Global modeling diurnal gross primary production from OCO-3 solar-induced chlorophyll fluorescence

Abstract

Photosynthesis plays a crucial role in regulating the global carbon cycle and mitigating climate change. The diurnal variation in photosynthesis provides key information on the responses of ecosystems to environmental drivers, but there is a critical gap in the large-scale estimation of diurnal photosynthesis. In the last decade, satellite estimates of solar-induced chlorophyll fluorescence (SIF) have been found to mimic the seasonality of photosynthesis. Recently, the deployment of the Orbiting Carbon Observatory-3 (OCO-3) on the International Space Station has provided the opportunity to retrieve SIF at different times of the day. Here we utilized OCO-3 measurements to estimate and analyze diurnal cycles of SIF and gross primary production (GPP) at the global scale. We first mitigated the sun-sensor geometry effects on nadir-mode OCO-3 SIF (SIFnadir) at the sub-diurnal scale (hourly) by deriving the total canopy SIF emission (SIFtotal) using radiative transfer theory. Next, we generated the spatially and temporally continuous hourly SIFnadir and SIFtotal using artificial neural networks under clear-sky conditions, whose extrapolation ability was evaluated using the data from independent years. Compared with SIFnadir, the diurnal relationship between clear-sky SIFtotal and GPP from 38 homogeneous flux sites had smaller variations in the slope (the coefficient of variation was 0.07 vs 0.19). In addition, a correction to account for the bias between clear-sky and overcast conditions was used to estimate all-sky GPP from clear-sky SIFtotal and the resulting GPP was strongly correlated with tower GPP (R2 = 0.75; RMSE = 3.53 μmol/m2/s). Our results demonstrated that the new OCO-3 SIF trained GPP product (GPPSIF) was able to depict the diurnal pattern of photosynthesis globally, capturing also the physiologically hysteresis or afternoon depression of photosynthesis. By doing so, hourly GPPSIF has the potential to improve the modeling of terrestrial photosynthesis and the projection of the global carbon cycle under climate change.