Tracing the invisible footprint of mining in Iran: spectral mapping of heavy metal contamination in arid soils with a remote sensing approach

This research applies an interdisciplinary framework to investigate the interactions between mining activities, heavy metal contamination, and vegetation response in the Sangan mining areas of Khaf County, Iran. Geochemical analyses of 24 surface soil samples were integrated with remote sensing data from Landsat 8-OLI to develop a robust methodology for monitoring stressed ecosystems. Chemical analysis revealed substantial accumulation of cadmium (Cd, barium (Ba) and arsenic (As). The mean Cd concentration (26.5 mg kg⁻¹) was about 88 times higher than the global shale average (0.3 mg kg⁻¹), which is likely associated with to intensive iron ore mining. This finding underscores the risk of these metals entering the food chain and threatening human health, especially in children, who exhibit higher absorption rates. From a remote-sensing perspective, soil-adjusted vegetation indices such as RVI ( R = 0.82) and MSAVI2 ( R = 0.79) outperformed NDVI ( R = 0.40) in arid environments with sparse vegetation, providing a methodological advance for ecological monitoring. Correlation analyses between heavy metals and vegetation indices revealed a strong negative association between copper (Cu) and most indices, particularly IPVI ( R = − 0.69), indicating copper’s phytotoxic effects on chlorophyll and biomass. In contrast, chromium (Cr) showed a weak positive correlation with NDVI ( R = 0.36). These results highlight the need to select vegetation indices according to local environmental conditions and offer a practical framework for enhancing remote sensing–based monitoring of contaminated ecosystems. The study improves understanding of mining impacts and provides a replicable model for sustainable management of mining-affected landscapes worldwide.