Spectral noise removal is a necessary process for remote sensing regional applications, and the noise removal effect can directly affect the monitoring accuracy of regional surface information. To study and analyze the decomposition mechanism of the discrete wavelet algorithm on spectral data and explore the spectral noise information removal and spectral processing method based on the discrete wavelet algorithm, this study takes the winter wheat canopy spectra and leaf water content as the data source, and then denoises the spectral data using the discrete wavelet algorithm with the wavelet base of Meyer; and then separates the information of the denoised spectral data by using the wavelet bases of Meyer, Sym2, and Coif2, and constructs the spectral data by combining the correlation analysis algorithm and partial least squares algorithm. Then, we separated the information of the denoised spectral data with Meyer, Sym2, and Coif2 as the wavelet bases and constructed a model for estimating the water content of winter wheat leaves by combining the correlation analysis algorithm and partial least squares algorithm. The studys conclusions are as follows: (1) Under the discrete wavelet algorithm, with the increasing number of merging scales of the merging spectral curves, the original spectral curves local large, medium, and small features were highlighted in order. The correction amplitude of the merging curves was also gradually reduced with the joining of the decomposition scales of H10—H1. With the sequential addition of H10—H1 decomposition scales, the magnitude of the correction of the decomposition information to the merged curves is also gradually weakened, in which the merged spectral curves are almost unchanged after the sequential merging of H3—H1. (2) The denoising method proposed in this paper can change the sensitivity of the spectra to the water content of winter wheat leaves and the band positions of the sensitive bands to a certain extent: in the 1~3 scale, the sensitivity of the spectra to the water content of winter wheat leaves is reduced, and the distribution of the band positions of the sensitive bands is changed. Within 4~10 scales, it can significantly enhance the sensitivity of the spectrum to the water content of winter wheat leaves (Coif2); the denoising method proposed in the study can enhance the sensitivity of the local bands to the water content of winter wheat leaves (Sym2). (3) The denoising method proposed in this study can significantly improve the stability of the spectrum to the model. It can improve the accuracy and stability of the optimal model within the Sym2 and Coif2 wavelet bases, in which the validation accuracy is improved by 8.6% (Sym2) and 34.1% (Coif2), which indicates that the denoising treatment proposed in this study is effective.