Fluctuations in reservoir water levels exert a strong triggering effect on landslides along reservoir banks, constituting a long-term concern in the safe operation of hydroelectric projects and in the prevention and management of geological disasters. While existing research has investigated the impact of periodic water level changes on the deformation of reservoir bank landslides, observation and detection of such deformation are challenging, with noticeable gaps in understanding how these deformations respond to water level changes during the water impoundment period. To address this, our study targets the Baihetan Reservoir, leveraging 567 ascending and descending LiCSAR data and LiCSBAS (the small-baseline subset within LiCSAR) technology to construct a time series of ground deformations in the study area from 2019 to 2023. The TLCC (Time Lag Cross Correlation) model was employed to examine the time-lag response pattern of reservoir bank landslide deformations to reservoir water level changes during the impoundment period. Our findings indicate a clear time-lag response in reservoir bank landslide deformations to water level changes during the impoundment process. The rise in water levels emerged as a primary factor influencing the instability of reservoir bank landslides. During the half-year impoundment period of the Baihetan Reservoir, a time lag of 5–7 days was observed between landslide deformations and increases in water levels, with landslides on the eastern and western banks exhibiting differing time-lag response patterns. Our study illuminates the time-lag effect between water level changes during reservoir impoundment and reservoir bank landslide deformation monitoring. By proposing a quantitative analysis methodology utilizing LiCSBAS technology and the TLCC model, our findings can inform decision-making in the field of disaster prevention and reduction in reservoir engineering.

Time-series Interferometric Synthetic Aperture Radar (InSAR) technology, renowned for its high-precision, wide coverage, and all-weather capabilities, has become an essential tool for Earth observation. However, the quality of the interferometric baseline network significantly influences the monitoring accuracy of InSAR technology. Therefore, optimizing the interferometric baseline is crucial for enhancing InSAR’s monitoring accuracy. Surface vegetation changes can disrupt the coherence between SAR images, introducing incoherent noise into interferograms and reducing InSAR’s monitoring accuracy. To address this issue, we propose and validate an optimization method for the InSAR baseline that considers changes in vegetation coverage (OM-InSAR-BCCVC) in the Yuanmou dry-hot valley. Initially, based on the imaging times of SAR image pairs, we categorize all interferometric image pairs into those captured during months of high vegetation coverage and those from months of low vegetation coverage. We then remove the image pairs with coherence coefficients below the category average. Using the Small Baseline Subset InSAR (SBAS-InSAR) technique, we retrieve surface deformation information in the Yuanmou dry-hot valley. Landslide identification is subsequently verified using optical remote sensing images. The results show that significant seasonal changes in vegetation coverage in the Yuanmou dry-hot valley lead to noticeable seasonal variations in InSAR coherence, with the lowest coherence in July, August, and September, and the highest in January, February, and December. The average coherence threshold method is limited in this context, resulting in discontinuities in the interferometric baseline network. Compared with methods without baseline optimization, the interferometric map ratio improved by 17.5% overall after applying the OM-InSAR-BCCVC method, and the overall inversion error RMSE decreased by 0.5 rad. From January 2021 to May 2023, the radar line of sight (LOS) surface deformation rate in the Yuanmou dry-hot valley, obtained after atmospheric correction by GACOS, baseline optimization, and geometric distortion region masking, ranged from −73.87 mm/year to 127.35 mm/year. We identified fifteen landslides and potential landslide sites, primarily located in the northern part of the Yuanmou dry-hot valley, with maximum subsidence exceeding 100 mm at two notable points. The OM-InSAR-BCCVC method effectively reduces incoherent noise caused by vegetation coverage changes, thereby improving the monitoring accuracy of InSAR.