Accurate identification of HMs contamination in mine tailings is essential for understanding pollution and supporting remediation. However, conventional laboratory monitoring is labor-intensive, time-consuming, and spatially discontinuous, while UAV hyperspectral inversion is limited by spectral inconsistency, unstable performance under small-sample conditions, and insufficient interpretability. Here, we developed an interpretable UAV–laboratory synergistic framework for Cd and Pb mapping in the Yitong open-pit mine. Forty site-level soil samples, composited from 200 subsamples, were linked with UAV hyperspectral observations. Direct Standardization was used to harmonize UAV and laboratory spectra. A weighted voting ensemble (RF, GBDT, and XGBoost) achieved the best performance (R2 = 0.85), outperforming the individual models and showing slightly higher stability than CNN (R2 = 0.84). Environmental covariates (pH, SOM, SMC) revealed distinct metal-specific prediction patterns: Cd was mainly associated with pH–SOM interactions, whereas Pb was more strongly associated with SOM–SMC coupling. SHAP and Grad-CAM identified sensitive spectral regions, with Cd linked to the 440–580 nm range and Pb to the 720–740 nm range. Overall, this study provides an integrated framework that combines spectral transfer correction, stable multi-model inversion, and mechanism-oriented interpretability for HMs monitoring in complex mining environments.
Yu et al. (Fri,) studied this question.