Unveiling hidden heavy metal hotspots in mining landscapes using integrated hyperspectral remote sensing

Regional ecological risk assessments typically rely on interpolated toxic heavy metal (THM) surfaces derived from sparse field samples. However, this approach is fundamentally constrained by sampling density and interpolation errors, resulting in high spatial uncertainty that masks fine-scale contamination heterogeneity. Hyperspectral remote sensing offers a scalable alternative by enabling continuous spatial characterization of soil properties. This study evaluates an integrated ground-satellite hyperspectral framework to map THMs and ecological risks in the Baixintan (BXT) and Lubei (LB) mining areas (early-stage extraction). We developed a robust inversion workflow combining spectral transformations, band optimization, and machine learning algorithms, which effectively mitigated background noise and enhanced feature extraction. After calibrating satellite spectra with ground-based measurements, estimation accuracy for Cu, Ni, and Cr improved by 31%, 10%, and 34%, respectively, compared to uncorrected baselines. Based on these optimized models, we generated spatially continuous maps for four pollution indices (I_geo, INI, E_i, and RI). Results revealed distinct spatial patterns: while Cu and Ni showed mild-moderate enrichment (I_geo = 0.40-2.01), Cr exhibited severe enrichment (I_geo = 3.24-3.53). Crucially, the high-resolution mapping identified localized high-risk hotspots extending beyond documented mining footprints, likely driven by natural transport mechanisms (e.g., topography and wind) interacting with mining activities. Although the overall ecological risk remained predominantly low to moderate (mean RI of 61.4 in BXT and 82.5 in LB), the detection of these off-site contamination zones demonstrates the superior capability of hyperspectral inversion in capturing spatial nuances missed by traditional monitoring. This framework provides actionable, high-precision data for early-stage risk management and boundary-spanning pollution control.