## 4 Conclusions  
In this study, we took the contaminated soils in the areas of the cities of Daye and Honghu in Jianghan Plain, China, as the research objects. Based on four different model construction methods, we focused on the use of soil reflectance spectral curves to invert the soil As content. The main conclusions are as follows:  
(1) The data quality has a significant impact on the modeling effects. By comparing the different regression models based on the CARS and CARS-SCA feature selection algorithms, it was found that CARS coupled with the SCA characteristic band selection algorithm can effectively eliminate irrelevant and redundant information, and can greatly improve the correlation between the spectra and soil heavy metal As content. It was also found that the correlation is greatly improved after the pretreatment, which overcomes the shortcomings of the traditional feature pre-selection algorithm, without changing the original data.  
(2) By comparing the four regression methods in the two research areas, it was found that the GBRT model shows a high inversion accuracy and a good generalization ability, indicating that the model has high stability and obtains a high prediction accuracy, which can satisfy the actual forecasting requirements.  
(3) The experiments showed that it is feasible to use the CARS-SCA-GBRT algorithm for the spectral analysis of soil heavy metal As content. By analyzing the measured spectra of soil, the problem of information redundancy and poor prediction accuracy in the field of spectral inversion is solved, which will provide a basis for large-scale inversion of soil heavy metal content in the future. In addition, the regional construction of hyperspectral inversion models will also provide a reference for unmanned aerial vehicle, aerospace, and aerial remote sensing.  
[PERSON] and [PERSON] were responsible for the overall design of the study and contributed to the proofreading of the manuscript. [PERSON] performed the experiments, analyzed and interpreted the data, wrote the manuscript, and helped with the proofreading of the manuscript. [PERSON] and [PERSON] contributed to designing the study and the proofreading of the manuscript. [PERSON] contributed to the chemical analysis. [PERSON] analyzed and interpreted the data. All authors read and approved the final manuscript.  
This research was funded by the National Key Research and Development Program of China (2017 YFB0504202), the National Natural Science Foundation of China (41622107), the Special Projects for Technological Innovation in Hubei (2018 ABA078), the Open Fund of the Key Laboratory of Ministry of Education for Spatial Data Mining and Information Sharing (2018 LSDMIS05), and the Open Fund of the Key Laboratory of Agricultural Remote Sensing of the Ministry of Agriculture (20170007).  
Figure 9: A comparison between the measured values and predicted values of the different regression models for Daye. (**a**) Gradient boosting regression tree model. (**b**) Random forest regression model. (**c**) Linear regression model. (**d**) Support vector machine regression model.  
**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 (RSMEEI) helped to process the data.  
**Conflicts of Interest:** The authors declare no conflicts of interest.