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Estimation of litchi (Litchi chinensis Sonn.) leaf nitrogen content at different growth stages using canopy reflectance spectra

文献类型： 外文期刊

作者： Li, Dan 1 ; Wang, Congyang 1 ; Liu, Wei 1 ; Peng, Zhiping 2 ; Huang, Siyu 1 ; Huang, Jichuan 3 ; Chen, Shuisen 1 ;

作者机构： 1.Guangzhou Inst Geog, Guangdong Key Lab Remote Sensing & GIS Applicat, Guangdong Open Lab Geospatial Informat Technol &, Guangzhou 510070, Guangdong, Peoples R China

2.Guangdong Acad Agr Sci, Inst Agr Resources & Environm, Guangzhou 510640, Guangdong, Peoples R China

3.Guangdong Acad Agr Sci, Inst Agr Resources & Environm, Guangzhou 510640, Guangdong, Peoples R

关键词： Litchi;Canopy reflectance;Leaf nitrogen content;Continuum removal;Growth stage

期刊名称：EUROPEAN JOURNAL OF AGRONOMY （ 影响因子：5.124； 五年影响因子：5.567 ）

ISSN：

年卷期：

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收录情况： SCI

摘要： The estimation of crop nitrogen status in fresh vegetation leaf using field spectroscopy is challenging due to the weak responses on leaf/canopy reflectance and the overlapping with the absorption features of other compounds. Although the spectral indices were proposed in the literature to predict leaf nitrogen content (LNC), the performance of selected spectral indices to estimate the LNC is often inconsistent. Moreover, the models for nitrogen content estimation changed with the growth stage. The goal of this study was to evaluate the performance of published indices, ratio of data difference index (RDDI) and ratio of data index (RDI) developed by band iterative-optimization algorithm in LNC estimation. The correlation analysis, linear regression and cross validation were used to analyze the relationship between spectral data and LNC and construct the best performed estimation model. The study was conducted by the data of five growing seasons of litchi from the orchards in Guangdong Province of China. Results showed that the relationship between chlorophyll (Chl) related spectral indices and LNC varied with the growth stage. Even in flower bud morphological differentiation stage and autumn shoot maturation stage, there were not significant correlations between the proposed spectral indices and LNC. Besides it is difficult to estimate the LNC by the general model across the growth stages due to the integrated effects of cultivar, biochemical, canopy structure, etc. The band iterative-optimization algorithm can improve the sensitivity of spectral data to LNC to some extent. The optimal RDDI performed better than other indices for the synthetic dataset and the dataset in each growth stage. And the sensitive bands selected in the optimal indices at each growth stage are not consistent, which are not only related to the Chl absorption but also other biochemical components, such as starch, lignin, cellulose, protein, etc. In general, the LNC can be estimated by the optimized CR-based RDDI indices in autumn shoot maturation stage, flower spike stage, fruit maturation stage, and flowering stage with the R-2 > 0.50 and RMSE < 0.14. Although there were the significant relationship between RDIs and RDDIs in flower bud morphological differentiation stage, the highest Res of the model developed by RDDIs and RDIs were less than 0.50 in cross validation. This study indicated that the applicability of canopy reflectance to estimate litchi LNC was closely related to the growth stage of litchi. Growth stage-specific models will be preferred for estimating litchi LNC estimation. (C) 2016 Elsevier B.V. All rights reserved.

  • 相关文献

[1]Estimation the leaf phosphorus concentration of litchi (Litchi chinensis Sonn.) at different growth stages by canopy reflectance. Wang, Chongyang,Chen, Shuisen,Li, Dan,Liu, Wei,Huang, Siyu,Peng, Zhiping. 2015

[2]Effect of Application Ratio of Potassium over Nitrogen on Litchi Growth and Fruit Quality. Li, G. L.,Yang, B. M.,He, Z. H.,Zhou, C. M.,Tu, S. H.. 2014

[3]Dynamic changes of nutrition in litchi foliar and effects of potassium-nitrogen fertilization ratio. Li, G. L.,He, Z. H.,Zhou, C. M.,Yao, L. X.. 2015

[4]Effect of Application Ratio of Potassium over Nitrogen on Litchi Fruit Yield, Quality, and Storability. Li, Guoliang,Yang, Shaohai,He, Zhaohuan,Zhou, Changmin,Yao, Lixian.

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