Wang Yulong, Zou Bin, Li Sha, Tian Rongcai, Zhang Bo, Feng Huihui, Tang Yuqi
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China.
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China; Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring of Chinese Ministry of Education, Changsha 410083, China.
J Hazard Mater. 2024 Nov 5;479:135699. doi: 10.1016/j.jhazmat.2024.135699. Epub 2024 Aug 28.
Promising hyperspectral remote sensing exhibits substantial potential in monitoring soil heavy metal (SHM) contamination. Nevertheless, the local spatial perturbation effects induced by environmental factors introduce considerable variability in SHM distribution. This engenders non-stationary relationship between SHM concentrations and spectral reflectance, posing challenges for accurate inversion of SHM globally. Addressing this gap, a novel Hierarchical Residual Correction-based Hyperspectral Inversion Method (HRCHIM) is proposed for SHM, considering their spatial heterogeneity. Initially, a global model is constructed using ground hyperspectral data to predict SHM concentration, capturing overarching contamination trends. Subsequently, four hierarchical levels, segmented by residual standard deviation (SD) intervals, identify critical environmental factors via Geodetector. These factors inform local residual correction models, refining global model predictions. HRCHIM aims to synergize global trends and local stochasticity to enhance prediction accuracy and interpretation of SHM spatial heterogeneity. Validated through a case study of a Cadmium(Cd)-contaminated mine area, six critical environmental factors were identified, exhibiting significant differences across hierarchical levels. By incorporating hierarchical correction models, HRCHIM demonstrated superior inversion performance compared to other conventional methods, achieving optimal prediction accuracies (Rv = 0.94, RMSEv = 0.21, and RPDv = 4.11). This innovative method can facilitate more precise and targeted strategies for preventing and controlling SHM contamination.
有前景的高光谱遥感技术在监测土壤重金属（SHM）污染方面具有巨大潜力。然而，环境因素引起的局部空间扰动效应导致SHM分布存在相当大的变异性。这使得SHM浓度与光谱反射率之间呈现非平稳关系，给全球范围内SHM的准确反演带来挑战。为解决这一差距，考虑到SHM的空间异质性，提出了一种基于分层残差校正的高光谱反演方法（HRCHIM）用于SHM。首先，利用地面高光谱数据构建一个全局模型来预测SHM浓度，捕捉总体污染趋势。随后，通过残差标准差（SD）区间划分的四个层次级别，利用地理探测器识别关键环境因素。这些因素为局部残差校正模型提供信息，优化全局模型预测。HRCHIM旨在协同全局趋势和局部随机性，以提高预测精度并解释SHM的空间异质性。通过对一个镉（Cd）污染矿区的案例研究进行验证，识别出六个关键环境因素，不同层次级别之间存在显著差异。通过纳入分层校正模型，HRCHIM与其他传统方法相比展现出卓越的反演性能，实现了最佳预测精度（Rv = 0.94，RMSEv = 0.21，RPDv = 4.11）。这种创新方法可为预防和控制SHM污染提供更精确、有针对性的策略。
