Regional mapping of soil heavy metals via a novel deep learning approach: A case study in the Yangtze River Delta,...

Accurate regional mapping of soil heavy metals is increasingly supported by multi-source environmental covariates, yet predictive performance is often limited by sparse field sampling that cannot capture regional heterogeneity. We developed DL_TL, a novel deep learning framework that integrates kriging-based virtual sample augmentation with transfer learning to improve mapping under limited observations. For six metals (Zn, Cu, Cr, Cd, Pb, and As) in the Yangtze River Delta, China, DL_TL outperformed benchmark models, improving R² by 17.36-42.99% over an augmented deep learning baseline, 24.00-95.34% over ordinary kriging (OK), and 34.50-133.93% over random forest (RF). Optimal performance was achieved with 6-km virtual sampling, full-network fine-tuning, and terminal-layer replacement. The shapley additive explanations (SHAP) analysis revealed that soil properties (37-42%) and climate (23-32%) dominated model explanations, with element-specific contributions from anthropogenic indicators and PM_2.5. The resulting 1-km resolution maps better delineated localized hotspots, identifying a larger fraction of high ecological-risk areas (3.20% vs 0.07% for RF) while reducing false-positive exceedance of childhood carcinogenic risk (0.09% vs 1.50% for OK). DL_TL provides a scalable solution for regional contamination mapping and risk screening in covariate-rich but sample-poor settings.