Three-dimensional mapping of soil organic carbon using soil and environmental covariates in an arid and semi-arid region of Iran

This study focus on modeling and mapping soil organic carbon (SOC) at high spatial resolution and at four standard depths in an arid and semi-arid region of Iran. The SOC data includes 850 soil samples collected from 278 observation profiles. In parallel, a wide range of environmental covariates (n = 62) were obtained from multiple sources. Six individual machine learning (ML) algorithms were compared to modeling and predicting SOC. Two scenarios were investigated. The first one accounts for soil and environmental covariates (S1) while the second one only accounts for environmental covariates (S2). Our results show that accounting for soil variables in the prediction (S1) leads to a twofold increase of R2 for all ML algorithms, while random forest (RF) outperformed the other ML approaches at all depths. Whenever possible, using additionally the soil variables that are at hand in a study area is thus beneficial for improving SOC predictions.