Abstract: Hyper spectral data is the most vulnerable to environmental noise (such as soil background) when monitoring wheat stripe rust. The first- and second-order differential processing of spectral data can be used to eliminate part of the noise, but it is easy to ignore the detailed information of stripe rust. In this study, a fractional-order differential spectral index was proposed to process the hyperspectral data of wheat canopy under the stress of stripe rust. Three two-band and three three-band fractional-order spectral differential indices were constructed after the band combination optimization, according to the current six types of spectral index. Gaussian regression was also applied to estimate the severity of stripe rust disease, compared with the commonly-used reflectivity spectral index. The results showed that the correlation between the fractional-order differential spectrum and the disease index of stripe rust was more significant than that of the original spectrum, where the most obvious significance was found in the range of 0.3-1.3 order differential spectrum. The correlation coefficient was the largest for the 481 nm band of 1.2 order differential spectrum with the severity of wheat stripe rust, 20.9%, 3.9%, and 20.5% higher than that of the original reflectance spectrum, the first-, and the second-order differential spectrum, respectively. Two-band fractional-order differential spectral indices were determined by the maximum correlation coefficient. Specifically, the values of the best order for the fractional-order differential-difference index, ratio index, and normalized difference index were 0.4, 1.3 and 1.2, respectively, where the band combination was 481 and 475 nm, 478 and 622 nm, as well as 481 nm and 673 nm, respectively. In the three-band fractional-order differential-difference index, the best order of fractional-order differential improved difference index was 1.1, and the band combination was 481, 442, and 454 nm. The best order of fractional-order differential improved ratio index was 1.2, and the band combination was 880, 670, and 481 nm. The best order of fractional-order differential photochemical reflectance index was 0.5, and the band combination is 646, 400, and 955 nm. The correlation between the three-band fractional-order differential spectral index and the severity of wheat stripe rust was better than that of the two-band fractional-order differential spectral index, where the fractional-order differential photochemical reflectance index presented the highest correlation with the severity of wheat stripe rust. Furthermore, the Gaussian regression model using the fractional-order differential spectral index indicated a better prediction accuracy for the stripe rust disease index than that for the reflectance spectral index. The determination coefficient between the predicted and measured values of Disease Index (DI) in the training and validation data set increased by 3.8% and 19.1%, respectively, where the Root Mean Square Error (RMSE) decreased by 13.0% and 33.5%, respectively, compared with the reflectance spectral index. Consequently, the fractional-order differential spectral index can be expected to improve the remote sensing detection accuracy of wheat stripe rust. This finding can provide a promising feasible way for the hyper spectral remote sensing to monitor the wheat stripe rust, thereby realizing the large-scale high-precision remote sensing monitoring of crop health.