## 4 Discussion
### Impact of Different Remote Sensing FPAR on GPP  
As an \"internal\" factor, FPAR can potentially determine a very important part in remote sensing-based GPP model and directly related to the primary mechanism of the canopy photosynthetic process [30]. In MODIS-GPP algorithm, light, temperature, and water are just the \"external\" environmental stresses, which will limit maximum light use efficiency, while FPAR as remote sensing forcing data can determinate the seasonal change of GPP through adjusting PAR absorbed by the vegetation. In experiment (3), after we replaced MODIS-FPAR with GLASS-FPAR, \(R^{2}\) values between estimated GPP and flux derived GPP increased by 0.16 (XSBN) \(\sim\)0.40 (QYZ), implying that the error of MODIS-FPAR significantly affects estimated GPP in experiment (2). By comparing MODIS-FPAR with GLASS-FPAR, the results show \(R^{2}\) values for evergreen forests (DHS, QYZ and XSBN) are lower than 0.16 and RMSE values are greater than 0.23 (Table 3). In contrast, \(R^{2}\) values of MODIS-FPAR and GLASS-FPAR in grasslands (HB, XLGL and DX) are more than 0.80, and RMSE and bias are close to 0. Therefore, the accuracy of estimated GPP using MODIS-FPAR is comparable with that using GLASS-FPAR. Generally, the estimated GPP based on GLASS-FPAR more approaches flux derived GPP. For mixed forests and croplands, MODIS-FPAR also gives a good performance with higher \(R^{2}\) and lower bias. Thus, when using GLASS-FPAR to calculate GPP, the result only performed a little improvement compared with estimating GPP based on MODIS-FPAR. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **CBS** & **DHS** & **QYZ** & **XSBN** & **YC** & **HB** & **XLGL** & **DX** \\ \hline Bias & 0.01 & –0.28 & –0.12 & –0.12 & 0.04 & 0.00 & 0.03 & 0.00 \\ \hline RMSE & 0.17 & 0.38 & 0.26 & 0.23 & 0.13 & 0.07 & 0.06 & 0.06 \\ \hline \(R^{2}\) & 0.71 & 0.06 & 0.16 & 0.01 & 0.72 & 0.96 & 0.88 & 0.80 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison between MODIS-FPAR and GLASS-FPAR. Where, CBS in mixed forests site; DHS and XSBN in evergreen broadleaf forests site; QYZ in evergreen needleleaf forests site; YC in croplands; HB, XLGL and DX in grasslands site. As shown in Figure 4, the flux derived GPP fluctuation is consistent with that of FPAR during the vigorous growth period in CBS, especially for GLASS-FPAR (\(R^{2}=0.91\)). It can be concluded that the differences between MODIS-GPP and flux derived GPP are mainly caused by MODIS-FPAR during these period (Figure 4a). In subtropical regions, clouds can often impact satellite observations. Therefore, FPAR appears very large fluctuation for each eight-day in subtropical evergreen forests. Although the fluctuation of MODIS-FPAR has been processed by temporal filtering method in MODIS-GPP algorithm [17], our study finds that the impact of contamination MODIS-FPAR on GPP still exists. Statistical analysis suggests that such MODIS-FPAR fluctuation leads to MODIS-GPP fluctuation and then results in the large bias and RMSE of MODIS-GPP compared with flux derived GPP. In Figure 4b-d, the relationship between flux derived GPP and GLASS-FPAR gives higher \(R^{2}\) ranging from 0.56 to 0.80, however, \(R^{2}\) of flux derived GPP and MODIS-GPP only ranges from 0.03 to 0.17. Our results also reveal that MODIS-FPAR/GLASS-LAI in evergreen forests (Figure 4b,d) showed a saturation phenomenon with most values centralized in the range from 0.8 to 0.9. In the non-growing season, MODIS-FPAR is greater than 0.2 in croplands (Figure 4e); while it is generally lower than 0.1 in grasslands (Figure 4f-h). As MODIS-FPAR in croplands can explain 70% variations for flux derived GPP, the overestimation in non-growing season and underestimation in growing season of MODIS-GPP compared with the flux derived GPP can basically attributed to MODIS-FPAR error which was overestimated/underestimated by remote sensing data in corresponding seasons. GLASS-FPAR explains 88% variations for flux derived GPP in croplands.