Predictive Soil Mapping of Key Soil Properties in Western Ghats, India - S. Dharumarajan (Journal of the Indian Society of Soil Science, 2023)
A study was conducted to map the key soil properties such as pH, organic carbon (OC), cation exchangecapacity (CEC), sand, silt, clay and bulk density (BD) in part of Western Ghats, South India using threemachine learning algorithms (random forest, cubist and support vector machine). Primary and secondaryterrain attributes, vegetation indices and bioclimatic variables were used as environmental variables forprediction of soil properties. Equal-area quadratic splines were fitted to 173 soil profile datasets collectedover the study area to estimate the soil properties at six soil depths (0-5, 5-15, 15-30, 30-60, 60-100 and100-200 cm) as per GlobalSoilMap specifications. The models were calibrated using 80% of the samples(138) and validated using remaining 20% of the samples (35). The accuracy of the performance wasassessed based on coefficient of determination (R2), concordance correlation coefficient (CCC), root meansquare error (RMSE) and mean error (bias). The random forest model outperformed other two models withhigh R2 and minimal RMSE for most of the soil properties. The model explained 41, 42, 31 and 36% ofvariation for surface pH, CEC, OC and BD, respectively. The high resolution (250 m) predicted soilproperties aid the policy makers to revert the land degradation process and to preserve soil quality byexecuting suitable land use policies.