Estimate of soil heavy metal in a mining region using PCC-SVM-RFECV-AdaBoost combined with reflectance spectroscopy

Soil contamination with heavy metals is a relatively serious issue in China. Traditional soil heavy metal survey methods cannot meet the demand for rapid and real-time large-scale area soil heavy metal surveys. We chose a typical mining area in Henan Province as the study area, collected 124 soil samples in the field and obtained their soil hyperspectral data indoors using a spectrometer. After different spectral transformations of the soil spectral curves, Pearson correlation coefficients (PCC) between them and the heavy metals Cd, Cr, Cu, and Ni were calculated, and after correlation evaluation, the best spectral transformations for each heavy metal were determined and preselected characteristic wavebands were obtained. Then the support vector machine recursive feature elimination cross-validation (SVM-RFECV) is used to select among the preselected feature wavebands to obtain the final modeled wavebands, and the Adaptive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Partial Least Squares (PLS) methods were used to establish the inversion model. The results showed that the PCC-SVM-RFECV can effectively select characteristic wavebands with high contribution to modeling from high-dimensional data. Spectral transformations methods can improve the correlation of spectra with heavy metals. The location and quantity of characteristic wavebands for the four heavy metals were different. The accuracy of AdaBoost was significantly better than that of GBDT, RF, and PLS (i.e., Ni: R_AdaBoost^2 = 0.735, R_GBDT^2 = 0.679, R_RF^2 = 0.596, R_PLS^2 = 0.510 ). This study can provide a technical reference for the use of hyperspectral inversion models for large-scale monitoring of soil heavy metal content.