## 4 Discussion
### Impact of Different Remote Sensing LAI/FPAR on ET  
Many studies have shown that LAI/FPAR and ET have a very good relationship via _in-situ_ observations and it has been widely applied in ecosystem process models. For example, in BEPS-EASS model, canopy LAI is directly involved in the calculation process of canopy ET [27, 36, 37]. In addition, canopy ET usually occupies a large proportion in total ET. The relationship between LAI/FPAR and ET can be explained biophysically that LAI changes will affect stomatal conductance, further affect vegetation transpiration rate [38]. On the basis of an analysis of the carbon sequestration, vegetation controls the size of stomatal opening to absorb more carbon dioxide from the surrounding air, but at the same time it will also consume more water [31]. As assimilated more carbon is fixed as GPP, the corresponding allocation to leaf production also increases and further promotes LAI increasing and water consumption [39, 40]. Without restriction of other environmental factors, the change of ET agrees with that of LAI/FPAR, since LAI/FPAR plays an important and positive role in calculating ET [27].  
Figure 4: Relationship between flux derived GPP (with gC/m\({}^{2}\)/8-day) and GLASS-FPAR (pink)/MODIS-FPAR (blue) for each site. (**a**) in mixed forests site; (**b**) in evergreen broadleaf forests site; (**c**) in evergreen needleleaf forests site; (**d**) in evergreen broadleaf forests site; (**e**) in croplands site; (**f**,**g**,**h**) in grasslands site.  
As shown in Figure 5, statistical analysis between flux observed ET and MODIS-LAI at site scale shows that MODIS-LAI can explain more than 50% variations of flux observed ET in five sites (CBS, YC, HB, XLGL, and DX, \(p<0.01\)). For evergreen forests, GLASS-LAI with flux observed ET gives a higher \(R^{2}\) when comparing with that of MODIS-LAI. In particular, \(R^{2}\) increased by more than 30% in DHS and QYZ, owing to that GLASS-LAI values aggregated more relative to the scatter of MODIS-LAI. For evergreen broadleaf forests (Figure 5b,d), the fluctuation of MODIS-LAI is very large in the vigorous growth period, so the quality of MODIS-LAI directly impacted on the final ET. Thus, the consistency of MODIS-ET and flux measured ET is relatively poor. The reason of overestimated ET in croplands (Figure 5e) is hard to explain only by analyzing the relationship between LAI and flux observed ET, which is probably coming from the setting of key parameters in ET algorithm. [PERSON] _et al._[41] indicated that when using MODIS-ET algorithm to calculate cropland's vapor flux, the estimated ET was about 20% more than the measured ET during the growing season of winter wheat. After they calibrated the crop physiological temperature parameters and coefficients for calculating the aerodynamic resistance and the fractional vegetation cover, the estimated results became more close to the measured values.  
Figure 5: Relationship between flux observed ET (with mm/8-day) and GLASS-LAI (pink)/MODIS-LAI (blue) for each site. (**a**) in mixed forests site; (**b**) in evergreen broadleaf forests site; (**c**) in evergreen needleleaf forests site; (**d**) in evergreen broadleaf forests site; (**e**) in croplands; (**f,g,h**) in grasslands site.  
Although \(R^{2}\) varies from different land cover types, MODIS-LAI and flux observed ET generally show the same fluctuations at each site. The above results indicated that the accuracy of remote sensing LAI might directly impact the accuracy of ET.