## 1 Introduction  
Gross primary production (GPP), which is defined as the overall photosynthetic fixation of carbon by plants, is an important variable in studies of the carbon balance between the atmosphere and biosphere [1, 2]. GPP is also the basis for essential ecosystem services such as food, fiber, fuel and construction materials [3]. Thus, the quantification of GPP has become a topic of wide concern in global change studies [4, 5, 6, 7]. By using optical and near-infrared spectral wavelengths, GPP can be estimated from satellite remote sensing [8, 9]. In particular, the Moderate Resolution Imaging Spectroradiometer (MODIS) primary production products (MOD17A2) are the first regular, near-real-time GPP datasets for the repeated monitoring of global vegetation at a 1-km resolution every eight days [10]. [PERSON] _et al._ (2005) [11] had demonstrated that MODIS GPP fitted well with GPP derived from 12 flux towers over North America, indicating MOD17A2 were reliable products. However, for MODIS GPP products, there are still exist many potential sources of errors that arise from the input datasets, the parameters used to describe the biophysical behavior of vegetation, and the algorithm itself [12]. First, there is a large disparity between the spatial resolution of meteorological data and the resolution of MODIS products, which will provide inaccurate atmospheric conditions at scales consistent with land surface heterogeneities [13]. Second, the MODIS leaf area index (LAI) has a poor correlation with the ground measurements, which will lead to an erroneous estimation for fraction of photosynthetically active radiation (FPAR) [11]. Third, the accuracy of MODIS land cover classification is not very satisfactory, and most mistakes are between similar classes [14]. Finally, it does not conform to reality to assign a constant value of maximum light use efficiency (LUE) to the same biome type [5]. As a result, each of these error sources (_i.e._, meteorology, LAI/FPAR, land cover and LUE) requires a corresponding validation procedure and must be examined separately [15]. The error analysis of MODIS GPP products is a challenging task because of the difficulty in making direct measurements of GPP values. One widely used approach uses the eddy covariance (EC) technique, which measures the fluxes of carbon, water and energy between the atmosphere and land [16; 17]. A number of validation efforts have been established to evaluate the accuracy of MOD17A2 products that use time series comparisons between MODIS-based and EC flux tower-based GPP for one or more 1-km\({}^{2}\) cells centered on towers [18; 19]. [PERSON] _et al._ (2003) [20] evaluated the MODIS GPP products at two sites: a temperate forest site and a boreal forest site. Their results showed that, relative to the flux tower measurements, MODIS overestimated the GPP by 35% at the boreal forest site, but the MODIS estimates were comparable to the tower results for the temperate forest site. [PERSON] _et al._ (2006) [14] carried out a comprehensive evaluation of MOD17A2 by comparing MODIS-based GPP to flux tower-based GPP at 15 research sites in six different biome classes across North America. The authors reported that the MODIS GPP products overestimated the tower-based calculations by 20%-30% on average. They further found that the use of MODIS GPP products with DAO (NASA'S Data Assimilation Office) meteorology overestimated the annual GPP, whereas the use of tower-specific meteorology in the MODIS GPP calculations led to underestimates. The performance of MODIS GPP in Africa has also been evaluated using _in situ_ measurements of meteorology and flux tower GPP for 12 sites. The study indicated that MOD17A2 agreed well with the tower-based GPP for wet sites, whereas the estimates were too small for dry sites [21]. Based on EC flux data provided by the Chinese FLUX Observation and Research Network (ChinaFLUX), several studies have assessed the performances of MODIS GPP in Chinese different biome types [22; 23].