Remote sensing of sun-induced fluorescence to improve modeling of diurnal courses of gross primary production (GPP)

Terrestrial gross primary production (GPP) is an important parameter to explore and quantify carbon fixation by plant ecosystems at various scales. Remote sensing (RS) offers a unique possibility to investigate GPP in a spatially explicit fashion; however, budgeting of terrestrial carbon cycles based on this approach still remains uncertain. To improve calculations, spatio-temporal variability of GPP must be investigated in more detail on local and regional scales. The overarching goal of this study is to enhance our knowledge on how environmentally induced changes of photosynthetic light-use efficiency (LUE) are linked with optical RS parameters. Diurnal courses of sun-induced fluorescence yield (F-Syield) and the photochemical reflectance index of corn were derived from high-resolution spectrometric measurements and their potential as proxies for LUE was investigated. GPP was modeled using Monteith's LUE-concept and optical-based GPP and LUE values were compared with synoptically acquired eddy covariance data. It is shown that the diurnal response of complex physiological regulation of photosynthesis can be tracked reliably with the sun-induced fluorescence. Considering structural and physiological effects, this research shows for the first time that including sun-induced fluorescence into modeling approaches improves their results in predicting diurnal courses of GPP. Our results support the hypothesis that air-or spaceborne quantification of sun-induced fluorescence yield may become a powerful tool to better understand spatio-temporal variations of fluorescence yield, photosynthetic efficiency and plant stress on a global scale.