## 4 Discussion
### Model Comparison and Error Source Analysis  
Recently, using the LUE models to simulate GPP plays an important role in the global carbon budget research, but our understanding of relative advantages and disadvantages of different models is still limited [67]. The VPM model simulated the trends of the coastal wetland phenology well  
Figure 8: Linear regression analysis between the estimated gross primary production from EC tower (\(\text{GPP}_{\text{EC}}\)) and simulated gross primary production by VPM (\(\text{GPP}_{\text{VPM}}\)) in (a) 2009; (b) 2010; and (c) 2009–2010.  
and precisely predicted GPP (\(R^{2}\) = 0.73) in this study. The MODIS-Photosynthesis (PSN) model also can simulate the global GPP and generate the Terra /MODIS GPP project (MOD17A2) [40; 68]. Here, eight-day MODIS GPP product (GPPMOD17A2) data during 2009-2010 were downloaded from the Oak Ridge National Laboratory's Distributed Active Archive Center (DAAC) website ([[http://daac.ornl.gov/MODIS](http://daac.ornl.gov/MODIS)]([http://daac.ornl.gov/MODIS](http://daac.ornl.gov/MODIS))) [69; 70]. Then, we compared the differences between GPP\({}_{\text{EC}}\), GPP\({}_{\text{VPM}}\), and GPP\({}_{\text{MOD17A2}}\) in our studied coastal wetland. A Taylor diagram was also used to display the quality of model predictions against the observations [71]. We found that both GPP\({}_{\text{EC}}\) and GPP\({}_{\text{VPM}}\) were substantially higher than GPP\({}_{\text{MOD17A2}}\) in terms of the magnitudes. The underestimated range of GPP\({}_{\text{MOD17A2}}\) is probably 40-80% (see Figure 9). Some previous studies also reported that the MODIS-PSN model underestimated GPP in different ecosystems [15; 40; 72; 73; 74]. However, [PERSON] et al., found that GPP\({}_{\text{MOD17A2}}\) was significantly lower than GPP\({}_{\text{EC}}\) during the prophase of the growing season, but higher than GPP\({}_{\text{EC}}\) during the late growing season at broadleaf deciduous woodland [39].  
The discrepancies between GPP\({}_{\text{MOD17A2}}\) and GPP\({}_{\text{EC}}\) may result from two underestimated sources. First, GPP\({}_{\text{MOD17A2}}\) uses global meteorological reanalysis dataset, including average and minimum Ta, incident PAR, and specific humidity. The input weather data of MODIS-PSN model may not match the local air temperature data. The second source is the parameter of\(\epsilon_{0}\) in MODIS-PSN [66; 75]. \(\epsilon_{0}\) is a very important parameter in the PEM models to estimate GPP and differs significantly among vegetation types [76]. However, the \(\epsilon_{0}\) and some other biome-specific physiological parameters are not differentiated from the different performance of a specific biome [77]. The EC technique  
Figure 9: Comparisons between the estimated gross primary production from the flux tower (GPP\({}_{\text{EC}}\)) and simulated gross primary production by VPM (GPP\({}_{\text{VPM}}\)) and MODIS global GPP data product (GPPMOD17A2) in the typical coastal wetland during 2009–2010. Taylor diagram provides a visual comparison between two model results, or, most commonly, between model results and observations [71].  
provides an appropriate method to accurately estimate the parameter \(\varepsilon_{0}\) by using a large number of measured data [23; 30]. In this study, we estimated the \(\varepsilon_{0}\) value by fitting the nonlinear model between observed NEE and PAR in 2009 and 2010. Then, we used the site-specific climate data, the individual \(\varepsilon_{0}\) estimated from CO\({}_{2}\) flux data, and vegetation indices (EVI, LSWI) as input data and the parameters of VPM to simulate the GPP dynamics of coastal wetland. Therefore, future works should pay more attention to the local climate data and biome-specific physiological parameters in using GPPMOD17A2 for regional analysis.