Modeling and Mapping of Soil Salinity and Alkalinity Using Remote Sensing Data and Topographic Factors: a Case Study in Iran

Soil salinity and alkalinity seriously threaten crop production, soil productivity, and sustainable agriculture, especially in arid and semi-arid areas, leading to land degradation. Therefore, the spatial distribution of these parameters is really important for the successful management of such areas. The salinity and sodium adsorption ratio (SAR) of soil surface have been modeled in this article. Auxiliary data were terrain attributes derived from the digital elevation model (DEM), remote sensing spectral bands, and indices of vegetation and salinity derived from the Landsat 8 OLI satellite. In total, 118 soil samples were collected from a depth of 0–15 cm in homogenous units at Doviraj plain in the southern part of Ilam province, western Iran. Saturated electrical conductivity (ECe), SAR, and other soil properties were analyzed and calculated. To model ECe and SAR parameters with the auxiliary data, stepwise multiple linear regression (SMLR) and random forest (RF) regression were applied. The highest accuracy was obtained through the RF model with validation coefficient of determination ( R 2 ) = 0.82 and 0.83 and validation root mean square error (RMSE) = 7.40 dS/m and 11.20 for ECe and SAR, respectively. Furthermore, results indicated that the strongest influence on the prediction of soil salinity was obtained with Band10, principal component analysis (PC3), vertical distance to channel network (VDCN), and analytical hill-shading (AH). Also, Band10, Band11, flow accumulation (FA), and topographic wetness index (TWI) were the important covariates in alkalinity prediction through the RF model. Finally, it is suggested that similar techniques can be used to map and monitor soil salinity and alkalinity in other parts of arid regions.