Using Optimized Spectral Indices and Machine Learning Algorithms to Assess Soil Copper Concentration in Mining Areas

Soil copper (Cu) contamination in mining areas poses a serious threat to the surrounding environment and human health. Timely determination of Cu concentrations is crucial for the ecological protection of mining areas. Hyperspectral remote sensing technology, with its non-destructive monitoring advantages, is essential for monitoring soil Cu pollution and achieving sustainable agricultural development. Using the hyperspectral technique for assessing soil Cu concentration, four machine learning models (support vector regression (SVR), random forest (RF), partial least squares regression (PLSR), and artificial neural network (ANN)), combined with three types of input variables (the full-band, sensitive bands, and optimized spectral indices (Opt-TBIs)) were employed. The hyperspectral reflectance of 647 soil samples from an abandoned tailings mine in western Inner Mongolia, China was collected. The sensitive bands were extracted using the successive projections algorithms (SPA), and 12 Opt-TBIs were selected. Results showed that the regions with higher soil Cu concentration extracted by SPA and Opt-TBIs were concentrated in the red edge and near-infrared regions. Compared with the full spectrum and SPA-sensitive bands, models based on Opt-TBIs successfully predicted soil Cu concentrations. The Opt-TBIs-RF model provided higher accuracy in estimating soil Cu among the four models. Using only four Opt-TBIs as input variables, the model maintained a stable performance in estimating Cu concentrations in different mining areas (R 2 Val = 0.72, RPD Val = 1.90). In conclusion, Opt-TBIs as input variables demonstrate good predictive capabilities for soil Cu concentrations in the study area, providing a basis for the formulation of sustainable strategies for soil reclamation and environmental protection in Inner Mongolia.