Long-Term Landslide Inventory of the Xiluodu Reservoir, Downstream Jinsha River, China: Fusing C- and L-Band SAR Observations for Pre- and Post-Impoundment Monitoring in a High-Mountain Canyon Region

Reservoir-induced landslides are one of the most significant geological hazards in mountainous regions, especially in large hydropower areas. The fluctuation of reservoir water levels, combined with precipitation, plays a critical role in controlling slope stability and deformation processes. However, due to the lack of long-term, high-resolution observational datasets, the dynamic relationship between reservoir water level variations, rainfall, and landslide deformation is still not fully understood. To address this issue, this dataset provides a multi-source dataset for investigating the coupled effects of hydrological factors on landslide deformation. The study area is located in a typical reservoir region characterized by complex terrain and active slope deformation. The area is influenced by periodic reservoir water level fluctuations and seasonal rainfall, making it an ideal site for studying hydrological impacts on landslides. To derive landslide deformation over the study area, the SBAS-InSAR technique was applied. Both pre-impoundment and post-impoundment SAR data, including C-band observations from ENVISAT ASAR and Sentinel-1 with both ascending and descending orbits, as well as L-band data from ALOS PALSAR, were preprocessed using the GAMMA software, followed by time-series analysis and error correction implemented in MintPy. This processing workflow enables the retrieval of reliable surface deformation time series for landslide monitoring in the reservoir area.  In addition, precipitation data from Global Precipitation Measurement (GPM) and reservoir water level records were collected to investigate the hydrological controls and deformation response mechanisms of landslides. The dataset can support further studies on landslide mechanisms, time-series analysis, and the development of data-driven models for deformation prediction.