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dc.contributor.authorKudryavtsev, Vladimir
dc.contributor.authorFan, Shengren
dc.contributor.authorZhang, Biao
dc.contributor.authorChapron, Bertrand
dc.contributor.authorJohannessen, Johnny Andre
dc.contributor.authorMoiseev, Artem
dc.date.accessioned2024-11-08T11:48:38Z
dc.date.available2024-11-08T11:48:38Z
dc.date.created2023-03-04T12:53:23Z
dc.date.issued2023
dc.identifier.issn0196-2892
dc.identifier.urihttps://hdl.handle.net/11250/3164073
dc.description.abstractThis article proposes a Doppler velocity (DV) model based on dual co-polarized (co-pol) decomposition of a normalized radar cross section of an ocean surface on polarized Bragg scattering and nonpolarized (NP) radar returns from breaking wave components. The dual co-pol decomposition provides a quantitative description of resonant and NP scattering, as well as their dependence on the incident angle, azimuth, and wind speed. Subsequently, the contributions of the facet (resonant Bragg waves and breakers) velocities, tilt, and hydrodynamic modulations due to long waves to the resulting DV can be quantified. The tilt modulation contributions to DV are estimated using the measured/empirical tilt modulation transfer function (MTF). The hydrodynamic modulations are mostly dominated by wave breaking and are estimated using a semiempirical model based on in situ measurements. In addition to the VV and HH radar data, which are required for dual co-pol decomposition and tilt MTF estimates, the surface wave spectrum is required in the DV determination for a given radar observation geometry. In this article, qualitative and quantitative consistencies are presented between the model simulations and the empirical CDOP model. In a companion paper, a DV analysis is presented to analyze the Sentinel-1 synthetic aperture radar measurements and collocated in situ measurements of surface wind and wave spectra.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.titleOn the use of dual co-polarized radar data to derive a sea surface Doppler model-part 1: Approachen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionacceptedVersionen_US
dc.rights.holderCopyright 2023 IEEEen_US
dc.source.articlenumber4201013en_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode2
dc.identifier.doi10.1109/TGRS.2023.3235829
dc.identifier.cristin2131169
dc.source.journalIEEE Transactions on Geoscience and Remote Sensingen_US
dc.identifier.citationIEEE Transactions on Geoscience and Remote Sensing. 2023, 61, 4201013.en_US
dc.source.volume61en_US


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