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Estimation of Vertical Leaf Nitrogen Distribution Within a Rice Canopy Based on Hyperspectral Data

Accurate estimations of the vertical leaf nitrogen (N) distribution within a rice canopy is helpful for understanding the nutrient supply and demand of various functional leaf layers of rice and for improving the predictions of rice productivity. A two-year field experiment using different rice vari...

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Autores principales: He, Jiaoyang, Zhang, Xiangbin, Guo, Wanting, Pan, Yuanyuan, Yao, Xia, Cheng, Tao, Zhu, Yan, Cao, Weixing, Tian, Yongchao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7031418/
https://www.ncbi.nlm.nih.gov/pubmed/32117352
http://dx.doi.org/10.3389/fpls.2019.01802
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author He, Jiaoyang
Zhang, Xiangbin
Guo, Wanting
Pan, Yuanyuan
Yao, Xia
Cheng, Tao
Zhu, Yan
Cao, Weixing
Tian, Yongchao
author_facet He, Jiaoyang
Zhang, Xiangbin
Guo, Wanting
Pan, Yuanyuan
Yao, Xia
Cheng, Tao
Zhu, Yan
Cao, Weixing
Tian, Yongchao
author_sort He, Jiaoyang
collection PubMed
description Accurate estimations of the vertical leaf nitrogen (N) distribution within a rice canopy is helpful for understanding the nutrient supply and demand of various functional leaf layers of rice and for improving the predictions of rice productivity. A two-year field experiment using different rice varieties, N rates, and planting densities was performed to investigate the vertical distribution of the leaf nitrogen concentration (LNC, %) within the rice canopy, the relationship between the LNC in different leaf layers (LNC(Li), i = 1, 2, 3, 4), and the relationship between the LNC(Li) and the LNC at the canopy level (LNC(Canopy)). A vertical distribution model of the LNC was constructed based on the relative canopy height. Furthermore, the relationship between different vegetation indices (VIs) and the LNC(Canopy), the LNC(Li), and the LNC vertical distribution model parameters were studied. We also compared the following three methods for estimating the LNC in different leaf layers in rice canopy: (1) estimating the LNC(Canopy) by VIs and then estimating the LNC(Li) based on the relationship between the LNC(Li) and LNC(Canopy); (2) estimating the LNC in any leaf layer of the rice canopy by VIs, inputting the result into the LNC vertical distribution model to obtain the parameters of the model, and then estimating the LNC(Li) using the LNC vertical distribution model; (3) estimating the model parameters by using VIs directly and then estimating the LNC(Li) by the LNC vertical distribution model. The results showed that the LNC in the bottom of rice canopy was more susceptible to different N rates, and changes in the LNC with the relative canopy height could be simulated by an exponential model. Vegetation indices could estimate the LNC at the top of rice canopy. R(705)/(R(717)+R(491)) (R(2) = 0.763) and the renormalized difference vegetation index (RDVI) (1340, 730) (R(2) = 0.747) were able to estimate the parameter “a” of the LNC vertical distribution model in indica rice and japonica rice, respectively. In addition, method (2) was the best choice for estimating the LNC(Li) (R(2) = 0.768, 0.700, 0.623, and 0.549 for LNC(L1), LNC(L2), LNC(L3), and LNC(L4), respectively). These results provide technical support for the rapid, accurate, and non-destructive identification of the vertical distribution of nitrogen in rice canopies.
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spelling pubmed-70314182020-02-28 Estimation of Vertical Leaf Nitrogen Distribution Within a Rice Canopy Based on Hyperspectral Data He, Jiaoyang Zhang, Xiangbin Guo, Wanting Pan, Yuanyuan Yao, Xia Cheng, Tao Zhu, Yan Cao, Weixing Tian, Yongchao Front Plant Sci Plant Science Accurate estimations of the vertical leaf nitrogen (N) distribution within a rice canopy is helpful for understanding the nutrient supply and demand of various functional leaf layers of rice and for improving the predictions of rice productivity. A two-year field experiment using different rice varieties, N rates, and planting densities was performed to investigate the vertical distribution of the leaf nitrogen concentration (LNC, %) within the rice canopy, the relationship between the LNC in different leaf layers (LNC(Li), i = 1, 2, 3, 4), and the relationship between the LNC(Li) and the LNC at the canopy level (LNC(Canopy)). A vertical distribution model of the LNC was constructed based on the relative canopy height. Furthermore, the relationship between different vegetation indices (VIs) and the LNC(Canopy), the LNC(Li), and the LNC vertical distribution model parameters were studied. We also compared the following three methods for estimating the LNC in different leaf layers in rice canopy: (1) estimating the LNC(Canopy) by VIs and then estimating the LNC(Li) based on the relationship between the LNC(Li) and LNC(Canopy); (2) estimating the LNC in any leaf layer of the rice canopy by VIs, inputting the result into the LNC vertical distribution model to obtain the parameters of the model, and then estimating the LNC(Li) using the LNC vertical distribution model; (3) estimating the model parameters by using VIs directly and then estimating the LNC(Li) by the LNC vertical distribution model. The results showed that the LNC in the bottom of rice canopy was more susceptible to different N rates, and changes in the LNC with the relative canopy height could be simulated by an exponential model. Vegetation indices could estimate the LNC at the top of rice canopy. R(705)/(R(717)+R(491)) (R(2) = 0.763) and the renormalized difference vegetation index (RDVI) (1340, 730) (R(2) = 0.747) were able to estimate the parameter “a” of the LNC vertical distribution model in indica rice and japonica rice, respectively. In addition, method (2) was the best choice for estimating the LNC(Li) (R(2) = 0.768, 0.700, 0.623, and 0.549 for LNC(L1), LNC(L2), LNC(L3), and LNC(L4), respectively). These results provide technical support for the rapid, accurate, and non-destructive identification of the vertical distribution of nitrogen in rice canopies. Frontiers Media S.A. 2020-02-13 /pmc/articles/PMC7031418/ /pubmed/32117352 http://dx.doi.org/10.3389/fpls.2019.01802 Text en Copyright © 2020 He, Zhang, Guo, Pan, Yao, Cheng, Zhu, Cao and Tian http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
He, Jiaoyang
Zhang, Xiangbin
Guo, Wanting
Pan, Yuanyuan
Yao, Xia
Cheng, Tao
Zhu, Yan
Cao, Weixing
Tian, Yongchao
Estimation of Vertical Leaf Nitrogen Distribution Within a Rice Canopy Based on Hyperspectral Data
title Estimation of Vertical Leaf Nitrogen Distribution Within a Rice Canopy Based on Hyperspectral Data
title_full Estimation of Vertical Leaf Nitrogen Distribution Within a Rice Canopy Based on Hyperspectral Data
title_fullStr Estimation of Vertical Leaf Nitrogen Distribution Within a Rice Canopy Based on Hyperspectral Data
title_full_unstemmed Estimation of Vertical Leaf Nitrogen Distribution Within a Rice Canopy Based on Hyperspectral Data
title_short Estimation of Vertical Leaf Nitrogen Distribution Within a Rice Canopy Based on Hyperspectral Data
title_sort estimation of vertical leaf nitrogen distribution within a rice canopy based on hyperspectral data
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7031418/
https://www.ncbi.nlm.nih.gov/pubmed/32117352
http://dx.doi.org/10.3389/fpls.2019.01802
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