## 5 Conclusions  
In this study, we methodically evaluated the eight-day, seasonal and annual MODIS GPP using EC flux measurements at eight sites in five various biome types across China from 2003 to 2005. The sensitivity of MOD17A2 algorithm to meteorological data and LAI/FPAR products were examined by introducing site meteorological measurements and improved GLASS LAI products. We also assessed the potential error contributions from land cover and \(\epsilon_{\max}\). Each of these validation steps would help to isolate and identify sources of error. The main conclusions can be summarized as follow:1. The standard MOD17A2 product (_i.e._, MOD_NCEP GPP) performed better at estimating the annual GPP (R\({}^{2}\) = 0.76) and agreed well with the tower GPP during the autumn (R\({}^{2}\) = 0.80) and winter (R\({}^{2}\) = 0.92). However, the effectiveness of the MOD_NCEP GPP algorithm for estimating GPP over eight days was poor (R\({}^{2}\) = 0.55) and even worse during the summer (R\({}^{2}\) = 0.46). In addition, the MOD_NCEP GPP algorithm was ineffective when estimating the annual GPP for mixed forests, evergreen needleleaf forests and cropland.
2. Replacing the NCEP-DOE Reanalysis II meteorology data with the site meteorology data (_i.e._, MOD_Tower GPP) only slightly improved the correlation with the tower GPP over eight days (R\({}^{2}\) = 0.56). However, substantial improvements in estimating the tower GPP over eight days (R\({}^{2}\) = 0.65) and during the summer (R\({}^{2}\) = 0.52) were achieved by substituting GLASS_FPAR for MODIS_FPAR (_i.e._, NCEP_GLASS GPP). For cropland, the GPP estimates were more accurate after the FPAR data were improved. When the meteorology inputs and FPAR data were simultaneously replaced with improved data (_i.e._, Tower_GLASS GPP), the effectiveness in estimating the tower GPP was improved significantly for mixed forests and evergreen needleleaf forests.
3. There are four potential error sources related to the inputs of the MOD17A2 algorithm: meteorology, LAI/FPAR, land cover and \(\varepsilon_{\max}\). The NCEP-DOE Reanalysis II meteorology data failed in estimating the tower measured VPD (R\({}^{2}\) = 0.31), and MODIS_FPAR underestimated the improved FPAR data at most sites. Although MCD12Q1 succeeded in classifying most of the sites correctly, the values of MOD17A2 \(\varepsilon_{\max}\) were much smaller than the optimized \(\varepsilon_{\max}\) values for all five biome types discussed in this paper.  
From above analysis, we suggest that the qualities of the meteorological data and LAI/FPAR products need to be improved and the BPLUT parameters should be adjusted to provide better GPP estimates using MOD17A2 for Chinese ecosystems. In future research, additional high-resolution LAI/FPAR and GPP products should be considered, such as MOD15A2H and MOD17A2H, which have been recently released.  
We are grateful for the availability of the tower flux data from ChinaFLUX. We also acknowledge the China Meteorological Administration (CMA), which provided the observational meteorological 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] analyzed the data; [PERSON] wrote the manuscript; and [PERSON] and [PERSON] corrected and modified the manuscript. The authors declare no conflict of interest.