Internal wave parameters retrieval from space-borne SAR image

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.

Keywords

synthetic aperture radar / internal wave / retrieval

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Acknowledgements

We would like to thank Remote Sensing Ground Station of China, Chinese Academy of Sciences (CAS) and European Space Agency for providing the ERS-1/2 SAR, ENVISAT ASAR and Radarsat-1 SAR images, the CISL Research Data Archive (RDA) for providing the NCEP reanalysis wind data, both http://www.nodc.noaa.gov/ and http://iridl.ldeo.columbia.edu for providing the Levitus98 data and SODA data, and Dr. R. Romeiser for sharing the radar microwave backscatter imaging model of M4S. This research is jointly supported by the National Natural Science Foundation of China (Grant Nos. 41106155 and 41471227) and under the Open Fund of State Key Laboratory of Satellite Ocean Environment Dynamics (No. SOED1407). This work is also supported by General Research Fund of Hong Kong Research Grants Council (RGC) under grants CUHK 402912 and 403113. We also would like to thank the anonymous reviewers’ comments to improve the original manuscript.

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg