The potential of satellite remote sensing time series to uncover wetland phenology under unique challenges of tidal setting
Remote Sensing
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By: Gwendolyn Joelle Miller, Iryna Dronova, Patricia Oikawa, Sara Helen Knox, Lisamarie Windham-Myers, Julie Shahan, and Ellen Stuart-Haëntjens
Edited by: Charles R. Bostater Jr.
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Abstract
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.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | The potential of satellite remote sensing time series to uncover wetland phenology under unique challenges of tidal setting |
| Series title | Remote Sensing |
| DOI | 10.3390/rs13183589 |
| Volume | 13 |
| Issue | 18 |
| Year Published | 2021 |
| Language | English |
| Publisher | MDPI |
| Contributing office(s) | California Water Science Center, WMA - Earth System Processes Division |
| Description | 3589, 28 p. |
| First page | 1 |
| Last page | 28 |
| Country | United States |
| State | California |
| City | San Francisco |
| Other Geospatial | San Francisco Bay Estuary |
| Google Analytic Metrics | Metrics page |