The current study was established for predicting some selected heavy metals (HMs) including Zn, Mn, Fe, Co, Cr, Ni, and Cu, by applying random forest (RF) and a set of environmental covariates at watershed scale. The objectives were to find out the most effective combination of variables and controlling factors on the variability of HMs in a semiarid watershed in central Iran. One hundred locations were selected in the given watershed in the hypercube manner and soil samples from a surface 0–20 cm depth and concentration of HMs and some soil properties were measured in the laboratory. Three scenarios of input variables were defined for HMs prediction. The results revealed that the first scenario (remote sensing + topographic attributes) explained about 27–34% of the variability in HMs. Inclusion of a thematic map to the scenario I, improved the prediction accuracy for all HMs. Scenario III (remote sensing data+ topographic attributes + soil properties) was the most efficient scenario for prediction of HMs with R2 values ranging from 0.32 for Cu to 0.42 for Fe. Similarly, the lowest nRMSE was found for all HMs in scenario III, ranging from 0.271 for Fe to 0.351 for Cu. Among the soil properties, clay content and magnetic susceptibility were the most important variables, and also some remote sensing data (Carbonate index, Soil adjusted vegetation index, Band2, and Band7) and topographic attributes (mainly control soil redistribution along the landscape) were the most efficient variables for estimating HMs. We concluded that the RF model with a combination of remote sensing data, topographic attributes, and assisting of thematic maps such as land use in the studied watershed could reliably predict HMs content.

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