文献类型： 外文期刊

作者： Li, Xuqing ¹ ; Gu, Huitao ¹ ; Tang, Ruiyin ¹ ; Zou, Bin ⁴ ; Liu, Xiangnan ⁵ ; Ou, Huiping ² ; Chen, Xuying ¹ ; Song, Yubin ¹ ; Luo, Wei ¹ ; Wen, Bin ¹ ;

作者机构： 1.North China Inst Aerosp Engn, Sch Remote Sensing & Informat Engn, Langfang 065000, Peoples R China

2.Guangxi Acad Agr Sci, Agr Resources & Environm Res Inst, Guangxi Key Lab Arable Land Conservat, Nanning 530007, Peoples R China

3.Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

4.Cent South Univ, Sch Geosci & Info Phys, Changsha 410083, Peoples R China

5.China Univ Geosci, Sch Informat Engn, Beijing 100083, Peoples R China

关键词： hyperspectral image; spectral response features; soil heavy metal; farmland; XGB-Boruta; XGBoost

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2025 年 15 卷 3 期

页码：

收录情况： SCI

摘要： Heavy metal pollution of farmland is worsened by the excessive introduction of heavy metal elements into soil systems, posing a substantial threat for global food security and human health. The traditional laboratory-based methods for monitoring soil heavy metals are limited for large-scale applications, while hyperspectral imagery data-based methods still face accuracy challenges. Therefore, a fusion XGBoost model based on the superposition of ensemble learning and packaging methods is proposed for large-scale monitoring with high accuracy of soil heavy metal using hyperspectral imagery. We took Xiong'an New Area, Hebei Province, as the study area, and acquired heavy metal content using chemical analysis. The XGB-Boruta-PCC algorithm was used for precise feature selection to obtain the final modeled spectral response features. On this basis, the performance indicators of the Optuna-optimized XGBoost model were compared with traditional linear and nonlinear models. The optimal model was extended to the entire region for drawing the spatial distribution map of soil heavy metal content. The results suggested that the XGB-Boruta-PCC method effectively achieved double dimensionality reduction of high-dimensional hyperspectral data, extracting spectral response features with a high contribution, which, combined with the XGBoost model, exhibited greater general estimation accuracies for heavy metal (Pb) in farmland (i.e., Pb: R2 = 0.82, RMSE = 11.58, MAE = 9.89). The results of the mapping indicated that there were exceedances for the southwest and parts of the west over the research region. Factories and human activities were the potential causes of heavy metal Pb contamination in farmland. In conclusion, this innovative method can quickly and accurately achieve monitoring large-scale soil heavy metal contamination in farmland, with ZY-1-02E spaceborne hyperspectral imagery proving to be a reliable tool for mapping soil heavy metal in farmland.