## 1 Introduction  
Terrestrial ecosystems can absorb up to 33% of total anthropogenic carbon emissions (from both fossil fuel combustion and land use), contributing to the reduction of atmospheric CO\({}_{2}\) levels [1]. Gross primary production (GPP) refers to the amount of CO\({}_{2}\) fixed by terrestrial vegetation per unit time through the reduction of CO\({}_{2}\) into organic compoundsvia photosynthesis, representing the primary pathway of carbon input into ecosystems and the fundamental mechanism underlying terrestrial carbon sequestration [2; 3]. Accurately characterizing photosynthesis (i.e., GPP) at spatial and temporal scales provides crucial information on the timing, location, and amount of carbon absorbed [4; 5]. This is a critical component of terrestrial carbon budgets. Changes in vegetation structure, growing season, and climate-responsive functions across ecosystems indicate that GPP is undergoing global changes [6]. A precise understanding of GPP is fundamental for comprehensively evaluating the role of terrestrial ecosystems in the global carbon cycle and climate change, yet global GPP remains difficult to quantify accurately. GPP estimation primarily relies on eddy covariance (EC) flux observations and greenness-based remote sensing models. EC flux towers are considered one of the most accurate methods for quantifying canopy-scale biomass productivity [7; 8; 9]. By measuring the net ecosystem exchange (NEE) of CO\({}_{2}\) between vegetation and the atmosphere, EC reflects the overall carbon exchange processes within the flux footprint, thereby enhancing our understanding of the terrestrial carbon cycle [10; 11]. However, due to the limited number of observation sites and their uneven spatial distribution, the representativeness and coverage of EC flux towers at the global scale are constrained. Greenness models based on remote sensing vegetation indices (e.g., NDVI) serve as an important approach for GPP estimation and have been widely applied in monitoring large-scale carbon fluxes [12; 13; 14]. These models estimate potential photosynthetic activity based on the correlation between vegetation indices and photosynthetic capacity, but they are limited in capturing instantaneous photosynthesis or the dynamic nature of actual GPP [15; 16]. Moreover, GPP estimations based on light use efficiency (LUE) models are highly dependent on the accuracy of meteorological input data, and uncertainties in model structure and parameterization further limit their applicability in complex ecosystems [17]. In recent years, solar-induced chlorophyll fluorescence (SIF) has gained increasing attention as a physiological indicator of GPP [18]. Unlike reflectance-based vegetation indices, SIF originates directly from the photosynthetic process and can more accurately reflect plant photosynthetic activity [19]. Therefore, SIF is regarded as a more sensitive indicator of vegetation's carbon uptake capacity [20]. With the advancement of satellite remote sensing technologies, large-scale retrieval of SIF has become increasingly feasible. Studies have shown that SIF can be integrated into land surface models to improve GPP estimation, offering a novel pathway for remote-sensing-based GPP monitoring. Early studies using low-spatial-resolution satellite SIF data (tens of kilometers) revealed a strong linear relationship between SIF and GPP [21]. The emergence of high-resolution satellite platforms, such as NASA's Orbiting Carbon Observatory-2 (OCO-2) with a spatial resolution of 1.2 \(\times\) 2 km, has made it possible to match satellite-derived SIF data with ground-based EC observations of GPP [22]. Research has demonstrated a high degree of consistency between SIF and GPP across diurnal, seasonal, and interannual timescales, supporting the stability and scalability of SIF as an indicator of GPP [23]. To address the scale mismatch between satellite remote sensing and ground-based observations, an increasing number of studies have focused on the relationship between SIF and GPP at the canopy scale.