## 1 Introduction  
Gross primary production (GPP) is the total amount of carbon fixed by vegetation through photosynthesis, contributing the largest global carbon flux and driving ecosystem functions [1; 2]. The carbon cycle of terrestrial ecosystems is complicated and the uncertainty of terrestrial ecosystem flux remains high, resulting in the so-called \"mystery of missing carbon\" [3; 4]. This means that there is an imbalance between carbon sink and carbon sources [5]. GPP is a key index that can be used to estimate the carbon flux of terrestrial ecosystems [6; 7]. Therefore, the accurate estimation of GPP is important for studies on the carbon cycle and climate change. There are four methods commonly used to estimate GPP: (1) the eddy covariance (EC) technique [8], (2) the light use efficiency (LUE) model [9], (3) the process-based model [10], and (4) sun-induced chlorophyll fluorescence (SIF) [11; 12; 13; 14]. The EC method is considered the most accurate of the four methods [15; 16; 17]. However, it requires the establishment of flux towers. At present, the distribution of flux towers is uneven and sparse, making it impossible to obtain GPP globally with a high accuracy. The LUE model is widely used to estimate GPP. The LUE approach can be expressed as the product of PAR (the photosynthetically active radiation received by the plant canopy), fPAR (the proportion of PAR absorbed by plant canopies), and LUE\({}_{\text{P}}\) (light use efficiency). MODIS GPP products (e.g., MOD17A2, MYD17A2, MOD17A2H, and MYD17A2H) are estimated using the LUE model [18]. However, the LUE model depends on multiple types of ground parameters. Therefore, the accuracy of each parameter will affect the accuracy of the estimated GPP [19; 20; 21]. One of the process models commonly used is the soil-canopy observation of photosynthesis and energy (SCOPE) balance model. The SCOPE model is a one-dimensional vertical model. It is assumed that the canopy is homogeneous in the horizontal and vertical directions [22]. In addition, the photosynthesis, stomatal adjustment, and atmospheric disturbance module parameters need to be set with reference to the measured data. Setting a large number of parameters brings great uncertainty to the estimation of GPP. In recent years, SIF has brought new prospects for estimating GPP and monitoring vegetation growth states [11; 12; 23; 24]. SIF is a by-product of photosynthesis [25]. Therefore, compared with the traditional vegetation index, SIF can be used to reflect the growth state of vegetation and helps estimate the GPP more accurately [26]. Many studies have investigated performances of estimating GPP using SIF products derived from greenhouse gases observing satellite (GOSAT) and global ozone monitoring experiment-2 (GOME-2) [27]. Because of the low spatial resolution of GOSAT and GOME-2 (GOME-2: 40 \(\times\) 80 km; GOSAT: 10 km diameter), it is impossible to estimate GPP at an ecosystem scale [13; 28]. With the launch of orbiting carbon observatory-2 (OCO-2), which provides SIF products with a fine spatial resolution (1.3 \(\times\) 2.2 km), the spatial resolution of estimated GPP has been significantly improved [13]. However, the footprint of OCO-2 is very sparse and cannot match the flux tower well, which generates huge challenges in estimating the global GPP using OCO-2 SIF [19; 23]. To solve this problem, [PERSON] and [PERSON] have developed a new SIF product based on OCO-2 SIF and meteorological data, named 'GOSIF', which has a time resolution of eight days and a spatial resolution of 0.05\({}^{\circ}\)[18]. The data of OCO-2 SIF, MODIS GPP, and GOSIF all have fine resolutions and have the potential to estimate GPP at an ecological scale. However, many results have demonstrated that MODIS GPP (MOD17A2) underestimates vegetation GPP, especially in Africa [29]. Other results have shown that MOD17A2 has a limited capacity for representing the temporal and spatial variation of annual GPP in croplands [8]. Furthermore, only a few studies have validated MODIS GPP (MYD17A2H) with in-situ GPP measurements.