## 1 Introduction  
Earth's climate has changed dramatically in the past century and will keep changing during the next few centuries. The IPPC AR6 report revealed that the global annual near-surface temperature has been rising steadily from the latter part of the 19 th century and places the year 2020 as one of the three warmest years on record [1]. Such changes have exerted substantial effects on terrestrial ecosystem processes and functions [2, 3], whereas human-induced activities, especially greenhouse gases emitted from fossil fuel combustion,contributed a lot to this warming [4; 5]. Fortunately, as an important part of terrestrial ecosystems, forests make up 30% of the Earth's land surface, which plays a crucial role in regulating atmospheric CO\({}_{2}\) concentrations under the influence of both photosynthesis and respiration [6; 7; 8]. Essentially, the carbon sequestration potential of terrestrial forests is jointly affected by climate change and human-induced perturbation through land-use changes including deforestation and afforestation [9; 10; 11]. It is therefore imperative to accurately evaluate the dynamics of terrestrial forest carbon cycles in order to predict the feedback on Earth's climate system. With the development of the eddy covariance-based flux measurements (EC) in recent decades, the ongoing land-atmosphere carbon and water exchanges can be directly observed [12]. Currently, over 900 EC-based flux towers on the Earth are operated in the long run and jointly form the global network FLUXNET [13]. These sites are located in various ecosystems such as tropical and subtropical forests, temperate conifer and broadleaf (evergreen and deciduous) forests, croplands, wetlands, grasslands, and tundra. Forest flux sites occupy over a half of theFLUXNET2015 dataset. These EC-based carbon flux measurements (NEE) can be further separated into gross primary production (GPP) and ecosystem respiration (R\({}_{\rm e}\)) through the daytime-based [14] approach and the nighttime-based [15] partitioning method. Nevertheless, these tower-based fluxes were generally observed at site-level with small areas (<1 km\({}^{2}\)), and long-term financial support is necessary to construct and maintain these instruments [16; 17]. In order to quantify the spatial and temporal patterns of terrestrial GPP across large areas, these spatially sparse flux measurements need to be upscaled to spatially continuous estimates. Various methods, including the satellite-based vegetation indices or leaf area index and process-based and light-use efficiency GPP models, were developed and compared to evaluate the spatio-temporal dynamics of vegetation productivity [18; 19]. Despite that, large uncertainties still exist in these GPP estimates, owing to the model structure and different input data sets such as the fractional photosynthetically active radiation (FPAR) product, the potential error contributions from land cover classification and maximum light use efficiency (\(\epsilon_{\rm max}\)) [20; 21]. In recent years, satellite-based sun-induced vegetation chlorophyll fluorescence (SIF) observations, like the space-borne SIF retrievals of GOSAT [22], GOME-2 [23], and OCO-2 [24], have been recognized as a new proxy for vegetation GPP over various terrestrial ecosystems. Because of its inherent connection with photosynthesis, as well as the simple and robust relationship between SIF signals and GPP observations [25; 26; 27], these SIF products are crucial to examining the dynamics of ecosystems and responses to climatic change. Even so, the tremendous applicable values were severely constrained by such spatially sparse SIF signals from site to regional or global levels. A high-resolution, spatially contiguous global SIF product from space-borne SIF retrievals can fulfill the need for repeatedly and precisely quantifying vegetation productivity over different time and space scales [28; 29], which can bridge the gaps between site-level EC-based flux towers and satellite-driven SIF data sets.