Internal wave parameters retrieval from space-borne SAR image View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2015-12

AUTHORS

Kaiguo Fan, Bin Fu, Yanzhen Gu, Xingxiu Yu, Tingting Liu, Aiqin Shi, Ke Xu, Xilin Gan

ABSTRACT

Based on oceanic internal wave SAR imaging mechanism and the microwave scattering imaging model for oceanic surface features, we developed a new method to extract internal wave parameters from SAR imagery. Firstly, the initial wind fields are derived from NCEP reanalysis data, the sea water density and oceanic internal wave pycnocline depth are estimated from the Levites data, the surface currents induced by the internal wave are calculated according to the KDV equation. The NRCS profile is then simulated by solving the action balance equation and using the sea surface radar backscatter model. Both the winds and internal wave pycnocline depth are adjusted by using the dichotomy method step by step to make the simulated data approach the SAR image. Then, the wind speed, pycnocline depth, the phase speed, the group velocity and the amplitude of internal wave can be retrieved from SAR imagery when a best fit between simulated signals and the SAR image appears. The method is tested on one scene SAR image near Dongsha Island, in the South China Sea, results show that the simulated oceanic internal wave NRCS profile is in good agreement with that on the SAR image with the correlation coefficient as high as 90%, and the amplitude of oceanic internal wave retrieved from the SAR imagery is comparable with the SODA data. Besides, the phase speeds retrieved from other 16 scene SAR images in the South China Sea are in good agreement with the empirical formula which describes the relations between internal wave phase speed and water depths, both the root mean square and relative error are less than 0.11 m∙s–1 and 7%, respectively, indicating that SAR images are useful for internal wave parameters retrieval and the method developed in this paper is convergent and applicable. More... »

PAGES

700-708

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11707-015-0506-7

DOI

http://dx.doi.org/10.1007/s11707-015-0506-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1003529270


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