The Permian Basin, encompassing ~170,000 km² of southeastern New Mexico and west Texas, consists of ancient marine rocks underlain by water-soluble rocks and multiple hydrocarbon-rich formations. Densely populated oil and gas producing facilities have impacted the stability of the solid-Earth, inducing long-lasting surface subsidence and uplift and the formation of sinkholes and fissures. The ground instability and the associated geohazards threaten the safe operation of key infrastructure such as roads, hydrocarbon facilities, pipelines, and water management facilities. Using multi-temporal and multi-band interferometric synthetic aperture radar (InSAR) datasets, we have mapped temporal behaviors of the geohazards on a weekly to monthly basis. The time-lapse InSAR measurements are compared to collected human activity data to reveal and correlate the causality of the geohazards with the type of anthropogenic perturbation (e.g., wastewater injection, CO₂ flooding, abandoned well, salt dissolution, mining, etc.). We have quantified the impacts of human activities on the stability of the solid-Earth through numerical poroelastic modeling, which simulates the induced stress/pressure distribution in the strata and the resulting surface subsidence/uplift. By identifying the triggering factor(s) behind human-induced geohazards that have already occurred, our in-depth study provides insights for the mitigation of environmental impacts and assists the decision-making of public authorities and private oil and gas companies as they strive to minimize negative environmental impact and financial risk while supporting the sustainable growth of the Permian Basin’s petroleum industry.

How to cite: Lu, Z., Zheng, W., Karanam, V., and Kim, J.: Human-induced geohazards in Permian Basin, USA revealed by InSAR and numerical modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2152, https://doi.org/10.5194/egusphere-egu23-2152, 2023.

The Kenyan Rift system hosts various forms of land use, including residential, commercial, and agricultural. In addition, the geology of the Kenyan Rift, the geodynamic setting of the quaternary volcanoes along the rift axis, and the high temperatures associated with the hot asthenosphere along the Kenyan Rift system are favourable for the occurrence of geothermal fields, some of which have already been harnessed for the generation of electricity. The interplay of human activities along the Kenyan Rift system can cause deformation, which is also prone to deformation due to geophysical activities such as volcanism and magmatism. In our study, we utilized both conventional and optimized multitemporal InSAR analyses based on the SBAS method to quantify human-induced deformation along the Kenyan Rift. By directly estimating the tropospheric delay from Sentinel-1 SAR data, the optimized approach can reduce errors in InSAR derived displacement measurements. Nairobi, located on the eastern flank of the Kenyan Rift, has experienced significant deformation caused by urbanization and the overexploitation of groundwater. A maximum subsidence rate of approximately 55 mm/yr. was observed in one of the eight deformation units that are mainly located in residential areas. Njoro town and Nakuru town industrial zone have also been shown to be undergoing land subsidence of approximately 20 mm/yr. and 10 mm/yr., respectively, both of which are associated with the overexploitation of groundwater resources. In addition, land subsidence in the range of 20 mm/yr was observed at several flower farms in Naivasha, which can also be attributed to the overexploitation of groundwater. At Olkaria, we observed land subsidence in the seven geothermal fields in the range of 22-50 mm/yr., we also observed at Menengai Crater Land subsidence and uplift of approximately 8 mm/yr. and 6 mm/yr. respectively. There is a significant deformation in the Kenyan Rift as a result of human activities, and these results indicate that InSAR can be used to monitor deformation in regions that were previously unmonitored due to the associated costs of using other geodetic monitoring techniques. Similarly, correct estimation of tropospheric delay in InSAR not only leads to better time-series displacement estimation with a more apparent temporal trend but also reveals subtle deforming regions that are otherwise obscured by tropospheric delay in the conventional method.

How to cite: Kirui, P. K., Riedel, B., and Gerke, M.: Determination of anthropologically induced deformation along the Kenyan Rift system using Multitemporal InSAR analysis with Sentinel-1 data. , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12996, https://doi.org/10.5194/egusphere-egu23-12996, 2023.

Satellite Interferometric Synthetic Aperture Radar (InSAR) is widely used for topographic, geological and natural resource investigations. However, most of the existing InSAR studies of ground deformation are based on relatively short periods and single sensors. This paper introduces a new multi-sensor InSAR time series data fusion method for time-overlapping and time-interval datasets, to address cases when partial overlaps and/or temporal gaps exist. A new Power Exponential Knothe Model (PEKM) fits and fuses overlaps in the deformation curves, while a Long Short-Term Memory (LSTM) neural network predicts and fuses any temporal gaps in the series. Taking the city of Wuhan (China) as experiment area, COSMO-SkyMed (2011-2015), TerraSAR-X (2015-2019) and Sentinel-1 (2019-2021) SAR datasets were fused to map long-term surface deformation over the last decade. An independent 2011-2020 InSAR time series analysis based on 230 COSMO-SkyMed scenes was also used as reference for comparison. The correlation coefficient between the results of the fusion algorithm and the reference data is 0.87 in the time overlapping region and 0.97 in the time-interval dataset. The correlation coefficient of the overall results is 0.78, which fully demonstrates that the algorithm proposed achieves a similar trend as the reference deformation curve. Based on the long time series settlement results obtained by fusion, we analyze the causes of settlement in detail for several subsidence zones. The subsidence in Houhu is caused by soft soil consolidation and compression. Soil mechanics are therefore used to estimate when the subsidence is expected to finish and to calculate the degree of consolidation for each year. The COSMO-SkyMed PSInSAR results indicate that the area has entered the late stage of consolidation and compression and is gradually stabilizing. The subsidence curve found for the area around Xinrong shows that the construction of an underground tract of subway Line 21 caused large-scale settlement in this area. The temporal granularity of the PSInSAR time series also allows precise detection of a rebound phase following a major flooding event in 2016. The experimental results demonstrate the accuracy of the proposed new fusion method to provide robust time series for the analysis of long-term land subsidence mechanisms and unveil previously unknown characters of land subsidence in Wuhan, thus clarifying the relationship with the urban causative factors.

How to cite: Jiang, H., Balz, T., Cigna, F., Tapete, D., and Li, J.: Land Subsidence in Wuhan Revealed Using a Multi-Sensor InSAR Time Series Fusion Approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9638, https://doi.org/10.5194/egusphere-egu23-9638, 2023.

 The Himalayan region is prone to natural disasters, including land deformation caused by tectonic activity, earthquakes, landslides, and human activities such as construction of large infrastructural projects. In the Joshimath region, located at the base of Middle Himalayas, there has been a significant number of reported cases of visible deformation in roads and buildings over the past six months i.e. between July 2022 to December 2022, with 610 buildings, including houses and hotels etc., showing cracks in their walls and foundations. This poses a danger to both the community and infrastructure in the area. To study this deformation, time-series synthetic aperture radar interferometry was used to monitor land subsidence in the region. An analysis of land subsidence in the entire Joshimath region was conducted using Time-Series Synthetic Aperture Radar Interferometry, and the land deformation velocity for was calculated using a PsInSAR approach, which measured the displacement velocity in mm/year. The results indicated that the rate of displacement, measured in Line of Sight (LOS) deformation velocity, was in the range of +187.55 mm/year to -84.65 mm/year. A positive sign indicates movement away from the SAR sensor, while a negative sign represents movement towards the sensor. The highest rate of subsidence was observed in the northwest region of the Joshimath that is in the range +103.22 mm/year to +187.55 mm/year, while areas in the north and central region also experienced high to moderate subsidence of +63.73 mm/year to +103.22 mm/year. In contrast, the southwest region was found to have experienced expansion measuring −84.65 mm/year to -13.13 mm/year. Additionally, the southeast region of the town had a rapid land subsidence ranging from -13.13 mm/year to -5 mm/year towards the lower part of the town. The potential causes of deformation in the Joshimath region are believed to include an inadequate drainage system in the town, high levels of erosion caused by the Alaknanada river, which is impacting the stability of the slope on which the town is situated, and recent development of large infrastructure projects in this disaster-prone area, that include construction of a hydropower project tunnel by NTPC and the expansion of the Chardham Highway. The fact that the ridge on which the town sits is composed of debris from past landslides further exacerbates these issues, as the terrain formed by such debris has a lower bearing capacity, making it a poor foundation for heavy infrastructure development.

How to cite: Awasthi, S. and Jain, K.: Analyzing the Land Subsidence activity in the Joshimath Region of Indian Himalayas Using Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11307, https://doi.org/10.5194/egusphere-egu23-11307, 2023.

Multi-temporal InSAR (MT-InSAR) technique has been widely used in the earth observation field. However, there are still challenges in the high-resolution monitoring and interpreting of urban infrastructures, especially for long-term time series analysis. We develop a data-driven post-processing method to provide a new solution to using MT-InSAR analyses in urban infrastructure health monitoring. We use a deep learning-based clustering method to classify different displacement temporal evolution patterns along Shanghai maglev from 7 years of TerraSAR-X observations (2013 to 2020). Our study region is observed by the satellite with alternating viewing angles between consecutive passes. We jointly estimate the orbital error per epoch to combine the two interweaving time series with different viewing geometries. We include spatial information of observation points for more reliable clustering. We then interpret the cluster results with maglev structural knowledge and surrounding groundwater level change. Different from previous classification methods, the deep learning-based clustering method is independent of predefined deformation models, allowing the identification of previously unknown types of deformation signals. Our preliminary results highlight the potential of applying deep clustering for MT-InSAR time series analyses for future automated structural health monitoring.

How to cite: Wang, R., Hooper, A., Gaddes, M., and Liao, M.: Monitoring and Interpreting Shanghai Maglev Deformation Using Deep Clustering on MT-InSAR Analyses, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15690, https://doi.org/10.5194/egusphere-egu23-15690, 2023.

In recent years, land subsidence has been intensively studied due to its severe impacts on urban communities and the environment. Amongst others, groundwater withdrawal is suspected to be the main trigger for such land subsidence. A prominent example is the Red River Delta in Northeastern Vietnam, where Hanoi is located. Radar remote sensing for mapping ground movement has been successfully applied for Hanoi City to quantify the land subsidence. Specifically, SAR data at the X, C, and L bands have been used, mainly based on the small baseline subset (SBAS) and Persistent Scatterer InSAR (PSInSAR) methods for extracting deformation in the urban setting of Hanoi from 1995 to the present. In these previous studies, line-of-sight land deformation was converted into the vertical direction with the assumption that horizontal movement is insignificant. However, a detailed analysis of the hydro-mechanical processes triggering the subsidence would strongly benefit from more complete InSAR deformation data, accounting also for horizontal movement. Therefore, the study applies PSInSAR to process both ascending and descending Sentinel-1 data acquired from 2017 to the end of 2019 to extract both vertical and horizontal (along the east-west direction) deformation in the study area. Our results show that in some areas, total displacement adds up to 32 mm/y in the vertical (subsidence) and 17 mm/y in the horizontal direction, indicating that horizontal movements are not negligible when it comes to interpreting deformation and relating it to hydro-mechanical processes in a heterogeneous subsurface. An interdisciplinary workflow is introduced that illustrates how remotely sensed subsidence data can be interpreted with the help of a geological subsurface model and coupled hydro-mechanical simulations conducted with numerical multi-physics software. We present the data basis and model set-up for the planned modeling study with the open-source software platform OpenGeoSys.

How to cite: Tran, H. H., Busch, W., and Butscher, C.: A workflow to study land subsidence based on InSAR analysis and hydro-mechanical modeling: A case study of Hanoi, Vietnam, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2633, https://doi.org/10.5194/egusphere-egu23-2633, 2023.

The Indo-Gangetic plain (IGP) in Northern India is one of the crucial aquifer systems, which depicts a declining trend of groundwater levels due to anthropogenic activities over a period from 2000 to 2012 (Mcdonald et al., 2016). The Gravity Recovery and Climate Experiment (GRACE) satellite have already illustrated a substantial decline in total water storage from 2000 to 2008 over in the northwestern part of India (Rodell et al., 2009). However, this study focused on Mohali and Chandigarh study areas, one of the emerging metropolitan planned cities of East Punjab and the union territory of India, for understanding groundwater dynamics using an advanced satellite radar interferometry technique (InSAR). Here, we explored Synthetic Aperture Radar (SAR) datasets with ascending and descending orbital tracks of Sentinel-1A/B sensors of the European Space Agency (ESA) to compute vertical land motion (VLM) during the study period from November 2015 to August 2022. 175 acquisitions of ascending and 170 imageries of descending orbital paths were used for generating the InSAR data processing. For ascending datasets, 638 suitable interferograms were generated with suitable temporal-spatial baseline thresholds of 75 days and 80 meters, respectively. Similarly, 574 interferograms were considered for descending datasets with temporal-spatial baseline thresholds of 80 days and 100 meters, respectively. The data processing has been carried out using Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technique using the Small BAseline Subset (SBAS) algorithm on GMTSAR software for generating a Line of Sight (LOS) velocity map (Sandwell et al., 2011). Further, the InSAR-derived results from both tracks were combined to compute the VLM of the study area (Fuhrmann et al., 2019). The observation shows a significant deformation signal in the Mohali and Chandigarh regions. In particular, about 18 cm/yr of VLM rate was noticed in Mohali, 16cm/yr in Kharar, 17cm/yr in Dera Bassi, 12 cm/yr in Lalru of SAS Nagar districts, and 8cm/yr in the south-eastern part of Chandigarh during the study period. However, the water level shows a total 6.45 meters below ground level (mbgl) with a declining trend of 0.645 mbgl/yr in Mohali compared to surrounding regions, whereas Chandigarh city exhibits 0.593 mbgl/yr GW rate with a total head level change of 5.93 mbgl during the observation period of 2011 to 2021, which demonstrates a good correlation with the InSAR VLM. Our ongoing investigation is carried out to understand further the groundwater dynamics of the aquifer system and local scale subsidence over different parts of the cities.

REFERENCES        

•  MacDonald, A. M., et al. "Groundwater quality and depletion in the Indo-Gangetic Basin mapped from in situ observations." Nature Geoscience 9.10 (2016): 762-766.
• Rodell, I. Velicogna, and J. S. Famiglietti, "Satellite-based estimates of groundwater depletion in India," Nature, vol. 460, no. 7258, pp. 999-1002, Aug 20, 2009.
• Sandwell, David, et al. "Gmtsar: An InSAR processing system based on generic mapping tools." (2011).
• Fuhrmann, Thomas, and Matthew C. Garthwaite. "Resolving three-dimensional surface motion with InSAR: Constraints from multi-geometry data fusion." Remote Sensing 11.3 (2019): 241.

How to cite: Chawla, S., Ojha, C., and Shirzaei, M.: Subsidence Due To Groundwater Exploitation Using InSAR Technique Over Chandigarh-Mohali Regions Of Northern India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-599, https://doi.org/10.5194/egusphere-egu23-599, 2023.

The change in ice thickness plays a primary role in measuring the glacier mass balance. Remote sensing techniques, such as optical and SAR instruments are regarded as powerful tools to monitor glacier thickness with different resolutions at a multi-scale. Despite great interesting, the temporal resolution becomes a main limitation to provide the continuous monitoring. In this paper, we for the first time integrate Sentinel-1/2 and GF-3 time-series dataset to enhance the temporal resolution. Also, the increased degrees of freedom from multi-source integration allow the full evaluation of three-dimensional glacier motion. Using data set ranging from January 2018 to December 2020, we use this methodology to explore the change in Siachen glacier thickness over the eastern Himalayas. More concretely, after estimating pixel offsets from individual sensors, the full three-dimensional flow velocity of glacier is first estimated by L1-norm minimization, followed by least-squares. The vertical velocity is then decomposed into Surface-Parallel Flow (SPF) of the glacier's movement along the glacier surface slope and non-Surface-Parallel Flow (nSPF) of the internal ice deformation and glacier thickness change. The seasonal and interannual variation of the flow velocity is also observed. We found a maximum thickness thinning velocity up to 23 cm/day in the lower middle portion of the glacier. The extracted time series demonstrate a remarkable temporal variability in flow velocities. Compared with the results estimated from the individual sensors, the integration improves the three-dimensional flow velocity by 26%, 19% and 4% in the east-west, north-south and vertical direction respectively. This study is of great significance for obtaining high temporal resolution and high accuracy glacier thickness changes using multi-source remote sensing data, and mitigating the disasters caused by glaciers.

How to cite: Zhang, R., Jiang, M., and Li, G.: Exploring Siachen glacier thickness change over eastern Himalayas by integrating multispectral and SAR time-series dataset, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15654, https://doi.org/10.5194/egusphere-egu23-15654, 2023.

We report on countrywide InSAR deformation mapping of Iceland using all the available Sentinel-1 radar data (Summer/Fall 2015-2021) from three parallel and overlapping descending and three ascending orbit tracks, yielding a complete countrywide coverage for both look directions. The total number of satellite passes for each of the six orbit tracks is about 170, meaning that over 1000 data sets were used, from which we processed around 8700 interferograms (multilooked to 100 m x 100 m pixels). Atmospheric signals in the data were reduced using a two-step correction approach based on global atmospheric model outputs and information about the stochastic characteristics of atmospheric noise. We then solved for the time-series of each of the six data sets and inverted for near-east and near-vertical time-series, assuming that north ground displacements are small. Large-scale displacements in Iceland are dominated by the plate motion and by glacio-isostatic adjustment. The results show how the width of the plate-boundary zone varies from being relatively narrow in Reykjanes to more distributed deformation in the Eastern Volcanic Zone. The glacio-isostatic uplift reaches a maximum of ~3 cm/year in central Iceland and appears to accelerate during the observation period. These large-scale horizontal and vertical displacements can be removed with a model of the plate motion, plate-boundary deformation and glacio-isostatic adjustment, leaving only local deformation signals in the residual displacement rate map, e.g., at central volcanoes and areas of geothermal exploitation. Widespread slope movements are also evident in the residual deformation map. Almost all east-facing slopes are moving eastward and west-facing slopes westward. This deformation is seen all over Iceland and amounts to a few mm/year, with faster rates at some known landslides. Example areas include northwestern and central-north Iceland where 5-10 mm/year movement rate is found on many of slopes, as well as the Western Fjords and Snæfellsnes peninsula. Recent slope failures in North Iceland in 2021 and 2022, which resulted in mudslides with road closures and some structural damage, occurred on slopes that can be seen moving during the years before the failures. However, no anomalous motion is detected at these slopes in the months before the failures; they are just slowly creeping like many other slopes in this area. In Summary, our results show that InSAR data are effective to map country-wide ground velocities and velocity changes as well as local deformation signals and transients at volcanoes and geothermal areas.  The results also show that slopes all over Iceland are subject to steady gravitational soil creep amounting to several mm/year, with higher rates observed in many areas where geomorphologically landslides can be identified in the landscape.

How to cite: Jónsson, S. and Cao, Y.: Widespread Slope Movements in Countrywide InSAR Mapping of Iceland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6485, https://doi.org/10.5194/egusphere-egu23-6485, 2023.

Using synthetic aperture radar (SAR) backscatter imagery can enable faster detection of landslides compared to optical images, particularly where there is persistent cloud cover or shadows. However, SAR images are underutilised for this purpose. This is partly due to the more complicated pre-processing requirements, and also due to the less intuitive interpretation of landslide signatures in SAR, relative to optical images. The problem of landslide identification in SAR backscatter imagery is complex. Landslides can occur in almost any land cover type and their expression in the environment can vary significantly depending on the material type and failure mechanisms. How this affects the expression of landslides in SAR backscatter data has so far not been well understood. In this study, we attempt to reduce this knowledge gap by investigating the physical basis for the expression of landslides in SAR backscatter data.

This involved identifying trends in the spatial and temporal signatures of landslides in 30 case studies around the world, representing diverse physiographical and landslide types. Morphometric features of landslides (scarp, transport and deposition zone) were mapped separately, and quantitative analysis of their pixel values in multi-temporal Sentinel-1 SAR backscatter images was performed. The role of environmental factors including the orientation of the landslide with respect to the sensor (local incidence angle), land cover, seasonal variations, and water content were also analysed.

The terrain influenced whether or not landslides were detectable, while the presence or absence of woody vegetation determined if there would be an increase or decrease in backscatter intensity. Landslides in non-forested areas that produce an increase in surface roughness, are best observed using VV polarisation and show increased backscatter intensity. Deposit zones also tend to show increased backscatter intensity, unless very fine material was deposited as a smooth flat surface (e.g. from non-turbulent mudflows). Removal of the forest is best viewed in VH polarisation, and produces a recognisable pattern of both decreased (due to radar shadow, and change from volumetric to surface scattering) and increased (due to direct and double bounce reflection from vertical tree trunks and scarp surface) backscatter intensity. Landslides that occur in mixed vegetation types, and those that do not significantly change the scattering properties of the ground surface, did not produce a detectable change in the C-band SAR images.  

The findings were summarised in a conceptual model, based on SAR theory and empirical evidence. This can be used to help interpret landslides in SAR backscatter change images, and to design representative or synthetic datasets for training automatic landslide detection models. 

How to cite: Lindsay, E., Devoli, G., Reiche, J., Nordal, S., and Frauenfelder, R.: Landslide expression in C-band SAR backscatter change images: a physically- and empirically-based conceptual model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6316, https://doi.org/10.5194/egusphere-egu23-6316, 2023.

Rainfall induced landslides have been the No.1 geohazards in Hong Kong (HK). In coastal subtropical monsoon regions, air temperature and humidity vary substantially and frequently. Tropospheric delays (TDs) limit the accurate detection of slow slope motion (which is the common case in HK) using interferometric synthetic aperture radar (InSAR).  In this presentation, we introduce a new TD correction method for InSAR line-of-sight (LOS) measurements at individual slope scale. The TD signal was estimated from LOS time series through a blind source separation (i.e., independent component analysis). The stratified TD was isolated according to a spatially elevation-linked and temporally periodic independent-component (IC), which was determined via a correlation test and power spectrum analysis. Therefore, the TD correction was not relying on any external weather products/meteorological data and had unprecedented spatiotemporal details equivalent to the SAR images.

A case study in Tai O, HK was conducted to verify the proposed method using 63 descending CosmoSkyMed (CSK) and 143 ascending Sentinel-1 (SNT-1) images. We used meteorological data of air temperature, humidity and weather products of ECMWF (European Centre for Medium-Range Weather Forecasts) ERA-5 and GACOS (Generic Atmospheric Correction Online Service) to validate the estimated TD. We estimated up to 3-4 cm spatiotemporally relative TD in the LOS directions of CSK and SNT-1, corresponding to a slope elevation change of ~ 400 m. The estimated TD was largely affected by specific air conditions (e.g., temperature and humidity) on the SAR image-acquisition days. In addition, we found the relative TD exhibited a slower increment rate than that of the slope elevation, suggesting the TD was not linearly related to the elevation. Ground deformation measurements from prisms and records of rainfall and tide were used to validate and interpret the InSAR deformation time series. It is interesting to find different hydrological forcings regulate the seasonal deformations in the slope (~ 10mm) and the reclamation (~ 15mm) in Tai O. Downslope movement (due to increase in pore-water pressure) occurred when rainfall accumulated from a dry season to a wet season, whereas upslope rebound (due to soil shrinkage) occurred when rainfall decreased from a wet season to a dry season. However, the periodic deformation of the reclamation substrate seemed to be more related to the sea level, instead of rainfall. The TD correction has reduced the root-mean-square error (RMSE) by 42.3%, such that InSAR LOS time series with millimeter-level accuracy (potentially 1-3 mm) were obtained in the Tai O case.

How to cite: Shi, G.: Millimeter slope seasonal deformation from multitemporal InSAR with a tropospheric delay correction: a case in Hong Kong, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1732, https://doi.org/10.5194/egusphere-egu23-1732, 2023.

Slow-moving landslides are an important erosional geomorphic process that shape hillslopes and transport large amounts of sediment material to river channels. They may have potentially catastrophic consequences for infrastructure and human life. Identifying the spatiotemporal pattern of hillslope deformation is essential for understanding the kinematic evolution of hillslope failure and mitigating associated hazards. InSAR (Interferometric Synthetic Aperture Radar) is an effective geodetic method for mapping landslide deformation with high spatiotemporal resolution and precision, especially where direct access to the hillslope areas is difficult.
The study area in the south-central Andes is characterized by steep climatic and topographic gradients. The low-elevation eastern foreland areas with dense vegetation cover change to semi-arid and arid, near-vegetation-free high-elevation areas. InSAR phase estimation and landslide mapping in such a complex region can be affected by spatial and temporal variations of soil moisture, vegetation cover, and atmospheric regime.  
In this study, we extract InSAR time series from the C-band ascending and descending track of Sentinel-1A/B data acquired between 2014 and 2022 and the L-band ascending track of ALOS1 PALSAR data acquired between 2006 and 2011 in the south-central Andes of northwest Argentina. We compare Sentinel deformation time series and maps derived from the linear small baseline subset technique with different numbers of connections in sequential interferogram formation with non-linear phase inversion techniques. We assess the phase bias contribution of short-temporal baseline interferograms for the time series analysis and propose several correction techniques tailored to this study area. Statistical and weather based models are used to reduce the impact of tropospheric delay on the deformation signal, especially during convective events controlled by the South American Monsoon and the large fluctuation of topographic relief effects on the tropospheric phase delay. We investigate the difference between tropospheric correction methods. We further implement a double-difference filter with different local and regional spatial filters to reduce the tropospheric delay on the InSAR time series. After additional filtering steps to remove further ionospheric noise in the time series, we identify the landslide spatial extent and their dynamic through spatial analyses. 
 Our results reveal multiple landslides, including three transitional bodies with downslope velocities of 5-10 cm/yr that demonstrate the importance of carefully filtering InSAR time series for slow-moving landslide detection.

How to cite: M.Aref, M., Bookhagen, B., and R. Strecker, M.: Using InSAR time series to characterize landslide deformation dynamics in the south-central Andes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11528, https://doi.org/10.5194/egusphere-egu23-11528, 2023.

In this work we present a processing chain we implemented to calculate, in a completely automatic way, the seismic source with distributed slip starting from the Differential Synthetic Aperture Radar Interferometry (DInSAR) coseismic displacement maps generated through the EPOSAR service.

EPOSAR is a scientific service of EPOS (European Plate Observing System) Research Infrastructure, developed by CNR-IREA, that provides coseismic displacement maps at global scale. In particular, following the occurrence of an earthquake of a) magnitude greater and b) depth smaller than selected thresholds, EPOSAR automatically retrieves and process all the Copernicus Sentinel-1 data necessary to generate all the possible DInSAR coseismic maps within a monthly time window, so that the earthquake can be analyzed from different satellite paths. We further remark that the EPOSAR service is currently operative and the generated DInSAR products are freely available to the scientific community through the EPOS infrastructure.

In this work we present the implementation of an automatic new modeling chain, by acting in cascade to the EPOSAR service, with a twofold aim: revealing the seismic source at the occurrence of every new event detectable through DInSAR and providing a complete database of sources that includes all the earthquakes occurred since the launch of Sentinel-1 satellites.

The procedure starts from DInSAR data, produced by the EPOSAR service, and a focal mechanism automatically retrieved from several catalogs (USGS, Global CMT, INGV-TDMT). The non-linear inversion is implemented with two stages, coarse and refined, to get a robust and well centered, uniform slip solution; this source is then extended and subdivided into small elements to get the slip distribution via linear inversion. For every single step, a number of algorithms, based on two decades of experience in modeling at INGV, were implemented to face the large number of options and conditions usually handled by an expert user: image selection, setup and iterative update of the input parameters, definition of the regularization strength,  detection of specific conditions (point-source, poorly constraining data, etc.). The model is also automatically updated with the availability of new DInSAR data, always balancing the contribution from ascending and descending acquisitions.

The developed tool is designed to deploy a service aimed at providing a quick and reliable automatic fault model solution and it has been tested and validated on hundred up to date events, characterized by different magnitudes, rupture mechanisms and locations. In this work, we present the main algorithm aspects and performances, addressing also the potentialities arising with the availability of a complete and homogeneous database of DInSAR-based source models: definition of updated scaling factors, systematic bias, etc.

We finally remark that our tool will be soon operative and integrated within the EPOS infrastructure, thus allowing the user community to access the generated results and benefit from quick and reliable products on the source mechanisms of the more significant seismic events.

How to cite: Atzori, S., Antonioli, A., Monterroso, F., De Luca, C., Svigkas, N., Lanari, R., Manunta, M., and Casu, F.: Automatic generation of seismic source models using Sentinel-1 DInSAR coseismic maps obtained through the EPOSAR service, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7469, https://doi.org/10.5194/egusphere-egu23-7469, 2023.

In recent years, ground-based radar has been widely used as an emerging deformation monitoring technology. Compared with traditional satellite-based radar, ground-based radar has many unique advantages, such as high temporal resolution and independence from atmospheric and ionospheric influences. Scholars usually assume that the radar's position is fixed every day when making continuous observations of the target area for multiple days. However, when experimental conditions do not allow us to temporarily fix the radar on the ground, a baseline error arises between two days of measurements, which causes the attitude of the radar to change. Based on the upper and lower dual antenna design adopted by the GAMMA portable Radar, this paper proposes a DEM model generated using the upper and lower antennas to back-calculate the baseline difference between the two measurements and eliminate the baseline difference by the model to ensure the accuracy of the experimental results. The results of the measured data processing demonstrate the effectiveness of the method mentioned in this paper in eliminating baseline errors

How to cite: Zhang, Z., Ding, X., Wu, S., and Zhao, J.: Ground-based Radar baseline correction based on the DEM model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13954, https://doi.org/10.5194/egusphere-egu23-13954, 2023.

Earthquake-induced landslides often pose a great threat to the safety of human life and property, especially in seismic active regions. This has motivated plentiful studies with a focus on coseismic landslides that collapsed during or shortly after an earthquake. However, long-term seismic effects that activated unstable landslides but without causing failures/collapse even after a long period since the earthquake (months to years) are typically ignored due to the minor ground changes caused compared to collapsed slopes. These landslides respond to seismic stress disturbances differently from failed coseismic/post-seismic landslides and their movements are typically accelerated with increased sliding velocity after earthquakes. The acceleration phenomenon of these earthquake accelerated landslides (EALs) could be maintained for a long time and they may generate continuous damage to the ground and develop into catastrophic failures in the future.

As a new type of landslides associated with earthquakes, EALs have been largely neglected by the emerging research. In our study, we used satellite radar (Sentinel-1) observations from October 2014 to August 2020 to detect and investigate EALs in Central Italy. Distinguished from previous studies based on single or discrete landslides, we established a large EAL inventory and statistically quantified as a whole their spatial clustering features against a set of landslide conditioning factors. Results show that EALs did not rely on strong seismic shaking or hanging wall effects to occur and larger landslides were more likely to accelerate after earthquakes than smaller ones. We also discovered their accelerating-to-recovering sliding dynamics, and how they differed from the collapsed coseismic landslides. These investigations serve as an important supplement to the complete picture of the landslide inducing mechanism by earthquakes and contribute to a more comprehensive long-term assessment of landslide risk.

How to cite: Song, C., Yu, C., Li, Z., Utili, S., Frattini, P., Crosta, G., and Peng, J.: Detection and characterization of earthquake accelerated landslides (EALs) using InSAR observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4886, https://doi.org/10.5194/egusphere-egu23-4886, 2023.

The railways and highways along the Qinghai Tibet Engineering Corridor (QTEC) were established on the frozen ground. Intensive engineering construction and human activities have significantly disturbed the permafrost environment. The melting of ice-rich permafrost closely relates to the carbon release. Beyond that, elevated pore fluid pressures may initiate or enlarge retrogressive thaw slumps (RTSs), which may further damage the foundation of critical transportation lifelines. However, the precise locations and margins of hundreds of RTSs around QTEC have not been systematically identified and delineated due to their remote locations and divergent surface features. The development of deep learning makes it possible to automatically and accurately identify and delineate the margins of RTSs. However, inventorying multi-temporal and large-scale RTS is challenged by the low generalization of deep learning model. Here we will apply the DeepLabv3+ segmentation algorithm to decipher 3-meter resolution PlanetScope optical images for a RTSs detection model. Fine-tuning, CycleGAN, and domain adversarial training will be used to improve the model's generalization ability. The time-dependent metric changes of RTSs will be investigated based on 2018-2022 multi-temporal RTS inventories. We will further extract the ground displacements over the mapped RTS using European Space Agency’s Copernicus Sentinel-1 satellite images and time-series Interferometric Synthetic Aperture Radar (InSAR) analysis. Our study leverages remote sensing big data and deep learning methods for hazard mitigation over the frozen ground in high environmental vulnerability.

How to cite: Lin, Y., Mirzadeh, S. M. J., Hu, X., and Liu, J.: Generalized Image Segmentation Model for Multi-annual Retrogressive Thaw Slumps Mapping, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4267, https://doi.org/10.5194/egusphere-egu23-4267, 2023.

The aim of this work is to present RASTOOL (EGMS RASTOOL: European ground motion risk assessment tool), a project co-financed by the EU-Union Civil Protection Mechanism. The Copernicus European Ground Motion Service (EGMS) represents a remarkable source of knowledge for the geohazard community. It provides consistent, regular, and reliable information on natural and anthropogenic ground motion phenomena over Europe, with millimetric accuracy (https://land.copernicus.eu/pan-european/european-ground-motion-service). The EGMS provides satellite interferometric products, with an annual updating, and a free and open policy. The availability of this vast amount of data is valuable for the scientific community but difficult to be exploited in the regular activities of territorial managers or Civil Protection Authorities. In fact, interferometric data interpretation might be challenging and time consuming, demanding a high level of expertise and a specific background. In this context, RASTOOL aims to develop a set of tools for simplifying the usage of the EGMS products, to automatically analyse them and to generate maps to support hazard, exposure, and risk-assessment against geohazards (both natural and anthropogenic). The tools developed in the frame of previous projects (Safety, U-Geohaz, Momit) will be improved to be easily applied to the EGMS products and integrated with new ones.

How to cite: Barra, A., Cuevas-González, M., Palamà, R., Gao, Q., Shahbazi, S., Béjar Pizarro, M., Ezquerro, P., Bru Cruz, G., Crosetto, M., and Monserrat, O.: RASTOOL project, tools for the Copernicus European Ground Motion Service exploitation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7576, https://doi.org/10.5194/egusphere-egu23-7576, 2023.

Since the beginning of the 1960s, the urban area of Bologna has experienced land subsidence due to excessive groundwater withdrawals. Ground deformation reached its peak during the 70s of the last century when maximum displacement rates of about 10 cm/year were documented, and significant damage to structures and infrastructures occurred. This process has been intensively monitored over the years, and extensive ground displacement measurements were collected employing increasingly sophisticated techniques, ranging from topographic levelling to GNSS surveys and, since 1992, satellite interferometry. Satellite data, in particular, has given a substantial contribution to the reconstruction of the subsidence process in more recent times, with a progressively higher spatial and temporal resolution towards the newer surveys. The available interferometric data are the results of four consecutive SAR campaigns undertaken by local authorities: 1992 – 2000 (ERS), 2002 – 2006 (ENVISAT), 2006 – 2011 (RADARSAT), 2011 – 2016 (RADARSAT and COSMO-SkyMed), and a fifth survey performed by the UniBo spin-off “Fragile” from the free SENTINEL1 2014 – 2020 data. As long-term data are essential to comprehensively understand the ongoing subsidence process evolution, within this work, a methodology was developed to integrate ground-based and remotely sensed monitoring data collected over the years, and produce continuous cumulative ground displacement time series and maps, depicting the long-term temporal evolution and spatial distribution of the subsidence process, respectively. The results obtained through the adopted processing chain highlight that the long-term ground displacement field well agrees with the 3D geological model of the area and that the cumulative subsidence and displacement rates temporal evolution nicely matches the pluriannual trend of the piezometric and groundwater pumping time series.

How to cite: Zuccarini, A., Di Paola, G., Giacomelli, S., Martini, A., Severi, P., and Berti, M.: Integration of topographic and InSAR surveys for studying the long-term evolution of the land subsidence process in an urban area, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5131, https://doi.org/10.5194/egusphere-egu23-5131, 2023.

Flooding is a natural disaster that can have devastating impacts on communities and individuals, causing significant damage to infrastructure, loss of life, and economic disruption. The Global Flood Monitoring (GFM) system of the Copernicus Emergency Management Service (CEMS) addresses these challenges and provides global, near-real time flood extent masks for each newly acquired Sentinel-1 Interferometric Wide Swath Synthetic Aperture Radar (SAR) image, as well as archive data from 2015 on, and therefore supports decision makers and disaster relief actions. The GFM flood extent is an ensemble product based on a combination of three independently developed flood mapping algorithms that individually derive the flood information from Sentinel-1 data. Each flood algorithm also provides classification uncertainty information as flood classification likelihood that is aggregated in the same ensemble process. All three algorithms utilize different methods both for flood detection and the derivation of uncertainty information.
The first algorithm applies a threshold-based flood detection approach and provides uncertainty information through fuzzy memberships. The second algorithm applies a change detection approach where the classification uncertainty is expressed through classification probabilities. The third algorithm applies the Bayes decision theorem and derives uncertainty information through the posterior probability of the less probable class. The final GFM ensemble likelihood layer is computed with the mean likelihood on pixel level. As the flood detection algorithms derive uncertainty information with different methods, the value range of the three input likelihoods must be harmonized to a range from low [0] to high [100] flood likelihood.
The ensemble likelihood is evaluated on two test sites in Myanmar and Somalia showcasing the performance during an actual flood event and an area with challenging conditions for SAR-based flood detection. The findings further elaborate on the statistical robustness when aggregating multiple likelihood layers.
The final GFM ensemble likelihood layer serves as a simplified appraisal of trust in the ensemble flood extent detection approach. As an ensemble likelihood, it provides more robust and reliable uncertainty information for the flood detection compared to the usage of a single algorithm only. It can therefore help interpreting the satellite data and consequently to mitigate the effects of flooding and accompanied damages on communities and individuals.

How to cite: Krullikowski, C., Chow, C., Wieland, M., Martinis, S., Chinni, M., Matgen, P., Bauer-Marschallinger, B., Roth, F., Wagner, W., Stachl, T., Reimer, C., Briese, C., and Salamon, P.: A likelihood analysis of the Global Flood Monitoring ensemble product, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8774, https://doi.org/10.5194/egusphere-egu23-8774, 2023.

Flood hazards result in enormous casualties and huge food losses worldwide every year. Therefore, monitoring floods in flood-prone areas is crucial to better understand the flooding patterns and characteristics. Previous studies utilizing hydrological data were unsuccessful in identifying flooding patterns in the regions where the hydrological stations are sparse. In addition, studies based on optical satellite images did not successfully monitor floods in cloudy areas. To improve flood monitoring methods, Synthetic aperture radar (SAR) imaging was proposed to monitor floods. The Sentinel-1 SAR sensor, with a spatial resolution of 10 m and a swath of up to 400 km, has free access and a short revisit period. This study will use multi-temporal Sentinel-1 SAR data to monitor flood dynamics in large-scale flood-prone areas with a focus on croplands to identify the inundated duration of flooded croplands, which would be valuable to assess the flood damage on crops and flood impact on food security.

How to cite: Qiu, J. and Tarolli, P.: High-resolution mapping of flood dynamics in cropland areas using multi-temporal Sentinel-1 SAR, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9702, https://doi.org/10.5194/egusphere-egu23-9702, 2023.

With the rapid development of modern Interferometric Synthetic Aperture Radar (InSAR) missions, SAR images with wider geographic coverage can be used to monitor ground deformation from local to continental scale. In such a large-scale application scenario, the ocean tide loading displacement introduces a long-wavelength error, which increases with distance, from millimetre to decimetre-level in InSAR interferograms over coastal areas. Despite great efforts being made to investigate the impacts of OTL on InSAR, these works are limited to individual interferograms and seldomly used in time-series analysis. In this study, we fully explore the OTL effects on Sentinel-1 InSAR time-series data along the western coast of the UK. We adopted wavelet analysis to indicate that the OTL displacement creates periodic signals with major cycles of 15 and 64 days under the 6- and 12-day Sentinel-1 sampling rates, respectively. These periodic signals are responsible for high noise magnitude of time-series displacement up to ~1cm and ~1 cm/yr bias on estimated velocities. An example is shown in Figure 1, where large velocity bias (a1-a3) and time-series standard deviation (b1-b3) can be seen along the western coastline of non-OTL corrected deformation fields, which are considerably eliminated after OTL correction. Our further validation against GNSS observations reveals that OTL correction improves the accuracy of large-scale InSAR time-series analysis by 25%.

How to cite: Wu, Z., Jiang, M., and Xiao, R.: Impacts of Ocean Tide Loading displacement on Large-scale InSAR Time-series analysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13764, https://doi.org/10.5194/egusphere-egu23-13764, 2023.

Detecting, characterizing and monitoring ground deformation are relevant tasks for natural risk assessments. The Synthetic Aperture Radar-SAR Interferometry (InSAR) technique stands out as a widely applied method to survey ground movements due its ability to resolve small-magnitude displacements. However, uncertainties associated with atmospheric delays prevent the detection of very small deformation signals that may be overprinted by noise. Common corrections applied either to single interferograms or time series analysis do not completely mitigate the influence of delayed signals, but they allow to distinguish between causes of delay , i.e. tropospheric or ionospheric.

In this study, we explore options to minimize the impact of any delay signal in time series analysis by using subsets of available SAR scenes. We analyzed 5 years, from 2018 to 2022, of Sentinel 1 A/B data in ascending (Track 76) and descending (Track 10) orbits. The scenes cover the Eastern Cordillera in the Northwestern Argentina, the easternmost range of the Central Andes. For each date, atmospheric delays are estimated using modern processing techniques: (i) tropospheric delays are investigated from global atmospheric models and, (ii) ionospheric delays are estimated from Split Range-Spectrum technique. Then we carefully remove noisy scenes and perform InSAR time series analysis. We evaluate our method by comparing displacements from the 5.8Mw earthquake that occurred on 29-Nov-2020 with an epicenter near Quebrada de Humahuaca. The analysis is expanded to the time-motion history retrieved from landslides in this area, which also serves to study the relationship between displacements rates and the earthquake. Finally, we explore how the quality of InSAR pairs precipitates into coherence, errors of phase unwrapping, and estimation of topographic residuals. Our results suggest that image quality assessment and subsequent SAR-scene removal is an effective tool for improving the quality of the time series.

How to cite: Viotto, S., Bookhagen, B., Torrusio, S., and Toyos, G.: Mitigating tropospheric and ionospheric uncertainties in InSAR Time Series Analysis: the 5.8Mw Earthquake in the Eastern Cordillera (Central Andes), Northwestern Argentina, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10451, https://doi.org/10.5194/egusphere-egu23-10451, 2023.

In China, there are nearly 100,000 earth and rock-filled dams, which are essential water conservancy facilities for agricultural irrigation, food security, flood controlling, and power generation and so on. The periodically deformation monitoring for those dams are the key criterion to evaluate their safety. However, traditional surveying technologies such as levelling, total station and GNSS and some inner sensors (like fibers) for continuous real-time monitoring are costly and require large amounts of human resources.

In this paper, we evaluate the ability of the currently operating X-band and C-band SAR sensors on dam deformation monitoring and propose a conversion parameter to retrieve the dam consolidation settlement. According to the earth and rock-filled dam post-construction settlement mechanism, a dam settles due to the compaction effect of filling earth in the dam. While in SAR geometry, only the line-of-sight projection of the slope surface deformation is visible along the radar beam. In order to convert the deformation from SAR geometry to real 3D geometry, we proposed a method for converting the dam post-construction deformation from SAR light of sight to vertical direction, based on the geometrical parameters of the dam and the SAR sensor.

The experiments by both simulated and real cases in the Gongming reservoir of Shenzhen city in china are utilized to compare the deformation monitoring capability of various SAR sensors with different resolutions and to demonstrate the applicability of deformation conversion method. The simulation results show that the foreshortening of the slope greatly affects the slope deformation retrieval, for the small earth-rock filled dams with axis being parallel to the SAR heading direction, the conversion parameter between two slopes of dam maybe differ than 2~3 times. The real experiments, by both the differential interferogram and time series analysis, based on TSX, CSK and Sentinel-1 data in Shenzhen Gongming reservoir show that the high-resolution data have more precise results. The time series analysis of multi-SAR data from 2017 to 2021 are used to show full processing of the dam post-construction settlement. For the small dam which is nearly 30 meters high and 200 meters long, With the 1~3 meters resolution of TSX and CSK data, we can retrieve the dam  settlement from both the cross-section profile and the axis section profile. The Sentinel TOPS data sets with low resolution cannot fully retrieve the dam settlement and the results are underestimated.

How to cite: Liu, J., Li, T., Ma, S., Dan, Q., and Jiang, W.: Study on the earth and rock-filled dam settlement monitoring in multi-SAR interferometry, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3767, https://doi.org/10.5194/egusphere-egu23-3767, 2023.