{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T13:14:43Z","timestamp":1773234883558,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T00:00:00Z","timestamp":1639699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100007842","name":"Ministry of Foreign Affairs","doi-asserted-by":"publisher","award":["SINOGRAIN II, CHN-17\/0019"],"award-info":[{"award-number":["SINOGRAIN II, CHN-17\/0019"]}],"id":[{"id":"10.13039\/100007842","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology","doi-asserted-by":"publisher","award":["2016YFD0200600; 2016YFD0200602"],"award-info":[{"award-number":["2016YFD0200600; 2016YFD0200602"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000268","name":"Biotechnology and Biological Sciences Research Council","doi-asserted-by":"publisher","award":["BB\/P004555\/1"],"award-info":[{"award-number":["BB\/P004555\/1"]}],"id":[{"id":"10.13039\/501100000268","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005825","name":"National Institute of Food and Agriculture","doi-asserted-by":"publisher","award":["State project 1016571"],"award-info":[{"award-number":["State project 1016571"]}],"id":[{"id":"10.13039\/100005825","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate assessment of crop nitrogen (N) status and understanding the N demand are considered essential in precision N management. Chlorophyll fluorescence is unsusceptible to confounding signals from underlying bare soil and is closely related to plant photosynthetic activity. Therefore, fluorescence sensing is considered a promising technology for monitoring crop N status, even at an early growth stage. The objectives of this study were to evaluate the potential of using Multiplex\u00ae 3, a proximal canopy fluorescence sensor, to detect N status variability and to quantitatively estimate N status indicators at four key growth stages of maize. The sensor measurements were performed at different growth stages, and three different regression methods were compared to estimate plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). The results indicated that the induced differences in maize plant N status were detectable as early as the V6 growth stage. The first method based on simple regression (SR) and the Multiplex sensor indices normalized by growing degree days (GDD) or N sufficiency index (NSI) achieved acceptable estimation accuracy (R2 = 0.73\u20130.87), showing a good potential of canopy fluorescence sensing for N status estimation. The second method using multiple linear regression (MLR), fluorescence indices and GDDs had the lowest modeling accuracy (R2 = 0.46\u20130.79). The third tested method used a non-linear regression approach in the form of random forest regression (RFR) based on multiple sensor indices and GDDs. This approach achieved the best estimation accuracy (R2 = 0.84\u20130.93) and the most accurate diagnostic result.<\/jats:p>","DOI":"10.3390\/rs13245141","type":"journal-article","created":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T02:40:32Z","timestamp":1639968032000},"page":"5141","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Canopy Fluorescence Sensing for In-Season Maize Nitrogen Status Diagnosis"],"prefix":"10.3390","volume":"13","author":[{"given":"Rui","family":"Dong","sequence":"first","affiliation":[{"name":"College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8419-6511","authenticated-orcid":false,"given":"Yuxin","family":"Miao","sequence":"additional","affiliation":[{"name":"Precision Agriculture Center, Department of Soil, Water and Climate, University of Minnesota, St. Paul, MN 55108, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinbing","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6979-0029","authenticated-orcid":false,"given":"Fei","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Geography, Minnesota State University, Mankato, MN 56001, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6302-3707","authenticated-orcid":false,"given":"Krzysztof","family":"Kusnierek","sequence":"additional","affiliation":[{"name":"Center for Precision Agriculture, Norwegian Institute of Bioeconomy Research (NIBIO), Nylinna 226, 2849 Kapp, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1093\/jxb\/erq297","article-title":"Understanding plant response to nitrogen limitation for the improvement of crop nitrogen use efficiency","volume":"62","author":"Kant","year":"2011","journal-title":"J. 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