SAR imagery plays a crucial role in geological and environmental monitoring, particularly in highland mountainous regions. However, inherent geometric distortions in SAR images often undermine the precision of remote sensing analyses. Accurately identifying and classifying these distortions is key to analyzing their origins and enhancing the quality and accuracy of monitoring efforts. While the layover and shadow map (LSM) approach is commonly utilized to identify distortions, it falls short in classifying subtle ones. This study introduces a novel LSM ground-range slope (LG) method, tailored for the refined identification of minor distortions to augment the LSM approach. We implemented the LG method on Sentinel-1 SAR imagery from the tri-junction area where the Xiaojiang, Pudu, and Jinsha rivers converge at the Yunnan-Sichuan border. By comparing effective monitoring-point densities, we evaluated and validated traditional methods—LSM, R-Index, and P-NG—against the LG method. The LG method demonstrates superior performance in discriminating subtle distortions within complex terrains through its secondary classification process, which allows for precise and comprehensive recognition of geometric distortions. Furthermore, our research examines the impact of varying slope parameters during the classification process on the accuracy of distortion identification. This study addresses significant gaps in recognizing geometric distortions and lays a foundation for more precise SAR imagery analysis in complex geographic settings.

The Central Yunnan Water Diversion Project (CYWDP) is currently under construction and represents China’s most extensive and geologically challenging water transfer infrastructure, facing significant geohazard risks induced by intensive engineering activities, posing severe threats to its entire lifecycle safety. Therefore, monitoring and spatiotemporal evolution analysis of surface deformation along the CYWDP is critically important. This study presents the first integrated analysis of geometric distortions and multi-dimensional spatiotemporal deformation characteristics along the CYWDP, utilizing both ascending and descending orbit data from Sentinel-1. First, by integrating the Layover-Shadow Mask (LSM) model and R-Index method, we identified geometric distortion types in SAR imagery and evaluated their suitability for deformation monitoring. Subsequently, SBAS-InSAR technology was employed to derive line-of-sight (LOS) deformation information from 124 images (ascending) and 90 images (descending) acquisitions (2022–2024), enabling the identification of significant deformation zones and analyzing their spatial distribution characteristics. Finally, two-dimensional (2D) deformation fields were obtained through the joint inversion of ascending and descending orbit data in typical deformation zones. The results reveal that geometric distortions in Sentinel-1 imagery along the CYWDP are dominated by foreshortening effects, accounting for 35.3% of the study area in the ascending-orbit data and 37.9% in the descending-orbit data. A total of 10 significant deformation-prone areas were detected, and the most pronounced subsidence, amounting to −164 mm/y, was observed in the northern Jinning District (Luoci-Qujiang section), showing expansion trends toward water conveyance infrastructure. This study reveals surface deformation’s multi-dimensional spatiotemporal evolution patterns along the CYWDP. The findings support geohazard mitigation and provide a methodological reference for safety monitoring of major water conservancy projects in complex geological environments.