Digital soil mapping of key GlobalSoilMap properties in northern Karnataka plateau

Abstract

Accurate and quantitative information on soil properties of each and every location is essential for site specific sustainable management of land resources. A study was conducted to predict the different key soil properties of Northern Karnataka as per GlobalSoilMap specifications using Quantile Regression Forest (QRF) Model. Along with Sentinel-2 data, terrain attributes such as elevation, slope, aspect, topographic wetness index, topographic position index, plan and profile curvature, multi-resolution index of valley bottom flatness, multi-resolution ridge top flatness and vegetation factors like NDVI and EVI were used as covariates. Equalarea quadratic splines were fitted to soil profile datasets to estimate soil properties viz. pH, OC, CEC, clay, sand, silt, field capacity and permanent wilting point at six standard soil depths (0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm) as per GlobalSoilMap specifications. The coefficient of determination (R2), mean error (ME) and root mean square error (RMSE) were calculated in order to assess model performance. Prediction interval coverage percentage (PICP) was calculated to evaluate the associated uncertainty predictions. The predicted soil properties are reliable with minimum errors and the QRF model captured maximum variability for most of the soil properties.