{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T14:39:27Z","timestamp":1777991967515,"version":"3.51.4"},"reference-count":60,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T00:00:00Z","timestamp":1674518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41971383"],"award-info":[{"award-number":["41971383"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42171394"],"award-info":[{"award-number":["42171394"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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, SIFyield (normalized by absorbed photosynthetically active radiation), fluorescence yield (\u03a6F) retrieved by NIRvP (non-physiological components of canopy SIF) and relative fluorescence yield (\u03a6F-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 &lt; 0.001) with disease severity levels (SL) under comprehensive experimental conditions (SL without dividing the experimental samples) and light disease conditions (SL &lt; 20%). The strongest correlation between NDVI and SL (R = 0.69) was observed under the comprehensive experimental conditions, followed by NIRv (R = 0.56), \u03a6F-r (R = 0.53) and SIF (R = 0.51), and the response of \u03a6F (R = 0.45) and SIFyield (R = 0.34) to SL was weak. Under lightly diseased conditions, \u03a6F-r (R = 0.62) showed the strongest response to disease, followed by SIFyield (R = 0.60), SIF (R = 0.56) and NIRv (R = 0.54). The weakest correlation was observed between \u03a6F 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, \u03a6F-r estimated by the newly proposed \u2018NIRvR approach\u2019 (which uses SIF together with NIRvR (i.e., SIF\/ NIRvR) as a substitute for \u03a6F) 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 \u03a6F-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.<\/jats:p>","DOI":"10.3390\/rs15030693","type":"journal-article","created":{"date-parts":[[2023,1,25]],"date-time":"2023-01-25T04:22:16Z","timestamp":1674620536000},"page":"693","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["An Improved Approach to Monitoring Wheat Stripe Rust with Sun-Induced Chlorophyll Fluorescence"],"prefix":"10.3390","volume":"15","author":[{"given":"Kaiqi","family":"Du","sequence":"first","affiliation":[{"name":"College of Geomatics, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xia","family":"Jing","sequence":"additional","affiliation":[{"name":"College of Geomatics, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yelu","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qixing","family":"Ye","sequence":"additional","affiliation":[{"name":"College of Geomatics, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingyu","family":"Li","sequence":"additional","affiliation":[{"name":"College of Geomatics, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0341-1983","authenticated-orcid":false,"given":"Jianxi","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Land Science and Technology, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s12571-020-01016-z","article-title":"Pathogens Which Threaten Food Security: Puccinia Striiformis, the Wheat Stripe Rust Pathogen","volume":"12","author":"Chen","year":"2020","journal-title":"Food Secur."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s11119-007-9038-9","article-title":"Identification of Yellow Rust in Wheat Using In-Situ Spectral Reflectance Measurements and Airborne Hyperspectral Imaging","volume":"8","author":"Huang","year":"2007","journal-title":"Precis. 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