Sun-induced chlorophyll fluorescence (SIF) has shown potential in quantifying plant responses to environmental changes by which abiotic drivers are dominated. However, SIF is a mixed signal influenced by factors such as leaf physiology, canopy structure, and sun-sensor geometry. Whether the physiological information contained in SIF can better quantify crop disease stresses dominated by biological drivers, and clearly explain the physiological variability of stressed crops, has not yet been sufficiently explored. On this basis, we took winter wheat naturally infected with stripe rust as the research object and conducted a study on the responses of physiological signals and reflectivity spectrum signals to crop disease stress dominated by biological drivers, based on in situ canopy-scale and leaf-scale data. Physiological signals include SIF, SIF_yield (normalized by absorbed photosynthetically active radiation), fluorescence yield (Φ_F) retrieved by NIRvP (non-physiological components of canopy SIF) and relative fluorescence yield (Φ_F-r) retrieved by near-infrared radiance of vegetation (NIRvR). Reflectance spectrum signals include normalized difference vegetation index (NDVI) and near-infrared reflectance of vegetation (NIRv). At the canopy scale, six signals reached extremely significant correlations (P < 0.001) with disease severity levels (SL) under comprehensive experimental conditions (SL without dividing the experimental samples) and light disease conditions (SL < 20%). The strongest correlation between NDVI and SL (R = 0.69) was observed under the comprehensive experimental conditions, followed by NIRv (R = 0.56), Φ_F-r (R = 0.53) and SIF (R = 0.51), and the response of Φ_F (R = 0.45) and SIF_yield (R = 0.34) to SL was weak. Under lightly diseased conditions, Φ_F-r (R = 0.62) showed the strongest response to disease, followed by SIF_yield (R = 0.60), SIF (R = 0.56) and NIRv (R = 0.54). The weakest correlation was observed between Φ_F and SL (R = 0.51), which also showed a result approximating NDVI (R = 0.52). In the case of a high level of crop disease severity, NDVI showed advantages in disease monitoring. In the early stage of crop diseases, which we pay more attention to, compared with SIF and reflectivity spectrum signals, Φ_F-r estimated by the newly proposed ‘NIRvR approach’ (which uses SIF together with NIRvR (i.e., SIF/ NIRvR) as a substitute for Φ_F) showed superior ability to monitor crop physiological stress, and was more sensitive to plant physiological variation. At the leaf scale, the response of SIF to SL was stronger than that of NDVI. These results validate the potential of Φ_F-r estimated by the NIRvR approach to monitoring disease stress dominated by biological drivers, thus providing a new research avenue for quantifying crop responses to disease stress.

Solar-induced chlorophyll fluorescence (SIF) has great advantages in the remote sensing detection of crop stress. However, under stripe rust stress, the effects of canopy structure and leaf physiology on the variations in canopy SIF are unclear, and these influencing factors are entangled during the development of disease, resulting in an unclear coupling relationship between SIF_canopy and the severity level (SL) of disease, which affects the remote sensing detection accuracy of wheat stripe rust. In this study, the observed canopy SIF was decomposed into NIR_VP, which can characterize the canopy structure, and SIF_tot, which can sensitively reflect the physiological status of crops. Additionally, the main factors driving the variations in canopy SIF under different disease severities were analyzed, and the response characteristics of SIF_canopy, NIR_VP, and SIF_tot to SL under stripe rust stress were studied. The results showed that when the severity level (SL) of disease was lower than 20%, NIR_VP was more sensitive to variation in SIF_canopy than SIF_tot, and the correlation between SIF_tot and SL was 6.6% higher than that of SIF_canopy. Using the decomposed SIF_tot component allows one to detect the stress state of plants before variations in vegetation canopy structure and leaf area index and can realize the early diagnosis of crop diseases. When the severity level (SL) of disease was in the state of moderate incidence (20% < SL ≤ 45%), the variation in SIF_canopy was affected by both NIR_VP and SIF_tot, and the detection accuracy of SIF_canopy for wheat stripe rust was better than that of the NIR_VP and SIF_tot components. When the severity level (SL) of disease reached a severe level (SL > 45%), SIF_tot was more sensitive to the variation in SIF_canopy, and NIR_VP reached a highly significant level with SL, which could better realize the remote sensing detection of wheat stripe rust disease severity. The research results showed that analyzing variations in SIF_canopy by using the decomposed canopy structure and physiological response signals can effectively capture additional information about plant physiology, detect crop pathological variations caused by disease stress earlier and more accurately, and promote crop disease monitoring and research progress.