###### Abstract  
Hyperspectral remote sensing can be used to effectively identify contaminated elements in soil. However, in the field of monitoring soil heavy metal pollution, hyperspectral remote sensing has the characteristics of high dimensionality and high redundancy, which seriously affect the accuracy and stability of hyperspectral inversion models. To resolve the problem, a gradient boosting regression tree (GBRT) hyperspectral inversion algorithm for heavy metal (Arsenic (As)) content in soils based on [PERSON]'s rank correlation analysis (SCA) coupled with competitive adaptive reweighted sampling (CARS) is proposed in this paper. Firstly, the CARS algorithm is used to roughly select the original spectral data. Second derivative (SD), Gaussian filtering (GF), and min-max normalization (MMN) pretreatments are then used to improve the correlation between the spectra and As in the characteristic band enhancement stage. Finally, the low-correlation bands are removed using the SCA method, and a subset with absolute correlation values greater than 0.6 is retained as the optimal band subset after each pretreatment. For the modeling, the five most representative characteristic bands were selected in the Honghu area of China, and the nine most representative characteristic bands were selected in the Daye area of China. In order to verify the generalization ability of the proposed algorithm, 92 soil samples from the Honghu and Daye areas were selected as the research objects. With the use of support vector machine regression (SVMR), linear regression (LR), and random forest (RF) regression methods as comparative methods, all the models obtained a good prediction accuracy. However, among the different combinations, CARS-SCA-GBRT obtained the highest precision, which indicates that the proposed algorithm can select fewer characteristic bands to achieve a better inversion effect, and can thus provide accurate data support for the treatment and recovery of heavy metal pollution in soils. Keywords:soil heavy metal pollution; competitive adaptive reweighted sampling; gradient boosting regression tree; characteristic bands +
Footnote †: journal: Applied Softcan dissolve red blood cells, destroy normal cells, and are both carcinogenic and teratogenic. Arsenic (As) pollution can directly lead to a decrease in soil fertility and crop yield reduction. Furthermore, planting crops in contaminated soil or using contaminated surface water to irrigate crops causes the crops to absorb large amounts of toxic substances [3; 4]. The traditional soil heavy metal detection methods are photometry, chemical analysis, atomic fluorescence spectrometry, inductively coupled plasma optical emission spectroscopy, and surface-enhanced Raman spectroscopy [5]. Although these methods can achieve a high level of precision, they do require a lot of manpower, material resources, equipment, and time. The development of remote sensing technology, especially the development of hyperspectral analysis technology, has made it possible to use continuous, high-resolution spectral bands to predict the heavy metal content in soil, and large-area rapid detection can be achieved, avoiding complex chemical analysis steps. [PERSON] et al. [6] analyzed the iron (Fe), As, and copper (Cu) content of soil samples taken near Baoshan Mine in Hunan province, China. After measuring the spectral reflectance of the soil in laboratories and establishing a partial least squares (PLS)-based soil heavy metal concentration regression model, it was concluded that the soil heavy metal content could be indirectly evaluated by the soil spectral reflectance. [PERSON] et al. [7] proposed a multivariate calibration method based on support vector machine regression (SVMR) to establish the relationship between visible-near-infrared (VIS-NIR) regional reflectance spectra and the manganese (Mn), Cu, cadmium (Cd), zinc (Zn), and lead (Pb) content in soil. [PERSON] et al.