SAR Polarimetry for Sea Ice Monitoring

1. SAR Polarimetry for Sea Ice Monitoring Wolfgang Dierking1,2, Henning Skriver2, and Preben Gudmandsen3 1 Alfred Wegener Institute for Polar and Marine Research, Germany 2Ørsted-DTU, Dept. of Electromagnetic Systems, Technical University of Denmark 3 Technical University of Denmark

2. ESA POLSAR ProjectSea Ice Study Our presentation focusses on two questions: • Do we gain anything by utilising polarimetric data (intensities + phase differences) in sea ice classification ? • What is the optimal strategy for sea ice classification ?

3. Data Sets We utilised airborne SAR imagery from the flight campaigns listed below: • AIRSAR: Beaufort Sea (March 1988), L+C-band • EMISAR: Greenland Sea (March 1995), C-band • EMISAR: Baltic Sea (March 1995), L+C-band (EMAC campaign) (Winter conditions, ice regimes at the 3 sites are different from one another.)

4. Polarimetric Phase HHVV • Including HHVV and HHVV in a sea ice classification scheme does not significantly improve the classification accuracy. (Rignot and Drinkwater, 1994) • Thin ice areas often reveal a HHVV significantly different from zero. Is HHVV linked to the thickness of thin ice ? (Winebrenner et al., 1995; Thomsen et al., 1998a,b; Thomsen, 2001) • A fully polarimetric model (intensity and phase) has been used to retrieve thin ice thickness by means óf a neural network. Result: C-band data seems to work for thicknesses 0-10cm, L-band is less sensitive. Contribution of individual polarization coefficients ? (Kwok et al., 1995)

5. Intensity R-0XP, G-0HH, B-0VV Co-polarisation Ratio 0VV / 0HH Phase HHVV Versus Intensity Example 1 Greenland Sea C-Band Depolarisation Ratio 0HV / (0HH + 0VV) Phase Difference HHVV

6. Intensity R-0XP, G-0HH, B-0VV Co-polarisation Ratio 0VV / 0HH Phase Difference HHVV Phase HHVV Versus Intensity- Example 2 Greenland Sea, C-Band

7. Phase difference HHVV Observations 1

8. Phase difference HHVV - Observations 2

9. Thin Ice Radar Signatures C-band scatterometer measurements over growing ice in a cold room at CRREL: Backscattered intensity at like- and cross- polarisation increased by 6-10 dB as the ice thickened from 3cm to 11cm. (Nghiem et al., 1997) TH1 TH2 TH3 TH4

10. Phase Difference HHVV • Improves classification: discrimination thin ice – open water • Linked with thin ice thickness ? (classification, heat and salt fluxes) • Needs further research: what determines the magnitude of HHVV ? (brine inclusions, anisotropic volume – scattering from the ice-water interface, dielectric profile)

11. Sea Ice Classification Our choice is a hierarchical scheme (knowledge-based approach) WHY ? • Results of measurements and theoretical modelling of sea ice radar signatures, and the experience gained from field campaigns can be considered. • Decision boundaries at the individual levels in the hierarchy can be determined by means of statistical methods. (A similar procedure is applied at the ASF, Kwok et al., 1991)

12. Methodology: Step 1 • We determined „typical“ values of various polarimetric parameters for different ice types visually (subjectively) identified in the radar images. • Polarimetric parameters: • Covariance matrix: intensities VV, HH, XP, correlation and phase • difference HHVV, co- and depolarisation ratio, symmetry. • Decomposition (coherency matrix): entropy, alpha, anisotropy. Visual classification on the basis of • a 3-layer image format representing only intensities (R-HV, G-HH, B-VV) • complementary data (photos, videos, in-situ spot measurements, meteorological data).

13. Greenland Sea Baltic Sea Polarimetric parameters of different ice types - Example Beaufort Sea

14. Methodology: Step 2 For the classification scheme, polarimetric parameters have been selected for which distance between ice type data clusters is largest

15. Methodology: Step 3 We devised classification rules for each test site and radar band Classification Rules for Greenland Sea Ice (Winter)

16. Classification, Greenland Sea, C-Band Hierarchical Approach, ISODATA thresholds Intensity R(HV) G(HH) B(VV)

17. Classification, Greenland Sea, C-Band Used Parameters: 0HV, HHVV 0VV / 0HH 0HV / (0VV + 0HH) Classes: 1 Ridged ice (39) 2 MY ice 1(141) 3 MY ice 2 (98) 4 Thin ice 1-3 (101) 5 Thin ice 4 (49) 6 Open Water (33) Confusion matrix for hierarchical classification: 1 2 3 4 5 6 1 100.0 0.0 0.0 0.0 0.0 0.0 2 0.0 98.5 0.7 0.7 0.0 0.0 3 0.0 7.9 92.1 0.0 0.0 0.0 4 0.0 0.9 13.4 84.8 0.9 0.0 5 0.0 0.0 0.0 9.4 90.6 0.0 6 0.0 0.0 0.0 0.0 0.0 100.0 Confusion matrix for ISODATA with hierarchical classification as initialisation: 1 2 3 4 5 6 1 100.0 0.0 0.0 0.0 0.0 0.0 2 0.0 99.3 0.0 0.7 0.0 0.0 3 0.0 4.5 93.3 2.2 0.0 0.0 4 0.0 0.9 11.6 86.6 0.9 0.0 5 0.0 0.0 1.9 5.7 92.5 0.0 6 0.0 0.0 0.0 0.0 0.0 100.0

18. Classification: Beaufort Sea

19. Classification: Baltic Sea

20. Classification: Decomposition • Indicates scattering • mechanisms from sea ice: • Z9, Z6: surface scattering • with an increasing amount • of secondary scattering • contributions • Z8, Z5: volume scattering • from inclusions with a • decreasing correlation of • their orientation • Z2: noise-like scattering • from randomly oriented • scatterers

21. Sea Ice Classification • Regional and seasonal differences in ice cover characteristics require different „optimal“ sequences of classification rules (are they stable for a particular region and season ?).

22. Which frequency, which polarisations ? X-band (intensity) is very similar to C-band

23. Thank you for your attention !