## 1 Introduction  
The gross primary production (GPP) of a terrestrial ecosystem is the maximum global carbon flux between the land surface and the atmosphere [1]. It refers to the amount of organic carbon fixed by green plants through photosynthesis in unit time and determines the initial substance and energy entering terrestrial ecosystems [2; 3]. Accurate and quantitative estimation of spatial and temporal variations of GPP is important for a clear understanding of ecosystem functions and carbon budgets in response to global change [4; 5]. Over the past decades, many approaches for terrestrial GPP estimations have been developed. Among them, the concept of light-use efficiency (LUE) is generally accepted for both its theoretical basis and practicality, as well as the advantage gained from connecting the eddy covariance (EC) observations of carbon fluxes and remotely sensed data [5]. Therefore, LUE-based models are widely used for terrestrial photosynthesis modeling [6]. In recent years, sun-induced chlorophyll fluorescence (SIF)-based GPP modelling is another practical and state-of-the-art method providing a new perspective for evaluating vegetation photosynthesis and simulating regional GPP due to its inherent connection with the photosynthetic process [7; 8; 9; 10; 11]. Since the first truly global SIF observations were derived from a hyper-spectrum obtained by the Japanese Greenhouse Gases Observing Satellite (GOSAT) mission launched in 2009 [12; 13], there have been multitudinous SIF retrievals carried out using other satellite devices [14; 15; 16; 17; 18]. [PERSON] et al. [14] showed that even moderate spectral resolution can be used to infer SIF using either a very broad Fraunhofer line at 866 nm (using Scanning Imaging Absorption Spectrometer for Atmospheric Chattorography, SCIAMACHY) or applying a larger spectral range from 715 to 780 nm using Global Ozone Monitoring Experiment-2 (GOME-2) [15]. Based on the methodology of [PERSON] et al. [15], a global monthly averaged time series of SIF (from January 2007 to June 2015) was then derived from GOME-2 spectral measurements [16]. Later, the differential optical absorption spectroscopy technique was used to exploit narrow spectral structures resulting from the filling-in of the Fraunhofer Fe I line for SCIAMACHY and GOME-2 [17]. However, there is generally a tradeoff between spectral resolution and spatiotemporal resolution [8]. The SIF retrieved from the Orbiting Carbon Observatory-2 (OCO-2) satellite, with smaller ground-pixels (\(1.3\times 2.25\) km\({}^{2}\)), can alleviate some of the drawbacks of current SIF observations and combine the advantages of both GOSAT and GOME-2 [8]. Variations in SIF provide more insight into plant physiological functioning at the canopy- and ecosystem scale compared to reflectance-based vegetation indices [19; 20]. Studies on SIF from leaf to canopy scales have exhibited moderate to strong linear correlations (R\({}^{2}\) = 0.45-0.80) with photosynthesis across all ecosystems [7; 11; 21; 22]. SIF can also capture the seasonal hysteresis of vegetation response temperature changes across boreal ecosystems throughout a growing season [19]. In addition, [PERSON] et al. [23] and [PERSON] et al. [24] suggested that SIF can capture the decreased GPP directly through radiative emission by the photosynthetic machinery in a drought-stressed forest even as the leaf area remained constant, confirming that the passive measurements of SIF can be used to track changes in physiological activity at large scales in the absence of changes in greenness or structure. Based on the various algorithms and their simulation results, there have been a lot of evaluations and developments reported. For LUE-based models, early studies paid attention to simulations using different input data and uncertainties generated from input data [25; 26; 27]. In recent years, some studies focused on the model structure. [PERSON] et al.