The main objectives of this paper were 1) to estimate soil organic carbon (SOC) using remote sensing covariates, soil properties, and topographic factors , and 2) to evaluate the interaction and the relative influence of the selected factors on the spatial variation of SOC. Thirteen factors were considered for digital mapping of SOC in the west Urmia Lake in Iran. To quantify multicollinearity among the predictor variables, Variance Inflation Factor (VIF) was calculated. Among them, nine independent factors were remained including silt, sand, slope, enhanced vegetation index (EVI), brightness, wetness, land cover, and latitude and longitude. A machine learning algorithm called Gradient Boosting Machine (GBM) was calibrated for understanding the spatial dynamic and prediction of SOC. Model performance showed that GBM explained 43.5% (R2) of the SOC variation, and root mean square error (RMSE) was 0.23%. Results showed that EVI and sand were the most influential factors of the SOC variation while slope and land cover were the least important ones. Furthermore, significant interaction among EVI-wetness-SOC and EVI-sand-SOC was detected. On the other hand, 45.2% of SOC variation was estimated by remote sensing covariates. These results suggested that GBM was a promising approach for an in-depth understanding of the SOC variation over space.
