## 4 Discussion  
Combining satellite data and EC tower data is a common approach to estimate ecosystem-scale or regional-scale GPP. Remote sensing data are valuable when used with EC data because they allow us to model and map GPP beyond the tower footprint. Even though the LUE-based GPP model is considered a robust approach, simpler empirical approaches such as the correlation between GPP and vegetation indices are often valuable [16], in part because they do not require many parameters and often are geographically scalable [23]. [PERSON] et al. [24] demonstrated the problems (i.e., dampened phenology) with standard vegetation indices derived from MODIS surface reflectance data for wetlands and proposed a tide flagging method as a solution. Based on that method, we developed several cloud-free and tide-filtered vegetation composites to be correlated with tower derived GPP in a simple empirical model. Our best model was able to explain 46% of the variation in tidal wetland GPP in the  
Figure 6: The modeled GPP vs. flux GPP for the 2014 validation set (**a**) and the residual of the model fit for the same data, plotted against time (**b**). Figure 7: The modeled and flux GPP for 2014 validation data, where all MODIS datasets are 8 day composites and GPP is 8 day sums. RMSE (g C m\({}^{-2}\)) for predicting flux GPP for each MODIS dataset is also indicated. validation data and had a lower RMSE than MOD17. Therefore, this represents an improvement over the existing method for tidal wetland GPP calculation. Accounting for tidal effects in wetland remote sensing data is important. We found that 20-40% of the MODIS daily surface reflectance data were affected by either clouds or tides or both in GA wetlands (Table 1). Remote sensing derived vegetation indices in wetlands have several other problems, namely reduced NIR reflectance due to saturated soil background and lack of a pronounced red-edge due to scattering at the red-absorption band [14]. These problems often introduce significant errors in remote sensing based biophysical or GPP models [1, 14, 17]. Moreover, the errors will increase sharply if we do not filter out tide and cloud affected pixels [20]. The procedure presented in this paper overcomes these problems by demonstrating how to tidally-filter daily surface reflectance information from MODIS, and subsequently generate a higher quality MODIS GPP model within tidal wetlands than the existing MODIS product. The best vegetation index for estimating GPP in our study was the Normalized Difference Moisture Index (NDMI). Compared to other indices, NDMI served as a reasonable spatial proxy for wetland GPP but only after accounting for tidal conditions. We observed that NDMI, a NIR, and shortwave-IR (SWIR) based index performed better than red-NIR based indices (Table 2). As noted in [PERSON] et al. (2017)[24], even though the tide affected pixels are removed, the red-NIR contrast is lower compared to NIR-SWIR contrast. This is because of the perpetually moist soil background in wetlands. Longer wavelength bands are attenuated by water and soil moisture more rapidly than visible wavelengths. Therefore, indices composed of both visible and longer wavelengths are very noisy within wetlands because the soil moisture and water depth vary over time. Further, as noted above, NDMI is composed of bands related LAI (e.g., the NIR band) as well as bands related to plant moisture content [43]. The amount of leaf area in the canopy and the water status of these leaves is positively related to plant photosynthesis rates in _S. alterniflora_[44]. Perhaps consequently, NDMI has a strong phenological signal and was high during summer when plant biomass was high and low during the winter when plants were dormant (Figure 5). Therefore, NDMI showed a stronger correlation with GPP compared to other indices, likely through its numerical robustness to tidal flooding and its physical relationship to plant physiology.