## 5 Conclusions  
The present study represents a first attempt to design and calibrate simple MODIS GPP models for salt marsh ecosystems through the integration of remotely sensed information, flux tower observations, and tidal data. The study describes how to composite MODIS daily surface reflectance data or derived products within tidal marshes, allowing us to model tidal wetland productivity. We certified that LUE-based MOD17A2 does not accurately reflect tidal wetland GPP compared to a simple empirical vegetation index-based model where tidal influence was accounted for. The results of this study also point to the need for more robust calculations of the tidal effects on ecosystem gross primary productivity. Mapping such productivity data gives insight into ecosystem function and can establish sites as sinks or sources of carbon in the environment. The MODIS-based models developed in this study can serve as a baseline for developing satellite-based models of GPP of the tidal wetlands. This is particularly important because these productive coastal ecosystems are vulnerable to climate change induced sea level rise and perpetual developmental pressures. One of the limitations of this study is that we used a single MODIS pixel to calibrate and validate our model. Although a single pixel does not represent the Georgia wetlands, any study aimed at developing a coast-wide GPP product needs to start at a pixel-level. The initial pixels used to parameterize such a model need to be those in the footprints of EC towers so that the tower data can serve as ground-truth data for parameterizing models and estimating model error. We used the best such pixel in this study. The biggest factor that affects MODIS surface reflectance and, hence, the GPP product is tide. Unless we have a robust tide flagging mechanism to filter the tide affected pixels, the supposedly robust LUE models are always going to be noisy, no matter how much re-parametrization is done. Again, the tide-flagging model needs to start at a pixel level where we have tower-based and phenocam observations to serve as ground-truth data. Fortunately, MODIS produces a dense temporal dataset and even if the tower pixel is affected by cloud and tide 20-40% of the time, we will still be left with a good size dataset to calibrate and validate the GPP model. With that in mind, our goal in this study was to examine the effect of tide on the performance of a simple GPP model before investing time on a complex but more robust LUE based GPP model. Our goal was not to develop a coast-wide tide-filtered GPP map with a simple empirical model which may have other problems (site- or species-specific etc.). For that, we need more pixel-based marsh inundation observations across multiple sites. Only then can we start re-parametrizing LUE based model for the coast-wide application. This will allow us to produce a coast-wide GPP map for salt marshes and revisit the vast MODIS archive to generate an 18+ year time-series composite. It also would be valuable to extend these time series to investigate environmental drivers and patterns of change in marsh GPP and the phenology of GPP at this site and across the broader GA coast. Conceptualization, J.T., D.M. and J.O. ; Methodology, J.T. ; Validation, J.T., J.O. and D.L.C. ; Formal Analysis, J.T., J.O. ; Resources, D.M. ; Data Curation, M.L., H.B.N., G.Z., R.P., J.O., J.T., and D.L.C. ; Writing--Original Draft Preparation, J.T., and D.R.M. ; Writing--Review & Editing, J. T., J.O., D.M., D.L.C., M.L., H.B.N., G.Z., R.P. ; Supervision, D.R.M. ; Project Administration, D.R.M. ; Funding Acquisition, D.R.M., M.L. This work was partially supported by Natural Science Foundation of Hubei Province (The Spatial-temporal variations of vegetation dynamics of Jianghan Plain during 2001 and 2016). This project was also funded by [PERSON]'s NASA grant#NNX14 AR30G and the Georgia Coastal Ecosystems ITER, which is supported by the National Science Foundation (OCE12-37140). **Acknowledgments:** We would thank GCE LTER project for providing the flux data.