Spatial modeling of soil erosion in the Teesta River Basin in Bangladesh using RUSLE and remote sensing data in Google Earth Engine - Butar Butar, Erni Saurmalinda; Thepvongsa, Jedtavong (Journal of Degraded and Mining Lands Management, 2026)
Soil erosion posed a significant environmental challenge in river basin environments, threatening agricultural productivity, compromising water quality, and eroding ecosystem integrity. The Teesta River Basin, an ecologically sensitive and economically important region, is increasingly affected by erosion driven by natural and anthropogenic factors. This study employed the Revised Universal Soil Loss Equation (RUSLE) alongside Google Earth Engine (GEE) and Geographic Information System (GIS) tools to evaluate the spatial distribution of soil erosion. The model incorporates rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C), and support practices (P), processed with high-resolution remote sensing datasets. Results indicate strong spatial variability, with average soil loss estimated at 11.25 t/ha/yr. About 44% of the basin experiences very low erosion, 21% low, 6% moderate, 10% severe, and 7% very severe erosion. Agricultural land, the dominant cover type (391,796.9 ha), shows the highest average soil loss (112 t/ha/yr), largely due to continuous tillage, residue removal, and unsustainable practices. Nearly 59% of cropland faces high erosion risk compared to other land covers. Prioritization of erosion-prone areas reveals that 7% of the basin falls into high priority (very severe), 10% medium priority (severe), and over 70% low priority (low and very low). These findings offer crucial guidance for implementing targeted soil conservation measures and informing sustainable land use planning. The study highlights the effectiveness of integrating RUSLE with GEE for large-scale erosion assessment and watershed management.