## 1 Introduction  
Gross primary production (GPP) is plant-driven carbon captures from atmosphere, and thus abundant organic carbon stores in the natural ecosystems [1; 2], which forms the basis of all biosphere functions [3]. After GPP, ecosystem respiration (Re) is the second main carbon flux between ecosystems and the atmosphere [4], whose two components, autotrophic and heterotrophic respiration (Ra and Rm), may be controlled in different ways by abiotic and biotic factor [5; 6]. For example, response of Ra and Rm to temperature may exhibit different temperature sensitivity (Q\({}_{10}\)) [7], which is the key parameter of the climate-carbon feedback, usually expressed as the percentage of Re increase for a 10 \({}^{\circ}\)C rise in temperature [8]. Climate-carbon feedback depends on whether Rm increases to warming are offset by corresponding increases in GPP [9]. This urges us to give a better understanding of the relative contribution of Ra and Rm components [10]. Recent studies have suggested that the contribution of Ra or Rm is approximately 0.5 [11; 12]. However, the average estimates generally mask considerable uncertainties arising from different ecosystems, estimating techniques and time scales [5]. Therefore, accurate quantification of GPP and Re, and, further, effectively partitioning the components of Re, is critical to evaluating potential influence of climate change, especially warming, on ecosystem carbon cycling. Alpine meadow ecosystems, with about 11.3 Pg of the soil carbon pool in China, is among the ecosystem with the highest soil carbon density (18.2 \(\mathrm{Kg}\)\(\mathrm{m}^{-2}\)) [13; 14]. In addition, it is one of the most sensitive ecosystems to climate change across the globe [15]. This ecosystem occurs on the Qinghai-Tibetan Plateau, a unique geography mostly situated at 3000 m above sea level, and covers the largest areas (approximately 1/5) of alpine grass ecosystems (2.5 \(\times\) 10\({}^{6}\) km\({}^{2}\)) in this Plateau [16]. Climatic prediction suggests a more intensive climate change, about twice of the average observed global warming rate (increase 0.2 \({}^{\circ}\)C per decade) [17], is ongoing on the Tibetan Plateau [15; 18]. With its widespread areas and more intense climate change, a clearer understanding of climate-carbon cycle processes on this special ecosystem is necessary for it to serve as a sensitive indicator of regional and global carbon cycles [19; 20]. Eddy covariance (EC) is a state-of-the-art method to measure net ecosystem CO\({}_{2}\) exchange (NEE) between atmosphere and terrestrial ecosystems [21; 22]. Further improvement of regional and global FLUXNET data benefits the needed GPP and Re estimations in EC-based NEE partitions [23; 24]. Indeed, EC-based carbon cycle studies increasingly focus on the alpine meadow ecosystem. However, their results are spatially heterogeneous even in evaluating the carbon budgets in this specific ecosystem, which has showed carbon sink [6; 14; 19; 25], carbon neutral [26], or carbon source [27]. Thus, accurate spatial extrapolation from site-specific footprints to regional and global scales is full of challenges. Satellite-based products provide another pathway to capture quasi-continuous observations of vegetation structure and the resultant GPP estimations across broad temporal and spatial scales [2; 28; 29; 30]. For example, six satellite-based GPP models, MODIS GPP version 5 [31] and 6 products (GPP_MOD5 and GPP_MOD) [32], revised GPP_MOD (GPP_MOD) [33], vegetation photosynthesis model (VPM) [28; 29], photosynthetic capacity model (PCM) [34], and the alpine vegetation model (AVM) [35], were widely used to estimate GPP at local, regional, and global scales [30; 36]. In general, key parameter inputs of these models, such as the maximum light use efficiency (LUE) [28; 29; 31; 32] or the conversion coefficient [34; 35], and the performance of these models are site-specific [30; 33; 37; 38; 39; 40].