## 1 Introduction  
Terrestrial gross primary production (GPP), which is the total amount of organic carbon fixed by green plants through photosynthesis at an ecosystem scale, determines the initial amount of energy and material entering the terrestrial ecosystem [1; 2]. Terrestrial ecosystems can partly mitigate global warming and offset increasing CO\({}_{2}\) emissions through GPP [3]. Therefore, accurately quantifying GPP is essential for assessing global climate variation and carbon cycles [4]. The eddy covariance (EC) technique is the most accurate approach to measure the net CO\({}_{2}\) exchange (NEE) between the atmosphere and the terrestrial ecosystem [5]. NEE then partitions into ecosystem respiration and GPP using different modeling methods. However, EC provides a limited carbon flux measurements scope, ranging from a hundred meters to several kilometers around the flux tower depending on the height of the tower, canopy characteristics, wind velocity, and homogeneity of the fetch [6; 7]. This limitation necessitates upscaling flux tower data to regional, continental, or global scales to reflect terrestrial carbon cycling [8]. Satellite remote sensing (RS) provides a potentially viable tool for upscaling efforts. This technology has played an increasing role in the estimation of GPP that can offset the restricted coverage of flux tower observations [9; 10]. A number of RS-based models have been developed to quantify the GPP of terrestrial ecosystems because they are relatively simple and efficient [11; 12; 13]. The prevalent RS-based GPP models can be categorized into four groups [14]: light use efficiency (LUE) models [15; 16], vegetation index (VI) driven models [17; 18], process-based models [13], and machine learning models [19; 20]. In the aforementioned models, LUE and VI-driven models have been most frequently used on account of their simple conceptual algorithm and practicality [21]. Therefore, only LUE and VI-driven models are discussed in this study. The foundation of LUE models is based on the LUE concept proposed by [PERSON] [22], which is determined as the ratio of GPP to absorbed photosynthetically active radiation (APAR). APAR is defined as the product of photosynthetically active radiation (PAR) and the fraction of absorbed photosynthetically active radiation (FAPAR). LUE is regulated under environmental stresses such as temperature stress and water availability [23]. Without environmental stress conditions, maximal LUE (\(\varepsilon_{\text{max}}\)) is specified for different biome types. Under environmental stresses, the \(\varepsilon_{\text{max}}\) is down-regulated by scalars (\(f\)), which varies from 0 to 1 and which represents the reduction of LUE relative to \(\varepsilon_{\text{max}}\) due to environmental stresses. The definitions of the scalars (\(f\)) are various in different LUE models [24]. Numerous LUE models have been proposed, including the Carnegie-Ames-Stanford approach (CASA) [1], the global production efficiency model (GLO-PEM) [25], the moderate resolution imaging spectroradiometer GPP algorithm (MODIS GPP) [10], the vegetation photosynthesis model (VPM) [16; 26], and the eddy covariancelight use efficiency (EC-LUE) model [12]. At present, the first operational, near-real-time GPP dataset, which is based on the MODIS GPP algorithm, is the MOD17 product at an eight-day interval [27]. The primary input data of the MOD17 product are the FAPAR product (MOD15) and meteorological data. Three collections of the MOD17 GPP product (5.0, 5.5, and 6.0) are currently freely available to users. The essential improvement of the latest collection 6.0 is focused on the spatial resolution of the FAPAR (MOD15A2H) and GPP (MOD17A2H) products from 1000 to 500 m compared with the previous collections [28]. Although the MOD17 GPP products have been widely used as the recognized standard global GPP products, some discrepancies between this product and insitu measured GPP remain [29; 30; 31; 32]. Among the problems are the uncertainties in FAPAR estimates [28; 33; 34].