Photosynthesis is a major process in controlling the global carbon cycle. Solar-induced chlorophyll fluorescence (SIF), detected by ground and satellite observations, has been extensively used to estimate the Gross Primary Production (GPP) at the ecosystem scale. There are only a few simulation models to calculate GPP from SIF based on the physiology of photosynthesis; however, they suffer from large uncertainty in parameterization mainly due to a lack of field-based observation.
In the modeling approach, the SCOPE model can simulate GPP from a correlation between maximum carboxylation rate (Vcmax) and SIF, which is obtained by inversion approaches by the SCOPE model. However, there are still theoretical gaps in SIF and photosynthesis and the use of SIF in the physiological models is still halfway and challenging. To reduce the uncertainty for modeling, we focus on the three calculation processes of GPP from SIF; the expansion factor of observed near-infrared SIF to broad-band SIF, the fraction of emissions from photosystem II in total SIF, and the escape ratio to observation direction from the canopy and investigate these three uncertain values in the relationship between GPP and observed SIF and developed the semi-analytical relationship between them.
In this study, we investigated these three uncertain values in the GPP and the observed SIF relationship and explored their semi-analytical relationship. The model parameter to bridge the knowledge gap between SIF measurements and GPP was calibrated by the ground-based data and remote sensing index. We performed the study in a cool-temperate deciduous forest in Takayama, central Japan (AsiaFlux: JP-TKY) as a first case study site because of a rich ground-based observation including tree census, physiological, optical measurements, near-surface remote sensing measurements including SIF, and eddy covariance fluxes to derive GPP.
As a result, the estimated GPP from our approach exhibits a better seasonal trend to the observed GPP than that of a general sun/shade canopy photosynthesis model.
Our model employed a physiological approach in estimating GPP. In future studies, investigating other ecosystem types is necessary to evaluate the accuracy and seasonal behavior of semi-analytical parameters.