Zhou Yanan, Liu Chang, Wang Jie, Zhang Mei-Wei, Wang Xiaoqing, Zeng Ling-Tao, Cui Yu-Pei, Wang Huili, Sun Xiao-Lin
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China.
School of Environment Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China.
J Hazard Mater. 2025 Feb 15;484:136689. doi: 10.1016/j.jhazmat.2024.136689. Epub 2024 Nov 28.
Accurate and effective monitoring of potentially toxic elements (PTEs) in soil across vast regions is crucial for environmental modeling and public health. While remote sensing (RS) technology provides a promising approach by detecting soil spectrum, dense and persistent vegetation cover in subtropical agricultural areas hinders acquisition of bare soil signals, limiting soil PTEs monitoring. To address this challenge, the present study proposed an innovative method for monitoring soil arsenic (As) content by using vegetation characteristics retrieved from RS data as proxy variables, given soil-vegetation interactions. The method was evaluated in a densely vegetated cropland of southern China, where 104 surface soil samples were collected. Vegetation information was extracted both individually and synergistically using time-series Sentinel-2 multispectral and Sentinel-1 synthetic aperture radar (SAR) images throughout the entire growing season, and an unmanned aerial vehicle (UAV) hyperspectral image during the crop maturity. Multiple machine learning algorithms, including Random Forest, Support Vector Regression, CatBoost, and Stacking were applied to model the relationship between soil As and vegetation variables. The SHapley Additive exPlanation (SHAP) technique was introduced for identifying key variables and corresponding thresholds indicating significant accumulation of soil As. Results showed that time-series satellite-multispectral images outperformed other single RS data types in terms of prediction accuracy. Moreover, the synergy of optical and SAR images significantly improved model accuracy. Particularly, the combination of time-series satellite multispectral and SAR data using the stacking algorithm achieved the best results, with a coefficient of determination (R) of 0.71 and a root mean square error (RMSE) of 20.22 mg/kg. Key predictive variables included red-edge vegetation index (RENDVI3) on August 7 and May 26, and the blue band on October 26, with values below 0.018, 0.013 and 0.052, respectively, indicating the As accumulation in soil. In summary, the proposed method of using multiple RS data to retrieve vegetation characteristics for inferring soil PTEs in densely vegetated areas was convenient, cost-effective, and reliable, offering new insights and technical support for environmental monitoring.
准确有效地监测广大区域土壤中的潜在有毒元素（PTEs）对于环境建模和公众健康至关重要。虽然遥感（RS）技术通过检测土壤光谱提供了一种有前景的方法，但亚热带农业地区茂密且持久的植被覆盖阻碍了裸土信号的获取，限制了土壤PTEs的监测。为应对这一挑战，本研究提出了一种创新方法，利用从RS数据中提取的植被特征作为替代变量来监测土壤砷（As）含量，考虑到土壤 - 植被相互作用。该方法在中国南方植被茂密的农田中进行了评估，在那里采集了104个表层土壤样本。在整个生长季节，利用时间序列哨兵 - 2多光谱和哨兵 - 1合成孔径雷达（SAR）图像单独和协同提取植被信息，并在作物成熟期利用无人机（UAV）高光谱图像提取植被信息。应用了多种机器学习算法，包括随机森林、支持向量回归、CatBoost和堆叠算法，来建立土壤As与植被变量之间的关系模型。引入了SHapley加法解释（SHAP）技术来识别关键变量以及表明土壤As显著积累的相应阈值。结果表明，在预测准确性方面，时间序列卫星多光谱图像优于其他单一RS数据类型。此外，光学和SAR图像的协同作用显著提高了模型准确性。特别是，使用堆叠算法将时间序列卫星多光谱和SAR数据相结合取得了最佳结果，决定系数（R）为0.71，均方根误差（RMSE）为20.22mg/kg。关键预测变量包括8月7日和5月26日的红边植被指数（RENDVI3）以及10月26日的蓝波段，其值分别低于0.018、0.013和0.052时，表明土壤中As的积累。总之，所提出的利用多种RS数据检索植被特征以推断植被茂密地区土壤PTEs的方法方便、经济高效且可靠，为环境监测提供了新的见解和技术支持。
