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Remote Sens. 2015, 7(10), 14179-14199;

Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian Inversion

School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, China
Department of Agriculture-Agricultural Research Service, Crop Production Systems Research Unit, 141 Experiment Station Road, Stoneville, MS 38776, USA
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, Enschede 7500 AE, The Netherlands
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China
Author to whom correspondence should be addressed.
Academic Editors: Tao Cheng, Clement Atzberger and Prasad S. Thenkabail
Received: 31 March 2015 / Revised: 20 October 2015 / Accepted: 22 October 2015 / Published: 27 October 2015
(This article belongs to the Special Issue Recent Advances in Remote Sensing for Crop Growth Monitoring)
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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·m2·sr1·nm1, respectively, for backward fluorescence, and 0.92 and 3.84 × 10−6 W·m2·sr1·nm1 for forward fluorescence. Based on results, the inverted values of the fluorescence parameters were analyzed, and the potential of this method was investigated. View Full-Text
Keywords: chlorophyll fluorescence; FluorMODleaf; model inversion; Bayesian approach; hyperspectral remote sensing; radiative transfer chlorophyll fluorescence; FluorMODleaf; model inversion; Bayesian approach; hyperspectral remote sensing; radiative transfer

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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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Zhao, F.; Guo, Y.; Huang, Y.; Verhoef, W.; van der Tol, C.; Dai, B.; Liu, L.; Zhao, H.; Liu, G. Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian Inversion. Remote Sens. 2015, 7, 14179-14199.

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