Monitoring soil heavy metal content around a lead–zinc mining area in Yunnan, China using GF-5A and Sentinel-2B remote sensing with consideration of pollution sources

To address the challenges of scarce exposed soils, strong surface heterogeneity, and insufficient synergistic monitoring using multi-source remote sensing in high-altitude Pb-Zn mining areas, this study used laboratory spectra as the reference and developed a workflow consisting of direct standardization (DS)-based correction of GF-5A bare-soil imagery, spectral enhancement, Boruta-based band selection, and XGBoost inversion. A parallel comparison with Sentinel-2B (S2B) was also conducted. By integrating SHapley Additive exPlanations (SHAP)-based feature attribution, Bootstrap uncertainty analysis, and the geographic detector to identify the sources of heavy metal contamination, an integrated analytical framework of ‘inversion – reliability-source’ was established. The results showed that DS tended to shift the spectra of-different land-cover types towards the ‘soil spectral space’ as a whole, while derivative transformation exhibited superior performance in enhancing heavy metal-sensitive bands. GF-5A provided more complete coverage over key diagnostic spectral regions, particularly within 2200–2450 nm, and achieved test-set R2 values of 0.694, 0.720, and 0.722 for Zn, Pb, and Ni, respectively, outperforming S2B overall. Although S2B yielded lower quantitative accuracy, it was more advantageous for delineating the fine details of pollution boundaries. Distance from the mining area was the dominant controlling factor for Zn and Pb contamination, whereas Ni was more strongly controlled by parent material background, and GF-5A exhibited lower overall uncertainty. This method is applicable to the remote sensing monitoring of heavy metals in bare soils of complex high-altitude mining areas and provides a basis for the integration of GF-5A and S2B.