## 4 Discussion
### Sensitivity and Uncertainty for Vegetation Photosynthesis Model Simulations  
In general, there are always some errors in model simulations. The analysis of sources and uncertainties for the errors are very important for future improvement and the development of these models [78; 79]. In this study, the VPM simulation results show that the VPM model can correctly simulate the seasonal variations of GPP. The modeled results can explain about 85% of GPP\({}_{\text{EC}}\) changes over the two study years. However, there still exist some differences between seasonal GPP\({}_{\text{VPM}}\) and GPP\({}_{\text{EC}}\) in this study, such as higher GPP\({}_{\text{VPM}}\) from late July to early August in 2009 and 2010, and lower GPP\({}_{\text{VPM}}\) in late May in 2010 (Figure 7). The discrepancies between GPP\({}_{\text{EC}}\) and GPP\({}_{\text{VPM}}\) can be attributed, in part, to the estimation error of tower-based GPP\({}_{\text{EC}}\)[36; 80]. GPP\({}_{\text{EC}}\) is calculated as the difference between ecosystem respiration and NEE [15; 36]. The estimation error of daytime ecosystem respiration, which is the difference between ecosystem respiration and observed nighttime ecosystem respiration, may lead to the error of estimation (either overestimation or underestimation) of GPP\({}_{\text{EC}}\)[15; 36; 39; 40]. Another error source may be attributed to the modeling of GPP\({}_{\text{VPM}}\) from VPM [33]. To further clarify the sources and uncertainty of the GPP\({}_{\text{VPM}}\) errors, we conducted a sensitivity analysis for some parameters and variables of the model (Figure 10). The alternative sensitivity scenarios of input parameter were set by increasing Ta, \(\varepsilon_{0}\), PAR, EVI, and LSWI by 10%, respectively. When compared with the base scenario, the sensitivity of \(\varepsilon_{0}\) and PAR are relatively larger than that of Ta in this coastal wetland (Figure 10a,d). The potential conversion efficiency of absorbed photosynthetically active radiation is largely determined by \(\varepsilon_{0}\), which varies with vegetation types and study regions [23; 39], and it may even affect the accuracy of GPP simulation [40; 81]. PAR is an important variable in the GPP simulation, lower/higher PAR values may result in the under/over-estimation of GPP for the assumed relationship between GPP and PAR in VPM model [15; 82]. Therefore, the accurate estimation of \(\varepsilon_{0}\) and development of PAR observation would substantially improve the simulated accuracy of the VPM model and other models [36]. The source of uncertainty also come from the MODIS time series vegetation indices, such as the effects of cloud, cloud shadow, aerosols, and other atmospheric condition [40]. The EVI is more sensitive than LSWI to GPP simulation during 2009-2010 in this typical coastal wetland (Figure 10b,d). Future work should focus on how to avoid and mitigate the effects of angular geometry on surface albedo and to reconstruct the time series data of vegetation indices in order to improve the accuracy of vegetation indices, especially EVI. The simulation uncertainties of VPM also come from two intermediate variables that are related to water (\(W_{\text{scalar}}\)) and temperature (\(T_{\text{scalar}}\)) [39]. We use the two intermediate variables to set three sensitivity scenarios, including without \(T_{\text{scalar}}\) (\(\varepsilon_{\text{g}}=W_{\text{scalar}}\times P_{\text{scalar}}\times \varepsilon_{0}\)), without \(W_{\text{scalar}}\) (\(\varepsilon_{\text{g}}=T_{\text{scalar}}\times P_{\text{scalar}}\times \varepsilon_{0}\)), and without \(T_{\text{scalar}}\) & \(W_{\text{scalar}}\) (\(\varepsilon_{\text{g}}=P_{\text{scalar}}\times\varepsilon_{0}\)). When there is no \(T_{\text{scalar}}\) in the VPM, the model overestimated GPP by about 10%, 39%, and 25% in 2009, 2010, and 2009-2010, respectively. In the absence of \(W_{\text{scalar}}\) in VPM, the model overestimated GPP by about 18%, 36%, and 27% in 2009, 2010, and 2009-2010, respectively.