Soil erodibility parameters are quantitative indicators used to describe soil susceptibility to erosion by raindrop impact and surface runoff. Soil erodibility is a key factor in understanding and predicting soil loss under varying environmental conditions. The present study aimed to estimate soil erodibility parameters such as inter-rill erodibility (K-ib), rill erodibility (K-rb), critical shear stress (tau(cb)), and the K-factor. In this way, remote sensing data extracted from Sentinel-2 imagery and topographic data extracted from digital elevation models (DEMs) were applied as input variables in the Lake Urmia region of northwestern Iran. A total of 96 soil samples were collected and analyzed for key soil properties. Soil erodibility parameters were calculated using WEPP (Water Erosion Prediction Project) sub-models and RUSLE (Revised Universal Soil Loss Equation) equations. Three modeling scenarios were evaluated: Scenario I) topography, Scenario II) remote sensing, and Scenario III) a combination of both. Results showed that the integrated model (Scenario III) provided the most accurate predictions (R-2 values of 0.618 for K-ib, 0.337 for K-rb, 0.629 for tau(cb), and 0.503 for the K-factor). It was concluded that combining spectral and terrain data significantly improves the estimation of soil erodibility and offers a reliable and scalable approach for erosion risk assessment in semi-arid landscapes. The findings provide practical applications for land managers and policymakers to target erosion control strategies, improve watershed planning, and improve the identification of vulnerable zones in the deteriorating Lake Urmia ecosystem. WEPP (Water Erosion Prediction Project) sub-models and RUSLE (Revised Universal Soil Loss Equation) equations. Three modeling scenarios were evaluated: Scenario I) topography, Scenario II) remote sensing, and Scenario III) a combination of both. Results showed that the integrated model (Scenario III) provided the most accurate predictions (R-2 values of 0.618 for Kib, 0.337 for K-rb, 0.629 for tau(cb), and 0.503 for the K-factor). It was concluded that combining spectral and terrain data significantly improves the estimation of soil erodibility and offers a reliable and scalable approach for erosion risk assessment in semi-arid landscapes. The findings provide practical applications for land managers and policymakers to target erosion control strategies, improve watershed planning, and improve the identification of vulnerable zones in the deteriorating Lake Urmia ecosystem.