Hyperspectral quantitative retrieving of soil iron oxide and Zn content combining feature selection and machine learning algorithms

Abstract

Hyperspectral reflectance provides a pathway for estimating soil iron oxide and heavy metal zinc(Zn) content. The method and process for retrieving soil physicochemical properties from soil reflectance spectra mainly include spectral preprocessing—feature wavelength selection—machine learning modeling. To find the optimal model combination, this study first applies conventional spectral transformations (Continuum Removal, CR; Standard Normal Variate, SNV; First Derivative, FD and Second Derivative, SD) to the original soil spectra, then uses competitive adaptive reweighted sampling (CARS) and the Boruta algorithm to select sensitive bands, and finally constructs four machine learning models (Partial Least Squares Regression, PLSR; Support Vector Machine, SVM; Back Propagation Neural Network, BPNN and Extreme Gradient Boosting, XGBoost). The results show that spectral transformations (CR、SNV、FD and SD) can reduce the interference of external environments on soil spectra, effectively highlighting absorption and reflection features in the spectral curve, thus improving the accuracy of feature band selection and the prediction accuracy of the model. Among the feature selection methods, CARS is more suitable for soil iron oxide, while Boruta is more suitable for heavy metal Zn. In machine learning methods, both linear and nonlinear models can well explain the relationship between soil iron oxide and spectral reflectance, while the relationship between soil heavy metal Zn and spectral reflectance is nonlinear. The best retrieval model combination for soil iron oxide is FD_CARS_SVM, with R_C² = 0.878, RMSE_C = 4.395, R²_V = 0.849, RMSE_V = 4.478, and RPD_V = 2.576. The best retrieval model combination for heavy metal zinc is FD_Boruta_XGBoost, with R_C² = 0.999, RMSE_C = 0.102, R²_V = 0.682, RMSE_V = 2.697, and RPD_V = 1.772.