Prediction of Soil Depth in Karnataka Using Digital Soil Mapping Approach

Abstract

Spatial information of soil depth in regional and national level is essential for arriving crop suitability decisions. In the present study, high-resolution (250 m) soil depth map of Karnataka is prepared using digital soil mapping approach. A total of 5174 Soil legacy datasets studied by NBSS&LUP over a period of 30 years is collected and organized for mapping. Quantile regression forest (QRF) and regression kriging (RK) algorithm is tested to predict the soil depth in Karnataka. Topographic attributes derived from digital elevation model, normalized difference vegetation index, landsat-8 data and climatic variables are used as covariates. For model calibration, 80% of soil depth data is used and 20% of data is used for validation. The classical uncertainty estimates such as coefficient of determination (R2) and root mean square error (RMSE) and bias were calculated for the validation datasets in order to assess the model performance. RK model explained maximum variability for prediction of soil depth (R2 = 30%, RMSE = 34 cm) compared to QRF (R2 = 17%, RMSE = 37 cm). Lithology and elevation are found to be most important variables for prediction of soil depth in Karnataka. The predicted soil depth in Karnataka is ranged from 22 to 173 cm, and the present high-resolution (250 m) soil depth maps are useful in different hydrological, crop modelling and climate change studies.