Improving model performance in mapping cropland soil organic matter using time-series remote sensing data

文献类型： 外文期刊

第一作者： Zhang, Xianglin

作者： Zhang, Xianglin;Chen, Songchao;Wang, Zheng;Chen, Xueyao;Xiao, Yi;Shi, Zhou;Xue, Jie;Chen, Songchao;Zhuo, Zhiqing;Shi, Zhou

作者机构：

关键词： cropland; soil organic matter; digital soil mapping; machine learning; feature selection; model averaging

期刊名称：JOURNAL OF INTEGRATIVE AGRICULTURE （ 影响因子：4.6； 五年影响因子：4.5 ）

ISSN： 2095-3119

年卷期： 2024 年 23 卷 8 期

页码：

收录情况： SCI

摘要： Faced with increasing global soil degradation, spatially explicit data on cropland soil organic matter (SOM) provides crucial data for soil carbon pool accounting, cropland quality assessment and the formulation of effective management policies. As a spatial information prediction technique, digital soil mapping (DSM) has been widely used to spatially map soil information at different scales. However, the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human disturbance. To overcome this limitation, this study systematically assessed a framework of "information extraction-feature selection-model averaging" for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou, China in 2021. The results showed that using the framework of dynamic information extraction, feature selection and model averaging could efficiently improve the accuracy of the final predictions (R-2 : 0.48 to 0.53) without having obviously negative impacts on uncertainty. Quantifying the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM, which improved the R-2 of random forest from 0.44 to 0.48 and the R-2 of extreme gradient boosting from 0.37 to 0.43. Forward recursive feature selection (FRFS) is recommended when there are relatively few environmental covariates (<200), whereas Boruta is recommended when there are many environmental covariates (>500). The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average uncertainty. When the structures of initial prediction models are similar, increasing in the number of averaging models did not have significantly positive effects on the final predictions. Given the advantages of these selected strategies over information extraction, feature selection and model averaging have a great potential for high-accuracy soil mapping at any scales, so this approach can provide more reliable references for soil conservation policy-making.

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