Unveiling Erosion Dynamics: Integrating PSInSAR into Length and Steepness Factor in RUSLE Model.

Soil erosion poses a significant global challenge, with an estimated 25–40 billion tons of soil lost annually, threatening food security, water quality, and ecological balance. In Europe alone, soil losses exceed 970 million tons annually, emphasizing the urgent need for precise modeling to assess and mitigate erosion risks. The Revised Universal Soil Loss Equation (RUSLE) serves as a widely used empirical model for quantifying soil erosion risk, incorporating key parameters such as rainfall erosivity (R), soil erodibility (K), cover and management (P and C), and the Length and Steepness factor (LS). The LS factor, which accounts for slope gradient and length effects on runoff velocity and soil detachment, is critical but often constrained by static Digital Elevation Models (DEMs) that lack the temporal and spatial resolution to capture dynamic topographical changes.  This study introduces the integration of Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR), a cutting-edge remote sensing technique, with high-resolution Sentinel-1 SAR data and DEMs to enhance LS factor predictions within the RUSLE framework. PSInSAR enables millimetre-scale monitoring of terrain displacement over time, identifying subtle ground deformations that influence soil stability. Applying this approach to two watersheds in Israel—Yarkon-Ayalon and HaBsor—the study refines slope length and steepness estimates with sub-centimetre vertical accuracy, addressing the limitations of conventional DEM-based methods. Time-series displacement analyses derived from Sentinel-1 SAR data (2017–2023) reveal slope deformations, with subsidence rates reaching -14.49 mm/year and uplift rates up to 4.60 mm/year. Stable areas exhibited negligible displacement trends, validating the precision of the method. These displacement trends, supported by statistically significant p-values (< 0.01), highlight the spatial variability of erosion potential and topographical changes. The enhanced LS factor significantly improves soil erosion risk assessments under diverse climatic and land-use conditions. By integrating PSInSAR with the RUSLE model, this study advances soil erosion research and supports sustainable land management and policy development in erosion-prone areas. The findings provide actionable insights for reducing erosion risks and promoting soil sustainability on a broad scale.

Keywords: Sentinel-1, Synthetic Aperture Radar, Soil erosion modelling, Interferometric SAR, SNAP, Persistant Scatterer Interferometry, Digital Elevation Model.