Monitoring soil arsenic content in densely vegetated agricultural areas using UAV hyperspectral, satellite multispectral and SAR data.

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.