Abstract
Recent developments of winds and waves retrieval from synthetic aperture radar (SAR ) are introduced. winds were retrieved using the geophysical model function (GMF) together with polarization ratio (PR) model. An ocean surface wave retrieval algorithm, named Parameterized First-guess Spectrum Method (PFSM), is used to extract wave parameters from X-band TerraSAR-X /TanDEM-X (TS-X/TD-X ) and C-band Sentinel-1 (S-1) SAR images over whole ocean, in particular, there are several cases at China seas . This theoretic-based algorithm relies on the first-guess wave spectrum produced by SAR -derived wind speed. More recent, several studies have made an attempt to retrieve significant wave height (SWH) and mean wave period (MWP) based on the cutoff wavelength in azimuth (or satellite flight) direction. A new semi-empirical algorithm for wave parameters retrieval at C-band, which depends on the radar incidence angle, wave propagation angle relative to range direction and azimuthal cutoff wavelength, has been implemented for S-1 SAR images and then validated against moored buoys. Although existing waves retrieval algorithms work for C- and X-band SAR with about 0.6 m error of SWH, the accuracy of retrieval waves is expected to be improved in the future.
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Acknowledgements
The X-band TS-X/TD-X images are kindly provided by German Aerospace Center (DLR) through several AOs (No. OCE1656 and OCE2256). We appreciated European Space Agency (ESA) provides C-band S-1 SAR images for free via https://scihub.copernicus.eu. NOAA buoy measurements were accessed via http://www.ndbc.noaa.gov/. ECMWF reanalysis data were openly accessed via http://www.ecmwf.int. Wave data from the WaveWatch-III model were kindly provided by the professional Institut Francais de Recherche pour Exploitation de la MER (IFREMER) group and the data were collected through the server: ftp.ifremer.fr.
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Shao, W., Li, X., Yang, X. (2019). Retrieval of Winds and Waves from Synthetic Aperture Radar Imagery. In: Barale, V., Gade, M. (eds) Remote Sensing of the Asian Seas. Springer, Cham. https://doi.org/10.1007/978-3-319-94067-0_16
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DOI: https://doi.org/10.1007/978-3-319-94067-0_16
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