To explore the feasibility of using fractional differentials in the hyperspectral estimation of wheat leaf water content, we select the Fukang experimental science base of Xinjiang University as the study area. Based on springtime wheat-field spectral data and wheat leaf water content data, we calculate the fractional differentials of the spectrum from the 0-order to the 2-order in 0.2-order steps; further, we analyze their correlations with the water content of the wheat leaves. We then use the successive projection algorithm (SPA) to select the optimal combination of bands for estimating the leaf water content from bands passing the 0.01 significance test. Finally, we establish a back propagation (BP) neural network model for estimating the water content of spring wheat leaves. The results show that fractional differentials can refine the trend of correlation between wheat leaf water content and the wheat leaves' spectral data. After fractional differential processing, the number of bands for which the correlation coefficients pass the 0.01 significance test first increases and subsequently decreases; in addition, the optimal order of fractional differentials is also different in different bands. Sensitive bands selected by the SPA are mainly concentrated in the red and near infrared bands, and the number of water sensitive bands is highest (reaching 13) after 1.2-order differential processing. Among the models considered herein, the BP neural network model with the 6-4-1 structure based on the 1.8-order differential is the best model, with the following specifications: the root-mean-square error of the modeling group is 0.701, the determination coefficient of the modeling group is 0.751, the root-mean-square error of the verification group is 0.227, the determination coefficient of the verification group is 0.917, and the relative analysis error of the verification group is 3.253. These conclusions show that the stability and predictive ability of the model using fractional differentials are better than those of integer differentials, and it provides a well-defined reference for the quantitative inversion of hyperspectral data to estimate the water content of spring wheat.