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Multi‐Sensor Airborne Remote Sensing for Calibrating Hydrodynamic and Sediment Transport Models in Coastal Louisiana Wetlands (Atchafalaya–Terrebonne).

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Title: Multi‐Sensor Airborne Remote Sensing for Calibrating Hydrodynamic and Sediment Transport Models in Coastal Louisiana Wetlands (Atchafalaya–Terrebonne).
Authors: Payandeh, Ali Reza1 (AUTHOR) ali.reza.payandeh@jpl.nasa.gov, Simard, Marc1 (AUTHOR), Jones, Cathleen E.1 (AUTHOR), Christensen, Alexandra1 (AUTHOR), Jensen, Daniel1 (AUTHOR), Denbina, Michael1 (AUTHOR), Oliver‐Cabrera, Talib1 (AUTHOR)
Source: Water Resources Research. Apr2026, Vol. 62 Issue 4, p1-21. 21p.
Subjects: Calibration, Sediment transport, United States. National Aeronautics & Space Administration, Remote sensing, Hydrodynamics, Ground vegetation cover, Remote sensing by radar, Coastal wetlands
Geographic Terms: United States, Louisiana, Mississippi River Delta (La.)
Abstract: This study integrates high resolution remote sensing data from NASA's Delta‐X mission with a process based hydrodynamic and sediment transport model to improve predictions of water levels and suspended sediment dynamics in the Mississippi River Delta, in coastal Louisiana, USA, focusing on Atchafalaya and Terrebonne basins. A two dimensional Delft3D Flexible Mesh model was implemented using spatially variable bottom friction maps derived from optical imagery (AVIRIS‐NG and Sentinel‐2) to represent vegetation heterogeneity. Hydrodynamic calibration leveraged airborne interferometric radar measurements (Airborne Surface Water and Ocean Topography (AirSWOT) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR)) of water surface elevations and water level changes, while validation used in situ tide gauge records from spring and fall 2021. Model comparison with AirSWOT measurements in channels showed that spatially explicit roughness parameterizations substantially improved performance. Results were notably better for the spring acquisitions (root mean square error = 0.09 m; R2 = 0.82), likely due to higher wind speeds and steeper water surface slopes that increased surface roughness and improved radar retrieval performance. UAVSAR provided additional spatial constraints on transient water level changes across wetlands at ∼30‐min intervals, further informing roughness calibration. Model deviation from UAVSAR were typically within ± $\pm $4 cm, although performance degraded in forested and densely vegetated areas due to reduced radar coherence. Validation with in situ tide gauges confirmed model performance for tidal and subtidal variability. Sediment transport calibration using AVIRIS‐NG total suspended solids allowed refinement of settling velocity and critical shear stress. Overall, the integration of remote sensing data into model calibration and parameterization led to measurable improvements in hydrodynamic and sediment predictions. Key Points: Radar‐based water‐surface measurements constrain calibration best when wind/flow create sufficient roughness and water‐level gradientsInterferometric radar measurements degrade in dense vegetation (e.g., forested wetlands and aquatic‐vegetation zones) raising uncertaintyIn frictional wetlands like coastal Louisiana, vegetation‐based spatial roughness improves hydrodynamic skill over simple friction models [ABSTRACT FROM AUTHOR]
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Abstract:This study integrates high resolution remote sensing data from NASA's Delta‐X mission with a process based hydrodynamic and sediment transport model to improve predictions of water levels and suspended sediment dynamics in the Mississippi River Delta, in coastal Louisiana, USA, focusing on Atchafalaya and Terrebonne basins. A two dimensional Delft3D Flexible Mesh model was implemented using spatially variable bottom friction maps derived from optical imagery (AVIRIS‐NG and Sentinel‐2) to represent vegetation heterogeneity. Hydrodynamic calibration leveraged airborne interferometric radar measurements (Airborne Surface Water and Ocean Topography (AirSWOT) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR)) of water surface elevations and water level changes, while validation used in situ tide gauge records from spring and fall 2021. Model comparison with AirSWOT measurements in channels showed that spatially explicit roughness parameterizations substantially improved performance. Results were notably better for the spring acquisitions (root mean square error = 0.09 m; R2 = 0.82), likely due to higher wind speeds and steeper water surface slopes that increased surface roughness and improved radar retrieval performance. UAVSAR provided additional spatial constraints on transient water level changes across wetlands at ∼30‐min intervals, further informing roughness calibration. Model deviation from UAVSAR were typically within ± $\pm $4 cm, although performance degraded in forested and densely vegetated areas due to reduced radar coherence. Validation with in situ tide gauges confirmed model performance for tidal and subtidal variability. Sediment transport calibration using AVIRIS‐NG total suspended solids allowed refinement of settling velocity and critical shear stress. Overall, the integration of remote sensing data into model calibration and parameterization led to measurable improvements in hydrodynamic and sediment predictions. Key Points: Radar‐based water‐surface measurements constrain calibration best when wind/flow create sufficient roughness and water‐level gradientsInterferometric radar measurements degrade in dense vegetation (e.g., forested wetlands and aquatic‐vegetation zones) raising uncertaintyIn frictional wetlands like coastal Louisiana, vegetation‐based spatial roughness improves hydrodynamic skill over simple friction models [ABSTRACT FROM AUTHOR]
ISSN:00431397
DOI:10.1029/2025WR042357