Introduction: Continuous spatial data of environmental variables are often required for environmental sciences and management. However, information for environmental variables is usually collected by point sampling. Thus, the methods generating such spatially continuous data from the point samples become essential tools for many environmental analyses. Spatial interpolation is the procedure of estimating the value of un-sampled points using existing observations. The methods for spatial interpolation can be classified into two main categories as deterministic and geostatistical. Deterministic interpolation techniques including Inverse Distance Weighting (IDW), Radial Basis Function (RBF), and so on calculate the values of un-sampled areas based on the known values of the sampled points and create surfaces from measured points. However, Geostatistical interpolation techniques, e. g. Kriging use statistical properties of the measured points to quantify the spatial autocorrelation among the measured points and account for the spatial configuration of the sample points around the estimation location...