Integration of RUSLE model with remotely sensed data over Google Earth Engine to evaluate soil erosion in Central Indus Basin

Abstract Soil erosion presents a substantial environmental obstacle for farmers, especially in the plains of the Indus Basin, which are characterised by rainfall scarcity. This study utilised remotely sensed data on Google Earth Engine (GEE) to estimate the yearly soil erosion by implementing the Revised Universal Soil Loss Equation (RUSLE) model in the Central Indus Basin. The study's primary objective was to determine the order of importance and execute conservation strategies. The input datasets were processed on GEE to produce essential factors, including soil erosivity ( R ), soil erodibility ( K ), slope length and steepness ( LS ), land cover ( C ) and land management techniques ( P ), which are required for the model. The yearly soil erosion in the study area varied from 1 to 26.2 t ha −1 year −1 . The combined area of regions with low, moderate, high, and extremely high rates amounted to 1 445 397 ha. More precisely, 8670 (0.6%), 263 062 (18.2%) and 468 310 ha (32.4%) were allocated as first, second and third‐class priority areas, respectively. These areas were geographically dispersed across the northwest and eastern regions of the basin, including sandy dunes and infrequent agricultural cultivation. This study highlighted the usability of remotely sensed data on GEE for reliable soil erosion estimation on a large scale. This methodology amplifies the effectiveness of planning and conservation endeavours.