Iryna Dronova Patricia Oikawa Sara Helen Knox Lisamarie Windham-Myers Julie Shahan Ellen Stuart-Haëntjens Charles R. Bostater Jr. Gwendolyn Joelle Miller 2021 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. application/pdf 10.3390/rs13183589 en MDPI The potential of satellite remote sensing time series to uncover wetland phenology under unique challenges of tidal setting article