A comparative study of interpolation methods for mapping soil properties

The choice of an optimal interpolation technique for estimating soil proper ties at unsampled locations is an important issue in site-specific manageme nt. The objective of this study was to evaluate inverse distance (InvD) wei ghting, ordinary kriging (KO), and lognormal ordinary kriging (KOlog) to de termine the optimal interpolation method for mapping soil properties, Relat ionships between statistical properties of the data and performance of the methods were analyzed using soil test P and K data from 30 agricultural fie lds. For InvD weighting, we used powers of 1, 2, 3, and 4, The numbers of t he closest neighboring points ranged from 5 to 30 for the three methods. Th e results suggest that KOlog can improve estimation precision compared with KO for lognormally distributed data. The criteria helpful in deciding whet her KOlog is applicable for the given data set were the Kolmogorov-Smirnov goodness-of-fit statistic, coefficient of variation, skewness, kurtosis, an d the size of the data set, Careful choice of the exponent value for InvD w eighting and of the number of the closest neighbors for both InvD weighting and kriging (KO or KOlog) significantly improved the estimation accuracy ( P less than or equal to 0.05), However, no a priori decision could be made about the optimal exponent and the number of the closest neighbors based on the statistical properties of the data. For the majority of the data sets, kriging with the optimal number of the neighboring points, a carefully sel ected variogram model, and appropriate log-transformation of the data perfo rmed better than InvD weighting. Correlation coefficients between experimen tal data and estimated results of kriging were higher than those of InvD fo r 57 out of a total of 60 data sets, kriging mean absolute errors were lowe r for 44 data sets, and kriging mean errors were lower than those of InvD w eighting for 31 data sets.