insights into how complex alpine slopes respond to external forcing, demonstrating a highly effective approach for operational hazard monitoring in steep terrain

What is the most suitable fault model for slip inversions, given a distribution of InSAR-measured displacements and its corresponding spatial correlation information? To answer this question, we develop a quantitative method to determine the optimal smoothing and weighting for correlated InSAR data as well as the optimal fault discretization by adopting an Akaike Bayesian Information Criterion (ABIC)-minimizing approach, and apply it to multiple tracks of InSAR data covering the creeping central San Andreas fault. Although InSAR offers surface displacement measurements with fine spatial resolution (40 m or less), since both the geophysical signals and the atmospheric noise in the data are highly spatially correlated, the measurements are almost certainly not independent of each other. Inversions for fault slip from InSAR data can be strongly affected by spatially-correlated atmospheric noise. Without properly taking into account such correlations when formulating the inverse problem, for example by weighting the data based on their expected covariances, such models can be biased and poorly resolved. Additionally, since the data is correlated, the actual information content the data offers is significantly smaller than the number of data points. Often, one downsamples the data to reduce the redundancy of data points and has to regularize the inversion (e.g. by applying a smoothing constraint) due to the inversion being ill-posed. The common way to downsample the data is through quadtree downsampling, which is based on the variance of the measurements in each downsampled block; however to correctly account for spatially correlated noise, the downsampling process should also account for its correlation lengthscale. Beyond the method of downsampling data, several factors and choices made in designing a slip inversion can affect the final result. Traditional methods for choosing the strength of smoothing, like the L-curve, are somewhat subjective, relying on human interpretation. Another key factor is how the fault is discretized. Often this is done with resolution in mind, which is also related to the spatial distribution of the data. For slip models constrained with surface observations, we typically find resolution is good for shallower portions of the fault, motivating the use of smaller shallow fault elements, but degrades appreciably as depth increases, motivating the use of larger deep fault elements. Therefore, together with the consideration of data covariance and its spatial distribution, there ought to be an optimal fault discretization for the information content the data contains. Thus, we build into our approach a method for testing multiple candidate discretizations. In this study, we seek to quantify the effects that data distribution and covariance has on slip inversion, and explore the optimal fault model that they permit. Our target is to model the creep distribution on the central San Andreas fault, using data from UAVSAR (14 tracks) and Sentinel-1 (3 tracks). We estimate the covariance structure of the noise in each InSAR dataset by first removing a preliminary estimate of the tectonic signal and calculating the autocorrelation function of the residuals, and its radial average. We approximate the covariance as a function of distance by fitting an exponential function to this averaged autocorrelation. Second, we downsample the InSAR measurements and generate perturbed data by adding spatially correlated noise with the same covariance. Next, we randomly generate thousands of fault geometries, with the goal of determining the “best” one. We fix the strike and dip angle, and the total length and width of the whole fault. For each geometry realization, we first randomly draw the number of layers and the number of patches. Second, we draw the width of the first layer within a bound and enforce the width of each layer to grow exponentially that will eventually be constrained by the total width and the number of layers. The randomness of the generation should capture all combinations of the given constraint considering a sufficient sample size. After generating the fault geometries, we calculate the ABIC value for each fault model. The ABIC formulation takes into account of 1) the relative weighting of the smoothing with respect to the data, 2) number of independent model parameters and 3) the effective number of data points. The model with the smallest ABIC value represents the optimal geometry, smoothing and slip. Lastly, we invert for fault slip based on the optimal fault model geometry. Our results show significant strike-slip, consistent with the fast creeping behavior of the central San Andreas fault. Creep peaks around Bitterwater at rates of around 3.2 cm/yr which tapers down to both the north (~1.6 cm/yr, partitioned onto the Calaveras fault) and south (~0.5 cm/yr, with partial locking at Parkfield). However, we find that modeling only the strike-slip component of fault slip is insufficient to explain the data – we identify significant fault-perpendicular as well as vertical surface deformation signals that require modeling both the tensile opening/closing and the dip-slip components of motion. The dip-slip component is mostly seen around the Parkfield segment, which could result from accommodating the partial locking at depth. For the tensile deformation, we find a general closing rate around 0.5 cm/yr along the fault except for the Bitterwater section, which shows tensile opening at a similar rate. Possible mechanisms for such tensile deformation could be the interplay between regional stress orientation and the fault geometry, where the fault is clamped by stress acting on a higher angle and relaxed when the angle is more gentle, or could be the response of changes in pore fluid pressure at depth. Detailed investigation is needed to address the actual physical mechanism acting on the fault.

Co-seismic surface deformation mapped with interferometric Synthetic Aperture Radar (InSAR) may provide valuable insights into earthquake mechanics, revealing information about the rupture in areas where traditional seismological methods are not efficient due to lack of suitable seismic network geometry. Conventional geodetic approaches utilize complex, non-linear inversions of InSAR images to estimate geometric (e.g., azimuth, width, dip) and kinematic (e.g., dip-slip, strike-slip throws) quantities. However, these methods may prove computationally expensive to apply, while manual parameter bounding introduces biases. Herein, we seek to explore the potential of a deep-learning approach to reframe this problem as a faster computer vision classification and regression task.   We aim to train ESPI-ResNet (Zhao et al., 2021) on a synthetic dataset to evaluate its applicability and performance. ESPI-ResNet is a deep-learning framework designed to extract earthquake fault parameters from InSAR imagery. It utilizes a deep residual convolutional network, employing skip connections to retain interferometric phase gradients, which splits in two branches (one per sub-task). First, it aims to solve a classification problem, i.e., identify whether the image represents a normal, reverse right-lateral or left-lateral strike-slip fault type. This is achieved by the deep convolutional neural network which extracts spatial deformation features from the interferogram. Second, the regression branch of the framework processes the extracted features through a sequence of dense layers to estimate the geometric parameters of the rupture. To produce the synthetic dataset, we used the Okada model (Briole, 2017), as real images that depict the sought-after surface deformation are too scarce to compose a very large data volume, necessary for training, validation and testing. A uniform slip distribution was assumed, with parameters configured according to seismological data of medium-to-strong earthquakes. High-resolution 1024-by-1024 images were produced. Even though, as explained next, the network expects a coarser 224-by-224 resolution, we decided to maintain initially high resolutions for posteriority and dataset completeness. The final synthetic dataset comprised ~47k interferograms of the four fault types, covering 50-by-50 km areas. Before training, we followed an augmentation scheme to add diversity to the dataset and avoid a completely idealized input. Therefore, our augmentation was constrained to normalization, geometric, i.e., rotations, flipping and mirroring of the images, and pixel-level, i.e., brightness, contrast and color changes, operations. Through this, we sought to teach the model spatial invariance and make it resistant to changes in coherence levels. For training, we used a split with 70% of data for training, 10% for validation and 20% for testing. The model was optimized using the Adam optimizer, with an exponentially decaying learning rate. To prevent overfitting on the synthetic data and better allocate computing resources, we implemented an early stoppage mechanism which monitored validation loss (with a patience value of 5 epochs). This enabled training to stop early (before the 100 epochs) if the loss metric plateaued. A batch size of 128 was used. Preliminary evaluation on the 20% test set demonstrated the network’s capacity for classifying fault types and identifying their respective geometric parameters. Our augmentation pipeline may have reduced classification accuracy and increased geometric errors but introduces variability in the learning procedure and prepares the model for application in imperfect conditions. In the future, we aim to incorporate a complex noise augmentation step, representing the expected noise sources in real InSAR imagery. References Briole, P., 2017. Modelling of earthquake slip by inversion of GPS and InSAR data assuming homogenous elastic medium. https://doi.org/10.5281/ZENODO.1098399 Zhao, X., Wang, C., Zhang, H., Tang, Y., Zhang, B., Li, L., 2021. Inversion of seismic source parameters from satellite InSAR data based on deep learning. Tectonophysics 821, 229140. https://doi.org/10.1016/j.tecto.2021.229140

Abstract : This study examines the seismotectonic characteristics of the 2023 Al Haouz earthquake (Mw 6.8) in the Western High Atlas, Morocco. The research combines seismic catalog analysis with Differential Interferometric Synthetic Aperture Radar (DInSAR) observations to investigate the deformation pattern and identify the associated seismogenic structures. Sentinel- 1 SAR data were processed using the SNAP software to generate ground displacement maps related to the earthquake. The results reveal clear surface deformation patterns consistent with the regional tectonic framework and provide new insights into the geometry of the fault responsible for the event. The analysis suggests that the Tizi n’Test fault was the main tectonic structure reactivated during this earthquake. These findings contribute to a better understanding of earthquake mechanisms in the High Atlas and demonstrate the value of integrating seismic data with satellite geodesy for seismic hazard assessment in mountainous regions.

We communicate the development of a fully automated workflow, by the Aristotle University of Thessaloniki (AUTh) for the generation of SAR interferometric products from the Copernicus Sentinel-1 mission following strong earthquakes in Greece. Differential SAR Interferometry (DInSAR) has long been recognized as an effective technique for mapping ground deformation associated with earthquakes. In recent years, the systematic availability of data from the Copernicus programme has allowed several institutions to develop services for automatic response to co-seismic deformation. We present a fully unsupervised, event-driven DInSAR processing chain triggered by recordings from the AUTh Seismological Station (https://seismo.auth.gr) for the rapid generation of co-seismic deformation products. The service is integrated into the AUTh HPC infrastructure, ensuring high performance and scalability while enabling the efficient execution of the automated processing workflow. When an earthquake exceeding a predefined magnitude is detected, the workflow is automatically activated and registered users are notified of the expected dates of upcoming post-event satellite acquisitions. At this stage the system already identifies suitable pre-event Sentinel-1 SAR acquisitions covering the affected region. The area of interest is defined by a radius of several kilometers around the earthquake epicenter and considers all possible Sentinel-1 tracks intersecting this region. While remaining on hold for one earthquake, the system remains responsive to new earthquakes as independent triggers. Once post-event imagery is disseminated through dedicated portals, the download and ingestion process is initiated. Upon completion of processing, notification messages are sent including links for accessing the results. The outputs include wrapped differential interferograms, interferometric coherence levels, and displacement values, all geocoded to the GGRS87 (EPSG 2100) map projection. Particular emphasis is placed on operational readiness and FAIR-by-design principles, ensuring that the resulting deformation products are not only timely, but also transparent, interoperable, and reusable. All generated products are distributed in widely adopted open formats accompanied by structured metadata and provenance documenting processing parameters is systematically recorded to ensure full reproducibility. The final datasets are intended to be published in FAIR-enabling repositories, ensuring long-term discoverability, accessibility, and reuse. The workflow is demonstrated through a representative earthquake case, illustrating its potential to support both scientific analysis and rapid-response applications. Future developments will focus on automated fault modelling as well as enhancing user interaction and accessibility through the integration of the generated products within an interactive platform for intuitive visualization and exploration. Acknowledgements The authors acknowledge the OSCARS project, which has received funding from the European Commission’s Horizon Europe Research and Innovation programme under grant agreement No. 10112975.

The 2019 Mw 6.4 Durrës earthquake struck Albania on 26 November, causing several fatalities and severe economic loss. This earthquake, which concluded a longer seismic sequence that began in September 2019, reminds us that Albania faces one of the highest seismic hazards in Europe, despite low interseismic strain accumulation, with rates of only 10-20 nstrain/yr measured along the Adriatic-European plate boundary (e.g. Jouanne et al., 2012; D’Agostino et al. 2020; Métois et al., 2025). The earthquake occurred in the Peri-Adriatic foredeep of the Albanides belt, where compression related to subduction dominates. Recent geodetic studies (e.g. Piña Valdés et al., 2022; Meridi et al., in review) indicate that this compressional deformation spans from the coastline to the external-internal Albanides boundary, marking the transition to an extensional regime further east. This depression is characterized in the literature by a fold-and-thrust belt system involving crustal basement, active SW-verging reverse faults and conjugate back-thrusts, some of which are currently active (Teloni et al., 2021). To the west, deeper crustal SE-dipping faults, blind and oriented parallel to the inferred subduction zone, concentrate instrumental seismogenic activity at depth, accommodating the NNE motion of the Apulia microplate (4 mm/yr) relative to stable Eurasia. Despite several seismological and geodetic studies, the fault geometry of the 2019 Mw6.4 earthquake remains debated, with two candidates for the fault plane : (i) a steep SW-dipping backthrust (71°) corresponding to the Vorë fault, or (ii) a low-angle NE-dipping (15°) subduction interface plane (Govorčin et al., 2021). Moreover, transient signals, including potential Slow Slip Events (SSEs), seem to have been observed several months after the main shock near the epicentral area, that remain to be confirmed (Matraku et al. 2024). Understanding the timing and distribution of slip during this complex sequence is of paramount importance to better assess seismic hazard in the most populated region of Albania. In this study, we take advantage of several continuous datasets: (i) 2014-2021 InSAR time series (so up to 14 months after the earthquake) processed by the FLATSIM CNES/Formaterre service (Thollard et al., 2021) over the Balkans (240 m resolution, 14 ascending and descending tracks), (ii) permanent and campaign GNSS time series from Matraku et al. (2024) covering the pre and post-earthquake period, and (iii) catalog of relocated aftershocks starting 15 days after the mainshock from Schurr et al. (2026) to reconstruct the surface deformation and slip history associated with this earthquake. The postprocessing conducted on the FLATSIM time-series allows for extracting a clean coseismic pattern, and for exploring the existence of afterslip or other postseismic processes. First, we use parametric decomposition to extract the coseismic displacement in the LOS that is then inverted jointly with GNSS, strong motion, and teleseismic data to constrain the fault geometry and slip distribution . Our postseismic analysis combines three complementary approaches. We first map cumulative postseismic deformation (after removing the pre-seismic trend) to reveal its spatial footprint, then examine time series along profiles perpendicular to potential source faults to track temporal evolution. Preliminary observations suggest shallow slip on a secondary fault, possibly linked to shallow aftershocks on the Vorë fault. We therefore perform a moment budget analysis comparing aftershock moments with InSAR-derived slip, to discriminate between afterslip on the main fault or triggered slip on the Vorë fault. In addition, we apply careful regional referencing of seasonal terms to a null-deformation polynomial surface. This allows us to analyse their spatial expression, with amplitudes reaching up to 20 mm/yr locally in the Tirana and Dürres basins, and to evaluate whether they might be misinterpreted as tectonic transients. Finally, we compare InSAR and GNSS time series at stations potentially impacted by SSEs, providing an independent test for the presence of transient deformation. References : Jouanne et al. (2012), 10.1016/j.tecto.2012.06.008 D’agostino et al. (2020), 10.1016/j.epsl.2020.116246 Govorčin et al. (2021), 10.1029/2020GL088990 Teloni et al. (2021), 10.1093/gji/ggaa582 Thollard et al. (2021), 10.3390/rs13183734 / Dataset FLATSIM, 10.24400/253171/FLATSIM2020 Piña-Valdés et al. (2022), 10.1029/2021JB023451 Matraku et al. (2024), 10.1093/gji/ggad101 Métois et al. (2025), 10.55575/tektonika2025.3.1.99 Schurr et al., (2026) 10.5880/GFZ.FPXO.2026.001 Meridi et al., (2026) (in review)

Generating large-scale, high-accuracy velocity fields from Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) data remains a challenge due to long-wavelength orbital errors, atmospheric delays, and frame inconsistencies between adjacent tracks. In this work, we present a method for calibrating and combining Line-of-Sight (LOS) velocities across multiple processed LiCSAR products using a spatially weighted surface fitting approach integrated with 3D GNSS measurements. Our study area encompasses complex tectonic regimes in Türkiye, including the North Anatolian Fault Zone, East Anatolian Fault Zone, and Central Anatolian Block. To model and correct for long-wavelength InSAR deviations, we utilize a Continuously Operating Reference Station (CORS) network (~200 stations) for model training and a much denser campaign-based GNSS network (~1000 stations) dedicated to independent validation. Before initiating calibration, we first project the ENU (East, North, Up) GNSS velocity components onto a specific InSAR line-of-sight (LOS) geometry. This projection is obtained by using the average incidence and heading angles derived from coherent InSAR pixels located within a 1 km radius of each GNSS station, providing a geometric match between two different datasets. Next, we calculate the velocity difference between the GNSS-projected LOS and the raw InSAR LOS to detect the deviation at each station location. We propagate this bias surface across the frame using a Weighted Least Squares (WLS) approach based on GNSS and InSAR uncertainty measures. By incorporating the observation uncertainties of the GNSS stations into the algorithm, we tested interpolation models such as Quadratic Surface Fitting and Thin Plate Spline (TPS). To merge different frames with overlapping areas, we first reference each InSAR frame separately to the same GNSS network. By doing this, the velocity differences present in the overlapping regions of adjacent tracks are reduced before the merging process. Then, a pixel-based point mapping is applied to these overlapping regions. Adjacent tracks are merged by calculating the average velocity differences of the overlapping pixels. In other words remaining discrepancies are minimized by using a weighted average approach, thus linking the frames together. The performance of the tested interpolation algorithms was evaluated using an independent campaign-based GNSS network. Both standard Root Mean Square Error (RMSE) and Weighted RMSE (WRMSE) metrics were calculated for this evaluation. In conclusion, this study provides a basis for extending the calibration process to align geometries of multiple overlapping frames in both ascending and descending directions.

Advances in space geodesy have revealed slow deformation transients and complex interactions between aseismic and seismic slip along major strike-slip faults. The Haiyuan fault (northeast China) provides a documented example of this dual behavior, combining sections that ruptured during Mw ~8 earthquakes with a persistent creeping section [1, 2]. We investigate this ~35 km-long creeping section located between the western end of the rupture of the 1920, Mw 7.9, Haiyuan earthquake and the eastern end of the so-called « Tianzhu seismic gap » revisiting both its spatial distribution and temporal evolution using ERS, Envisat, and Sentinel-1 InSAR data. We analyze in more detail Sentinel-1 displacement time series processed with the FormaTerre FLATSIM service operated by CNES [3], spanning the period 2014–2020, for one ascending and two descending tracks. Linear and seasonal components are estimated pixel-by-pixel from the LOS time series. The linear term is then decomposed into horizontal, fault-parallel, and vertical velocity components using a multi-track inversion. Creep signature is visible in all LOS and decomposed horizontal velocity fields. Surface creep reaches values up to ~5 mm yr⁻¹ in the fault-parallel direction, with significant variations along fault strike. Localized subsidence of ~8 mm yr⁻¹ is observed within the Jingtai pull-apart, a major extensional relay zone between the fault section ruptured in 1920 and the seismic gap. To assess the long-term persistence of the aseismic slip, we compare fault-perpendicular profiles from ERS (1993–1998), Envisat (2003–2009), and Sentinel-1 (2014–2020) fault-parallel velocity maps. These profiles show that the localization of the steep velocity gradient at the mapped fault trace is maintained over nearly three decades, despite differences in data temporal sampling, with a significant gain in signal to noise ratio with Sentinel-1. We invert the InSAR velocity fields using the CSI software [4], jointly constrained by GNSS data, to model the distribution of shallow slip along the seismogenic zone. An Independent Component Analysis reveals the slip partitioning within the various fault strands around the Jingtai pull-apart basin and suggest the existence of episodes of slip acceleration along the creeping section. To further investigate such temporal variability, we construct (from the Sentinel-1 dataset) cumulative fault-parallel and vertical displacement time series from combined LOS data on common spatial and temporal grids. Series of fault-perpendicular profiles along fault and through time are used to estimate slip rates between consecutive acquisitions and to build time–longitude representations of slip evolution. The results indicate that creep is not steady but intermittent, evolving through alternating phases of increased and reduced fault-parallel slip rate. Transient episodes are spatially confined to the creeping section and involve both horizontal creep accelerations and subsidence-rate increases within the relay zone. These observations constrain the partitioning of deformation within the creeping step-over and document time-dependent variations in shallow slip, the underlying mechanical processes of which we discuss. References:[1] Gaudemer et al., 1995, doi.org/10.1111/j.1365-246X.1995.tb01842.x [2] Jolivet et al., 2012, doi.org/10.1029/2011JB008732 [3] Thollard et al., 2021,and FLATSIM Data Products. CNES. (Dataset) doi.org/10.3390/rs13183734 [4] Jolivet et al., 2020, doi.org/10.1029/2019GL085377

Understanding how crustal strain is accumulated and released between seismic and aseismic processes is essential for a better assessment of seismic hazard. Monitoring the surface deformation allows us to infer processes occurring in tectonically-active areas during the different phases of the seismic cycle. With the incipient arc-continent collision between the Philippine Sea Plate and the Eurasian Plate, southwestern Taiwan is becoming an important area for observing such fault movements. Southwestern Taiwan, characterized by a complex fold-and-thrust belt, exhibits high strain rates (> 1 strain/yr) despite low seismic activity in the shallow crust (< 10km). This apparent discrepancy raises key questions regarding the proportion of deformation accommodated elastically, with important implications for future large earthquakes. Due to the lack of shallow seismic activity in this area, most of the deformation appears to be aseismic. Geodetic observations represent the primary means of quantifying surface deformation over the past decades. To obtain high-resolution surface deformation in space and time, and to achieve three-dimensional decomposition, we plan to integrate complementary datasets from multiple satellite missions using InSAR and GNSS observations. We processed Sentinel-1 and ALOS2 dataset using a small baseline approach. the Sentinel-1 dataset (2014-2025) using the NSBAS processing chain, including the atmospheric and unwrapping error corrections to ensure signal integrity in Taiwan’s challenging subtropical environment. Furthermore, to overcome temporal decorrelation over the densely vegetated foothills, we used the ALOS-2 dataset covering from 2015 to 2025, processed with ISCE2 and Mintpy software to address some sources of uncertainty and time series inversion. Preliminary results demonstrate the complementarity of Sentinel-1 and ALOS-2 in terms of spatial coverage across the study area. While the Sentinel-1 dataset provides spatial detail over the coastal and Pingtung plains, revealing new fine details in the active tectonic structures, the most active structures exhibiting the largest deformation are located east of the coastal plain densely vegetated regions. In these challenging areas, the L-band ALOS-2 data proved to be critical to retrieve the deformation. The detailed deformation field will facilitate more reliable observations for understanding the creeping behaviors, identifying the process causing deformation, and separating tectonic from non-tectonic signals (e.g., hydrological signals from significant seasonal groundwater fluctuations). These deformation products will help distinguish between elastic and non-elastic deformation components in further analysis, providing an opportunity to evaluate the contribution of aseismic processes to the observed deformation field.

The 2023 Mw 7.8 and Mw 7.6 Kahramanmaraş earthquake doublet in southeastern Turkey generated exceptionally complex regional deformation patterns, providing a rare natural laboratory for investigating how large strike-slip earthquakes influence fault activity outside the main rupture zone. In this study, we employ time-series Interferometric Synthetic Aperture Radar (TS-InSAR) to quantitatively invert coseismic and postseismic surface deformation in the epicentral area and surrounding regions, with particular emphasis on independent fault structures that were previously widely regarded as inactive or only weakly active. Leveraging high-temporal-resolution InSAR time series, we not only capture the spatial continuity of deformation but also track its temporal evolution from months to years after the mainshocks, thereby distinguishing persistent postseismic deformation signals from short-lived responses and residual coseismic effects. Our results show that, beyond the primary rupture zones, multiple independent faults distributed around the broader earthquake-affected region exhibit clear postseismic deformation. In these areas, deformation continues to accumulate after the mainshocks and deformation rates increase markedly, supporting an interpretation of earthquake-triggered deformation on these faults rather than delayed release or residual effects of coseismic displacement. The observed postseismic deformation is heterogeneous: some independent faults display rapidly decaying early postseismic transients, whereas others exhibit sustained, quasi-steady deformation that persists for months and in some cases extends to multi-year timescales. To further investigate the physical mechanisms driving the earthquake-triggered deformation of these independent faults, we compare the deformation patterns inverted from InSAR with modeled static Coulomb stress changes, as well as with the spatial extent of dynamic stress perturbations induced by strong ground shaking. The results indicate that independently triggered faults show a pronounced spatial correspondence with regions of positive Coulomb stress increase and are also commonly located within areas experiencing strong dynamic shaking. This correspondence suggests that static stress transfer and dynamic shaking likely acted synergistically: static stress loading may have brought favorably oriented faults closer to failure, while dynamic perturbations may have accelerated nucleation processes or reduced effective strength, thereby facilitating triggering and prolonging postseismic deformation. Overall, this study emphasizes that independent faults outside the main rupture zone can accommodate a non-negligible fraction of postseismic strain release and play an important role in regional deformation partitioning. This insight has direct implications for seismic hazard assessment, as conventional fault–earthquake frameworks centered on major seismogenic faults often underestimate the potential contribution of such independent faults. By integrating TS-InSAR time-series observations with fault modeling, our results highlight the need to explicitly consider distributed, earthquake-triggered deformation on faults outside the main rupture zone when investigating postseismic processes and evaluating regional seismic hazard in tectonically complex settings.

The Mw 6.5 Norcia earthquake, which struck Central Italy on October 30, 2016, represents the climactic and most destructive event of the recent Apennine seismic sequence. Nucleating within the complex, extensional Mt. Vettore-Bove Fault System (MVBFS), this mainshock ruptured a critical structural gap between the historical 1997-1998 Colfiorito and 2009 L’Aquila earthquake sequences. Despite extensive geodetic monitoring over the past years, precisely resolving the three-dimensional surface deformation field and interpreting the intricate subsurface source geometry in this topographically rugged region remains a significant challenge for both geological engineering and geophysics. To accurately capture the coseismic displacement, we utilized multi-orbit Synthetic Aperture Radar (SAR) datasets acquired by the C-band Sentinel-1 and L-band ALOS-2 satellite missions. Traditional Differential Interferometric SAR (DInSAR) processing often struggles with severe decorrelation in high-gradient epicentral zones and relies heavily on subjective, empirical weighting when fusing heterogeneous multi-source data. To overcome these limitations, we implemented the advanced Strain-Model Variance Component Estimation (SM-VCE) framework. This technique incorporates a sophisticated spatial strain model to mathematically characterize the physical deformation correlations between adjacent ground pixels. Concurrently, it employs the VCE algorithm to iteratively and objectively determine the optimal variance components and contribution weights for each respective dataset based on their stochastic properties. This fusion strategy effectively mitigated atmospheric artifacts and successfully preserved critical near-fault deformation signals, yielding highly reliable two-dimensional (East-West and Vertical) coseismic displacement fields. The SM-VCE derived surface deformation maps reveal a pronounced and highly asymmetric kinematic pattern. In the horizontal plane, the fault zone accommodated a net East-West extension of approximately 60 cm. The vertical displacement field is characterized by a massive subsidence trough localized in the hanging wall, reaching maximum downward displacements of 70 to 80 cm. This strongly contrasts with the minor uplift of only 10 to 14 cm observed in the footwall block. Based on these high-precision measurements, we conducted a rigorous 3D volumetric integration. The calculations exposed an extreme volumetric unbalance, demonstrating that the subsided rock volume is approximately 14 times larger than the uplifted volume. This severe mass deficit poses a direct challenge to standard elastic rebound paradigms and indicates complex crustal interactions. To demystify the mechanical origins of this profound asymmetry and volume deficit, we applied the Defsour® (Free-geometry Multi-Source 3D Inversion) algorithm. Unlike conventional kinematic inversions that artificially constrain slip onto predefined, idealized planar faults, Defsour® adopts a purely data-driven, free-geometry strategy. It performs a global optimization across a dense 3D subsurface grid to simultaneously adjust arbitrary pressure and dislocation sources without a priori geometric assumptions. This autonomous inversion successfully reconstructed the primary slip distribution along the main southwest-dipping normal fault while independently identifying a distinct, east-northeast-dipping antithetic fractured zone. The incorporation of this antithetic structure significantly improved the consistency between the simulated and observed data, effectively resolving the misfit commonly encountered in single-fault models.

The Northern Anatolian Fault Zone (NAFZ) is the major strike-slip boundary between the Anatolian and Eurasian plates, extending for approximately 1500 km and accommodating dextral motion at an average rate of 20–30 mm/yr. The western portion of the NAFZ, near the city of Izmit, was the site of a Mw 7.6 earthquake in August 1999 that produced over 120 km of surface rupture across several fault segments. This devastating event was one of the most recent in a mostly westward-migrating sequence of large earthquakes along the NAFZ since 1939. If this westward progression continues, the next significant earthquake could occur near Istanbul and other densely populated centers. In order to better estimate earthquake potential and seismic risk in this region, it is critical to develop a robust understanding of the seismic cycle and fault dynamics specific to the western NAFZ. This master’s thesis aims to quantify fault slip, model fault geometries, and analyze locking depths within this region of the NAFZ, using a combination of geodetic and seismological datasets. The study will employ joint inversion methods and time-series analysis to determine slip deficits and improve understanding of the coseismic and interseismic phases associated with the Izmit earthquake. Building on the work of Delouis et al. (2000, 2002), this research will use tools such as GMTSAR and Pyrocko to reanalyze the Izmit earthquake and provide new insight into uncertainty estimates for fault slip and slip deficits from the time of the event until 2025. Early results of the ERS data reanalysis show that SAR pixel offset measurements provide a complementary dataset that can help fill the gaps of SAR interferometry where coherence is low and improve data quality. Initial earthquake source analysis results are presented here, showing slip distributions and fault geometries. This thesis anticipates producing an updated kinematic model of the Izmit event derived from several data sources and analyzing time series data to estimate slip deficit along the western portion of the NAFZ. Ultimately, this research will provide additional uncertainty constraints on the fault slip experienced in this region and underscore the importance of integrating diverse datasets to enhance seismic-cycle and earthquake source analyses.

On March 28th, 2025, a Mw 7.7 earthquake struck central Myanmar, approximately 16 km west of Mandalay, along a segment of the Sagaing Fault, a major right-lateral strike-slip fault system. The shallow rupture generated severe ground shaking, resulting in more than 4,900 fatalities and approximately 6,000 injuries. Seismic effects were reported at distances exceeding 1,000 km from the epicentral area, with the collapse of a high-rise building in Bangkok (Thailand) potentially attributable to site-specific amplification associated with predominantly loose, unconsolidated sedimentary deposits (Shahzada et al., 2025). This study aims to identify the coseismic ground displacement field induced by the earthquake and to constrain the geometry and slip distribution of the seismogenic source. To this end, we integrated satellite-based Synthetic Aperture Radar (SAR) techniques, including Pixel Offset Tracking (POT), Multiple Aperture Interferometry (MAI), and Interferometric SAR (InSAR). These techniques were applied to a dataset of 12 C-band Sentinel-1 SAR images acquired in Interferometric Wide Swath (IWS) mode, covering the broad region affected by the seismic event. The resulting displacement fields were subsequently inverted through a two-step analytical modeling approach to retrieve the fault geometry and key source parameters (Atzori et al., 2009; Atzori and Antonioli, 2011; Atzori et al., 2019). The use of multiple Sentinel-1 datasets ensured full spatial coverage of the rupture zone. In fact, the results revealed a rupture extending approximately 490 km across three distinct fault segments, characterized by a nearly vertical, north–south orientation and predominantly right-lateral strike-slip mechanism, consistent with the regional tectonic setting. The maximum coseismic slip identified by the seismic source modeling reached about 5 m in the central portion of the fault, corresponding to the segment previously identified by Hurukawa and Maung Maung (2011) as the seismic gap. This study highlights that the integration of multiple SAR-based techniques—each characterized by distinct sensitivity and accuracy levels—proved effective in capturing the full deformation field over a wide area, with strong consistency observed across the different techniques. Furthermore, this approach also enabled the retrieval of both horizontal (east–west and north–south) and vertical displacement components, thereby increasing the robustness of the source model inversion, including its slip distribution. Overall, this comprehensive analysis provides important insights into the rupture extent and kinematic behavior of the earthquake, contributing to a more robust characterization of the seismogenic source and improving our understanding of rupture processes and seismic hazard in tectonically active regions. REFERENCES Atzori, S., Hunstad, I., Chini, M., Salvi, S., Tolomei, C., Bignami, C., Stramondo, S., Trasatti, E., Antonioli, A., & Boschi, E. (2009). Finite fault inversion of DInSAR coseismic displacement of the 2009 L’Aquila earthquake (central Italy). Geophysical Research Letters, 36(15). https://doi.org/10.1029/2009GL039293 Atzori, S., & Antonioli, A. (2011). Optimal fault resolution in geodetic inversion of coseismic data. Geophysical Journal International, 185(1), 529–538. https://doi.org/10.1111/j.1365-246X.2011.04955.x Atzori, S., Antonioli, A., Tolomei, C., De Novellis, V., De Luca, C., & Monterroso, F. (2019). InSAR full-resolution analysis of the 2017–2018 M>6 earthquakes in Mexico. Remote Sensing of Environment, 234, 111461. https://doi.org/10.1016/j.rse.2019.111461 Shahzada, K., Noor, U. A., & Xu, Z.-D. (2025). In the wake of the March 28, 2025 Myanmar earthquake: A detailed examination. Journal of Dynamic Disasters, 1(2), 100017. https://doi.org/10.1016/j.jdd.2025.100017

Quantifying high-resolution interseismic strain rates is a key input for assessing fault-related seismic hazard and is commonly derived from geodetic observations, particularly Interferometric Synthetic Aperture Radar (InSAR) velocity fields. However, this process is complicated by noise inherent to both SAR data and subsequent InSAR processing workflows, necessitating the spatial filtering of the raw velocity field. Existing filtering approaches, such as Gaussian and median filters, often struggle to balance noise suppression with the preservation of sharp strain gradients. Gaussian filtering tends to introduce excessive spatial blurring ("blobbiness"), leading to systematic underestimation of peak on-fault strain magnitudes. In contrast, median filtering can cause spatial shifts in the location of concentrated fault strain in the presence of data gaps (e.g., data edges, decorrelation zones, or masked non-tectonic deformation). It may also generate Gibbs-ringing artifacts—oscillating sub-parallel signals adjacent to primary tectonic gradients—which can be misinterpreted as distributed deformation or spurious strain across multiple faults. Here, we present a new nested bilateral filtering framework designed to more faithfully recover on-fault strain magnitudes while effectively suppressing off-fault noise. The method employs a bilateral filter that jointly leverages spatial-domain and value-domain weighting. To enhance numerical stability and reduce parameter sensitivity, a localized small-window spatial kernel is embedded within the value-domain weighting scheme, forming a nested structure that stabilizes edge preservation while maintaining robust noise attenuation. We first evaluate its performance using synthetic datasets based on a 2D screw dislocation model, demonstrating clear improvements over commonly used Gaussian and median filters. A systematic grid-search analysis further provides practical guidance for parameter selection. We then apply the method to recently compiled trans-continental-scale geodetic velocity fields across the Alpine–Himalayan Belt (AHB), encompassing diverse tectonic settings including the Pamirs, Tibet, Anatolia, Makran, and the Hindu Kush. The results more effectively capture both the magnitude and spatial localization of strain along major faults and reveal previously unresolved strain features in several tectonically active regions. Finally, we discuss the implications of these improvements for geodetically informed seismic hazard assessment and continental deformation studies. These include: (1) more reliable identification of creeping fault segments; (2) improved quantification of slip-deficit rates and seismic moment accumulation rates; and (3) clearer characterization of on-fault versus off-fault strain partitioning across different tectonic domains.

Rapid and reliable post-seismic damage assessment is critical for effective emergency response, situational awareness, and resource prioritization following large earthquakes. Synthetic Aperture Radar (SAR) imagery offers significant advantages for such applications due to its ability to acquire data under all weather and illumination conditions. This study investigates the use of C-band Sentinel-1 SAR backscatter change detection for rapid delineation of urban damage following the 6 February 2023 Mw 7.8 Türkiye earthquake. Two severely affected urban areas, Hatay and Kahramanmaras, were analyzed using ascending (Track 14) and descending (Track 21) Sentinel-1 GRD acquisitions. Field surveys were conducted to identify completely collapsed buildings within the selected study areas. These data were used for quantitative validation, including 6,401 collapsed buildings in Hatay and 1,205 in Kahramanmaras. A multi-temporal pre-event baseline was constructed from five images acquired before the earthquake. Processing included the application of precise orbit files, thermal and border noise removal, radiometric calibration, speckle filtering using a Lee Sigma filter, and geometric correction based on the Copernicus Digital Elevation Model. Backscatter coefficients were converted to a decibel scale and organized into pre- and post-event image stacks. Post-event changes were calculated by subtracting the mean pre-event reference from two post-event acquisitions: the first available image immediately after the earthquake (February 2023) and a later acquisition approximately two months afterward (April 2023). Significant negative anomalies were identified using an adaptive threshold based on the global mean and standard deviation of the change image. The results indicate clear temporal variations in detection performance. For the first post-event acquisition (February), Building Detection Rates (BDR) ranged from 4.9% to 16.1%, while for the April acquisition, they ranged from 9.5% to 16.1%, demonstrating improved performance in most analyzed scenarios. In Hatay, BDR increased from 4.9% to 12.8% in ascending geometry and from 7.1% to 16.1% in descending geometry. Descending observations consistently showed superior performance, suggesting an influence of urban orientation relative to radar viewing direction. To mitigate effects associated with spatial resolution and geometric uncertainties, a 15 m buffer was applied during the spatial intersection analysis. The application of the buffer significantly improved area-based precision, reaching up to 58.4%, showing the sensitivity of spatial evaluation to sensor resolution. Although individual building detection remains constrained by the spatial resolution of Sentinel-1 products, the results confirm that SAR backscatter change detection provides a rapid, automated, and scalable approach for large-area post-seismic screening, with potential for further improvement through the integration of higher spatial and temporal resolution datasets. These findings highlight the operational value of Sentinel-1 SAR data for rapid, automated, and scalable large-area post-seismic damage screening, particularly in the critical early response phase.

A M 6.0 earthquake occurred on 31 August 2025 in northeastern Afghanistan in the Pamir-Hindu Kush region. The Pamir-Hindu Kush is among one of the most seismically active regions of the western Himalayan syntaxis, formed and driven by the ongoing continental collision between the Indian and Eurasian plates.This complex continental collision has produced compression and crustal shortening, resulting in continental crust thickening, uplift, and the development of complex fault systems. Afghanistan has limited local seismic network data and field access is limited, meaning that remote sensing, especially InSAR, is the best method for studying earthquakes in this area. This moderate-sized earthquake had a significant impact – it resulted in ~2200 fatalities and economic losses of over $100 million. Estimates of the source time function from teleseismic data and also the InSAR deformation pattern as we describe below, indicate that this was a complex rupture with multiple subevents. In this study, we use InSAR data from four Sentinel-1 tracks, processed using the ISCE software, to constrain and model the complex multi-segment rupture. The InSAR data shows two regions of significant displacement – a broad region with moderate decrease of range with a smaller, higher amplitude area of range decrease superimposed at the southern edge of the first (Figure 1). These are seen in data from both ascending and descending tracks, indicating that these are two regions of uplift, and our interpretation is that each of them could be associated with a reverse fault. To improve the coseismic signal and reduce unwrapping errors, we construct a preliminary two-fault model based on hand-digitized data to flatten the interferogram prior to unwrapping. By examining the connected components mask we identify isolated areas of the unwrapped interferogram that have phase jumps at their edges and then manually add or subtract multiples of 2pi to those areas until no phase jumps remain. Once corrected, we downsample each interferogram using a quadtree decomposition and model the fault geometry (e.g. fault length, fault width, slip, fault depth, strike, dip, rake) using rectangular dislocations in an elastic half space (Okada, 1985), determining the best-fitting parameters through nonlinear optimization (a Powell algorithm with multiple Monte Carlo restarts). Our preliminary results suggest that the rupture involved near-simultaneous rupture of two faults – a primary ENE-striking south dipping structure in the north and a secondary WSW-striking north dipping structure, fringing the area of additional uplift located ~9 km to the southeast. Both faults exhibit inward-dipping oblique thrust faulting mechanisms with minor right-lateral strike-slip components, suggesting that the earthquake is accommodating regional tectonic compression. To explore model fit to the observed data, we model the event with one, two and three fault segments. The reference figure shows results from Sentinel-1 descending track 005, including the data, model, and residuals of each of these models. We use a statistical test, the Bayesian Information Criterion (BIC), that evaluates whether adding more complexity (i.e. more fault segments) to a model results in improvements in fit to the data that are better than expected given the larger number of free parameters. In our preliminary analysis, the two-fault model has the lowest BIC, indicating that it is the most robust, and our preferred model. The northern fault segment has a fault length of ~4.7 km and a downdip width of ~7.0, with a uniform slip of ~1.0 m, centered at a depth of ~ 5.5 km. The secondary fault segment has a fault length of ~3 km and a downdip width of ~3.5, with a uniform slip of ~0.8 m centered at a depth of ~2 km. These inward dipping structures do not connect at depth as the secondary fault is shallower by ~3.5 km, however it may be a back thrust. The moment magnitude estimated from the preferred model is 6.02, consistent with seismic estimates.

Understanding how deformation is accommodated within the accretionary prism is important for accurately characterizing the seismic potential during large megathrust events. Observations from large subduction zone earthquakes, such as the 2011 Tohoku earthquake, have shown that faults within the accretionary prism can rupture together during the coseismic event. In addition, some accretionary prisms may accommodate deformation inelastically through folding, as seen in the Zagros fold-and-thrust belt in Iran. Identifying the contribution of the different deformation mechanisms is important as they influence how strain accumulates and is released with time, changing the seismic and tsunami hazard in subduction zones. However, studying deformation within the accretionary prism in most subduction zones can be challenging, given the distance from land-based GNSS and the sparse distribution of GNSS-A. In this study, we use observations from ALOS-2 wide-swath from 2015 to 2022 to analyze the deformation on the overriding plate of the Rakhine-Bangladesh megathrust, a unique subaerial accretionary prism, but which has proven difficult to study with InSAR due to dense vegetation and strong ionospheric influence. We produced interferometric time-series from 49 descending ALOS-2 scenes using ISCE and Mintpy, incorporating ionospheric correction using the split-spectrum method. We developed an enhanced workflow to improve the removal of the ionospheric signal by reducing the number of looks by half and doubling the Goldstein filtering patch size. We successfully identified localized ground deformation signals, including anticlines with interseismic uplift signals up to 2 mm/yr. We were unable to fit the observations using fault-based models without having the fault slip rate exceed the total convergence rate in the region, which is physically implausible. Instead, we propose that these interseismically uplifting anticlines could be explained by a process of active aseismic folding within the accretionary prism above a shallow decollement, and show that the anticlinal spacing is consistent with this ductile mechanism given the depth to the decollement. These results suggest that the strain energy available to drive coseismic rupture could be lower than predicted by purely elastic models, highlighting how accurately constraining the potential for ductile deformation in the overriding plate is an important consideration for improving future seismic hazard assessments.

Understanding low-magnitude crustal deformation in intraplate regions remains a major scientific challenge, particularly where tectonic signals are subtle yet seismically significant. We present an integrated GNSS–InSAR framework to investigate active deformation along the Delhi–Aravalli Ridge (DAR), a prominent Proterozoic orogenic belt within the northwestern Indian Shield. Despite its classification as part of a stable continental interior, the DAR experiences recurrent low-to-moderate seismicity, especially beneath the densely urbanized Delhi–National Capital Region (NCR). The coexistence of ancient structural inheritance, ongoing Himalayan convergence, and intense anthropogenic modification makes this region ideal for multi-sensor geodetic investigation. Continuous and campaign-mode GNSS observations across the DAR reveal low horizontal deformation rates, generally below 2 mm/yr. Although small in magnitude, these velocities are spatially coherent and geodetically robust, indicating measurable strain accumulation along inherited Proterozoic shear zones and fault systems. The orientation of velocity vectors suggests differential motion between crustal blocks bordering the ridge system, consistent with stress transmission from the Himalayan collision zone to the north. Rather than behaving as a rigid block, the DAR appears to act as a mechanically weak corridor that accommodates distributed intraplate strain. These findings reinforce the concept that stable continental regions may host slow but persistent deformation along pre-existing zones of weakness. While GNSS provides precise three-dimensional velocities at discrete points, it is limited in spatial density. To overcome this limitation, we employ multi-temporal InSAR time-series analysis to derive high-resolution surface displacement fields across the Delhi–NCR and adjoining sectors of the DAR. InSAR observations reveal pronounced vertical deformation patterns, with localized subsidence reaching approximately 15 mm/yr in certain urban and peri-urban zones. Much of this deformation correlates with groundwater extraction, sediment compaction, and infrastructure loading. However, deformation gradients frequently align with mapped basement faults and lithological boundaries, suggesting structural control over the spatial distribution of subsidence. The synergy between GNSS and InSAR is central to resolving the origin of observed deformation. GNSS establishes a stable regional reference frame and constrains long-wavelength horizontal strain accumulation. InSAR, in contrast, resolves short-wavelength vertical displacements at meter-scale spatial resolution. By integrating GNSS-derived velocities with InSAR line-of-sight time series, we separate horizontal tectonic strain from vertical subsidence signals and minimize ambiguities related to orbital errors and atmospheric artifacts. This joint inversion approach enhances the reliability of deformation estimates in a region where tectonic signals are subtle and easily masked by anthropogenic effects. The combined dataset reveals that deformation in the DAR is neither purely anthropogenic nor purely tectonic. Instead, surface displacement reflects the superposition of long-term strain accumulation along reactivated shear zones and short-term hydrological and infrastructural loading. GNSS data indicate slow accumulation of elastic strain across structural corridors, while InSAR detects localized compaction within sedimentary basins bounded by faults. In several locations, subsidence compartments appear structurally confined, implying that ancient fault geometries influence present-day groundwater-driven deformation. Such structural modulation may alter local stress fields, potentially influencing seismic hazard in densely populated areas. From a tectonic perspective, the deformation pattern supports a model involving differential motion between adjacent cratonic domains and reactivation of Proterozoic shear systems within the DAR. Far-field compressional stresses associated with ongoing Himalayan convergence propagate southward into the Indian Shield, exploiting mechanically weak zones. Although strain rates are low, the cumulative effect over decades to centuries may contribute to intraplate seismicity. Seasonal hydrological loading further modulates stress conditions, introducing temporal variability that may influence earthquake triggering in critically stressed faults. For the built environment, the implications are significant. The Delhi–NCR hosts critical infrastructure, high-rise development, and lifeline systems that are sensitive to millimeter-scale ground motion. InSAR-derived deformation maps enable identification of subsidence hotspots and infrastructure vulnerability zones, while GNSS ensures long-term stability monitoring. Together, they provide a scientifically rigorous framework for hazard assessment, urban planning, and risk mitigation in slowly deforming continental interiors. This study demonstrates that the integration of GNSS and InSAR is essential for detecting and interpreting subtle intraplate deformation. In regions like the Delhi–Aravalli Ridge, where tectonic signals are small yet societally important, multi-sensor geodesy bridges the gap between deep crustal processes and surface impacts. The proposed framework advances the science and applications of SAR interferometry in earthquake studies, seismic hazard evaluation, and infrastructure monitoring, offering a transferable methodology for stable continental regions worldwide.

Damage Proxy Mapping (DPM) is a powerful remote sensing tool to effectively deploy emergency resources (technical, personal, or financial) in the aftermath of natural disasters. Organizations such as the United Nations Satellite Center (UNOSAT) or the European Union's Copernicus Emergency Management Service (Copernicus EMS) produce such damage maps by comparing pre- and post-event optical and radar imagery acquired from air- or spaceborne sensors. The maps are then distributed to governments and first responders of the affected regions. Due to the urgent nature of the product, the focus in generating DPMs is on speed and reliability, using the often-limited datasets at hand rather than producing the highest-quality output, which could take months or even years to generate.Typically, DPMs are created through manual mapping from high resolution optical imagery, which offers easy visual interpretation. However, machine learning approaches are progressively being adopted to further automate and significantly accelerate production and response workflows. A key limitation of optical imagery, however, is its reliance on clear skies. To address this, methods based on Synthetic Aperture Radar (SAR) have been proposed as SAR provides an all-weather and day-and-night data acquisition capability. Yet, SAR-based damage analysis is hindered by its lower resolution (at least regarding spaceborne sensors), higher computational demands, and the complexity of interpreting radar data.In recent years, deep-learning based DPMs have shown great potential. These methods handle the large volume of radar datasets effectively and produce interpretable results rapidly once trained. In particular, Stephenson et al. (2022) used a Recurrent Neural Network (RNN) on transformed coherence timeseries to produce DPMs for various study regions affected by earthquakes. A key factor for their successful performance was the use of the entire coherence timeseries available up until the event date, which could span several years. However, this required the preprocessing of large data collections for each single study area, including the coregistration of stacks of single look complex (SLC) imagery and the computation of a coherence timeseries in the highest possible spatial and temporal resolution. The significant storage and computational demands of these steps likely contributed to the method never being widely adopted by operational DPM providers (to the knowledge of the authors).In this study, we present initial steps to adapt the Stephenson et al. (2022) method to the terrabyte High Performance Computing (HPC) cluster, a cooperation by the Leibniz Supercomputing Center (LRZ) and the German Aerospace Center (DLR). The goal of terrabyte is to enable both operational Earth observation data processing as well as research and development. This infrastructure is also used by DLR’s Center for Satellite Based Crisis Information (ZKI), which produces DPMs and other disaster-related products from SAR imagery (though not yet based on RNN-techniques). Our study aims to facilitate future integration of an RNN-based workflow into cluster-based operational disaster response processors. Among the improvements we present are data-parallel training, the integration of coherence timeseries generation into operational SAR processors, the preprocessing of high seismic risk areas in preparation of potential future earthquake events, and the incorporation of ancillary datasets (e.g., precipitation, topography, and land use) into the training algorithm to assess their effect on the assessment accuracy.

Detecting millimeter-order surface displacements by InSAR-related techniques like SAR time series analysis is still one of challenges due to several noises such as the atmospheric propagation delay effect, errors in phase unwrapping, and spatial and temporal decorrelation. Because of recent increase of SAR earth observation satellites and improvement of the recurrence period of each SAR satellite, now we can utilize over 100 SAR images for the time series analysis and thus can achieve millimeter-order displacement velocity detection, even though most of such events were limited to having the permanently displacing nature. However, SAR time series now struggles to detect transient, short time scale displacement like a slow slip event (SSE), which is one of urgent issues to be tackled with. Here we are trying to develop an anomaly detection model that is optimized for the InSAR time series analysis for the purpose of automatically detecting unknown SSEs.Our proposed anomaly detection model uses the sliding window k-nearest neighbor method to detect surface deformation signal objectively. The sliding window k-nearest neighbor method is one of machine learning methods for a general time series data. In this method, one time series data was grouped together with multiple adjacent observations and represented as a collection of multiple partial time series as vector. Then, the multiple partial time series before SSE were used as a reference. The minimum distance between the partial time series for anomaly detection and reference partial time series were defined as the anomaly level.To evaluate the performance of the proposed model, we prepared a pseudo time series dataset by simulating InSAR observations. As for a pseudo InSAR image generation, we simulated a displacement signal and a turbulent delay signal caused by the neutral atmosphere. Because state-of-the-art atmospheric delay correction methods exhibited effective removal for the stratified delay effect and the ionospheric phase disturbance, we did not consider them in this experiment. The turbulent component of the atmospheric delay was produced by applying a two-dimensional Gaussian random field. The displacement signal was simulated using the Okada rectangular dislocation model with a reverse-type slip, which is a typical situation of the SSE. We prepared several scenarios with different maximum displacement amplitudes: from 0.0 cm to 3.0 cm with an interval of 0.1 cm. In the InSAR time series simulation, we considered that SAR observations were conducted 50 times every 14 days by referring ALOS-2/-4, and that the SSE continued for 2 months with the constant slip velocity. To make the pseudo InSAR time series data more realistic, we applied spatial and temporal gaussian filtering to suppress the noise effect before anomaly calculation. To assess the detection ability statistically, we prepared 100 InSAR time series datasets for each displacement scenario. The sliding window k-nearest neighbor method successfully detected anomaly where the signal to noise ratio of over 0.4 (ratio of the maximum displacement amplitude to the maximum noise amplitude). Looking at the transition of anomaly in one time series, during the period when the transient displacement was occurring, the anomaly level also showed increase associated with increasing the accumulated displacement. After finishing the displacement, the anomaly level kept high because the displacement was a secular one. To suppress the residual delay noise effect in the estimated anomalies, we applied the stacking procedure in the time dimension to the pseudo InSAR time series. After the stacking was applied to the simulation data, the high anomaly area associated with the displacement was enhanced. Visually, we clearly detected high anomaly value when the maximum displacement amplitude over 1.3 cm. By statistical assessment with a parametric and non-parametric tests (Welch's t-test and Mann-Whitney U test), our proposed anomaly detection model could significantly detect displacements with 0.9 cm.

Earthquakes originating near Earth’s surface pose significant hazards to human safety and infrastructure, as their associated surface deformation can result in widespread structural damage and loss of life. Accurate characterization of coseismic displacement, the ground-surface motion caused by earthquakes, can provide critical insight into fault geometry and surface rupture processes as well as actionable information for disaster response and mitigation. Large-magnitude, shallow earthquakes often affect spatially extensive and remote regions, where both the scale of impact and limited accessibility hinder comprehensive ground-based characterization. Synthetic aperture radar (SAR) interferometry (InSAR), as well as pixel offset tracking of both SAR and optical imagery, can provide detailed measures of coseismic displacement within days of an earthquake and over broad regions, enabling more rapid characterization of the impacted region and associated emergency response. Despite the rapidly growing availability of SAR and optical satellite products, InSAR and pixel offset tracking processing routines present computational and data storage challenges to global-scale earthquake analyses. Additionally, displacement estimates derived from instruments with different operating frequencies complicates their integration and interoperability in surface deformation analyses and downstream modeling workflows. These barriers highlight a critical need for standardized, analysis-ready products that can be seamlessly analyzed within GIS platforms by end-users spanning a range of disciplines and technical backgrounds. The Advanced Rapid Imaging and Analysis (ARIA) project at the NASA Jet Propulsion Laboratory has developed a global archive of accessible, standardized, and analysis-ready coseismic displacement products derived from spaceborne SAR (Sentinel-1A/B/C) and optical (Sentinel-2A/B/C) imagery to facilitate more comprehensive studies of earthquake rupture processes and improve estimates for downstream rapid response efforts. The archive currently includes coseismic displacement products for all large (M 6.0 and greater) and shallow (30 km or less) onshore and near-shore earthquakes from October 2014 to present, and is continually updated with products corresponding to new events meeting these significance criteria. This dataset will soon be expanded to include coseismic displacement products derived from ALOS-2 PALSAR-2 ScanSAR data to provide coseismic displacement estimates in densely vegetated regions where shorter-wavelength sensors often lose coherence. Our product archive is unique from existing coseismic displacement product databases in terms of the data provided, data format, and data accessibility. First, our 30-meter resolution products are sensor-agnostic and provided in standardized units; this allows for the simultaneous use of multiple data sources and enables more rapid integration into GIS platforms and modeling workflows. Second, our product generation workflow integrates multiple scenes along the satellite track to capture the full rupture zone, eliminating the need for downstream mosaicking. Third, the availability of complementary SAR and optical coseismic displacement estimates for each earthquake provides increased sensitivity to surface displacement via pixel offset tracking. Finally, correction layers for solid-earth tides and ionospheric propagation path delays are embedded directly within the analysis-ready products for the end-user. Our workflow leverages the existing ARIA-HyP3 framework and capabilities to cost-effectively generate coseismic products for hundreds of earthquakes in the cloud. For future events meeting our significance criteria, our workflow implements an auto-trigger mechanism to generate coseismic displacement products at 90-meter resolution as soon as post-seismic data are available, delivering results with low latency (

The 2016 Mw 7.8 Kaikōura earthquake was characterized by exceptionally complex rupture behavior involving multiple fault segments and multidirectional displacement over a total rupture length of approximately 170 km. We build on previous studies of the three-dimensional (3D) coseismic displacement associated with this event by reconstructing a refined and comprehensive 3D deformation field using an extensive suite of ALOS-2 and Sentinel-1 synthetic aperture radar (SAR) observations. We derive both interferometric phase and pixel-offset measurements from carefully screened, artifact-free datasets, and apply additional atmospheric and ionospheric corrections. The SAR processing is carried out using modern Python-based workflows that incorporate atmospheric corrections, selective dataset exclusion, coherence-based masking, and optimized spatial downsampling strategies to reduce noise while preserving deformation signals. Six independent satellite tracks from ascending and descending viewing geometries are jointly analyzed to revisit the coseismic deformation nearly a decade after the earthquake, providing substantially improved spatial coverage and robustness relative to earlier reconstructions. Line-of-sight and azimuth displacement measurements from all tracks are combined using a weighted least-squares inversion to resolve displacement in the east, north, and vertical directions, followed by the removal of planar ramps and residual topography-correlated signals to mitigate long-wavelength orbital and systematic artifacts. This integrated approach significantly improves the signal-to-noise ratio of the 3D offset measurements relative to previous studies and enhances internal consistency across sensors and viewing geometries. The resulting 3D displacement field reveals pronounced spatial variability consistent with the highly segmented, multi-fault rupture geometry of the Kaikōura earthquake, with clearer expression of individual fault strands and improved constraints in remote and poorly accessible regions. Updated displacement estimates are obtained for key fault segments, highlighting variations in slip magnitude and direction that reflect complex rupture kinematics. The derived displacement field is validated against independent field observations, photogrammetry-derived 3D point-cloud data, and GNSS measurements, demonstrating strong agreement across all displacement components. Overall, this study demonstrates that integrating multi-sensor, multi-track InSAR data with advanced preprocessing, masking, and inversion strategies substantially improves 3D coseismic displacement estimation and provides a robust foundation for future earthquake source characterization and subsurface fault slip analysis.

On 2021 January 18, a blind mid-crustal Mw ∼6.4 earthquake occurred near San Juan, Argentina. The observation of associated ground deformation with single interferograms is obscured by strong tropospheric signals. We apply appropriate corrections to the data and reconstruct the deformation field associated to the event through InSAR time-series approach. We show it is possible to retrieve this signal to invert the fault parameters. The observed ground deformation is consistent with a high angle NW-dipping fault plane at a centroid depth of ∼19 km. The geometry of this fault supports the reactivation of pre-existing structures within the Cuyania Terrane, suggesting a direct structural connection and strain transfer to the actively deforming, east-vergent Precordillera front. We analyse our findings to deduce a static friction coefficient ≤ 0.3 for mid-crustal faults of the region.

Tierra del Fuego island is crossed by the South America-Scotia plate boundary, with a relative motion of ~5-6 mm/year between them. The boundary is mainly represented by the Magallanes-Fagnano Fault (MFF), which held a historic M ~7.5 earthquake doublet in 1949. While publication of new data in the present century has improved our understanding of the MFF, its seismogenic potential and possible segmentation remain poorly constrained. We present new geodetic evidence that allows for the first estimation of strain accumulation along the entire inland portion MFF from Seno Almirantazgo to the Atlantic ocean shore. Data from two GNSS stations deployed ~8 km north and south of the fault trace, as well as ascending and descending velocity fields derived from Sentinel-1 SAR data, record interseismic velocity of ~6 mm/year along the entire inland portion of the plate boundary, in agreement with previous estimations. However, Bayesian back-slip dislocation models at different longitudes along the fault show along-strike variations of the locking-depth, suggesting the MFF is indeed segmented. We compare our results with current geodetic and geomorphic evidence discussing potential segmentation from Holocene until the present, incluiding potential discrepancies on the previously proposed rupture estension of the 1949 events.

Active deformation in the Eastern Central Andes is caused by east-west shortening. Geologic and instrumental records indicate active compressional structures that potentially host large earthquakes, as indicated by significant geodetic strain. Our study area covers a latitudinal extent of approximately 1,000 km, from southwestern Bolivia (~16°S) to northwestern Argentina (~26°S). Our aim is to quantify and localize thin-skinned (Bolivia) vs. thick-skinned (Argentina) shortening patterns across the region. We analyzed ten years (2015-2025) of Interferometric Synthetic Aperture Radar (InSAR) time-series to measure surface deformation rates. We rely on descending ALOS-2 radar imagery (L-band) acquired in wide-swath (~350 km) ScanSAR mode. We used the "alos2stack" workflow in the ISCE-2 software and substantially downsampled the interferograms to suppress noise, resulting in a ground-range pixel spacing of ~136 m. We generated deformation time-series with the MintPy software and applied corrections for topography, Solid Earth tides, plate motion, and stratified tropospheric signal delay using ERA5 weather models. We further applied a split-spectrum method to suppress the ionospheric phase contribution. The resulting rate maps are complemented by published, pointwise displacement rates from accurate positioning (GNSS) projected into the satellite line-of-sight (LOS). We then compare the LOS rates along multiple cross-orogen transects with geology, fault databases, seismicity, and topography. The deformation rates exhibit kinematics from the Puna Plateau through the Eastern Cordillera to the highly vegetated Subandes. They reveal a variety of active ongoing processes in the eastern Central Andes, including inflation at Cerro Overo volcano (~1.5 cm/yr LOS). The dataset also reveals the dynamics of salars, with LOS motion toward the satellite at Salar de Arizaro (~0.7 cm/yr LOS) and LOS motion away from the satellite at Salar de Olaroz (~-2.6 cm/yr LOS). Significant motion away from the satellite (interpreted as subsidence) occurs in agricultural areas of Bolivia, including Punata (~-2.3 cm/yr LOS) and Cochabamba (~-1.2 cm/yr LOS). The deformation rates also exhibit ~8 cm of coseismic displacement associated with the 2020 magnitude Mw 5.8 Humahuaca normal-faulting earthquake in the Argentine Andes. Finally, we identify several landslide-related deformation signals, predominantly in Bolivia, concentrated in steep, deeply incised valleys along the eastern flank of the Eastern Cordillera and in the Interandean Zone.

The 14 August 2021 Mw 7.2 Nippes, Haiti earthquake ruptured a major portion of the Enriquillo–Plantain Garden Fault (EPGF) system along the southern peninsula of Haiti, producing widespread damage and complex patterns of coseismic and postseismic deformation. Post-earthquake geodetic investigations using L‑band ALOS‑2 and C‑band Sentinel‑1 InSAR revealed several instances of triggered shallow creep on both the primary EPGF strands and nearby thrust faults located within, or immediately adjacent to, the rupture zone. These early observations suggested that fault creep may have played a role in accommodating part of the coseismic strain release, but the spatial extent, duration, and kinematics of this creeping behavior remained incompletely characterized. Using a comprehensive Sentinel‑1 time‑series analysis, we demonstrate that the triggered creep was significantly more persistent and spatially extensive than previously understood. Several EPGF segments continued to creep for up to ~300 days after the mainshock, indicating prolonged shallow fault afterslip and a sustained redistribution of stress along the fault system. The creeping zones extend westward from the main rupture area deep into the Miragoâne pull‑apart basin, and their distribution exhibits pronounced along‑strike variability, revealing clear evidence of structural segmentation within the EPGF. This segmentation likely reflects variations in lithology, fault frictional properties, and near-surface structural complexity, all of which influence the extent to which individual segments undergo stable sliding versus stick‑slip rupture. To assess the relationship between creep and seismicity, we compare the spatiotemporal evolution of the observed postseismic slip with the relocated aftershock catalog of Douilly et al. (2023) and with the sequence of M 5+ earthquakes that occurred on 24 January 2022, approximately four months after the mainshock. This comparison shows that the triggered creep was not entirely aseismic; instead, the slip on several shallow, off‑fault structures appears to have initiated the January 2022 earthquake sequence. That sequence, in turn, appears to have reactivated creep on the main EPGF strand. This cascade of creep–seismicity interactions strongly indicates velocity‑weakening behavior on portions of the EPGF and highlights the potential for triggered slip to modulate loading rates on adjacent fault segments. These findings carry important implications for understanding postseismic strain redistribution, assessing the seismogenic potential of off‑fault structures, and evaluating the frictional and mechanical properties governing the broader EPGF fault system.

The Pamir and Tianshan orogens are two major tectonic units in western China and Central Asia, both of which have experienced direct or far-field effects from the India–Eurasia collision during the Cenozoic. Although both regions are actively deforming, they exhibit significant differences in their deformation mechanisms. Deformation in the Pamir Plateau is likely driven by a dual intracontinental subduction system, whereas the Tianshan orogen deforms primarily through crustal shortening and thickening. Existing GNSS stations, though sparsely distributed, reveal substantial strain accumulation across the orogens; however, they provide limited information on vertical motion due to the scarcity of observations. InSAR offers a more effective approach for mapping surface deformation in this challenging environment, providing improved spatiotemporal sampling. Therefore, accurate extraction of surface deformation from SAR data is essential for understanding the contrasting deformation mechanisms of these two tectonic units and their potential interactions. By exploiting the extensive archive of Sentinel-1 SAR data over the Pamir–Tianshan orogenic system, we are able to map tectonic deformation at an unprecedented resolution of ~200 m—a scale unattainable using GNSS alone in such a tectonically active region. To mitigate decorrelation effects inherent in C-band SAR time series spanning approximately ten years, especially over rugged topography with few man-made targets, we apply a persistent scatterer InSAR approach (modified version of the StaMPS PS-InSAR workflow) to process the data while preserving long time-series coherence. We integrate ascending and descending SAR data acquired from 28 orbital tracks to ensure dense PS coverage across the entire region, particularly over the Pamir Plateau, where even horizontal GNSS measurements are scarce. The PS-InSAR method offers advantages over conventional small-baseline (SBAS) algorithms, which rely on multilooked, filtered images and retain only the most coherent signals over short intervals. In contrast, the PS approach utilizes more complete time-series records, yielding higher accuracy for both horizontal and vertical deformation estimates. Given that the PS method operates on full-resolution SAR images with extended time-series acquisitions and concatenated frames, we parallelize the entire workflow on a cluster to efficiently process both conventional single-look interferograms and StaMPS-derived PS targets. Cluster-based processing enables us to analyze surface deformation with efficiency comparable to SBAS methods, but with a more complete spatial-temporal coverage. To better constrain Line-of-Sight (LOS) deformation, we incorporate established error correction techniques originally developed for SBAS into our PS processing framework. These include corrections for solid Earth tides (SET), tropospheric delays based on ECMWF ERA5 models, and ionospheric delays using JPL’s GIM models. Residual errors are further mitigated through the common scene stacking (CSS) technique, allowing the final time-series to support both average LOS velocity estimation and temporal displacement analysis—essential for vertical deformation studies. Our results demonstrate promising PS coverage across the entire region, including the Pamir Plateau. Due to the absence of GNSS vertical deformation data over such a vast area, we develop a simple fusion method to derive east and vertical surface deformation by combining PS-derived LOS deformation with interpolated horizontal GNSS velocities. GNSS data are interpolated onto the PS network to estimate north–south deformation, yielding a high-resolution (~200 m) horizontal deformation field across the study area. This high-resolution field is subsequently averaged to resolutions of ~1000–5000 m for horizontal strain mapping. Our results highlight the potential of PS-InSAR for analyzing deformation in large orogens, maximizing the value of SAR data for both tectonic and non-tectonic applications (e.g., aquifer and groundwater studies). While conventional SBAS methods efficiently capture first-order deformation patterns across broad spatial scales—such as across tectonic units—the PS approach offers enhanced capability for vertical deformation mapping, especially across active deforming zones and their boundaries. We employ horizontal PS velocities for 2D kinematic modeling of permanent strains and elastic deformation across the Pamir and Tianshan orogens. Our findings align broadly with previous fault slip rates and locking depths, while revealing greater detail along a refined fault network. Although the results over the Tianshan region are well explained by 2D models, the Pamir Plateau presents a more complex scenario, where intracontinental subduction is inferred from multiple geophysical imaging studies. Accordingly, we are now applying a 3D model integrating both horizontal and vertical PS velocities to better understand Pamir’s deformation dynamics. Detailed results and interpretations will be presented at the workshop.

Earthquakes are among the most destructive natural disasters. Approximately 1 million deaths have been caused by earthquakes between 2000 and 2020. It is still difficult to forecast earthquake events spatially and temporally. Post-event damage assessment aims to mitigate the human health risk of earthquakes. Rapid assessment of building damage is critical to understand the scale of civil protection response required, for example to allocate funds and set up emergency shelter. Moreover, rapid damage assessment is crucial for post-disaster rescue. 75% of the casualties in disasters are caused by building damage, and the maximum survival time of people trapped in collapsed buildings rarely exceeds 4-6 days.Ground surveys provide reliable building-level damage severity assessment, but may take weeks or months to complete. Optical and Synthetic Aperture Radar (SAR) remote sensing imagery are commonly used for large-scale earthquake damage assessment on short timescales. Post-event SAR imagery is often available within one to two days resulting from recent satellite launches (e.g., ALOS-4, NISAR, Sentinel-1 and commercial constellations such as Capella Space and ICEYE). In contrast to the more commonly applied optical data, SAR can provide rapid disaster response data regardless of light or cloud conditions. However, SAR models often overestimate damage due to temporal decorrelation and are typically outperformed by optical models in comparative studies [1,2]. Furthermore, distinguishing low and moderate damage severity with remote sensing data is still challenging. In our work, we present a Sentinel-1 SAR coherence timeseries model for rapid earthquake damage assessment and investigate multimodal fusion of SAR, shaking intensity and building taxonomy data. We apply our methods to the Central Italy 2016–2017 sequence (Mw>6) and Zagreb March 2020 earthquake (Mw 5.4) to test model effectiveness. We validate results against ground survey data and compare performance to manual assessment of high-resolution optical data by the Copernicus Emergency Management Service. Our SAR change detection model shows similar agreement with ground survey data as the manual interpretation of optical satellite data. Both medium-resolution SAR and sub-meter resolution optical data are found to be most valuable for identifying clusters of collapsed buildings. In contrast, empirical fragility curves and feature importance analysis of a fusion model reveal that building construction period and construction type data are valuable for identifying low to moderate damage grades, complementing remote sensing data assessments. Our findings have implications for rapid damage assessment data acquisition strategies, fusion model implementation and model validation approaches.[1] Stramondo, S., Bignami, C., Chini, M., Pierdicca, N., & Tertulliani, A. (2006). Satellite radar and optical remote sensing for earthquake damage detection: results from different case studies. International Journal of Remote Sensing, 27(20), 4433-4447.[2] Sun, Y., Wang, Y., & Eineder, M. (2024). QuickQuakeBuildings: Post-earthquake SAR-Optical Dataset for Quick Damaged-building Detection. IEEE Geoscience and Remote Sensing Letters, 21, 1-5.

The Mw 8.8 Kamchatka megathrust earthquake of 29 July 2025 ruptured a structurally complex segment of the Kuril-Kruzenstern subduction zone and propagated over more than 300 km along strike. The event initiated within a rough portion of the plate interface associated with the subduction of the Kruzenstern fracture zone and subsequently expanded southward into a comparatively smoother and more strongly locked megathrust domain. The mainshock was preceded by an Mw 7.0 earthquake in August 2024 and an Mw 7.5 event nine days earlier, and followed by two major aftershocks of Mw 7.4 and Mw 7.8 on 13 and 18 September 2025, respectively. Notably, all Mw ≥ 7 earthquakes in this sequence nucleated within the fracture zone. This unusual spatiotemporal evolution challenges the prevailing view that structurally rough megathrust segments primarily act as rupture barriers and rarely host the initiation of giant earthquakes. Understanding the generation environments of great earthquakes in global subduction zones is fundamental to earthquake physics and hazard assessment. Great megathrust events have traditionally been linked to relatively smooth plate interfaces, often overlain by thick trench sediments or lacking pronounced structural heterogeneity. Classic examples include the 2011 Mw 9.0 Tohoku-oki, the 1960 Mw 9.5 Chile, and the 2004 Mw 9.2 Sumatra earthquakes, all of which ruptured extensive and comparatively smooth megathrust segments. In contrast, rough fault domains, characterized by subducted seamounts, fracture zones, or strong bathymetric and density contrasts, are widely interpreted as segmentation boundaries that promote small to moderate earthquakes, host seismic swarms or slow-slip events, and impede the propagation of neighboring large ruptures. Geodetic and seismic studies from subduction systems including Sumatra, Peru, Chile, and northern Mexico have documented rupture termination or locking segmentation associated with fracture-zone subduction. Reports of giant earthquakes initiating within such rough segments are rare, rendering the 2025 Kamchatka event a compelling story. The fracture zone in Kamchatka is marked by strong along-strike variations in isostatic anomaly and persistent shallow seismic swarms that have previously been proposed to inhibit large coseismic rupture. Nevertheless, the Mw 8.8 mainshock evolved into a giant event. Rapid source analyses indicate that rupture propagated predominantly southward beyond the fracture zone into a smoother segment characterized by thicker sediment cover and reduced large-scale roughness. This southern domain previously hosted the 1952 Mw 9.0 earthquake and appears to be locked to a higher degree, or over a broader area, than the northern segment. The 2024–2025 earthquake sequence therefore provides a unique opportunity to examine whether a structurally rough segment can act not only as a barrier, but also as a gateway that facilitates cascading rupture into an adjacent smooth and highly stress-accumulated patch. Here we reconstruct the rupture processes of the 2024–2025 Kamchatka earthquake sequence by integrating comprehensive inland geodetic observations with teleseismic waveform modeling. Our joint inversions resolve a mosaic of discrete slip patches within the fracture zone that collectively bridged to a large, high-slip region in the smoother southern megathrust. The results demonstrate that rupture nucleation within a rough structural domain can dynamically connect to and trigger extensive failure on an adjacent smooth segment. These findings provide the first well-constrained evidence of a giant earthquake initiating on a rough megathrust patch and subsequently evolving into a large-scale cascading rupture. The observed gateway behavior calls for a systematic reassessment of structural controls on megathrust segmentation worldwide and offers new insights into earthquake nucleation, rupture dynamics, and integrated seismic and tsunami hazard assessment.

Landslides constitute a recurrent geomorphic hazard in the mountainous regions of Vietnam, where intense monsoonal rainfall, deeply weathered lithologies, and active tectonics promote widespread slope instability. In these humid tropical environments, dense vegetation cover, rapid biomass regeneration, and seasonal canopy variability introduce substantial challenges for satellite-based deformation monitoring. Temporal decorrelation caused by vegetation dynamics significantly reduces interferometric coherence, particularly in C-band systems, limiting the reliability of long-term displacement retrieval in forested terrain. These constraints are especially relevant for slow-moving landslides, where deformation rates are subtle and require high phase stability for robust detection. In this study, we applied multi-year Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) time-series analysis to reconstruct the spatiotemporal evolution of slow-moving landslides in a selected region of northern Vietnam with low vegetation cover. Differential interferograms were generated using the cloud-based Hybrid Pluggable Processing Pipeline (HyP3) and processed through a Small Baseline Subset (SBAS) workflow implemented in the open-source MintPy software. Both ascending and descending Sentinel-1 datasets were analyzed to enhance the robustness of displacement estimates and to reduce line-of-sight (LOS) directional bias. Daily precipitation data were integrated to evaluate the relationship between deformation patterns and the Southeast Asian monsoon regime. Given the dense tropical vegetation across many Vietnamese slopes, careful selection of interferometric pairs and strict baseline thresholds were implemented to minimize temporal and spatial decorrelation. Coherence filtering and masking strategies were applied to exclude low-quality pixels, resulting in deformation estimates primarily derived from partially vegetated slopes, exposed scarps, road cuts, and sparsely forested sectors. Although these measures improved signal reliability, coherence loss during peak monsoon months remained significant, leading to localized data gaps and increased uncertainty in some forested sectors. Consequently, deformation estimates in heavily vegetated zones should be interpreted conservatively, and the results highlight the inherent limitations of C-band InSAR in humid tropical settings. Despite these challenges, the InSAR time series reveals sustained, low-magnitude slope deformation across all investigated sites, confirming ongoing activity. Distinct periods of acceleration were observed during the main monsoon season (typically September-October), with peak displacement rates occurring in months characterized by cumulative rainfall maxima. While deformation does not exhibit a strict one-to-one correspondence with individual rainfall events, multi-week-to-seasonal rainfall accumulation appears to act as a conditioning and amplifying factor, indicating a hydrologically modulated response typical of deep-seated, slow-moving landslides. Outside the monsoon season, displacement rates decrease but do not cease entirely, demonstrating persistent creep behavior. This study demonstrates that, despite coherence limitations imposed by dense tropical vegetation, cloud-based InSAR processing combined with open-source SBAS time-series analysis can provide valuable quantitative insights into landslide kinematics in monsoon-dominated environments. The findings contribute to improved hazard characterization, seasonal risk assessment, and long-term infrastructure management strategies in Vietnam's landslide-prone regions. The study has been supported by the National Science Center (project no 2023/49/B/ST10/02879).

Vertical motion from ocean tides causes a significant horizontal displacement in the range-direction of offset-tracked ice speed measurements on ice shelves from Synthetic Aperture Radar (SAR) data. This geophysical signal can be usefully used to differentiate between grounded and floating ice, but it is not helpful for studies investigating ice-velocity change and interpretation of ice-dynamical processes. This effect is particularly important in coastal Antarctica, where large tidal amplitudes of up to 9 meters in the Ronne Filchner Ice Shelf, and floating ice around 74 % if the ice sheet margin led to strong tidal signals in SAR ice speed measurements. There is a need for robust algorithms to be developed to tidally correct ice speed measurements so the signal can be removed for applications that do not require it. However, converting tidal height variations into radar range displacement remains a major challenge because the tidal response of ice shelves depends on ice thickness, grounding-line proximity, flexural behaviour and ice rheology, all of which vary spatially. In this study we use an ocean tide model, inverse barometer corrections from ERA5 data and information on SAR imaging geometry to calculate a tide correction that can be applied to Sentinel-1 SAR offset-tracking measurements of ice speed. We then investigate two alternative approaches to model the tidal correction in the complex grounding zone region. The first method follows the ESA Antarctic Ice Sheet Climate Change Initiative (CCI) approach and applies an elastic beam model to estimate the relationship between tidal vertical displacement and radar range motion using ice thickness and distance from the grounding line. This approach provides a physics-based description of tidal flexure but relies on assumptions about ice thickness and buoyancy in the vicinity of the grounding zone which are poorly constrained. The second approach uses the output from the tidal motion offset correlation (TMOC) method of identifying tidal displacement and uses this result to derive a data-driven approach to correcting tide motion in the grounding zone. Our results show that tide amplitude and the associated ice speed displacement can be accurately obtained from an ocean model and SAR imaging geometry data. We present results from the methods application across the Antarctic Peninsula and Amundsen Sea, and other floating ice in Antarctica. When we compare the spatial distribution and magnitude of tide corrections, we find that the tide correction ranges from 46 m/y ice speed displacement on the Larsen-C ice shelf for a relatively large 1.5 m tide amplitude, through to a 20 m/yr velocity correction on Pine Island Glacier for a more modest 0.9 m tide amplitude. Our investigation into the two difference approaches for correcting speed measurements in the grounding zone shows that there are differences between the physic-based and data-driven tidal-response estimates. This shows that while there are modest differences between the two approaches to correcting grounding zone data, the performance of the data-driven method is likely stronger in regions where ice thickness and stiffness measurements are poorly known, such as in East Antarctica, where assumptions in the elastic beam approach are poorly constrained. In conclusion, tide corrections are an important processing step for SAR derived ice speed measurements over ice shelves, especially when dense time series of speed measurements are required. The choice of tidal-response model in the critical grounding zone region is important. In the future, ice speed in situ calibration validation campaigns will provide important validation data to help further reduce the error on ice speed measurements from space. The results provide guidance for future generation satellite velocity products and contribute to improving the accuracy of earth observation datasets used to monitor the Antarctic Ice Sheet.

Landslides represent some of the most destructive natural hazards encountered in unstable mountainous regions, such as the Western Ghats in India. The examination of landslides has garnered significant global attention due to their profound impacts on socio-economic activities. The utilization of remote sensing and geographic information systems has proven valuable for integrating the spatial factors that contribute to landslide occurrences. In this study, satellite imagery from Sentinel 1-C Band has been employed, and further interferometry techniques for detecting the landslide event in 2024 in the Wayanad district, Kerala. Leveraging Artificial Intelligence Techniques in RADAR remote sensing such as machine learning algorithms, particularly the Random Forest (RF) model, were utilized to classify the study area into affected and non-affected regions. The findings also indicate the affected land use and land cover in the given study area. In the end, it can be concluded that significant landslides on 30th July, 2024 in the Wayanad district were primarily precipitated by anthropogenic interventions, compounded by heavy precipitation and unstable topography. Activities such as stone quarrying and infrastructure development emerged as critical factors contributing to these landslides. This research provides valuable insights aimed at mitigating landslide hazards in the Wayanad district, thereby fostering sustainable development.

Landslide hazards in New Zealand are responsible for significant economic and social damage every year with an estimated cost between NZ$ 250 – 300 million and more than 1500 landslide related deaths since 1760. The most common trigger of landslides in New Zealand is heavy rainfall. Extreme weather events such as Cyclone Gabrielle in 2023 triggered an estimated 800,000 landslides, mainly across the north island. Historically, monitoring landslides has been done with ground-based instrumentation. However, these methods are time-consuming and costly in nature and are unsuitable for a nationwide landslide study. The launch of the Sentinel-1 mission in 2014 has accelerated research surrounding the application of satellite data to monitor geohazards such as landslides. Interferometric Synthetic Aperture Radar (InSAR), from satellites such as Sentinel-1, is widely used in studies that identify and map landslide deformation due to the nature of freely available data, frequent revisit time, global coverage, and its ability to measure accurate deformation on the scale of millimetres. However, solely relying on InSAR data makes estimating the 3D displacement of landslides challenging to resolve. This is because of InSAR’s relative insensitivity to north-south movement, estimates from InSAR observations are often limited to deriving the downslope component of motion. This typically assumes that movement is either along the same direction as the steepest slope, or that there is zero movement perpendicular to the maximum slope direction. However, for many landslides the assumption of downslope movement may not always be true. In the case of rotational landslides, there will be spatial variations in the slip vector with near vertical movement at the head and in some cases uplift of the toe. Here we exploit a suite of high-resolution, multi-mission InSAR datasets, digital elevation models and, where available, a-priori surface displacement datasets to help constrain the 3D-component of landslide movement across New Zealand. Through our analysis, we capture widespread, large-scale slope movements across New Zealand and find complex internal deformation within many of the landslide masses. Within individual landslides, there can be significant variations in their horizontal and vertical rates of movement highlighting zones of compression and extension. These data will be used to develop modelling tools for the rapid assessment of landslide behaviour in support of more complex numerical models.

Landslides are a major environmental and socio-economic hazard, with their frequency and intensity expected to increase under changing climatic conditions. Their sudden occurrence and complex geological, hydrological, climatic, and human interactions make reliable prediction and monitoring challenging. In this context, systematic multi-source monitoring of surface changes and the identification of slope instability precursors are key components of landslide risk mitigation strategies. This study evaluates the potential of integrating multi-source remote sensing data to monitor and identify landslide precursors. The research was conducted in two landslide-affected areas, combining radar and multispectral satellite data with very high-resolution RGB and multispectral imagery acquired using unmanned aerial vehicles (UAVs). Surface displacements were calculated using radar-based Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) and UAV-photogrammetry based on orthomosaics and digital terrain models (DEMs). The comparison of both methods revealed high agreement (RMSE = 0.017 m), confirming that PS-InSAR and UAV-photogrammetry can provide complementary monitoring of terrain deformation at regional and local scales. In addition, multispectral data from Sentinel-2 and Landsat-9 satellites, as well as UAV platforms, were used to derive vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), and Normalized Difference Water Index (NDWI). Mean vegetation indices derived from satellite observations were compared with high-resolution UAV results to assess their reliability for detecting early signs of slope instability at a regional scale. Quantitative analyses showed a strong correlation between satellite- and UAV-based results in the studied areas (r ≥ 0.88). Spatial analysis further confirmed that publicly available satellite observations are effective for regional-scale vegetation monitoring, while UAV imagery enables the detection of localized anomalies indicative of early slope destabilization. A two-stage framework for landslide monitoring and precursor identification is introduced. The first stage focuses on regional-scale assessment using multi-temporal satellite radar and multispectral data to analyse surface displacements and vegetation index changes, supported by machine learning algorithms for automated preliminary identification of high-risk areas. The second stage involves detailed investigations of selected sites using high-resolution RGB and multispectral UAV data, enabling precise local-scale characterization of terrain deformation and surface changes. The framework also incorporates the analysis of historical data from stable periods, enabling the determination of accuracy thresholds for each technique. Changes exceeding these thresholds can then be identified as anomalies potentially indicating landslide processes. By integrating multiple sensors and methods, the approach leverages the strengths and mitigates the limitations of individual techniques, enabling scalable and comprehensive monitoring strategies with potential applications in operational landslide early warning systems.

In mountainous landscapes, bedrock landslides represent a major natural hazard that threatens the safety of populations and infrastructure. In Himalaya, they are also widely recognized as the primary erosion mechanism driving hillslope erosion and landscape evolution. Diversity in rock weakening and geologic setting leads to a large diversity of landslides types, involving distinct depths and geometry of the failure surface. It also leads to a wide range of deformation rates, from rapid and catastrophic landslides to extremely slow deformations (a few m/kyr). While rapid landslides have been the subject of numerous studies, particularly because of the natural hazards they pose, slow-moving landslides have been investigated much less. However, slow-moving landslides can have a significant impact both on infrastructure and as precursors to rapid landslides: understanding their dynamics and the factors controlling their deformation therefore represents a significant research challenge. Deep seated slow-moving landslides in the Himalaya reveals different types: Deep Seated Gravitational Slope Deformation (DSGSD), characterized by very slow movements, with a pervasive zone of deformation at depth; Deep seated slides, which affect the whole hillslope and displace panels over hundreds of meters, with intense fracturing and loss of cohesion; Secondary slides developing within the cohesionless mass of the previous type and associated with rapid retrogressive incision of a talweg into the cohesionless mass. When they undergo phases of activity, the last two types present average deformation rates ranging from cm/a to tens of m/a. However, they can display high spatial and temporal variabilities, controlled by external factors including earthquake and monsoon. In Himalayan setting, the ability to identify, map on large scale and monitor slow deformations on hillslopes relies on remote sensing techniques. However, the many difficulties associated with widespread cloud cover during the monsoon season, steep slopes, and atmospheric problems for radar interferometry require the use of multiple sensors and methods to achieve the high-precision monitoring required. Here, we demonstrate that a combination of optical and radar datasets over the Bhote Koshi Valley (Central Nepal), leveraging both image correlation and Sentinel-1 InSAR processing, is effectively capable of characterizing the ground displacement associated with slow-moving landslides in a non-favorable environment. We established displacement records of several landslides, from ERS and ENVISAT radar archives (since 1992), high return time Sentinel-1 and 2 constellations since 2015 and 2016, as well as very high resolution optical Pléiades images (since 2014) and stripmap PAZ radar images (for the 2025 monsoon). Using image correlation and VHR Pléiades DSM differences, we show the effective detection of ground displacements from metric trends over several years for lower resolution sensors up to a fully characterizable displacement of tens of cm/yr for very high-resolution sensors. The high variability of the speckle pattern in Sentinel-1 images is mitigated using image stacking over 50 days and correlation refinement in frequency domain, effectively enhancing the signal-to-noise ratio of the correlation’s results. Different trends can be isolated and associated with annual events including monsoon, as well as punctual events like the 2015 Mw7.8 Gorkha earthquake, therefore allowing estimation of the response to landslide triggers. Additionally, we processed Sentinel-1 InSAR data with additional steps to mitigate the low coherence of mountainous areas and the high deformation gradients associated with acceleration phases of landslides such as monsoon. These steps include multi-looking and filtering of interferograms integrating a proxy reflecting potential biais and coherence variation from the humidity, snow and vegetation. Besides, we apply a series of correction on the wrapped phase to reduce the variability of the phase and thus enable unwrapping. The corrections include: (1) empirical flattening of the interferogram in range and azimuth directions, (2) empirical phase-elevation estimations to remove stratified delays, (3) empirical removal of the deformation during monsoon. This last step relies on the correlation results deformation model to effectively reduce the wrapped phase gradient during landslide activity and therefore reduce aliasing of the phase. By combining image correlation and de-ambiguated InSAR results over this unique dataset, 29 moving landslides were detected on the 760km2 study area with areas ranging from 0.1 to 10km2, including 13 detectable from image correlation. Displacement characterization can be achieved for all landslide velocities even during acceleration periods, from cm/yr for InSAR detection and tens of cm/yr for high-resolution image correlation. One of the largest landslides detected, located in the Balephi Valley northeast of Kathmandu and covering an area of 2.2 km2, has an average annual displacement of 1 to 2 m/y. This displacement is not continuous but mostly occurs during the monsoon season. Data from a GNSS station positioned on this landslide confirm that in 2025 the landslide responded to hydrology, with displacement limited to the very end of the monsoon and following the maximum elevation of the water table along the hillslope. These large displacements of 1 to 2 m are also systematically underestimated by conventional InSAR processing due to phase aliasing. This type of landslide with intermediate deformation rate clearly illustrates the complementary nature of the InSAR and image correlation approaches: the first method identifies active slopes, while the second accurately documents their spatial extent and average speed. Combining the two methods provides in addition precise information on temporal dynamics of the slow-moving landslides.

Landslide disasters are a global geohazard phenomenon. Conducting early identification of landslides prior to their occurrence, along with timely detection and monitoring of surface deformation, constitutes a crucial technical approach for effective landslide prevention. However, with global warming and the expansion of human engineering activities, the frequency of landslide events has been increasing annually, and an increasing number of ancient landslides are being reactivated. This situation imposes new requirements on the timeliness of landslide deformation detection. Time-series Interferometric Synthetic Aperture Radar (InSAR) offers the advantages of all-day, all-weather, and large-scale monitoring, and has therefore been widely applied in early landslide identification and monitoring applications. Nevertheless, due to the complex processing workflow of time-series InSAR, unavoidable phase unwrapping errors, and the uncertainties associated with error propagation, the accuracy, efficiency, and reliability of landslide detection results in wide-area complex mountainous regions often fall short of meeting engineering application requirements. For early landslide detection, obtaining only the boundary extent of the landslide feature field and the trend of deformation magnitude can suffice to locate potential hazard zones and assess danger levels, thereby enabling ground-based monitoring and prevention. Consequently, the Stacking method was proposed, which rapidly estimates landslide deformation rates by stacking time-series interferometric phases. Its straightforward and fast processing strategy has been widely adopted in early landslide detection. However, the Stacking method also necessitates phase unwrapping and struggles to effectively mitigate vertically stratified atmospheric disturbances in complex mountainous areas. Therefore, rapid landslide detection methods based on phase gradient stacking have gained further expansion and application in recent years. Compared to traditional Stacking deformation rate estimation methods, landslide detection using phase gradient stacking still faces the following issues. Firstly, the results of phase gradient stacking lack a clearly corresponding physical quantity (such as deformation magnitude or rate), and the errors inherent in gradient stacking cannot be assessed. Consequently, the detection outcomes from gradient stacking fail to provide quantitative deformation accuracy. Secondly, the gradient field only reflects, to a certain extent, the boundary characteristics of the deforming area and does not correspond to a complete deformation field. This makes it difficult to identify areas of uniform or slow deformation within the landslide body. Moreover, as the gradient step increases, the boundaries of the stacked gradient field become increasingly blurred. Finally, since phase gradient stacking requires stacking the entire time-series InSAR interferometric phases, the landslide detection results lose temporal observation capability, thus failing to dynamically capture newly developed landslides. Addressing the various difficulties encountered in rapid landslide detection using phase gradients, this paper proposes a landslide deformation detection method based on phase gradient analysis and stacking. This method extracts genuine deformation gradients from interferometric phases and recovers the spatial absolute deformation field through spatiotemporal double-difference phase analysis and time-series progressive gradient stacking. This approach circumvents phase unwrapping processing, enabling efficient extraction of the surface deformation field. Furthermore, the errors inherent in the phase gradient stacking process are also assessed for accuracy, facilitating fast, robust, and wide-area landslide deformation detection.

Landslides frequently result in severe casualties and substantial economic losses worldwide, making reliable deformation monitoring a critical component of hazard mitigation and risk management. Interferometric synthetic aperture radar (InSAR) has emerged as a powerful remote sensing technique for landslide deformation monitoring. Nevertheless, phase unwrapping (PU) under high-gradient deformation remains a fundamental challenge. Conventional single-baseline (SB) PU methods predominantly rely on the phase continuity assumption, which is frequently violated in areas characterized by rapid deformation, such as active landslides induced by underground mining activities. In contrast, existing multi-baseline (MB) PU approaches are primarily developed for topographic phase recovery and typically operate in a pixel-wise manner, limiting their effectiveness in resolving severe deformation gradients. To address these limitations, we propose improved multi-temporal baseline PU approaches specifically designed for high-gradient deformation scenarios and validate them using both simulated experiments and real landslide cases. Assuming that the deformation rate is approximately linear over short temporal intervals, we establish relationships among multi-temporal interferograms on Arcs. The initial ambiguity gradients on each Arc are determined via integer programming to ensure global consistency, after which the absolute phases of selected pixels from representative interferograms are optimally estimated using an Lp-norm optimization model, enhancing robustness against noise and local inconsistencies. To further improve computational efficiency, a quadtree segmentation strategy is introduced to adaptively partition the study area according to wrapped phases. Simulation experiments demonstrate that the proposed method improves phase recovery accuracy by approximately 23 times and computational efficiency by nearly one order of magnitude compared with conventional approaches. Furthermore, a typical mining-induced high-gradient landslide located in the mountainous region of southwestern China is selected as a real-case study using Sentinel-1 data. The proposed approach is systematically compared with existing one-dimensional to three-dimensional PU methods. Results indicate that our method is capable of recovering larger-magnitude and more reliable deformation signals under severe gradient conditions. Second, we address the challenge of an unknown feasible solution space, which can significantly degrade the performance of multi-temporal baseline PU when deformation gradients exceed the assumed bounds. We analytically demonstrate that inaccurate FSS estimation results in solution instability, particularly under extreme deformation conditions. To mitigate this problem, a constrained FSS is constructed using a priori deformation information derived from SAR offset-tracking, thereby providing physically meaningful bounds for ambiguity estimation. By incorporating this constraint into the multi-temporal PU framework, the stability and robustness of phase recovery are substantially improved. A representative landslide case located in the mountainous region of southwestern China is further selected for validation using high-resolution ALOS/PALSAR-2 data. Performance comparisons in terms of single-interferogram phase quality, deformation rate estimation, and time-series reconstruction consistently confirm the superiority and reliability of the proposed constrained approach under high-gradient deformation conditions. Unlike conventional PU approaches that depend on phase continuity assumptions, the proposed framework is tailored for deformation environments where such assumptions fail. Experiments on simulated and real datasets verify its robustness and scalability under varying gradient conditions. Although developed for landslide monitoring, the strategy is readily transferable to other severe deformation settings, including mining subsidence and earthquake-induced deformation, thereby broadening the applicability of multi-temporal InSAR for geohazard assessment and deformation monitoring.

Rock glaciers in the arid tropical Andes represent important cryospheric water reservoirs and sensitive indicators of high-elevation permafrost dynamics. In regions with low precipitation, they provide one of the few persistent sources of water, supporting local ecosystems and communities. Their debris-covered surface insulates the internal ice from atmospheric warming, enhancing their resilience to climate change. Consequently, rock glaciers are expected to play an increasingly significant role as natural water reservoirs in arid Andean environments, particularly in regions where other high-altitude ice sources are scarce. Despite their importance, the seasonal kinematic behavior of rock glaciers in arid mountain environments remains poorly constrained. We investigate rock glacier deformation in the Sajama region of northwestern Bolivia (18°S) using Sentinel-1 Persistent Scatterer Interferometry (2014–2022, extendable to 2026). Sajama is a region of the Bolivian Andes bordering Chile, where rock glaciers are located at high elevations between 4,500 and 5,600 meters above sea level. The climate of the Sajama region is characterized by cold temperatures and pronounced seasonality in precipitation. Monthly mean temperatures remain close to freezing throughout the year. Precipitation is strongly seasonal, with most of the rainfall occurring during the austral summer months (100 mm/month) and a pronounced dry season in winter from May-August. This climatic setting is favorable for continuous InSAR time-series analysis, due to lack of snow cover making it an ideal environment for studying the seasonal kinematic behavior of rock glaciers. Ascending and descending line-of-sight (LOS) time series were analyzed to derive multi-year velocity fields and temporally resolved displacement signals. Because a complete rock glacier inventory does not exist for the region, individual rock glaciers were manually identified through visual inspection of their distinct surface morphologies, including both previously known and newly discovered rock glaciers in the analysis. Actively deforming areas were then identified using velocity thresholding and density-based clustering. To investigate seasonal behavior, LOS time series were decomposed using harmonic modeling to extract annual amplitude and phase metrics. The study assesses whether deformation magnitude and seasonal behavior vary systematically with elevation, slope orientation, and regional wet–dry climatic cycles. By combining multi-year InSAR analysis with detailed geomorphometric and climatic context, we aim to better constrain the climatic sensitivity and hydrological relevance of tropical rock glaciers in the Bolivian Andes, providing a foundation for future studies on high-elevation permafrost dynamics and cryospheric water resources.

Systematic monitoring of rock glacier kinematics through Interferometric SAR (InSAR) is an essential component for investigating climate change impacts on mountain permafrost, as recognized by the inclusion of Rock Glacier Velocity (RGV) as an associated product of the Essential Climate Variable (ECV) Permafrost. Compared to field-based methods such as GNSS or total station surveys, InSAR offers broader spatial coverage but trades the accuracy and directness of point-based measurements for an area-wide, line-of-sight displacement signal that requires careful interpretation. Optical remote sensing approaches based on feature tracking can provide true 2D surface displacement fields and are less sensitive to temporal decorrelation, but are constrained by cloud cover, illumination conditions, and the availability of trackable surface features. Furthermore, obtaining optical imagery with sufficient spatial resolution (< 1 m) and regular temporal sampling remains operationally challenging. InSAR complements these methods by providing spatially dense displacement measurements with consistent revisit capability, though its utility depends strongly on coherence, which can be limited by for example snow cover or rapid surface change. In this contribution, we present results from the development and application of best-practice guidelines for Sentinel-1 C-band InSAR-based RGV production across several rock glaciers in the Swiss Alps. Processing the complete Sentinel-1 archive (2014–2025) with the GAMMA software, our workflow encompasses SLC co-registration and multi-looking, interferogram generation, adaptive filtering, atmospheric correction, phase unwrapping, and geocoding. The core of the approach is a structured quality assessment framework incorporating coherence-based temporal filtering with frequency- and baseline-appropriate thresholds, pixel-based observation count criteria, and restriction to a consistent summer observation window (July–September) to ensure inter-annual comparability. Filtering parameters are configured independently per rock glacier and orbit/baseline combination. By systematically excluding pixels and interferograms affected by unwrapping errors or low coherence, the framework yields reliable, reproducible RGV time series. Applied across our study sites, we find an overall acceleration trend from 2017–2020, followed by stabilization or slight deceleration through 2023 and renewed acceleration in 2024–2025, consistent with independent field GNSS measurements. For rock glaciers where velocities regularly exceed the C-band unwrapping thresholds, approximately 1.7 m/yr for 6-day and 0.8 m/yr for 12-day temporal baselines, the operational approach does not provide any data. In such cases, we investigated the potential of a manual point-selection approach using 2–4 strategically placed monitoring points per rock glacier with systematic unwrapping error correction. This extends the measurable velocity range and recovers periods otherwise lost, but requires substantial operator intervention and is not scalable to large inventories. In some cases this approach is also subjective. It is therefore best suited to individual rock glaciers of particular scientific or monitoring interest, where the effort is justified by the value of a continuous record. To investigate the potential of next-generation L-band missions such as NISAR and ROSE-L, we additionally processed already available SAOCOM-1 data (23.5 cm wavelength) at 16-, 32-, and 48-day temporal baselines for some of the same sites. L-band interferograms maintain high coherence over surfaces where C-band fully decorrelates, particularly on fast-moving fronts, and the longer wavelength substantially raises the phase aliasing threshold, reducing data gaps on fast rock glaciers. At Muragl, a particularly dynamic, polymorphic rock glacier with annual creep rates locally exceeding 1 m/yr, both C-band and L-band velocity estimates agree well with GNSS-derived velocities when accounting for line-of-sight projection geometry, validating both approaches. The limited temporal density of the current SAOCOM-1 archive constrains the statistical robustness of L-band time series at the moment. Together, the two frequencies are complementary: C-band provides reliable rock glacier velocities and benefits from the depth of the Sentinel-1 archive, while L-band extends the observable velocity range and improves coherence on challenging surfaces. The convergence of C-band and L-band SAR constellations, combined with standardized processing workflows and quality frameworks makes InSAR a useful tool for operational rock glacier monitoring across mountain regions worldwide.

Ice shelves exert a crucial buttressing force on marine terminating glaciers, influencing outlet glacier dynamics. When these shelves fracture or thin, this resistive force diminishes, sometimes triggering ice flow speedups upstream and increasing ice discharge to the ocean. Crevassing can also play a role in surface and dynamic mass balance, however the spatial distribution and temporal evolution of crevasses across large ice sheets is poorly quantified. Improved records on the distribution of crevasses over catchments of interest and on an ice sheet wide scale is essential to improve our understanding of these complex relationships. This work aims to begin addressing this knowledge gap, particularly as climate change driven changes to ablation zones may alter future fracture patterns. Interferometric SAR (InSAR) is a powerful tool for examining fracture processes of individual events. Interferograms enable the detection of small surface displacements, this sensitivity to sub‑wavelength motion (~5.5 cm for Sentinel‑1) makes InSAR uniquely suited for detecting the early stages of fracture propagation and for characterising mechanical changes that precede calving or major rupture. Coherence maps further reveal dense fracture networks that are often invisible in intensity backscatter imagery, reflecting structural weakening. While InSAR captures incredibly high-resolution crevasse propagation, monitoring crevasses over wider spatial and temporal scales requires automated, scalable methods. To this end, we apply deep learning approaches to Sentinel‑1 SAR backscatter imagery for crevasse detection over Greenland, adapting U‑Net convolutional neural networks originally developed for Antarctica. We apply two different neural net models for two categories of crevasse: ‘Type A’ crevasses which are large, multiple pixels in width and visible from many satellite look-angles; ‘Type B’ crevasses which are finer features that can be a single pixel in width and are most visible when look-angle is perpendicular to the crevasse. Training dedicated models for each type enables detection of major rifts, which on their own can have implications for buttressing, alongside wider patterns of distributed damage. Our primary case study is Petermann Glacier in NW Greenland, host to one of the last major floating ice tongues on the island. Petermann Glacier has long been regarded as dynamically stable. The glacier’s ice shelf underwent three major calving events during 2010-2012, with the large 2010 calving event not producing notable changes to grounding line position or ice flow speed. Petermann Glacier has recently undergone another major calving event. Early indications suggest a subtle speedup coincident with this event, raising questions about the glacier’s long‑term stability. With a large catchment, significant ice volume above flotation, and susceptibility to ocean forcing, Petermann represents a critical system for examining the links between fracture development and dynamic response. By combining event‑scale InSAR observations with deep‑learning‑based crevasse mapping across the full Sentinel‑1 mission period, we reconstruct the evolution of distributed damage throughout Petermann’s floating tongue and grounded ice. This framework enables assessment of whether recent changes reflect natural variability or a shift away from the glacier’s historically stable behaviour. Planned extensions include application of the methodology across wider areas of the Greenland Ice Sheet.

Grounding lines are flux gates through which ice discharges into the ocean and are essential for estimating of ice sheet mass balance. Their position reflects ice sheet stability, retreating landward or advancing seaward in response to changes in melting and accumulation, while also exhibiting ephemeral movement driven by tidal flexure of floating ice. While grounding lines derived from Differential Interferometric SAR (DInSAR) phase are regarded as the most accurate (Rignot et al., 2011), most existing products lack formal uncertainty estimates. Errors in their positions directly propagate into ice discharge calculations and can bias estimates of ice mass loss and sea level rise (Rignot et al., 2011). A further limitation is that DInSAR grounding lines are derived from interferograms combining three or four SAR acquisitions, such that each estimated position represents a superposition of multiple tidal states, making it difficult to establish causal links between their position and tidal state. Our proposed framework addresses both of these issues by obtaining grounding lines from single-difference SAR range offsets and simultaneously providing position uncertainties. We used a time series of Sentinel-1 range offsets at a temporal sampling of 6 days for the Larsen C Ice Shelf. The range offsets are part of the operational processing pipeline used by ENVEO IT to produce monthly and annual Sentinel-1 ice-velocity maps, and were computed by tracking features between consecutive SAR backscatter images (Nagler et al., 2015, Wuite et al., 2026). Grounding line positions were estimated by fitting the range offsets to a one-dimensional Euler–Bernoulli elastic beam model (Holdsworth, 1969) and performing Bayesian inversion using the cross entropy based importance sampling for Bayesian updating (CEBU) algorithm (Engel et al., 2023). This novel algorithm allows for the incorporation of external datasets as priors on model parameters while also accounting for errors in SAR data accrued during offset tracking. The resulting dataset provides a dense and high-frequency time series of grounding line points for Larsen C. Each point includes a Bayesian estimate of uncertainty, allowing a quantitative assessment of positional confidence. The grounding line points have a mean distance of 348.07 m from contemporaneous Sentinel-1 DInSAR grounding lines. Because the dataset is derived from SAR backscatter rather than interferometric phase, it is robust to coherence loss and can be used to fill gaps in DInSAR grounding line products over fast-flowing outlet glaciers and ice streams. The modular nature of this framework permits easy substitution of ice flexure models and data, thereby offering a promising pathway to derive ice‑thickness and ice‑elasticity estimates as well. Model comparison is also facilitated by the framework’s provision of model evidence, enabling rigorous model intercomparision. References 1. Rignot, E., Mouginot, J. & Scheuchl, B. Antarctic Grounding Line Mapping from Differential Satellite Radar Interferometry: GROUNDING LINE OF ANTARCTICA. Geophysical Research Letters 38. ISSN: 00948276. (2011).2. Nagler, T., Rott, H., Hetzenecker, M., Wuite, J. & Potin, P. The Sentinel-1 Mission: New Opportunities for Ice Sheet Observations. Remote Sensing 7, 9371–9389 (2015).3. Wuite, J., Nagler, T., Hetzenecker, M. & Rott, H. Ten Years of Polar Ice Velocity Mapping Using Copernicus Sentinel-1. Remote Sensing of Environment 332, 115092. ISSN: 00344257. (2026).4. Holdsworth, G. Flexure of a Floating Ice Tongue. Journal of Glaciology 8, 385–397. ISSN: 0022-1430, 1727-5652. (1969).5. Engel, M., Kanjilal, O., Papaioannou, I. & Straub, D. Bayesian Updating and Marginal Likelihood Estimation by Cross Entropy Based Importance Sampling. Journal of Computational Physics 473, 111746. ISSN: 00219991. (2023).

Subglacial lakes are bodies of water that exist beneath ice sheets, and their filling and drainage can induce uplift and subsidence of the ice-sheet surface. In fast-flowing glacier regions, differential interferometric synthetic aperture radar (DInSAR), which can detect ice surface elevation changes by differencing two interferograms under the assumption of steady glacier flow, has been used to identify subglacial lake activity and investigate its temporal behavior. However, because the timing, magnitude, and spatial pattern of lake-related elevation changes vary among lakes, the reference interferogram used for differencing has typically been selected separately for each lake. This has limited the application of DInSAR to catchment-scale analyses of interactions among subglacial lakes that may be driven by subglacial water flow. If a catchment-scale reference interferogram representing the glacier-flow component can be defined, DInSAR observations based on this reference could be used to detect ice elevation changes associated with subglacial lake activity more consistently and to extend the analysis to the catchment scale. Furthermore, when combined with the Small Baseline Subset (SBAS) approach, the statistical noise in interferograms can be reduced. By using a multi-temporal baseline network, the SBAS approach can also reduce the risk of gaps in SAR observations while enabling more continuous reconstruction of ice elevation changes associated with subglacial lake activity. In this study, we generated time-series interferograms with 12-day, 24-day, and 36-day temporal baselines from Sentinel-1 SAR images acquired over David Glacier, East Antarctica. We then estimated a reference interferogram representing the horizontal displacement associated with glacier flow as the median of the 12-day interferograms and subtracted it from the remaining interferograms to perform DInSAR. SBAS was then applied to the resulting DInSAR interferograms to investigate time-series ice-sheet surface displacement anomalies over David Glacier. A clear line-of-sight (LOS) displacement anomaly associated with subglacial lake activity was identified over a subglacial lake. In addition, several localized displacement anomalies were observed, suggesting the possible presence of previously undetected subglacial lakes. LOS displacement anomalies were also detected over a wide area along the main flow corridor of David Glacier. These anomalies may reflect acceleration of glacier flow or elevation changes associated with subglacial water flow along the main trunk of the glacier.

Glacial Lake Outburst Floods (GLOFs) are emerging as one of the most destructive high-mountain hazards in the Himalaya, where rapid glacier retreat and the proliferation of moraine-dammed lakes have intensified disaster risk. Sikkim, which hosts the highest concentration of glaciers in the Eastern Himalayas, has witnessed a rapid increase in glacial lake formation and recent catastrophic events, including the 2023 South Lhonak GLOF. Identifying lakes most susceptible to failure and detecting precursory instability remain challenging due to rugged terrain, persistent cloud cover, and limited field accessibility. This study develops an integrated multi-sensor remote-sensing framework to assess GLOF hazard in Sikkim using Synthetic Aperture Radar (SAR), Interferometric SAR (InSAR) time series, optical satellite imagery, digital elevation models (DEMs), and climatic indicators. PS- and SBAS-InSAR analyses from Sentinel-1 and TerraSAR-X datasets map millimeter-scale ground deformation, coherence loss, and slope creep around moraine dams, revealing instability zones and precursory deformation patterns. High-resolution optical time series (PlanetScope, Sentinel-2) capture seasonal to multi-year lake expansion, while DEM differencing between SRTM (2000) and Copernicus GLO-30 (2020) quantifies elevation change in dam crests and proglacial basins. Climatic parameters including extreme rainfall and melt-season anomalies are integrated to interpret hydrological loading and lake-level fluctuations. By synthesizing deformation rates, morphometric instability, lake-area evolution, and meteorological drivers, the study classifies lakes into risk tiers and identifies those most prone to failure. Results demonstrate that combining SAR-derived deformation precursors with optical and DEM-based lake evolution significantly enhances forecasting capability and forms a robust pathway for early warning. The proposed framework enables a shift from post-event assessment to proactive monitoring, strengthening disaster-risk reduction efforts across the vulnerable Himalayan basins.

The Mw 6.5 Norcia earthquake, which struck Central Italy on October 30, 2016, represents the climactic and most destructive event of the recent Apennine seismic sequence. Nucleating within the complex, extensional Mt. Vettore-Bove Fault System (MVBFS), this mainshock ruptured a critical structural gap between the historical 1997-1998 Colfiorito and 2009 L’Aquila earthquake sequences. Despite extensive geodetic monitoring over the past years, precisely resolving the three-dimensional surface deformation field and interpreting the intricate subsurface source geometry in this topographically rugged region remains a significant challenge for both geological engineering and geophysics. To accurately capture the coseismic displacement, we utilized multi-orbit Synthetic Aperture Radar (SAR) datasets acquired by the C-band Sentinel-1 and L-band ALOS-2 satellite missions. Traditional Differential Interferometric SAR (DInSAR) processing often struggles with severe decorrelation in high-gradient epicentral zones and relies heavily on subjective, empirical weighting when fusing heterogeneous multi-source data. To overcome these limitations, we implemented the advanced Strain-Model Variance Component Estimation (SM-VCE) framework. This technique incorporates a sophisticated spatial strain model to mathematically characterize the physical deformation correlations between adjacent ground pixels. Concurrently, it employs the VCE algorithm to iteratively and objectively determine the optimal variance components and contribution weights for each respective dataset based on their stochastic properties. This fusion strategy effectively mitigated atmospheric artifacts and successfully preserved critical near-fault deformation signals, yielding highly reliable two-dimensional (East-West and Vertical) coseismic displacement fields. The SM-VCE derived surface deformation maps reveal a pronounced and highly asymmetric kinematic pattern. In the horizontal plane, the fault zone accommodated a net East-West extension of approximately 60 cm. The vertical displacement field is characterized by a massive subsidence trough localized in the hanging wall, reaching maximum downward displacements of 70 to 80 cm. This strongly contrasts with the minor uplift of only 10 to 14 cm observed in the footwall block. Based on these high-precision measurements, we conducted a rigorous 3D volumetric integration. The calculations exposed an extreme volumetric unbalance, demonstrating that the subsided rock volume is approximately 14 times larger than the uplifted volume. This severe mass deficit poses a direct challenge to standard elastic rebound paradigms and indicates complex crustal interactions. To demystify the mechanical origins of this profound asymmetry and volume deficit, we applied the Defsour® (Free-geometry Multi-Source 3D Inversion) algorithm. Unlike conventional kinematic inversions that artificially constrain slip onto predefined, idealized planar faults, Defsour® adopts a purely data-driven, free-geometry strategy. It performs a global optimization across a dense 3D subsurface grid to simultaneously adjust arbitrary pressure and dislocation sources without a priori geometric assumptions. This autonomous inversion successfully reconstructed the primary slip distribution along the main southwest-dipping normal fault while independently identifying a distinct, east-northeast-dipping antithetic fractured zone. The incorporation of this antithetic structure significantly improved the consistency between the simulated and observed data, effectively resolving the misfit commonly encountered in single-fault models.

Mountain hazards such as avalanches, extreme snowfall, and landslides threaten people and infrastructure in alpine regions worldwide throughout the year. These hazards can damage lifelines, houses, agricultural areas, and limit sustainable community development. Due to the remoteness of many mountain locations and the large spatial extent of hazardous processes, ground-based monitoring and early warning systems are often difficult to implement for practical and economic reasons. Moreover, mountain hazards typically affect wide areas, requiring spatially distributed monitoring capabilities. To address these challenges, we present the activities carried out within the ESA-funded project SUMMIT (Satellites-based Utility for Monitoring Mountains Integrated Transitions). The project integrates multiple data sources, including Sentinel-1 SAR, Sentinel-2 optical imagery, Galileo GNSS technology, and Automatic Weather Stations (AWSs) records, to enable near real-time monitoring and the historical characterization of multiple and cascading mountain hazards. The system is demonstrated in Livigno, an alpine village located in the central Italian Alps. This area is characterized by intense winter and summer tourism and is therefore particularly exposed to natural hazard impacts. Beginning in autumn, snow accumulation over mountain slopes plays a critical role in avalanche forecasting and hydrological management. To address this, SUMMIT integrates automatic detection of avalanche deposits, snowpack depth mapping, and wet snow extent monitoring during the melt season. Furthermore, climate change is significantly affecting ground deformation in periglacial environments during the summer season, which requires timely and continuous monitoring to detect early signs of instability and support risk mitigation strategies. Snow avalanches represent one of the primary winter hazards in Europe, causing more than 100 fatalities annually and damage to infrastructures worldwide. Avalanche detection is achieved through a novel deep learning algorithm capable of automatically identifying avalanche deposits from both wet and dry snow events using Sentinel-1 SAR backscatter imagery in a change detection framework. The system is demonstrated for both near real-time monitoring and historical reconstruction of spatial and temporal avalanche activity distribution. The system’s performance is evaluated against observed avalanche records available for the study area, including an assessment of the main limitations and external interferences affecting SAR-based avalanche detection. Snowpack depth mapping is enabled by a novel mathematical model based on a dual-polarimetric SAR index variation, achieving a spatial resolution of 50 m with an RMSE of 22.4 cm and MAE of 18.1 cm. The model accounts for the influence of the Local Incidence Angle (LIA) on SAR signal depolarization, a factor shown to significantly affect retrieval accuracy. During the melting season, we exploit Sentinel-1 backscatter changes to map and quantify the spatial and temporal occurrence of wet snow. This, combined with the snow depth monitoring, provides key inputs for downstream flood susceptibility assessment and drought risk evaluation, as well as additional information for avalanche forecasting activities. During summer, ground deformation in periglacial environments is monitored through SBAS InSAR, which enables both near real-time tracking and multi-year reconstruction of surface displacements associated with rock glacier dynamics and deep-seated landslides. These SAR-based components, complemented by cost-effective Galileo-enabled GNSS receivers, AWS records, and the SNOWPACK model, enable a multi-scale synergetic integration across sensors with different spatial resolutions and acquisition frequencies. Using the 2023/2024 and 2024/2025 seasons as case studies, characterized by exceptionally thick winter snowpacks, high liquid water content, significant avalanche cycles, and a subsequent rock glacier collapse, we demonstrate how these monitoring approaches can be combined into a unified seasonal hazard framework. This integration reveals cascading hazard dynamics that would remain undetected by any single monitoring approach, offering a replicable model for SAR-based risk management in remote alpine communities.

The lack of regular, spatially detailed observations of snow mass (snow water equivalent, SWE) is a main gap in cryosphere monitoring. Spaceborne SAR systems offer various options for resolving this deficiency, as these sensors provide high spatial resolution and the signals penetrate snow as long as the snowpack is dry. A promising method for mapping SWE is differential radar interferometry (DInSAR), measuring the path delay of a radar signal propagating through a snow layer. C-band and L-band radar frequencies are well suitable for this approach, as the observed phase delay is only marginally affected by snow microstructure and density of dry snow. Critical issues for DInSAR SWE retrievals are the temporal decorrelation of the interferometric signal and the 2p phase ambiguity. We studied methods and performance for spatially distributed SWE retrievals in mountainous terrain from C-band and L-band interferometric data of airborne and spaceborne C-band and L-band SAR sensors. The airborne campaign was conducted over a test site in high-Alpine terrain, where multiple polarimetric and interferometric repeat pass data in C- and L-band were acquired with the F-SAR system of DLR, spanning snowfall events of different intensity. Coherence, interferometric phase and computed snow accumulation (delta SWE) images were computed for the snowfall events and were evaluated in connection with comprehensive field measurements on snow properties. The two frequencies exhibit differences in the susceptibility to phase ambiguities, in the exposure to temporal decorrelation caused by snowfall and in the phase sensitivity in respect to SWE. L-band has lower phase sensitivity than C-band which is, at least partly, compensated by higher coherence. C-band is affected by lower coherence and 2p phase ambiguities but has higher phase sensitivity. These characteristics confirm the complementarity of interferometric data of the two frequencies for setting up a reliable and robust SWE monitoring system. In order to further the synergistic use of C-and L-band data for SWE monitoring, we analysed interferometric Sentinel-1 C-band 6-day and ALOS PALSAR L-band 14-day repeat data over Alpine test sites. The interferometric processing workflow includes coherence estimation, removal of the atmospheric phase screen (e.g., using ETAD products), and topographic phase flattening based on the Copernicus DEM with 10 m spatial resolution. As reference points, targets with known changes in snow accumulation are used, such as in situ meteorological stations or dedicates snow stations located within contiguous areas. Six-day repeat-pass Sentinel-1 C-band SAR data show sufficient coherence for detecting moderate snow mass accumulation in open terrain. In forested area, the C-band signal tends to decorrelate. These regions are therefore masked out in the analysis. Short-repeat cycles of C-band data with sub-daily repeat would be provided by the proposed Earth Explorer 12 Candidate Mission Hydroterra+, ameliorating the decorrelation problem at least in open terrain. The L-band phase signal is less affected by temporal changes in surface properties. Even in forested areas, phase coherence is largely preserved, providing spatial continuity between open and forested areas. However, correction for the attenuation of the surface signal within the forest canopy is required. With shorter revisit intervals, such as the six-day repeat cycle expected from the upcoming ROSE-L mission, further improvements for SWE retrievals are to be expected. We present results from ongoing studies on methods and performance of SWE retrievals using Sentinel-1 C-band and ALOS PALSAR L-band data over Alpine sites, as well as a brief overview on the airborne campaign . Finally, open challenges and remaining steps toward an operational SWE monitoring service are discussed.

The grounding line location (GLL) is a geophysical product of the Antarctic Ice Sheet Climate Change Initiative (AIS_cci) ESA project. It has been derived for major ice streams and glaciers around the continent through the InSAR technique, covering the period 1994 – 2025 from ERS-1/2 era to Sentinel-1. The AIS_cci GLL product’s metadata annotations include information about model-based ocean tide levels and air pressure at satellite acquisition times for meaningful and interpretable comparison of GLLs. The position of the grounding line shifts in two distinct ways: (a) over short timescales, it experiences temporary migration caused by vertical uplift of ice shelves due to ocean tides, and (b) over longer timescales, it undergoes more consistent migration, typically retreating landward as a result of ice thinning. Recent grounding line products have acknowledged the short-term variation of the grounding line position, annotating a grounding zone instead of single grounding lines (Rignot et al., 2023). In line with the need to quantify the grounding line movements, we designed an additional parameter for the AIS_cci GLL, the Grounding Line Migration (GLM). The AIS_cci GLM product aims to provide short-term temporal variations within the grounding zone and separate this short-term position change from a long-term climatic-induced relocation of the grounding zone. Time stamped grounding lines appear as fragmented segments with various lengths depending on the data coverage and SAR interferometric coherence preservation. We use the time-annotated AIS_cci GLLs to derive an average grounding line for a certain period (e.g. one year) which will be further used to calculate migration. Our custom procedure fills the gaps with grounding lines from manual and machine-learning delineations (Ramanath et al., 2025) of temporally close Sentinel-1 DInSAR interferograms and external datasets. For GLM generation various metrics have been investigated to quantify the distance between two or more GLLs acquired in different periods. Here we plan to show GLM products over relevant sites prone to grounding line retreat with a dense time series of GLLs and outline the specifications and contents of the new GLM product. References: Rignot, E., Mouginot, J. & Scheuchl, B. (2023). MEaSUREs Grounding Zone of the Antarctic Ice Sheet. (NSIDC-0778, Version 1). Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/HGLT8XB480E4. Date Accessed 03-02-2026. Ramanath, S., Krieger, L., Floricioiu, D., Diaconu, C.-A., and Heidler, K.: Automatic grounding line delineation of DInSAR interferograms using deep learning, The Cryosphere, 19, 2431–2455, https://doi.org/10.5194/tc-19-2431-2025, 2025.

The grounding line – the interface where a floating ice shelf detaches from its bedrock – is a critical marker of ice sheet dynamics and a key indicator of climate‑induced sea‑level rise. It has been classified as an essential climate variable (ECV) within ESA's Antarctic Climate Change Initiative program (AIS-cci). Grounding Line Location (GLL) products derived from differential interferometric synthetic aperture radar (DInSAR) have enabled continuous monitoring of this feature for major Antarctic ice streams and Ice Shelves since the ERS era through to the current Sentinel‑1 mission. Two temporal processes affect the location of the grounding line. Changes in ocean tide move the grounding line back and forth on sub‑hour to monthly timescales, and long‑term, climatologically driven migration (typically retreat) is driven by ice thinning. Distinguishing these regimes is essential for accurate interpretation of ice sheet stability, yet current GLL products often delineate only single lines making it impossible to separate short‑term variations from long‑term trends. In an AIS‑cci optional activity a Grounding Line Migration (GLM) product will be defined, which explicitly separates the short‑term temporal variability within the grounding zone from the long‑term migration that relocates this zone. A core challenge is to define a distance metric between two spatially complex GLLs that captures both local changes and global shift of the grounding zone. Three candidate metrics will be evaluated for a DInSAR grounding line time-series over the Getz Ice Shelf. The approaches include the area‑averaged displacement calculated by the Box‑Method (Moon & Joughin, 2009), individual line-to-line distances by the Point‑to‑Line (PoLiS) method (Avbelj et al., 2014) and a retreat along glacier centerlines/flowlines. We will address the methods’ abilities to capture representative retreat rates for an entire ice shelf vs. localized regions prone to grounding line retreat and discuss possible representations of the derived retreat rates in a future AIS-cci GLM product. Moon, T., & Joughin, I. R. (2008). Changes in ice front position on Greenland’s outlet glaciers from 1992 to 2007. Journal of Geophysical Research: Earth Surface, 113(F2). https://doi.org/10.1029/2007JF000927 Avbelj, J., Müller, R., & Bamler, R. (2015). A Metric for Polygon Comparison and Building Extraction Evaluation. IEEE Geoscience and Remote Sensing Letters, 12(1), 170–174. IEEE Geoscience and Remote Sensing Letters. https://doi.org/10.1109/LGRS.2014.2330695

Ground-based interferometric radar systems such as the GAMMA Portable Radar Interferometer (GPRI-II) are commonly used for slope and infrastructure monitoring. However, their stationary configuration leads to range-dependent degradation of cross-range resolution due to the limited real aperture length. In contrast, satellite-based synthetic aperture radar (SAR) provides wide-area deformation monitoring but remains constrained by revisit intervals and fixed acquisition geometry, limiting its flexibility for localized, rapid-response measurements. To address these limitations, we explore a car-borne Ku-band SAR configuration aimed at flexible, high-resolution terrestrial deformation monitoring under variable acquisition geometries. By synthesizing the aperture along the vehicle trajectory, the system achieves a theoretical azimuth resolution of approximately 0.12 m, largely independent of range, representing a substantial improvement over conventional rotating GPRI systems, whose cross-range resolution deteriorates with increasing distance. A roof-mounted Ku-band (≈17.25 GHz) frequency-modulated continuous-wave (FMCW) radar, integrated with INS/GNSS units, was deployed to acquire data along arbitrary paths and curved road segments. Additionally, vibration-isolating rubber mounts were installed between the vehicle and the radar mounting frame to reduce vehicle-induced vibration and enhance phase stability during motion. Compact patch-type antennas were adopted to ensure a lightweight, low-profile design suitable for mobile operation while maintaining sufficient radiometric performance. Motion parameters derived from post-processed navigation data were incorporated into a Time-Domain Back-Projection (TDBP) scheme, enabling precise slant-range calculation for each pulse. Geocoded multi-look intensity images were generated directly on a DEM grid, facilitating geometrically consistent map-projected products without requiring intermediate reprojection from slant-range coordinates. High-resolution imaging with extended along-track coverage was successfully achieved, enabling continuous mapping beyond the spatial footprint of stationary installations. Repeat-pass interferometric processing produced stable phase measurements, although coherence was reduced under the present experimental conditions, likely due to the short Ku-band wavelength and trajectory irregularities. Despite these limitations, the results suggest that car-borne Ku-band SAR offers considerable promise for deformation monitoring in roadside and suburban settings. Ongoing work focuses on characterizing variability in coherence under different motion and acquisition geometries to improve interferometric robustness. With further refinement, the approach may provide a practical alternative to fixed ground-based installations and support mobile radar interferometry across a broader range of applications. Keywords : Car-Borne, SAR, motion compensation, TDBP

Spaceborne Multi-Temporal InSAR (MT-InSAR) has evolved into a well-established operational tool for mapping ground deformation, although its performance remains strongly sensitive to sensor resolution, radar wavelength, acquisition geometry, and land cover characteristics [1,2]. C-band ESA Sentinel-1 offers near-global coverage, but its effectiveness is limited by temporal decorrelation in densely vegetated areas. This limitation becomes even more pronounced at higher frequencies, such as those used by the X-band Italian Space Agency (ASI) COSMO-SkyMed constellation, which, despite providing considerably finer spatial resolution, is particularly susceptible to decorrelation over vegetated terrain. In contrast, L-band sensors — including SIASGE SAOCOM-1, JAXA ALOS-2, the recently launched NASA/ISRO NISAR and the upcoming ESA ROSE-L missions — operate at longer wavelengths (λ ≈ 23 cm) that are inherently less affected by volumetric decorrelation. This allows them to penetrate vegetation canopies more effectively, preserving coherence over extended spatial and temporal baselines in both vegetated and non-urbanised environments. Although L-band MT-InSAR has been successfully demonstrated using ALOS data [e.g., 3], the development and adaptation of algorithms specifically tailored to the operational exploitation of SAOCOM-1 for MT-InSAR applications remains relatively unexplored [4]. In the framework of the SIASGE Earth Observation programme, the Argentine SAOCOM‑1 L‑band constellation is currently being tasked by ASI to collect systematic Stripmap acquisitions over Italy, enabling country‑wide MT-InSAR analyses. We present the adaptation of the existing MT-InSAR algorithm implemented in the SPINUA chain [5] to process interferometric stacks of SAOCOM-1 SAR data, to the aim of providing insights into the capabilities that L-band MT-InSAR can enable for natural and anthropogenic hazards. We assess MTInSAR performance on an InSAR dataset obtained from the processing of an interferometric stack of 38 SAOCOM‑1 images (2020‑2024) covering a test site in the Gargano Promontory (Puglia, Italy), within the ASI-funded GEORES project framework [6]. We benchmark the results against C‑band Sentinel‑1 and X‑band COSMO‑SkyMed/COSMO-SkyMed Second Generation (CSK/CSG) stacks. We report on persistent scatterer (PS) spatial densities with respect to land cover and geomorphic parameters, and provide examples illustrating geocoding accuracy and displacement time‑series consistency, highlighting the complementary value of L‑band observations for multi‑hazard ground motion monitoring in southern Italy. The test case confirms theoretical expectations that longer wavelengths maintain coherence in vegetated environments, and may mitigate ambiguity problems in displacement estimation. The gain in PS density directly translates into improved sampling of non‑urban hazards (coastal cliff retreat, karst sinkholes). While CSK‑CSG sensors still provides superior PS densities due mainly to their higher spatial resolution, L-band consistently outperforms C-band data over most terrain classes. Strategically, then, the addition of a regular plan with SAOCOM‑1 strengthens the multi-frequency SAR data sources over the Italian territory and thus enables the retrieval of surface deformation estimates in different land cover/land use conditions. Remaining challenges with SAOCOM-1 data include radio frequency interference affecting some frames and the coarser azimuth sampling impacting small‑scale feature detection. Acknowledgements Research performed in the framework of the GEORES project - Agreement ASI – UNIBA n. 2023-42-HH.0 – CUP F93C23000240005, funded by the Italian Space Agency (ASI) in the framework of the “Innovation for Downstream Preparation for Science” (I4DP_SCIENCE) programme. References [1]      Zebker, H.A., Villasenor, J. 1992. Decorrelation in interferometric radar echoes. IEEE Trans. Geosci. Remote Sens., 30(5), 950–959. [2]      Sabater, J.R., Duro, J., Arnaud, A., Albiol, D., Koudogbo, F.N., 2011. Comparative analyses of multifrequency PSI ground deformation measurements, in: ESA FRINGE Conference on ERS SAR Interferometry, ESA SP-697. p. 81790M. https://doi.org/10.1117/12.898916 [3]      Wegmüller, U., Magnard, C., Strozzi, T., Caduff, R., & Jones, N. 2024. Landslide velocity mapping using ALOS-2 PALSAR-2 ScanSAR data. Procedia Computer Science, 239, 2278–2285. https://doi.org/10.1016/J.PROCS.2024.06.419. [4]      De Luca, C. et al., 2025. SAOCOM-1 L-Band DInSAR Time Series Generation Through the P-SBAS Approach: Algorithm Extension and Products Analysis, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 18, pp. 2680-2703, 2025, https://doi.org/ 10.1109/JSTARS.2024.3507554. [5]      Bovenga, F., Nutricato, R., Refice, A., Wasowski, J. 2004. SPINUA: a flexible processing chain for ERS/ENVISAT long‑term interferometry. Proc. ESA‑ENVISAT Symposium, Salzburg, Austria, 6‑10 Sep 2004. [6]      Lafortezza, R., Nutricato, R., Refice, A., Capolongo, D., Sacco, P., Tapete, D. 2024. The GEORES Project: Geospatial Application in Support of Environmental Sustainability and Resilience to Climate Changes in Urban Areas. IGARSS 2024, Athens, Greece, pp. 1384–1387. https://doi.org/10.1109/IGARSS53475.2024.10642728

In this study we evaluate the potential of the SARvey open-source InSAR time series analysis software for performing InSAR time-series analysis on high-resolution SAR data acquired by the commercial Capella Space constellation. The objective is to assess the capability of SARvey to process Capella data and to investigate deformation signals over a rapidly subsiding urban environment using high-resolution commercial data. We analyze a dataset composed of 18 single look complex (SLC) images acquired by Capella over Mexico City between 26 June 2024 and 15 August 2024, corresponding to a temporal span of approximately 1.5 months. The data has a high spatial resolution with approximately 60 by 100 cm spacing in range and azimuth, respectively. The study area is well known for significant land subsidence driven exceeding 30 cm/yr at locations primarily due to groundwater extraction. This making it an ideal test site for evaluating InSAR processing strategies. We perform the preprocessing of the data using the GAMMA Software. All acquisitions are coregistered to a common reference scene and resampled to generate a stack of SLC images. The Copernicus digital elevation model is used to remove the topographic phase contribution and for geocoding. The resulting coregistered stack is subsequently processed with SARvey for interferometric time-series analysis. Different processing configurations are tested to evaluate the robustness of the workflow for Capella data. These include multi-looked and full-resolution processing as well as different interferometric network strategies, specifically a star network and a small-baseline network. Furthermore, multiple phase unwrapping approaches are investigated in order to assess their influence on the stability and quality of the deformation estimates. Initial results suggest a deformation signal of up to approximately 4 cm in the radar line-of-sight direction over the observation period. The spatial pattern of the detected deformation is consistent with the well-documented subsidence processes affecting Mexico City. Despite the relatively short observation window, the time-series analysis demonstrates that Capella SAR data can capture ground displacement at high spatial resolution. These results indicate on one hand that SARvey provides a viable framework for processing commercial high-resolution SAR data and extracting meaningful deformation signals. On the other hand, the study highlights the potential of Capella observations for monitoring rapid urban subsidence when combined with flexible InSAR time-series processing approaches.

The rapid growth of high-resolution commercial SAR constellations has created new opportunities for stereo radargrammetry beyond the traditional constraints of interferometric processing. We propose a robust pyramidal stereo radargrammetric framework for accurate Digital Surface Model (DSM) generation from multi-acquisition SAR datasets characterized by significant diversity in range and azimuth viewing geometries.The method is designed for challenging environments where interferometric coherence is degraded and optical stereo approaches are limited by illumination, weather, or surface conditions. Rather than performing computationally expensive all-versus-all matching across highly heterogeneous acquisitions, the proposed strategy relies on pairwise close-angle matching. Only geometrically compatible image pairs are matched, improving triangulation stability and reducing ambiguity while still exploiting the angular diversity provided by modern acquisition campaigns.A coarse-to-fine pyramidal estimation scheme ensures computational efficiency and robustness. At each pyramid level, regularization constraints are introduced to suppress outliers, stabilize disparity estimation, and prevent error propagation across scales. The framework naturally supports cross-sensor integration among SAR platforms with comparable spatial resolution. Geometry-aware weighting and radiometric normalization allow heterogeneous commercial datasets to be jointly processed within a unified estimation model.To address layover and geometric distortions inherent to SAR imaging, two complementary operating modes are considered: (i) distributed-target dense reconstruction for DSM generation, and (ii) a bright-target configuration focusing on isolated high-reflectivity scatterers. The latter enables highly accurate 3D point cloud extraction suitable for ground control point generation in a Fan-SAR configuration, where relaxed angular separation constraints can be applied due to improved localization precision.The proposed approach maximizes the geometric information content of multi-angle commercial SAR acquisitions, providing a scalable and robust solution for precise 3D reconstruction in scenarios where conventional interferometric and optical methods are unreliable.

Four Mw 6.5 earthquakes struck northwest of Herat, Afghanistan, in a complex sequence of thrust faulting between 7 and 15 October 2023. This event raises important questions about the conditions that favor single-fault earthquakes, complex multi-fault ruptures, as well as tectonic stress conditions and structural factors that controlled the progression of this sequence.  We investigated these events using Sentinel-1 and ALOS-2 InSAR, burst overlap interferometry (BOI) and pixel offset line-of-sight (LOS) and pixel offset along-track surface deformation data. Our slip distribution models indicate that two events on 7 October were generated by two WNW–ESE-trending, NNE-dipping blind thrust faults that are overlap by ~1 km. Their geometric overlap produced a static stress shadow on the more westerly segment, potentially contributing to the delayed triggering. In contrast, the 11 and 15 October events were generated by NE–SW-trending, NW-dipping blind thrust faults that overlap by ~9 km. The overlap between these faults produced a static stress shadow along the edges of the 15 October fault, which may also have contributed to its delayed triggering. In addition, the intersection between the more easterly 7 October fault and the 11 October fault, which differ in strike of ~43o, generated a static stress shadow that may have contributed to the delayed triggering of the 11 October event. We also modeled an E–W–trending, north-dipping thrust fault that slipped aseismically from shallow depths up to the surface following the 11 October event. Postseismic slip persisted for at least one year after the earthquake. Static Coulomb stress change calculations suggest that the 7–11 October events may have promoted slip on this fault. Our quasi-dynamic 3D numerical simulations using aging-type rate-and-state friction laws suggest that delayed triggering occurs more frequently between faults with orientation similar to more easterly 7 October and 11 October faults, which differ in strike of ~43o, than between faults whose interaction is primarily controlled by geometric overlap. These results highlight an important implication for earthquake sequence studies: each faulting event in the sequence may contribute to the delayed triggering of subsequent events, and changes in strike angle may be more susceptible to multiplet formation than faults interacting mainly through geometric overlap.

Dike intrusions accommodate most of the plate extension in magmatic rifts. However, as the crust extends by diking, normal faulting also occurs, forming morphologically clear graben. Here, we analyze Sentinel-1 DInSAR coseismic products available in the EPOSAR earthquake catalog [1] to investigate deformation patterns associated with the seismic crisis and the dike intrusion that occurred between September and November 2024, within the Fentale–Dofen segment of the Main Ethiopian Rift. We compute spatial gradients of interferograms and LOS displacement maps using a geodetically consistent approach that accounts for latitude-dependent pixel spacing, enabling quantitative analysis of deformation gradients [2–3]. The resulting gradient field and its magnitude reveal sharp spatial variations along the edges of the deformation field, delineating the fault planes that slipped more clearly than displacement data alone. Application of this method allows the identification of discrete fault segments that progressively develop and lengthen during dike propagation. We plan to derive the vertical and horizontal displacements along the identified faults and compare the patterns to the dyke emplacement and the seismic moment release. This study demonstrates that quantitative analysis of LOS displacement gradients provides a robust framework for identifying deformation boundaries, improving interpretation of geodetic observations in magmatic rift settings. [1] EPOS, European Plate Observing System, [Online]. Available at https://www.epos-ip.org /tcs/satellite-data. [2] Hofmann, B., Magee, C., & Wright, T. J. (2025). Throw distribution across the Dabbahu–Manda Hararo dike-induced fault array: Implications for rifting and faulting. Geology, 53(2), 161–165. https://doi.org/10.1130/G52665.1 [3] Argo Galih, S., Atriyon, J. InSAR-derived surface displacement gradients unveil subseismic faults of the 2022 Cianjur earthquake. Model. Earth Syst. Environ. 11, 338 (2025). https://doi.org/10.1007/s40808-025-02502-z

Spaceborne Interferometric Synthetic Aperture Radar (InSAR) is increasingly demonstrating transformative value beyond the research domain, enabling actionable, real-world solutions to pressing societal and environmental challenges. We present two complementary success stories that illustrate the breadth and maturity of InSAR technology: rapid disaster damage assessment following a catastrophic earthquake sequence, and satellite-based carbon emissions monitoring over tropical peatlands. Success Story 1: Earthquake Damage Mapping after the 2023 Kahramanmaraş, Türkiye Earthquakes Following a disaster, responders need to rapidly assess the extent of the damage. The prevailing view is that very-high-resolution optical satellite images can provide more accurate estimates of building damage than lower-resolution synthetic aperture radar (SAR). However, we demonstrate that SAR- based damage proxy maps we produced after the 2023 Kahramanmaraş Türkiye earthquakes outperformed comparable maps derived from optical imagery. This finding held true for both a state- of-the art machine learning method and for visual interpretation. The SAR-based maps achieved an F1 performance score approximately twice as high as the optical-based maps (0.47 vs 0.24, 0.23 and 0.15). Additionally, SAR offers established advantages in both coverage and timeliness: SAR was able to image the entire affected area within ten days, whereas the very-high-resolution optical dataset covered only 5.4% of the SAR-covered area during the same timeframe. Using the largest ground dataset that we know of for any event, we show that the damage distribution captured by our SAR- based maps strongly correlates with the ground observations over a wide range of spatial scales from neighbourhoods (~1 km2) to provinces (~10,000 km2). Based on this experience, we argue that any reliable remote sensing-based damage assessment system should incorporate radar to complement other techniques. (Ainscoe et al., 2025) Success Story 2: Satellite Radar for Carbon Emissions Accountability over Tropical Peatlands Carbon markets face growing criticism over unreliable measurements of carbon credits. Tropical peatlands, which contain some of Earth’s most concentrated carbon, represent a huge, untapped opportunity for emission reductions, but remain excluded from the market due to challenges in measuring emissions due to degradation. Here, we demonstrate satellite L-band Interferometric Synthetic Aperture Radar as a solution to estimate carbon dioxide emissions by accurately measuring peat subsidence. Our framework accounts for major radar noise sources in tropical environments that were previously unaddressed, and is validated against high-rate ground-measured peat motion in both space and time. The radar results capture episodic peat motion linked to dry-wet cycles across different land uses, and long-term rates accurate up to 0.6 mm yr−1, equivalent to 0.97 t CO2 ha−1 yr−1 in emissions. This scalable, cost-effective approach provides a robust tool for Monitoring, Reporting, and Verification, benefitting carbon markets, local regulation, and global climate mitigation efforts. (Tay et al., 2025) Outlook Together, these two applications – disaster response and climate accountability – demonstrate the maturity of spaceborne InSAR as a tool for societally critical monitoring at scale. Both studies highlight the unique advantages of SAR: all-weather, day-and-night imaging, broad spatial coverage, and high sensitivity to subtle surface changes. With the combination of L-band and C-band global coverage (e.g., NISAR, Sentinel-1, ALOS-2/4), the temporal and spatial sampling capacity for both applications will improve substantially, reinforcing the role of InSAR as an indispensable component of global Earth observation infrastructure. References Ainscoe, E.A., Swaminathan, R., Way, L., Modugno, S., Chin, S., Panta, N., Crevoisier, T., Yun, S., “Earthquake damage mapped more comprehensively and accurately by radar satellites”, Communications Earth & Environment, DOI: 10.1038/s43247-025-02623-4, 2025. Tay, C., Jovani-Sancho, A. J., Yulianti, L., Evans, C., Callaghan, N., Jaya, A., Salman, R., Zheng. Y., Susilo, S., Dohong, S., Yun, S. H., “Satellite radar advances carbon emissions accountability over tropical peat”, Communications Earth & Environment, 10.1038/s43247-025-02926-6, 2025.

Spaceborne SAR tomography has entered an operational phase with the BIOMASS missions providing multi-baseline and repeat-pass interferometric observations over forested regions. A central assumption underlying the tomographic inversion schemes is that the vertical reflectivity distribution remains invariant during the acquisition interval. While this hypothesis is often justified for airborne campaigns acquired within a single flight, it becomes increasingly fragile for repeat-pass satellite systems, even when temporal baselines are short. Subtle variations in canopy structure induced by wind, moisture dynamics, or precipitation can modify the effective scattering distribution, leading to systematic phase inconsistencies across interferometric pairs. These inconsistencies propagate into tomographic reconstructions, degrading vertical focusing and biasing phase-center estimation. In this contribution, we revisit the concept of interferometric closure phase as a diagnostic observable for assessing temporal reliability in SAR tomography. Closure phase, defined over closed loops of interferometric measurements, is traditionally interpreted in InSAR as an indicator of decorrelation or multi-scattering effects. However, in volumetric media such as forests, non-zero closure phase arises naturally from the coherent superposition of multiple vertical scattering contributions. Consequently, the mere presence of closure phase does not imply temporal instability. We propose instead to exploit the structural symmetries of closure phase under a stable reflectivity profile as a consistency constraint for tomographic stacks. We analyze the dependence of closure phase on forest structural parameters and TomoSAR geometry. The analysis reveals that, for a temporally stable vertical reflectivity profile, closure phases exhibit invariant behavior. Based on this principle, we introduce a circular dispersion metric. Unlike conventional interferometric coherence, which conflates structural decorrelation and temporal effects, the proposed metric is designed to be insensitive to purely volumetric decorrelation under stationary conditions. Instead, it selectively responds to deviations from reflectivity-profile invariance, thereby offering a targeted indicator of temporal reliability for tomographic inversion. Beyond diagnostic analysis, we demonstrate how closure-phase residuals can be mapped onto pairwise phase correction terms for the interferometric data. The resulting correction framework enforces loop consistency. This approach aims to mitigate systematic phase biases that accumulate across interferometric pairs and to restore internal consistency prior to tomographic inversion. The methodology is assessed using airborne P-band data and also BIOMASS acquisitions over forest regions. The closure-based dispersion index highlights areas impacted by temporal variability, in agreement with coherence patterns but with enhanced selectivity. In pixels exhibiting low dispersion, tomographic reflectivity profiles before and after data correction remain nearly indistinguishable. In contrast, higher-dispersion areas show moderate redistribution of vertical power after correction, including adjustments in sidelobe structure and empowering the secondary scatterer (e.g. ground or canopy) and, in some cases, shifts of the dominant phase center. Importantly, the correction does not introduce artificial structural distortions, but rather reconciles inconsistencies already present in the interferometric stack. The results indicate that closure-phase offers a physically interpretable and geometry-aware tool for evaluating the temporal consistency of SAR tomographic stacks. As multi-baseline acquisitions from missions such as BIOMASS and Sentinel-1 continue to expand, closure-driven consistency analysis may play a key role in quality control, data screening, and pre-inversion process for operational SAR tomography

The exceptional meteorological sequence that hit the Emilia-Romagna region in May 2023 triggered an unprecedented hydrogeological crisis. This crisis was not characterised by a single isolated event, but by two consecutive extreme rainfall pulses that affected the central-eastern sector of the Apennines. The first event (Event 1) occurred between 1 and 3 May, with cumulative rainfall exceeding 200 mm in 48 hours. This is a value with a historical return period of over 100 years in several basins. It induced rapid soil saturation and an initial wave of landslides and river flooding. This initial phase was followed by a minor intermediate event on 10 May that prevented slope drainage and maintained high saturation levels. The sequence culminated in the catastrophic Event 2 on 16–17 May, which matched the intensity of Event 1 by delivering an additional 200–250 mm of rain onto already compromised terrain. This brought the cumulative effect of the sequence to an estimated return period of over 500 years. The geomorphological response was documented in the RER2023 inventory, which maps over 80,000 landslides through the manual interpretation of high-resolution (0.2 m) aerial orthophotos acquired after the emergency. While this inventory is spatially accurate, it does not inherently distinguish between landslides triggered during Event 1 or Event 2 and landslide reactivations. Temporal attribution is hindered by persistent cloud cover that rendered optical satellite monitoring unusable during the critical weeks. To overcome this limitation, the present study proposes a three-phase, hierarchical classification workflow integrating hourly rainfall data and soil surface moisture (SSM) variations derived from Sentinel-1 satellite data, with weighting based on landslide typology. In Phase 1, we analyse the dominance of rainfall intensity by comparing the maximum 48-hour cumulative rainfall recorded in the two time windows for each landslide centroid. This is based on the assumption that, for runoff-driven failures, the trigger coincides with the peak in precipitation. Phase 2 introduces a hydrological refinement using 500 m resolution SSM data from the Copernicus Global Land Service, calculating the anomaly relative to a dry baseline from April 2023 in order to detect areas where soils reached saturation during Event 1 without draining prior to Event 2. Phase 3 involves applying typological weighting by categorising landslides as either runoff-dominated (debris flows and debris slides), which respond rapidly to intensity peaks, or infiltration-dominated (earth flows and deep-seated slides), which are more sensitive to antecedent saturation and deep pore pressure. Applying this workflow to the 80,997 mapped failures reveals Event 2 to be dominant, triggering 52.5% of cases (~42,500 landslides) and consistent with the most catastrophic phase of the emergency. Event 1 triggered 25.8% of the landslides (approximately 20,900), concentrated in the western sector (the Sillaro and Idice basins), where the cumulative rainfall during the first storm was higher locally than during the second. 21.7% of the inventory (approximately 17,600 landslides) was classified as 'uncertain', which does not represent a modelling error, but rather the physical signature of slopes where instability was initiated by Event 1 (loading phase and reduction of suction forces) and finalised by Event 2. Integrating SSM data enabled the correction and reallocation of over 30% of landslides that had initially been classified ambiguously or incorrectly based solely on rainfall. This was particularly evident in regions where the intensity of Event 2 was lower than that of Event 1, yet the soils remained critically saturated. The analysis confirms that mechanical properties dictate the temporal response. 'Fast' landslides show strong synchronisation with Event 2 rainfall peaks, while 'slow' and deep-seated landslides exhibit a 'hydrological memory' that blurs the distinction between the two triggers. Validation through official ARPAE reports showed total concordance between model attributions and documented field activations in early May (e.g. Casamento and Monte Trebbio), confirming the algorithm's ability to recognise early activations despite subsequent heavy rainfall. In conclusion, this study shows that using rainfall thresholds alone is not enough for sequential events, and that integrating radar-derived soil moisture metrics is vital for reducing uncertainty and improving future regional susceptibility models.

Inversion of slip-surface geometry and subsurface property of slow-moving landslides remains a significant challenge. Especially using SAR interferometry due to the underdetermined nature of surface measurements and the complex, nonlinear mechanics governing soil and rock mass behaviour. To address this gap, this research builds a hybrid inversion framework for surface deformation observed from INSAR that couples a Bayesian Physics-Informed Neural Network (PINN) with a finite-element (FEM) representation of visco-elastoplastic landslide mechanics. The FEM component enforces dynamic momentum balance using a Drucker–Prager viscoplastic constitutive law and a frictional basal slip interface. It computes the weak-form residuals of the governing equations over a 3D domain. The PINN parameterises the displacement field and unknown rheological parameters, enabling joint inference of kinematics and material properties while respecting the physics at every collocation point via automatic differentiation. A Bayesian treatment of the rheological parameters within the PINN allows for the quantification of uncertainty and propagation of observational noise through to subsurface estimates. I demonstrate the method on UAVSAR datasets and show that the hybrid approach accurately recovers displacement, yield zones, and posterior distributions of constitutive parameters. This framework seamlessly integrates multi-source SAR data (such as potential fusion of Sentinel-1 and NISAR) into a physics-consistent inversion, offering a path toward improved hazard assessment and mechanistic interpretation of landslide processes.

Multi-temporal (MT) Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR) [1-5] has become a cornerstone technique for monitoring ground displacements associated with tectonic processes, volcanic unrest, subsidence, infrastructure stability, and slope instabilities. By exploiting long-term sequences of SAR acquisitions, MT-DInSAR provides spatially dense deformation time series with sub-centimeter accuracy over wide areas. However, its performance in vegetated and geomorphologically complex environments remains constrained by decorrelation effects [6], which reduce coherent pixel density and degrade displacement measurement reliability, particularly at shorter radar wavelengths, such as C- and X-band. In this context, the increasing availability of L-band SAR sensors has opened new perspectives for extending coherent, long-term deformation analysis to challenging scenarios. In particular, owing to their longer wavelength (~23 cm), L-band systems exhibit enhanced vegetation penetration capabilities, enabling improved temporal coherence over long-term observation periods in vegetated or agricultural areas, wetlands, and environments affected by seasonal snow/ice cover, where surface scattering properties may significantly change between successive SAR images. Furthermore, L-band radar data are inherently less sensitive to phase unwrapping errors, a key advantage for the investigation of rapid and/or large deformation phenomena in geohazard risk scenarios, such as slope instabilities and landslides. These intrinsic advantages have driven important international investments in L-band SAR constellations specifically designed for multi-temporal interferometric applications. Operational L-band missions include the Japanese ALOS-2 and ALOS-4 satellites, and the Argentine SAOCOM-1 constellation, while the recently launched NASA-ISRO NISAR mission [7], the forthcoming ESA ROSE-L system [8], and the "National L-band Radar Mission" of the Italian Space Agency (ASI) will further strengthen global capabilities with systematic, wide-coverage datasets for reliable MT-DInSAR analyses. These missions mark a strategic step toward systematic, global, long-term L-band SAR acquisitions, opening unprecedented opportunities for robust ground deformation monitoring at national and continental scales. Among these, the twin-sensor, full-polarimetric SAOCOM-1 system [9] plays a prominent role in long-term deformation monitoring over the Italian territory, thanks to its flexible acquisition modes, systematic DInSAR-oriented acquisition program over the European territory, and favourable revisit times [10]. In this work, we investigate the capabilities of the SAOCOM-1 L-band SAR data for extended MT DInSAR analysis in densely vegetated and landslide-prone areas of the Italian territory. Specifically, we evaluate the coherence preservation over vegetated slopes and the temporal continuity of displacement time series in environments typically affected by rapid decorrelation at shorter wavelengths. To this aim, we apply the Parallel Small BAseline Subset (P-SBAS) processing chain [10-12] to large L-band (SAOCOM-1) and C-band (Sentinel-1) datasets acquired over densely vegetated, geomorphologically complex areas. In particular, the SAOCOM-1 P-SBAS results are systematically compared with Sentinel-1 products, which are widely used for operational monitoring, to assess spatial coverage, coherent pixel density, and large-scale deformation mapping capability, especially in areas where shorter-wavelength SAR systems exhibit significant limitations. Finally, we present a quantitative validation of SAOCOM-1 P-SBAS time series accuracy by exploiting independent GNSS measurements across diverse geodynamic test sites, thus evaluating reliability, robustness, and performance under varying geological, land-cover, and deformation conditions. REFERENCES [1]     Ferretti, A., C. Prati, and F. Rocca, “Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry,” IEEE Trans. Geosci. Remote Sens., vol. 38, no. 5 I, 2000, doi: 10.1109/36.868878. [2]     Berardino, P., Fornaro, G., Lanari, R., and Sansosti, E., “A new Algorithm for Surface Deformation Monitoring based on Small Baseline Differential SAR Interferograms”. IEEE Trans. Geosci. Remote Sens, 40, pp.2375-2383, 2002. [3]     Werner, C., U. Wegmüller, T. Strozzi, and A. Wiesmann, “Interferometric Point Target Analysis for Deformation Mapping,” in International Geoscience and Remote Sensing Symposium (IGARSS), 2003. doi: 10.1109/igarss.2003.1295516. [4]     Lanari, R., Mora, O., Manunta, M., Mallorquí, J.J., Berardino, P., and Sansosti, E., ”A small baseline approach for investigating deformations on full resolution differential SAR interferograms”. IEEE Trans. Geosci. Remote Sens., 42, 1377-1386, 2004. [5]     Hooper A. J., “A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches,” Geophys Res Lett, vol. 35, no. 16, 2008, doi: 10.1029/2008GL034654. [6]     H. A. Zebker and J. Villasenor, "Decorrelation in interferometric radar echoes," in IEEE Transactions on Geoscience and Remote Sensing, vol. 30, no. 5, pp. 950-959, Sept. 1992, doi: 10.1109/36.175330. [7]     Kellogg K., et al. (2020). NASA-ISRO synthetic aperture radar (NISAR) Mission. in Proc. IEEE Aerosp Conf., Big Sky, MT, USA, 2020, pp. 1–21, doi: 10.1109/AERO47225.2020.9172638. [8]     Rostan F., K. Mak, M. von Alberti, A. Bauleo and N. Gebert (2024). Rose-L SAR Instrument Development Status and Mission Performance Prediction. IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 6618-6620, doi: 10.1109/IGARSS53475.2024.10642943. [9]     Delgado, F., T. Shreve, S. Borgstrom, P. León-Ibanez, J. Castillo, and M. Poland, “A global assessment of SAOCOM-1 L-band stripmap data for InSAR characterization of volcanic, tectonic, cryospheric, and anthropogenic deformation,” IEEE Trans. Geosci. Remote Sens., vol. 62, 2024, Art. no. 5216821, doi: 10.1109/TGRS.2024.3423792. [10]  De Luca, C, et al. "SAOCOM-1 L-band DInSAR Time Series generation through the P-SBAS approach: algorithm extension and products analysis." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 18 (2025): 2680-2703. [11]  F. Casu et al., “SBAS-DInSAR parallel processing for deformation time-series computation,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 7, no. 8, 2014. [12]  M. Manunta et al., “The Parallel SBAS Approach for Sentinel-1 Interferometric Wide Swath Deformation Time-Series Generation: Algorithm Description and Products Quality Assessment,” IEEE Trans. Geosci. Remote Sens., vol. 57, no. 9, pp. 6259–6281, 2019.

Slow mass movements, including landslides and permafrost-related slope processes, are characterized by diverse mechanisms depending on the materials involved and the geomorphological setting. Alpine environments are extensively affected by deep-seated rock and debris slides and active periglacial features, whereas fluvial-dominated mountain ranges are typically characterized by rockslides and long-lived earthflows. These processes span a wide range of volumes (10³–10⁸ m³), deformation patterns, and displacement rates (from mm/yr to m/yr), and pose risks either through slow movements or catastrophic collapse impacting lives and infrastructure. Enhanced capabilities for rapid, wide-area detection, classification, and monitoring of mass movements are therefore essential to support land-use planning, protect life and property, and reduce disaster risk—particularly in mountain regions increasingly subjected to anthropogenic pressures under climate change. Traditional regional-scale approaches for detecting and characterizing active mass movements rely on manual geomorphological mapping, supported by field observations and remote sensing data. Although accurate and process-oriented, these methods are time-consuming and difficult to update over wide areas. Synthetic Aperture Radar (SAR) sensors provide systematic acquisitions and millimetre-scale deformation measurements; however, the massive data volume and technical complexity have so far limited their full regional-scale exploitation. Furthermore, mass movements are often too fast to be captured by widely-used multitemporal InSAR products, yet too slow to be detected through optical imagery or SAR amplitude analysis. Dual-pass satellite wrapped DInSAR products offer a valuable alternative through the analysis of interferometric fringes, without requiring sophisticated multitemporal analysis. Despite still affected by noise and artefacts, these products don’t need phase unwrapping, thereby circumventing errors typically occurring in steep topography and decorrelated regions. To exploit the information content of wrapped DInSAR interferograms, we propose MIRAGE*, a deep learning framework for the automated detection and classification of active mass movements, using wrapped SAR interferograms derived from free, routinely available Sentinel-1 imagery. We adopted a YOLO-based convolutional neural network architecture, designed to detect and classify mass movements from deformation fringes within multi-scale spatial contexts. To emulate expert-based geomorphological interpretation, the model input layers include: (i) the DInSAR wrapped phase, (ii) a SAR signal reliability index integrating geometric sensitivity and coherence, and (iii) a composite geomorphometric variable derived from principal component analysis (PCA) of relevant morphometric parameters. Raw filtered and geocoded SAR interferograms were generated from Sentinel-1 ascending and descending acquisitions with temporal baselines ranging from 6 to 365 days, capturing mass movements across multiple spatial and temporal scales. To train the deep learning model, we constructed a geomorphologically constrained library of approximately 5000 labeled wrapped-phase signals, associated with different mass movement types identified by expert interpretation across two wide areas, representative of contrasting environmental and geomorphological conditions. A 1900 km² sector of the Italian Central Alps was selected to represent high-relief, para-periglacial environments with widespread rock glaciers, rockslides and debris slides. In contrast, a 1200 km² sector of the Italian Apennines represents lower-elevation, fluvial-dominated terrain developed on clay-shale and flysch lithologies, where earthflows, earth slides, and rockslides predominate. These areas encompass diverse mass movement types, topographic conditions, land cover characteristics, and deformation rates, thereby enabling robust training and testing aimed at model generalization. The network was trained using data augmentation and random dataset-splitting strategies. The trained model outputs objectness scores, bounding box offsets, and class confidence scores. The final bounding boxes are predicted using thresholds. The network demonstrates the ability to intercept mass movement signals directly within raw interferograms across multiple scales. Detection accuracy exceeds 0.75–0.8, and classification performs well even when interferogram components usually regarded as noise are included. In alpine environments with rugged high-relief topography and complex overlapping mass movement processes, detection performance decreases and classification becomes more challenging. However, in these settings, geomorphological assessment of false positives often reveals incompleteness in the training dataset, which negatively affects quantitative metrics. Although further developments are required to enhance model generalization and classification robustness in complex geomorphological contexts, the results demonstrate the significant potential of exploiting raw InSAR products to streamline the rapid detection of mass movements over a wide range of sizes and velocities. This approach supports inventory updating, local monitoring strategies, and integration into landslide modelling frameworks. * Funded by the European Union-Next Generation EU, Mission 4, Component 2, CUP H53D23001660006 (PRIN22 Project "MIRAGE: Mass movement Investigation and prediction through geomorphology, Remotesensing and Artificial intelligence").