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In order to reduce the influence of canopy geometry and other factors on the sensor detected solar-induced chlorophyll fluorescence (SIF), the response characteristics of red SIF (RSIF) fluorescence under stripe rust stress were discussed. Simple linear regression (SLR) and non-linear regression (NLR) models for remote sensing monitoring of wheat stripe rust were constructed with RSIF as the independent variable. The results showed that the leaf scale RSIF had a significant advantage in the remote sensing monitoring of wheat stripe rust, with a 13.2% higher correlation with the severity level (SL) of wheat stripe rust compared with far-red SIF(FRSIF). Compared with FRSIF, the R2 between predicted DSL and measured DSL was increased by 9.8% and 38.9%, and the RMSE was decreased by 23.1% and 36.4%, respectively, using the linear regression model and non-linear regression model constructed with the blade scale RSIF as the independent variable. In addition, downscaling can improve the accuracy of RSIF monitoring of wheat stripe rust. The R2 between leaf scale RSIF and DSL was increased by 126.3% compared with the canopy scale. The R2 between DSL and measured DSL predicted by SLR and NLR models using leaf scale RSIF as independent variable was increased by 114.3% and 233.3%, respectively, compared with the canopy scale, and RMSE was decreased by 16.7% and 15.4%, respectively. The research results were of great significance for improving the remote sensing monitoring accuracy of wheat stripe rust, and also had certain reference value for remote sensing monitoring of other stresses.