1. SWARP KO Bergen, 4 Feb. 2014 WP4 : Satellite remote sensing of wave in ice www.photofromtheworld.com

2. SWARP KO Bergen, 4 Feb. 2014 OceanDataLab (ODL) IFREMER spin-off incorporated in Brest, april 2013. Principal objective: Develop tools for multimodal synergy analysis (multisensors, models, in-situ) 4 persons : 3 research engineers and 1 software engineer. Role in SWARP : provide SAR wave spectra retrieval in the marginal ice zone and participate to the validation effort.

3. SWARP KO Bergen, 4 Feb. 2014 WP4 Outline Objective To develop and implement remote sensing methods for observation and model validation of waves in the MIZ. Tasks 1.Ice type recognition from coarse resolution scatterometry (Ifremer) 2.Ice type recognition from high resolution SAR and optical images (NIERSC) 3.Waves-in-ice retrieval methodology review and implementation (ODL) 4.Acquisition and analysis of collocated SAR, optical and CryoSat altimeter data (NERSC) 5.Analysis of observed waves-in-ice evolution relative to sea ice type (ODL)

4. Task 4.1 : Ice type recognition from coarse resolution scatterometry Fanny GIRARD ARDHUIN, IFREMER

5. Sea ice roughness from scatterometer sensors Canada 18-02-2007 Grid. resolution : 12.5 km, 25 km Daily (weekly for ERS) Since 1991 QuikSCAT for both Arctic & Antarctic areas ERS-1&2 : 1991-2001 NSCAT : 1996-1997 ASCAT : 2007-present QuikSCAT : 1999-2009 Ifremer/CERSAT unique time series Low res. scatterometer data : daily maps for both pole areas

6. Robinson (2004) Multi year ice (MY) First year ice (FY) 18-02-2007 QuikSCAT FYMY Roughness is linked with sea ice age MY/FY ice can be detected Ice type from scatterometer OCT MAY MARDEC Example of backscatter time series during a winter

7. MY extent time series Backscatter values over sea ice depend on frequencies, polarisation, incidence angle but also on ice type, salinity in the ice, etc... QuikSCAT method to adapt to ASCAT data for recent period (since 2009) for SWARP project and to validate → need SAR ice type detection Example of MY area time series with QuikSCAT data (1999- 2009) (consistent with Kwok's results) Swan & Long, 2009 Example of QuikSCAT backs. time series and ice type classification FY and MY areas can be quantified applying a moving back. threshold value

8. SWARP KO Bergen, 4 Feb. 2014 Task 4.2 Sea ice classification from SAR data Nansen International Environmental and Remote Sensing Centre, St.Petersburg, Russia Vladimir Volkov Natalia Zakhvatkina

9. SWARP KO Bergen, 4 Feb. 2014 Objective  To develop sea ice classification algorithm using high-resolution SAR images in order to classify the MIZ in selected test areas  To map ice edge and the details of the ice cover like ice types, open water and various stages of new and first-year ice, leads, polynyas, and others;  Implement the developed technique for the determination of the zone of broken-up floes in the MIZ, which will be used to validate the floe size distribution given by sea ice models

10. SWARP KO Bergen, 4 Feb. 2014 Data I. Envisat’s ASAR (Advanced Synthetic Aperture Radar) – ASAR operated in the C band in 5 modes; we worked mostly with Wide Swath mode images of 405 km swath and 150 m resolution. – ESA announced the end of Envisat's mission on 9 May 2012. II. Radarsat-2 SAR – multiple modes of operation, – HH, HV, VV and VH polarized data can be acquired, – its highest resolution is 3 m in Very High Resolution mode, – we work with data of ScanSAR Wide Beam mode that has a nominal swath width of 500 km and an imaging resolution of 100 m. III. Optical data ?????????????

11. SWARP KO Bergen, 4 Feb. 2014 Radarsat-2 ScanSAR Wide mode images We use RADARSAT-2 data received in ScanSAR Wide (SCW) mode at HH (horizontally transmitted and horizontally received) and HV (horizontally transmitted, vertically received) polarizations. This mode assembles wide SAR image from several narrower SAR beams, resulting to an image of 500 × 500 km with 100 m resolution. HHHV Fram Strait, 20/02/2012 OWr OW Ice OWr OW Ice Ice and water pixels can be separated OW rough OW calm Sea Ice

12. SWARP KO Bergen, 4 Feb. 2014 Sea ice classification using SAR images Two RADARSAT-2 SAR ScanSAR Wide images: HH and HV polarizations SAR images calibration, angular dependence correction Noise correction of HV dual-polarization SAR image Image features calculation: mean backscatter, texture characteristics Image classification using Support Vector Machines technique Sea ice charts

13. SWARP KO Bergen, 4 Feb. 2014 Noise reduction in HV polarization The effect is reduced by subtracting the noise floor level from the HV image values. Left image - raw HV polarization image, right image – noise reduced image. Blue curve shows the sigma0 value profile of the raw HV channel image over the horizontal line, the red curve depicts the noise floor level and the green curve is the result of subtraction

14. SWARP KO Bergen, 4 Feb. 2014 Support Vector Machines algorithm TEACHING (klusterization)

15. SWARP KO Bergen, 4 Feb. 2014 Support Vector Machines CLASSIFICATION

16. SWARP KO Bergen, 4 Feb. 2014 Automated classification of Radarsat-2 data The described technique has been used in the development of automated sea ice / water classification.

17. SWARP KO Bergen, 4 Feb. 2014 18 Jan 2014

18. SWARP KO Bergen, 4 Feb. 2014 Vilkitskiy Stright, 23, 26 Aug 2013 HHHVSVM

19. SWARP KO Bergen, 4 Feb. 2014 28 Aug 2013

20. SWARP KO Bergen, 4 Feb. 2014 Task 4.3 Waves-in-ice retrieval methodology review and implementation Task 4.3 Waves-in-ice retrieval methodology review and implementation Fabrice Collard (OceanDataLab)

21. SWARP KO Bergen, 4 Feb. 2014 Wave modulation modification Incident swell MIZ

22. SWARP KO Bergen, 4 Feb. 2014 Wave modulation modification Incident swell MIZ

23. SWARP KO Bergen, 4 Feb. 2014 Wave modulation modification Incident swell MIZ

24. SWARP KO Bergen, 4 Feb. 2014 MIZ OPEN OCEAN ASAR data © ESA 2011

25. SWARP KO Bergen, 4 Feb. 2014 Wave modulation modification MIZ

26. SWARP KO Bergen, 4 Feb. 2014 Wave modulation modification MIZ

27. SWARP KO Bergen, 4 Feb. 2014 Work to be done Update Modulation transfer functions to cope with ice roughness and dynamical properties Validate retrieved SAR wave spectra using wave buoys in the MIZ.

28. SWARP KO Bergen, 4 Feb. 2014 Task 4.4 Acquisition and analysis of collocated SAR, optical and CryoSat altimeter data (NERSC)

29. SMOS ice Thickness 2013 02 19

30. SWARP KO Bergen, 4 Feb. 2014 Task 4.5 Analysis of observed waves-in-ice evolution relative to sea ice type (ODL)

31. SWARP KO Bergen, 4 Feb. 2014 Attenuation analysis WARNING !!! : Uncalibrated retrieved wave height in the MIZ MIZ OPEN OCEAN