## 4 Discussion  
### The Representativeness of Sites and Results  
In this study, the flux observed or derived data of eight sites were used for examining the performances of MODIS-GPP and ET products. The eight sites of ChinaFLUX were selected to represent major ecosystem and climate types in China [20]. Four forests sites (CBS, DHS, QYZ and XSBN) locate in the eastern part of China and they are affected by monsoon climate. Among them, CBS, QYZ and DHS are distributed along the North-South Transect of Eastern China. Three grasslands sites (XLGL, HB and DX) spanned from Daxinganling Mountain Range in the northeast to the Tibetan Plateau in the southwest along the Temperate-Alpine Rangeland Transect. CBS and XLGL sites are distributed along the North East Chinese Transect, which is a bridge between forests transect and grasslands transect. A cropland site (YC) is located in the North China Plain in which the dominant land cover is irrigated croplands of double-cropping winter wheat and summer maize. All eight sites are critical in regulating climatic change on the Eurasian continent and the selection of all sites is a trade-off between micrometeorological criteria and ecological considerations [20]. Given the above-mentioned sites representativeness, the flux observed data of eight sites may well represent the net ecosystem exchanges of CO\({}_{2}\), H\({}_{2}\)O and heat in major ecosystems.  
Ideally, GPP validating sites should cover as many as land cover types and climate regimes as possible. However, limited available filed dataset generally cannot meet the requirement of validation. In this study, the flux towers are located across China from 20\({}^{\circ}\)N to 45\({}^{\circ}\)N and represent five land cover types: mixed forest, evergreen needleleaf forests, evergreen broadleaf forests, grasslands and croplands. This study shows MODIS-GPP underestimates flux derived GPP with \(R^{2}\) of 0.58, bias of -6.7 gC/m\({}^{2}\)/8-day and RMSE of 19.4 gC/m\({}^{2}\)/8-day for all sites. Compared with results of [PERSON] _et al._[17], we find that MODIS-GPP has a better performance in USA (\(R^{2}\) is about 0.70 and relative error is 19%) than in China. In their validation, the land cover types mainly include deciduous broadleaf forests, evergreen needleleaf forests, mixed forests, shrublands and grasslands across North America from the middle latitude to the polar region. The differences of land cover types in both regions can potentially affect the validation results. In addition, we find that the MODIS-GPP accuracy for evergreen broadleaf forests (Figure 2b,d) is the worst among all land cover types, suggesting that the performances in evergreen broadleaf forests have yet to be further optimized.  
[PERSON] _et al._[15] indicated that bias significantly increased by 24.1%, RMSE also slightly increased, but the correlation coefficients of the measured ET with estimated ET decreased to 0.58 (\(R^{2}\) is about 0.34) across 46 towers, when they utilized the DAO meteorological data to run the improved ET algorithm in America. Their validated results are close to our results that present that MODIS-ET usually overestimates the observed ET with \(R^{2}\) of 0.36, bias of 6 mm/8-day and RMSE of 11 mm/8-day for all eight sites.