## 1 Introduction  
Gross primary productivity (GPP) is the total amount of organic matter produced by terrestrial plants through photosynthetic uptake of atmospheric CO\({}_{2}\)[1; 2]. It plays an important role in the global carbon cycle and has a significant impact on mitigating climate change [3; 4]. Enhancing GPP is an important way to achieve sustainable development [5], and consequently, the accurate estimation of GPP is of vital importance in the current changing world [6]. At present, GPP has been widely quantified by different methods, e.g., estimations from the machine-learning upscaling approach [7; 8], light-use-efficiency (LUE) models [9; 10], and ecosystem processed-based models [11; 12; 13]. However, due to the complex models and many influencing factors, the quantification of GPP still shows considerable uncertainties [14; 15]. Recent advances in satellite-based direct proxies for GPP, such as near-infrared reflectance (NIRv), provide new opportunities for the accurate estimation of GPP on a large scale [16; 17]. As the product of the normalized different vegetation index (NDVI) and NIR reflectance [16], NIRv is easily acquired. Moreover, NIRv can effectively isolate vegetation signals and reduce the effect of soil noise on the vegetation spectrum [16]. Previous studies have shown that NIRv has a strong correlation with GPP at different time scales [17; 18], and NIRv could be used for global GPP estimation with long time series [19; 20; 21]. Compared with traditional models of GPP quantification, using NIRv as a proxy to estimate GPP is simpler, and it does not need to input environmental information, such as temperature, precipitation, etc. [21; 22]. Notably, as one of the most important processes of global environmental change, land cover (LC) has important impacts on GPP estimation [23]. The effects of land cover on GPP can be discussed from two aspects, namely, the dynamic land cover change (LCC) and the discrepancies in multisource products (DMP) [24; 25]. On the one hand, LCC is the most important driving force of land surface processes, and it has an especially profound impact on the global carbon cycle [26]. With changes in time, the land cover types (LCTs) of pixels may change, which would lead to uncertainties in GPP estimation. In theory, GPP quantification by NIRv would be highly dependent on LCTs because of NIRv's simple parameters (linear parameters for different land cover) [22; 26]. Taking the LC of a certain year as the predictive parameter, [PERSON] et al. used NIRv to dynamically estimate GPP based on different land cover types, and the results were satisfactory [22]. However, the impact of LC on the relationship between GPP and NIRv has not been systematically explored. In previous studies focusing on the estimation of GPP by NIRv, little attention has been paid to the parameters of land cover and its changes. On the other hand, the discrepancies in multisource products would cause differences in the expression of land cover heterogeneity [27], which would further influence the GPP estimation based on NIRv. Accurately capturing the GPP changes caused by LCC requires high-quality land cover data [26]. Previous studies usually used the Moderate-resolution Imaging Spectroradiometer (MODIS) land cover product classified by the International Geosphere Biosphere Programme (IGBP) (MCD12C1) with long time series and various land cover types. This product, however, has the problem of relatively rough resolution. The study of [PERSON] et al. pointed out that the accuracy of the quantization of GPP could be improved by using high-resolution land cover maps [28]. The European Space Agency (ESA) Climate Change Initiative (CCI) Maps (ESACCI) show a high accuracy and resolution [29]. In theory, due to the great differences between MCD12C1 and EASCCI, the GPP estimations based on these two products would also be different [27; 30]. However, for the emerging method of estimating GPP by NIRv, few studies systematically pay attention to the impact of land cover products.