Digital soil mapping based on wavelet decomposed components of environmental covariates

Abstract Multi-scale soil variations are increasingly employed to improve the accuracy for digital soil mapping (DSM). In this study, we attempted to explore a methodology of wavelet analysis on this topic. The terrain attributes of a study area were decomposed using the wavelet analysis, and the resulted components were applied to map soil organic carbon (SOC) content, pH and clay content using multiple linear regression (MLR) and regression kriging (RK). The results showed that the wavelet components strengthened soil-landscape relationships in terms of correlation coefficients, enhanced soil-landscape modelling in terms of MLR modelling coefficients of determination (R2). Compared with several standard DSM approaches, i.e., ordinary kriging (OK), MLR and RK with the original terrain attributes, the use of wavelet components improved the prediction accuracy at some scales, but not all the scales. Most of the improvements were at the slight to moderate levels, e.g., 3.66–14.24% increases in the accuracy based on mean error, mean absolute error, root mean square error and R2. Maps made with wavelet components were relatively smooth and sometimes contained hotspots due to characteristics of wavelet components, which differed a lot from those made by the standard DSM methods. The potential benefits of using wavelet components as predictors in DSM may be further revealed in the future when more predictor selection approaches and mapping methods are considered.

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