Surface displacement measurements from satellite Synthetic Aperture Radar (SAR) have become a critical observational tool for understanding a wide range of natural and anthropogenic processes, including tectonic deformation, landslides, and coastal subsidence. Increasing revisit frequency and data availability now enable systematic monitoring across diverse spatial and temporal scales.
We present an application-focused assessment of InSAR displacement monitoring across multiple hazard contexts, drawing on examples from California, Texas, Hawaii, Alaska, and New York in the USA. These case studies demonstrate how InSAR time-series observations can disentangle overlapping deformation signals associated with tectonics, slope instability, volcanic unrest, groundwater-related and coastal subsidence. These examples are framed in the context of practical applications by state and federal agencies, including hazard assessment, infrastructure planning, and coastal risk analysis, highlighting the importance of spatially consistent, operationally usable displacement products. Specifically, we show how variable coastal subsidence impacts present and future sea level estimates for policy decision making in California. We assess the exposure of critical infrastructure, such as petroleum above ground storage tanks, to subsidence and flooding during hurricane events in Houston, Texas. In New York City, we demonstrated natural and anthropogenic vertical land motion impacts on local communities. In volcanic settings, displacement time series are being evaluated by volcano observatories for operational use to detect anomalous trends and characterize evolving surface deformation associated with active and re-awakened systems, including Mauna Loa and Kilauea volcanoes in Hawaii, as well as Mount Edgecumbe volcano in Alaska. Lastly, we demonstrate the use of the displacement time-series to map the spatial extent and slope instability of the Palos Verdes landslide in Los Angeles, California, adding additional observational context for informed decision making by local authorities.
This work is performed using OPERA Surface Displacement (DISP) products, which provide spatially consistent, large-scale InSAR displacement fields derived from C-band Sentinel-1 data over North America beginning from 2016. Analyses are supported by advanced and state-of-the-art spatial time-series algorithms designed to support continental-scale processing and a newly developed global tropospheric correction dataset based on the ECMWF High-Resolution Forecast (HRES) numerical weather model. Looking forward, OPERA will incorporate observations from the L-band NASA-ISRO SAR (NISAR) mission, providing continuity and enhanced capability in challenging environments for future displacement monitoring. All OPERA datasets are freely available through NASA archives, and the associated algorithms are developed in open-source repositories, enabling broad scientific reuse, reproducibility, and application.
How to cite: Bekaert, D., Govorcin, M., Buzzanga, B., Sangha, S., Handwerger, A., Staniewicz, S., Mirzaee, S., Bato, M. G., and Maurer, J.: Surface Displacement Monitoring for Natural and Anthropogenic Hazard Applications: From Tectonics to Urban Subsidence , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22006, https://doi.org/10.5194/egusphere-egu26-22006, 2026.
While phase bias in interferometric synthetic aperture radar (InSAR) can provide useful insights into temporal changes in geophysical variables such as soil moisture and vegetation dynamics, it can also introduce systematic errors in the interferometric phase. These biases can severely distort displacement time series and lead to unreliable velocity estimates, particularly when using short-baseline multilooked interferograms. Phase linking (PL) techniques can mitigate InSAR phase biases, but their applicability is often limited in regions with low long-term coherence, such as densely vegetated or seasonally dynamic landscapes.
In this work, we apply our recently developed InSAR phase bias correction algorithm—originally validated over selected test sites—to the entire Italian peninsula, demonstrating its robustness and scalability in operational contexts. The algorithm estimates bias terms from short-term wrapped interferograms using calibration factors derived from long-term interferograms and includes a temporal smoothing constraint to manage time-series gaps. This large-scale implementation enables us to analyse the spatial and temporal behaviour of phase bias across diverse land cover types and climatic zones.
We systematically examine how phase bias varies across forests, agricultural lands, and urban regions, and how its characteristics evolve seasonally. Our results show that phase bias effects are most pronounced in vegetated and moisture-sensitive regions during wet seasons, often manifesting as false subsidence or uplift in uncorrected velocity fields. Corrected velocity maps demonstrate strong alignment with those from PL methods in high-coherence areas while preserving meaningful deformation signals in regions where PL fails due to decorrelation.
This study presents a large-scale quantification and correction of InSAR phase bias using a non-PL-based strategy, offering a practical alternative for deformation monitoring in challenging environments. Our findings highlight the importance of incorporating phase bias correction in regional-scale InSAR applications, particularly for tectonic, volcanic, and hydrological hazard monitoring in areas where long-term coherence cannot be guaranteed.
How to cite: Maghsoudi, Y., Hooper, A., Wright, T., and Pinheiro, M.: Large-Scale Characterisation and Correction of the InSAR Phase Bias: Insights from Nationwide Analysis in Italy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21217, https://doi.org/10.5194/egusphere-egu26-21217, 2026.
Persistent Scatterer Interferometric SAR (PS-InSAR) is a widely used time-series technique for estimating surface deformation from multi-temporal SAR data. The rapidly increasing volume and resolution of SAR acquisitions from modern satellite missions pose significant challenges for scalability and extensibility. Meanwhile, novel algorithms developed by the InSAR community call for the improvement of maintainability of existing PS-InSAR software.
We present DePSI, an open-source Python software package for PS-InSAR analysis designed to efficiently handle large InSAR datasets while adhering to modern Python software engineering standards. DePSI is based on the established DePSI algorithm originally implemented in MATLAB (van Leijen, 2014), and extends it with a scalable, modular, and community-oriented architecture.
To address the challenges of large-scale 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.
How to cite: Ku, O., van Leijen, F., van Diepen, S., Alidoost, F., Lumban-Gaol, Y. A., Brouwer, W., Wang, Y., Lăpădat, A., van Lankveld, T., and Hanssen, R.: DePSI: An Open-Source Python Software Package for PS-InSAR Analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13118, https://doi.org/10.5194/egusphere-egu26-13118, 2026.
Monitoring ground surface displacement is critical for understanding geophysical processes and mitigating natural hazards, yet conventional Synthetic Aperture Radar (SAR) techniques are often limited by decorrelation, complex terrain, and heterogeneous motion. We present deep learning based-offset tracking (DeepOT), an adaptable deep-learning framework for estimating pixel-level ground surface displacement directly from SAR amplitude image pairs. The framework is enabled by a synthetic-to-real training strategy in which controlled displacement fields are embedded into real SAR imagery, allowing large-scale supervised training without reliance on ground truth displacement measurements or offset-tracking-derived labels. We evaluate DeepOT using multiple deep-learning models and apply it to contrasting landslide settings, including the Slumgullion landslide in Colorado and the Barry Arm landslide in Alaska. The framework supports time-series displacement construction and is evaluated using independent extensometer measurements at Slumgullion. Results show that DeepOT recovers spatially coherent displacement patterns under challenging conditions where interferometric coherence is limited and conventional offset tracking is sensitive to surface heterogeneity. Qualitative comparisons in earthquake case studies further indicate applicability to large-scale, high-gradient deformation. DeepOT is designed as a modular and extensible framework, providing a foundation for future advances in data-driven SAR-based displacement monitoring.
How to cite: Lu, Z., Kim, J., and Jung, H.-S.: DeepOT: A Deep Learning Framework for Pixel-Level Ground Surface Displacement Estimation from SAR Amplitude Imagery, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15409, https://doi.org/10.5194/egusphere-egu26-15409, 2026.
The development of scalable AI-based solutions for oil spill detection, discrimination, and characterization is crucial for advancing marine protection, disaster response, and sustainable ocean governance. Due to the attenuation of sea surface roughness, oil spills are detected as low backscattering regions in Synthetic Aperture Radar (SAR) imagery, being a strategic data source for operational services dedicated to marine pollution monitoring. Near-real-time information on the location, extent, and shape of oil-covered areas represents the primary SAR-derived input for oil spill response (OSR). In a subsequent stage, characterizing relative oil thickness variations within slicks becomes critical for improving cleanup effectiveness, which is higher over thicker oil layers commonly referred to as “actionable oil”.
A strong contrast between dark features and the surrounding sea-surface is required for reliable detection and represents a key property for improving discrimination and characterization using data-driven approaches. In this context, the Damping Ratio (DR) has been demonstrated as a meaningful SAR-based feature for enhancing sea surface contrast. Compared to the Normalized Radar Cross Section (NRCS), DR is less affected by incidence angle and wind intensity, offering strong potential for the development of robust, operational AI-based systems for oil slick detection and characterization.
Existing deep learning approaches for oil spill detection rely exclusively on NRCS, which has shaped available SAR datasets toward pre-processed products unsuitable for oil slick characterization. The project “Searching for Oil Spills on Sea Surfaces” (SOSeas) proposes a two-stage AI-based framework, in which deep learning is used for oil spill detection and delineation, followed by the thematic characterization of relative oil thickness within intra-slicks, both utilizing DR as the primary feature. Achieving this objective required the construction of the SOSeas.Dataset, a new, large-scale, field-validated oil spill benchmark that goes beyond NRCS, providing SAR-derived products from Sentinel-1, especially DR, in raw format to preserve the sea-surface backscattering properties important for characterization.
A proof-of-concept dataset comprising 143 oil spills, field-validated by the Bonn Agreement and primarily located in the North Sea, was used to train, test, and validate a UNet–based semantic segmentation model for oil spill detection. Two identically configured models were trained using either DR or NRCS as input to directly compare their detection performance. To evaluate the discriminative power of DR versus NRCS, binary oil spill masks were used as labels to distinguish polluted water (PW) from non-oiled (NO) areas, which include clean ocean and lookalikes. Models trained with DR consistently outperformed NRCS-based models, achieving higher Intersection over Union (NRCS: 0.4058; DR: 0.5491) and F1-scores (NRCS: 0.58; DR: 0.71) for the polluted water class.
The validation of the DR as a better feature for oil detection lays the foundation for developing the second stage as a future perspective, integrating DR and deep learning for oil slick characterization. Finally, the SOSeas.Dataset lays the groundwork for developing new AI-driven solutions capable of processing large volumes of SAR data, identifying patterns, and extracting useful information in near-real time, supporting operational agencies while enhancing monitoring and OSR actions for ocean protection.
How to cite: Genovez, P. C., Mareto, R., Persello, C., Chang, L., Hadjuch, G., Kerbaol, V., Bleriot, R., Jones, C. E., and Holt, B.: SAR-based Oil Spill Detection and Characterization using Damping Ratio and Semantic Segmentation to Advance Operational AI-based solutions for Oceanic Monitoring and Protection, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21440, https://doi.org/10.5194/egusphere-egu26-21440, 2026.
Sinkholes represent an escalating geohazard across the central Najd Plateau, a region in Saudi Arabia characterized by soluble sedimentary units and subject to both natural and anthropogenic influences. Subsidence often begins as a slight and barely visible settling of the surface, only later becoming apparent and damaging roads, services, and new developments. This is a concern because urban and infrastructural expansion is taking place across the Najd Plateau, where the sedimentary cover locally contains soluble carbonates and evaporites. When groundwater levels change or surface water is added, these units can dissolve, and the overlying ground loses support, leading to subsidence. In such geologically sensitive environments, this needs to be monitored and interpreted early.
This study presents a comprehensive, multi-temporal analysis of ground deformation from 2017 to 2024, utilizing interferometric synthetic aperture radar (InSAR) data acquired by European Space Agency (ESA) Sentinel-1 C-band SAR data. Interferograms were generated using the InSAR Scientific Computing Environment version 2 (ISCE2) processing framework, and timeseries analysis was performed to identify patterns of subsidence related to sinkhole activity.
Four sites located near the city of Riyadh on the Najd Plateau were studied for sinkhole-related subsidence. These sites are in sinkhole-prone areas, and each site represents distinct geological and hydrological contexts. Some of the sites have developed sinkholes, while others have shown indications of potential sinkholes.
Primary time series analysis of Sentinel-1 InSAR data reveals correlations between the InSAR analysis and optical satellite images at certain sites. In some locations, the data indicate that the ground subsided even before collapse events and continues to do so afterwards, which aligns with a scenario where a roof gradually gives way over a dissolution cavity. This matches the field mapping, which showed that the collapse features enlarged in subsequent years.
Subsidence in this region is primarily attributed to soluble sedimentary units that are affected by local changes in groundwater or surface water. The 2017–2024 InSAR time series indicates that this deformation occurs well before any collapse is visible at the surface. This information can now be used to flag locations that require follow-up in the field and to refine the sinkhole-susceptibility maps for the broader Najd Plateau region.
How to cite: Alroudhan, A., Aljurbua, A., K. Alshammari, R., Albesher, Z., Alzahrani, A., Alsubaie, T., A. Almusaylim, Z., Melebari, A., and Alothman, A.: Analysis of Sinkholes on the Najd Plateau Using InSAR, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-185, https://doi.org/10.5194/egusphere-egu26-185, 2026.
With advancements in and increased standardization of SAR hardware and processing algorithms, major InSAR errors have been largely mitigated, leading to substantial improvements in measurement precision and accuracy. The identification of new error sources will further enhance InSAR measurements and promote new and emerging InSAR applications. In this talk, we present two new mechanisms that cause InSAR and closure phase errors.
The first mechanism is associated with range misregistration. High-quality InSAR measurements require precise and accurate range coregistration both between the reference and secondary SAR images, and between the reference SAR image and the DEM used for computing the topographic phase. However, this requirement is not always satisfied due to range misregistration arising from various sources that have been largely overlooked. The range misregistration can occur between the reference image and the DEM, between the reference and secondary images, or due to the inhomogeneity within the range resolution cell. Our analysis reveals that, apart from decorrelation, the effects of all three types of misregistration ultimately reduce to that of an equivalent misregistration between the reference image and the DEM, which manifests as a phase error. Moreover, closure phase errors can be induced by InSAR phase errors arising from range misregistration between the reference and secondary images, as well as from the inhomogeneity within the range resolution cell. These InSAR and closure phase errors are confirmed by simulations and experiments with real data.
The second mechanism is associated with along-track ionospheric variations within the synthetic aperture. To analyze their effects, we decompose the along-track Total Electron Content (TEC) using a Taylor series expansion. An analysis of the matched filtering process reveals that odd-order components shift the target peak position, while even-order components introduce a phase error. Moreover, all components, except the linear one, can cause target defocusing. These effects can lead to non-negligible InSAR and closure phase errors. The resulting InSAR and closure phase errors are also confirmed by simulations and experiments with real data. These errors are particularly significant in the current golden age of L-band satellite SAR missions.
How to cite: Liang, C., Yang, F., and Wang, Y.: InSAR and Closure Phase Errors: Two New Mechanisms, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9261, https://doi.org/10.5194/egusphere-egu26-9261, 2026.
Rapid urban and industrial development in reclaimed coastal cities of the Korean Peninsula has imposed persistent anthropogenic loading and vibration on thick marine sediment, leading to frequent geotechnical hazards and exacerbating vulnerability to compound coastal hazards. Although preconstruction ground improvement techniques were implemented adequately, these thick sediment layers continue to undergo long-term consolidation and secondary compression, resulting in progressive subsidence that is often overlooked by MTInSAR-based ground subsidence monitoring approaches. Therefore, to characterize land subsidence dynamics and associated cascading hazards in reclaimed cities, we develop an ensemble kinematic and physical modeling framework using time-series SAR interferometry. For experimental purposes, we selected three major reclaimed coastal cities, i.e., Incheon, Mokpo, and Busan in South Korea. Initially, the multi-temporal Sentinel-1 SAR (2017-2023) data were processed using the Persistent Scatterer Interferometry (PSInSAR) technique to derive vertical displacement (VD) time series at persistent scatterer (PS) locations with temporal coherence >0.7. Thereafter, the VD time series of each PS point was smoothed using a Savitzky-Golay local polynomial regression to reduce noise while preserving long-term displacement signals. Consequently, subsidence kinematics were characterized by identifying the temporal regime using the Pruned Exact Linear Time (PELT) algorithm with an L2 loss function, and segment-wise first-order linear regression (R²>0.9) was applied to quantify phase-dependent displacement rates. The results exhibit that the VD rates in the reclaimed regions of Mokpo, Busan, and Incheon vary from -9.78 to 3.59 mm/yr, -41.19 to 1.85 mm/yr, and -9.89 to 1.79 mm/yr, with mean rates of -0.64, -4.15, and -0.94 mm/yr, respectively. We observed multiple VD phases characterized by a shift from quasi-linear settlement to episodic acceleration at each PS in all three cities, indicating that reclaimed sediments are still undergoing consolidation and stress redistribution within strata. Furthermore, the VD time series of each PS was modeled using hyperbolic settlement formulations to characterize the nonlinear consolidation behavior of reclaimed sediments. The strong agreement between hyperbolic model predictions and PSInSAR-derived VD indicates that land subsidence in reclaimed areas within these cities is predominantly consolidation-controlled. The modeled subsidence characteristics were further validated through analysis of in-situ borehole geotechnical data using the Casagrande plasticity chart. Moreover, the velocity decay ratios derived from the hyperbolic settlement model exhibit relatively high in Busan (0.733) and Incheon (0.603), indicating sustained, long-term settlement associated with secondary compression and transitional consolidation stages. On the other hand, the Mokpo reclaimed region exhibits a substantially lower decay ratio (0.341), indicating a rapid attenuation of subsidence velocity and near completion of primary consolidation, which is also consistent with its decade-old land reclamation history. Notably, it was observed that subsidence-related geohazards (i.e., sinkhole occurrences) have been reported more frequently in the reclaimed areas of Incheon and Busan than in Mokpo, providing independent evidence that further supports the modeled subsidence mechanism. The proposed ensemble framework exhibits that integrating kinematic segmentation with physical modeling of PSInSAR-derived VD time histories facilitates a transition from passive monitoring to predictive urban subsidence hazard assessment, which is crucial for long-term infrastructure planning in reclaimed coastal megacities under global climate change scenarios.
How to cite: Das Adhikari, M., Song, M.-S., Kim, S.-W., and Yum, S.-G.: Assessing Progressive Subsidence Hazards in Reclaimed Coastal Cities Using Ensemble Kinematic and Physical Modeling of PSInSAR Displacement Time Series, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4023, https://doi.org/10.5194/egusphere-egu26-4023, 2026.
Flood maps derived from remote sensing data, especially synthetic aperture radar (SAR), are crucial for situational awareness and risk assessment during flood events. In recent years, deep learning models, such as U-Net have been applied successfully to flood mapping based on SAR data. However, these models typically require large amounts of labeled training data. Earth observation (EO) foundation models offer a promising alternative. By pretraining a neural network encoder on large, diverse remote sensing datasets, using self-supervised learning, they enable efficient fine‑tuning of small decoders for specific downstream tasks, potentially requiring only limited amounts of annotated data.
In this study, we evaluate THOR, a pretrained EO foundation model, for flood mapping and compare its performance against a U‑Net baseline with a pretrained ResNet backbone. To assess the dependence on training dataset size, we prepare multiple datasets of varying scales using Sentinel‑1 SAR data and water body masks from Norway. These datasets are used both to train the U‑Net model and to fine‑tune a decoder on top of THOR. The resulting models are tested on an independent dataset of flood events and systematically compared.
We analyze how model performance changes with decreasing dataset size and identify conditions under which the foundation model outperforms the U‑Net baseline. In particular, we investigate the threshold at which THOR becomes advantageous for limited-data scenarios. Finally, we assess whether the performance achieved by the foundation-model-based approach is sufficient for operational flood mapping when only small, labeled datasets are available.
How to cite: Rudjord, Ø., Solberg, R., Luppino, L. T., and Line Forgaard, T. J.: Flood mapping using EO foundation model with limited data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11716, https://doi.org/10.5194/egusphere-egu26-11716, 2026.
Soil degradation can intensify abruptly during armed conflict due to heavy machinery traffic and earthworks that compact cropland soils and disrupt surface structure. We develop field-scale, remote-sensing indicators of conflict-driven soil compaction in agricultural land in the Western Negev (Israel) using the Sentinel-1 InSAR time series. Sentinel-1A IW ascending and descending acquisitions (2017-2024) were processed with LiCSAR interferograms and LiCSBAS time-series analysis to estimate line-of-sight (LOS) velocity and cumulative displacement. To isolate conflict-related impacts, the time series was analyzed in two periods: pre-war (before 7 October 2023) and post-war (from 7 October 2023 onward), treating this date as a structural change point. InSAR-derived metrics were linked to Ministry of Agriculture field and damage polygons to extract per-field velocity trends, cumulative displacement, and incremental displacement between consecutive acquisitions. Pre-war conditions were largely stable, with most fields exhibiting LOS velocities within ±2 mm yr⁻¹. Post-war maps reveal spatially coherent subsidence in reported damaged parcels, frequently exceeding -10 mm yr⁻¹ and locally reaching below -30 mm yr⁻¹, consistent with severe soil compaction and disturbance. Time series show abrupt step changes and negative displacement “shock” events after the onset of the conflict, while adjacent parcels often remain stable, highlighting strong heterogeneity at the field scale. The proposed indicator set, velocity shifts, cumulative displacement changes, and incremental deformation anomalies provide a rapid, scalable framework for screening soil degradation, prioritizing remediation, and tracking recovery trajectories in data-scarce, crisis-affected agricultural landscapes.
Keywords: soil degradation, soil compaction, Sentinel-1, InSAR time series, LiCSBAS, conflict impacts, agricultural damage mapping
How to cite: Paul, A., Kenigswald, M., Zahavi, M., and Paz-Kagan, T.: Monitoring conflict-driven ground deformation in agricultural land using Sentinel-1 LiCSBAS time series., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13949, https://doi.org/10.5194/egusphere-egu26-13949, 2026.
Increased availability of high-resolution synthetic aperture radar (SAR) data has resulted in SAR speckle tracking becoming an important tool for measuring surface deformations that are too large to be captured reliably with interferometric SAR (InSAR). Speckle tracking finds local two-dimensional offsets for a pair of images by maximizing the normalized cross-correlation between shifted chips from one image with stationary reference chips from the other image. This approach has several drawbacks. It is computationally intensive because the optimum matching problem is solved independently for each reference chip and each image pair, produces displacement maps at a resolution coarser than the input imagery, and often requires significant post-processing cleanup to remove frequent mismatches and noise artifacts.
In this study, we are proposing an alternative approach to traditional speckle tracking that uses unsupervised machine learning (ML) for the non-rigid co-registration of a pair of approximately (globally) preregistered SAR images to derive two-dimensional displacement fields typical of faster composite landslides. With prior global co-registration, the output local offset field directly captures local deformation. Using an ensemble of sufficiently large, sensor-specific datasets from representative displacement test sites as training input, the fully trained ML network can then ingest any SAR image pair acquired by the same sensor, whether previously seen or unknown, and produce a local vector offset field that accurately aligns the images.
The resulting deformation field represents local movements between the two images analogous to the two-dimensional offset maps produced by conventional speckle tracking. Compared to traditional speckle-tracking workflows, the proposed approach is computationally more efficient (e.g., approximately twice as fast when applied to a stack of seven images), as the trained network evaluates a learned parametric function to directly map one (globally pre-registered) SAR image to another, rather than relying on repeated chip-to-chip optimization. Our proposed method also yields substantially cleaner displacement estimates, with reduced noise and approximately 84% fewer outliers. Finally, the resulting two-dimensional offset (deformation) maps nearly preserve the original spatial resolution of the input SAR images.
How to cite: Hosseini, F. and Rabus, B.: An unsupervised machine learning approach to derive two-dimensional displacement from repeat-pass SAR images, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15433, https://doi.org/10.5194/egusphere-egu26-15433, 2026.
Interferometric Synthetic Aperture Radar (InSAR) has emerged as a powerful tool for landslide hazard detection, yet topographic residuals arising from outdated Digital Elevation Models (DEMs), dynamic terrain changes, and unknown scatterer positions pose significant challenges. These residuals, scaled by perpendicular baselines, can introduce substantial biases in deformation rate estimates, leading to overlooked hazards in techniques such as Stacking, Small Baseline Subset (SBAS), and Persistent Scatterer (PS)/Distributed Scatterer (DS) InSAR.
We present an enhanced Stacking methodology that eliminates topographic residual contributions through baseline normalization without directly estimating DEM errors. By leveraging the linear relationship between DEM error phase and spatial baseline, our approach performs phase normalization by baseline magnitude and applies sign-balancing transformations to ensure equal numbers of positive and negative perpendicular baselines. This preserves the simplicity, efficiency, and robustness of traditional Stacking while significantly improving deformation velocity estimation accuracy.
Additionally, we discuss complementary strategies including near-zero baseline InSAR approaches through interferogram integer combination and non-parametric Independent Component Analysis (ICA) methods for enhanced topographic residual estimation under complex deformation scenarios.
This work provides practical solutions for improving InSAR-based landslide hazard identification in dynamic terrain environments, with significant implications for geological disaster monitoring and early warning systems.
How to cite: Zhang, L., Song, X., and Liang, H.: Mitigating Topographic Residual Effects in InSAR-Based Landslide Detection: An Enhanced Stacking Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17306, https://doi.org/10.5194/egusphere-egu26-17306, 2026.
The Caspian Sea has experienced an accelerated decline in water level over the recent decade, leading to obvious shoreline retreat, ecosystem stress, and increasing socio-economic impacts along coastlines. While climate-driven factors such as rising air temperature, enhanced evaporation, and changes in regional hydrology are widely recognized as primary drivers of this decline, the potential contribution of tectonic processes remains insufficiently explored. This study investigates the interaction between Caspian Sea level change and coastal dynamics, with a particular focus on the role of tectonically driven vertical land motion along the coastline. Using multi-temporal InSAR analysis of Sentinel-1 data between 2014 and 2025, we quantify coastal vertical deformation patterns across key sectors of the Caspian shoreline, including areas affected by subsidence, uplift, land reclamation, and rapid shoreline migration. These deformation signals are analyzed together with observed coastline changes derived from optical satellite imagery, enabling the separation of relative sea-level effects from absolute water-level variations. Preliminary results reveal spatially heterogeneous deformation along the coast, with localized uplift and subsidence rates that are comparable in magnitude to the observed rate of sea-level decline. In some regions, shoreline retreat coincides with uplifted coastal segments, suggesting that tectonic processes may amplify the apparent rate of relative sea-level fall.
The findings show that Caspian Sea coastal changes cannot be completely clarified by climatic forcing alone, and that both tectonic deformation and broader geodynamic processes contribute to the observed sea-level and shoreline trends. This study demonstrates that climate forcing alone is insufficient to explain the decline of the Caspian Sea, highlighting the role of tectonic deformation along the coast. Integrating geodetic deformation measurements with coastal change analysis provides independent evidence of vertical land motion influencing relative sea-level and shoreline trends, with important implications for hazard assessment, coastal management, and future projections under ongoing climate change.
How to cite: Ahadov, B., Fielding, E., and Kadirov, F.: Is Climate Forcing Alone Sufficient to Explain the Decline of the Caspian Sea Level? Space-Geodetic Evidence of Tectonic Deformation Along the Neftchala-Lankaran Coast of Azerbaijan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16740, https://doi.org/10.5194/egusphere-egu26-16740, 2026.
The 30 July 2024 Wayanad landslide in the Western Ghats represents one of the most destructive rainfall-induced mass movements in India, characterised by an ~8 km runout and catastrophic downstream impacts. To advance process understanding for hazard assessment, we integrate hydrometeorological forcing, geotechnical constraints from published field investigations, and Sentinel-1 SAR interferometry. Extreme monsoonal precipitation (~586 mm in 48 h) combined with sustained 15-day antecedent rainfall repeatedly exceeded global intensity-duration thresholds, indicating prolonged saturation and pore‐pressure accumulation. The landslide source area comprises 2-8 m thick lateritic soil mantles over weathered and fractured gneiss, where laboratory evidence from recent studies shows high saturated hydraulic conductivity and marked reductions in unsaturated shear strength under 30-40 kPa suction.
We processed pre- and post-event Sentinel-1 (IW mode) interferograms, applying coherence-based masking and zero-reference correction to quantify line-of-sight deformation. The InSAR signal exhibits distinct displacement concentration at the crown zone coincident with a documented pre‐existing fracture system, and spatially continuous deformation aligned with the observed debris-flow channel. These patterns corroborate a failure mechanism involving rainfall‐induced saturation of lateritic covers, mobilisation along structurally weakened bedrock interfaces, and rapid transformation into a fluidised debris flow.
The results demonstrate the utility of spaceborne InSAR for characterising pre‐ and post-failure kinematics in inaccessible terrain and highlight the need to couple rainfall-soil moisture thresholds with routine SAR-based monitoring for early warning in the monsoon-dominated Western Ghats.
How to cite: Anand, N., Parekattuvalappil Shaju, A., and Gali, M. L.: Investigating Rainfall-Induced Instability in Wayanad through Sentinel-1 SAR Interferometry and Geotechnical Context, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-787, https://doi.org/10.5194/egusphere-egu26-787, 2026.
Intensive and repeated underground coal mining in the Upper Silesian Coal Basin (USCB), Poland – the largest coal mining region in Europe – causes large and rapid surface subsidence. The deformation rate often exceeds the maximum detectable displacement gradient of satellite radar interferometry (InSAR). Phase aliasing makes it impossible to correctly detect large subsidence, leading to underestimations of 80–90%. Therefore, effective phase aliasing correction methods are essential for using InSAR in mining areas of the USCB. The aim of this study is to present a practical application of an InSAR phase aliasing correction method – the Linear Dependency (LD) method – applied in the subsidence area of the Bobrek coal mine located in the northern part of the USCB. The method was developed at the Central Mining Institute – National Research Institute (GIG-PIB) in 2023.
We used 10.5 years of Sentinel-1 satellite images processed with the SBAS method, including both ascending and descending passes. The two LOS displacement components were decomposed into vertical and east–west (E-W) directions. Detailed analysis and LD correction were applied only to the last year of the time series (June 2024 – June 2025), for which four quarterly vertical displacement maps were generated. At the same time, quarterly RTN-GNSS measurements were carried out at 5 control points located in the areas of the largest subsidence.
A comparison of GNSS and SBAS results confirmed clear and spatially extensive phase aliasing in the study area. The largest difference between the methods was about 300 mm. Maximum subsidence measured by GNSS reached 403 mm, while SBAS detected only 106 mm, resulting in an underestimation of 65%. RMSE values at individual points reached up to 158 mm, and the average RMSE before LD correction was 122 mm.
The LD method corrects InSAR underestimation by defining a local linear relationship between the monthly subsidence rate measured by GNSS and the differences between GNSS and SBAS results at each control point. Then applying this relationship proportionally across the entire subsidence basin. After that, full reconstruction of the subsidence time series was obtained. The deformation amplitude increased significantly. After correction, maximum subsidence at the control points ranged from 174 mm to 371 mm. The largest differences after correction were 126 and 172 mm (35% underestimation), which is about twice lower than before correction. At the other points, the final differences did not exceed 22 mm.
The results clearly show that phase aliasing in the USCB is a common effect strongly related to rapid subsidence, and that standard SBAS processing cannot correctly identify high deformation rates. The LD method provides an effective way to correct phase aliasing in InSAR data on a large spatial scale, covering entire subsidence basins. This approach significantly improves the use of satellite radar interferometry for monitoring fast mining-induced subsidence in the USCB.
How to cite: Apanowicz, B.: High-Rate Subsidence Monitoring in the Bobrek mine in the Upper Silesian Coal Basin Using InSAR and GNSS Technology, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-989, https://doi.org/10.5194/egusphere-egu26-989, 2026.
Road construction sites often involve complex ground conditions, such as cut-and-fill slopes, rapid surface modification, and spatially heterogeneous deformation. While conventional in-situ monitoring systems, including inclinometers and GNSS, provide accurate point-based measurements, their applicability is limited by spatial coverage, particularly along long or inaccessible construction corridors. This study explores the feasibility of using spaceborne Synthetic Aperture Radar (SAR) data to support qualitative monitoring of ground deformation and slope behavior at road construction sites.
High-resolution X-band SAR data from TerraSAR-X and ICEYE were analyzed over a highway construction site in South Korea. Different observation geometries, including ascending and descending orbits as well as left- and right-looking configurations, were examined to assess slope visibility under varying terrain orientations. Geocoded gamma-nought and multilooked intensity images were used to qualitatively evaluate slope detectability and surface change patterns at different construction stages.
The analysis shows that SAR observation geometry strongly influences slope visibility in road construction environments. The availability of multiple viewing geometries from ICEYE improves the observation of slopes with diverse orientations along linear infrastructure corridors. High-resolution Spotlight imagery enables identification of small-scale cut slopes and construction-related surface changes, supporting site-scale qualitative monitoring. However, variations in incidence angle limit the suitability of ICEYE data for consistent quantitative deformation analysis. In contrast, TerraSAR-X provides more stable observation geometry, making it more appropriate when quantitative displacement assessment is required.
These results indicate that SAR satellites can serve as an effective wide-area screening and complementary monitoring tool for road construction site management. SAR-based observations can assist in early identification of potentially unstable slopes, prioritization of field inspections, and integration with ground-based monitoring systems for infrastructure safety management.
How to cite: Kim, J., Jung, H. C., Kim, D. M., and Lee, D. Y.: Using SAR Satellite Data to Support Ground Deformation Monitoring at Road Construction Sites, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1657, https://doi.org/10.5194/egusphere-egu26-1657, 2026.
Rapid population concentration and infrastructure overload have intensified traffic congestion in urban areas. In response, South Korea has promoted underground transportation systems such as the Great Train Express (GTX) in the Seoul metropolitan area and large-scale underground roads in Busan. However, underground construction poses significant risks to surface stability, particularly ground subsidence, highlighting the need for monitoring systems that provide wide-area coverage with high accuracy at reasonable cost.
This study applies time-series interferometric synthetic aperture radar (InSAR) techniques to monitor surface displacement associated with construction of the Mandeok–Centum underground road in Busan, which connects the eastern and western parts of the city. A total of 165 Sentinel-1 A/B SAR images acquired between May 2015 and January 2021 were analyzed using both Small Baseline Subset (SBAS) and Persistent Scatterer InSAR (PSInSAR) approaches.
The study area spans mountainous terrain and densely urbanized subsurface zones, underlain primarily by Cretaceous andesite and granodiorite, with alluvial deposits in low-lying urban areas. Major geological structures include the Yangsan Fault in the western section and the Dongnae Fault in the central section, both trending predominantly NNE–SSW. Using descending orbit path 61 imagery, 705 interferograms were generated for SBAS analysis with temporal baselines limited to 60 days and perpendicular baselines constrained to 2% of the critical baseline.
Ground Control Points (GCPs) were established at 68 locations sufficiently distant from the tunnel alignment and assumed to be stable. Most GCPs exhibited displacement within ±10 mm, consistent with typical Sentinel-1 DInSAR accuracy, while three GCP clusters showed variations up to ±20 mm, suggesting possible excavation-related effects. Points of Interest (POIs) were selected along a 500 m-wide corridor centered on the tunnel route to assess excavation influence.
Results indicate that most POIs exhibited near-linear displacement trends with magnitudes up to ±50 mm. Localized anomalies were detected at vegetated and construction-affected sites, with abrupt displacement changes observed in 2016 and 2018. PSInSAR results were generally consistent with SBAS-derived trends, though spatial coverage was limited in vegetated and water-covered areas. Notable subsidence of up to ±20 mm was identified near the Minam Intersection and along both banks of the Suyeong River.
Although fault-related displacement was not clearly detected in the urban environment, time-series InSAR effectively captured temporal surface deformation patterns at intervals of several weeks. The results demonstrate that satellite SAR-based monitoring is well suited for preliminary site investigation, design evaluation, construction-phase monitoring, and operational surveillance of underground transportation infrastructure. Areas exhibiting cumulative displacement of several centimeters or deformation rates exceeding several millimeters per year should be prioritized for complementary ground-based monitoring.
This study contributes to the development of cost-effective wide-area surface displacement monitoring techniques for the safe construction and management of underground transportation infrastructure in complex urban environments.(KICT project No. 20250285-001, second year).
How to cite: Kim, W., Hwang, S., and Park, B.: Surface Displacement Monitoring for Urban Underground Transportation Infrastructure Construction Using Satellite SAR Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2673, https://doi.org/10.5194/egusphere-egu26-2673, 2026.
Inadequate post-closure management of waste landfill facilities in South Korea has led to various environmental and social challenges. This study explores the feasibility of using freely available high-resolution satellite Synthetic Aperture Radar (SAR) imagery for continuous monitoring of landfill stability. SAR technology offers clear advantages for monitoring remote or inaccessible sites where permanent on-site personnel deployment is impractical and holds particular promise for developing countries requiring long-term ground stability assessment.
Landfill surfaces pose significant observation challenges for SAR due to vegetation cover and surface objects that degrade measurement quality. To overcome these limitations, artificial corner reflectors were installed at the upper sections of a landfill facility to enhance signal strength and measurement precision. Two types of corner reflectors with different geometries—a conventional triangular trihedral reflector and a cubic reflector—were designed, fabricated, and deployed at a waste landfill site in Pohang, South Korea.
Multi-source SAR datasets were analyzed, including 19 Sentinel-1A images acquired between October 2023 and October 2024, and 7 TanDEM-X images captured at 11-day intervals from September 2024 to January 2025 along ascending orbits. Ground displacement was estimated using Interferometric SAR (InSAR) techniques, with time-series InSAR methods applied to assess temporal deformation patterns.
Comparative evaluation of reflector performance demonstrated that the cubic corner reflector achieved significantly higher signal detection rates than the triangular trihedral design. The cubic reflector exhibited superior radar cross-section characteristics, more stable phase coherence, and lower sensitivity to installation misalignment, indicating its suitability for operational landfill monitoring.
To determine optimal monitoring strategies, various combinations of SAR data sources and processing tools were evaluated. High-resolution commercial SAR data (TerraSAR-X) consistently provided more accurate displacement estimates than freely available Sentinel-1 data across all processing approaches. Nevertheless, Sentinel-1's frequent revisit capability offers substantial advantages for continuous, long-term monitoring applications.
The results confirm that satellite SAR monitoring augmented with appropriately designed corner reflectors represents a practical and cost-effective solution for continuous landfill stability assessment. The integration of ascending and descending orbit data, together with strategically deployed cubic corner reflectors, enables robust three-dimensional ground displacement monitoring. This approach demonstrates strong potential for technology transfer to developing countries where conventional monitoring infrastructure is limited (KICT project No. 20250285-001, second year).
How to cite: Hwang, S., Kim, W., and Park, B.: Applicability Analysis of Corner Reflectors for Satellite SAR Data Collection in Waste Landfill Facility Maintenance, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2683, https://doi.org/10.5194/egusphere-egu26-2683, 2026.
The 2023 landslide event in Joshimath, Uttarakhand Himalaya, signifies the coupling between hydrological cycles and anthropogenic modifications in controlling long-term slope instability of slow moving landslides (SMLs). Using multi-temporal Interferometric Synthetic Aperture Radar (InSAR) data spanning from 2017 to 2023, combined with field observations, land use land cover analysis, and numerical modeling, we quantify the temporal evolution, driving mechanisms, and potential failure scenarios of the Joshimath landslide and adjacent Hailang and Kalpeshwar slopes. InSAR displacement time series reveal the onset of slow creep in 2018, followed by pronounced acceleration after extreme precipitation in October 2022 in the hillslope of Joshimath. Spectral and cross-correlation analysis between InSAR derived LOS displacement, rainfall, equivalent water height, and modeled vertical hydrological loading (LSDM) after long term trend removal demonstrate a dominant annual (~12-month) deformation cycle with a rainfall-deformation lag of 0 to 3 months, consistent with delayed pore-pressure propagation in the subsurface. Concurrently, land use change mapping indicates a >25% decline in forest canopy between 2000 and 2022, attributed to urban expansion and deforestation. Numerical slope stability modeling confirms that the factor of safety reduces through decreased root cohesion and increased surface saturation. While Hailang and Kalpeshwar exhibit hydrologically modulated creep, Joshimath displays an additional long-term acceleration trend, suggesting progressive failure behavior under compounded hydro-mechanical forcing. Runout simulations using the D-Claw framework highlight that a potential slope failure event could severely impact the downstream Tapovan Vishnugad Hydropower Project. Collectively, our results demonstrate that the interplay between seasonal cyclic hydrological loading and anthropogenic land-cover alteration exerts first-order control on deformation dynamics of SMLs, emphasizing the necessity of integrating hydro-geomechanical monitoring for anticipatory hazard assessment in rapidly urbanizing mountain terrains.
How to cite: Dalal, P., Hari, M., and Kundu, B.: Seasonal and Anthropogenic Controls on Slow-moving Landslides: A Case Study of Uttarakhand Himalaya, India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3256, https://doi.org/10.5194/egusphere-egu26-3256, 2026.
Extreme rainfall events are becoming more frequent and intense due to climate change. Accurate flood detection is therefore essential to prevent or reduce future flood risks. In the United Arab Emirates (UAE), three consecutive rainfall events in February, March, and April 2024 set unprecedented records, surpassing all observations since rainfall measurements began in 1930. The April 2024 event led to one of the most severe flood episodes in the country’s history, with flash floods reported across most wadi systems. In arid regions, it is often difficult to detect flooded areas using traditional methods that rely solely on optical or SAR images acquired during or after flood events, primarily due to decorrelation effects. To overcome this problem, this study proposes a new approach that generates maps from Interferometric Synthetic Aperture Radar (InSAR) using coherence change detection (CCD) integrated with Principal Component Analysis (PCA) to reduce the impact of decorrelation. This approach evaluates InSAR-CCD using Principal Component Analysis (PCA) for multitemporal Sentinel-1 SLC data to map flood inundation in the UAE. Three coherence layers for pre-flood, peak-flood, and post-flood phases were computed and transformed through PCA to isolate dominant variance patterns linked to inundation. The Feature Preserve Smoothing filter was applied to CCD-PCA to reduce noise and ensure consistent resolution. The method was compared with the Change Difference Threshold (CDT). Results showed that filtered CCD obtained from PCA produced continuous and topographically consistent flood extents in urban plains, wadis, and salt-flat areas (sabkha). The observed coherence loss captured not only standing water but also saturated soil, erosion, and sediment transport. Thus, optical imagery was used to compare and cross-validate the CCD-PCA and CDT by choosing random points on the map to ensure they represented water bodies rather than sediment transport or soil moisture. The filtered CCD derived from PCA showed an overall accuracy (OA) of 0.84 and a Kappa (κ) value of 0.71, while CDT showed an OA of 0.65 and a κ of 0.20. The filtered CCD-PCA product showed perfect sensitivity, and no flooded pixels were missing. The results highlighted the sensitivity and accuracy of flood detection in arid environments using InSAR, which has great potential for flood detection and future mitigation strategies in arid regions.
How to cite: Alghafli, K., Ebraheem, A. A., and Gulzar, H.: InSAR–Based Flood Detection of the 2024 UAE Rainfall Events Using Principal Component Analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6420, https://doi.org/10.5194/egusphere-egu26-6420, 2026.
Height estimation from a single Synthetic Aperture Radar (SAR) image has demonstrated a great potential in real-time environmental monitoring and scene understanding. However, recovering 3D information from 2D image is a mathematics ill-posed problem. Moreover, in mountainous regions, severe layover causes signal aliasing and great loss of geometric information. This research presents a single-image SAR height estimation framework that explicitly addresses layover-induced distortions by integrating physics-based modeling with deep learning. The proposed approach first reconstructs SAR backscattering in layover regions by establishing a one-to-many mapping between radar slant-range pixels and ground cells using SAR imaging simulation and a coarse digital elevation model. Mixed backscattered energy in layover pixels is then reallocated to individual ground locations according to physically derived contribution ratios, yielding a reconstructed SAR image with a more rational radiometric distribution.
Based on the reconstructed SAR data, an enhanced U-Net architecture with attention and selective-kernel mechanisms is employed for height estimation. Large-kernel selective modules enable adaptive multi-scale feature extraction to capture both local terrain details and long-range topographic context, while efficient channel attention emphasizes height-relevant feature channels. In addition, sparse elevation priors and Euclidean distance maps are incorporated to further constrain the inversion process. The datasets are constructed using Sentinel-1A SAR imagery and ground truth height maps derived from the Shuttle Radar Topography Mission (SRTM). The study focuses on three distinct regions characterized with different topography: Yumen in Gansu Province, China; Shule Nanshan in Qinghai Province, China; and the San Juan National Forest in the United States.
Experiments conducted demonstrate that the proposed framework substantially improves height estimation accuracy compared with conventional single-image SAR methods. Specifically, the reconstruction module mitigates signal aliasing by establishing a one-to-many mapping between slant-range and ground cells, successfully restoring a rational backscattering distribution in layover areas. This restoration alone reduces RMSE of the estimated height by 5.6%, 24.1%, and 25.3% across the three datasets. Complementing this, the ASK-UNet leverages LSK and ECA modules to capture multi-scale features, further refining the estimation accuracy. Compared with the baseline network, the ASK-UNet yields additional RMSE reductions of 7.3%, 5.5%, and 8.3% respectively. Overall, experimental results demonstrate that mSAR2Height achieves state-of-the-art performance with a total RMSE reductions of 12.4%, 28.2%, and 31.4%. The results indicate that combining physics-based layover reconstruction with attention-guided deep learning provides an effective and reliable solution for single SAR image height estimation in complex terrain, with high potential for rapid mapping and disaster response applications.
How to cite: Luo, Q., Wang, J., Chen, H., Gan, J., Zhao, J., and Zhong, L.: SAR2HEIGHT: Height Estimation from A Single SAR Image via Layover Backscattering Reconstruction and Deep Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8644, https://doi.org/10.5194/egusphere-egu26-8644, 2026.
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.
How to cite: Li, J., Yu, C., and Hu, X.: Orbit-specific tropospheric effects on Sentinel-1A/B interferometric synthetic apertureradar observations: insights for deformation analysis and future mission design, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8878, https://doi.org/10.5194/egusphere-egu26-8878, 2026.
The 2023 Mw 7.8 and Mw 7.6 Kahramanmaraş earthquake doublet produced complex deformation patterns across southeastern Turkey, offering a rare opportunity to investigate the response of non-main fault structures to large strike-slip earthquakes. Using time-series Interferometric Synthetic Aperture Radar (TS-InSAR) analysis, we quantify both coseismic and postseismic deformation in the epicentral region, with a particular focus on off-fault structures that were previously considered inactive. Our results reveal that, beyond the primary rupture zones, several off-faults exhibit significant postseismic deformation characterized by increased deformation rates, indicating fault reactivation rather than residual coseismic effects. To explore the driving mechanisms of this off-fault activation, we compare the observed deformation patterns with modeled static Coulomb stress changes and dynamic stress perturbations associated with the earthquake doublet. The spatial distribution of activated off-faults shows a strong correlation with areas experiencing positive Coulomb stress changes and regions affected by strong dynamic shaking. Temporally, the deformation signals display heterogeneous behavior, ranging from rapid early postseismic transients to sustained deformation persisting for months to years after the mainshocks. These observations suggest that the combined effects of static and dynamic stress transfer played a key role in triggering off-fault deformation following the Kahramanmaraş earthquakes. Our study highlights the importance of off-fault structures in accommodating postseismic strain and emphasizes their potential contribution to regional seismic hazard, which is commonly underestimated in traditional fault-based assessments.
How to cite: Hu, X., Yu, C., and Li, J.: Off-fault damage and deformation triggered by the 2023 Kahramanmaraş earthquake doublet revealed by InSAR time series, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8880, https://doi.org/10.5194/egusphere-egu26-8880, 2026.
Reservoir-induced landslides pose a significant threat to the safety of nearby residential areas and infrastructure. Understanding the relationship between reservoir water level fluctuations and landslide deformation is therefore critical for effective hazard assessment and early warning. In this study, we investigate the spatiotemporal evolution of the Wangjiasha Landslide using a multi-sensor remote sensing approach. Sentinel-1 (C-band) and TerraSAR-X (X-band) Synthetic Aperture Radar (SAR) data were combined to monitor surface deformation over different temporal scales, with Sentinel-1 observations spanning from 2017 to 2025 and TerraSAR-X data covering the period from 2022 to 2024. In addition, Sentinel-2 optical imagery was processed on the Google Earth Engine (GEE) platform to extract variations in reservoir water surface area. By integrating InSAR-derived deformation measurements with water body dynamics, we analyze the spatial patterns of slope instability and examine the relationship between reservoir water area changes and landslide motion. Particular attention is given to the influence of reservoir water level fluctuations on landslide kinematics, including potential variations in deformation rate and spatial distribution. The results demonstrate the effectiveness of multi-sensor remote sensing for characterizing reservoir-induced landslide dynamics and provide valuable insights for deformation monitoring and hazard assessment.
How to cite: Zhu, Y. and Motagh, M.: Assessing Reservoir-Induced Landslide Dynamics Using Integrated Sentinel-1, TerraSAR-X, and Optical Remote Sensing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11404, https://doi.org/10.5194/egusphere-egu26-11404, 2026.
The Budryk–Knothe model is one of the fundamental tools used for predicting surface deformations induced by underground mining. Among the model parameters, the exploitation parameter a, describing the degree of deformation, and the time coefficient c, characterizing the temporal development of deformation, are of particular interest.
Both parameters show a strong dependence on geological and mining conditions, the mining system, and the mechanical properties of the rock mass. In practice, the time coefficient c is determined by fitting the time function to observed subsidence data, whereas the parameter a is derived from final deformations as a proportionality coefficient between the volume of extracted material and the size of surface deformation. Assuming constant values of both parameters throughout the entire mining period does not always allow for accurate representation of the temporal and spatial evolution of deformations, which may lead to reduced forecast accuracy.
The literature indicates that InSAR time series can provide information that is not available in classical measurements, particularly with regard to changes in the rate of subsidence and temporal variations in direct and secondary deformations. This creates the possibility of simultaneous analysis of c and a parameters based on observed displacements.
Despite the growing number of studies using InSAR time series to analyze mining-induced deformation, their application to the formal calibration of parameters c and a in the Budryk–Knothe model remains insufficiently recognized. The aim of this study is to assess whether the use of satellite-based InSAR time series can improve the accuracy and precision of surface deformation forecasts in this model.
We present the results of calculations performed for the Legnica–Głogów Copper Belt (LGOM) area using InSAR time series derived with the SBAS method. Observed vertical displacements were used to estimate local, spatially variable values of the time coefficient c, allowing an assessment of the variability of subsidence dynamics under different geological and mining conditions. The results indicate the potential of InSAR time series as a tool to support the calibration of Budryk–Knothe model parameters and improve the quality of surface deformation forecasts.
How to cite: Łańduch, A. and Milczarek, W.: Application of SBAS time series for spatial estimation of the time coefficient c in the Budryk–Knothe model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11465, https://doi.org/10.5194/egusphere-egu26-11465, 2026.
Homogeneous pixel selection (HPS) is a critical component in distributed scatterer interferometric synthetic aperture radar (DS-InSAR) processing, and it directly affects the accuracy and stability of phase linking and deformation retrieval. Conventional HPS approaches mainly rely on statistical goodness-of-fit tests applied to amplitude time series (e.g., Kolmogorov–Smirnov (KS), Anderson–Darling (AD), and ttest) to determine homogeneity; however, they often suffer from insufficient detection in areas with limited image numbers or complex scattering mechanisms. 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.
How to cite: Wang, S., Chen, L., Zhao, J., Lu, Z., and Chen, Y.: DLHPS: A novel DS-InSAR Homogeneous Pixel Selection Method Based on Prior Constraints and Consistency Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11894, https://doi.org/10.5194/egusphere-egu26-11894, 2026.
How to cite: Aslan, G.: ITAS: An Open-Source Toolbox for Interpreting InSAR Deformation Time Series in Slope Instabilities , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12982, https://doi.org/10.5194/egusphere-egu26-12982, 2026.
Monitoring dam deformation is critical for mitigating geohazards and ensuring the safety of both water-retaining and tailings dam infrastructure. Conventional in situ monitoring techniques provide accurate point-based measurements but are spatially sparse and do not cover the whole dam structure. Interferometric Synthetic Aperture Radar (InSAR) complements the traditional dam monitoring techniques, providing observations of surface deformation over the entire structure. In this study, we present an InSAR-based monitoring and prediction framework applied to two dams: the Oldman River dam in Alberta, Canada, and the Córrego do Feijão Tailings Dam I in Minas Gerais, Brazil. Sentinel-1 SAR data were processed using an InSAR time-series technique to derive detailed deformation patterns over the two dam sites. At the Oldman River Dam, semi-vertical deformation velocities revealed consistent subsidence along the dam crest, with rates ranging from 5.08 to 6.23 mm/yr. The observed deformation exhibited a temporal relationship with fluctuations in reservoir water levels, including accelerated crest deformation during the drawdown period. In contrast, pre-failure deformation analysis of the Córrego do Feijão Tailings Dam I revealed pronounced deformation behind the crest, with line-of-sight velocities reaching up to −69 mm/yr prior to the catastrophic failure in January 2019. To address the limitations of conventional time-series prediction approaches, particularly their inability to account for spatial dependencies among InSAR measurement points, a graph-based deep learning architecture that explicitly models spatial relationships was introduced. Specifically, spatiotemporal Graph Attention Network (GAT)–based recurrent models, namely GAT-Long Short-Term Memory (GAT-LSTM) and GAT-Gated Recurrent Unit (GAT-GRU), were proposed to jointly capture spatial dependencies and temporal dynamics in InSAR deformation data. The proposed models outperformed equivalent non-graph recurrent neural network baselines (i.e., LSTM and GRU) in deformation forecasting. Overall, the results demonstrated the robustness and transferability of InSAR-driven, graph-based predictive frameworks for diverse dam environments. The proposed approach provides a scalable pathway for deformation monitoring and early warning systems, enabling proactive risk management for critical dam infrastructure worldwide.
How to cite: Farhadiani, R., Mirzadeh, S. M. J., and Homayouni, S.: Spatiotemporal Dam Deformation Monitoring and Prediction using InSAR and Deep Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15240, https://doi.org/10.5194/egusphere-egu26-15240, 2026.
Expansive clays can undergo pronounced seasonal oscillations and long-term trend deformation driven by rainfall infiltration and soil-moisture fluctuations, posing persistent potential threats to urban infrastructure. Taking Houston, a representative region with widespread expansive-clay deposits, as a case study, this paper proposes an expansive-clay hazard monitoring and interpretation framework that integrates heterogeneous-data-based atmospheric correction and signal-separation techniques to address tropospheric-delay contamination and complex deformation-signal mixing. First, we develop a Point–Grid Attention U-Net (PGAU-Net) that fuses high-temporal-resolution GNSS-ZTD observations with the spatially continuous ERA5 background field to reconstruct a high-accuracy tropospheric delay correction field, significantly suppressing atmospheric phase noise in interferometric synthetic aperture radar (InSAR) time-series analysis. Using this correction, we retrieve a five-year (2018–2023) surface deformation time series for the Houston area. The results show that expansive-clay deformation exhibits a pronounced periodic component together with a linear subsidence trend. We further apply wavelet analysis to decompose the deformation into periodic and trend components. The periodic oscillations agree well with the rainfall time series, while the overall deformation indicates an evident subsidence trend, with an average annual deformation rate of approximately −14 mm/yr. Moreover, we investigate the coupling between periodic parameters of expansive-clay deformation and rainfall cycles, estimating a deformation lag relative to rainfall of about Δt = 23 days, and discuss its implications for soil-moisture diffusion and interlayer seepage processes. Finally, we cross-validate the InSAR-derived deformation using GNSS deformation time series at different burial depths, thereby revealing differences between shallow and deep soil layers in periodic response amplitude and phase lag. Overall, the proposed framework can stably extract the periodic–linear deformation characteristics of Houston expansive clays while effectively mitigating atmospheric errors, providing a verifiable technical pathway for long-term monitoring and mechanistic analysis of urban expansive-clay hazards.
How to cite: Shang, Z., Si, J., and Lu, Z.: Expansive-Clay Deformation Monitoring and Rainfall-Lag Analysis in Houston Using Multi-Source Data Constraints and PGAU-Net, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15806, https://doi.org/10.5194/egusphere-egu26-15806, 2026.
On 1 January 2024, a magnitude 7.5 earthquake struck Japan's Noto Peninsula. Thoroughly elucidating the seismic mechanism and tectonic activity characteristics of this event holds significant importance for assessing regional seismic hazard. Existing studies predominantly rely on ALOS-2 PALSAR-2 imagery and geodetic data, overlooking the unique role of optical remote sensing data in reconstructing horizontal displacement fields and the potential of Sentinel-1 intensity information for extracting azimuthal deformation. Consequently, this study comprehensively utilises multi-source remote sensing data from ALOS-2 PALSAR-2 and Sentinel-1/2. Employing Differential Interferometric Synthetic Aperture Radar (D-InSAR), Synthetic Aperture Radar Pixel Offset Tracking (POT), and Optical Image Correlation (OIC) techniques to obtain a high-precision co-seismic deformation field. This enabled the inversion of fault slip distributions, revealing earthquake rupture characteristics and stress effects.
This study successfully obtained the complete three-dimensional co-seismic deformation field of the earthquake, revealing significant deformation characteristics both along the fault strike and in the normal direction. The slip distribution inversion results clarified the geometric parameters and motion characteristics of the primary rupture fault, demonstrating spatially concentrated slip distribution. Furthermore, analysis based on co-seismic Coulomb stress changes indicated a significant spatial correlation between co-seismic stress perturbations and aftershock distribution. This suggests that static stress triggering plays a dominant role in aftershock activity, while also identifying stress-loading zones with potentially high seismic hazard for the future.
The application of multi-source remote sensing technology effectively compensates for the monitoring shortcomings of single techniques in regions with large deformation gradients. It significantly enhances the informational completeness and spatial continuity of the co-seismic deformation field, providing a reliable method for obtaining high-precision, multi-dimensional surface displacement data. The subsequent inversion of slip distribution and Coulomb stress analysis, based on multi-source deformation data, not only provided a detailed characterisation of the Noto earthquake's fault geometry and rupture behaviour but also further elucidated the triggering mechanisms and spatial control exerted by co-seismic stress perturbations on the aftershock sequence. These findings have deepened our understanding of the seismic rupture dynamics in this region and offer crucial insights for assessing post-seismic hazard risks and identifying potential precursory phenomena.
How to cite: Chengbin, H., Zhao, J., Niu, Y., and Zhou, Z.: 3D Coseismic Deformation and Slip Distribution Inversion of the 2024 Noto Mw 7.5 Earthquake, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16790, https://doi.org/10.5194/egusphere-egu26-16790, 2026.
In April 2024, an extreme flash-flood event occurred in northern Oman. It was prompted by rainfall exceeding one to two years of the regional average within 24 hours. This study assesses three remote-sensing approaches for mapping flood-activated channels in an arid environment: Sentinel-2 Tasseled Cap Transformation (TCT) Brightness, Sentinel-1 amplitude change detection (ACD), and Sentinel-1 interferometric coherence change detection (CCD). The analysis encompassed multi-temporal optical and SAR datasets as well as hydrological terrain indices derived from TanDEM-X elevation data.
TCT and ACD were conducted via the Google Earth Engine API using the harmonized Sentinel-2 surface reflectance collection and radiometrically and terrain corrected Sentinel-1 GRD data. The CCD processing was implemented using a hybrid workflow combining the pyroSAR Python API and the Sentinel Application Platform (SNAP), integrated within an Open Data Cube environment. Long temporal baseline coherence was estimated using annual November acquisitions from 2015–2023. Flood-induced changes were isolated using short (12-day) temporal baseline SAR coherence centred on the April 2024 event and compared to InSAR coherence under stable conditions.
Results show that CCD provides the clearest and most spatially consistent delineation of flood-activated channels. Coherence differences within active channels decreased by up to 0.6 compared to stable conditions, clearly distinguishing disturbed surfaces. The robustness of CCD was verified through a sensitivity analysis. It is less affected by noise than ACD and is effective in integrating flood-related changes over time into a single product. TCT Brightness successfully highlighted bleaching of alluvial deposits under clear-sky conditions, while ACD was most informative where surface water persisted at the time of SAR acquisition.
The combined analysis demonstrates that Sentinel-1 CCD, supported by optical data and terrain metrics, offers a robust and transferable approach for post-event flood mapping in arid regions. Its compatibility with Sentinel-1 acquisition strategies makes it particularly suitable for rapid flood assessment in the context of increasingly frequent extreme rainfall events in arid environment. Integrating DEM-derived morphometrics with event-based observations will allow for identification of where DEM-based channel predictions remain robust and where morphological updating is required.
How to cite: Ullmann, T., Obrecht, L., Löw, J., Plank, S., Hadidi, A., and Sahwan, W.: Post-Flood Channel Mapping in Arid Northern Oman: A comparison of Optical and SAR based approaches, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16866, https://doi.org/10.5194/egusphere-egu26-16866, 2026.
The Emilia-Romagna region (Italy) is characterized by widespread landslide activity, representing a major challenge for land-use planning and risk mitigation. Recent extreme rainfall events have further highlighted the regional susceptibility to slope instabilities (Berti et al., 2024), emphasizing the need for systematic tools to characterize landslide activity at regional scale. In this context, comprehensive monitoring approaches are required to support the Hydrogeological Asset Plan (PAI), particularly under evolving climate conditions.
The University of Bologna is actively involved in the observation and analysis of several landslide sites, in collaboration with the Civil Protection Agency, the Regional Geological Service, and the Po River Basin Authority. With over 80,000 mapped landslides across the region, it is impractical to monitor all of them using traditional ground-based geodetic methods. Therefore, satellite-based Differential Interferometric Synthetic Aperture Radar (DInSAR) data is proposed as a key resource, offering broad spatial coverage and the capability to detect millimetric ground displacements over time.
However, several challenges must be addressed, particularly in rural and mountainous environments affected by complex types of mass movements such as earth flows, earth slides, debris flows, rock slide and rock fall. These phenomena can destroy or displace radar scatterers, reducing the quality and density of DInSAR measurements.
The purpose of this work is to evaluate the feasibility of using satellite interferometry for landslide monitoring in the Emilia-Romagna region and to identify which landslide types are most suitable for DInSAR analysis by combining radar data with the regional landslide inventory prior to the 2023 flood events. Furthermore, the assessment of landslide activity derived from the analyses represents a key outcome of the project and provides valuable support to the PAI.
The SAR acquisitions are from the Sentinel 1 mission, covering the period from 2018 to 2022, and processed using Small Baseline Subset (SBAS) (Berardino et al., 2003) algorithm to derive deformation time series and compute the velocity maps. Geospatial analisys was carried out taking into account the spatial distribution and the density of radar scatterers through a clustering process based on the DBSCAN algorithm (Ester et al., 1996).
Preliminary results indicate that less than 20% of the landslides can be monitored; however, this fraction still corresponds to several thousand landslides. Satellite interferometry therefore represents a valuable tool to be used complementarily with other satellite-based, airborne, and ground-based instrumentation.
The satellite data were processed using the Earth Console service by Progressive Systems, supported by ESA NoR sponsorship.
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(11), 2375-2383.
Berti, M., Pizziolo, M., Scaroni, M., Generali, M., Critelli, V., Mulas, M., ... & Corsini, A. (2024). RER2023: the landslide inventory dataset of the May 2023 Emilia-Romagna event. Earth System Science Data Discussions, 2024, 1-24.
Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996, August). A density-based algorithm for discovering clusters in large spatial databases with noise. In kdd (Vol. 96, No. 34, pp. 226-231).
How to cite: Bartola, M., Berti, M., Zuccarini, A., Dal Seno, N., Rani, R., Ciccarese, G., and Simonelli, T.: Assessing landslide activity in the Emilia-Romagna Region (Italy) through DInSAR analysis to support the Hydrogeological Asset Plan, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19049, https://doi.org/10.5194/egusphere-egu26-19049, 2026.
InSAR time series are widely used to characterize long-term surface deformation, yet coseismic steps can distort displacement histories and bias velocity estimates if they are not explicitly identified and separated. We present a catalog-independent framework to detect and remove multiple coseismic steps from large InSAR time series datasets by exploiting a characteristic earthquake signature: many pixels exhibit near-synchronous displacement steps, while step amplitudes vary spatially. After reducing slowly varying components (e.g., linear trend and seasonal terms), we identify a sparse set of shared changepoint times across displacement histories using a multi-signal shared-changepoint model, enabling recovery of multi-event sequences within a single observation period. For each detected changepoint, we estimate pixel-wise step amplitudes using robust windowed statistics and/or step regression, and then regularize each event’s step-amplitude field on a spatial neighborhood graph using total-variation regularization to enforce spatial consistency, suppress outliers, and preserve sharp gradients expected near faults. Subtracting the regularized steps from the original time series yields de-evented displacement histories and updated long-term deformation rates. The approach is scalable, supports repeated and closely spaced events via joint estimation of multiple steps, and does not require prior event timing information. Applied to multi-year regional InSAR products, the method produces cleaner time series, reduced residual variance, and more stable velocity estimates, improving characterization of gradual deformation in tectonic and volcanic settings.
How to cite: Chen, X., Floris, M., Rosi, A., and Catani, F.: Catalog-independent detection and removal of coseismic steps in InSAR time series using shared changepoints and spatial regularization, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19196, https://doi.org/10.5194/egusphere-egu26-19196, 2026.
Urban tunneling projects pose significant geotechnical challenges, especially in densely populated regions where heterogeneous subsurface conditions increase the risk of ground displacements. Monitoring these displacements is therefore essential to ensure infrastructure safety and minimize potential impacts on surrounding communities. Traditional geotechnical monitoring methods, such as ground-based sensors, achieve sub-millimeter precision with high temporal resolution but are limited in spatial coverage and incur high operational costs. In addition, they may interrupt construction activities and disturb neighborhoods, restricting their deployment to areas directly above critical infrastructure. This limitation often results in incomplete datasets and contributes to legal disputes over alleged tunneling-induced damage. This work investigates the application of Persistent Scatterer Interferometry (PSI) as a complementary technique for settlement monitoring in the expansion of São Paulo Metro Line 2. This large-scale project is expected to benefit approximately 1.2 million people, with a public investment of R$ 13.4 billion. The construction, which began in 2021, is being excavated in Paleogene sediments of the São Paulo and Resende formations of the São Paulo Basin, as well as Quaternary alluvial deposits. Owing to the rift-related tectonic heritage that originated this basin, the local geology is highly heterogeneous, which may result in differential settlement and further reinforces the need for comprehensive monitoring strategies.
Using high-resolution X-band images (1m resolution) from the ICEYE microsatellite constellation, this study employs SARPROZ to evaluate the dataset's coherence and baseline characteristics and assesses the potential of PSI for wide-area monitoring in a dense urban environment. The preliminary results demonstrated the significant challenges inherent in processing high-resolution X-band data from emerging constellations. Specifically, the large perpendicular baselines present in the dataset increased the sensitivity to topographic phase errors and geometric decorrelation, which, combined with strong atmospheric phase screen (APS) effects, hindered the isolation of the deformation signal through conventional linear phase modeling. These findings highlight the critical role of baseline optimization and advanced APS mitigation strategies when applying PSI to microsatellite constellations in tropical urban settings. Despite these constraints, this study provides valuable insights into the feasibility of integrating satellite-based SAR data with in situ monitoring for tunneling projects, offering a pathway toward more comprehensive, reliable, and cost-effective settlement monitoring frameworks to support informed decision-making in large-scale infrastructure development.
How to cite: Bortoluzzi Miraya, B., Moraes Arraut, E., Massayuki Kuwajima, F., Pettersson, M., Javadi, S., and Machado, R.: Measuring vertical ground displacement from São Paulo Line 2 subway perforation with PSInSAR and ICEYE data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20520, https://doi.org/10.5194/egusphere-egu26-20520, 2026.
Interferometric synthetic aperture radar (InSAR) is widely used to measure surface deformation associated with processes such as slow-moving hillslope instability. Although InSAR time series can reach millimetre-level precision under favourable conditions, the practical reliability of deformation maps is often difficult to assess without calibration and validation using in situ displacement measurements, which are rarely available in the remote mountainous settings where landslides commonly occur. This limitation means that key processing choices in operational workflows are frequently set based on user preference and computational constraints, with limited quantitative insight into how they influence the final deformation products and the resulting interpretation. Here we evaluate the sensitivity of Sentinel-1 time-series deformation results produced with the GMTSAR workflow for a study area in northern Pakistan where slow-moving landslides have been reported in the literature. We systematically vary controlling parameters including temporal and perpendicular baseline thresholds, multilooking factors, reference area selection, coherence thresholds, and the length of the time stack. For each configuration, we apply an identical post-processing procedure to detect hillslope deformation anomalies and delineate candidate slow-moving landslides, enabling a consistent comparison of the resulting inventories. We show that these processing choices can substantially affect the mapped landslide population and inferred spatial extent, while also changing the processing effort required to reach a stable solution. The outcomes provide practical guidance for selecting InSAR processing parameters for landslide mapping in data-sparse regions where ground calibration is not feasible.
How to cite: Munir, M. B., Tanyas, H., Chang, L., and van Westen, C.: GMTSAR SBAS Sensitivity for Landslide Mapping in a Mountain Test Site, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21073, https://doi.org/10.5194/egusphere-egu26-21073, 2026.
Ultra-high voltage (UHV) transmission towers are critical infrastructures for the stability and resilience of power systems. Their structural integrity is constantly challenged by conductor tension, thermal expansion, and external loads. Conventional inspection techniques, including UAV surveys and in-situ sensors, are costly and spatially constrained. In contrast, spaceborne synthetic aperture radar (SAR) provides a cost-effective means for wide-area, millimeter-level deformation monitoring.
This study proposes a tower persistent scatterer (PS) simulation and interferometric elevation-phase modeling method that integrates three-dimensional LiDAR-derived tower models with the radar range–Doppler equation. Using high-resolution C-band SAR data from China’s Fucheng-1 satellite, 78 ascending and descending scenes were analyzed over two 500 kV transmission lines in Chongqing. Corner reflectors (CRs) were installed at both tower bases and on tower bodies to provide accurate geometric calibration parameters and high-confidence CR-PS points for tower deformation analysis.
Results demonstrate that CR-based calibration achieved sub-pixel geometric accuracy and millimeter phase precision. The base CRs revealed approximately 8 mm of vertical subsidence over nine months. Tower-body CRs exhibited height-dependent small deformations corresponding to differential thermal expansion at different structural levels.
Two types of deformation estimation were performed: (1) Based on one-year short-baseline interferometric pairs, tower deformation ranges were empirically derived under various temperature intervals, indicating that straight towers exhibited larger deformation amplitudes than strain towers, with descending-track results exceeding 6 rad when ΔT > 25 °C. (2) Using the proposed differential interferometric approach that removes simulated elevation phases, continuous deformation patterns consistent with the empirical thresholds were retrieved, validating the physical effectiveness of the model.
In conclusion, this study confirms the feasibility of using China's high-resolution C-band SAR satellites for long-term, high-precision monitoring of UHV transmission tower deformation. The proposed methodology validates the capability of meter-resolution SAR systems to capture subtle structural deformations and provides a methodological foundation for assessing large-scale infrastructure responses to geological hazards, earthquakes, and typhoons in complex environments.
How to cite: Ma, S., Li, T., Liu, Y., Ren, W., Liu, Y., Yang, Z., Xu, Y., and Xiao, J.: Small deformation monitoring of Ultra-High Voltage transmission towers using China’s high-resolution C-band SAR satellites, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11441, https://doi.org/10.5194/egusphere-egu26-11441, 2026.
Posters virtual: Mon, 4 May, 14:00–18:00 | vPoster spot 3
EGU26-18221 | ECS | Posters virtual | VPS12
PS-InSAR based Slope Deformation Monitoring in the Bhagirathi Valley, Uttarakhand HimalayaMon, 04 May, 14:06–14:09 (CEST) vPoster spot 3
Slow-moving, deep-seated landslides represent a significantly underestimated geologic hazard, incurring huge economic loss and persistent long-term risk to communities annually. Further, they have the potential to evolve into catastrophic events, which necessitates continuous monitoring to better understand their dynamics, minimize potential losses, and implement appropriate mitigation measures. The present study aims at understanding the dynamics of the slow-moving slopes housing villages such as Bhatwari, Raithal, and Barsu in the Bhagirathi Valley, Uttarakhand Himalaya, by means of PS-InSAR techniques. A total of 129 ascending-pass and 114 descending-pass scenes of Sentinel-1, from January-2021 up to March-2025, have been utilized to estimate slope velocities along the radar line-of-sight (LOS) for each pass, using open-source tools such as ISCE and StaMPS. Further, these LOS velocities were decomposed to obtain vertical (up-down) and horizontal (east-west) velocities. The results reveal that Raithal (elevation ~2150 m), on middle of the slope, is subsiding at ~3 mm/year with an eastward movement of ~5 mm/year. Bhatwari (1650 m), on the lower slope, shows eastward creep at ~4 mm/year and upliftment at ~2 mm/year, suggesting rotational landslide activity. Barsu (2262 m), situated at a slope ~3 km upstream, exhibits eastward movement at ~6 mm/year and subsidence at ~3 mm/year. Field investigations corroborate these findings, revealing features such as scarps, cracks, tilted structures, disrupted roads, and longitudinal and transverse ponds. The persistent creeping suggests the potential for sudden slope failure during heavy rainfall or earthquakes, which may dam the Bhagirathi River, and the impoundment may further trigger cascading downstream hazards. Therefore, there is a need for a comprehensive investigation integrating the PS results with the slope stability analysis that assesses the role of geology, rainfall, and earthquakes. This integration shall assist in estimating the risk posed by the failure and further help in mitigation planning.
How to cite: Gupta, A. K., Luirei, K., Gupta, V., and Shawez, M.: PS-InSAR based Slope Deformation Monitoring in the Bhagirathi Valley, Uttarakhand Himalaya, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18221, https://doi.org/10.5194/egusphere-egu26-18221, 2026.
EGU26-19054 | Posters virtual | VPS12
Urban Landslide Monitoring Using PS-InSAR Sentinel-1 Data in Chișinău, Republic of Moldova (2019-2025)Mon, 04 May, 14:09–14:12 (CEST) vPoster spot 3
Chișinău, the capital of the Republic of Moldova, faces significant geohazard challenges due to its unique geological setting on loess-covered plateaus dissected by river valleys and ravines. Urban expansion and infrastructure development have intensified landslide susceptibility in this region, threatening residential areas, transportation networks, and critical infrastructure. This study presents a comprehensive analysis of urban landslides in Chișinău using Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique applied to Sentinel-1 satellite data spanning the last five years (2019-2025).
The PS-InSAR methodology provides millimeter-level precision in detecting and monitoring ground deformation over time, making it particularly suitable for identifying slow-moving landslides and ground subsidence in urban environments. We processed ascending and descending Sentinel-1 SAR imagery to generate time-series deformation maps and identify persistent scatterers across the Chișinău metropolitan area. The analysis revealed multiple zones of significant ground displacement, with deformation rates ranging from -15 to +25 mm/year, concentrated primarily in areas with steep terrain, proximity to water courses, and urban development on historically unstable slopes.
The susceptibility map derived from our analysis indicates high-risk zones in the northern and western sectors of Chișinău, particularly around suburb localities Vatra, Ghidighici, and Durlești, where loesslike deposits on valley slopes are subjected to both natural erosion processes and anthropogenic pressures. The southeastern areas near locality Bubuieci also show elevated landslide susceptibility, correlating with urban expansion into previously undeveloped terrain. Integration of PS-InSAR results with geological maps, digital elevation models, and land-use data enabled the development of a comprehensive landslide susceptibility assessment framework.
Key findings reveal that ground deformation patterns in Chișinău exhibit strong seasonal variations, with accelerated movement during spring months corresponding to snowmelt and precipitation events. Urban infrastructure, including roads, buildings, and utilities, located within identified high-risk zones, shows structural damage consistent with slow-moving landslide activity. The study identifies critical infrastructure corridors, including major transportation routes (E583, E581) traversing the study area, that require enhanced monitoring and mitigation measures.
Acknowledgements: This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS – UEFISCDI, project number 40PCBROMD within PNCDI IV.
How to cite: Sandric, I., Nicoara, I., Spian, C., Tambur, A., Ilinca, V., Jeleapov, V., Irimia, R., Daia-Creinicean, T., and Alexandru, N.: Urban Landslide Monitoring Using PS-InSAR Sentinel-1 Data in Chișinău, Republic of Moldova (2019-2025), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19054, https://doi.org/10.5194/egusphere-egu26-19054, 2026.
