Unstable Slope Identification and Monitoring Using Polarization-Enhanced DS-InSAR: A Case Study in the Bailong River Basin

Interferometry Synthetic Aperture Radar (InSAR) technology has been widely applied to the identification and monitoring of unstable slopes. Recent studies have demonstrated that polarization information can enhance the quality of interferometric phase and increase the density of monitoring points. In this study, we propose a distributed scatterer InSAR (DS-InSAR) method centered on efficient polarization channel optimization and the construction of DS target covariance matrices to improve surface deformation monitoring in mountainous regions. This approach utilizes time-series InSAR based on polarimetric SAR data to enhance phase quality and monitoring point density. Specifically, the method first determines the optimal polarization channels for PS and DS points using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method with auxiliary land cover classification data, targeting amplitude dispersion and coherence. Next, the similarity-weighted approach is applied to estimate the sample covariance matrix (SCM) for DS points. Finally, regularization parameters are introduced to further refine the optimal phase of DS points. Real-data experiments conducted in the Bailong River Basin of China, using Sentinel-1 ascending and descending data, demonstrate the effectiveness of the method through qualitative and quantitative analyses. Compared to traditional DS-InSAR techniques, the proposed method achieves a 10% improvement in monitoring point density and identifies 29 unstable slopes in the study area. Additionally, incorporating polarimetric data enhances the accuracy of displacement evolution over time.