Belhadj-aissa et al. (2025) Mapping salt marsh hydroperiod using Synthetic Aperture Radar time series

Identification

• Journal: Remote Sensing Applications Society and Environment
• Year: 2025
• Date: 2025-12-22
• Authors: Saoussen Belhadj-aissa, Marc Simard, Adriana Parra Ruiz, Jordi Palacios, Sergio Fagherazzi
• DOI: 10.1016/j.rsase.2025.101850

Research Groups

• The Jet Propulsion Laboratory, California Institute of Technology, United States of America
• Department of Earth and Environment, Boston University, United States of America

Short Summary

This study develops a novel methodology integrating Synthetic Aperture Radar (SAR) time series with in-situ water level measurements to map salt marsh hydroperiod at high spatial and temporal resolution. The method, validated against a LiDAR-derived 'bathtub' model, demonstrates strong agreement and provides a robust tool for monitoring wetland vulnerability to sea-level rise.

Objective

• To develop and validate a methodology that integrates Synthetic Aperture Radar (SAR) time series with high-frequency in-situ water level measurements to reconstruct salt marsh hydroperiods at hourly resolution.

Study Configuration

• Spatial Scale: Salt marshes of Plum Island Sound, Massachusetts, USA, covering approximately 40 square kilometers. SAR data has a 10 meter pixel spacing, and the LiDAR-derived Digital Terrain Model (DTM) has a 3 meter resolution.
• Temporal Scale: SAR data acquired for the year 2021. In-situ water level measurements are hourly. Hydroperiod is calculated for an annual period, with SAR acquisitions from October to early May used for classification to minimize vegetation effects.

Methodology and Data

• Models used:
  • SAR Processing: Alaska Satellite Facility’s Hybrid Pluggable Processing Pipeline (HyP3) Python SDK, GAMMA software for Radiometric Terrain Corrected (RTC) products, GLO30 Copernicus DEM for terrain slope correction, Enhanced Lee filter (7x7 pixel window) for speckle noise reduction.
  • Flood Classification: Dynamic image selection based on bimodal histogram distribution of SAR backscatter intensity (𝜎0), peak-detection algorithm, and thresholding at the local minimum of the filtered 𝜎0 histogram.
  • Hydroperiod Calculation: SAR-fusion approach, integrating SAR flood classifications with hourly in-situ water level measurements using a pixel-specific flood threshold (𝑊threshold).
  • Validation: "Bathtub" model, simulating flooding extent by filling a LiDAR-derived Digital Terrain Model (DTM) with hourly in-situ water level measurements.
• Data sources:
  • Satellite: Sentinel-1 A and Sentinel-1 B time series (VV polarization) for the year 2021.
  • Observation (in-situ): Hourly water level measurements from Ipswich Bay, near the mouth of the Plum Island Estuary, obtained from the PIE LTER Environmental Data Initiative (EDI) data portal.
  • Ancillary: PIE land-cover map (from the National Wetlands Inventory), 3 meter LiDAR-derived topographic–bathymetric Digital Terrain Model (DTM) from 2014 (Cooperative Institute for Research in Environmental Sciences (CIRES), 2014, NOAA National Centers for Environmental Information).

Main Results

• 46 out of 53 Sentinel-1 acquisitions (87%) were retained for analysis due to exhibiting a clear bimodal backscatter distribution, indicating distinct flooded and non-flooded classes.
• The SAR-only hydroperiod effectively distinguished inundation regimes across wetland types (tidal flats, low marshes, high marshes) and captured key hydrogeomorphic gradients within the Plum Island Estuary.
• Seasonal filtering of SAR acquisitions (October to early May) significantly improved the negative correlation between VV backscatter (𝜎0) and water level across all wetland types, enhancing the accuracy of inundation detection by minimizing vegetation-induced variability.
• The SAR-fusion hydroperiod product showed strong overall agreement with the independent 'bathtub' model hydroperiod, with an R² of 0.92 and a Root Mean Square Error (RMSE) of approximately 12.3%.
• Discrepancies were observed, with the SAR-fusion hydroperiod tending to overestimate flooding in isolated depressions, narrow creeks, and tidal flats. This overestimation is attributed to SAR's sensitivity to microtopography and potential misclassification of saturated mudflats as flooded.
• The SAR-fusion approach successfully identified small ponds and isolated water bodies, which are ecologically significant features often not represented in the bathtub model.
• Performance varied by wetland class: Tidal flats (R² = 0.69, RMSE = 18.2%), Low marshes (R² = 0.57, RMSE = 12.3%), and High marshes (R² = 0.48, RMSE = 7.9%).

Contributions

• Developed a novel and straightforward methodology that integrates sparse Synthetic Aperture Radar (SAR) time series with high-frequency in-situ water level measurements to reconstruct continuous, high-resolution (hourly, 10 meter) salt marsh hydroperiods.
• Demonstrated the capability of SAR time series to provide spatially extensive and high-resolution estimates of hydroperiod, overcoming limitations of traditional in-situ measurements (logistical challenges, cost, sparsity) and optical remote sensing (cloud cover, limited vegetation penetration).
• Validated the SAR-fusion approach against an independent LiDAR-derived 'bathtub' model, showing strong agreement and highlighting SAR's unique sensitivity to microtopography and small, isolated water bodies crucial for ecological assessment.
• Provided a robust method for large-scale monitoring of seasonal and interannual variations in salt marsh hydrology, which is critical for assessing wetland vulnerability and resilience in the face of accelerating sea-level rise.
• Addressed a significant data gap for spatially detailed datasets on coastal marsh hydrodynamics, thereby enhancing the applicability of existing marsh response models (e.g., SLAMM, HYDRO-MEM).

Funding

• NASA Earth System Science for Building Coastal Resilience program (award 80NSSC23K0129, NNH22ZDA001N-COASTAL)
• Research Opportunities in Space and Earth Sciences (ROSES) 2022
• U.S. National Aeronautics and Space Administration (NASA) contract to the Jet Propulsion Laboratory, California Institute of Technology

Citation

@article{Belhadjaissa2025Mapping,
  author = {Belhadj-aissa, Saoussen and Simard, Marc and Ruiz, Adriana Parra and Palacios, Jordi and Fagherazzi, Sergio},
  title = {Mapping salt marsh hydroperiod using Synthetic Aperture Radar time series},
  journal = {Remote Sensing Applications Society and Environment},
  year = {2025},
  doi = {10.1016/j.rsase.2025.101850},
  url = {https://doi.org/10.1016/j.rsase.2025.101850}
}