## 1 Introduction  
Estimates of Gross Primary Productivity (GPP) are useful for understanding local and global carbon dynamics and are often scaled from sites to landscapes using satellite data [1; 2; 3; 4]. A common data source for this effort is the Moderate Resolution Imaging Spectroradiometer (MODIS), which has a coarse spatial resolution but collects daily global observations. Information from MODIS has been packaged into the MOD17A2 GPP product, which is a cumulative composite of daily GPP estimates. The MOD17 algorithm uses the radiation-use efficiency concept first proposed by [PERSON] [3] and provides a method for estimating pixel-wise GPP and Net Primary Productivity (NPP) globally [5]. Satellite-derived GPP, such as from MODIS, is useful because it helps model terrestrial energy, carbon, and water cycle processes. However, the algorithm for MOD17 was calibrated for upland biomes and cannot be directly applied to wetlands with any accuracy. Consequently, there is a knowledge gap concerning the spatial and temporal variability of wetland GPP. Tidal marshes, the wetland type that is the focus of this study, are ecologically productive ecosystems [6; 7] and are at risk to loss from threats such as land-use change and sea level rise [6]. Estimations of tidal marsh GPP can improve our understanding of feedback mechanisms that regulate carbon dynamics in the context of global change and facilitate climate policy-making that might preserve coastal habitats. Remote sensing techniques provide an efficient method for estimating GPP and MODIS is a useful data source for this kind of monitoring. While MODIS is spatially coarse (pixel spatial resolution of 250 m to 1 km), its daily return frequency is especially useful for estimating plant and ecosystem properties within tidal marshes [8; 9]. Daily observations allow sufficient data density after cloudy and tidally influenced observations are removed. Further, MODIS products often are used to estimate GPP in terrestrial systems [10; 11] and parameterizing a similar product for carbon-rich productive tidal marshes allows for landscape-scale comparisons among terrestrial and coastal systems through MODIS data. MODIS can only assist with monitoring spatiotemporal variability in wetlands if the influence of tides on vegetation reflectance can be isolated. Water or saturated soil background in wetlands reduce the intensity of the near-infrared (NIR) reflectance, change the slope and position of red-edge, and alter the magnitude of vegetation indices such as Enhance and Normalized Vegetation Index (EVI and NDVI) and other red-edge-type indices [12; 13; 14]. Consequently, GPP models used for terrestrial ecosystems cannot be readily applied to wetlands without accounting for the effect of tides on surface reflectance [2; 4; 15]. The MODIS GPP product, MOD17A2, while useful for upland monitoring, cannot be used for tidal wetlands because it has not been parameterized for this habitat. The MOD17A2 product employs a Production Efficiency Model (PEM) to estimate GPP. PEMs are based on the original logic of [PERSON] [3], who suggested that GPP is linearly related to absorbed photosynthetically active radiation (APAR), such that  
\[\text{GPP}\propto\varepsilon\times\Sigma\text{ (fAPAR}\times\text{PAR}_{\text{in}}) \tag{1}\]  
where, \(\text{PAR}_{\text{in}}\) is the incident photosynthetically active radiation, fAPAR is the fraction of \(\text{PAR}_{\text{in}}\) absorbed by the canopy, and \(\varepsilon\) is light use efficiency (LUE). The MOD17A2 product estimates GPP by assuming a linear relationship between fAPAR and NDVI and substitutes LUE as a vegetation biome-specific constant obtained from the lookup tables [16]. However, tidal marshes are not one of the eleven biomes implemented within MOD17A2 [17], perhaps because, compared to terrestrial systems, the processes that regulate ecosystem-atmosphere carbon dioxide (CO\({}_{2}\)) fluxes, including GPP and LUE in wetlands are not well understood [18].