Many potential landslides occured in the Baihetan reservoir area before impoundment. After impoundment, these landslides may still slide, affecting the safe operation of the reservoir area (e.g., causing barrier lakes and floods). Identifying the locations of landslides and their distribution pattern has attracted attention in China and globally. In addition, due to the rolling terrain of the reservoir area, synthetic aperture radar (SAR) imaging will affect the interactive synthetic aperture radar (InSAR) deformation results. Only by obtaining effective deformation information can active landslides be accurately identified. Therefore, the banks of the Hulukou Xiangbiling section of the Baihetan reservoir area before impoundment in the Jinsha River Basin were studied in this paper. Using terrain data and the satellite parameters from Sentinel-1A ascending and descending orbits and ALOS PALSAR ascending orbit, the line-of-sight visibility was quantitatively analyzed, and an analysis method was proposed. Based on the SAR data visibility analysis, the small baseline subset (SBAS) technique was used to process the SAR data to acquire effective deformation. InSAR deformation data was combined with Google Earth imagery to identify 25 active landslides. After field verification, 21 active landslides (14 new) were determined. Most of the active landslides are controlled by faults, and the strata of the other landslides are relatively weak. This InSAR analysis method based on SAR data visibility can provide a reference for identifying and analyzing active landslides in other complicated terrain.

The Baihetan Hydropower Station, the second largest in the world, is currently impounded, posing significant challenges to reservoir slope stability and nearby community’s safety. Thus, continuous monitoring as well as updation of landslide inventory is pressing requirements. InSAR technology, with its mm-scale precision and round-the-year usability, will be highly effective in this region. However, single-source SAR data are limited for long-term detection, and traditional atmospheric models in InSAR grapple with attenuating the external atmospheric disturbances caused by impoundment, affecting InSAR accuracy. Therefore, we used multi-source SAR data (ALOS PALSAR and Sentinel-1A/B), and used time-series InSAR with the GACOS atmospheric correction model to accurately detect and update landslide inventory before and during impoundment. The results show that a total of 52 landslides were detected, including 31 newly detected during impoundment. Among them, 22 landslides have toe slopes in direct contact with water. Comparing landslides before and during impoundment, deformations exhibit three behaviors: new deformation emergence, weakening of existing deformation, and continuous increase. These landslides mainly develop in landforms with an inclination angle of 30° to 40°, trending northeast and northwest, and an altitude of 800 to 1200 m. Most landslides reside in non-massive rock strata and are modulated by fault zones, and their frequency diminishes with increasing distance from the reservoir boundary. Moreover, the deformation time series results show that intense summer rainfall and rapid reservoir water level rise are key factors accelerating deformation in active landslides and reactivating unstable slopes. Thus, this research can be directly used for landslide prevention and mitigation in the Baihetan reservoir area, providing an important reference for detecting similar reservoir landslides in atmospherically influenced areas.

Interferometric synthetic aperture radar (InSAR) is a key tool for monitoring landslide deformation in reservoir regions. However, when only ascending and descending line-of-sight (LOS) observations are available, 3D deformation inversion over complex hillslopes remains challenging because of slope-geometry priors and the anisotropic observation sensitivity. This study focuses on hillslopes in the Baihetan Reservoir area after impoundment. We use 340 ascending and descending Sentinel-1A images acquired from April 2021 to October 2024, generating LOS displacement time series using the extended small baseline subset (E-SBAS) technique. We propose a two-track InSAR 3D inversion framework centered on sensitivity-constrained anisotropic regularization (SC-Aniso). In this framework, a local-gradient surface-parallel flow model (LGSPFM) serves as a supporting pixel-scale topographic prior for representing local slope geometry. SC-Aniso constitutes the primary methodological innovation by mapping the inverse joint LOS sensitivities of the E, N, and U components to component-wise regularization weights. This design suppresses noise amplification in weakly constrained directions. Results show that the Baihetan Reservoir area is generally stable, with localized anomalies mainly in typical reservoir-bank landslide zones. The inverted 3D fields reveal coupled subsidence, horizontal displacement and downslope creep in the L01–L03 landslides. GNSS validation shows vertical RMSEs below 5.29 mm, mean 3D rate differences below 4 mm/yr, and an average component-wise rate difference of 2.56 mm/yr. At the optimal regularization parameter, SC-Aniso reduces north–south dispersion in stable areas by 41.9% compared with isotropic regularization. Wavelet analysis indicates a 288–384 day seasonal period for nonlinear displacement of the Xiaomidi landslide, with lags of 24 and 90 days relative to precipitation and reservoir water level, respectively. This study provides support for accurately recovering 3D deformation and interpreting movement mechanisms of landslides under limited two-track LOS observations.