## 1 Introduction  
Arsenic (As) is a ubiquitous element in nature, and can be found in rocks, soils, sediments, fossil fuels, plants, and almost all living organisms, including the biota of aquatic ecosystems [1]. Worldwide total arsenic (TAs) levels in soils have been reported to range between 2 and 5 mg/kg [2, 3]. However, TAs can be very harmful due to excessive accumulation in agricultural soils [4, 5]. Firstly, the transfer of TAs from soil to human beings through the food chain poses a potential disease risk [6, 7]. Secondly, excess TAs entering the pedosphere can affect the quality of cultivated land and reduce productivity [7, 8]. Research has suggested that the TAs can be accumulated due to human activities such as mining and smelting, industrial processes, and agricultural fertilizers. Most countries have been confronted with the soil contamination caused by heavy metals has become a worldwide issue [9, 10, 11, 12, 13]. Traditional chemical-based methods are destructive, time-consuming, and expensive. Therefore,nondestructive, cheap, and rapid methods for detecting soil TAs content, such as hyperspectral imaging, are needed to avoid human health risks and achieve soil protection [14].  
Visible/near infrared (VNIR), X-ray fluorescence spectroscopy (XRF), and laser-induced breakdown spectroscopy (LIBS) do not need too much sample preparation and utilization of chemicals to evaluate TAs concentration in soil [15; 16]. Hyperspectral imaging utilizes the VNIR spectrum and is used under laboratory conditions to acquire high spectral resolution images of soil, through its advantages of being fast, effective, non-destructive, and low cost [17; 18]. Prediction of TAs content is made possible by correlating the spectral data extracted from the hyperspectral images to their corresponding chemical concentrations [19]. Previous studies showed that the partial least squares (PLS) model can be used to determine the TAs concentration in soil samples (\(R^{2}_{\text{P}}=0.75\), \(\text{RMSE}_{\text{P}}=153.77\)) [20].  
In recent years, the development of machine learning algorithms also allowed their application for the prediction of element concentration in soil by the use of hyperspectral imaging (400-2500 nm) [21; 22; 23]. Compared with support vector machine regression (SVMR), the random forest (RF) model is a more effective machine learning method for developing diagnosis models [23]. Feature selection and machine learning methods are now important methods of predicting total nitrogen, total zinc, and total magnesium [24; 25]. Selecting the sensitive bands based on Spearman's rank correlation coefficients is a common approach when estimating soil concentration content [24; 25], but there is only a limited number of studies regarding the estimation of TAs content in soil using hyperspectral imaging technology. In addition, distribution maps obtained using hyperspectral imaging techniques are now widely used in agricultural studies, forestry, meat quality testing, etc. [26; 27; 28; 29]. Meanwhile, the use of hyperspectral imaging techniques for generating soil TAs concentration distribution maps using machine learning models remains to be studied [30].  
The objectives of this study were to investigate the use of VNIR hyperspectral imaging technology in the prediction of TAs concentration in soil. Preprocessing methods were used for selecting the characteristic bands in hyperspectral imaging technology, based on [PERSON]'s rank correlation coefficients. We also compared the machine learning techniques of PLR, SVMR, RF, and extremely randomized trees regression (ETR) in the prediction of TAs concentration in soil. The estimation of soil TAs content was then achieved based on the best-performing regression model for the prediction of soil TAs concentration distribution map.