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Abstract: Current advances and future outlook of bathymetry prediction utilising SWOT altimetry and machine learning (AGU25)

Current advances and future outlook of bathymetry prediction utilising SWOT altimetry and machine learning

Bjarke Nilsson1, Benjamin Phrampus2, Farshad Salajegheh3, Biao Liu4, Yao Yu5, David T Sandwell6, Hugh Harper7, Ole Baltazar Andersen4, Walter H F Smith8, Paul Elmore9, Jonathan Kirby4, James Beale10, Jamie Roberts11 and Luis Altamirano12, (1)Technical University of Denmark, Kgs. Lyngby, Denmark, (2)Naval Research Lab Stennis Space Center, Ocean Sciences Division, Stennis Space Center, United States, (3)University of Newcastle, Callaghan, Australia, (4)DTU Space, Lyngby, Denmark, (5)Scripps Institution of Oceanography, Institute of Geophysics and Planetary Physics, La Jolla, United States, (6)Scripps Institution of Oceanography, University of California San Diego, Institute of Geophysics and Planetary Physics, La Jolla, United States, (7)Appalachian State University, Boone, United States, (8)NOAA Lab Satellite Altimetry, College Park, MD, United States, (9)Naval Research Lab Stennis Space Center, Stennis Space Center, United States, (10)National Geospatial Intelligence Agency, St. Louis, United States, (11)National Geospatial Intelligence Agency, Springfield, United States, (12)The University of Southern Mississippi, School of Ocean Science and Engineering, Stennis Space Center, United States
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