Precise and accurate geolocation of (point) scatterers is crucial for the correct interpretation of InSAR time series in the built environment, since this allows the scatterers to be linked to physical objects. The precision and accuracy of the geolocation is dependent on orbit precision, sub-pixel scatterer localization within the SAR images, as well as a range of geophysical, SAR processor, and instrument-related corrections.

Focusing on the abundantly available Sentinel-1 SAR acquisitions, previous studies on 40 corner reflectors in  Australia, with 30 acquisitions aligned towards ascending orbits (Garthwaite et al., 2015), and georeferenced using annual GNSS campaigns, yielded a positioning dispersion (1sigma) of 6~cm in range and 26~cm in azimuth, and residual offsets of 3~cm (range) and 18~cm (azimuth) (Gisinger et al., 2021).  

Here we report on new results applied on the network of Integrated Geodetic Reference Stations (IGRS) in the Netherlands, which currently consists of 80 corner reflectors on 40 stations (i.e., ascending and descending) on the same physical construction, equipped with permanent GNSS stations.   The already developed end-to-end methodology for SAR geolocation is revised, and applied to Sentinel-1 interferometric wide swath (IW) data from 257 ascending and 263 descending acquisitions.  Our results confirm the validity of the applied corrections.

How to cite: Hanssen, R. and Bazzocchi, P.: Geolocation accuracy and precision for InSAR point positioning; validation using the Dutch IGRS network, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21827, https://doi.org/10.5194/egusphere-egu24-21827, 2024.

In InSAR time series analysis for displacement studies, the essential parameter estimation part is performed on arcs between a reference point and an evaluation point. Usually, both points are scatterers that satisfy certain optimality conditions. The set of parameters to be estimated in the functional model can be different for each arc. Especially in the built environment, individual points may behave rather differently. Conventional approaches to estimate average displacement velocities for the entire time series length are therefore often sub-optimal. However, deviation from a single uniform parameterization for all arcs implies that for each arc the optimal model needs to be selected.

Chang and Hanssen (2016) proposed a method to select the optimal functional model, i.e. parameterization, for each arc using multiple hypothesis testing (MHT). The selection was based on rejecting the conventional null hypothesis of linear steady-state displacement, satisfying Newton’s first law, in favor of an alternative hypothesis that is chosen from a library of canonical models. This procedure required the a priori selection of a significance level (related to the impact of the erroneous rejection of the null hypothesis), the discriminatory power (related to the impact of erroneously sustaining the null hypothesis), and the stochastic model of the arc observations. For the latter, a conservative uniform approximation was chosen.

Recently, Brouwer and Hanssen (2023) developed a methodology to approximate the stochastic model for each scatterer in an InSAR time series analysis, based on amplitude behavior. By combining both approaches, i.e., applying the MHT approach for functional model selection using a point- and epoch-specific stochastic model, we significantly reduce both Type-1 and Type-2 errors, leading to the improved identification of dynamic mechanisms in a complex environment. We report on the mathematical background, the level of improvement in practical case studies as well as the numerical consequences of the approach.

References:

W.S. Brouwer, Y. Wang, F.J. van Leijen, and R.F. Hanssen. ”On the stochastic model for InSAR single arc point scatterer time series.” In IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium, pp. 7902-7905. IEEE, 2023.

L. Chang and R.F. Hanssen, ”A Probabilistic Approach for InSAR Time-Series Postprocessing,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 1, pp. 421-430, Jan. 2016, doi: 10.1109/TGRS.2015.2459037.

How to cite: Brouwer, W., Chang, L., and Hanssen, R.: Selecting optimal displacement models using an improved stochastic model in InSAR arc-based time series analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21683, https://doi.org/10.5194/egusphere-egu24-21683, 2024.

Satellite missions have delivered a wealth of SAR images for Earth monitoring applications since the 1990s. Due to the complex nature of SAR images and a limited amount of accessible SAR labeling data, these images remain underutilized in providing reference information for machine learning. In response to this gap, we designed a SAR feature creation workflow in an operational framework by releasing Jupyter tools to the public.  The workflow is developed upon Doris-5 and consists of two streams. The first stream utilizes SAR images to generate basic SAR and SAR interferometric and polarimetric features. The second stream capitalizes on other available geospatial datasets, such as optical images, cadastral and geological maps, to generate additional features for SAR data that can be treated as reference data. They are first radar-coded to align with the extracted SAR features and then geo-coded in geographic coordinates. All SAR features are concatenated as separate layers in the NetCDF data format, which contains STAC (spatio-temporal asset catalogs) for the data querying.

For the demonstration, an area in the province of Groningen, the Netherlands, was selected as the test site. Seven ascending Sentinel-1A images in VV and VH modes on track 15 between January and March 2022 were used, along with the topographic base map – TOP10NL dataset as a reference. The extracted features encompass VV amplitude, VH amplitude, VV interferometric phase, VV coherence, intensity summation, intensity difference, intensity ratio, cross-pol correlation coefficient, cross-pol cross product, entropy, buildings, roads, water and railways. The first ten features were created via the first stream, while the last four features via the second stream. By applying a random forest classifier to these fourteen SAR features, the model resulted into four types of classified SAR images: building, road, water and railway. The overall accuracy was 0.8558, 0.9939, 0.9065, and 0.8191, with corresponding F1-scores of 0.9191, 0.9669, 0.9490, and 0.9006, respectively.

We conclude that the created SAR features well facilitate machine learning, and that even a simple random forest classification can yield relatively high-accuracy results. In addition, our workflow to create SAR features is well suited to prepare labeled features for machine learning analyses that are even friendly to a user with limited knowledge of SAR.  

How to cite: Zhang, X., Chang, L., and Stein, A.: An operational way of SAR feature creation to facilitate machine learning analyses, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2017, https://doi.org/10.5194/egusphere-egu24-2017, 2024.

Synthetic Aperture Radar (SAR) remote sensing has become indispensable for monitoring natural and anthropogenic hazards. Here we present a novel application of SAR technology in the ecological domain. Focusing on Zavodovski Island, home to the world's largest penguin colony, we address the challenges posed by limited optical satellite observations due to persistent cloud cover and rare ground surveys due to the extreme remoteness of the study site.

Our proposed approach involves the application of an AI-driven classification algorithm to high spatiotemporal resolution X-band radar interferometric coherence data. Despite the inherent limitations of optical observations, we showcase the potential of dual-polarimetric, high-resolution SpotLight SAR in measuring phenology and its effectiveness in detecting, mapping, and monitoring penguin colonies on Zavodovski Island. The study leverages temporally dense SAR data, utilizing the TerraSAR-X and PAZ satellite systems, and spatially high-resolution data gathered during two field campaigns in February and December 2023.

This research not only highlights the innovative use of SAR in ecological monitoring but also underscores the broader applicability of SAR technology in diverse domains. By contributing to the understanding of penguin colony dynamics, our study exemplifies the transformative impact of SAR remote sensing on ecological health indices. This contribution demonstrates the capabilities of SAR technology in addressing unique challenges and expanding its utility beyond traditional hazard applications.

How to cite: Richter, N., Schade, M., Cartus, O., and Hart, T.: AI-Driven Classification of X-band Radar Dual-Polarimetric Coherence Data for Mapping and Monitoring Penguin Colonies on Zavodovski Island, South Sandwich Islands, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22189, https://doi.org/10.5194/egusphere-egu24-22189, 2024.

Since the launch of the Sentinel-1 mission and with the upcoming NISAR mission, SAR data is continuously and freely available, which can be used for geodetic monitoring and assessing hazards related to infrastructure. Continous deformation monitoring requires a regular update of the displacement time series based on newly acquired images to assess the current structural health status of the observed infrastructure. Existing InSAR time series methods, in particular PSI methods, however, are designed for a fixed SAR stack size of a particular study period and unwrap the phase time series for pixels which exhibit a coherent signal over the whole study period. Therefore, before phase unwrapping, it is essential to assess if the pixels preserve phase coherence in the interferograms related to the new image.

Previous studies proposed an amplitude change detection which divides the study period into two subsets and tests whether the amplitude distributions of the subsets stem from different Rayleigh distributions and, hence, from different scatterers. All possible splits of the stack into two subsets are tested and the split with the highest test score is selected as the change point. This approach has the drawback that the changes can hardly be identified if one of the subsets contains only very few images, which would be the case for a sequential update for continuous monitoring purposes. Moreover, other studies demonstrated that the time of change in the amplitude might not coincide with the time of change in the coherence of the phase. Therefore, they suggested an unwrapping-based change point refinement to the amplitude-based method to identify the change point in the temporal coherence of the phase.

Here, we propose a new method for assessing the decorrelation in a sequential updating framework using a Kalman filter.
Our approach estimates the temporal coherence for the newly acquired image. We extend the Temporal Phase Coherence (TPC) approach from Zhao and Mallorqui (2019) which approximates the phase noise of a scatterer by subtracting the spatial low-pass filtered phase of the immediate neighbourhood of the scatterer in each interferogram. To assess the coherence of the new image, we connect the new image to all previous images within a fixed time of e.g. one year. We use the Kalman filter to predict and update the TPC for each new image and apply a threshold on the TPC to distinguish coherent from incoherent pixels. This approach comes with the advantage that neither amplitude analysis nor unwrapping of the phases is required to assess the coherence of a scatterer in the new image.

We perform a case study in Nordrhein-Westfalen, Germany, along the Sauerland-Autobahn to demonstrate the effectiveness of the proposed Kalman filter for sequential coherence estimation. Along the Sauerland-Autobahn, several highway bridges need to be rebuilt due to structural health problems arising from their ageing process. We coregister a stack of Sentinel-1 images using the InSAR Scientific Computing Environment (ISCE).
The dataset covers eight years of data from ascending and descending orbits. We compare our proposed phase-based coherence estimation with the results from amplitude-based change detection.

How to cite: Haghshenas Haghighi, M. and Piter, A.: Kalman filter for sequential temporal coherence estimation for multi-temporal InSAR, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11830, https://doi.org/10.5194/egusphere-egu24-11830, 2024.

Differential Synthetic Aperture Radar Interferometry (DInSAR) products are often contaminated by atmospheric contributions, frequently referred to as atmospheric artifacts or atmospheric phase screen (APS) signals [1]. Specifically, the velocity and, consequently, the path length of the radar signal propagating through the inhomogeneous atmosphere layers can vary among repeat-pass SAR acquisitions. Therefore, these variations can be erroneously interpreted as due to deformation signals. Accordingly, filtering out the APS component from DInSAR products, for correctly retrieving deformation measurements, represents a challenging issue.

In this work we present a statistical analysis to investigate the performance of the Sentinel-1 (S1) Extended Timing Annotation Dataset (ETAD) data for mitigating the APS component in both DInSAR interferograms and deformation time-series. The ETAD product consists of different correction layers that provide the azimuth and range timing shifts for each S1 burst to achieve a precise geolocation with centimetre accuracy [2]. It is worth noting that, although the ETAD dataset is not primarily designed for the interferometric phase filtering, some of its correction layers, specifically, the tropospheric, the ionospheric, and the solid Earth tidal displacement ones, may be effectively used to filter the APS signal component affecting the DInSAR measurements [2].

For the presented experimental analisys we used 104 S1 images acquired along ascending orbits over Central/Southern Italy, during the 2018-2020 time-span. The exploited DInSAR products have been generated at medium spatial resolution (about 40 m) and are represented by 278 interferograms and by the corresponding displacement time series retrieved through the P-SBAS processing chain [3].

To quantitively evaluate the ETAD APS correction performance, we analyzed first their impact on DInSAR interferograms. In particular, we considered the following statistical metrics for the entire dataset of unwrapped interferograms, before and after the application of the ETAD APS correction: i) standard deviation of the interferograms at different spatial scales and ii) empirical variograms of the unwrapped phase fitted with a parametric exponential model. Subsequently, to investigate the impact of the ETAD APS correction on the deformation time-series, we analyzed the behavior of their variance before and after the application of the ETAD correction.

The measured metrics results, although still preliminary, demonstrate the effectiveness of the ETAD APS corrections for both the DInSAR interferograms and the deformation time-series in more than 90% of the cases.

[1]  I. Zinno et al., "On The Exploitation of ETAD Data for the Atmospheric Phase Screen Filtering of Medium/High Resolution DInSAR Products," IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 2023, pp. 7882-7885.

[2]  C. Gisinger et al., "The Extended Timing Annotation Dataset for Sentinel-1Product Description and First Evaluation Results,"  IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-22, 2022, Art no. 5232622

[3]  M. Manunta et al., "The parallel SBAS approach for Sentinel-1 interferometric wide swath deformation time-series generation: Algorithm description and products quality assessment," IEEE Transactions  on Geoscience and Remote Sensing, vol. 57, no. 9, pp. 6259-6281, 2019

How to cite: Casamento, F., Lanari, R., and Zinno, I.: On the exploitation of ETAD data to filter out atmospheric artifacts from Sentinel-1 medium-resolution DInSAR products: a performance evaluation analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16359, https://doi.org/10.5194/egusphere-egu24-16359, 2024.

Synthetic aperture radar (SAR) interferometry (InSAR) is a well-established technology for precise monitoring of ground motions (due to subsidence, landslides, volcanic and seismic phenomena) with millimeter accuracy from time series of satellite SAR images. A crucial aspect of this technology involves identifying points exhibiting interferometric phase coherence across acquisitions in an image stack, typically corresponding to man-made structures, rocks, or bare soil, irrespective of the scattering mechanism (point-like or distributed). Coherent point identification faces challenges, particularly due to atmospheric and other systematic disturbances affecting the phase. Various techniques have been presented in the scientific literature, relying on statistics of stack image amplitudes (such as amplitude dispersion and signal-to-clutter ratio) and/or phases in spatial and temporal domains.

This work presents a new algorithm we have recently developed, named Point Coherence Estimation (PCE), for identification of coherent points. The temporal coherence (related to phase noise) of each point is derived from the coherences between pairs of points, directly calculable, through an effective and clean procedure, without the need for spatial averages, amplitude/phase calibrations, or critical assumptions.

The algorithm begins by examining phase differences between neighboring points, within tens or hundreds of meters. As known, temporal coherences of these point pairs can be estimated exploiting the cancellation of spatially correlated components in phase differences (such as atmospheric and orbital artifacts, large scale motions) and determining temporally correlated components related to ground motion and elevation. The temporal coherence of each point pair primarily depends on the phase noises (temporal, spectral, geometric decorrelations, thermal noises) of the two points.

Assuming statistically independent phase noises in neighboring points (possibly excluding the nearest neighboring pixels if the images are oversampled), the expected value of temporal coherence for each pair is shown to be the product of the temporal coherence expected values for the two paired points. By taking the logarithm of these equations, an overdetermined system of linear equations is derived, which can be solved by minimizing the equation residuals according to the L1 or L2 norm, using existing efficient solvers such as linear or quadratic (LP or QP) programming solvers. The solution provides a reliable estimate of the temporal coherence for each point.

Importantly, the PCE method operates without assuming any probability distribution of phase noise. Moreover, the PCE algorithm can be applied to full-resolution data as well as to data with degraded resolution for a previous multi-look or distributed scattering processing. In addition, the algorithm provides consistent results if applied to preselected candidate coherent points to reduce computational time (however absolutely affordable even without point preselection).

Extensive testing, including stacks of Sentinel-1 interferometric SAR images over different scenarios, showcased the method effectiveness. PCE provided reliable measurements of temporal coherence and phase noise variance for each point, enabling detection of coherent points with minimal missing or false detections. The tested areas, affected by diverse displacement phenomena, showcased the algorithm applicability across different land cover types and geological features.

The PCE method will be made available to the geoscience community as software as a service (SaaS).

How to cite: Costantini, M., Minati, F., Vecchioli, F., and Zavagli, M.: Point Coherence Estimation (PCE) in SAR interferometry, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13336, https://doi.org/10.5194/egusphere-egu24-13336, 2024.

The Nile Delta represents the most critical part of Egypt, hosting more than 50% of the population and approximately two-thirds of the nation’s agricultural lands. During the last decades, the Nile Delta has suffered from significant surface deformation that has led to damage to transport networks and infrastructures, thus becoming a significant risk in the area. This deformation is mainly due to environmental changes and anthropogenic activities such as the over-extraction of groundwater for different purposes and the building of dams inside and outside Egypt along the river Nile. These activities have led to shortage of water inflow, changing the discharge rates, reduced sedimentation in the delta and changes in the water recharge rates of the Nile, the primary source of water for the Nile Delta aquifers. Regarding the environmental changes, the shifts in climate patterns, including variations in precipitation, rising temperatures, and rising sea levels, have all altered the hydrological balance of the Nile Delta, consequently leading to variations in surface deformation patterns. Many studies have studied the rate and patterns of land deformations in the Nile Delta based on geodetic tools such as Global Positioning System (GPS) and Interferometric Synthetic Aperture Radar (InSAR). But still, there is a gap in understanding the relationship between surface deformation rates and the causative factors. Such understanding could potentially enable the estimation of system response for future scenarios.

In this research, we present the results of a system that uses Sentinel-1 SAR data characterized by VV polarization, with ascending and descending orbital directions, acquired between 2015 and 2023 along the Nile Delta. We utilized open-source LiCSBAS tools to analyze the surface deformation rates from InSAR Sentinel-1 data. Then, we calculated the vertical deformation velocity over time by decomposing the ascending and descending LOS data. Then, we analyzed the surface deformation results obtained with the present methodology against the freely available geospatial data, which represents the possible causative factors (such as rainfall, water body change, total terrestrial water storage, land use-landcover, temperature, etc.), to understand their relations and their impact. By linking the surface deformation to its causative factors through machine learning techniques such as Random Forest, our research aims to provide a better understanding of the system dynamics and an appropriate model for prediction. This model can be utilized by decision-makers to consider and manage risks associated with severe surface deformation for possible future scenarios. The results should enable the design of future mitigation actions to protect Egyptian society and make it more resilient to the consequences of land deformation over the Nile Delta.

How to cite: Zaki, A., Manzella, I., Lazecky, M., Hooper, A., Chang, L., Meijde, M. V. D., and Fadel, I.: Surface deformation along the Nile Delta, Egypt: From monitoring towards prediction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8929, https://doi.org/10.5194/egusphere-egu24-8929, 2024.

The Vietnamese Mekong Delta (VMD) is facing several environmental challenges, including coastal erosion and land subsidence. Subsidence rates of up to several centimeters per year have been reported, which are an order larger than the regional sea level rise of about 3.3 mm/yr. The associated risks are an increased vulnerability to flooding, salinization of water resources and permanent inundation. Precise monitoring of land subsidence with high spatial and temporal resolution is essential to support the study of the associated causes and hazards as well as appropriate countermeasures.

Here, we present results of land subsidence monitoring between 2017 and 2022 in the VMD based on Sentinel-1 Persistent Scatterer Interferometry (PSI). We used Sentinel-1 scenes from ascending and descending orbits and applied an advanced PSI approach. The advancements of the algorithm include the integration of Temporary Persistent Scatterers to derive the best possible Persistent Scatterer network for long time series. Furthermore, we developed a method to optimally integrate reference pixels in order to suppress spatially correlated noise in the subsidence time series. Due to a lack of well distributed geodetic references, we made use of an infrastructural reference network consisting of large bridges with deep foundation depths, which are nearly unaffected by subsidence.

We present the derived subsidence rates and exemplary subsidence time series across the study area. Additionally, we highlight specific spatial and temporal features in the subsidence, which can be associated with land use characteristics and environmental influences.

How to cite: Dörr, N., Schenk, A., and Hinz, S.: Recent Land Subsidence in the Vietnamese Mekong Delta derived from Advanced Sentinel-1 SAR Interferometry, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20069, https://doi.org/10.5194/egusphere-egu24-20069, 2024.

The availability of displacement maps across extensive regions, based on Multi-Temporal SAR satellite interferometry techniques (MT-InSAR), has notably increased in recent years. The launch of the Copernicus Sentinel-1 satellites in 2014, ensuring global and regular acquisitions under an open and free data distribution policy, marked a turning point in both exploitation and application. A significant outcome of this progress is the development of regional, national, and continental ground motion services, providing comprehensive displacement maps that offer highly detailed information regarding both human activities and natural phenomena. Since 2022, the European Ground Motion Service (EGMS) freely provides billions of displacement Measurements Points (MP) updated annually, covering nearly the entire European territory, characterized by high reliability and millimetric precision. Despite the full potential usefulness of these maps for territorial management and risk assessment, these data remain still underexploited. With the aim of improving the operational use of this extensive catalogue, there is a critical need for automated tools that can simplify and accelerate the extraction, analysis, and interpretation processes. The production of derived and simplified maps is crucial for the uptake of the EGMS products by both expert and non-expert InSAR users. In response to this need, projects such as the European RASTOOL (DG-HECHO, UCPM-PJG-101048474) and the Spanish SARAI (MCIN/AEI) have made concerted efforts to explore both artificial intelligence and deterministic approaches. In this context, we present the deterministic methodologies and the developed tools, called ADATools (i.e., Active Deformation Areas Tools). Designed to be flexible, adaptable, and user-friendly, these tools aim to support territorial and risk management, with specific attention given to compatibility with EGMS data formats. The first tool, ADAFinder, is an improvement of a previously consolidated tool developed in the frame of previous European projects. ADAFinder allows the automatic extraction and selection of most significant ADAs, a crucial initial step in transitioning from a multitude of individual MPs to a manageable number of polygons to be further analysed or used. Following the identification of moving areas or ADAs, the ADAClassifier permits a preliminary evaluation of the processes likely causing the displacement. Incorporating auxiliary data, each ADA receives a preliminary characterization, allowing to visualize associations with landslides, subsidence, sinkhole, construction settlements, or uplift. Furthermore, a temporal characterization based on the automatic analysis of the time series from all MPs within each ADA is provided. Both temporal and phenomena characterizations are then used for an initial ranking of ADAs, considering their potential impact on structures and infrastructures, using the ADAImpact. In this presentation, examples of results obtained by applying the ADATools to the EGMS data will be presented, emphasizing strengths, and outlining perspectives for future improvements. This work is part of the Spanish Grant SARAI, PID2020-116540RB-C21, funded by MCIN/AEI/ 10.13039/501100011033.

How to cite: Barra, A., Cuevas-González, M., Navarro, J., Béjar-Pizarro, M., Ezquerro, P., Bianchini, S., Zezere, J. L., Medici, C., Del Soldato, M., Palamà, R., Shahbazi, S., Mateos, R. M., Poyiadji, E., Alfonso Jorde, D., Crosetto, M., and Monserrat, O.: ADATools: free and user-friendly tools to semiautomatically extract and analyse wide PSI displacement maps. Applications to the European Ground Motion Service (EGMS). , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20708, https://doi.org/10.5194/egusphere-egu24-20708, 2024.

Severe subsidence poses a significant threat to coastal river deltas, bringing substantial hazards. The Yellow River Delta, being the youngest of such deltas, is particularly vulnerable due to its unstable sedimentary structure. It has experienced significant subsidence as a result of frequent human activities. The InSAR technology facilitates large-area, long-term deformation monitoring of the Yellow River Delta region. Current research has identified that subsidence in the Yellow River Delta is primarily influenced by groundwater extraction and oil exploitation. However, there is a lack of comprehensive study on the spatiotemporal patterns of subsidence induced by different human activities. To acquire extensive and long-term surface subsidence information in the Yellow River Delta region and analyze its causes, this study utilizes 92 ascending Sentinel-1 satellite images and 79 descending Sentinel-1 satellite images. Applying the SBAS-DInSAR technique, the research investigates ground subsidence in the area from January 2019 to April 2022. The study includes an integration of interferometric fringes and DInSAR deformation monitoring results, along with ascending and descending orbit deformation rates for internal accuracy cross-checking. Additionally, it examines the spatiotemporal evolution characteristics and causes in different human-impacted areas. The results indicate that the deformation rates from ascending and descending orbits show high consistency, with a correlation coefficient exceeding 0.8. Significant subsidence areas in the Yellow River Delta are concentrated in the eastern coastal regions, with the maximum subsidence rate reaching approximately -255 mm/year. The primary human-induced factors contributing to subsidence include the extraction of underground brine, oil, and groundwater. Additionally, natural factors like temperature, precipitation, and evaporation also impact subsidence. Notably, the deformation rate changes induced by precipitation exhibit a delayed response. Human activities, with their varying types and intensities, impart distinct temporal characteristics to subsidence. In areas like wetlands, urban regions, and farmlands, deformation is influenced by changes in groundwater levels, resulting in smaller deformation magnitudes and fluctuating deformation rates affected by variations in rainfall, temperature, and evaporation. Oil extraction deformation exhibits long-term change characteristics, influenced by the volume of oil extracted and water injection; from January 2019 to July 2020, a subsidence trend was observed, followed by a slow rebound after July 2020. Subsidence induced by groundwater extraction is characterized by rapid and stable deformation rates. Temporally, significant linear rate subsidence occurred from January to August 2019, with varying degrees of rebound from June 2019 to March 2020, followed by a return to subsidence. Influenced by heavy rainfall, there is a minor rebound each year from July to August.

How to cite: Yu, B. and Ma, D.:  Spatiotemporal Patterns of Human-Induced Subsidence in the Yellow River Delta Region revealed by Ascending and Descending Orbit Time-Series InSAR , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20003, https://doi.org/10.5194/egusphere-egu24-20003, 2024.

In 2018, NASA funded a project to develop and mature automated oil spill detection and thickness estimates from synthetic aperture radar (SAR) and optical imagery, based on focused field testing combined with in situ oil sampling and airborne imaging with the UAVSAR L-band SAR.  The goal was to implement these new algorithms and databases in a semi-automatic system that NOAA uses operationally to detect and assess oil spills and post-storm offshore damage and debris.  The study's field validation site was the Coal Oil Point seep field, an area of natural seep activity located in the Santa Barbara channel, California, which leaks approximately 100 barrels of crude oil per day.  There were three campaigns to collect calibration/validation data, in May 2021, October 2021, and June 2022, during which UAVSAR overflew boat crews collecting optical images from a drone and some water samples for thickness and viscosity analysis.  The data was combined with available satellite SAR and optical imagery collected around the same time and used to develop an algorithm for classifying oil by relative thickness based upon contrast with clean ocean.  One algorithm is tailored for Sentinel-1 data and uses Machine Learning (ML) methods, and the other is an analytical analysis that can be applied to any radar frequency's data.  The data acquired in June 2022 has been used additionally to evaluate differentiation of mineral oil slicks from low wind radar-dark areas using a series of rapid repeat SAR images. In this talk, the study, data collections, and developed algorithms and methods will be presented.

How to cite: Jones, C., Monaldo, F., DeChamps, B., Di Pinto, L., Garcia-Pineda, O., Graettinger, G., Helfrich, S., Holt, B., Johansson, M., Quigley, C., Ramirez, E., Staples, G., and Tulis, D.: Overview and Outcome of the NASA Marine Oil Spill Thickness (MOST) Project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13793, https://doi.org/10.5194/egusphere-egu24-13793, 2024.

The long-term over-extraction of groundwater in the North China Plain(NCP) has led to disasters such as ground subsidence, ground fissures, and seawater intrusion. These have posed serious threats to infrastructure, aquifer systems, and the ecological environment. By establishing a functional model of surface deformation and groundwater changes, we can enhance our understanding of the mechanisms behind ground subsidence and quantitatively assess how groundwater storage evolves under the dual influences of human activities and natural processes. InSAR (Interferometric Synthetic Aperture Radar) has proven to be the most effective tool for long-term、wide-area ground deformation monitoring and underground hydrological parameter inversion. Considering the characteristics of ground subsidence such as long-term, progressive, and wide distribution, in this study, eight stacks of 1496 Sentinel-1A/1B SAR scenes spanning from 2017 to 2023 were acquired in ascending mode along tracks T40 and T142. Through methods like (InSAR) time series analysis method、phase unwrapping correction and spatio-temporal smoothing fitting, we obtained a long-time-series high-precision LOS deformation field for the NCP. Secondly, by introducing the spatial domain network adjustment method, we can implement joint adjustment and correction of wide-area multi-map results to obtain a unified spatio-temporal reference for the NCP's wide-area InSAR vertical ground deformation field. Finally, we use the InSAR-VSM model based on elastic half-space and the one-dimensional poroelastic model considering elastic unloading to obtain independent quantitative estimates of groundwater loss in NCP. For the first time, we have reduced the spatial resolution of groundwater reserves inversion in the NCP from hundreds of kilometers to several kilometers, breaking through the bottleneck of insufficient spatial resolution in groundwater hydrological research. By integrating the South-to-North Water Diversion Project, groundwater extraction policies in the NCP, meteorological datasets, and groundwater level data, we have found that there are significant differences in the response mechanisms of groundwater and land subsidence between piedmont plains and flood plains in the NCP. Our data and results have enhanced our understanding of changes in groundwater storage in the North China Plain and provide scientific support for scientifically managing groundwater resources and carrying out related work on preventing and mitigating ground subsidence disasters.

How to cite: zhang, X. and hu, J.: Wide-area deformation surveying and Groundwater Volume Loss assessment in the North China Plain by Multi-track InSAR Observations and mechanical models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8212, https://doi.org/10.5194/egusphere-egu24-8212, 2024.

Geohazards related to ground motion are widespread in mountainous regions. Time series of spaceborne SAR data are commonly used to retrieve maps of ground motion with extensive spatial coverage. However, there are situations where terrestrial radar systems are more suitable or even necessary for measuring ground motion. Such situations include slopes facing north or south, where the line of sight of current space-based SAR systems is nearly perpendicular to the prevalent direction of ground motion; slopes that lie in radar shadow or layover for current spaceborne SAR geometries; fast-moving landslides requiring shorter interferometric measurement intervals; and cases demanding higher spatial resolution or higher frequencies (e.g., Ku-band) with better sensitivity to line-of-sight motion.

Terrestrial stationary radar/SAR systems, typically operating at Ku- or X-band, have been employed for many years to address these challenges. However, their limited synthetic aperture (or antenna size in the case of real-aperture radars) result in a constant angular resolution in the azimuth direction, leading to a  reduced spatial azimuth resolution with increasing distance.

Monitoring a landslide from a moving car or a UAV with a longer synthetic aperture allows using lower frequencies such as L-band, offering good spatial resolution at the meter level. Aperture synthesis from a car or UAV at higher frequencies (e.g., Ku-band) with smaller radar antennas can significantly improve the azimuth resolution to sub-meter or even decimeter level compared to stationary terrestrial radar systems which typically have azimuth resolutions in the order of 10m and more at range distances of several kilometers.

In our previous work, we had demonstrated mobile mapping of ground motion using a compact repeat-pass L-band interferometric SAR system on a car and a UAV. Recently, a Ku-band SAR system (a modified version of the Gamma Portable Radar Interferometer (GPRI)) was added to the car-borne InSAR measurement setup. The new configuration allows simultaneous data acquisitions at both frequencies during repeat-pass SAR measurements while driving along a road.

Frequency diversity proves to be advantageous in mountainous areas with varying land cover and motion processes with different velocities and scales. In this contribution, we present recent results from car-borne mobile mapping campaigns in the Swiss Alps showcasing the dual-frequency car-borne SAR setup (Gamma L-band SAR and modified GPRI at Ku-band). In particular, we present ground motion measurements of the Brinzauls landslide in Switzerland taken in fall 2023 at both frequencies, Ku- and L-band, and at different time intervals. The case study strikingly shows the complementary properties of the two frequencies in terms of sensitivity to motion and temporal decorrelation. The unprecedented high-resolution SAR imagery and interferograms obtained with the car-borne Ku-band SAR (decimeter-level azimuth resolution) allows discriminating different bodies of the landslide moving at different velocities in detail.

How to cite: Frey, O., Werner, C., and Caduff, R.: Dual-frequency high-resolution mobile mapping of ground motion of the Brinzauls landslide in Switzerland with a car-based interferometric SAR system at L- and Ku-band, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17107, https://doi.org/10.5194/egusphere-egu24-17107, 2024.

The planned soil dumping in Shenzhen has been insufficient for a long time, and improper disposal of excess soil would pose significant safety hazards such as soil settlement and landslides. Therefore, analyzing the causes and potential impacts of soil dumping disasters is crucial for effective risk prevention and control. Interferometry Synthetic Aperture Radar (InSAR) is an effective land surface deformation monitoring technology with unique advantages, including low costs, large-scale implementation potential, and a high coherence level in the settlement analysis of soil dumps without vegetation.

This study investigated a soil dump with the highest risk potential in the Shenzhen-Shanwei Special Cooperation Zone, processing 91 Sentinel-1 images from 2019 to 2022 for deformation monitoring. An improved Small Baseline Subset-InSAR (SBAS-InSAR) method was utilized to analyze the soil dump’s time-series deformation, and multi-source remote sensing data were used for auxiliary interpretation. The experimental results indicate that rainfall, high temperature, and construction vibrations may cause instability of the soil dump. When the monthly rainfall is 200 mm/month and the temperature is 30℃, the meteorological conditions significantly impact the soil dump’s stability. In addition, vegetation and drainage procedures can help resist the impact of high temperatures and rainfall. Activities such as slope excavation, earthwork filling, and gravel production are also the main causes of settlement fluctuation in the soil yard. However, with artificial excavation, unloading, and comprehensive management, the soil dump’s LOS velocity decreases by 10% to 45%.

The Chishi soil dump remains stable during the original period and does not show a subsidence trend until the soil dump is formed. The overall settlement rate is around -33.3mm/yr, and most severe settlement occurs near the north slope with a maximum deformation rate of about -51mm/yr. The closer to the landfill’s center, the greater the soil thickness and the more severe the settlement. Moreover, with similar soil thicknesses, the settlement rate on the slope is higher than that at the top of the soil dump.

The limit equilibrium analysis of the soil dump indicates a risk of instability under continuous heavy rainfall. Therefore, a depth-integrated continuous medium model was introduced to simulate the surface process and analyze the potential landslide risk. The landslide simulation results demonstrate that a landslide will likely occur, harm personnel, and damage the buildings on the north side of the soil dump when it is saturated (≥ 0.50). This research can provide case references for the analysis, interpretation, disaster prevention, and control evaluation of similar soil dumps.

How to cite: Qin, X., Huang, Y., Xie, L., Shi, X., and Wang, C.: Settlement and Landslide Risk Analysis of Temporary Soil Dump Based on InSAR and Continuous Medium Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2957, https://doi.org/10.5194/egusphere-egu24-2957, 2024.

In the dynamic landscape of the northwest Indian Himalayas, the Leo Pargil Landslide stands as a monumental example of slope instability. This study marks the first detection of this giant landslide through Interferometric Synthetic Aperture Radar (InSAR) techniques. Spanning an impressive 55 km² and mobilizing approximately 25 km³ of rock material towards the Spiti River at a rate of 80 mm/year, it poses significant risks to several villages, towns, and the NH505 highway located atop and along its path.

Deep-Seated Gravitational Slope Deformations (DSGSDs), such as the Leo Pargil Landslide, are pivotal in shaping mountainous landscapes. These giant landslides significantly influence topographic evolution, particularly in regions marked by rapid rock uplift in steep terrain. Understanding these processes is crucial, given their geological significance and the natural hazards they pose to communities in tectonically active regions. However, their inherent unpredictability, influenced by factors like geology, geomorphology, climate, and seismic activities, makes evaluating landslide dynamics a challenging task.

The Leo Pargil Landslide, bounded by the northeast-trending Leo Pargil Shear Zone (LPSZ) and incorporating several brittle normal faults, is conditioned by at least three geological factors: steep slope terrain, the bedding structure of the rock formation, and deep river incision at the base of the landslide. Our study investigates the factors conditioning the landslide, the driving forces behind it, and its evolution, offering new insights into the underlying mechanisms of failure.

In this study, we utilized Sentinel 1A/B satellites, applying Persistent Scatterer (PS) InSAR processing techniques to analyze the active dynamics of the Leo Pargil landslide. By combining InSAR derived velocity field data with the local geology, geomorphological features of the slope and previously published geochronological data we tried to elucidate the possible mechanisms involved in the initiation and development this landslide.

The findings underscore the role of dome exhumation as a geomechanical driver of slope dynamics. The transition in stress fields, tectonic and structural influences, and the interplay between erosion, river incision, and monsoon precipitation anomalies are highlighted as significant factors in the landslide's development. This comprehensive understanding is vital for slope stability assessments and risk mitigation strategies in the Himalayan region.

In conclusion, the study links geomechanical, geochronological, and geomorphological analyses to unravel the complexities of the Leo Pargil Landslide. It emphasizes the significance of the Leo Pargil Dome, not merely as a backdrop but as an active contributor to landslide dynamics, highlighting the critical need to consider both local and broader tectonic contexts in understanding slope instability

How to cite: Aslan, G., de Michele, M., Redfield, T., van-Boeckel, M., Hermanns, R., Noël, F., and Dehls, J.: InSAR Insights into the Massive Himalayan Leo Pargil Landslide, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4011, https://doi.org/10.5194/egusphere-egu24-4011, 2024.

When shallow landslides are triggered by sequences of earthquakes or storms, we need to know when in the sequence they occurred and whether they were later reactivated in order to better understand and model the hazard. In tropical environments, cloud cover often obscures all or part of the optical imagery acquired during the sequence, so that it is necessary to use images acquired after the sequence of events to map the landslides. Because of this, we often cannot tell which earthquake or storm triggered a particular landslide. This limits our understanding of how landslide hazard and mass wasting evolve in space and time during such sequences.

Synthetic Aperture Radar (SAR) images can be acquired through cloud cover and since 2015, Sentinel-1 has acquired data globally every 6-12 days. Landslides alter the scattering properties of the Earth’s surface and so can be detected in SAR images. SAR amplitude time series can be used to constrain the timings of individual landslides. We apply these methods to three sequences of triggers in order to better understand how landsliding evolved during them: the 2018 Lombok, Indonesia earthquake sequence; the 2019 Cotabato – Davao del Sur earthquake and the earthquake-hurricane sequence that occurred in Haiti in 2021.

The 2018 Lombok earthquake sequence also offers an ideal opportunity to test new methods of using InSAR coherence (the level of noise in an interferogram) to detect multi-stage failure. High resolution cloud-free images were acquired halfway through this sequence of four Mw > 6.0 earthquakes and many landslides mapped after the first two earthquakes were then observed to have grown in size or changed shape by the end of the sequence. We demonstrate that for large, favourably oriented landslides, InSAR coherence can be sensitive to this reactivation and so could potentially be used in the future for cases where no cloud-free images are available.

How to cite: Burrows, K., Sridharan, A., and Ferrario, M. F.: Timing landslides and identifying reactivations during sequences of earthquakes and storms with Sentinel-1 amplitude and coherence, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11881, https://doi.org/10.5194/egusphere-egu24-11881, 2024.

This study presents a new application of the Sentinel-1 dataset for the detection and measurement of earthflow displacements. The proposed methodology utilizes a multi-baseline interferometric hybrid approach, leveraging the backscattered radiation from both point-like and distributed radar targets. The analysis takes into consideration eight datasets acquired between 2017 and 2020, both on ascending and descending orbits, but it is restricted to seven months of the yearly acquisitions, spanning from late March to the beginning of November, to mitigate temporal decorrelation and minimize the impact of snow cover. The investigated area is part of the Dolomites Unesco World heritage site (Eastern Italian Alps). The results so far obtained are then compared with those derived from the European Ground Motion Service (EGMS) and in-situ Global Navigation Satellite System (GNSS) monitoring networks. Preliminary findings reveal the promising reliability of this approach, demonstrating its efficacy and accuracy. Furthermore, this methodology offers a notable advantage in terms of spatial sampling, resulting in the enhancement of the capability to identify and characterize earthflows movement. Overall, this study underscores the potential of utilizing Sentinel-1 data for monitoring landslides distinguished by significantly high rates of displacement, by loosening some of the well-known constraints of the interferometric analyses. The findings highlight the importance of space-borne SAR missions in providing valuable insights in landslide risk mitigation and management.

How to cite: Mantovani, M., Ballaera, A., Bossi, G., Ceccotto, F., Marcato, G., and Pasuto, A.: An effective method to detect and measure earthflow displacements using a hybrid interferometric approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9502, https://doi.org/10.5194/egusphere-egu24-9502, 2024.

Land subsidence has a great impact on coastal plains near sea level, leading to permanent inundation. The Shuguang oilfield, located in Liaohe River Delta (LRD), northeastern China, is one of the most significant subsidence areas as a direct consequence of oil production. We studied the production-induced deformation in the LRD region by Sentinel-1 radar images. Images from two ascending and two descending tracks are processed by an Interferometric Synthetic Aperture Radar (InSAR) time series analysis over the 2017 to 2021 period, providing deformation rate maps and time series in the radar line-of-sight (LOS) direction.

Previous researches carried out in this area assumed the oil production-induced deformation corresponds only to vertical deformation. Here, we proposed a method to retrieve the three-dimensional (3D) displacement field over the oilfield. We retrieved the vertical and east-west deformation components by combining the multiple InSAR geometries LOS observations and retrieved the north-south component based on the assumption of a physical relationship between the horizontal and vertical displacement.

The derived 3D displacement fields over Shuguang oilfield exhibit a circular subsidence bowl with a maximum subsiding rate reaching 212 mm/year, accompanied by a centripetal pattern of horizontal displacements with maximum rates up to 50-60 mm/year moving towards the subsidence center. The retrieved-3D displacements are in good agreement with predictions from the geomechanical modeling by assuming a disk-shaped reservoir subject to a uniform reduction in pore fluid pressure. Finally, we show the importance of knowing both the vertical and horizontal displacement in characterizing the lateral boundary of the subsurface reservoir.

The Liaohe River Delta region is often affected by heavy rainfall and storm surge in flood season, and flood disasters occur frequently in this low-lying coastal area. This deltaic region is vulnerable to floods not only from the extreme heavy rainfall, but also the land subsidence related to oil production. By spatial overlay analysis of the land subsidence distribution and the inundation extent of a flood event in August 2022, we reveal the impacts of land subsidence on flood inundation in this region. Our findings provide scientific support for oil production-related subsidence control and flood planning and designing in this deltaic region.

How to cite: Tang, W.: Three-dimensional deformation over Shuguang oilfield in Liaohe River Delta, China, from multi-track InSAR and its impacts on flood inundation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2842, https://doi.org/10.5194/egusphere-egu24-2842, 2024.

In this research, a comprehensive and sophisticated multi-step procedure is developed and implemented to perform quality assessment of the PS data points using vector-autoregressive-based spatio-temporal (VAR-ST-PS) modelling. Firstly, the PS points are classified into buildings and ground types using LoD2 building models. Multivariate PSI time series analysis is then carried out to understand the temporal behaviours of groups of PS points in local geometric patches. This involves modelling and analysing PSI time series to estimate deterministic and stochastic parameters such as offset, velocity, standard deviation, and corresponding distributional parameters. A spatio-temporal modelling is employed within the local geometric patches of PS points using a mathematical surface approximation model. A 95% confidence interval is estimated for the approximated surfaces using a bootstrapping approach. Subsequently, an appropriate quality model for the PS points is derived from the above-mentioned temporal and spatial modelling.

How to cite: Omidalizarandi, M., Shahryarinia, K., Mohammadivojdan, B., and Neumann, I.: Quality assessment of Persistent Scatterer Interferometry time series using vector-autoregressive-based spatio-temporal (VAR-ST-PS) modelling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17912, https://doi.org/10.5194/egusphere-egu24-17912, 2024.