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Due to the limitations inherent in SAR satellite imaging modes, utilizing time-series InSAR technology to process single-orbit satellite image data typically only yields one-dimensional deformation information along the LOS direction. This constraint impedes a comprehensive representation of the true surface deformation of landslides. Consequently, in this paper, after the SBAS-InSAR and PS-InSAR processing of the 30-view ascending and 30-view descending orbit images of the Sentinel-1A satellite, based on the imaging geometric relationship of the SAR satellite, we propose a novel computational method of fusing ascending and descending orbital LOS-direction time-series deformation to extract the landslide's downslope direction deformation of landslides. By applying this method to Baige landslide monitoring and integrating it with an improved tangential angle warning criterion, we classified the landslide's trailing edge into a high-speed, a uniform-speed, and a low-speed deformation region, with deformation magnitudes of 7~8 cm, 5~7 cm, and 3~4 cm, respectively. A comparative analysis with measured data for landslide deformation monitoring revealed that the average root mean square error between the fused landslide's downslope direction deformation and the measured data was a mere 3.62 mm. This represents a reduction of 56.9% and 57.5% in the average root mean square error compared to the single ascending and descending orbit LOS-direction time-series deformations, respectively, indicating higher monitoring accuracy. Finally, based on the analysis of landslide deformation and its inducing factors derived from the calculated time-series deformation results, it was determined that the precipitation, lithology of the strata, and ongoing geological activity are significant contributors to the sliding of the Baige land-slide. This method offers more comprehensive and accurate surface deformation information for dynamic landslide monitoring, aiding relevant departments in landslide surveillance and management, and providing technical recommendations for the fusion of multi-orbital satellite LOS-direction deformations to accurately reconstruct the true surface deformation of landslides. [ABSTRACT FROM AUTHOR]

Objectives: Lutan-1 (LT-1) satellites are the first scientific research satellite constellation with interferometric synthetic aperture radar (InSAR) measurement of surface deformation as its main features and focusing on long-term natural disaster monitoring and warning in complex areas. It consists of two Lband synthetic aperture radar (SAR） satellites with the same hardware parameters, and has high resolution, high revisit, and full polarization imaging capabilities. The constellation uses the bi-satellites “single-pass in close formation” mode to map terrain, and the bi-satellites “repeat-track” mode to measure surface deformation. Taking the Xieliupo landslide in Zhouqu County, Gansu Province, China as a demonstration example, the ability to monitor large deformation was tested using 4 d and 8 d high revisit observation data. Methods: The interferometric point target analysis (IPTA) method was used to perform InSAR deformation time-series analysis on the Xieliupo landslide. The artificial corner reflector (CR) and global navigation satellite system (GNSS) were installed at the same location of the landslide, and the position of GNSS ground equipment was identified and confirmed by the CR on LT-1 data. The observation results of GNSS were used to verify the accuracy of InSAR results. Results: The annual cumulative maximum deformation of the landslide body exceeds 3.4 m. The accuracy of InSAR deformation time-series obtained from LT-1 satellites reaches 7.5 mm in line of sight. The maximum deformation rates of the tail, middle, and foreside of the landslide exceed 25 mm/d, 11 mm/d, and 2 mm/d, respectively, which indicates the landslide is in the stage from slow sliding to very slow sliding. Conclusions: Through the comprehensive analysis of imaging mode, repeated observation interval, coherence change, baseline control range, and deformation detection ability, it can be found that the comprehensive efficiency of LT-1 satellite has reached the design indicators, and LT-1 satellite has good ability in regularized wide area surface deformation and monitoring. The high-frequency, strict regression, and consistent observation mode of LT-1 will play an important role in geological hazards identifying and monitoring on large-sca [ABSTRACT FROM AUTHOR]

Interferometry Synthetic Aperture Radar (InSAR) technology is widely employed in the identification of geological hazards. However, in regions characterized by high mountains, canyons, and dense vegetation, decorrelation often hampers effective identification. In this study, focusing on the landslide-prone area of Wenxian County in Longnan City with abundant vegetation, we propose a Multi-Temporal InSAR (MTI) monitoring method that integrates Persistent Scatterers (PS) points with Slowly-Decorrelating Filtered Phase (SDFP) points in order to effectively reduce decorrelation effects. Additionally, we conduct high-precision monitoring of landslide surface deformation in Wenxian County using the the Generic Atmospheric Correction Online Service for InSAR (GACOS). Based on cross-validation of results obtained from three different techniques and the comparison with GNSS monitoring results, we validate the accuracy of these identification results. Furthermore, we use time-series deformation analysis methods to analyze the dynamic evolution of the characteristics of landslides in Chengguan Town and Koutouba Township, Wenxian County, Longnan City, and investigate their relationship with different environmental factors, such as rainfall and temperature. Our findings demonstrate that the spatial distribution consistency of the surface deformation information identified by the three techniques is high. However, StaMPS-MTI technology has the highest spatial sampling rate in deformation results, which is 21.02% higher than that of StaMPS-PS and over 4.6 times higher than that of StaMPS-SBAS. Additionally, the errors and Root Mean Square Error (RMSE) of StaMPS-MTI results in comparison to GNSS monitoring results are also the smallest, with values of 5.35 mm and 9.38 mm. We also observe varying degrees of deformation in Level 1 landslide areas identified by the STAMPS-MTI method in Chengguan Town and Koutouba Township with distinct seasonal characteristics. The cumulative deformation in the period from March to June shows a noticeable acceleration, with landslide incidents caused by the freeze-thaw phenomenon due to rising temperatures in March and April while rainfall caused landslide incidents from April to June. This research provides essential data and scientific support for geological hazard monitoring, prevention, and early warning. [ABSTRACT FROM AUTHOR]

This study presents a long-term reactivation monitoring approach using multi-source satellite SAR and optical data. It also explores the relationship between landslide deformations and hydrological factors for reactivation assessment and management. More specifically, this approach is applied to investigate the long-term reactivation evolution of the Huangtupo landslide under the local engineering work conducted in four different phases. The spatial–temporal evolutions of the Huangtupo landslide were mapped by the multi-temporal optical images from the PlanetScope satellite, revealing that the building areas were gradually replaced by the vegetation covers. The long-term reactivated deformations were explored by the multi-source satellite SAR data from ALOS-2 and Sentinel-1A/B based on time-series InSAR analysis. Cross-validation between the multi-source InSAR results showed that the ALOS-2 and Sentinel-1A/B measurements are highly correlated. Long-term spatial–temporal deformations were investigated using the Sentinel-1A/B Track 11 data between 2016 and 2022. A long stack of SAR datasets was divided into four consecutive branches based on the local engineering work plan. A general slowing down deformation trend can be observed from the time series analysis, indicating the effectiveness of local measures for maintaining the slope stability. Most importantly, seasonal fluctuations caused by changes in rainfall and Three Gorges Reservoir (TGR) water level can be clearly observed from the time series evolutions of deformation during each phase. Through the gray correlation analysis and cross wavelet analysis, it is found that the lower part of the Huangtupo landslide shows a higher correlation with the TGR water level changes, while the upper slope mainly responds to rainfall. Importantly, it is found that the lowest common power was observed at P4 since August, 2020, suggesting that the upper part of the slope has been changed to be more rainfall erosion resistant due to the effort of the drainage system and vegetation restoration from the ecological restoration project. [ABSTRACT FROM AUTHOR]

Mining-induced surface deformation disrupts ecological balance and impedes economic progress. This study employs SBAS-InSAR with 107-view of ascending and descending SAR data from Sentinel-1, spanning February 2017 to September 2020, to monitor surface deformation in the Fa'er Coal Mine, Guizhou Province. Analysis on the surface deformation time series reveals the relationship between underground mining and surface shifts. Considering geological conditions, mining activities, duration, and ranges, the study determines surface movement parameters for the coal mine. It asserts that mining depth significantly influences surface movement parameters in mountainous mining areas. Increasing mining depth elevates the strike movement angle on the deeper side of the burial depth by 22.84°, while decreasing by 7.74° on the shallower side. Uphill movement angles decrease by 4.06°, while downhill movement angles increase by 15.71°. This emphasizes the technology's suitability for local mining design, which lays the groundwork for resource development, disaster prevention, and ecological protection in analogous contexts. [ABSTRACT FROM AUTHOR]

Urban ground subsidence, a major geo-hazard threatening sustainable urban development, has been increasingly reported worldwide, yet comprehensive investigations integrating multi-temporal ground deformation monitoring and predictive modeling are still lacking. This study aims to characterize the spatial-temporal evolution of ground subsidence in Tianjin's Jinnan District from 2016 to 2023 using 193 Sentinel-1 A ascending images and the advanced Interferometric Synthetic Aperture Radar (InSAR) techniques of Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR). The maximum cumulative subsidence reached − 326.92 mm, with an average subsidence rate of -0.39 mm/year concentrated in industrial, commercial, and residential areas with high population density. Further analysis revealed that subway construction, human engineering activities, and rainfall were the primary drivers of ground subsidence in this region. Simultaneously, this study compared the predictive capabilities of five machine learning methods, including Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), Extremely Randomized Tree (ERT), and Long Short-Term Memory (LSTM) neural network, for future ground subsidence. The LSTM-based prediction model exhibited the highest accuracy, with a root mean square error of 2.11 mm. Subdomain predictions generally outperformed the overall prediction, highlighting the benefits of reducing spatial heterogeneity. These findings provide insights into the mechanisms and patterns of urban ground subsidence, facilitating sustainable urban planning and infrastructure development. [ABSTRACT FROM AUTHOR]

Objectives: Land subsidence caused by long-term over-exploitation of groundwater is one of major problems in Beijing. Since the opening of the South-to-North Water Transfer Project, the problem of water shortage in Beijing has been greatly alleviated, and the pressure of land subsidence has been reduced to a certain extent. Methods: In order to analyze the development of land subsidence after the start of the South-to-North Water Transfer in Beijing, ascending and descending time-series interferometric synthetic aperture radar (InSAR) technique is used to monitor land subsidence in Beijing. First, the mean deformation velocity and cumulative deformation in line of sight in Beijing from January 2015 to December 2020 is obtained by the small baseline subset InSAR. Second, the robust least square fitting method is used to fuse the deformation results of the lifting rail, after that the global positioning system monitoring data are compared with the fusion results of lifting rail. Finally, the variation trend between the deformation results obtained by the robust least quadratic fitting and groundwater data is analyzed. Results: The deformation results show that the center of Beijing is basically stable and the deformation distribution is not uniform. The maximum ascending annual deformation velocity and the maximum ascending cumulative deformation amount reach −134 mm/a and −697 mm respectively. The maximum descending annual deformation velocity and the maximum descending cumulative deformation amount reach −135 mm/a and −734 mm respectively. And the fusion results obtained by the least square fitting method has reliability and accuracy. Conclusions: The subsidence rate in Beijing shows a decreasing trend with the gradual increase of groundwater level. In general, the middle route of South-to-North Water Transfer Project has alleviated the expansion trend of land subsidence in Beijing to a certain extent. [ABSTRACT FROM AUTHOR]

On 4 October 2023, a glacier lake outburst flood (GLOF) occurred at South Lhonak Lake in the northwest of Sikkim, India, posing a severe threat to downstream lives and property. Given the serious consequences of GLOFs, understanding their triggering factors is urgent. This paper conducts a comprehensive analysis of optical imagery and InSAR deformation results to study changes in the surrounding surface of the glacial lake before and after the GLOF event. To expedite the processing of massive InSAR data, an InSAR processing system based on the SBAS-InSAR data processing flow and the AI Earth cloud platform was developed. Sentinel-1 SAR images spanning from January 2021 to March 2024 were used to calculate surface deformation velocity. The evolution of the lake area and surface variations in the landslide area were observed using optical images. The results reveal a significant deformation area within the moraine encircling the lake before the GLOF, aligning with the area where the landslide ultimately occurred. Further research suggests a certain correlation between InSAR deformation results and multiple factors, such as rainfall, lake area, and slope. We speculate that heavy rainfall triggering landslides in the moraine may have contributed to breaching the moraine dam and causing the GLOF. Although the landslide region is relatively stable overall, the presence of a crack in the toparea of landslide raises concerns about potential secondary landslides. Our study may improve GLOF risk assessment and management, thereby mitigating or preventing their hazards. [ABSTRACT FROM AUTHOR]

Landslide hazards are a common occurrence on the Loess Plateau of China, and the use of time-series interferometric synthetic aperture radar (InSAR) can effectively identify potential landslides and monitor their deformation, thus allowing to mitigate the risks associated with such hazards. In this study, we used Sentinel-1A ascending and descending orbit images to identify 74 significantly deformed areas in Xining city, of which 35 were confirmed as active landslides, using two time-series InSAR algorithms: small baseline subset (SBAS) and permanent scatterer (PS). Compared to PS, SBAS yielded a higher point density and more continuous deformation rate results in space. Our correlation analysis indicated a high correlation between the deformation rate results obtained from SBAS and PS algorithms, demonstrating their high consistency. Furthermore, by comparing the monitoring results of SBAS and PS algorithms with the GNSS monitoring data, we observed that the cumulative landslide deformation detected by PS algorithms was more consistent with the GNSS monitoring data. Finally, we analyzed the relationship between landslide deformation and the main influencing factors, which showed that the combined effect of precipitation, fault, slope, and engineered rock formations was the primary cause of landslide activity in the region during the study period. In conclusion, time-series InSAR algorithm is a valuable tool for identifying and monitoring loess landslides, and provides a scientific basis for regional disaster prevention and control. [ABSTRACT FROM AUTHOR]

Ground subsidence often occurs over a large area. Although traditional monitoring methods have high accuracy, they cannot perform wide-area ground deformation monitoring. Synthetic Aperture Radar (SAR) interferometry (InSAR) technology utilizes phase information in SAR images to extract surface deformation information in a low-cost, large-scale, high-precision, and high-efficiency manner. With the increasing availability of SAR satellite data and the rapid development of InSAR technology, it provides the possibility for wide-area ground deformation monitoring using InSAR technology. Traditional time-series InSAR methods have cumbersome processing procedures, have large computational requirements, and rely heavily on manual intervention, resulting in relatively low efficiency. This study proposes a strategy for wide-area InSAR multi-scale deformation monitoring to address this issue. The strategy first rapidly acquires ground deformation information using Stacking technology, then identifies the main subsidence areas by setting deformation rate thresholds and visual interpretation, and finally employs advanced TS-InSAR technology to obtain detailed deformation time series for the main subsidence areas. The Turpan Basin in Xinjiang, China, was selected as the study area (7474.50 km2) to validate the proposed method. The results are as follows: (1) The basin is generally stable, but there is ground subsidence in the southern plain area, mainly affecting farmland. (2) From 2016 to 2019, the maximum subsidence rate in the farmland area was approximately 0.13 m/yr, with a maximum cumulative subsidence of about 0.25 m, affecting a total area of approximately 952.49 km2. The subsidence mainly occurred from late spring to mid-autumn, while lifting or subsidence mitigation occurred from late autumn to early spring. The study also analyzed the impacts of rainfall, geographical environment, and human activities on subsidence and found that multiple factors, including water resource reduction, overexploitation, geological characteristics, and the expansion of human activities, contributed to the subsidence problem in the Turpan Basin. This method contributes to wide-area InSAR deformation monitoring and the application of InSAR technology in engineering. [ABSTRACT FROM AUTHOR]

The Baihetan reservoir region is characterized by complex geomorphology, significant altitude differences, and rugged terrain. Geological hazards in such areas are often characterized by high concealment, wide distribution, and difficulty in field investigation. Traditional identification techniques are unable to detect and monitor geological hazards on a large scale with high efficiency and accuracy. In recent decades, interferometric synthetic aperture radar (InSAR) techniques, such as small baseline subset InSAR (SBAS-InSAR), have been widely applied to landslide identification. However, due to factors such as vegetation and the degree of landslide deformation, single-band synthetic aperture radar (SAR) still has certain limitations in detecting landslides. In this study, SBAS-InSAR was conducted based on ALOS-2 and Sentinel-1 ascending-descending images covering the Baihetan reservoir region. Deformation identification results were utilized to conduct a statistical analysis of the SAR detection performance and landslide characteristics, and the effect of vegetation on the detection effectiveness of different SAR bands was discussed. The study revealed that when surface vegetation coverage reaches a high degree, the percentage of areas with coverage greater than 0.6 is greater than 95%, the SAR coherence is mainly affected by vegetation thickness; the comparison of the difference change in the average coherence of the C/L bands among the four vegetation types shows that the ratio of the average coherence of the L-bands to the C-bands increases by a factor of three with the increase in thickness and the transition from crops to shrubs and trees. The results showed that the L-band has better detectability than the C-band in alpine-canyon terrain with vegetation coverage and complex vegetation composition. However, considering the high temporal resolution and accessibility of Sentinel-1 SAR data, it is still the main data choice for wide-area identification of landslides in the reservoir area, while other satellite-borne SAR data with different wavelengths and resolutions, such as ALOS, can be used to assist in the identification and monitoring of landslide hazards with significant magnitude of deformations and dense vegetation coverage. Therefore, the combined utilization of multi-band SAR data has the potential to enhance the dependability of landslide identification and monitoring, resulting in more accurate detection results. [ABSTRACT FROM AUTHOR]

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Abstract The Jiali-Chayu fault, situated on the eastern side of the eastern Himalayan syntaxis, is the southeastern margin of the large strike-slip fault zone of the Jiali Fault. The study of the distribution and activity within this fault zone is imperative for a comprehensive understanding of the tectonic movement patterns in the southeastern Tibetan Plateau. Previous studies have established that the kinematic characteristic of the Jiali-Chayu fault diverges significantly from that of other segments within the Jiali fault. Nonetheless, the current tectonic characteristics, including the slip sense, slip rate, and geometric deformation of this fault, are still not well resolved, leading to divergent interpretations regarding its contemporary activity intensity. This paper introduced an optimized time-series InSAR method with phase compensation designed for regions characterized by low coherence and exhibiting slow deformation. Using Sentinel-1 SAR data from both ascending and descending orbits spanning the period between 2017 and 2022, we successfully derived deformation rates for the middle part of the Jiali-Chayu fault at a spatial resolution of 150 m. The slip and dip rates of active faults are determined by considering the fault movement rates from two different observation angles, in conjunction with strike angle and the assumed dip angle of the fault. The results show that the deformation rates of the three branches are very different, with F2-1 and F2-2 exhibiting notable activity, while other areas exhibit relatively weaker activity. The strike-slip rates for F2-1 and F2-2 faults range between 3.6 and 5.3 mm/a and 3.05 to 5.13 mm/a, respectively, while their respective dip-slip rates fall within the range of 1.1–2.7 mm/a and 2.99–5.02 mm/a. In accordance with the fault slip directions, we classify the F2-1 fault as a sinistral (left-lateral) strike-slip fault and the F2-2 fault as a dextral (right-lateral) strike-slip fault. This study addresses a gap in remote sensing methods for detecting active fault activity in this region, providing a systematic foundation for identifying weak activity characteristics within the fault zone. Graphical Abstract

Due to the limitations inherent in SAR satellite imaging modes, utilizing time-series InSAR technology to process single-orbit satellite image data typically only yields one-dimensional deformation information along the LOS direction. This constraint impedes a comprehensive representation of the true surface deformation of landslides. Consequently, in this paper, after the SBAS-InSAR and PS-InSAR processing of the 30-view ascending and 30-view descending orbit images of the Sentinel-1A satellite, based on the imaging geometric relationship of the SAR satellite, we propose a novel computational method of fusing ascending and descending orbital LOS-direction time-series deformation to extract the landslide’s downslope direction deformation of landslides. By applying this method to Baige landslide monitoring and integrating it with an improved tangential angle warning criterion, we classified the landslide’s trailing edge into a high-speed, a uniform-speed, and a low-speed deformation region, with deformation magnitudes of 7~8 cm, 5~7 cm, and 3~4 cm, respectively. A comparative analysis with measured data for landslide deformation monitoring revealed that the average root mean square error between the fused landslide’s downslope direction deformation and the measured data was a mere 3.62 mm. This represents a reduction of 56.9% and 57.5% in the average root mean square error compared to the single ascending and descending orbit LOS-direction time-series deformations, respectively, indicating higher monitoring accuracy. Finally, based on the analysis of landslide deformation and its inducing factors derived from the calculated time-series deformation results, it was determined that the precipitation, lithology of the strata, and ongoing geological activity are significant contributors to the sliding of the Baige land-slide. This method offers more comprehensive and accurate surface deformation information for dynamic landslide monitoring, aiding relevant departments in landslide surveillance and management, and providing technical recommendations for the fusion of multi-orbital satellite LOS-direction deformations to accurately reconstruct the true surface deformation of landslides.

In the Three Gorges Reservoir area, landslide disasters occur frequently, making scientific monitoring and risk prediction crucial for disaster prevention and mitigation. However, most previous studies have been constrained by analysis of singular influencing factors. In this study, we employed multi-temporal InSAR techniques coupled with multivariate geospatial statistical analysis to monitor and analyze the dynamic evolution of the Muyuba landslide in Zigui County, Hubei Province, China from 2016 to 2023. The findings indicate that the Muyuba landslide was predominantly characterized by continuous, gradual subsidence. Key factors inducing deformation included well-developed drainage networks, gentle slopes of 15-30°, and the orientation of rock strata. Deformation rates in residential areas and along roadways exceeded background levels, implicating anthropogenic activities in the heightened landslide risk. A significant correlation was observed between landslide deformation and reservoir water level fluctuations, as opposed to rainfall patterns, highlighting reservoir regulation disturbances as a critical landslide triggering factor. [ABSTRACT FROM AUTHOR]

In order to solve the problems of low efficiency, multiple blind spots, and high risks in traditional debris flow exploration, two technical means were adopted: small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) and unmanned aerial vehicle (UAV) low altitude remote sensing (the former obtains highly reliable deformation of vegetation coverage areas through dense images, while the latter obtains high-resolution realistic 3D models through regional network adjustment and other processing) . Integrating deformation and image texture, a survey and analysis of debris flow valleys along a railway in a certain mountainous area was conducted to obtain the spatial and temporal distribution of geological hazards in the watershed. The results show that, through SBAS-InSAR technology, it was found that there were multiple seasonal active hazards in the debris flow valley, with a maximum deformation variable of 28.5 mm/a. Based on the 3D model of UAV, a detailed investigation was conducted on the spatial form, volume, occurrence, and development of the deformation hazard. The largest scale landslide had a volume of 165 000 to 180 000 cubic meters. On site investigations show that the comprehensive remote sensing technology can effectively overcome the challenges of complex mountainous terrain and difficult access for humans, providing a multidimensional perspective for disaster screening. It has the advantages of low personnel investment, wide monitoring area, fast disaster positioning, and high screening accuracy. [ABSTRACT FROM AUTHOR]

Due to the physicochemical properties of salt swelling and collapsibility, the potential geological hazards threaten the large-scale infrastructure and residential areas in the saline-soil region. Geodetic imaging and dynamic monitoring of the spatiotemporal evolution process of saline soil are vital to detect the potential geological hazards in a vast area. Therefore, this paper presents a coupled deformation model synthesizing environmental factors and trigonometric function model for estimating the time-series InSAR deformation and interpreting the dynamic evolutionary mechanism of the saline mudflat. To demonstrate the advantages of the proposed model, the sulphate saline mudflat is selected as the study area, which is traversed by the Tehran-Isfahan high-speed railway in Howz-e-Soltan Salt Lake, Qom, Iran. Moreover, 259 interconnecting interferograms were selected for the time-series InSAR estimation, composed of 87 Sentinel-1A images obtained from October 2014 through December 2018. Then, the time-series deformations, including the annual deformation velocity and the accumulated deformation, are extracted by the original estimation, polynomial, trigonometric and environmental models. The comparison results show that the environmental model's RMSEs (root-mean-square errors) are smaller than others. It should be noted that the linear deformation, periodic oscillation, and deformation derived from the environmental factors (i.e., precipitation and temperature) are extracted by the environmental model. Furthermore, the dynamic evolutionary mechanism of the saline mudflat is interpreted according to the physicochemical properties of sulphate/carbonate saline soil. The results derived from the environmental model suggested that the saline-soil deformation is directly related with the environmental changes: rapid collapsibility occurs when the precipitation increases from September to the next May, while uplift due to the salt swelling in the dry and rainless season from May to September every year. Our findings provide an opportunity to detect, prevent and control the potential geological hazards in saline mudflats.

On 4 October 2023, a glacier lake outburst flood (GLOF) occurred at South Lhonak Lake in the northwest of Sikkim, India, posing a severe threat to downstream lives and property. Given the serious consequences of GLOFs, understanding their triggering factors is urgent. This paper conducts a comprehensive analysis of optical imagery and InSAR deformation results to study changes in the surrounding surface of the glacial lake before and after the GLOF event. To expedite the processing of massive InSAR data, an InSAR processing system based on the SBAS-InSAR data processing flow and the AI Earth cloud platform was developed. Sentinel-1 SAR images spanning from January 2021 to March 2024 were used to calculate surface deformation velocity. The evolution of the lake area and surface variations in the landslide area were observed using optical images. The results reveal a significant deformation area within the moraine encircling the lake before the GLOF, aligning with the area where the landslide ultimately occurred. Further research suggests a certain correlation between InSAR deformation results and multiple factors, such as rainfall, lake area, and slope. We speculate that heavy rainfall triggering landslides in the moraine may have contributed to breaching the moraine dam and causing the GLOF. Although the landslide region is relatively stable overall, the presence of a crack in the toparea of landslide raises concerns about potential secondary landslides. Our study may improve GLOF risk assessment and management, thereby mitigating or preventing their hazards.

Synthetic aperture radar interferometry (InSAR) has emerged as an effective technique for monitoring potentially unstable landslides and has found widespread application. Nevertheless, in mountainous reservoir regions, the precision of time-series InSAR outcomes is often constrained by topography-dependent atmospheric delay (TDAD) effects. To address this limitation, we propose a novel InSAR time-series method that integrates TDAD correction. This approach employs advanced deep learning algorithms to individually model and mitigate TDAD for each interferogram, thereby enhancing the accuracy of small baseline subset InSAR (SBAS-InSAR) and stacking InSAR time-series analyses. Utilizing Sentinel-1 data, we apply this method to identify potential landslides in the Baihetan reservoir area, located in southwestern China, where we successfully identified 26 potential landslide sites. Comparative experimental results demonstrate a significant reduction (averaging 70% and reaching up to 90%) in phase standard deviation (StdDev) in the corrected interferograms, indicating a marked decrease in phase–topography correlation. Furthermore, the corrected time-series InSAR results effectively remove TDAD signals, leading to clearer displacement boundaries and a remarkable reduction in other spurious displacement signals. Overall, this method efficiently addresses TDAD in time-series InSAR, enabling precise identification of potentially unstable landslides influenced by TDAD, and providing essential technical support for early landslide hazard detection using time-series InSAR. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Baihetan reservoir region is characterized by complex geomorphology, significant altitude differences, and rugged terrain. Geological hazards in such areas are often characterized by high concealment, wide distribution, and difficulty in field investigation. Traditional identification techniques are unable to detect and monitor geological hazards on a large scale with high efficiency and accuracy. In recent decades, interferometric synthetic aperture radar (InSAR) techniques, such as small baseline subset InSAR (SBAS-InSAR), have been widely applied to landslide identification. However, due to factors such as vegetation and the degree of landslide deformation, single-band synthetic aperture radar (SAR) still has certain limitations in detecting landslides. In this study, SBAS-InSAR was conducted based on ALOS-2 and Sentinel-1 ascending-descending images covering the Baihetan reservoir region. Deformation identification results were utilized to conduct a statistical analysis of the SAR detection performance and landslide characteristics, and the effect of vegetation on the detection effectiveness of different SAR bands was discussed. The study revealed that when surface vegetation coverage reaches a high degree, the percentage of areas with coverage greater than 0.6 is greater than 95%, the SAR coherence is mainly affected by vegetation thickness; the comparison of the difference change in the average coherence of the C/L bands among the four vegetation types shows that the ratio of the average coherence of the L-bands to the C-bands increases by a factor of three with the increase in thickness and the transition from crops to shrubs and trees. The results showed that the L-band has better detectability than the C-band in alpine-canyon terrain with vegetation coverage and complex vegetation composition. However, considering the high temporal resolution and accessibility of Sentinel-1 SAR data, it is still the main data choice for wide-area identification of landslides in the reservoir area, while other satellite-borne SAR data with different wavelengths and resolutions, such as ALOS, can be used to assist in the identification and monitoring of landslide hazards with significant magnitude of deformations and dense vegetation coverage. Therefore, the combined utilization of multi-band SAR data has the potential to enhance the dependability of landslide identification and monitoring, resulting in more accurate detection results.

Ground subsidence often occurs over a large area. Although traditional monitoring methods have high accuracy, they cannot perform wide-area ground deformation monitoring. Synthetic Aperture Radar (SAR) interferometry (InSAR) technology utilizes phase information in SAR images to extract surface deformation information in a low-cost, large-scale, high-precision, and high-efficiency manner. With the increasing availability of SAR satellite data and the rapid development of InSAR technology, it provides the possibility for wide-area ground deformation monitoring using InSAR technology. Traditional time-series InSAR methods have cumbersome processing procedures, have large computational requirements, and rely heavily on manual intervention, resulting in relatively low efficiency. This study proposes a strategy for wide-area InSAR multi-scale deformation monitoring to address this issue. The strategy first rapidly acquires ground deformation information using Stacking technology, then identifies the main subsidence areas by setting deformation rate thresholds and visual interpretation, and finally employs advanced TS-InSAR technology to obtain detailed deformation time series for the main subsidence areas. The Turpan Basin in Xinjiang, China, was selected as the study area (7474.50 km2) to validate the proposed method. The results are as follows: (1) The basin is generally stable, but there is ground subsidence in the southern plain area, mainly affecting farmland. (2) From 2016 to 2019, the maximum subsidence rate in the farmland area was approximately 0.13 m/yr, with a maximum cumulative subsidence of about 0.25 m, affecting a total area of approximately 952.49 km2. The subsidence mainly occurred from late spring to mid-autumn, while lifting or subsidence mitigation occurred from late autumn to early spring. The study also analyzed the impacts of rainfall, geographical environment, and human activities on subsidence and found that multiple factors, including water resource reduction, overexploitation, geological characteristics, and the expansion of human activities, contributed to the subsidence problem in the Turpan Basin. This method contributes to wide-area InSAR deformation monitoring and the application of InSAR technology in engineering.

Time-series InSAR technology has been widely applied to the identification of various types of surface deformation information, but its traditional processing chain of being applied to the detection, extraction and monitoring of wide-area geohazards requires a large amount of disk space, computing resources and processing time and has high requirements for data, personnel and equipment, which makes it difficult to promote its application in wide-area geohazard identification. Based on this background, this study constructs a time-series InSAR processing chain that makes it easy to achieve fast detection and effective extraction and monitoring of wide-area geohazards. First, the well-coherent HyP3 InSAR online service interferometric dataset is selected for time-series InSAR processing to detect the wide-area surface deformation rate. Then, the deformation anomaly regions in the wide-area range are extracted by combining multi-threshold segmentation and aggregation point (polygon) analysis methods. And then, the original Sentinel-1A SLC data are subjected to interferometric processing and time-series InSAR analysis using the open-source desktop program EZ-InSAR in the extracted typical small-area deformation anomaly regions of interest to obtain more refined deformation information. Nanjian County, Yunnan Province, China, is used as the study area, and a total of 119 potential geohazards with deformation anomalies were detected and extracted in this region, approximately half of which coincided with the results of the field survey and visual interpretation of optical images. The processing chain considers the advantages of wide-area detection of InSAR online service datasets and the advantages of raw resolution of SAR SLC data, and the data and software used are open source with low hardware requirements, which is expected to provide an effective processing chain for wide-area creep geohazard identification. [ABSTRACT FROM AUTHOR]

Tunneling work, including the construction of municipal tunnels and metro lines, may disturb the structural health of aging buildings in densely built urban areas. Deformation monitoring and risk assessments of aging buildings are crucial to mitigate incidents and prevent losses of people's lives and properties. Time-series InSAR reveals spatio-temporal information about observed targets by extracting persistent scatterers of the structures, which can achieve the wide-range monitoring of buildings and infrastructure. However, solely relying on InSAR-derived general parameters (deformation rates and time series of specific points) cannot objectively assess the safety conditions of buildings. To address this issue, this study proposes an InSAR Nonuniform Settlement Index. First, the point targets of buildings are extracted through time-series InSAR processing. Then, using the points as inputs, the Nonuniform Settlement Index calculates the 3D settlement plane and the inclination angle of the plane corresponding to each building. In this way, the proposed Nonuniform Settlement Index acts as a subsequent analysis method of time-series InSAR to characterize the safety statuses of buildings. In our study, 147 scenes of COSMO-SkyMed images from 2013 to 2022 were used to inverse the nine-year deformation evolution of the tested area. After time-series InSAR processing and index analysis based on the above SAR datasets, cross-validation was implemented with static-level and manual investigation data. The approach was to use one aging, collapsed building affected by tunneling work, as well as the eight adjacent aging buildings. The results showed high consistency with the in situ data, which proves the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

The Lianjiang Plain, renowned for its position as 'China's textile hub' and characterized by its high population density, has experienced considerable subsidence due to excessive groundwater extraction in recent years. Although some studies have investigated short-term subsidence in this plain, research on long-term subsidence and rebound remain understudied. In this paper, the characteristics of surface deformation in the Lijiang Plain during two periods (2015–2017 and 2018–2021) have been investigated using the time-series interferometric synthetic aperture radar (TS-InSAR) technique, and the correlation with the changes in groundwater level, geological factors, and urban construction are discussed. The InSAR-derived results are cross-validated with the adjacent orbit datasets. Large-scale and uneven subsidence ranging from −124 mm/year to +40 mm/year is observed from 2015 to 2017. However, a significant decrease in the subsidence rate during 2018–2021, with local rebound deformation up to +48 mm/year in three regions, is also observed. Groundwater level changes are found to be the major cause of the ground deformation, and the intercomparison between groundwater level and ground displacement time series from TS-InSAR measurements also indicates a clear relationship between them during 2018–2021. Geological factors control the range of deformation area over the study period. The impact of urban construction on surface subsidence is evident, contributing to high deformation. Our findings could improve the understanding of how deformation is affected by groundwater rebound and offer valuable insights into groundwater management, urban planning, and land subsidence mitigation. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) has been effectively used to monitor surface deformation in a mining area with millimeter-level accuracy. On March 27, 2022, a tailings dam failure occurred in Shanxi Province, China, causing significant damage to surrounding houses, woodland, and roads below the tailings pond. We obtained the surface deformation map of the tailings dam before the catastrophic failure using the InSAR time-series method with a full resolution of Sentinel-1 A. This paper employed a GPU-assisted InSAR processing method for 91 Sentinel-1 images in Interferometric Wide Swath (IW) mode acquired from January 8, 2019, to March 17, 2022. The InSAR results show that during the 25 months preceding the tailings dam failure, Dam-II experienced an average cumulative LOS deformation of nearly 80 mm, while Dam-I experienced a significant deformation of more than 140 mm in the LOS direction. The analysis combining the deformation and rainfall results shows that rainfall significantly affects tailings pond deformation. In general, the deformation evolution has a high correlation with the rainfall annually, but the maximum deformation rate occurs with a delay of about one month compared to the peak rainfall. InSAR technology can significantly improve the monitoring capability of tailings dam failure and other landslide disasters, but it is limited by the observation frequency of the satellite. Thus, improving the temporal resolution of SAR data may assist in predicting tailings dam failure times more accurately. [ABSTRACT FROM AUTHOR]

The Middle Route of the South–North Water Diversion Project has changed the water resources pattern in China. As advanced equipment for the country, it is responsible for the water supply "lifeline" of Beijing, Tianjin, Hebei, Henan, etc. Ensuring its safe operation is a top priority to promote social stability and coordinated economic development between the North and the South. Used persistent scatterer interferometric synthetic aperture radar (PS-InSAR) technology to monitor the deformation of the high fill characteristic canal in Wenzhuang Village, Ye County, during the period from October 2016 to June 2017 for the South–North Water Diversion Project showed that there was significant deformation on the 1 km-long slope of the east bank of the canal, with the maximum deformation volume reaching 36 mm. Through the comparison and verification with the second order leveling data, there are more than 87% of the root mean square error of both less than ±2 mm. The correlation coefficient is 0.96, and the two were highly consistent in deformation trends and values. Through the vertical and cross-sectional analysis of the canal's east bank and four key monitoring sections, it was found that the east bank of the canal presents overall uneven subsidence, and the closer the canal is to the water, the greater the canal deformation, and vice versa. Further comparison of the PS-InSAR deformation results of the canal from October 2016 to February 2018 proves that this technology cannot only monitor the subsidence range and rate of the South–North Water Diversion canal but also accurately identify the subsidence sequence of the east and west banks. It can provide reliable technical support for the safety monitoring and disaster prevention of the South–North Water Diversion canal characterized by high fill and deep excavation. [ABSTRACT FROM AUTHOR]

Mapping and monitoring landslides in remote areas with steep and mountainous terrain is logistically challenging, expensive, and time consuming. Yet, in order to mitigate hazards and prevent loss of life in these areas, and to better understand landslide processes, high-resolution measurements of landslide activity are necessary. Satellite-based synthetic aperture radar interferometry (InSAR) provides millimeter-scale measurements of ground surface deformation that can be used to identify and monitor landslides in remote areas where ground-based monitoring techniques are not feasible. Here we present a novel InSAR deformation detection approach, which uses double difference time-series with local and regional spatial filters and pixel clustering methods to identify and monitor slow-moving landslides without making a priori assumptions of the location of landslides. We apply our analysis to freely available Copernicus Sentinel-1 satellite data acquired between 2014 and 2017 centered on the Trishuli River drainage basin in Nepal. We found a minimum of 6 slow-moving landslides that all occur within the Ranimatta lithologic formation (phyllites, metasandstones, metabasics). These landslides have areas ranging from 0.39 to 1.66 km2 and long-term dry-season displacement rates ranging from 2.1 to 8.8 cm/yr. Due to periods of low coherence during the monsoon season (June – September) each year, and following the 25 April 2015 Mw7.8 Gorkha earthquake, our time series analysis is limited to the 2014–2015 and 2016–2017 dry seasons (September–May). We found that each of the landslides displayed slightly higher rates during the 2014 period, likely as a result of higher cumulative rainfall that fell during the 2014 monsoon season. Although we do not have high quality InSAR data to show the landslide evolution directly following the Gorkha earthquake, the similar rates of movement before (2014–2015) and after (2016–2017) Gorkha suggest the earthquake had negligible long-term impact on these landslides. Our findings highlight the potential for region-wide mapping of slow-moving landslides using freely available remote sensing data in remote areas such as Nepal and future work will benefit from expanding our methodology to other regions around the world.

Land subsidence can be observed with time-series of Interferometric Synthetic Aperture Radar (InSAR) data. However, existing approaches only reveal subsidence signals that are multi-scale mixed, which is not conducive to the systematic analysis of subsidence of different mechanisms. A deformation signal decomposition (DSD) method based on spectral analysis is used to decompose the deformation extracted by time-series InSAR into three classes of deformation signals. They refer to large-scale deformation related to geological settings, medium-scale deformation caused more by group excavation, and small-scale deformation along linear infrastructures. TerraSAR-X datasets for Shanghai spanning April 2013 to September 2020, and Sentinel-1A datasets spanning January 2016 to September 2020 are used in this study. The results were cross-verified between the TerraSAR-X and Sentinel-1A datasets, and validated against levelling measurements. Subsidence signals caused by different mechanisms were automatically decomposed, which facilitates a systematic analysis for targeted diagnosis of land subsidence signals. A detailed analysis was conducted jointly at three scales of surface displacement, geological conditions, major construction activities, and subsidence mechanisms. It indicated that construction activities were the leading cause of land subsidence, and suggests that local authorities that wish to mitigate surface subsidence may benefit from primarily considering this process.

Hekoucun Reservoir is an important part of the flood control engineering system in the lower reaches of the Yellow River. Accurate and quick derivation of the deformation field in the reservoir with high temporal and spatial resolution is of great significance. Synthetic aperture radar interferometry has the advantages of high precision, large range, and spatiotemporal continuity, and proposes a new method for reservoir timing monitoring. This paper selected 24 Sentinel-1A orbit-raising images covering Hekoucun Village Reservoir from January 15, 2020 to December 4, 2020, and combined the particularity and complexity of the reservoir environment to develop time series SBAS-InSAR technology suitable for actual needs. The time series deformation information of the reservoir was obtained and the deformation characteristics of the rear dam area were mainly studied. Through ground GNSS monitoring data and field investigation, it was verified that InSAR technology has high applicability for reservoir monitoring, and can provide data support for the stability evaluation of subsequent reservoirs and the flood control pressure evaluation of the levee in the lower reaches of the Yellow River. [ABSTRACT FROM AUTHOR]

Since 2012, a mega-project, “Mountain Excavation and City Construction” (MECC), has been underway on the Chinese Loess Plateau, aiming to create extra flat ground for urban growth. However, this large-scale land creation has led to dramatic changes in the local geological environment, forming a large number of engineered loess slopes. This study aims to map and assess the boundaries and stability of these potentially unstable engineered slopes (potential landslides). Based on spatio-temporal deformation patterns of landslides prior to failure, a novel interferometric synthetic aperture radar (InSAR)-based procedure for potential landslide mapping and stability evaluation was constructed in this study. The proposed procedure was employed to map and evaluate the potential landslides in the Yan’an New District (YND), the largest land-creation area in the Loess Plateau. First, the spatial locations and boundaries of the potential landslides were determined by combining optical image interpretation with differential deformation maps derived from Sentinel-1 small baseline subset InSAR (SBAS-InSAR) analysis. Then, the kinematic evolution trend of the mapped potential landslide was evaluated by quantitative analysis of the time-series displacements using an exponential model. Finally, the potential influencing factors and failure modes of the potential landslide were examined and discussed. The results indicate that the InSAR-derived differential deformation maps reflect the external boundary of the potential landslides well, and a total of 18 potential landslides are identified and mapped for the first time in the large-scale land-creation area of the YND. Over 82% of the coherent targets (CTs) within these potential landslides showed primary or steady creep during the InSAR observation period; however, tertiary creep was also observed on several slopes. Thus, it is crucial to utilize multitemporal InSAR and/or ground-based equipment for the continuous monitoring of engineered slopes in large-scale land-creation areas. The high correlation between spatiotemporal deformation patterns and environmental factors indicates that the activity of potential landslides is mainly controlled by the thickness of filling loess, while rainfall accelerates the displacement of slopes. These findings can contribute to the development of landslide disaster prevention and mitigation measures in the YND.

There is an urgent demand for continuous detection and monitoring of active slopes in wide reservoir areas, as reservoir impoundments may activate unstable slopes. Although satellite time-series InSAR has been widely used in mapping active landslides, the tedious artificial interpretation of InSAR results limits the efficiency and reliability of landslides detection. We propose a set of procedures for deformation monitoring and continuous automatic landslide identification in wide reservoir areas. The local time window estimation can extract the nonlinear deformation in the time series InSAR signal, thus enhancing the deformation field. Spatial adaptive clustering enables the effective extraction of unstable slopes. The abnormal deformation trends of landslides can be updated through the continuous identification of new results and comparison with historical results. The procedure is used to continuously detect unstable slopes in the Lianghekou reservoir area before and after the impoundment. Combining the ascending and descending SAR data of Sentinel-1, 109 historically active landslides were found and 18 new landslides were activated within one year after the first water storage. The distribution and types of unstable slopes were analyzed, followed by two case studies. The first case shows the different effects of the two-stage water storage on slope deformations, indicating rising water levels' critical role in landslide activation. The second case shows the consolidation settlement of the embankment dam and the influence of water storage on slope deformation monitoring. The results demonstrate the effectiveness of the InSAR automatic landslide identification and this study provides a technical reference for similar reservoir area.

Earthquake‐triggered slow‐moving landslides are not well studied mainly due to a lack of high‐resolution in‐situ geodetic observations both in time and space. Satellite‐based interferometric synthetic aperture radar (InSAR) has shown potential in landslides applications, however, it is challenging to detect earthquake‐triggered slow‐moving landslides over large areas due to the effects of post‐seismic tectonic deformations, atmospheric delays, and other spatially propagated errors (e.g., unwrapped errors caused by decorrelation noises). Here, we present a novel InSAR phase‐gradient‐based time‐series approach to detect slow‐moving landslides that triggered by the 2016 Mw 7.8 Kaikōura earthquake. Twenty‐one earthquake‐triggered large (>0.1 km2) slow‐moving landslides are detected and studied. Our results reveal decaying characteristics of the temporal evolutions of these landslides, that averagely 3.9 years after the earthquake, their post‐seismic velocity will decay by 90% to reach approximately the pre‐seismic level. Our study opens new perspectives for understanding mass balance of earthquakes and helps reduce associated hazards. Plain Language Summary: Large shallow earthquakes in mountainous regions can trigger widespread landslides that cause major damage to infrastructure. Such landslides are typically identified using aerial imagery, optical satellite images, or fieldwork as such landslides tend to be associated with "fresh" scars and deposits of debris. However, another type of landslides, that are triggered by earthquake but move slowly, are difficult to find and monitor. Satellite‐based interferometric synthetic aperture radar (InSAR) provides an opportunity to monitor slow‐moving landslides, however, given the subtle and localized signals in the InSAR maps, the landslide displacements are easily contaminated by other signals in InSAR. To address this problem, we developed a new InSAR‐phase‐gradient based time‐series method. Using our new method, we find 21 large earthquake‐triggered slow‐moving landslides with average area of 0.84 km2. Through studying the spatio‐temporal displacements of these landslides, we find their movements gradually recover to pre‐seismic level in the years after the earthquake. Our study help to better understand the mechanism of earthquake triggered landslides, and thus contribute to reduce associated hazards. Key Points: A new interferometric synthetic aperture radar phase‐gradient based time‐series approach used to detect earthquake‐triggered slow‐moving landslidesTwenty‐one slow‐moving landslides triggered by the 2016 Mw 7.8 Kaikōura earthquake are detected and monitoredOur results reveal decaying characteristics of post‐seismic velocity of the earthquake‐triggered slow‐moving landslides [ABSTRACT FROM AUTHOR]

Potential landslide identification and monitoring are essential to prevent geological disasters. However, in mountainous areas where the surface gradient changes significantly, the leveling effect is not completely removed, affecting the deformation results. In this paper, the SBAS-InSAR and PS-InSAR time-series processing methods were combined to interfere with the SAR image data of the ascending orbit in the southern mountainous area of Ningxia and its surrounding regions. Based on the obtained surface deformation monitoring results and optical images, landslide hazard identification was successfully carried out within the coverage area of 3130 km2 in Xiji County. The results show that the whole study area presented a relatively stable state, most of the deformation rates were concentrated in the range of 0 mm/a to −10 mm/a, and the deformation in the southwest area was larger. A total of 11 large potential landslides (which were already registered potential danger points of geological disasters) were identified in the study area, including three historical collapses. The landslide identification results were highly consistent with the field survey results after verification. The timing analysis of the typical landslide point of the Jiaowan landslide was further carried out, which showed that the Jiaowan landslide produced new deformation during the monitoring time, but it was still in a basically stable state. It can do a good job in disaster prevention and reduction while strengthening monitoring. The results of this study have a guiding effect on landslide prevention and mitigation in the mountainous areas of southern Ningxia. [ABSTRACT FROM AUTHOR]

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In the past 30 years, the small baseline subset (SBAS) InSAR time-series technique has emerged as an essential tool for measuring slow surface displacement and estimating geophysical parameters. Because of its ability to monitor large-scale deformation with millimeter accuracy, the SBAS method has been widely used in various geodetic fields, such as ground subsidence, landslides, and seismic activity. The obtained long-term time-series cumulative deformation is vital for studying the deformation mechanism. This article reviews the algorithms, applications, and challenges of the SBAS method. First, we recall the fundamental principle and analyze the shortcomings of the traditional SBAS algorithm, which provides a basic framework for the following improved time series methods. Second, we classify the current improved SBAS techniques from different perspectives: solving the ill-posed equation, increasing the density of high-coherence points, improving the accuracy of monitoring deformation and measuring the multi-dimensional deformation. Third, we summarize the application of the SBAS method in monitoring ground subsidence, permafrost degradation, glacier movement, volcanic activity, landslides, and seismic activity. Finally, we discuss the difficulties faced by the SBAS method and explore its future development direction.

Synthetic aperture radar interferometry (InSAR) has emerged as an effective technique for monitoring potentially unstable landslides and has found widespread application. Nevertheless, in mountainous reservoir regions, the precision of time-series InSAR outcomes is often constrained by topography-dependent atmospheric delay (TDAD) effects. To address this limitation, we propose a novel InSAR time-series method that integrates TDAD correction. This approach employs advanced deep learning algorithms to individually model and mitigate TDAD for each interferogram, thereby enhancing the accuracy of small baseline subset InSAR (SBAS-InSAR) and stacking InSAR time-series analyses. Utilizing Sentinel-1 data, we apply this method to identify potential landslides in the Baihetan reservoir area, located in southwestern China, where we successfully identified 26 potential landslide sites. Comparative experimental results demonstrate a significant reduction (averaging 70% and reaching up to 90%) in phase standard deviation (StdDev) in the corrected interferograms, indicating a marked decrease in phase–topography correlation. Furthermore, the corrected time-series InSAR results effectively remove TDAD signals, leading to clearer displacement boundaries and a remarkable reduction in other spurious displacement signals. Overall, this method efficiently addresses TDAD in time-series InSAR, enabling precise identification of potentially unstable landslides influenced by TDAD, and providing essential technical support for early landslide hazard detection using time-series InSAR.

Landslides occur frequently in the western mountainous areas of China, causing huge losses every year. InSAR technology can efficiently and accurately identify potential landslides and is a powerful tool for landslide hazards mitigation. However, the successful application of InSAR technology is limited by several factors, such as geometric distortion and dense vegetation, especially in the area with alpine-canyon terrain. This study investigates the applicability of InSAR observations in identifying potential landslides of the middle section of the Yalong River, which is a typical alpine-canyon terrain area. Using time-series InSAR Sentinel-1 datasets, we detect six potential landslides, which are verified and analyzed by using optical remote sensing images. Then, the applicability analysis is performed considering geometric distortion and band suitability. The results reveal that combining ascending and descending data can increase the detectable area (not in the geometric distortion) from 70% to 92.9%. The comparison of the performance of C-band and L-band data in identifying potential landslides shows that the latter is able to detect potential landslides with high vegetation coverage but it may miss the area with slight displacement. This study demonstrates the use of InSAR for potential landslide identification in alpine-canyon terrain areas and reveals its applicability, which provides a deep understanding of SAR data selection and would play an important role in the InSAR-based landslide geohazard mitigation application.

Abstract Earthquake‐triggered slow‐moving landslides are not well studied mainly due to a lack of high‐resolution in‐situ geodetic observations both in time and space. Satellite‐based interferometric synthetic aperture radar (InSAR) has shown potential in landslides applications, however, it is challenging to detect earthquake‐triggered slow‐moving landslides over large areas due to the effects of post‐seismic tectonic deformations, atmospheric delays, and other spatially propagated errors (e.g., unwrapped errors caused by decorrelation noises). Here, we present a novel InSAR phase‐gradient‐based time‐series approach to detect slow‐moving landslides that triggered by the 2016 Mw 7.8 Kaikōura earthquake. Twenty‐one earthquake‐triggered large (>0.1 km2) slow‐moving landslides are detected and studied. Our results reveal decaying characteristics of the temporal evolutions of these landslides, that averagely 3.9 years after the earthquake, their post‐seismic velocity will decay by 90% to reach approximately the pre‐seismic level. Our study opens new perspectives for understanding mass balance of earthquakes and helps reduce associated hazards.

The metro system solves traffic congestion in urban transportation while bringing potential deformation risks, especially in tunnels built in typical soft deposits in coastal cities. Time-series Synthetic Aperture Radar Interferometry (TS-InSAR) is an effective tool for monitoring land subsidence in urban areas. However, the different structures of metro lines, such as elevated tracks, ground lines, and tunnels, were often neglected in previous studies. Here, we introduced a Station Based Monitoring (SBM) method combined with InSAR analyses for metro safety monitoring. TS-InSAR provided the measurements along the elevated rails and ground lines. For tunnels, the TS-InSAR and SBM were jointly performed to deduct partial deformation under tunnels caused by land subsidence. The proposed metro system monitoring strategy was demonstrated and validated over the Shanghai Metro and Tianjin Rail Transit in the coastal cities of China. The results show that most deformed sections are located on the metro network's periphery, with velocities around −5 mm/year in Shanghai and −25 mm/year in Tianjin. The spatial distribution and temporal evolution of deformation characteristics along the two cases were then explored and compared. The proposed hazard matrix combining geological conditions and passenger density provided a more comprehensive assessment of potential hazards. The study highlights that InSAR and SBM analyses could be potentially applied to monitoring metro systems considering different structures.

The main city of Kunming is located on the north bank of Dianchi Lake. The complex geological environment, large-scale construction, and expansion of the city in recent years have caused uneven land surface subsidence and threatened public safety. In this study, Sentinel-1 ascending and descending orbit datasets were collected for the period of February 2018 to May 2021. The characteristics of surface displacement in the Kunming downtown area were monitored using the time-series interferometric synthetic aperture radar (InSAR) technique, and attribution analysis was performed. It was found that areas with more severe surface settlement were concentrated in the International Exhibition Center area and the large, newly built communities near Dianchi Lake and the Xiaobanqiao Region. The multifactor attribution analysis results demonstrated that the subsidence areas are concentrated in urban villages and engineered, construction-intensive areas in the lakeside sedimentary layer area, with the maximum displacement rate reaching −23.12 mm/a in the line-of-sight direction of the Sentinel-1 ascending dataset. The reliability of the InSAR results was cross-validated with ascending and descending results. This study provides a scientific reference for urban development planning and potential geological disaster detection in Kunming. [ABSTRACT FROM AUTHOR]

Many ancient landslides in the upper reaches of the Jinsha River seriously threaten the safety of residents on both sides of the river. The river erosion and groundwater infiltration have greatly reduced the stability of the ancient landslides along the Jinsha River and revived many large landslides. Studying their deformation characteristics and mechanisms and predicting possible failure processes are significant to the safety of residents and hydropower projects. We used SBAS-InSAR and three-dimensional decomposition techniques in our study. Our results showed that the trailing edge and middle part of the landslide have rapidly deformed. The maximum vertical annual displacement rate was 12 cm/a period from July 2017 to July 2019. Correlation analysis showed that creep deformation is closely related to the river damming of the Baige landslide events and that the rising river level was an important factor in the resurrection and accelerated destruction of the Xiaomojiu landslide. As a result, we predicted the possible failure process of the Xiaomojiu landslide, which might have lasted 80 s and eventually formed a landslide deposit with a height of about 150 m, a length of approximately 1500 m, and an average width of 450 m. Our results provide data references for displacement monitoring and instability risk simulation of large landslides along the Jinsha River. [ABSTRACT FROM AUTHOR]

Tunneling work, including the construction of municipal tunnels and metro lines, may disturb the structural health of aging buildings in densely built urban areas. Deformation monitoring and risk assessments of aging buildings are crucial to mitigate incidents and prevent losses of people’s lives and properties. Time-series InSAR reveals spatio-temporal information about observed targets by extracting persistent scatterers of the structures, which can achieve the wide-range monitoring of buildings and infrastructure. However, solely relying on InSAR-derived general parameters (deformation rates and time series of specific points) cannot objectively assess the safety conditions of buildings. To address this issue, this study proposes an InSAR Nonuniform Settlement Index. First, the point targets of buildings are extracted through time-series InSAR processing. Then, using the points as inputs, the Nonuniform Settlement Index calculates the 3D settlement plane and the inclination angle of the plane corresponding to each building. In this way, the proposed Nonuniform Settlement Index acts as a subsequent analysis method of time-series InSAR to characterize the safety statuses of buildings. In our study, 147 scenes of COSMO-SkyMed images from 2013 to 2022 were used to inverse the nine-year deformation evolution of the tested area. After time-series InSAR processing and index analysis based on the above SAR datasets, cross-validation was implemented with static-level and manual investigation data. The approach was to use one aging, collapsed building affected by tunneling work, as well as the eight adjacent aging buildings. The results showed high consistency with the in situ data, which proves the efficiency of the proposed approach.

The Lianjiang Plain, renowned for its position as ‘China’s textile hub’ and characterized by its high population density, has experienced considerable subsidence due to excessive groundwater extraction in recent years. Although some studies have investigated short-term subsidence in this plain, research on long-term subsidence and rebound remain understudied. In this paper, the characteristics of surface deformation in the Lijiang Plain during two periods (2015–2017 and 2018–2021) have been investigated using the time-series interferometric synthetic aperture radar (TS-InSAR) technique, and the correlation with the changes in groundwater level, geological factors, and urban construction are discussed. The InSAR-derived results are cross-validated with the adjacent orbit datasets. Large-scale and uneven subsidence ranging from −124 mm/year to +40 mm/year is observed from 2015 to 2017. However, a significant decrease in the subsidence rate during 2018–2021, with local rebound deformation up to +48 mm/year in three regions, is also observed. Groundwater level changes are found to be the major cause of the ground deformation, and the intercomparison between groundwater level and ground displacement time series from TS-InSAR measurements also indicates a clear relationship between them during 2018–2021. Geological factors control the range of deformation area over the study period. The impact of urban construction on surface subsidence is evident, contributing to high deformation. Our findings could improve the understanding of how deformation is affected by groundwater rebound and offer valuable insights into groundwater management, urban planning, and land subsidence mitigation.

In recent years, increasing available synthetic aperture radar (SAR) satellite data and gradually developing interferometric SAR (InSAR) technology have provided the possibility for wide-scale ground-deformation monitoring using InSAR. Traditionally, the InSAR data are processed by the existing time-series InSAR (TS–InSAR) technology, which has inefficient calculation and redundant results. In this study, we propose a wide-area InSAR variable-scale deformation detection strategy (hereafter referred to as the WAVS–InSAR strategy). The strategy combines stacking technology for fast ground-deformation rate calculation and advanced TS–InSAR technology for obtaining fine deformation time series. It adopts an adaptive recognition algorithm to identify the spatial distribution and area of deformation regions (regions of interest, ROI) in the wide study area and uses a novel wide-area deformation product organization structure to generate variable-scale deformation products. The Turpan–Hami basin in western China is selected as the wide study area (277,000 km2) to verify the proposed WAVS–InSAR strategy. The results are as follows: (1) There are 32 deformation regions with an area of ≥1 km2 and a deformation magnitude of greater than ±2 cm/year in the Turpan–Hami basin. The deformation area accounts for 2.4‰ of the total monitoring area. (2) A large area of ground subsidence has occurred in the farmland areas of the ROI, which is caused by groundwater overexploitation. The popularization and application of facility agriculture in the ROI have increased the demand for irrigation water. Due to the influence of the tectonic fault, the water supply of the ROI is mainly dependent on groundwater. Huge water demand has led to a continuous net deficit in aquifers, leading to land subsidence. The WAVS–InSAR strategy will be helpful for InSAR deformation monitoring at a national/regional scale and promoting the engineering application of InSAR technology. [ABSTRACT FROM AUTHOR]

In this study, the probability integral method, Synthetic Aperture Radar Interferometry (InSAR), and the Okada dislocation model were collaboratively used to analyze deformation in the Alhada Pb-Zn mine. The predicted deformation values of the subsidence centers in three subsidence areas were 107 mm, 120 mm, and 83 mm, respectively, as predicted using the probability integral method. The coherent scatterer InSAR technique was used to analyze the time-series deformation of the mining area, and the same subsidence center locations and similar deformation values were observed. The Okada dislocation model was used to invert the optimal parameters of the underground-mining ore body causing the surface subsidence, indicating that the surface subsidence is mainly caused by the mining of ore bodies in the 888 and 848 middle sections. We further simulated ground deformation using the multi-source Okada model. The results showed that the predicted and modeled deformations are highly correlated with the observed deformation. Through the analysis and comparison of the InSAR results, it was concluded that the three subsidence areas do not threaten the stability of the main buildings in the mining area. Using theoretical, observational, and modeling methods, the development and evolution of the subsidence area in mines can be established, which could provide basic data for subsidence control work and guarantee mine production safety. [ABSTRACT FROM AUTHOR]

As an important indicator of permafrost degradation, surface deformation is often used to monitor the thawing and freezing process in the permafrost active layer. However, due to the large area of the continuous permafrost of the Qinghai–Tibet Plateau (QTP) and the large amount of data processed by conventional time-series InSAR, previous studies have mostly focused on local area investigations, and regional characteristics of surface deformation of the continuous permafrost area on the QTP are still unclear. In this paper, we characterized surface deformation in space and time over the main continuous permafrost area on the QTP, by analyzing 11 ascending and 8 descending orbits of Sentinel-1 SAR data acquired between 2018 and 2021 with the time-series InSAR processing system LiCSAR. The reliability of the InSAR deformation results was verified by a combination of leveling measurement data, the intercomparison of overlapping area results, and field verification. The results show that the permafrost regions of the central QTP exhibited the most significant linear subsidence trend. The subsidence trend of permafrost on the QTP was mainly related to the thermal stability of permafrost, and the regions with larger subsidence rates were concentrated in sub-stable, transitional and unstable permafrost areas. We also found that, according to analysis of time-series displacement, the beginning and ending times of permafrost thawing were highly spatially heterogeneous, with the time of maximum thawing depth varying between mid-October and mid-November, which was probably attributed to the active layer thickness (ALT), water content in the active layer, and vegetation cover in these regions. This study is of great significance for understanding the changing trend of permafrost on the QTP under the background of climate change. In addition, this study also demonstrates that combination of Sentinel-1 SAR images with the LiCSAR system has significant potential for detecting permafrost deformation with high accuracy and high efficiency at regional and global scales. [ABSTRACT FROM AUTHOR]

Drastic climate change has led to glacier retreat in southeastern Tibet, and the increased frequency and magnitude of heavy rainfall and intense snow melting have intensified the risk of ice avalanche-induced debris flows in this region. To prevent and mitigate such hazards, it is important to derive the pre-disaster evolutionary characteristics of glacial debris flows and understand their triggering mechanisms. However, ice avalanche-induced debris flows mostly occur in remote alpine mountainous areas that are hard for humans to reach, which makes it extremely difficult to conduct continuous ground surveys and optical remote sensing monitoring. To this end, synthetic aperture radar (SAR) images were used in this study to detect and analyze the pre-disaster deformation characteristics and spatial evolution in the Sedongpu Basin and to detect changes in the snowmelt in the basin in order to improve our understanding of the triggering mechanism of the ice avalanche-induced debris flows in this region. The results revealed that the maximum average deformation rate in the basin reached 57.3 mm/year during the monitoring period from January 2016 to October 2018. The deformation displacement in the gully where the ice avalanche source area was located was intimately correlated with the summer snowmelt and rainfall and was characterized by seasonal accumulation. Clear acceleration of the deformation was detected after both the most recent earthquake and the strong rainfall and snowmelt processes in the summer of 2018. This suggests that earthquakes, snowmelt, and rainfall were significant triggers of the Sedongpu ice avalanche-induced debris flows. The results of this study provide new insights into the genesis of the Sedongpu ice avalanche-induced debris flows, which could assist in disaster warning and prevention in alpine mountain regions. [ABSTRACT FROM AUTHOR]

The Middle Route of the South–North Water Diversion Project has changed the water resources pattern in China. As advanced equipment for the country, it is responsible for the water supply “lifeline” of Beijing, Tianjin, Hebei, Henan, etc. Ensuring its safe operation is a top priority to promote social stability and coordinated economic development between the North and the South. Used persistent scatterer interferometric synthetic aperture radar (PS-InSAR) technology to monitor the deformation of the high fill characteristic canal in Wenzhuang Village, Ye County, during the period from October 2016 to June 2017 for the South–North Water Diversion Project showed that there was significant deformation on the 1 km-long slope of the east bank of the canal, with the maximum deformation volume reaching 36 mm. Through the comparison and verification with the second order leveling data, there are more than 87% of the root mean square error of both less than ±2 mm. The correlation coefficient is 0.96, and the two were highly consistent in deformation trends and values. Through the vertical and cross-sectional analysis of the canal’s east bank and four key monitoring sections, it was found that the east bank of the canal presents overall uneven subsidence, and the closer the canal is to the water, the greater the canal deformation, and vice versa. Further comparison of the PS-InSAR deformation results of the canal from October 2016 to February 2018 proves that this technology cannot only monitor the subsidence range and rate of the South–North Water Diversion canal but also accurately identify the subsidence sequence of the east and west banks. It can provide reliable technical support for the safety monitoring and disaster prevention of the South–North Water Diversion canal characterized by high fill and deep excavation.