Integrating RUSLE Model in Google Earth Engine and Geographic Information System for Soil Erosion Assessment—A Case Study of Upper Mahi River Basin, India

Soil erosion poses a significant threat to environmental conservation and sustainable land management, especially in regions where land use, terrain, and rainfall patterns interact to accelerate degradation. This study aimed to assess soil erosion and prioritize watersheds for conservation in the upper Mahi River catchment. The main objectives were to estimate spatial soil loss, identify erosion-prone areas, and provide recommendations for targeted soil and water conservation measures. The Revised Universal Soil Loss Equation (RUSLE) model was integrated in cloud computing approach (Google Earth Engine) and Geographic Information System to analyse key factors influencing erosion, including rainfall erosivity, soil erodibility, slope length and steepness, crop management, and conservation practices. The value of R factor ranges from 852.62 to 1236.42. The K value ranges from 0 to 0.05. The higher erodibility values observed in watershed 1 and watershed 2 coincide with areas dominated by soils containing higher proportions of fine sand. The LS factor varied from 0 to 12.68, indicating considerable spatial variability in erosion susceptibility due to topographic influence and the C factor ranged between 0 to 1. Higher C factor values indicate greater susceptibility of soil to erosion, commonly associated with barren land and grazing areas where protective vegetation is minimal. The P factor value ranges from 0 to 1 indicates the less management practices in the study area. The integration of all the factors shows that estimated soil loss in the six watersheds ranged from 3.92 to 8.41 t/(ha yr), with watershed 3 exhibiting the highest erosion rate. These findings indicate that watershed 3 is the most vulnerable and requires urgent soil conservation interventions. Recommended practices include contour farming, cover cropping, agroforestry, grass waterways, mulching, riparian buffer zones, check dams, reduced or no-tillage farming, and diversion channels to effectively reduce soil loss and improve land productivity. The study demonstrates that integrating cloud-based GIS analysis provides an efficient and timely approach for supporting sustainable watershed management. It is recommended that these conservation practices be implemented in the most erosion-prone areas to mitigate degradation and support long-term environmental sustainability.