Retrieval algorithm of sea surface wind vectors for WindSat based on a simple forward model

WindSat/Coriolis is the first satellite-borne polarimetric microwave radiometer, which aims to improve the potential of polarimetric microwave radiometry for measuring sea surface wind vectors from space. In this paper, a wind vector retrieval algorithm based on a novel and simple forward model was developed for WindSat. The retrieval algorithm of sea surface wind speed was developed using multiple linear regression based on the simulation dataset of the novel forward model. Sea surface wind directions that minimize the difference between simulated and measured values of the third and fourth Stokes parameters were found using maximum likelihood estimation, by which a group of ambiguous wind directions was obtained. A median filter was then used to remove ambiguity of wind direction. Evaluated with sea surface wind speed and direction data from the U.S. National Data Buoy Center (NDBC), root mean square errors are 1.2 m/s and 30° for retrieved wind speed and wind direction, respectively. The evaluation results suggest that the simple forward model and the retrieval algorithm are practicable for near-real time applications, without reducing accuracy.

[1]  V. Etkin,et al.  The Dependence of Sea Brightness Temperature on Surface Wind Direction and Speed. Theory and Experiment , 1991, [Proceedings] IGARSS'91 Remote Sensing: Global Monitoring for Earth Management.

[2]  Stephen Cox,et al.  A nonlinear optimization algorithm for WindSat wind vector retrievals , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Christopher Ruf,et al.  An emissivity-based wind vector retrieval algorithm for the WindSat polarimetric radiometer , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[4]  R. Kwok,et al.  Polarimetric passive remote sensing of ocean wind vectors , 1994 .

[5]  D. Batens,et al.  Theory and Experiment , 1988 .

[6]  Alexey V. Kuzmin,et al.  The dependence of S-band sea surface brightness and temperature on wind vector at normal incidence , 1995, IEEE Trans. Geosci. Remote. Sens..

[7]  F. Wentz A well‐calibrated ocean algorithm for special sensor microwave / imager , 1997 .

[8]  A.J. Gasiewski,et al.  Airborne passive polarimetric measurements of sea surface anisotropy at 92 GHz , 1994, Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium.

[9]  Bertrand Chapron,et al.  The potential of QuikSCAT and WindSat observations for the estimation of sea surface wind vector under severe weather conditions , 2007 .

[10]  Niels Skou,et al.  Wind direction over the ocean determined by an airborne, imaging, polarimetric radiometer system , 2001, IEEE Trans. Geosci. Remote. Sens..

[11]  Peter W. Gaiser,et al.  A statistical approach to WindSat ocean surface wind vector retrieval , 2006, IEEE Geoscience and Remote Sensing Letters.

[12]  W. Linwood Jones,et al.  The WindSat spaceborne polarimetric microwave radiometer: sensor description and early orbit performance , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Jeffrey Piepmeier,et al.  High-resolution passive polarimetric microwave mapping of ocean surface wind vector fields , 2001, IEEE Trans. Geosci. Remote. Sens..

[14]  Simon Yueh,et al.  Polarimetric microwave wind radiometer model function and retrieval testing for WindSat , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Simon Yueh,et al.  Polarimetric measurements of sea surface brightness temperatures using an aircraft K-band radiometer , 1995, IEEE Trans. Geosci. Remote. Sens..

[16]  T. Meissner,et al.  Ocean retrievals for WindSat: radiative transfer model, algorithm, validation , 2005 .

[17]  David G. Long,et al.  A median-filter-based ambiguity removal algorithm for NSCAT , 1991, IEEE Trans. Geosci. Remote. Sens..

[18]  Martti Hallikainen,et al.  Fully polarimetric microwave radiometer for remote sensing , 2003, IEEE Trans. Geosci. Remote. Sens..