Predicting and Mapping of Soil Organic Carbon Stock Using Machin Learning Algorithm

Investigation of soil organic carbon stock (SOCS) in agricultural lands and the role of factors affecting its variability and digital modeling are important for predicting possible scenarios of future carbon stock. The purpose of this study was to investigate the spatial variability and to estimate SOCS at 0 to 100 cm depth based on two generation of machine learning approaches in a part of Qazvin plain. SOCS of about 211 legacy soil data were prepared. The environmental variables including 11 geomorphometric variables and 25 spectral indices with 10-meter spatial resolution were used. Further, the dataset was divided into two parts: 70% of data were chosen as training and 30% of data for model validation. Two algorithm were used for SOCS modeling in the study area. Validation results indicated that the QRF had a higher coefficient of determination than the RF. According to the results of the relative importance of environmental variables, DEM and Valley depth parameters are more important in the spatial modeling of SOCS than other variables. Generally, it is suggested to investigate hybrid models in the process of modeling secondary soil characteristics.