Unravelling long-term spatiotemporal deformation and hydrological triggers of slow-moving reservoir landslides with multi-platform SAR data

• Multiple SAR data are employed to uncover landslide kinematics and dynamics. • 15-year-long displacement evolution of landslides has been reconstructed. • Deformation response to hydrologic triggers is quantitatively characterized. Active landslides pose significant global risks, underscoring precise displacement monitoring for effective geohazard management and early warning. The Three Gorges Reservoir Area (TGRA) in China, a pivotal section of the world’s largest water conservancy project, has developed thousands of landslides due to unique hydrogeological conditions and reservoir operations. Many of these landslides are oriented north–south and covered by seasonal vegetation, which complicates the conventional remote sensing-based displacement monitoring, particularly in estimating the three-dimensional (3D) deformation and long-term time series displacement. To address these challenges, we propose an approach that integrates interferometric synthetic aperture radar (InSAR), pixel offset tracking (POT), stacking, and priori kinematic models to fully utilize the phase and amplitude information of multi-platform, multi-band SAR images (i.e., L-band ALOS-1, C-band Sentinel-1, and X-band TerraSAR-X). This approach is employed to scrutinize the long-term spatiotemporal deformation and evolution mechanism of two slow-moving, north-facing reservoir landslides in the TGRA. The results reveal for the first time the 15-year-long displacement evolution of these landslides before and after reservoir impoundment, highlighting the spatiotemporal heterogeneity of landslide deformation induced by hydrologic triggers. The impoundment in September 2008 induced transient acceleration in both landslides, followed by a relatively stable, step-like deformation pattern subject to rainfall and reservoir water level (RWL) fluctuations. Rainfall, with a lag of approximately 20 days, predominantly affects both landslides, while RWL fluctuations mainly influence the deformation at landslide toes. Notably, as the distance from the reservoir increases, the influence of RWL diminishes, with lag times increasing from 8 to about 40 days. This quantitative characterization of landslide responses to triggers represents a crucial step towards improved hazard mitigation capabilities.