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基于高光谱数据的水稻冠层叶片垂直氮素分布估算

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

作者信息

He Jiaoyang, Zhang Xiangbin, Guo Wanting, Pan Yuanyuan, Yao Xia, Cheng Tao, Zhu Yan, Cao Weixing, Tian Yongchao

机构信息

National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing, China.

Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Nanjing, China.

出版信息

Front Plant Sci. 2020 Feb 13;10:1802. doi: 10.3389/fpls.2019.01802. eCollection 2019.

Abstract

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, i = 1, 2, 3, 4), and the relationship between the LNC and the LNC at the canopy level (LNC). 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, the LNC, 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 by VIs and then estimating the LNC based on the relationship between the LNC and LNC; (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 using the LNC vertical distribution model; (3) estimating the model parameters by using VIs directly and then estimating the LNC 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/(R+R) (R = 0.763) and the renormalized difference vegetation index (RDVI) (1340, 730) (R = 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 (R = 0.768, 0.700, 0.623, and 0.549 for LNC, LNC, LNC, and LNC, respectively). These results provide technical support for the rapid, accurate, and non-destructive identification of the vertical distribution of nitrogen in rice canopies.

摘要

准确估算水稻冠层内叶片氮素的垂直分布,有助于了解水稻各功能叶层的养分供需情况,并提高对水稻生产力的预测。进行了一项为期两年的田间试验,采用不同水稻品种、施氮量和种植密度,以研究水稻冠层内叶片氮浓度(LNC,%)的垂直分布、不同叶层(LNC,i = 1、2、3、4)的LNC之间的关系,以及LNC与冠层水平的LNC之间的关系。基于相对冠层高度构建了LNC的垂直分布模型。此外,研究了不同植被指数(VIs)与LNC、LNC以及LNC垂直分布模型参数之间的关系。我们还比较了以下三种估算水稻冠层不同叶层LNC的方法:(1)通过VIs估算LNC,然后基于LNC与LNC之间的关系估算LNC;(2)通过VIs估算水稻冠层任一叶层的LNC,将结果输入LNC垂直分布模型以获得模型参数,然后使用LNC垂直分布模型估算LNC;(3)直接使用VIs估算模型参数,然后通过LNC垂直分布模型估算LNC。结果表明,水稻冠层底部的LNC对不同施氮量更敏感,LNC随相对冠层高度的变化可用指数模型模拟。植被指数可以估算水稻冠层顶部的LNC。R/(R + R)(R = 0.763)和重新归一化差异植被指数(RDVI)(1340,730)(R = 0.747)分别能够估算籼稻和粳稻LNC垂直分布模型的参数“a”。此外,方法(2)是估算LNC的最佳选择(LNC、LNC、LNC和LNC的R分别为0.768、0.700、0.623和0.549)。这些结果为快速、准确和无损识别水稻冠层氮素垂直分布提供了技术支持。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c051/7031418/072c41e7cc0a/fpls-10-01802-g001.jpg

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