Continental-Scale Mapping of Soil Nickel: Integration of Spectral Preprocessing and Machine Learning Approaches

Soil heavy metal contamination has attracted widespread concern globally, with nickel (Ni) posing distinct environmental and human health risks. However, high-precision prediction of soil Ni concentrations at a large scale remains inadequately explored. In this study, spectral data from 18,675 sampling points were compiled to investigate the prediction of Ni concentrations. Two widely applied and highly effective preprocessing techniques, namely first-order derivative and second-order derivative, were explored. Following spectral preprocessing, three advanced machine learning models, namely random forest, extreme gradient boosting, and light gradient boosting machine (LGBM), were constructed and compared for Ni prediction. These models exhibited robust predictive performance and excellent generalization capability. Among them, the optimal model integrating the second-order derivative and LGBM achieved a coefficient of determination (R2) of 0.582 on the training set, which was further improved to 0.613 after hyperparameter tuning. On the test set, the model yielded superior predictive results with R2 = 0.585, mean squared error (MSE) = 130.284, and mean absolute error (MAE) = 6.468. Feature importance analysis identified the critical spectral bands for Ni concentration prediction, including 508–509 nm, 894–895 nm, 2214.5–2215.5 nm, and 778.5–779.5 nm. This study establishes an efficient framework for predicting soil Ni concentrations, providing valuable insights for improving predictive accuracy. It also offers theoretical support for the sustainable management of soil environments and long-term soil heavy metal risk mitigation.