Advancing Soil Erosion Quantification in the Yamuna River Subbasin Through GEE and Empirical Models for Sustainable Development

This study presents an integrated geospatial approach for assessing soil erosion dynamics in the southern sub‐basin of the Yamuna River, India, using the Revised Universal Soil Loss Equation (RUSLE), Google Earth Engine (GEE), land surface temperature (LST), and the normalized difference moisture index (NDMI). The analysis employs multisource remote sensing datasets and spectral indices within the GEE platform to derive soil erosion parameters and map spatiotemporal degradation patterns. Results indicate that rainfall erosivity (R‐factor) values range from 5 to 90 MJ mm ha −1 h −1 yr −1 , with higher values concentrated in the southwestern region. Soil erodibility (K‐factor) varies from 0.01 to 0.5 Mg h MJ −1 mm −1 , while LS‐factors exceed 20 in steeper slopes of the southwestern area, highlighting terrain‐driven vulnerability. Crop management (C‐factor) ranges between 0.005 and 1, correlating with vegetation cover, and the support practice factor (P) is uniformly set to 0.5 due to resolution limitations. Integrated LST analysis shows temperatures between 25°C and 34°C, indicating thermal stress zones. NDMI values range from 0.001 to 0.8, with higher values near riparian zones and lower values in barren regions, suggesting erosion‐prone areas with reduced vegetation moisture. The spatial convergence of high LST and low NDMI zones aligns with areas of severe erosion risk. This integrated methodology provides a robust framework for identifying soil erosion hotspots, offering critical insights for achieving SDG 15 (Life on Land), SDG 13 (Climate Action), SDG 2 (Zero Hunger), and SDG 6 (Clean Water and Sanitation) by enabling sustainable land use planning and climate‐resilient agricultural practices.