## 4 Discussion
### Impacts of FPAR on GPP Estimations  
FPAR is an important input variable in light use efficiency model that directly modulates the essential energy source input to photosynthetic systems [9; 13; 16]. However, FPAR\({}_{\text{M}}\) often produce misleading signals in GPP estimations due to the contamination by atmospheric characteristics [28; 70]. Thus, compared with FPAR\({}_{\text{M}}\) among diverse seasons and different spatial biomes, realistic ground FPAR\({}_{\text{G}}\) could be either undervalued [34; 71], overvalued [25; 29; 72], or had a closest agreement [17; 34]. In this study, 22.2% (20.4% in 2011 to 23.8% in 2010) of the FPAR\({}_{\text{G}}\) derived from ground LAI\({}_{\text{MG}}\) was underestimated by the FPAR\({}_{\text{M}}\) (Figure 1d,e). A near study in an alpine meadow, however, demonstrated that the MODIS-based FPAR was about 14.70% higher than the FPAR\({}_{\text{G}}\), which could be attributed that the vegetation canopies in alpine meadow was much less than in this alpine wetland [25; 71; 73]. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Methods *} & \multicolumn{6}{c}{**RMSE (g C m\({}^{-2}\)) +**} & \multicolumn{6}{c}{**RPE (\%) +**} \\ \cline{2-11}  & **2009** & **2010** & **2011** & **2012** & **Mean** & **2009** & **2010** & **2011** & **2012** & **Mean** \\  & **(_n_ **46**)** & **(_n_ **45**)** & **(_n_ **46**)** & **(_n_ **45**)** & **Mean** & **2009** & **2010** & **2011** & **2012** & **Mean** \\ \hline GPP\_MOD & 2.90 & 2.59 & 2.14 & 2.26 & 2.47 & \(-\)70.4 & \(-\)72.0 & \(-\)63.7 & \(-\)65.7 & \(-\)68.0 \\ GPP\_MODR1 & 3.24 & 2.97 & 2.41 & 2.59 & 2.80 & \(-\)75.1 & \(-\)77.5 & \(-\)69.3 & \(-\)70.0 & \(-\)73.0 \\ GPP\_MODR2 & 2.84 & 2.55 & 2.04 & 2.21 & 2.41 & \(-\)63.8 & \(-\)66.4 & \(-\)56.7 & \(-\)57.8 & \(-\)61.2 \\ GPP\_MODR3 & 1.30 & 0.95 & 0.97 & 0.86 & 1.02 & \(-\)9.4 & \(-\)11.3 & \(-\)15.4 & \(-\)1.4 & \(-\)9.4 \\ GPP\_MODR4 & 2.01 & 1.75 & 1.55 & 1.37 & 1.67 & \(-\)37.9 & \(-\)40.4 & \(-\)39.9 & \(-\)29.9 & \(-\)37.0 \\ \hline \hline \end{tabular}  
* The MODIS-based GPP estimation methods are similar to Table 1;
* The RMSE and RPE are the root mean square error and the relative predictive error between MODIS-based daily GPP estimations and GPP_EC, respectively. The negative RPE represents the underestimation of MODIS-based daily GPP estimations compared to GPP_EC.  
\end{table} 
Table 3: Performances of MODIS-based GPP estimations methods on daily GPP estimations as compared to the insitu flux tower GPP (GPP_EC). The improving performance of FPAR\({}_{\text{G}}\) inputs, instead of FPAR\({}_{\text{M}}\), into GPP estimation model was different at distinct \(\varepsilon_{\text{max}}\) levels (Figure 6 and Table 3). With the default \(\varepsilon_{\text{max}}\) inputs, although GPP_MODR2 improved the agreement of GPP_MODR1 in daily GPP estimation by 11.8% of RPE with 0.38 g C m\({}^{-2}\) decrease in RMSE, the promoting effects was still not significant (Table 3). Nevertheless, when we used the optimized \(\varepsilon_{\text{max}}\) values from our estimation for this specific ecosystem, the effect of the introduction of FPAR\({}_{\text{G}}\) (GPP_MODR3) instead of FPAR\({}_{\text{M}}\) (GPP_MODR4) would generate an appreciable close to GPP_EC estimation by 27.6% of RPE with 0.58 g C m\({}^{-2}\) decrease in RMSE (Table 3). Therefore, the discrepancies of FPAR changes in GPP estimation models among two distinct \(\varepsilon_{\text{max}}\) levels implied that 15.8% of RPE would be related to the interaction of FPAR and \(\varepsilon_{\text{max}}\) (FPAR:\(\varepsilon_{\text{max}}\)) in GPP estimation models in this alpine swamp [74] (Figure 6).