The Three Gorges hydropower station in China creates a large reservoir by diverting water from the Yangtze River, increasing the risk of geological disasters, especially massive landslides along the reservoir shoreline. To mitigate these risks, improving geological monitoring and early warning systems is crucial. Interferometric Synthetic Aperture Radar (InSAR) is widely used to monitor reservoir bank landslides. However, its potential in early warning systems is limited due to temporal resolution constraints, preventing timely warnings. To address this, we propose integrating daily hydrological data (precipitation and water level observations) with historical InSAR deformation sequences using our deep learning-based multivariate united state estimation network, “MUSEnet.” This approach generates customized daily landslide deformation products for high-risk areas, greatly enhancing early warning capabilities by providing timely and accurate information on landslide occurrence and magnitude. We validated our method using 161 Sentinel-1 A images of the Xinpu landslide in the Three Gorges Reservoir area. Through statistical analysis, we identified different degrees of influence from rainfall and reservoir water level on the deformation of the Xinpu landslide at various locations. Additionally, we observed distinct lag times between deformation and corresponding rainfall and reservoir water level events. By utilizing deep learning, our method estimates nonlinear states by considering hysteresis and intelligently accounts for the impact of rainfall and reservoir water level, resulting in more accurate estimations compared to traditional models.

The Antarctic is one of the most sensitive areas to climate change, and ice velocity is a fundamental parameter for quantitatively assessing the glacier mass balance. Interferometric synthetic aperture radar (InSAR), a powerful tool for monitoring surface deformation with the advantages of having high precision and wide coverage, has been widely used in determining ice velocity in the Antarctic. However, the mapping of complete three-dimensional (3D) ice velocities is greatly limited by the imaging geometries and digital elevation model (DEM)-induced errors. In this study, we propose the integration of multibaseline and multiaperture InSAR measurements from the ENVISAT ASAR datasets to derive complete 3D ice velocities in the Grove Mountains area of the Antarctic. The results show that the estimated complete 3D ice velocities are in good agreement with MEaSUREs and GPS observations. Compared with the conventional 2D and quasi-3D ice velocities, the complete 3D ice velocities can effectively eliminate the effects of DEM errors and elevation changes and are also capable of retrieving the thickness change of the ice, which provides important information on the origin of mass transition.

Reservoir landslides represent a significant geological hazard that jeopardizes the safety of reservoirs. Deformation monitoring and numerical simulation are essential methodologies for elucidating the evolutionary patterns of landslides. Nonetheless, existing approaches exhibit limitations in revealing the potential deformation mechanism. Consequently, this study proposes an innovative strategy that incorporates Interferometric Synthetic Aperture Radar (InSAR) deformation characteristics alongside fluid-solid coupling stress analysis to investigate the deformation, focusing on the Shuizhuyuan landslide within the Three Gorges Reservoir area as a case study. Using Temporary Coherence Point InSAR (TCP-InSAR) technology, significant motion units were identified, with a maximum deformation rate of -60 mm/yr. The complete deformation time series reveals three independent components of landslide movement and their trigger factors geometrically. Subsequently, the saturation permeability coefficient of the sliding mass in the seepage analysis is modified with the assistance of InSAR deformation. Then, we coupled the seepage analysis results to FLAC3D model for stress and strain analysis, and determined the seepage-induced progressive failure mechanism and the deformation mode of the Shuizhuyuan landslide, driven by reservoir water level (RWL) drop. The numerical simulation results aid in interpreting the deformation mechanism of different spatial and temporal patterns of landslides from three aspects: hydrodynamic pressure from rainfall infiltration, groundwater hysteresis caused by RWL drop, and seepage forces from RWL rise. Furthermore, our findings reveal that the dynamic Factor of Safety (FOS) of landslide during InSAR observation period is highly consistent with the periodic fluctuations of the RWL. However, there is also a small trend of overall decline in FOS that cannot be ignored.