Solar-induced chlorophyll fluorescence (SIF) can sensitively reflect crop disease stress information, but the geometric structure of canopy and other factors seriously affected the ability of SIF to capture changes in photosynthetic function and stress status of vegetation. Therefore, in this paper, the normalized difference vegetation index (NDVI) and MERIS terrestrial chlorophyll index (MTCI), which can sensitively reflect crop biomass, were integrated with SIF_P (SIF_P-NDVI,SIF_P-MTCI,SIF_P-NDVI*MTCI), and the remote sensing monitoring accuracy of SIF on wheat stripe rust before and after the integration was compared and analyzed. The results show that: (1) at the O₂-B, O₂-A and H₂O absorption at 719 nm bands, integrated reflectance spectral indices of SIF_P-NDVI, SIF_P-MTCI and SIF_P-NDVI*MTCI showed different improvements in correlation with disease index (DI) than SIFP. The O₂-B band increased the most significantly, by 23.48%, 33.61% and 36.49% respectively, while the O₂-A band increased the least by 2.39%, 2.14% and 1.51%, respectively. (2) If SIF_P-NDVI and SIF_P-MTCI were regarded as independent variables respectively, the averaged R² value of the prediction model based on random forest regression (RFR) algorithm were increased by 1.15% and 4.02%, and the averaged RMSE value were decreased by 2.7% and 14.41%, respectively, compared to those with SIFP as the independent variable. (3) The prediction model based on SIF_P-NDVI*MTCI gave the best performance with an R² value 5.74% higher than that of SIF_P, and an RMSE value 22.52% lower than that of SIF_P. The results of this paper are of great significance to improve the accuracy of remote sensing monitoring of wheat stripe rust and have a certain reference value for disease monitoring of other crops.