Quantification of Soil Erosion Using Digital Soil Mapping and RUSLE Method for Coimbatore District, Tamil Nadu, India

The study predicted the soil erosion map of the Coimbatore district, Western part of Tamil Nadu, India, to assess the potential of Digital Soil Mapping (DSM) against the empirical model, Revised Universal Soil Loss Equation (RUSLE). A total of 386 surface soil samples were collected and their properties used to predict soil erosion spatially. The Geographical Information System (GIS) enabled RUSLE factors to be derived spatially and integrated for annual soil loss estimation. The soil loss values calculated based on the RUSLE factors ranged from 0 to 88.58 t ha–1 yr–1. The spatial soil erosion was further predicted based on the SCORPAN model using the decision tree methodology to compare the suitability of the approaches in erosion mapping. The modelling tool, Cubist, was adopted to model the continuous soil erosion properties with 33 environmental covariates as independent variables. The LS factor, profile curvature, geology, mean annual rainfall, maximum air temperature, land use, minimum air temperature, elevation, slope and geomorphology covariates were the most influencing variables in predicting soil loss. The Cubist model’s rules are applied to the NLCD-Cubist classifier to generate a soil erosion map, and the annual soil loss ranged from 0 to 52.54 t ha–1 yr–1. The spatial pattern of soil erosion classes indicated that the Coimbatore district’s western part has a high erosion risk. Based on the validation sample points (n = 190), the DSM approach showed a higher concordance correlation (r = 0.857), whereas the RUSLE model showed less agreement (r = 0.632). From the concordance correlation values, it could be inferred that the decision tree algorithm performed better than the RUSLE erosion model. However, from the error map generated, it is advisable to have more soil observations with suitable environmental covariates to improve digital soil erosion mapping accuracy. The DSM approach is exploited well with point-based soil erosion loss information. The RUSLE model would perform well if the R, K, LS, C, and P factor maps were available at uniform scale and spatial resolution.