## 4 Conclusions  
In this study, we collected 59 soil samples from the Daye City mining area of China. Hyperspectral imaging of the soil samples was undertaken using a hyperspectral imaging system (470-900 nm). Through the pretreatment methods of FD, SNV, and MSC, combined with the Spearman's rank correlation coefficients, the characteristic bands were selected, and the PLSR, SVMR, RF, and ETR models were compared. The ETR model was used to estimate the soil TAs content and generate the soil TAs distribution map. The main conclusions are as follows:  
1. Using the images acquired in the hyperspectral imaging system, bands selected according to different correlation coefficients are put into different models for prediction, it was found that the Spearman's rank correlation coefficients were an effective way to select the characteristic bands of TAs content. ETR (\(R^{2}\) = 0.81, RMSE = 0.38), RF (\(R^{2}\) = 0.78, RMSE = 0.42), SVMR (\(R^{2}\) = 0.78, RMSE = 0.42) models are capable of predicting total As content.
2. Soil TAs concentration distribution map shows, the [PERSON]'s rank correlation coefficients selected bands for ETR model, to predict the soil TAs distribution map generated by the pixel spectral of the hyperspectral image can be used as for estimation of TAs concentration in soil.  
The restriction on estimating total As could be considered a limitation of this present study. This is because not all forms of As are soluble and thus toxic. Therefore, in the context of toxicity, future research should focus instead on predicting the concentration of bioavailable As.  
[PERSON] and [PERSON] were responsible for the overall design of the study and contributed to the proofreading of the manuscript. [PERSON] performed the experiments. [PERSON] analyzed and interpreted the data and wrote the manuscript. [PERSON] and [PERSON] helped with the proofreading of the manuscript. [PERSON] contributed to designing the study and the proofreading of the manuscript. All authors read and approved the final manuscript.  
This research was funded by the \"National Key Research and Development Program of China\" (2019 YFB2102902, 2017 YFB0504202), the \"Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, MNR\" (KF-2019-04-006), the Opening Foundation of State Key Laboratory of Geo-Information Engineering (SKLGIE2018-M-3-3),the Central Government Guides Local Science and Technology Development Projects (2019 ZYYD050), the Opening Foundation of Hunan Engineering and Research Center of Natural Resource Investigation and Monitoring(2020-2), the \"Open Fund of the State Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University\" (18R02) and the \"Open fund of Key Laboratory of Agricultural Remote Sensing of the Ministry of Agriculture\" (20170007).  
Figure 7: HSI predicted TAs and measured TAs with the standard deviation as error bars.  
**Acknowledgments:** We gratefully acknowledge the help of the Data Extraction and Remote Sensing Analysis Group of Wuhan University (RSIDEA) in collecting the data. The Remote Sensing Monitoring and Evaluation of Ecological Intelligence Group of Hubei University (RSMEEI) helped to process the data. In addition, we are grateful to [PERSON] for the English editing.  
**Conflicts of Interest:** The authors declare no conflicts of interest.