In response to the demand for soil spatial information and in order to improve natural resource management outcomes through the development of soil suitability maps, the acquisition of digital auxiliary data and matching it to field soil observation is increasing. With the harmonization of these data sets, through computer based methods, so-called Digital soil Maps are increasingly being found to be as reliable as traditional soil mapping practices but without the prohibitive costs. Therefore, at present research, we have attempted to develop Regression Kriging (RK), Regression Tree (DTA), Artificial Neural Network (ANN), Artificial Neural Network-Algorithm Genetic (ANN-GA), Multi-Linear Regression (MLR), Kriging (OK), and Co-Kriging (CK) models for spatial prediction of soil salinity in an area covering 700 km 2 located in arid region of central Iran where traditional soil survey methods are very difficult to undertake. In this area, using the grid sampling method, 180 soil samples were selected, which then sampled and analysed. Auxiliary data used in this study were terrain attributes (derived from a digital elevation model), Landsat 7 ETM+ data, apparent electrical conductivity (ECa)—measured using an electromagnetic induction instrument (EMI)—and a polygon maps (i.e. Geology). Results showed that the RK (i.e. Regression tree and kriging) had the higher accuracy than other models for prediction of soil salinity. The RK model could find the strong relationship between soil salinity and ancillary data; root mean square error and R 2 were 12.10 dSm -1 and 0.92, respectively. Also, RK had relative improvement of 58% in comparison with kriging method confirming the importance of auxiliary data. Our results showed some auxiliary variables had more influence on predictive soil salinity model which included: apparent electrical conductivity, remote sensing indices, and wetness index. In general, results showed that RK model had higher accuracy than other models and also their results are more convenient for interpretation. With application of these rules, soil salinity map produced. Keywords :Soil Salimity, Spatial prediction, apparent electrical conductivity Regression Kriging, Ancillary data, DEM