A remote sensing-based strategy for mapping potentially toxic elements of soils: Temporal-spatial-spectral covariates combined with random forest
文献类型： 外文期刊
作者： Xu, Xibo 1 ; Wang, Zeqiang 1 ; Song, Xiaoning 3 ; Zhan, Wenjie 3 ; Yang, Shuting 4 ;
作者机构： 1.Beijing Normal Univ, Key Lab Environm Change & Nat Disaster, Minist Educ, Beijing 100875, Peoples R China
2.Harbin Normal Univ, Coll Geog Sci, Harbin 150025, Peoples R China
3.Zaozhuang Univ, Coll Tourism & Environm Resource, Zaozhuang 277160, Peoples R China
4.Ningxia Acad Agr & Forestry Sci, Inst Agr Sci & Informat Technol, Yinchuan 750002, Peoples R China
5.19 Xinjiekouwai St Rd, Beijing, Peoples R China
关键词： Soil; Pb; Random forest; Temporal-spatial-spectral covariates; Mapping strategy
期刊名称：ENVIRONMENTAL RESEARCH （ 影响因子：8.3； 五年影响因子：8.2 ）
ISSN： 0013-9351
年卷期： 2024 年 240 卷
页码：
收录情况： SCI
摘要： The selection of predictor variables is a crucial issue in building a digital mapping model of potentially toxic elements (PTEs) in soil. Traditionally, the predictor variables for mapping models of soil PTEs have been chosen from sets of spatial parameters or spectral parameters derived from geographical environmental data. However, the enrichment of soil PTEs exhibits significant variations in both spatial and temporal dimensions, with the temporal dimension often being overlooked in the selection of predictor variables for digital mapping models. This limitation hampers the robustness and generalizability of the models. Therefore, multi-source geographical data were used in this study to determine three temporal indices for characterizing the enrichment process of soil PTEs in temporal dimensions, and additionally to construct the temporal-spatial-spectral (TSS) covariate combinations. The random forest (RF) algorithm was used to map soil PTEs at a regional scale. Results showed that: (1) When using spatial parameters or spectral parameters as predictor variables and measured Pb content as the dependent variable, the values of the model performance indicator RPIQ (ratio of performance to inter-quartile range) were 2.66 and 2.27, respectively. After incorporating the temporal parameters into the model input, values of RPIQ for the RF model reached 3.55 (using spatial-temporal covariates) and 3.21 (using spectral temporal covariates), representing performance improvements of 33.46% and 41.41%, respectively. (2) The RF model constructed with the temporal-spatial-spectral covariates achieved satisfactory mapping accuracy (R2 = 0.85; RMSE = 0.80 mg kg- 1; RPIQ = 4.09). (3) The soil Pb content in the western and northeastern regions was relatively high, while the remaining areas exhibited lower Pb levels, mainly due to industrial activities. (4) The mapping results of Pb obtained in this study were superior to other mapping methods, such as ordinary kriging, artificial neural networks, and multivariate linear regression methods. The soil PTE mapping technique employed in this study that combined TSS covariates with the RF provided an effective methodological approach for preventing soil pollution, controlling environmental risk, and improving soil management.
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