Digital mapping of selected soil properties using machine learning and geostatistical techniques in Mashhad plain, northeastern Iran

Understanding the spatial variation of soil properties is essential to monitor land capabilities as well as sustainable management of soil resources. The aim of this study was to predict digital soil properties mapping using 23 environmental variables i.e. terrain attributes and remote sensing (RS) indicis through 1500 km 2 of Mashhad plain lands. To achieve this purpose, a total of 180 soil samples (0-10 cm) were taken. The random forest (RF) model combined with ordinary kriging (OK), as well as regression kriging (RK) were applied to relate environmental variables and the studied soil properties. The results revealed that RF-OK was the best model with R 2 and RMSE for silt (0.89 and 0.10%) followed by calcium carbonate equivalent (0.88 and 3.30%), clay (0.87 and 2.26%), soil organic carbon (0.86 and 0.24%), sand (0.84 and 4.21%), and pH (0.82 and 5.42%). The RS covariates including band 5 (B5), modified soil adjusted vegetation index (MSAVI), difference vegetation index (DVI), band 2 (B2), carbonate rock index (CRI2), gypsum index (GI) and enhanced vegetation index (EVI) and terrain attributes including topographic wetness index (TWI) and elevation (EL), and topographic position index (TPI) were the most important variables in modeling of different soil properties. Finally, RF-OK model with low cost and high accuracy can be applicable to use for predicting different soil properties as well as spatial information acquired from an effort to maps to managing agriculture in areas at different conditions. This method can be applied to other regions of similar characteristics and for similar purposes.