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首页> 外文期刊>Geoderma: An International Journal of Soil Science >Digital soil mapping based on wavelet decomposed components of environmental covariates
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Digital soil mapping based on wavelet decomposed components of environmental covariates

机译：基于小波分解组成的环境协变量数字土壤映射

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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 (R-2). 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 R-2. 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.
机译：多尺度土壤变化越来越多地用于提高数字土壤映射（DSM）的准确性。在这项研究中，我们试图探索对此主题的小波分析方法。使用小波分析分解研究区域的地形属性，并使用多元线性回归（MLR）和回归克里格（RK）来施加所得组分以映射土壤有机碳（SOC）含量，pH和粘土含量。结果表明，小波分量在相关系数方面加强了土壤景观关系，在MLR建模系数方面增强了土壤景观建模（R-2）。与若干标准DSM方法相比，即普通Kriging（OK），MLR和RK与原始地形属性，使用小波组件在某些尺度上提高了预测精度，但不是所有的尺度。大多数改进都在轻微到中等水平，例如，基于平均误差的准确性增加3.66-14.24％，意味着绝对误差，根均方误差和R-2。由于小波部件的特性，用小波分量制造的地图相对平滑，有时包含热点，这是由标准DSM方法制成的那些不同的热点。在考虑更多预测测量的选择方法和映射方法时，可以进一步揭示使用小波部件作为DSM中的预测器的潜在益处。

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