Machine Learning Approach for Soil Organic Carbon Prediction Using Auxiliary Environmental Variables in Agricultural Lands

The finding emphasized that the most crucial ancillary variables that significantly influence the prediction of spatial variation in SOC were CNBL, MRVBF, and NDVI, and the RF model can be considered an appropriate method for mapping soil properties, particularly SOC.