## 3 Results
### The Sensitivity of Estimates to Climate Factors and LAI/FPAR Data  
Through the comparison of the experiment (1) and (2), we can isolate the impact of climate cause on GPP. Table 2 shows that bias, RMSE and \(R^{2}\) between (1) and (2) present similar values, implying that  
Figure 3: Comparison between the flux observed ET and MODIS-ET for all sites, pink solid line and blue solid line represent the flux observation ET and MODIS-ET, respectively. (DOY: Day of year). (**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.  
DAO data may be similar to site flux meteorological data. By comparing flux meteorological data with DAO meteorological data, [PERSON] _et al._[16] indicated that meteorological data generally performed good agreement for VPD and minimum temperature, but a high bias for DAO PAR. However, the results in YC reveal a high bias. A linear regression method is applied to MODIS-GPP (x) and GPP_MOD (y): y = 0.95x - 18.29 (\(R^{2}=0.91\), \(p<0.001\)). The intercept value indicates this high bias. The noise existed in the forced data may result in this high bias when calculating MODIS-GPP products. [PERSON] _et al._[29] pointed out that the annual MODIS-GPP only accounted for 1/5-1/3 for croplands, because the key parameter, maximum light use efficiency, was underestimated for croplands. When using flux measurements and optimized maximum light use efficiency to calculate GPP, they found that the MODIS-GPP algorithm significantly improved the accuracy of GPP estimations. Therefore, Case 1 shows that the differences of climate factors may have little impact on GPP estimation.  
Through comparing the experiment (2) with (3), we can explain the impact of LAI/FPAR on GPP, since (2) and (3) have the same meteorological data but the different FPAR. Table 2 shows that GPP_GLA has lower bias and RMSE and a higher \(R^{2}\) than GPP_MOD. By substituting MODIS-FPAR with GLASS-FPAR in MODIS-GPP algorithm, it results in improving the accuracies of GPP estimations because of using the higher quality of GLASS-LAI/FPAR. The accuracies of GPP estimations improve by 2% for DHS to 43% for QYZ. Thus, accurate remote sensing parameters are important for achieving reliable estimations. The errors of MODIS-GPP/ET products are mainly caused by inaccuracy of LAI/FPAR.  
By comparison of the experiment (3) and (4), the MOD17 maximum light use efficiency is comparable with the optimized value based on flux observed calibration, especially in mixed forests, evergreen needleleaf  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **CBS** & **DHS** & **QYZ** & **XSBN** & **YC** & **HB** & **XLGL** & **DX** \\ \hline Bias(1) & \(-8.4\) & \(-2.5\) & \(-11.9\) & \(-17.6\) & \(-4.8\) & \(-3.2\) & \(0.4\) & \(-1.5\) \\ RMSE(1) & \(17.3\) & \(1  
forests and grasslands. Their bias and RMSE do not significantly change. For croplands, the bias significantly decreases from -24.7 gC/m\({}^{2}\)/8-day to 3.1 gC/m\({}^{2}\)/8-day and RMSE significantly decreases from 38.0 gC/m\({}^{2}\)/8-day to 15.2 gC/m\({}^{2}\)/8-day. Croplands generally have strong management, such as irrigation and fertilization, which significantly affect photosynthesis capacity, thus the maximum light use efficiency may have a large variation in different regions.