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Leaf Biochemistry Parameters Estimation of Vegetation Using the Appropriate Inversion Strategy

Biochemistry parameters of vegetation are important indicators of the photosynthetic process and provide a substantial amount of data about the status of ecosystems. Estimation of these parameters are greatly affected by the correlations of spectral bands and the sensitivity of each biochemistry par...

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Autores principales: Du, Lin, Yang, Jian, Sun, Jia, Shi, Shuo, Gong, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7326141/
https://www.ncbi.nlm.nih.gov/pubmed/32670300
http://dx.doi.org/10.3389/fpls.2020.00533
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author Du, Lin
Yang, Jian
Sun, Jia
Shi, Shuo
Gong, Wei
author_facet Du, Lin
Yang, Jian
Sun, Jia
Shi, Shuo
Gong, Wei
author_sort Du, Lin
collection PubMed
description Biochemistry parameters of vegetation are important indicators of the photosynthetic process and provide a substantial amount of data about the status of ecosystems. Estimation of these parameters are greatly affected by the correlations of spectral bands and the sensitivity of each biochemistry parameter to inversion models. Hence, reducing the spectral dimension and inefficient computation process using an appropriate inversion strategy is significant for biochemistry parameters’ estimation. In this work, we used band-selection-based artificial neural networks (ANNs) combined with feature weighting (FW) and principal component analysis (PCA) process to reduce the sensitive spectral correlations and to improve the inversion model predictability for four biochemistry parameters: chlorophyll a and b (Cab), carotenoid (Car), equivalent water thickness (EWT), and leaf mass per area (LMA). We analyzed the model performance by conducting different inversion strategies, including: (1) linking reflectance (R), transmittance (T), and R&T spectral properties in different numbers of band to four biochemistry parameters; (2) simultaneously and then separately inverting them using FW- and PCA-ANNs considering their sensitivity to the ANN model; and (3) choosing a spectral subset from R, T spectrum for EWT, and LMA inversion successively. The results show that: (i) the FW- and PCA-ANN models exhibit efficient improvements by selecting less spectral characteristics; (ii) concurrently inverting EWT and LMA can achieve a satisfactory R(2), while it is inappropriate for Cab and Car whose optimal R(2) are obtained by separately inverting all four biochemicals; (iii) the properties of R, T, and R&T spectra exhibit various performances on parameters inversion.
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spelling pubmed-73261412020-07-14 Leaf Biochemistry Parameters Estimation of Vegetation Using the Appropriate Inversion Strategy Du, Lin Yang, Jian Sun, Jia Shi, Shuo Gong, Wei Front Plant Sci Plant Science Biochemistry parameters of vegetation are important indicators of the photosynthetic process and provide a substantial amount of data about the status of ecosystems. Estimation of these parameters are greatly affected by the correlations of spectral bands and the sensitivity of each biochemistry parameter to inversion models. Hence, reducing the spectral dimension and inefficient computation process using an appropriate inversion strategy is significant for biochemistry parameters’ estimation. In this work, we used band-selection-based artificial neural networks (ANNs) combined with feature weighting (FW) and principal component analysis (PCA) process to reduce the sensitive spectral correlations and to improve the inversion model predictability for four biochemistry parameters: chlorophyll a and b (Cab), carotenoid (Car), equivalent water thickness (EWT), and leaf mass per area (LMA). We analyzed the model performance by conducting different inversion strategies, including: (1) linking reflectance (R), transmittance (T), and R&T spectral properties in different numbers of band to four biochemistry parameters; (2) simultaneously and then separately inverting them using FW- and PCA-ANNs considering their sensitivity to the ANN model; and (3) choosing a spectral subset from R, T spectrum for EWT, and LMA inversion successively. The results show that: (i) the FW- and PCA-ANN models exhibit efficient improvements by selecting less spectral characteristics; (ii) concurrently inverting EWT and LMA can achieve a satisfactory R(2), while it is inappropriate for Cab and Car whose optimal R(2) are obtained by separately inverting all four biochemicals; (iii) the properties of R, T, and R&T spectra exhibit various performances on parameters inversion. Frontiers Media S.A. 2020-05-20 /pmc/articles/PMC7326141/ /pubmed/32670300 http://dx.doi.org/10.3389/fpls.2020.00533 Text en Copyright © 2020 Du, Yang, Sun, Shi and Gong. 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
Du, Lin
Yang, Jian
Sun, Jia
Shi, Shuo
Gong, Wei
Leaf Biochemistry Parameters Estimation of Vegetation Using the Appropriate Inversion Strategy
title Leaf Biochemistry Parameters Estimation of Vegetation Using the Appropriate Inversion Strategy
title_full Leaf Biochemistry Parameters Estimation of Vegetation Using the Appropriate Inversion Strategy
title_fullStr Leaf Biochemistry Parameters Estimation of Vegetation Using the Appropriate Inversion Strategy
title_full_unstemmed Leaf Biochemistry Parameters Estimation of Vegetation Using the Appropriate Inversion Strategy
title_short Leaf Biochemistry Parameters Estimation of Vegetation Using the Appropriate Inversion Strategy
title_sort leaf biochemistry parameters estimation of vegetation using the appropriate inversion strategy
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7326141/
https://www.ncbi.nlm.nih.gov/pubmed/32670300
http://dx.doi.org/10.3389/fpls.2020.00533
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