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Kriging interpolation is frequently used for mapping soil properties in the analysis and interpretation of spatial variation of soil. Mapping quality could affect the performance of site‐specific management. Soil map–delineation in existing soil maps showing abrupt changes at the boundaries between different soil types can provide valuable categorical information for interpreting variation in soil properties. In this study, map units were used to group sampled observations, and the variation in soil properties was separated into two parts: (i) between soil types (i.e., soil type effect) and (ii) within each soil type (i.e., residual). A kriging model combined with soil map–delineation, taking into account the variation components of soil type effect and residual, was proposed. A comparison of performance of kriging combined with soil map–delineation (KSMD) and ordinary kriging (OK) was performed to assess the feasibility of KSMD for improving the interpolation of soil properties. Real data of soil properties (sand, silt, and clay contents; pH; and Mehlich‐3 Ca and P) in a field of midwestern Taiwan were used for illustration. The analysis of variance table revealed that the contribution of soil types is a significant source of the spatial variation of soil. The spatial variation components of soil type effect and residual were thus determined for KSMD. When comparing KSMD and OK, the mean errors of KSMD and OK estimations were similar. However, decreases in estimation of imprecision for KSMD relative to OK (DIP %) for the 127 validation locations for sand, silt, and clay contents; pH; and Mehlich‐3 Ca and P were 34, 40, 48, 20, 42, and 3%, respectively. These results suggested that the proposed KSMD method could use the information of soil map–delineation to increase precision of the interpolation of soil properties.

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Characterization of the spatial distribution of pollutants in contaminated soils is important for risk assessment and soil reclamation. In this study, three kriging methods using auxiliary variables, cokriging, kriging combined with regression, and kriging combined with Q‐mode factor analysis, were used for interpolation of heavy metal concentrations in a contaminated site. The three interpolation methods were evaluated for whether or not they could make better use of an auxiliary variable for estimation of the spatial distribution of heavy metals. A heavy metal contaminated site about 10 ha in area, situated in Taoyuan, Taiwan, was studied. The results demonstrate that better use of the auxiliary variable in interpolations of the target variable was made using kriging combined with regression compared to using cokriging or kriging combined with Q‐mode factor analysis. The results also show that the spatial distribution patterns of the target variables estimated using kriging combined with Q‐mode factor analysis were more similar to those estimated using kriging combined with regression than were those estimated using cokriging. In addition, kriging combined with regression and kriging combined with Q‐mode factor analysis could avoid negative estimates, which occur in cokriging. Moreover, both of them were more robust than cokriging. Simultaneous estimation of spatial distributions of several target variables using an auxiliary variable was demonstrated for the kriging combined with Q‐mode factor analysis procedure. However, for only one target variable, kriging combined with regression was simpler and less cumbersome than cokriging.

The spatial distribution of a pollutant in contaminated soils is usually highly skewed. As a result, the sample variogram often differs considerably from its regional counterpart and the geostatistical interpolation is hindered. In this study, rank-order geostatistics with standardized rank transformation was used for the spatial interpolation of pollutants with a highly skewed distribution in contaminated soils when commonly used nonlinear methods, such as logarithmic and normal-scored transformations, are not suitable. A real data set of soil Cd concentrations with great variation and high skewness in a contaminated site of Taiwan was used for illustration. The spatial dependence of ranks transformed from Cd concentrations was identified and kriging estimation was readily performed in the standardized-rank space. The estimated standardized rank was back-transformed into the concentration space using the middle point model within a standardized-rank interval of the empirical distribution function (EDF). The spatial distribution of Cd concentrations was then obtained. The probability of Cd concentration being higher than a given cutoff value also can be estimated by using the estimated distribution of standardized ranks. The contour maps of Cd concentrations and the probabilities of Cd concentrations being higher than the cutoff value can be simultaneously used for delineation of hazardous areas of contaminated soils.