The potential of satellite remote sensing time series to uncover wetland phenology under unique challenges of tidal setting

While growth history of vegetation within upland systems is well studied, plant phenology within coastal tidal systems is less understood. Landscape-scale, satellite-derived indicators of plant greenness may not adequately represent seasonality of vegetation biomass and productivity within tidal wetlands due to limitations of cloud cover, satellite temporal frequency and attenu-ation of plant signals by tidal flooding. However, understanding plant phenology is necessary to gain insight into aboveground biomass, photosynthetic activity, and carbon sequestration. In this study we use a modeling approach to estimate plant greenness throughout a year in tidal wet-lands located within the San Francisco Bay Area, USA. We used variables such as EVI history, temperature, and elevation to predict plant greenness on a 14-day timestep. We found this ap-proach accurately estimated plant greenness, with larger error observed within more dynamic restored wetlands, particularly at early post-restoration stages. We also found modeled EVI can be used as an input variable into greenhouse gas models, allowing for an estimate of carbon se-questration and gross primary production. Our strategy can be further developed in future re-search by assessing restoration and management effects on wetland phenological dynamics and through incorporating the entire Sentinel-2 time-series once it becomes available within Google Earth Engine.