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Remote Sens. 2018, 10(9), 1402; https://doi.org/10.3390/rs10091402

Analyzing the Effect of Fluorescence Characteristics on Leaf Nitrogen Concentration Estimation

1
Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, Hubei, China
2
Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, Hubei, China
3
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, Hubei, China
4
Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, Hubei, China
*
Author to whom correspondence should be addressed.
Received: 24 July 2018 / Revised: 23 August 2018 / Accepted: 30 August 2018 / Published: 3 September 2018
(This article belongs to the Special Issue Remote Sensing for Precision Nitrogen Management)
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Abstract

Leaf nitrogen concentration (LNC) is a significant indicator of crops growth status, which is related to crop yield and photosynthetic efficiency. Laser-induced fluorescence is a promising technology for LNC estimation and has been widely used in remote sensing. The accuracy of LNC monitoring relies greatly on the selection of fluorescence characteristics and the number of fluorescence characteristics. It would be useful to analyze the performance of fluorescence intensity and ratio characteristics at different wavelengths for LNC estimation. In this study, the fluorescence spectra of paddy rice excited by different excitation light wavelengths (355 nm, 460 nm, and 556 nm) were acquired. The performance of the fluorescence intensity and fluorescence ratio of each band were analyzed in detail based on back-propagation neural network (BPNN) for LNC estimation. At 355 nm and 460 nm excitation wavelengths, the fluorescence characteristics related to LNC were mainly located in the far-red region, and at 556 nm excitation wavelength, the red region being an optimal band. Additionally, the effect of the number of fluorescence characteristics on the accuracy of LNC estimation was analyzed by using principal component analysis combined with BPNN. Results demonstrate that at least two fluorescence spectral features should be selected in the red and far-red regions to estimate LNC and efficiently improve the accuracy of LNC estimation. View Full-Text
Keywords: laser-induced fluorescence; leaf nitrogen concentration; back-propagation neural network; principal component analysis; fluorescence characteristics laser-induced fluorescence; leaf nitrogen concentration; back-propagation neural network; principal component analysis; fluorescence characteristics
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Yang, J.; Song, S.; Du, L.; Shi, S.; Gong, W.; Sun, J.; Chen, B. Analyzing the Effect of Fluorescence Characteristics on Leaf Nitrogen Concentration Estimation. Remote Sens. 2018, 10, 1402.

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