Digital Mapping of Key Static Soil Attributes of Tamil Nadu, India using Legacy Soil Information

Abstract

Abstract Acquiring spatial soil information is pivotal for land resource management, environmental and soil modelling. Digital soil mapping approach helps in prediction of spatial soil properties by establishing the relationship between soil and environmental covariates. In the current study, we attempted to predict and map spatial distribution of soil depth, coarse fragments (CF), and soil textural classes over 0.13 million sq km area of Tamil Nadu, India. About 2105 samples were used for the prediction of soil attributes viz., soil depth and coarse fragments using random forest (RF) regression model, multiple linear regression (MLR), and support vector machine (SVM), while the same set of soil data was used to predict the soil textural classes as categorical variables using Random Forest classifier. Different environmental covariates such as derivatives of digital elevation models, IRS LISS-III data and bioclimatic variables were related for predicting the soil properties. The predicted soil depth and CF ranged from 46-200 cm and 1-42 per cent, respectively. The RF model outperformed by explaining the variability (R2) of 43% for soil depth and 21% for coarse fragments with root mean square error (RMSE) of 38 cm and 13%, respectively, whereas, MLR and SVM could achieve the maximum variability of R2 of 0.20 and 0.25 for soil depth and R2 of 0.07 and 0.09 for CF. With respect to soil textural classes, RF classifier performed well with overall accuracy of 63.8% and kappa index of 0.43. Variable importance ranking of Random Forest model showed that elevation, multiresolution valley bottom flatness (MrVBF), multiresolution ridge top flatness (MrRTF) and remote sensing variables (NDVI & EVI) displayed decisive role in prediction of the soil depth, coarse fragments and soil textural classes. In this study, 250 m resolution detailed soil depth, CF and textural class maps were prepared which will be useful for different environmental modelling and proper agricultural management purposes.