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This study assessed the soil erosion dynamics in Iran's Maharloo watershed using remote sensing indices (Normalized Difference Vegetation Index (NDVI) , Normalized Difference Salinity Index (NDSI), and Topsoil Grain Size Index (TGSI)) and machine learning models (RF, SVM, and BRT). Landsat 8 satellite images (2005–2024) were processed via the Google Earth Engine, with field validation ensuring accuracy. Among the indices, TGSI (R 2 = 0.86), NDSI (R 2 = 0.89), and NDVI (R 2 = 0.87) showed the strongest correlations with ground data (Rain, Soil and Vegetation). The RF outperformed the other models (AUC = 0.89), identifying the central and western regions as warning erosion zones. Breakpoint analysis revealed abrupt changes in NDVI and NDSI (2013), while early warning signals (autocorrelation, variance, and skewness) indicated an escalating erosion warning, particularly near wetlands and rainfed fields. Spatial trends highlighted significant NDVI declines (Kendall's τ = 0.69) in wetland peripheries and NDSI increased (τ = 0.52) in northern farmlands. These findings underscore the efficacy of integrating machine learning and remote sensing for erosion monitoring, providing actionable insights for land management and conservation strategies. • Integrated NDVI, NDSI, and TGSI with machine learning for erosion risk mapping. • RF model achieved top accuracy (AUC = 0.89) for erosion hotspot identification. • Detected 2013 breakpoints in NDVI and NDSI as early warning signals for erosion. • Revealed central and western watershed as high-risk zones near wetlands and fields. • Established a scalable framework for soil erosion monitoring using remote sensing.
Fathi et al. (Sat,) studied this question.