Detection of Wind Speed and Sea Ice Motion in the Marginal Ice Zone from RADARSAT-2 Images Alexander S. Komarov 1, Vladimir Zabeline 2, and David G. Barber 1 1 Centre for Earth Observation Science, University of Manitoba, Winnipeg, Canada 2 Canadian Ice Service, Environment Canada, Ottawa, Canada OBJECTIVE  To develop and validate an ocean surface wind speed retrieval model free of wind direction input for RADARSAT-2 HH-HV ScanSAR imagery  To explore the possibility of merging SAR-derived wind speed over the ocean surface and ice motion over the sea ice for better monitoring of sea ice dynamics in the Arctic Ocean Marginal Ice Zone (MIZ) WIND SPEED RETRIEVAL FROM HH-HV IMAGES WITHOUT INPUT WIND DIRECTION We built a database which contains 248 ocean buoy measurements coincident and collocated with RADARSAT-2 ScanSAR HH-HV observations over the Canadian west and east coasts. The time difference between SAR images and buoy data does not exceed 30 min. 100 km We propose a quadratic relationship in a regression model between buoy wind speed, HH normalized radar cross- sections (NRCS), cross-polarization variable and the incidence angle (no wind direction as a predictor): Fig. 1. Dependence of (a) HH normalized radar cross-section (NRCS) and (b) HV NRCS and noise equivalent sigma zero (NESZ) on the buoy wind speed for three ranges of incidence angles; 248 samples. Fig. 2. Dependence of the HV cross-polarization variable on the buoy wind speed for three ranges of incidence angles. where the cross-polarization variable is defined as follows: Threshold was chosen to be HH and HV NRCS, [dB] HV NRCS and NESZ, [linear] incidence angle, [degree] High thresholds exclude the cross-polarization signal from the wind retrieval model, which decreases the accuracy of retrieved wind speeds. Too low threshold values lead to the appearance of noise floor stripes in wind speed maps. wind speed, [m/s] ACKNOWLEDGEMENT Part of this study was conducted at the Meteorological Service of Canada, Environment Canada, within the National SAR Winds Project, which was supported by the Canadian Space Agency through the Government Related Initiatives Program. Thanks to the Canadian Ice Service and Dr. Roger De Abreu (now at the Canada Centre for Remote Sensing, Ottawa) for supporting ice motion tracking work and for providing RADARSAT-2 imagery AK research is supported by NSERC Canada Graduate Scholarship. REFERENCES 1.H. Hersbach, “Comparison of C-band scatterometer CMOD5.N equivalent neutral winds with ECMWF,” J. Atmos. Ocean. Technol., vol. 27, no. 4, pp. 721–736, Apr B. Zhang, W. Perrie, and Y. He, “Wind speed retrieval from RADARSAT-2 quad-polarization images using a new polarization ratio model,” J. Geophys. Res., vol. 116, no. C8, p. C08 008, A. S. Komarov and D. G. Barber, “Sea ice motion tracking from sequential dual-polarization RADARSAT-2 images,” IEEE Trans. Geosci. Remote Sens., in press, A. S. Komarov, V. Zabeline, and D. G. Barber, “Ocean surface wind speed retrieval from C-band SAR images without wind direction input,” IEEE Trans. on Geosci. and Remote Sens., in press, CONCLUSION 1.A new model for ocean surface wind speed retrieval from RADARSAT-2 ScanSAR HH-HV images was proposed. The independent variables of this model are HH NRCS, HV NRCS, NESZ and incidence angle. The new cross- polarization variable in the regression model was introduced as a combination of the HV signal and the noise floor. 2.The developed HH-HV model showed better performance (RMSE = 1.59 m/s) than the CMOD5.N with the SAD polarization ratio (RMSE = 2.19 m/s) on the independent dataset. The introduced cross-polarization variable compensates for the absence of the wind direction, which is required by CMOD-type models. 3.We proposed the ice motion – wind speed product, which appears to be promising for studying various dynamic processes in the Arctic Ocean MIZ. 4.More details on this study are in [4] which is currently in press. COMBINED WIND SPEED-ICE MOTION PRODUCT Wind speed and sea ice motion can be extracted from the same SAR dataset – RADARSAT-2 ScanSAR dual- polarization imagery. Fig. 6. Two examples of wind speed-ice motion products in MIZ. Sea ice drift vectors are overplayed on a wind speeds map calculated from the first HH-HV image. Color bar indicates wind speed (m/s). 1 pixel is 100 m. Note, that wind speed is valid over open water. Oct. 10 – Oct. 13, 2009 Banks Island Sep. 28 – Sep. 29, km Our ice motion tracking algorithm detects similar features in two sequential SAR images using the combination of cross- and phase-correlation matching techniques at different resolution levels. Full details of the method are in [3]. Fig. 5. SAR derived ice motion vectors vs GPS ice beacon data.Fig. 4. SAR-derived ice motion vectors from May 24-May 25, 2008 image pair. Green: high level of confidence, yellow: medium level of confidence, red: low level of confidence. SEA ICE MOTION TRACKING FROM SEQUENTIAL SAR IMAGES Training subset (130 samples)Testing subset (118 samples) CMOD5.N + SAD pol ratio HH-HV model without wind direction Fig. 3. HH-HV wind speed retrieval model without wind direction against wind speed retrieval using CMOD5.N + SAD pol ratio. 100 km VALIDATION OF HH-HV WIND SPEED RETRIEVAL MODEL Fig. 3 demonstrates that our HH-HV model performs significantly better than CMOD5.N [1]+SAD co-polarization ratio [2] which requires input wind direction. The developed wind speed retrieval model is particularly useful in the Arctic Ocean MIZ where numerical weather prediction (NWP) models are often not reliable.