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Evaluation of Leaf N Concentration in Winter Wheat Based on Discrete Wavelet Transform Analysis

1
College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, Shaanxi, China
2
Key Laboratory of Plant Nutrition and the Agri-Environment in Northwest China, Ministry of Agriculture, Yangling 712100, Shaanxi, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1331; https://doi.org/10.3390/rs11111331
Received: 19 April 2019 / Revised: 24 May 2019 / Accepted: 31 May 2019 / Published: 3 June 2019
(This article belongs to the Special Issue Remote Sensing for Precision Nitrogen Management)
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Abstract

Leaf nitrogen concentration (LNC) is an important indicator for accurate diagnosis and quantitative evaluation of plant growth status. The objective was to apply a discrete wavelet transform (DWT) analysis in winter wheat for the estimation of LNC based on visible and near-infrared (400–1350 nm) canopy reflectance spectra. In this paper, in situ LNC data and ground-based hyperspectral canopy reflectance was measured over three years at different sites during the tillering, jointing, booting and filling stages of winter wheat. The DWT analysis was conducted on canopy original spectrum, log-transformed spectrum, first derivative spectrum and continuum removal spectrum, respectively, to obtain approximation coefficients, detail coefficients and energy values to characterize canopy spectra. The quantitative relationships between LNC and characteristic parameters were investigated and compared with models established by sensitive band reflectance and typical spectral indices. The results showed combining log-transformed spectrum and a sym8 wavelet function with partial least squares regression (PLS) based on the approximation coefficients at decomposition level 4 most accurately predicted LNC. This approach could explain 11% more variability in LNC than the best spectral index mSR705 alone, and was more stable in estimating LNC than models based on random forest regression (RF). The results indicated that narrowband reflectance spectroscopy (450–1350 nm) combined with DWT analysis and PLS regression was a promising method for rapid and nondestructive estimation of LNC for winter wheat across a range in growth stages. View Full-Text
Keywords: leaf nitrogen concentration; discrete wavelet transform; partial least squares; hyper-spectra leaf nitrogen concentration; discrete wavelet transform; partial least squares; hyper-spectra
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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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Li, F.; Wang, L.; Liu, J.; Wang, Y.; Chang, Q. Evaluation of Leaf N Concentration in Winter Wheat Based on Discrete Wavelet Transform Analysis. Remote Sens. 2019, 11, 1331.

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