This study was conducted in order to digital mapping soil classes according to WRB system was conducted by boosted regression tree (BRT) method on about 60.000 hectares of Hirmand county lands. 108 soil profiles were dug and soil profiles were sampled and described based on WRB system. Twenty environmental covariates were used as estimators for soil mapping including terrain attributes and remote sensing covariates. Results showed that the young soil covered the study area and mostly influenced by flood sediment which classified as Fluvisol and Cambisol groups and Solonchak group in salt -affected area. The variable importance showed that the environmental attributes such as Multi-resolution Valley Bottom Flatness Index (MrVBF), Valley Depth, Convergence Index, Catchment Area and Salinity Index (NDSI) had the highest importance among all covariates for two levels of WRB prediction. The validation results showed that the BRT model could predict WRB1 and WRB2 levels with overall accuracy of 47 and 25%, respectively, and also from WRB1 to WRB2 levels the accuracy decreased. In low relief area and young soil that the low soils variability, digital soil mapping approach could be useful, efficient, and fast technique to produce and predict soil classes map.
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