Machine learning and remote sensing for soil heavy metal(loid) risk assessment: A systematic review of multi-source...

Soil heavy metal(loid) contamination threatens food security and public health, but conventional monitoring alone cannot provide the spatial coverage required for effective risk assessment and management. This PRISMA-compliant review synthesizes 245 eligible reports published between 2015 and 2025 on machine learning and remote sensing for soil contamination prediction and risk assessment. Across 536 eligible conventional R² estimates from 200 reports, the field-wide mean was 0.722 at the estimate level and 0.749 at the report level. Algorithm-family differences were descriptive and confounded by target element, sample size, input data, study context, validation design, and reporting quality. No report used spatial block cross-validation; 99 (40.4%) used random train-test splitting, 38 (15.5%) used random k-fold cross-validation, and 72 (29.4%) inadequately documented partition construction. Restricting analyses to documented protocols reduced the estimate-level mean R² to 0.707. Interpretability remained limited: 82 reports (33.5%) used no method, 46 (18.8%) provided insufficient documentation, and 19 (7.8%) used SHAP, which should be interpreted as a model attribution method rather than a causal inference method. Only 43 reports (17.6%) included health or ecological risk assessment, and 11 (4.5%) explicitly applied EPA HQ/HI frameworks. A six-item Minimum Reporting Standard is proposed to strengthen validation, transparency, interpretation, and risk integration.