Abstract: Wheat stripe rust, caused by Puccinia striiformis, is one of the most serious diseases on wheat yield. It is of great significance to timely and accurately detect the disease, in order to monitor and prevent the wheat stripe rust. The stripe rust can infect the internal physical and chemical characteristics and external morphological structure of wheat. Solar-induced chlorophyll fluorescence (SIF) can be expected for the remote sensing detection of crop stress. The red-band sunlight-induced chlorophyll fluorescence (RSIF) has more information about photosystem II (PSII), thus sensitively representing the photosynthetic physiological state of plants. The SIF escape rate is closely related to the canopy geometry, leaf optical properties, and light energy utilization efficiency of vegetation. In this study, field-measured data was used to invert and calculate the SIF and its escape rate (ε_CP) at different scales (canopy scale SIF_Canopy and photosystem scale SIF_PS) in the red and far-red band. The contents of four wheat pigments were obtained to combine the leaf area index (LAI) closely related to vegetation growth. The physiological basis of RSIF escape rate (Rε_CP) was determined to monitor the wheat stripe rust. Subsequently, the response characteristics of Rε_CP under stripe rust stress were explored to compare with the SIF and its derived parameters (fluorescence yield Ф_F, apparent SIF yield SIF_y) in the red and far-red light bands, the normalized difference vegetation index (NDVI), the MERIS terrestrial chlorophyll index (MTCI) and the simple ratio vegetation index (SR). We also systematically analyzed the response characteristics of Rε_CP to disease severity level (D_SL) under different D_SL and chlorophyll (Chl) levels. The results revealed that the correlations between nitrogen balance index (NBI), Chl, flavonoids (Flav), anthocyanins (Anth), LAI, and D_SL were all extremely significant, with the highest correlation observed between Chl and D_SL. Rε_CP showed extremely significant correlations with NBI, Chl, Flav, and Anth, outperforming RSIF and far-red Sun-induced chlorophyll fluorescence (FRSIF) at the photosystem scale and being superior to FRSIF at the canopy scale in relation to LAI. This indicates that Rε_CP better reflects crop physiological and canopy structural changes induced by disease stress. Among various characteristic variables such as canopy-scale FRSIF (FRSIF_Canopy), photosystem-scale FRSIF (FRSIF_PS), RSIF (RSIF_PS), apparent SIF yield in the red band (RSIF_y), its fluorescence yield (RФ_F), NDVI, MTCI, and SR, Rε_CP exhibited the highest correlation with D_SL. For both mild to moderate (0<D_SL≤45%) and severe (D_SL>45%) disease conditions, the correlation between Rε_CP and D_SL was higher than that of SIF, its derived parameters, and vegetation indices, all reaching extremely significant levels. Rε_CP was more sensitive to changes in D_SL, surpassing other parameters. Whether under low (Chl≤30) or medium-to-high (Chl>30) Chl content, Rε_CP demonstrated the most sensitive response to wheat stripe rust stress, with its correlation with D_SL superior to the extremely significant levels achieved by SIF and its derived parameters, as well as vegetation indices. Therefore, Rε_CP can serve as a suitable factor for remote sensing monitoring of wheat stripe rust, which is of great significance for disease prevention and yield enhancement. This study also provides a robust reference and tool for remote sensing monitoring of crops in agricultural production, incorporating RSIF and escape ratio into remote sensing monitoring to significantly enhance the detection and monitoring of plant health status.