## 5 Conclusions  
In our study, we systematically assessed both eight-day and annual MOD17A2H GPP using the latest FLUXNET2015 dataset at eighteen sites in six different ecosystems across the globe during 2001-2014. The sensitivities of the MOD17A2H algorithm to the meteorology reanalysis dataset and FPAR product was evaluated by introducing site-based meteorological observations and an upgraded GLASS FPAR product. We further explored the potential error contributions from land cover classification and \(\varepsilon_{max}\). Our principal conclusions are summarized as follows:  
1. The effectiveness of the standard MOD17A2H product (i.e., MOD_GMAO GPP) for estimating annual GPP was poor (R\({}^{2}\) = 0.62) and even worse over eight days (R\({}^{2}\) = 0.52). Replacing the GMAO meteorology reanalysis dataset with the site-based meteorological observations (i.e., MOD_TowerGPP) failed to improve the correlations with the flux-derived eight-day GPP (R\({}^{2}\) = 0.52) and annual GPP (R\({}^{2}\) = 0.56). However, great improvements in estimating the flux-derived annual GPP (R\({}^{2}\) = 0.79) were gained by replacing MODIS FPAR with GLASS FPAR (i.e., GMAO_GLASS GPP). This may indicate that in the current version of the MODIS GPP product, the error from FPAR was larger than that from the meteorological data. When the meteorological dataset and FPAR product were replaced by upgraded data simultaneously (i.e., Tower_GLASS GPP), the accuracy in estimating the flux-derived eight-day GPP (R\({}^{2}\) = 0.65) was significantly improved.
2. There are four possible sources of error related to the input data of MOD17A2H algorithm: meteorological reanalysis dataset, FPAR product, land cover classification result and the \(\varepsilon_{max}\) value. The GMAO meteorology reanalysis dataset commonly overestimated the site-measured PAR, and MODIS FPAR underestimated the upgraded FPAR in most flux tower sites. In addition, MCD12Q1 exhibited frequent misclassification errors, and the MOD17A2H \(\varepsilon_{max}\) values were much smaller than the inferred \(\varepsilon_{max}\) values for all six ecosystems discussed in our study.  
From the above analyses, the latest MODIS GPP product, MOD17A2H, still has some defects in estimating the actual value of GPP. We suggest that FPAR and land cover classification products need to be improved, and \(\varepsilon_{max}\) should be adjusted in the next versions of datasets for GPP estimation.  
This work was financially supported by the National Natural Science Foundation of China (No. 41601044) andthe Special Fund for Basic Scientific Research of Central Colleges, China University of Geosciences, Wuhan (No. CUGI50631, CUGL170401). We are grateful for the availability of the eddy covariance flux data from FLUXNET. We also acknowledge the Global Modeling and Assimilation Office (GMAO), which provided the meteorological reanalysis datasets, and we would like to thank the Center for Global Change Data Processing and Analysis of Beijing Normal University for providing the GLASS LAI datasets.  
[PERSON] and [PERSON] designed the research. [PERSON] performed the experiments. [PERSON] and [PERSON] analyzed the data. [PERSON] and [PERSON] wrote the manuscript. [PERSON] and [PERSON] corrected and modified the manuscript.  
The authors declare no conflict of interest.