## 1 Introduction  
Plant photosynthesis provides a wealth of ecosystem services that are essential for most life on Earth. Gross primary productivity (GPP), the largest planetary carbon dioxide (CO\({}_{2}\)) flux, is defined as carbon fixation by terrestrial plants via photosynthesis. Terrestrial GPP is the major driver of land carbon sequestration and may substantially affect the future climate trajectory [1]. As a result, spatially and temporally explicit estimation of GPP has important implications for understanding ecosystem functions, carbon cycling, and feedbacks to the climate. A variety of methods have been developed for the estimation of global GPP, and they are mainly divided into three categories [2]: (1)Satellite-based production efficiency models [3; 4; 5; 6], which combine satellite measurements of vegetation and meteorological variables through a modeling approach; (2) process-based models [7; 8; 9; 10], which simulate photosynthesis based on physiological and biogeochemical processes; and (3) data-driven models [11; 12; 13; 14], which upscale the GPP estimates from eddy covariance (EC) towers combining machine learning techniques and a suite of gridded ecosystem variables. However, these methods led to a wide range of global GPP estimates (e.g., 98 to 177 Pg C yr\({}^{-1}\)) [15; 16]. The large discrepancies in current global GPP estimates indicate that other independent data are needed for benchmarking these models. Over the last decade, a newly emerging satellite retrieval, solar-induced chlorophyll fluorescence (SIF), has opened a new perspective to directly measure plant photosynthetic activity from space globally [17; 18; 19; 20]. SIF, a 'glow' of plants under sunlight, is an energy flux emitted by plant chlorophyll molecules with wavelengths ranging from 600 to 800 nm [21]. Unlike satellite-derived vegetation indices, which are more indicative of vegetation 'greenness' [22], SIF is considered as an indicator for the functional status of actual plant photosynthesis [23]. Great advancements have been made in the interpretation of SIF based on modeling approaches and hyperspectral measurements. Model-based studies mainly illuminate what drives the simulated SIF signals and their relation with photosynthesis at the leaf or canopy scales [24; 25; 26; 27]. The ground-based and airborne hyperspectral measurements further reveal the tight relationship between SIF and photosynthesis at the ecosystem scale [28; 29; 26; 2; 30]. The global SIF observations from satellites have become available in recent years, which has led to tremendous developments in monitoring terrestrial photosynthesis (i.e., GPP) at various spatial and temporal scales. The main satellite platforms/instruments providing SIF observations to the scientific community include Greenhouse Gases Observing SATellite (GOSAT) [20; 31], Global Ozone Monitoring Experiment-2 (GOME-2) [18], SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) [32], and the Orbiting Carbon Observatory-2 (OCO-2) [17]. Using SIF data from GOSAT and/or GOME-2 and gridded GPP data, several studies reported a strong linear relationship between SIF and GPP, suggesting that SIF is a promising proxy for GPP at large scales [19; 20; 33; 34; 35; 36; 37; 38]. However, these satellite-derived SIF products have coarse spatial resolutions (e.g., GOME-2: 40 \(\times\) 80 km\({}^{2}\); GOSAT: 10 km diameter; SCIAMACHY: 30 \(\times\) 240 km\({}^{2}\)) and are insufficient for finer-scale or ecosystem-level analyses [39]. Currently, this problem can be partly addressed by the recent availability of finer-resolution SIF products from OCO-2 [17]. The OCO-2 SIF data, which have much smaller footprints (i.e., 1.3 \(\times\) 2.25 km), allow us to link satellite-derived SIF to flux tower-based GPP at the ecosystem scale [17]. Based on GPP from EC flux towers and SIF from OCO-2, several recent studies found strong SIF-GPP relationships for a variety of biomes [39; 40; 41; 42; 43].