## 5 Conclusions  
In this study, we incorporated climate and remote sensing data into a satellite-based VPM model to investigate the biophysical performance of different vegetation indices and to evaluate the applicability and accuracy of the VPM model for simulating GPP of coastal wetland in China. The results indicated that air temperature can explain most of the seasonal variation in vegetation indices and \(\text{GPP}_{\text{EC}}\) fluxes. Time series of greenness-related EVI and water-related LSWI can better delineate the green-up and senescence phases of coastal wetland vegetation, which are in accordance with the CUP, as defined by \(\text{GPP}_{\text{EC}}\). Further, both EVI and water-sensitive LSWI have higher predictive power for simulating the GPP in this coastal wetland. The EVI-based VPM model is able to simulate the U-shaped growth pattern of the coastal plant and accurately simulate the trajectories of the seasonal dynamics of \(\text{GPP}_{\text{EC}}\) fluxes, explaining about 85% of \(\text{GPP}_{\text{EC}}\) changes. Further development and improvement of VPM for nearby coastal wetlands and the spatial-temporal scale expansion are necessary for regional and continental simulations of GPP in the future.  
Figure 10: Parameter sensitivity analysis for VPM model in the targeted coastal wetland. GPP, gross primary production; \(\text{GPP}_{\text{EC}}\), estimated GPP from EC tower; \(\text{GPP}_{\text{VPM}}\), simulated GPP by VPM model; Ta, air temperature; \(\epsilon_{0}\), maximum light use efficiency; PAR, photosynthetically active radiation; EVI, enhanced vegetation index; LSWI, land surface water index; \(W_{\text{scalar}}\) and \(T_{\text{scalar}}\) are the downregulation scalars for the impacts of water and temperature on light use efficiency, respectively. Base, all of the variables do not change, \(\epsilon_{\text{g}}=T_{\text{scalar}}\times W_{\text{scalar}}\times P_{\text {scalar}}\times\epsilon_{0}\); Ta-(1 + 10%), increase in Ta by 10%; \(\epsilon_{0}\)-(1 + 10%), increase in \(\epsilon_{0}\) by 10%; PAR-(1 + 10%), increase in PAR by 10%; EVI-(1+10%), increase in EVI by 10%; LSWI-(1 + 10%), increase in LSWI by 10%; Without \(T_{\text{scalar}}\), \(\epsilon_{\text{g}}=W_{\text{scalar}}\times P_{\text{scalar}}\)\(\times\)\(\epsilon_{0}\); Without \(W_{\text{scalar}}\), \(\epsilon_{\text{g}}=T_{\text{scalar}}\times P_{\text{scalar}}\times\epsilon_{0}\); and, Without \(T_{\text{scalar}}\) & \(W_{\text{scalar}}\), \(\epsilon_{\text{g}}=P_{\text{scalar}}\times\)\(\epsilon_{0}\).  
**Author Contributions:** X.K. and L.Y. contributed equally to this work. All authors contributed to designing the study, developing the overall concept of data analysis, the preparation of figures and tables, and the revision of the manuscript.  
**Acknowledgments:** This work was supported by the National Key Research and Development Program of China (2017 YFC0506203), the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant and a Postdoctoral Fellowship to [PERSON] from the University of Quebec at Montreal (UQAM), Canada, the National Natural Science Foundation of China (Grant No. 31770511, 41701113, 41403102,41403073). We thank the principal investigators of the MODIS data products as well as the Oak Ridge National Laboratory's (ORNL) Distributed Active Archive Center (DAAC) and the Earth Observation and Modeling Facility (EOMF), the University of Oklahoma. We thank the anonymous reviewers and editors for their valuable comments and suggestions on the earlier versions of the manuscript. We also thank [PERSON] for the suggestion of statistical analysis.  
**Conflicts of Interest:** The authors declare no conflict of interest.