Abstract
The uncertainty of carbon fluxes of the terrestrial ecosystem is the highest among all flux components, calling for more accurate and efficient means to monitor land sinks. Gross primary productivity (GPP) is a key index to estimate the terrestrial ecosystem carbon flux, which describes the total amount of organic carbon fixed by green plants through photosynthesis. In recent years, the solar-induced chlorophyll fluorescence (SIF), which is a probe for vegetation photosynthesis and can quickly reflect the state of vegetation growth, emerges as a novel and promising proxy to estimate GPP. The launch of Orbiting Carbon Observatory 2 (OCO-2) further makes it possible to estimate GPP at a finer spatial resolution compared with Greenhouse Gases Observing Satellite (GOSAT), Global Ozone Monitoring Experiment-2 (GOME-2) and SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). However, whether the relationship between GPP and SIF is linear or non-linear has always been controversial. In this research, we proposed a new model to estimate GPP using SIF and the atmospheric CO2 concentration from OCO-2 as critical driven factors simultaneously (SIF-CO2-GPP model). Evidences from all sites show that the introduction of the atmospheric CO2 concentration improves accuracies of estimated GPP. Compared with the SIF-CO2-GPP linear model, we found the SIF-GPP model overestimated GPP in summer and autumn but underestimated it in spring and winter. A series of simulation experiments based on SCOPE (Soil-Canopy Observation of Photosynthesis and Energy) was carried out to figure out the possible mechanism of improved estimates of GPP due to the introduction of atmospheric CO2 concentrations. These experiments also demonstrate that there could be a non-linear relationship between SIF and GPP at half an hour timescale. Moreover, such relationships vary with CO2 concentration. As OCO-2 is capable of providing SIF and XCO2 products with identical spatial and temporal scales, the SIF-CO2-GPP linear model would be implemented conveniently to monitor GPP using remotely sensed data. With the help of OCO-3 and its successors, the proposed SIF-CO2-GPP linear model would play a significant role in monitoring GPP accurately in large geographical extents.
Highlights
The carbon flux of terrestrial ecosystems, with the greatest uncertainty, plays an important role in the carbon cycle [1]
Previous studies have demonstrated that solar-induced chlorophyll fluorescence (SIF)-estimated Gross primary productivity (GPP) is more accurate than that of light use efficiency (LUE) model mainly because SIF is a byproduct of photosynthesis
A plausible reason to explain such results would be that 771 nm is farther away from the peak of SIF spectrum than 757 nm
Summary
The carbon flux of terrestrial ecosystems, with the greatest uncertainty, plays an important role in the carbon cycle [1]. Gross primary productivity (GPP) is an important indicator for estimating terrestrial carbon fluxes. LUEP is generally determined as a fixed parameter in the LUE model according to specific vegetation types, resulting in large uncertainties of estimated GPP. Previous studies have demonstrated that SIF-estimated GPP is more accurate than that of LUE model mainly because SIF is a byproduct of photosynthesis. Along with the launch of the Orbiting Carbon Observatory 2 (OCO-2) in 2014, a new SIF product is available to estimate GPP at a finer spatial resolution (1.3 × 2.25 km), comparing with previous products from GOME-2 (40 × 40 km) and GOSAT (10km diameter) [22]. Current studies on estimating GPP using satellite-derived SIF products ignored the possible effect of atmospheric CO2 concentrations on modeling GPP.
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