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Using spectral chlorophyll fluorescence and the photochemical reflectance index to predict physiological dynamics

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Original languageEnglish
Pages (from-to)17
Number of pages30
JournalRemote Sensing of Environment
Early online date22 Jan 2016
Publication statusPublished - 1 Feb 2016


Observations of terrestrial chlorophyll fluorescence and the Photochemical Reflectance Index (PRI) from space have the potential to improve estimates of global carbon exchange. However the relationship between photosynthetic rate and these measurements is complicated by several factors that relate to the dissipation of absorbed light energy via both photochemical (photosynthesis) and non-photochemical pathways. Numerical simulations of physiological remote sensing signals require the coupling of physically-based radiative transfer models with models of physiological dynamics. These schemes provide the quantitative frameworks from which physiological information can be extracted from remote sensing observations of vegetation, helping to resolve the aforementioned complexities. We present such a framework that links physiological fluorescence theory with spectral remote sensing type measurements at the leaf scale. We show how a simple expression can be used to predict the quantum yield of photochemistry (ΦPSII), a proxy for photosynthetic efficiency, from spectral measurements and modelled non-photochemical quenching (NPQ). We tested two alternate models of NPQ; one process-based (PHOTOII) and the other empirical, based on visible region reflectance changes (the PRI). We used a Monte Carlo Radiative Transfer (MCRT) model to retrieve the separated yields of chlorophyll fluorescence from photosystems II and I. Measurements of dynamic spectral fluorescence, the PRI, hemispherical reflectance and transmittance, saturation pulse integrated fluorescence and pigment contents were collected from maple leaves and used to calibrate and validate the modelling framework. Both NPQ models reproduced the observed photochemical and non-photochemical dynamics. Future work is recommended to scale the framework across space, time and species.

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