Abstract: Abstract: The prevalence of wheat stripe rust has a significant impact on the production of winter wheat all over the world. An effective monitoring and warning of this disease is imperative to ensure the quality of wheat production. Remote sensing detection of wheat stripe rust is important for agriculture management and decision. The reflectance spectrum is closely related to the changes of biomass. It cannot, however, directly reveal the photosynthetic physiological state of vegetation. Solar-induced chlorophyll fluorescence(SIF) can sensitively reflect the photosynthetic vitality of crops, and the canopy's solar-induced chlorophyll fluorescence signal includes the fluorescence characteristics of physiological changes caused by plant disease stress. In order to improve detection precision of wheat stripe rust, this study made full use of the advantages of reflectance spectroscopy for the detection of crop biochemical parameters and the advantages of chlorophyll fluorescence in photosynthetic physiological diagnosis, a remote sensing study on the severity of wheat stripe rust was carried out by using random forest (RF) and other machine learning algorithms synergistic SIF and reflectance differential spectral index in the canopy of wheat. Firstly, based on Fraunhofer line principle, three bands fraunhofer line discrimination(3FLD) algorithm was used to predict the intensity of chlorophyll fluorescence in O2-A band (760 nm). Then 11 reflectance differential spectral indices, which are sensitive to the severity of wheat stripe rust disease were selected. Based on RF and back propagation(BP) neural network algorithm, a model for predicting the severity of wheat stripe rust with differential reflectance spectral index and canopy SIF was established. The study incorporated a cross-checking method based on measurements of control samples. Fifty-two raw crop samples were randomly divided into two parts three times, the first part including 39 datasets was used as the training set for the model building, and the remaining 13 data samples were used to evaluate the accuracy of the models. The results showed that: 1) There is a significant negative correlation between SIF and the disease severity of wheat stripe rust. Remote sensing detection of wheat stripe rust severity can both be realized using the differential spectral index alone or by using the differential spectral index and the solar-induced chlorophyll fluorescence in combination. However, the accuracy of the estimates made by the RF and BP neural network models using the combination of data from the differential spectral index and the solar-induced chlorophyll fluorescence were all higher than that for the models constructed using the differential spectral index alone. In the three sample groups, average determination coefficient between the estimated DI using the RF model and the BP neural network model and the measured DI increased by 4% and 14% respectively, and the average RMSE decreased by 33% and 28% respectively. The detection accuracy of wheat stripe rust severity can be improved using solar-induced chlorophyll fluorescence combined reflectance differential spectral index. 2) The canopy solar-induced chlorophyll fluorescence synergistic differential spectral index were used as sensitive factors, the coefficients of determination between the estimated DI using the RF model and the measured DI were 0.90, 0.93, and 0.98, respectively, which were greater than the coefficients produced when using the BP neural network model for the same group (0.88, 0.84, and 0.92). Similarly, the RMSEs were 0.09, 0.07, and 0.04, respectively, which were smaller than the RMSEs (0.10, 0.11, and 0.09) using the BP neural network model for the same group. Therefore, the model using the RF algorithm was better at estimating wheat stripe rust severity than the BP neural network-based model, and it is more suitable for the remote sensing detection of wheat stripe rust severity. These results have important significance for improving the accuracy of the real-world remote sensing detection of wheat stripe rust, and the analysis provides new ideas for further realizing large-area remote sensing monitoring of crop health.