Theoretical and Experimental Performance of Spatial Interpolation Methods for Soil Salinity Analysis

Abstract

Interpolation methods are required for analysis of soil salinity data by geographic information systems. This study was conducted to determine interpolation methods that are best suited to map soil salinity. Methods of closest neighbor, kriging, inverse-distance moving average, and thin plate smoothing splines were compared by cross-validation for precision and smoothing, using 341 measured values of electrical conductivity of saturated paste extract in a 16 000 ha area in southwest Iran. Interpolation precision of all methods were low, with a mean absolute difference between measured and predicted values ranging from 42 to 76% of the mean measured soil salinity. This was due to the large ratio of nugget effect to the sill of the variogram and to the high variability of data. Thin plate smoothing splines and ordinary kriging were the most precise methods, whereas closest neighbor was the least precise. The smoothing of the methods was assessed by comparing the dispersion standard deviation of interpolated values with that of observed values. The most precise methods were also those that performed an important smoothing. Ordinary kriging and thin plate smoothing splines produced contour maps that were much easier to interpret. A theoretical analysis of the performance of the methods (precision and smoothing) led to conclusions similar to those based on the cross-validation study. Such a theoretical analysis can be used to select an appropriate interpolation method without the need for time consuming cross-validation.