## 1 Introduction  
Climate change is causing permafrost melting [1], shrub cover expansion, growing season lengthening, and consequently, carbon flux changes in the Arctic [2]. Furthermore, the carbon cycle is also influenced by changes in vegetation phenology [3]. GPP, which is considered the biggest carbon flux of terrestrial ecosystems [4], not only plays a vital role in offsetting the concentration of greenhouse gases and mitigating global warming to a certain extent [5] but also builds a bridge between terrestrial and air carbon. In the context of the Arctic, the rate of climate warming is almost twice the global average, a phenomenon known as Arctic amplification [6, 7, 8, 9, 10, 2]. Therefore, quantifying the spatial, seasonal (phenological), and inter-annual variations of Arctic GPP is critical for comprehending the carbon cycle and its feedback to climate warming. Quantifying global or local GPP has received a great deal of attention in recent studies. Utilizing satellite-based near-infrared reflectance (NIRv) as the proxy of GPP and the revised light-use-efficiency model (i.e., EC-LUE model), [PERSON] et al. [11] and [PERSON] et al. [12] explored the global spatial patterns of GPP with a spatial resolution of 0.05 degrees. However, the annual average estimates of GPP were not consistent during the same period. [PERSON] et al. [11] reported a range of 128.3 \(\pm\) 4.0 Pg C year\({}^{-1}\) while [PERSON] et al. [12] reported a range of 106.2 \(\pm\) 2.9 Pg C yr\({}^{-1}\). Some studies have detected the GPP in the Arctic, but most paid attention to specific ecosystems (e.g., streams and moss communities) [13, 14] and few efforts [12, 15] have been devoted to investigating the specific situation of the Arctic GPP. Here, the MOD17A2H product was selected because it is one of the major official GPP products and has been most widely used in detecting the carbon cycle of terrestrial ecosystems [4; 16]. Additionally, its finer resolution (500 m) can reveal detailed GPP variations in the Arctic. Satellite products generally suffer from the uncertainty that results from complex data acquisition processes and limitations of retrieval algorithms. For this reason, different datasets lead to disparate results. MOD17 is based on the light-use-efficiency (LUE) concept, which is difficult to parameterize since it is influenced by land cover types, phenophases and different types of environmental stress. Furthermore, the maximal values of LUE are specified in the look-up tables for the same biota types, which might introduce uncertainties in GPP [17]. Although MOD17A2H, the v.6 product of MOD17, has overcome the limitation of the proposed year and filling method, the core issues caused by its algorithm still exist. In addition, MODIS (Moderate Resolution Imaging Spectroradiometer) products are inferred based on surface reflectance, which is only available when the relative accuracy of MODIS reflectance products can be determined [18]. Therefore, evaluating its performance is necessary before characterizing the spatiotemporal pattern of GPP. There have been many validation studies regarding the performance of MODIS GPP products at the global scale [19; 20; 21; 22] and their accuracies over different biomes (e.g., grassland and forest) have been quantified [23]. However, the validation pixels belonging to specific biomes are often combined together and there are few specialized studies that quantify the accuracy of MOD17A2H in the Arctic. In fact, the performance of the MODIS GPP algorithm shows reasonable variations with climate regions and factors [18; 19], species [24], and latitude [25]. Furthermore, the phenology (e.g., the peak timing of GPP) derived from satellite products is often mismatched in scale with in situ data [26]. Several studies have assessed the performance of phenological patterns of MOD17A2H in different regions or biomes [17; 27]. However, it is unclear whether MOD17A2H is suitable for the Arctic. Therefore, there is a pressing need to investigate the accuracy of MOD17A2H in the Arctic.