## 4 Discussion  
This study evaluated eight-day and annual WUE calculated from MODIS GPP and ET products in China using eddy covariance-measured GPP and ET at eight ChinaFLUX sites. Both MODIS- and eddy covariance-derived WUE values were within the range of previous studies (0.36-6.07 g C \(\cdot\) kg \({}^{-1}\) HzO) [9, 32]. They also show higher values in forest ecosystems than grassland ecosystems, which were consistent with previous studies of the variation of WUE across different ecosystem types [7, 8, 33]. Because we defined WUE as the ratio of GPP and ET, the performance of MODIS WUE depends on the accuracy of both GPP and ET estimation. Previous studies of the evaluation of MODIS GPP and ET products reported that MODIS GPP generally underestimates GPP and MODIS ET overestimates ET using eddy flux measurement from 2003-2005 at the same eight ChinaFLUX sites [17, 21]. This indicated that WUE tends to be underestimated by MODIS data when compared to WUE calculated from eddy flux measurements. That is the case for the four forests and one cropland site, as presented in this study. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Site** & **ET\_EC** & **ET\_MODIS\_** & **ET\_MODIS\_** \\ \hline DHS & 675 & 1051 & 715 \\ QYZ & 510 & 976 & 755 \\ XSBN & 564 & 1287 & 1117 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between flux-derived ET (ET_EC), the original MODIS-derived ET (ET_MODIS\_) and modeled ET using the MODIS ET algorithm and meteorological data measured in subtropical and tropical forests (ET_MODIS\_). However, for the three grassland ecosystems, MODIS WUE values are significant overvalued, mainly due to underestimated ET. The evaluation of the MODIS global ET product based on FLUXNET data showed that monthly ET is overestimated in forest with a mean bias error of 6 mm per month and underestimated in grassland and cropland with mean bias errors of \(-8\) and \(-10\) mm per month, respectively [18]. Many studies on the validation of MODIS LAI also demonstrated that the MODIS LAI product often overestimated LAI in forest, but underestimated it in grassland and cropland ecosystems [34, 35, 36, 37, 38, 39]. However, the bias in LAI might not influence the estimation of ET and WUE much, because neither ET nor WUE is sensitive to the change in LAI. Similar to the results in this study and in [21] based on the eddy flux data from ChinaFLUX, an assessment of MODIS GPP against eddy covariance measured GPP at 10 flux towers in northern China pointed out that MODIS underestimated GPP at nine sites, especially for all cropland sites [16]. MODIS GPP was also undervalued in an evergreen coniferous forest in Britain [40]. However, the MODIS product was found to have the tendency to generally overestimate GPP for low productivity and to underestimate it for high productivity in North America, compared to ecosystem-level measurements of GPP at eddy covariance flux towers from AmeriFlux and Fluxnet-Canada [15, 41, 42]. Moreover, MODIS GPP overestimates GPP in a deciduous forest in Korea [43], three subtropical forest ecosystems [41] and non-forest areas in North America [44]. The underestimation in GPP was associated with the low estimation of light use efficiency, especially for cropland [15, 21, 45, 46]. On the contrary, the overestimation in GPP might result from high FPAR [15]. Meteorological data also have a significant contribution to the uncertainty in MODIS GPP [22]. Besides, some bias of GPP may be derived from an imperfect model structure in the current MODIS GPP algorithm, which did not consider the contribution of shade leaf to canopy photosynthesis [41]. Although the eddy flux measurement has a larger footprint compared to the field measurements at certain points [47], it remains uncertain due to random measurement errors [48], gap-filling errors [49], the surface energy balance disclosure issue [50, 51] and the variation in the footprint [52].