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The continuous downward movement exhibited by the Lashagou landslide group in recent years poses a significant threat to the safety of both vehicles and pedestrians traversing the highway G310. By integrating geomorphological interpretation using multi-temporal optical images, interferometric synthetic aperture radar (InSAR) measurements, and continuous global navigation satellite system (GNSS) observations, this paper traced the formation period of the Lashagou landslide group, and explored its kinematic behavior under external drivers such as rainfall and snowmelt. The results indicate that the formation period can be specifically categorized into three periods: before, during, and after the construction of highway G310. The construction of highway G310 is the direct cause and prerequisite for the formation of the Lashagou landslide group, whereas summer precipitation and spring snowmelt are the external driving factors contributing to its continuous downward movement. Additionally, both the long-term seasonal downslope movement and transient acceleration events are strongly controlled by rainfall, and there is a time lag of approximately 1–2 days between the transient acceleration and heavy rainfall events. This study highlights the benefits of leveraging multi-source remote sensing data to investigate slow-moving landslides, which is advantageous for the implementation of effective control and engineering intervention to mitigate potential landslide disasters. [ABSTRACT FROM AUTHOR]

Land motions are significantly widespread in the Yellow River delta (YRD). There is, however, a lack of understanding of the delta-wide comprehensive deformation mode and its dynamic mechanism, especially triggered by groundwater extraction. This paper adopts an integrated analysis of multidisciplinary data of image geodesy, geophysics, geology and hydrogeology to provide insights into Earth surface displacement patterns and dynamics in the YRD. Delta-scale land motions were measured for the first time using L-band ALOS images processed using multi-temporal InSAR, illustrating multiple obvious surface sinking regions and a maximum annual subsidence velocity of up to 130 mm. Then, the InSAR-constrained distributed point source model with optimal kernel parameters, a smoothness factor of 10 and a model grid size of 300 m was established and confirmed to be rational, reliable and accurate for modeling analysis over the YRD. Remarkable horizontal surface displacements, moving towards and converging on a sinking center, were recovered by means of modeling and measured using InSAR, with a maximum rate of up to 60 mm per year, which can trigger significant disasters, such as ground fissures and building damage. In addition, the annual total water storage variation at the delta scale, the most meaningful outcome, can be calculated and reaches a total of approximately 12,010 × 103 m3 in Guangrao city, efficiently filling the gap of GRACE and in situ investigations for delta-wide aquifer monitoring. Finally, a comparative analysis of time series InSAR measurements, modeling outcomes, and fault and groundwater data was conducted, and the strong agreement demonstrates that faults control aquifer distribution and hence the spatial distribution of groundwater-withdrawal-related regional land subsidence. Moreover, the obvious asymmetric displacements, demonstrating a northeasterly displacement trend, further reveal that faults control aquifer distribution and Earth surface deformation. These findings are useful for understanding the land motion patterns and dynamics, helping to sustainably manage groundwater and control disasters in the YRD and elsewhere worldwide. [ABSTRACT FROM AUTHOR]

The Lashagou landslide group in Gansu Province, China, is a typical shallow loess landslide group caused by artificial slope cutting. In April 2018, local sliding of the landslide group damaged houses and blocked the G310 highway, leading to the relocation of the Lashagou village, which aroused widespread concern. Unfortunately, the spatiotemporal displacement characteristics and failure modes of the landslide remain unknown. In this study, a method for the estimation of two-dimensional deformation of landslides, based on the local parallel flow model, was presented. This method only needs two orbital synthetic aperture radar (SAR) images with different imaging geometries, and has high accuracy verified by global satellite navigation system (GNSS) observations. In practice, we first obtained the surface velocity and time series deformation of the ascending and descending orbits. The best-fit sliding direction and inclination of the landslide movement were then inverted by combining satellite imaging geometry and surface velocity. Furthermore, the two-dimensional deformation of the Lashagou landslide group in the sliding and normal directions was obtained. We found that the landslide was in the accelerated deformation stage during the wet season and the deformation was mainly concentrated in the northern part of the Lashagou village. The snowmelt and continuous rainfall were the main factors in the landslide deformation. In addition, the landslide surface displacement characteristics and deep stress states can be linked using a combination of two-dimensional deformation, combined deformation, and inclination, which provides evidence that landslide movement is controlled by one or more deep continuous structural planes. Our research shows that the two-dimensional deformation retrieval method can be applied to gravity-driven translational landslides to help prevent and mitigate landslide hazards. [ABSTRACT FROM AUTHOR]

Ankang Airport is constructed on an expansive soil-fill platform in Shaanxi Province, Central China. Since its completion in 2020, it has suffered surface deformation caused by the consolidation and settlement of the fill layer and instability of the expansive soil slope. Exploring the special deformation law of expansive soil regions by remote sensing and analyzing the deformation characteristics of airports in mountainous areas have always been key issues in related disaster research. Based on the intensity and phase observation data of 37 Sentinel-1 synthetic aperture radar images, this study obtained the spatio-temporal distribution of the deformation of Ankang Airport from May 2020 to October 2021. First, phase optimization was performed on the original interferograms. Second, the persistent scatterer synthetic aperture radar interferometry (PS-InSAR) method was applied to extract the surface deformation information of Ankang Airport, and the accuracy was evaluated. Finally, the singular spectrum analysis method was introduced to jointly analyze the deformation information obtained by the InSAR technology in combination with geological and climatic data. The results show that the excavation area of Ankang Airport was basically stable, the filling area had obvious surface and uneven deformation, and the expansive soil fill slope exhibits deformation characteristics strongly related to slope, rainfall, and fill depth. The deformation was mainly caused by consolidation and settlement, supplemented by the expansion and shrinkage deformation of the expansive soil. [ABSTRACT FROM AUTHOR]

In June 2019, an earthquake sequence comprising five M > 5 events occurred in a region of southwest China with fluid injection for both hydraulic fracturing and salt mining, which raised an extensive controversy on the cause. Here we use interferometric synthetic aperture radar (InSAR) observations to determine the source parameters of the sequence and to investigate the relationship with local injection activities. Both Sentinel‐1 and Advanced Land Observing Satellite 2 SAR images are collected to measure coseismic and preseismic surface deformation. Geodetic inversions with coseismic observations show that the sequence ruptured a previously unmapped southwest‐dipping thrust fault above 3 km depth, which intersects with the open‐hole sections of wells for solution mining of salt. In the 4 months before the sequence, cumulative line‐of‐sight displacements near the wells are around 1–2 cm after correcting seasonal‐like deformation. Our results indicate that water injection likely enhanced pore pressure within the fault zone and thus contributed to inducing the sequence. Plain Language Summary: Earthquakes are seldom reported in relation to fluid injection for salt mining. Here, we present a unique case of an earthquake sequence probably induced by such human activity in Changning (southwest China). The sequence began with a Ms 6.0 earthquake, which is an unprecedented magnitude and larger than the historical records of earthquakes induced by wastewater disposal and hydraulic fracturing. Due to the sparse distribution of seismic stations and the failure of a key station closest to injection wells for salt mining before the Ms 6.0 earthquake, available seismic data are not enough to constrain the spatial locations of events in the earthquake sequence. Here we instead use high spatial‐resolution interferometric synthetic aperture radar observations of coseismic surface deformation to image the causative fault of the sequence. We find that the open‐hole sections of injection wells intersect the seismogenic fault. Moreover, the cumulative surface displacements near the salt mine in the ~4 months before the sequence are 1–2 cm, which was mainly caused by ground uplift. Accordingly, we hypothesize that fluid injection increased pore pressure within the fault zone and brought it to rupture. Our interferometric synthetic aperture radar observations and inversion results present significant evidence toward the causality between salt mining and the earthquake sequence. Key Points: InSAR observations show significant ground deformation due to the Changning earthquake sequence near the salt mineInSAR inversions reveal that the Changning earthquake sequence ruptured a southwest‐dipping thrust fault above the depth of 3 kmThe open‐hole sections of wells injecting for solution mining of salt intersect the InSAR‐inferred seismogenic fault [ABSTRACT FROM AUTHOR]

Unsupervised change detection approaches, which are relatively straightforward and easy to implement and interpret, and which require no human intervention, are widely used in change detection. Polarimetric synthetic aperture radar (PolSAR), which has an all-weather response capability with increased polarimetric information, is a key tool for change detection. However, for PolSAR data, inadequate evaluation of the difference image (DI) map makes the threshold-based algorithms incompatible with the true distribution model, which causes the change detection results to be ineffective and inaccurate. In this paper, to solve these problems, we focus on the generation of the DI map and the selection of the optimal threshold. An omnibus test statistic is used to generate the DI map from multi-temporal PolSAR images, and an improved Kittler and Illingworth algorithm based on either Weibull or gamma distribution is used to obtain the optimal threshold for generating the change detection map. Multi-temporal PolSAR data obtained by the Radarsat-2 sensor over Wuhan in China are used to verify the efficiency of the proposed method. The experimental results using our approach obtained the best performance in East Lake and Yanxi Lake regions with false alarm rates of 1.59% and 1.80%, total errors of 2.73% and 4.33%, overall accuracy of 97.27% and 95.67%, and Kappa coefficients of 0.6486 and 0.6275, respectively. Our results demonstrated that the proposed method is more suitable than the other compared methods for multi-temporal PolSAR data, and it can obtain both effective and accurate results. [ABSTRACT FROM AUTHOR]