## 2 Materials and Methods
### The Moderate Resolution Imaging Spectroradiometer-Based Vegetation Photosynthesis Model
#### 2.4.2 Model Parameterization  
In order to normally run the VPM model for simulating GPP of coastal wetland using input data, such as climate observation and MODIS imagery, we conducted a series of model parameterization.  
Different vegetation types have a different \(\varepsilon_{0}\) value. It can be obtained from analysis of NEE and photosynthetic photon flux density of the EC tower site [52; 53]. In this study, we used the Michaelis-Menten function (Equation (13)) in order to estimate the \(\varepsilon_{0}\) of local vegetation type by the nonlinear model between the observed NEE and incident PAR, according to the flux data at 30 min intervals. Here, we took 2016.67 mg CO\({}_{2}\) mol\({}^{-1}\) PAR as \(\varepsilon_{0}\) during the study period:  
\[NEE=\frac{\varepsilon_{0}\times PAR\times GPP_{\text{max}}}{\varepsilon_{0} \times PAR+GPP_{\text{max}}}-R_{\text{eco}} \tag{13}\]  
Figure 2: Structure of the vegetation photosynthesis model (VPM) model. CO\({}_{2}\) flux and climate data (air temperature, PAR, precipitation, etc.) come from the eddy flux tower. Vegetation indices (VIs) were extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) datasets. \(\varepsilon_{0}\), maximum light use efficiency; \(T_{\text{scalar}}\), \(W_{\text{scalar}}\), \(P_{\text{scalar}}\), the scalars for the effects of temperature, water and leaf phenology on light use efficiency of vegetation, respectively; \(FPAR_{\text{chl}}\), the fraction of PAR absorbed by leaf chlorophyll; PAR, the photosynthetically active radiation; \(LSWI_{\text{max}}\), the maximum Land Surface Water Index (LSWI); \(T_{\text{max}}\), \(T_{\text{min}}\), and \(T_{\text{opt}}\), the maximum, minimum, and optimal air temperature, respectively.  
where \(GPP_{\text{max}}\) (mg CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)) is maximum gross primary production, \(R_{\text{eco}}\) (mg CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)) is ecosystem respiration, NEE (mg CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)) is the net ecosystem CO\({}_{2}\) exchange, PAR is the photosynthetically active radiation (\(\mu\)mol PPFD). Based on the diurnal GPP flux data, we chose the maximum value of GPP (0.63 mg CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)) during the study period as \(GPP_{\text{max}}\).  
Different vegetation types have different estimation values of \(T_{\text{scalar}}\), \(T_{\text{min}}\), \(T_{\text{opt}}\), and \(T_{\text{max}}\)[33]. The daily daytime mean temperature was calculated by using the daily maximum and minimum air temperature, rather than daily mean air temperature [33, 54]. In our study, if the air temperature of the study site falls below \(T_{\text{min}}\), \(T_{\text{scalar}}\) is set to zero. Because \(T_{\text{scalar}}\) is the scalar of temperature on plant photosynthesis activities, we consider the local meteorological conditions and plant growth (observed GPP) comprehensively. When considering the effect of low temperature on plant growth, we finally defined \(T_{\text{min}}\) as 0 \({}^{\circ}\)C (cold damage to plants). Based on the observed maximum temperature during the two study years, we finally defined \(T_{\text{max}}\) as 35 \({}^{\circ}\)C. We also defined site-specific \(T_{\text{opt}}\) as 26 \({}^{\circ}\)C by exploring the correlation between the observed vegetation indices, GPP and air temperature over 2009-2010.  
\(W_{\text{scalar}}\) can be derived from the water-sensitive vegetation index LSWI and \(LSW_{\text{max}}\). Site-specific \(LSW_{\text{max}}\) is estimated according to the time series MODIS data [26, 33, 36]. As a water-sensitive vegetation index, \(LSW_{\text{max}}\) varies across years under different water condition. Using the observed eight-day MODIS reflectance data, we calculated the site-specific LSWI values over 2009-2010 and chose the maximum value during the growing season as \(LSW_{\text{max}}\) (0.27 and 0.26 for 2009 and 2010, respectively).