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Remote Sens. 2015, 7(4), 3548-3564; doi:10.3390/rs70403548

Sea Surface Wind Retrievals from SIR-C/X-SAR Data: A Revisit

1
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2
Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, Guilin 541004, China
*
Author to whom correspondence should be addressed.
Academic Editors: Richard Gloaguen and Prasad S. Thenkabail
Received: 22 November 2014 / Revised: 16 February 2015 / Accepted: 17 March 2015 / Published: 26 March 2015
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Abstract

The Geophysical Model Function (GMF) XMOD1 provides a linear algorithm for sea surface wind field retrievals for the Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar (SIR-C/X-SAR). However, the relationship between the normalized radar cross section (NRCS) and the sea surface wind speed, wind direction and incidence angles is non-linear. Therefore, in this paper, XMOD1 is revisited using the full dataset of X-SAR acquired over the ocean. We analyze the detailed relationship between the X-SAR NRCS, incidence angle and sea surface wind speed. Based on the C-band GMF CMOD_IFR2, an updated empirical retrieval model of the sea surface wind field called SIRX-MOD is derived. In situ buoy measurements and the scatterometer data of ERS-1/SCAT are used to validate the retrieved sea surface wind speeds from the X-SAR data with SIRX-MOD, which respectively yield biases of 0.13 m/s and 0.16 m/s and root mean square (RMS) errors of 1.83 m/s and 1.63 m/s. View Full-Text
Keywords: X-band Synthetic Aperture Radar (SAR); SIR-C/X-SAR; sea surface wind field; retrieval X-band Synthetic Aperture Radar (SAR); SIR-C/X-SAR; sea surface wind field; retrieval
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Ren, Y.; Li, X.-M.; Zhou, G. Sea Surface Wind Retrievals from SIR-C/X-SAR Data: A Revisit. Remote Sens. 2015, 7, 3548-3564.

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