###### Abstract
[8] used soil samples to analyze the relationship between magnesium (Mg) content and soil reflectance spectra in soil samples, using principal component regression analysis, PLS regression analysis, and SVMR analysis. The modeling method was used to establish an estimation model of Mg content, and the final results showed that the estimation accuracy of the SVMR model was the highest. [PERSON] et al. [9] studied the inversion of heavy metals in the soil of reclaimed mining areas, and they introduced extreme learning machine (ELM) technology to inversion modeling, and compared this with the traditional PLS regression and SVMR methods. The results of [PERSON] et al. [6] showed that the ELM model can achieve a higher accuracy than the models of SVMR and PLS in the prediction of Zn, Cu, Cd, and Chromium (Cr). Hyperspectral remote sensing monitoring in the field of soil heavy metal pollution has the characteristics of high dimensionality and high redundancy, which seriously affect the accuracy and stability of the hyperspectral inversion models. Characteristic band selection algorithms can be used in hyperspectral inversion models to improve the model predictive ability. [PERSON] et al. [10] took the prediction of nickel (Ni) content in soils from Hengyang and Zhangzhou of Hunan province, China, as an example. The spectra of the heavy metal adsorption and immobilization materials in the soil were used to predict the heavy metal content with the genetic partial least squares method (GA-PLSR). This study laid a foundation for predicting soil heavy metal content using field-measured spectra and hyperspectral remote sensing images. [PERSON] et al. [11] selected two research areas in metal mining and coal mining areas in northern China, and used competitive adaptive reweighted sampling-partial least squares (CARS-PLS) to select the characteristic bands. The results showed that, compared with other linear models, the nonlinear CARS-PLS-SVMR model obtained the highest accuracy in soil heavy metal estimation in the different mining areas by using appropriate spectral feature extraction in the preprocessed spectra. [PERSON] et al. [12] took the city of Xinzheng in Henan province, China, as the research object, and selected five common hyperspectral characteristic bands of heavy metals which passed the P = 0.01 significance test as the independent variables of the inversion model. They then adopted the fixed-effect variable coefficient model based on the ordinary least squares estimation (OLS) method, which indicated that this panel data model could be used for rapid hyperspectral monitoring of heavy metals in the cultivated soil of Xinzheng. This paper takes the soils of the areas of the cities of Honghu and Daye in Hubei province, China, as the research objects, and describes the field soil sampling, laboratory chemical analysis, and spectral collection and pretreatment that was undertaken in this study. A characteristic band selection algorithm based on [PERSON]'s rank correlation analysis (SCA) coupled with competitive adaptive reweighted sampling (CARS) was used. In order to verify the generalization ability of the algorithm, gradient boosting regression tree (GBRT), random forest (RF), linear regression (LR), and support vector machine (SVM) models were used to build the model, and we compared the accuracies to find the best method for the inversion of heavy metals in the soil. This will be of great significance to the rapid monitoring of soil heavy metal content in this region, and will provide a new approach for large-scale real-time monitoring and early warning of soil heavy metal pollution.