Abstract
Abstract
Heavy metal contamination, particularly lead (Pb), poses significant risks to agricultural soil health and food safety. This study investigates Pb contamination in the Bahar plain, a key agricultural region in Iran, by integrating Sentinel-2 satellite imagery with some ground-based soil sampling and laboratory analysis. Forty topsoil samples were collected systematically and analyzed for Pb content, physicochemical properties, and spatial distribution relative to a major roadway. A predictive model was developed using spectral indices (e.g., Soil Adjusted Vegetation Index, and Weighted Difference Vegetation Index) and multivariate regression to estimate Pb levels from satellite data, achieving a strong Pearson correlation (r = 0.79) between lab-measured and model-predicted values. Results indicated that total Pb contents (15.17–42.62 mg/kg) were within permissible limits for calcareous soils, except near urban-industrial zones. Contrary to expectations, Pb levels increased with distance from the road, likely due to decades of chemical fertilizer use in central farmlands. The study demonstrates the efficacy of combining remote sensing and limited ground data for large-scale Pb monitoring, providing a framework for sustainable soil management and contamination risk mitigation.