Tidal Wetland Inundated Volume Estimates Using L-Band Radar Imagery and Synthetic Tide Gauging

Tidal inundation dynamics are a principal driver of hydrological and biogeochemical processes in coastal ecosystems, controlling the exchange of carbon, nutrients, and sediments between wetlands and estuaries. In this study, we assessed the utility of L-band radar imagery in deriving tidal wetland inundated volume estimates (pixel-wise water depths), which provide a more robust characterization of wetland–estuary exchange processes than the lateral inundation state estimates. Inundation state products derived using L-band radar were combined with digital elevation models (DEMs) and synthetic tide gauging to estimate the volume of inundation. Synthetic tide gauges, models of water level produced from combined short-term field measurements and long-term monitoring stations were employed to provide calibration and validation for satellite observations for times outside of the water level sensor monitoring period (August–December 2018). Ten synthetic gauges were established across the Charles H. Wheeler Wildlife Management Area (Connecticut, USA) in a regular grid and were used to validate the radar-based inundation state and inundated volume products. To generate volumetric inundation estimates from inundation state products, we employed two bathymetric fill approaches using a DEM to constrain water surface elevations. The first approach assumed a constant water elevation fill for all inundated pixels, while the second introduced a maximum water depth constraint. While both approaches showed strong correlations with synthetic gauges, the depth constraint approach was more accurate, increasing R2 from 0.87 to 0.98 and lowering RMSE from 0.79 m to 0.02 m. In this study, PALSAR-1/2 served as a proxy for the recently launched NISAR mission. Future research is planned to leverage the improved temporal sampling of the NISAR data record, combined with in-marsh water level observations (May 2025–present) and synthetic gauge estimates to improve wetland–estuary volumetric exchange characterization, which we demonstrate can be accurately estimated when paired with high-quality DEMs.