Addressing Spatial Variability in Estimating Cover Management Factor of Soil Erosion Models using Geoinformatics: A Case Study of Netravati Catchment, Karnataka, India

Abstract

Soil erosion is a significant threat to both agricultural productivity and natural resources. The most commonly applied soil erosion models are the Universal Soil Loss Equation (USLE) and its revised version (RUSLE), which rely heavily on the Cover Management factor (C factor) as a critical input parameter. This study aims to improve the accuracy of C factor estimates for the Netravati catchment present in the Western Ghats and Coastal Plains of India by using the Random Forest Algorithm and Sentinel 2 satellite data. The research examined five commonly used Normalized Difference Vegetation Index (NDVI) based C factor estimating equations and found that they inadequately represented local vegetation dynamics in the study area. To address this, a high-resolution Land Use Land Cover (LULC) map was generated using the Random Forest algorithm and in situ C factor values were assigned to LULC classes. A regression analysis between Sentinel 2-derived NDVI and the actual C factor yielded a novel equation. The proposed equation estimated C factor values ranging from 0.056 to 0.99, which closely align with actual observations and outperforming existing methods. The model’s performance was evaluated using statistical metrics, including a correlation coefficient of 0.984, mean absolute error of 0.048, root mean square error of 0.058, and Kling-Gupta efficiency of 0.921, indicating superior accuracy compared to existing methods. This study presents a region-specific approach for estimating the C factor, serving as a reliable tool for improving soil erosion predictions in the Western Ghats and Coastal Plains of India. Apart from highlighting the need for local parameterisation, the results have important implications for soil conservation planning, erosion risk management, and sustainable land use practices in the region.