Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian Inversion

In this study, backward and forward fluorescence radiance within the emission spectrum of 640–850 nm were measured for leaves of soybean, cotton, peanut and wheat using a hyperspectral spectroradiometer coupled with an integration sphere. Fluorescence parameters of crop leaves were retrieved from th...

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Main Authors: Feng Zhao, Yiqing Guo, Yanbo Huang, Wout Verhoef, Christiaan van der Tol, Bo Dai, Liangyun Liu, Huijie Zhao, Guang Liu
Format: Article
Language:English
Published: MDPI AG 2015-10-01
Series:Remote Sensing
Subjects:
Online Access:http://www.mdpi.com/2072-4292/7/10/14179
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spelling doaj-a284a1fde8644adb9d7e362556ab7cc82020-11-24T22:40:46ZengMDPI AGRemote Sensing2072-42922015-10-01710141791419910.3390/rs71014179rs71014179Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian InversionFeng Zhao0Yiqing Guo1Yanbo Huang2Wout Verhoef3Christiaan van der Tol4Bo Dai5Liangyun Liu6Huijie Zhao7Guang Liu8School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, ChinaSchool of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, ChinaUnited States Department of Agriculture-Agricultural Research Service, Crop Production Systems Research Unit, 141 Experiment Station Road, Stoneville, MS 38776, USAFaculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, Enschede 7500 AE, The NetherlandsFaculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, Enschede 7500 AE, The NetherlandsSchool of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, ChinaInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, ChinaSchool of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, ChinaInstitute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, ChinaIn this study, backward and forward fluorescence radiance within the emission spectrum of 640–850 nm were measured for leaves of soybean, cotton, peanut and wheat using a hyperspectral spectroradiometer coupled with an integration sphere. Fluorescence parameters of crop leaves were retrieved from the leaf hyperspectral measurements by inverting the FluorMODleaf model, a leaf-level fluorescence model able to simulate chlorophyll fluorescence spectra for both sides of leaves. This model is based on the widely used and validated PROSPECT (leaf optical properties) model. Firstly, a sensitivity analysis of the FluorMODleaf model was performed to identify and quantify influential parameters to assist the strategy for the inversion. Implementation of the Extended Fourier Amplitude Sensitivity Test (EFAST) method showed that the leaf chlorophyll content and the fluorescence lifetimes of photosystem I (PSI) and photosystem II (PSII) were the most sensitive parameters among all eight inputs of the FluorMODleaf model. Based on results of sensitivity analysis, the FluorMODleaf model was inverted using the leaf fluorescence spectra measured from both sides of crop leaves. In order to achieve stable inversion results, the Bayesian inference theory was applied. The relative absorption cross section of PSI and PSII and the fluorescence lifetimes of PSI and PSII of the FluorMODleaf model were retrieved with the Bayesian inversion approach. Results showed that the coefficient of determination (R2) and root mean square error (RMSE) between the fluorescence signal reconstructed from the inverted fluorescence parameters and measured in the experiment were 0.96 and 3.14 × 10−6 W·m−2·sr−1·nm−1, respectively, for backward fluorescence, and 0.92 and 3.84 × 10−6 W·m−2·sr−1·nm−1 for forward fluorescence. Based on results, the inverted values of the fluorescence parameters were analyzed, and the potential of this method was investigated.http://www.mdpi.com/2072-4292/7/10/14179chlorophyll fluorescenceFluorMODleafmodel inversionBayesian approachhyperspectral remote sensingradiative transfer
collection DOAJ
language English
format Article
sources DOAJ
author Feng Zhao
Yiqing Guo
Yanbo Huang
Wout Verhoef
Christiaan van der Tol
Bo Dai
Liangyun Liu
Huijie Zhao
Guang Liu
spellingShingle Feng Zhao
Yiqing Guo
Yanbo Huang
Wout Verhoef
Christiaan van der Tol
Bo Dai
Liangyun Liu
Huijie Zhao
Guang Liu
Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian Inversion
Remote Sensing
chlorophyll fluorescence
FluorMODleaf
model inversion
Bayesian approach
hyperspectral remote sensing
radiative transfer
author_facet Feng Zhao
Yiqing Guo
Yanbo Huang
Wout Verhoef
Christiaan van der Tol
Bo Dai
Liangyun Liu
Huijie Zhao
Guang Liu
author_sort Feng Zhao
title Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian Inversion
title_short Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian Inversion
title_full Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian Inversion
title_fullStr Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian Inversion
title_full_unstemmed Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian Inversion
title_sort quantitative estimation of fluorescence parameters for crop leaves with bayesian inversion
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2015-10-01
description In this study, backward and forward fluorescence radiance within the emission spectrum of 640–850 nm were measured for leaves of soybean, cotton, peanut and wheat using a hyperspectral spectroradiometer coupled with an integration sphere. Fluorescence parameters of crop leaves were retrieved from the leaf hyperspectral measurements by inverting the FluorMODleaf model, a leaf-level fluorescence model able to simulate chlorophyll fluorescence spectra for both sides of leaves. This model is based on the widely used and validated PROSPECT (leaf optical properties) model. Firstly, a sensitivity analysis of the FluorMODleaf model was performed to identify and quantify influential parameters to assist the strategy for the inversion. Implementation of the Extended Fourier Amplitude Sensitivity Test (EFAST) method showed that the leaf chlorophyll content and the fluorescence lifetimes of photosystem I (PSI) and photosystem II (PSII) were the most sensitive parameters among all eight inputs of the FluorMODleaf model. Based on results of sensitivity analysis, the FluorMODleaf model was inverted using the leaf fluorescence spectra measured from both sides of crop leaves. In order to achieve stable inversion results, the Bayesian inference theory was applied. The relative absorption cross section of PSI and PSII and the fluorescence lifetimes of PSI and PSII of the FluorMODleaf model were retrieved with the Bayesian inversion approach. Results showed that the coefficient of determination (R2) and root mean square error (RMSE) between the fluorescence signal reconstructed from the inverted fluorescence parameters and measured in the experiment were 0.96 and 3.14 × 10−6 W·m−2·sr−1·nm−1, respectively, for backward fluorescence, and 0.92 and 3.84 × 10−6 W·m−2·sr−1·nm−1 for forward fluorescence. Based on results, the inverted values of the fluorescence parameters were analyzed, and the potential of this method was investigated.
topic chlorophyll fluorescence
FluorMODleaf
model inversion
Bayesian approach
hyperspectral remote sensing
radiative transfer
url http://www.mdpi.com/2072-4292/7/10/14179
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