Abstract

Soil erosion represents one of the most critical environmental challenges globally, threatening agricultural productivity, ecosystem stability, and sustainable development. This study presents a comprehensive assessment of soil erosion risk using the integration of remote sensing technologies with the Revised Universal Soil Loss Equation (RUSLE) model. The research was conducted across a representative watershed covering 2,450 km² in a semi-arid region, utilizing multi-temporal satellite imagery from Landsat 8 OLI and Sentinel-2 MSI sensors spanning 2018-2023. The RUSLE model was employed to quantify annual soil loss by integrating five key factors: rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C), and support practice (P). Remote sensing data facilitated the derivation of critical parameters including vegetation indices (NDVI), land use/land cover classifications, and topographic variables from digital elevation models. Results indicated that 34.2% of the study area exhibited high to very high erosion risk (>15 t ha⁻¹ yr⁻¹), with agricultural lands and degraded forests showing the highest vulnerability. The spatial distribution of erosion risk demonstrated strong correlations with slope gradient (r = 0.78), vegetation cover (r = -0.82), and land use patterns. Integration of remote sensing with RUSLE proved highly effective for large-scale erosion assessment, providing accuracy levels of 85.3% when validated against field measurements. This integrated approach offers valuable insights for land managers, policymakers, and conservation practitioners in developing targeted soil conservation strategies and sustainable land management practices.