Quantitative Estimation of Fluorescence Parameters for Crop Leaves with Bayesian Inversion

Abstract

Abstract: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 OPEN ACCESSRemote Sens. 2015, 7 14180 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 (R 2 ) 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.

Difference and Potential of the Upward and Downward Sun-Induced Chlorophyll Fluorescence on Detecting Leaf Nitrogen Concentration in Wheat

“…The reason for the low accuracy of ↑FY739 and ↓FY739 for the LNC is the strong self-absorption of chlorophyll in the red region rather than in the far-red region. The red peak of SIF is more likely to be closely correlated to the LNC than the far-red peak, which agrees with previous study [52]. Although the downward SIF signal was weaker than that of the upward SIF, the correlation coefficient between the LNC and the downward SIF was higher than that with the upward SIF.…”

“…Figure 12A,B,C also show that the value of the downward SIF yield indices (↓FY687 and ↓FY739) are generally lower than that in the upward fluorescence (↑FY687 and ↑FY739). Due to the stronger absorption, scattering effect for the SIF emission [52], and more chlorophyll content around the upper epidermis, it was observed that the upward SIF radiance and SIF yield indices were higher compared with that of the downward SIF, especially for the SIF peak in the red region, which is consistent with [40,53]. Much of the light is absorbed by the palisade parenchyma, in the view of light propagation in the leaves.…”

FluorWPS: A Monte Carlo ray-tracing model to compute sun-induced chlorophyll fluorescence of three-dimensional canopy

“…Chlorophyll content (C ab , in unit of μg/cm 2 ), carotenoid content (C ca , in unit of μg/cm 2 ), water content (C w , in unit of g/cm 2 ), and dry matter content (C dm , in unit of g/cm 2 ), were measured for the leaves and used to evaluate the inverted results as introduced below. Detailed description of the measurement protocol and data processing can be found in Zhao et al (2015b).…”

Preface: Recent Advances in Remote Sensing for Crop Growth Monitoring

“…Zhao et al [1] showcase their attempt at inverting crop leaf fluorescence parameters from the leaf-level fluorescence model, FluorMODleaf. As a simpler and more straightforward approach, spectral indices have received more attention in this Special Issue.…”