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Remote measurement of solar-induced chlorophyll fluorescence (SIF) has opened a new perspective to assess plant actual photosynthesis at larger, ecologically relevant scales. Several recent researches have demonstrated the strong link between satellite based SIF and gross primary production (GPP) at large scales. However, understanding the underling mechanisms between SIF and GPP remains challenging before SIF used as a robust constraint for estimating GPP. In this study, we used a combination of remotely sensed data together with model analysis to assess the impact factors that determining the SIF-GPP relationship. We found that the Global Ozone Monitoring Experiment–2 (GOME-2) SIF is spatially corresponded to the Moderate Resolution Imaging Spectroradiometer (MODIS) product for GPP. We also noticed that the SIF-GPP relationship was ecosystem-specific and influenced by land surface temperature. The former is due to some structural and physiological characteristics related to each ecosystem and the latter can be attributed to the biochemical process influenced by temperature conditions. Our analysis using Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model also indicated the SIF-GPP relationship was complex and affected by some factors like shortwave irradiance, chlorophyll content and LAI. As SIF and GPP are influenced by a combination of several factors under natural conditions, we concluded that the simply linear regression relationship between SIF and GPP may be applicable only for some certain conditions and the their relationship may not be linear over natural conditions. Based on our analysis, we then suggested that dedicated strategies were required to compensate for the factors affecting SIF-GPP relationship before using SIF to estimate global GPP. The findings of this study contributes to a better understanding of the information inherent in remotely sensed SIF and its functional relationship to GPP.

Analyzing the Relationship between Solar-induced Chlorophyll Fluorescence and Gross Primary Production using Remotely Sensed Data and Model Simulation