Predictive soil mapping of key soil properties in Western Ghats, India Dharumarajan S.*, Kalaiselvi B., Lalitha M., Vasundhara R., Kumar K.S. Anil, Hegde Rajendra ICAR-National Bureau of Soil Survey and Land Use Planning, Regional Centre, Hebbal, Bengaluru, 560024, Karnataka, India *Corresponding author (Email: sdharmag@gmail.com)
Online Published on 10 January, 2023. Abstract A study was conducted to map the key soil properties such as pH, organic carbon (OC), cation exchange capacity (CEC), sand, silt, clay and bulk density (BD) in part of Western Ghats, South India using three machine learning algorithms (random forest, cubist and support vector machine). Primary and secondary terrain attributes, vegetation indices and bioclimatic variables were used as environmental variables for prediction of soil properties. Equal-area quadratic splines were fitted to 173 soil profile datasets collected over the study area to estimate the soil properties at six soil depths (0–5, 5–15, 15–30, 30–60, 60–100 and 100–200 cm) as per Global SoilMap 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 was assessed based on coefficient of determination (R2), concordance correlation coefficient (CCC), root mean square error (RMSE) and mean error (bias). The random forest model outperformed other two models with high R2 and minimal RMSE for most of the soil properties. The model explained 41, 42, 31 and 36% of variation for surface pH, CEC, OC and BD, respectively. The high resolution (250 m) predicted soil properties aid the policy makers to revert the land degradation process and to preserve soil quality by executing suitable land use policies. Top Keywords Predictive soil mapping, Random forest, Cubist model, Support vector machine, Key soil properties, Western Ghats. Top |