{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T01:10:03Z","timestamp":1722215403748},"reference-count":68,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,12,9]],"date-time":"2018-12-09T00:00:00Z","timestamp":1544313600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871339, 41501468, 41571354, 41601466"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"The vertical leaf nitrogen (N) distribution in the crop canopy is considered to be an important adaptive response of crop growth and production. Remote sensing has been widely applied for the determination of a crop\u2019s N status. Some studies have also focused on estimating the vertical leaf N distribution in the crop canopy, but these analyses have rarely considered the plant geometry and its influences on the remote estimation of the N vertical distribution in the crop canopy. In this study, field experiments with three types of maize (Zea mays L.) plant geometry (i.e., horizontal type, intermediate type, and upright type) were conducted to demonstrate how the maize plant geometry influences the remote estimation of N distribution in the vertical canopy (i.e., upper layer, middle layer, and bottom layer) at different growth stages. The results revealed that there were significant differences among the three maize plant geometry types in terms of canopy architecture, vertical distribution of leaf N density (LND, g m\u22122), and the LND estimates in the leaves of different layers based on canopy hyperspectral reflectance measurements. The upright leaf variety had the highest correlation between the lower-layer LND (R2 = 0.52) and the best simple ratio (SR) index (736, 812), and this index performed well for estimating the upper (R2 = 0.50) and middle (R2 = 0.60) layer LND. However, for the intermediate leaf variety, only 25% of the variation in the lower-layer LND was explained by the best SR index (721, 935). The horizontal leaf variety showed little spectral sensitivity to the lower-layer LND. In addition, the growth stages also affected the remote detection of the lower leaf N status of the canopy, because the canopy reflectance was dominated by the biomass before the 12th leaf stage and by the plant N after this stage. Therefore, we can conclude that a more accurate estimation of the N vertical distribution in the canopy is obtained by canopy hyperspectral reflectance when the maize plants have more upright leaves.<\/jats:p>","DOI":"10.3390\/rs10121995","type":"journal-article","created":{"date-parts":[[2018,12,10]],"date-time":"2018-12-10T08:36:41Z","timestamp":1544431001000},"page":"1995","source":"Crossref","is-referenced-by-count":21,"title":["Remote Estimation of Nitrogen Vertical Distribution by Consideration of Maize Geometry Characteristics"],"prefix":"10.3390","volume":"10","author":[{"given":"Huichun","family":"Ye","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Agricultural Big Data, Ministry of Agriculture, Beijing 100081, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1710-8301","authenticated-orcid":false,"given":"Wenjiang","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Earth Observation, Hainan Province, Sanya 572029, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0455-795X","authenticated-orcid":false,"given":"Shanyu","family":"Huang","sequence":"additional","affiliation":[{"name":"Institute of Geography, University of Cologne, 50923 K\u00f6ln, Germany"}]},{"given":"Bin","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Yingying","family":"Dong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Bei","family":"Cui","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Earth Observation, Hainan Province, Sanya 572029, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/S0378-4290(98)00105-1","article-title":"Critical nitrogen concentrations: Implications for high-yielding rice (Oryza sativa L.) cultivars in the tropics","volume":"59","author":"Sheehy","year":"1998","journal-title":"Field Crop Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"241","DOI":"10.2134\/agronj2009.0266","article-title":"Determination of a critical nitrogen dilution curve for spring wheat","volume":"102","author":"Ziadi","year":"2010","journal-title":"Agron. 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