Sun induced fluorescence at 760 nm (F 760 ) has shown to provide a valid approach to quantify gross primary production (GPP) at various scales, however the relationship between GPP and F 760 is influenced by the escape probability of fluorescence (Fesc), a variable which is not still fully understood. Combining radiative transfer modelling approaches, by means of the SCOPE model, and a data driven methodology based on variable selection methods we identify the predictors of Fesc, focusing on the effect of functional and structural traits. We show that Fesc is mainly predicted by structural variables such as fraction of grasses and near infrared reflectance. Building on the analysis of the predictor of Fesc, LUE p and LUE f we present a semi-empirical model formulation based only on optical data that significantly improves the GPP prediction.
Martini, D., Pacheco-Labrador, J., Perez-Priego, O., Van Der Tol, C., El Madany, T., Julitta, T., et al. (2018). Photosynthesis-sun induced fluorescence relationship in a Mediterranean grassland. In International Geoscience and Remote Sensing Symposium (IGARSS) (pp.5979-5982). Institute of Electrical and Electronics Engineers Inc. [10.1109/IGARSS.2018.8517362].
Photosynthesis-sun induced fluorescence relationship in a Mediterranean grassland
Julitta T.;Rossini M.;Migliavacca M.
2018
Abstract
Sun induced fluorescence at 760 nm (F 760 ) has shown to provide a valid approach to quantify gross primary production (GPP) at various scales, however the relationship between GPP and F 760 is influenced by the escape probability of fluorescence (Fesc), a variable which is not still fully understood. Combining radiative transfer modelling approaches, by means of the SCOPE model, and a data driven methodology based on variable selection methods we identify the predictors of Fesc, focusing on the effect of functional and structural traits. We show that Fesc is mainly predicted by structural variables such as fraction of grasses and near infrared reflectance. Building on the analysis of the predictor of Fesc, LUE p and LUE f we present a semi-empirical model formulation based only on optical data that significantly improves the GPP prediction.
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