This paper applied GIS based Revised Universal Soil Loss Equation (RUSLE), remote sensing and ground based data to develop the soil erosion risk mapping in lower Subarnarekha Watershed in India. The soil erosion input parameters were assessed in different ways: the R factor map was developed from the daily rainfall data and spatial distribution using Ordinary Kriging (OK) interpolation techniques, the K factor map was obtained from the soil map, the C factor map was generated based on a back propagation (BP) neural network model of Landsat ETM+ data with a correlation coefficient (r) of 0.921 to the ground truth collection and LS factor was derived from a digital elevation model (DEM) with a spatial resolution of 30 m. P factor map was generated using standard table proposed by USDA-SCS for conservation practices. By integrating the six factor maps in GIS platform through pixel-based computing, the spatial distribution of soil loss was obtained by the RUSLE model. The spatial distribution of erosion risk classes was 26.2 % (796.97 km2) very low erosion (<5 ton ha-1 year-1), 12.88 % (394.66 km2) low erosion (5–10 ton ha-1 year-1), 20.77 % (636.37 km2) moderate erosion (10–20 ton ha-1 year-1), 20.75 % (635.67 km2) high erosion (20–30 ton ha-1 year-1), and 19.58 % (599.71 km2) very high (>30 ton ha-1 year-1), soil erosion prone zone. The highest volume of very severe soil loss was observed in Keshiary > Dantan-I > Jaleswar > Sankrail blocks. However, the southern part of lower Subarnarekha watershed areas which are in the extremely severe level of soil erosion risk need immediate attention from soil conservation practices.
