Optimising interpolation as a tool for use in soil property mapping
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Inverse distance weighting (IDW) and kriging are robust and widely used estimation techniques in earth sciences (soil science). Variance of Kriging is often proposed as a statistical technique with superior mathematical properties such as a minimum error variance. However, the robustness and simplicity of IDW motivate its continued use. This research aims to compare the two interpolation techniques (Inverse Distance Weighting and Kriging), as well as to evaluate the effect of sampling density on mapping accuracy of soil properties with diverse spatial structure and diverse variability in a quest to improve interpolation quality for soil chemical property mapping. The comparison of these interpolation methods is achieved using the total error of crossvalidation and validation statistics. Mean Prediction Error and Root Mean Square Error are calculated and combined to determine which interpolator produced the lowest total error. The interpolator that produced the lowest total error portrays the most accurate soil property predictions of the study area. The finding of this study strongly suggests that the accuracy achieved in mapping soil properties strongly depends on the spatial structure of the data. This was clearly visible, in that, when the subset training data set was decreased, the total error increased. The results also confirmed that systematic sampling pattern provides more accurate results than random sampling pattern. The overall results obtained from the comparison of the two applied interpolation methods indicated that Kriging was the most suitable method for prediction and mapping the spatial distribution of soil chemical properties in this study area.