## 5 Conclusions  
In this study, the application requirements were taken as the foothold for developing spectrometry. Based on the soil moisture content inversion model previously constructed by the author and its reverse form, the sensitive wavelengths of soil moisture content, the best spectral resolution and the applicable SNR range were selected. This paper comprehensively considered the common influence of spectral range, spectral resolution and SNR on the estimation accuracy of soil moisture content. Firstly, the sensitive wavelengths of soil moisture were selected based on SA-SPA. Then the spectral resampling method was used to select the best spectral resolution in the corresponding sensitive wavelengths. Finally, under the selected sensitive wavelengths and spectral resolution, the SNR range suitable for soil moisture content inversion was explored. The research results show that the sensitive wavelengths of soil moisture content are 1010 nm, 1068 nm, 1915 nm, 1946 nm and 1988 nm. At the sensitive wavelengths, the best spectral resolution is 34 nm, and the applicable SNR ranges from 150 to 350. This study provides technical support for the efficient estimation of soil moisture content and the development of spectrometry.  
This study solves the following problems:  
1. A joint selection method of sensitive wavelength, optimal spectral resolution and signal-to-noise ratio of soil moisture content is proposed, which comprehensively considers the joint influence of spectral wavelength, spectral resolution and signal-to-noise ratio on the inversion accuracy, and solves the problem that only a certainspectral remote sensing parameter affects the inversion accuracy and ignores its joint influence.
2. The proposed sensitive wavelength selection method overcomes the problems that the traditional wavelength selection methods use a large amount of data, and the sensitive wavelength selection results are directly related to the data quality and representativeness.
3. When exploring the influence of spectral resolution on inversion accuracy, the spectral sampling interval is set to 1 nm to avoid the problem of large spectral sampling interval and poor research on the influence of spectral resolution on inversion accuracy.  
This study only preliminarily uses the laboratory measurement data to explore the effects of spectral band, spectral resolution and signal-to-noise ratio on the inversion accuracy of soil water content. The next research work is to deeply study the influence mechanism of spectral band, spectral resolution, signal-to-noise ratio and spatial resolution on inversion accuracy, and carry out remote sensing index parameter verification experiment combined with unmanned aerial vehicle.  
Conceptualization, J.Y.; methodology, J.Y.; software, J.Y.; validation, J.Y. and B.Y.; formal analysis, J.Y.; investigation, J.Y.; resources, C.Y. and J.Z.; data curation, J.Y.; writing--original draft preparation, J.Y.; writing--review and editing, B.Y., N.D. and Y.D.; visualization, J.Y.; supervision, C.Y. and J.Z.; project administration, C.Y. and J.Z.; funding acquisition, C.Y. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China under Grant 2016 YFF0103603; in part by the Technology Development Program of Jilin Province, China, under Grant 20180201012 GX; in part by the National Natural Science Foundation of China (NSFC) under Grant 61627819, Grant 61727818, Grant 6187030909, and Grant 61875192; in part by the National Natural Science Foundation of China Youth Fund under Grant 61805235; and in part by the STS Project of Chinese Academy of Sciences under Grant KFJ-STS-SCYD-212, Grant KFJ-STS-ZDTP-049, and Grant KFJ-STS-ZDTP-057.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.  
References