## 3 Results  
### Evaluation of Seasonal MODIS-Derived WUE  
Figure 1 shows the seasonal dynamics of WUE calculated based on eight-day GPP and ET estimated from MODIS data and eddy covariance data. Due to WUE having a similar seasonal pattern over different years, we calculated the annual mean values and standard deviations of eight-day WUE. For the deciduous forest (CBS) and grassland ecosystems, we only compared WUE in the growing season, because GPP in the non-growing season was assumed to be zero. WUE presents different seasonal patterns among eight ecosystems. Compared to WUE estimated from eddy covariance observations, WUE derived from MODIS GPP and ET product has different behaviors at these eight sites (Table 3). It underestimates WUE in four forest ecosystems and one cropland ecosystem with a bias from -0.36--2.28 g\(\cdot\)C\(\cdot\)kg\({}^{-1}\) H\({}_{2}\)O, while overestimating WUE in three grassland ecosystems with the bias from 0.26-1.11 g\(\cdot\)C\(\cdot\)kg\({}^{-1}\) H\({}_{2}\)O. Among the eight ChinaFLUX sites, MODIS WUE shows the best performance in the Haibei alpine grassland ecosystem (HBGC) with the highest R of 0.96 and the low RMSE of 0.42 g\(\cdot\)C\(\cdot\)kg\({}^{-1}\) H\({}_{2}\)O. The MODIS product has a better performance in estimating WUE in the CBS temperate mixed forest and the DHS subtropical evergreen forest than the QYZ subtropical evergreen forest and the XSBN tropical evergreen forest. It has a higher R and lower RMSE for CBS (R = 0.85, RMSE= 0.97 g\(\cdot\)C\(\cdot\)kg\({}^{-1}\) H\({}_{2}\)O) and DHS (R = 0.71, RMSE = 0.45 g\(\cdot\)C\(\cdot\)kg\({}^{-1}\) H\({}_{2}\)O) than for QYZ (R = 0.64, RMSE = 1.18 g\(\cdot\)C\(\cdot\)kg\({}^{-1}\) H\({}_{2}\)O). There is no significant linear relationship between MODIS- and flux-derived WUE in the XSBN tropical evergreen forest. The RMSE of WUE at XSBN is up to 2.53 g\(\cdot\)C\(\cdot\)kg\({}^{-1}\) H\({}_{2}\)O. Although MODIS WUE has a good correlation with flux-derived WUE in CBS, it underestimated the high water use efficiency during DOY 145-257 by 39% on average. The bias of WUE in summer (from June-August) is -1.62 g\(\cdot\)C\(\cdot\)kg\({}^{-1}\) H\({}_{2}\)O in CBS. The underestimation of WUE mainly resulted from the lower estimates in MODIS GPP in CBS in comparison to mean annual values observed in 2003-2008 (Figure 2). Systematic underestimates in MODIS WUE were found for QYZ with a bias of -1.13 g\(\cdot\)C\(\cdot\)kg\({}^{-1}\) H\({}_{2}\)O and at XSBN with a bias of -2.28 g\(\cdot\)C\(\cdot\)kg\({}^{-1}\) H\({}_{2}\)O, which are basically caused by undervalued GPP and high ET estimates (Figure 2). For example, in the summer season (from June-August) for XSBN, GPP was underestimated by 27%-36% and ET was overestimated by 62%-127%, which led to the underestimated WUE by 53%-74%. In the DHS subtropical evergreen forest, MODIS GPP and ET were higher than those derived from eddy covariance measurements by 55% and 77% (Figure 2) and, thus, led to an underestimate of WUE by 16% on average. MODIS WUE had a worse performance at the NM temperate grassland site than the other two alpine grassland sites. The bias of WUE derived from MODIS data at the NM temperate grassland site is 4.3- and 7.7-times larger than that at the HBGC alpine grassland site and the DX alpine grassland site, respectively. MODIS WUE also has a higher RMSE for NM (1.63 g\(\cdot\)C\(\cdot\)kg\({}^{-1}\) H\({}_{2}\)O) than HBGC (0.42 g\(\cdot\)C\(\cdot\)kg\({}^{-1}\) H\({}_{2}\)O) and DX (0.34 g\(\cdot\)C\(\cdot\)kg\({}^{-1}\) H\({}_{2}\)O). The overestimation of WUE in the three grassland ecosystems was due to different reasons. The significant high MODIS WUE for NM was generated by the \(\sim\)1.63 g\(\cdot\)C\(\cdot\)kg\({}^{-1}\) H\({}_{2}\)O. The overestimation of WUE in the three grassland ecosystems was due to different reasons. The significant high MODIS WUE for NM was generated by the \(\sim\)1.63 g\(\cdot\)C\(\cdot\)kg\({}^{-1  
overvalued GPP by 57% and underestimated ET by 40% in the growing season (Figure 2).