_applied sciences_  
_Article_  
**Strategies for the Efficient Estimation of Soil Moisture through Spectroscopy: Sensitive Wavelength Algorithm, Spectral Resampling and Signal-to-Noise Ratio Selection**  
[PERSON] 1, [PERSON] 1,2, [PERSON] 1,3,*, [PERSON] 1,4,*, [PERSON] 1,2 and [PERSON] 1,2  
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In recent years, spectral remote sensing technology has developed rapidly in China. Spectral remote sensing data plays an indispensable role in soil resources survey, environmental protection and other fields [8]. However, it is found that the parameters such as spectral range, signal-to-noise ratio (SNR) and spectral resolution directly affect the estimation accuracy, and the lack of research on the relationship between spectral remote sensing parameters and estimation accuracy restricts the prolongation of application [9]. Therefore, researchers have carried out the research on the relationship between these parameters and estimation accuracy, and achieved some scientific research results. Considering the selection method of sensitive wavelength for soil moisture content, some scholars used the correlation analysis method, and other scholars used the variable optimization method (Table 1) [10; 11; 12; 13; 14; 15]. Only a few scholars have made preliminary research about the influence of spectral resolution on the accuracy of soil spectral analysis. [PERSON] et al. resampled ASD FieldSpec.3 spectra with bandwidths from 1 to 10 nm and reported that 9 nm was the best bandwidth for estimating soil organic carbon [16]. [PERSON] et al. took black soil as the research object and pointed out that the best spectral resolution for the inversion of organic matter content in black soil is 50 nm [17]. [PERSON] et al. demonstrated that good and globally stable clay prediction results were obtained using laboratory spectroscopy with a spectral resolution from 3 to 60 nm [18]. The different methods used in model construction or the different selection of research objects may be the reasons for the differences in the above research results [19]. However, the current research has the following shortcomings: (i) the traditional wavelength selection method requires a large number of measurement data, and its performance is directly related to the quality and representativeness of \"training data\"; (ii) when determining the best spectral resolution, the spectral sampling interval is large, which is generally from ten nanometers to tens of nanometers; (iii) the influence of SNR on the estimation accuracy of soil moisture content has not been carried out; (iv) the estimation accuracy of soil moisture content is affected by modeling wavelength, spectral resolution and SNR. However, the current research does not take these three factors into account. In order to solve the above problems, firstly, a method based on the sensitivity analysis of soil radiative transfer model-successive projection algorithm (SA-SPA) was proposed to select the sensitive wavelengths.