1-km resolution is the MOD17A2 MODIS product [10; 11]. The MODIS GPP data correlated well with the GPP records from twelve flux towers across North America, indicating that MOD17A2 is a reliable data source [12]. Even so, there are still many potential error sources in MODIS GPP products arising from input data, the parameters describing the biophysical properties of vegetation and the algorithm itself [13; 14]. Firstly, the spatial resolution of meteorological reanalysis data is quite different from that of MODIS products, which introduces inaccuracies in estimating atmospheric conditions at a scale that matches heterogeneities in the land surface [15]. Secondly, estimation of the FPAR cannot be accurately achieved because the MODIS Leaf Area Index (LAI) is poorly correlated with ground measurements of the vegetation canopy [16]. Thirdly, the land cover classification provided by MODIS is not always accurate, and the misclassifications are quite common for similar land cover classes [17]. Finally, the use of a constant value attributed to the maximum value for light use efficiency (LUE) cannot be justified for the same biome type [18]. All sources of error mentioned above (meteorological data, FPAR product, land cover classification result and LUE value) require separate examination on an individual basis [19]. The analysis of errors arising from the MODIS GPP product is difficult because of inherent limitations in directly measuring GPP values. For the present, the eddy covariance (EC) technique is the frequently-used approach [20; 21; 22]. By performing comparisons between MODIS GPP products and EC flux-derived GPP measurements, much validation work has been conducted to evaluate the accuracies of previous MOD17A2 products [23; 24]. GPP values from MOD17A2 were comprehensively compared with GPP measurements from flux towers at fifteen research sites across North America by [PERSON] et al. (2006) [25]. Their results showed that MOD17A2 GPP on average overestimated the site-based GPP by 20-30%. Moreover, the effectiveness of MOD17A2 GPP in Africa was also evaluated by using site meteorological measurements and flux-derived GPP at twelve flux tower sites. The results suggested that GPP values from MOD17A2 agreed with those from the flux towers in wet sites, but were much smaller in dry sites [26]. For MODIS GPP products, many validation efforts have promoted updates and continuous improvements to MOD17A2 [27]. Note that the latest MOD17A2H product has essential advantages over the previous version (MOD17A2) because the resolution increased for the first time from 1000 m to 500 m. The major feature of MOD17A2H is its innovation in two sources of input data: meteorological data and FPAR products [28]. Firstly, GMAO (Global Modeling and Assimilation Office) reanalysis data substitutes DAO (Data Assimilation Office) reanalysis data to estimate the actual values of meteorological elements. Secondly, the latest MODIS FPAR product, MOD15A2H, substitutes the previous version, MOD15A2, to estimate the actual values of FPAR and to enable improvement in spatial resolution from 1000 m to 500 m.  
It was uncertain whether the improvements of MOD17A2H over MO17A2 could estimate GPP more accurately, which will attract great attention from researchers around the world. In this context, the evaluation of MOD17A2H GPP becomes an essential work. Currently, FLUXNET has released the latest global flux dataset, FLUXNET2015 [29]. It is clear that the latest EC flux dataset can provide a more accurate reference standard for the validation of MOD17A2H GPP. Therefore, it is of great significance to verify the latest MOD17A2H product using the latest FLUXNET2015 dataset. In this study, we evaluated MOD17A2H GPP using the FLUXNET2015 dataset at eighteen sites in six biome types around the world (Table 1). Not only the eight-day and annual MODIS GPP values were systematically verified against the flux-derived GPP measurements, but also the role of each input variable was carefully analyzed.