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Sun, XL;Wang, YD;Wang, HL;Zhang, CS;Wang, ZL
European Journal Of Soil Science
Digital soil mapping based on empirical mode decomposition components of environmental covariates
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In digital soil mapping (DSM) there is an increasing interest in incorporating multiscale variations of soil and environmental covariates to improve mapping accuracy. Empirical mode decomposition (EMD) is a frequently used method for multiscale analysis but has been rarely applied to DSM. This study examines if DSM with EMD covariates results in a more accurate map of a test area than DSM with original covariates. First, EMD was used to decompose terrain attributes of an area. These EMD components were then used to map soil organic carbon (SOC) concentration, pH, clay content and cation exchange capacity (CEC) by two DSM methods: multiple linear regression (MLR) and regression kriging (RK). The same two DSM methods were used with original covariates; in addition, ordinary kriging (OK) maps were produced without covariates. The resulting maps were compared by various accuracy measures. The use of EMD components for DSM improved correlations between soil properties and terrain attributes, and improved the goodness-of-fit of MLR models significantly and prediction accuracies by 2.5-18% of root mean square error (RMSE) for SOC, clay and CEC. By contrast, the use of EMD components for pH increased the goodness-of-fit of the MLR model slightly and decreased the prediction accuracy by 30% of RMSE. This study concluded that the use of EMD components for DSM improved mapping accuracies if the goodness-of-fit of the MLR models was significantly improved by EMD components. It is possible that sampling based on EMD components may provide a sample set that would improve mapping accuracy further. HighlightsEmpirical mode decomposition (EMD) components of terrain attributes were applied to soil mapping. EMD components improved correlations between soil properties and terrain attributes. EMD components improved the goodness-of-fit of multiple linear regression (MLR). Significant improved goodness-of-fit of MLR resulted in improved mapping accuracies.
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