Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China; Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou 510070, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China; Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou 510070, China; University of Chinese Academy of Sciences, Beijing 100049, China; Shaoguan Shenwan Low Carbon Digital Technology Co., Ltd., Shaoguan 512026, China.
J Hazard Mater. 2023 Mar 15;446:130722. doi: 10.1016/j.jhazmat.2023.130722. Epub 2023 Jan 3.
Widespread soil contamination endangers public health and undermines global attempts to achieve the United Nations Sustainable Development Goals. Due to the lack of relevant studies and low precision of spaceborne spectroscopy, estimating soil heavy metal concentrations is challenging. In this study, we developed a coupled retrieval to qualify the heavy metal nickel (Ni) concentration in agricultural soil from spaceborne hyperspectral imagery. The retrieval couples spectral feature extraction from multi-scale discrete wavelet transform (DWT) and dimension reduction (DR), optimal band combination algorithm to five machine learning retrieval models using tree-based ensemble learning, neural network-based, and kernel-based. The comparison between the retrievals and Ni measurements shows that the DWT combined with t-distributed stochastic neighbor embedding (tSNE) coupled extreme gradient boosting (XGboost) retrieval model exhibited the best prediction for the validation dataset. Moreover, due to the integration of six statistical indicators of model performance and the fitted slope of the regression line, the retrieval framework can produce more robust and accurate predictions than those that rely on correlation coefficients. The demonstrated potential of spaceborne hyperspectral remote sensing to provide accurate quantitative measurements of soil heavy metal concentrations will serve as a reference for agricultural plot applications worldwide.
广泛的土壤污染危害公众健康，并破坏全球实现联合国可持续发展目标的努力。由于缺乏相关研究和星载光谱学的精度低，估算土壤重金属浓度具有挑战性。在这项研究中，我们开发了一种耦合反演方法，可从星载高光谱图像中定量估算农业土壤中的重金属镍（Ni）浓度。该反演方法结合了多尺度离散小波变换（DWT）和降维（DR）的光谱特征提取、最优波段组合算法，以及基于树的集成学习、神经网络和核的五种机器学习反演模型。反演与 Ni 测量值的比较表明，DWT 与 t 分布随机邻域嵌入（tSNE）相结合的极端梯度提升（XGboost）反演模型在验证数据集上表现出最佳预测能力。此外，由于集成了模型性能的六个统计指标和回归线的拟合斜率，该反演框架可以产生比仅依赖相关系数更稳健和准确的预测。这项研究证明了星载高光谱遥感在提供土壤重金属浓度的准确定量测量方面的潜力，将为全球农业地块应用提供参考。
