1. SEA ICE CHARACTERISTICS IN THE SOUTHERN REGION OF OKHOTSK SEA OBSERVED BY X- AND L- BAND SAR Hiroyuki Wakabayashi (Nihon university) Shoji Sakai (Nihon university) Kazuki Nakamura (AIST) Fumihiko Nishio (CEReS/Chiba university) IGARSS2011 仙台 in Vancouver presented on Jul.28,2011

2. 2 Outline Study background and research objectives Test site and SAR data Ground truth experiment Data analysis Summary and future work

3. 3 Study background Role of sea ice monitoring  Sea ice extent and volume are related to local as well as global climate change  Sea ice acts as an insulator between air and water  Sign of decreasing sea ice extent in the Arctic Ocean Importance of SAR data  Microwave remote sensing plays an important role in monitoring sea ice in cryosphere due to its all weather capabilities  SAR data from TerraSAR-X and ALOS were available during wintering period in 2010

4. 4 Research objectives Objectives  Possible use of SAR data to monitor sea ice in the southern region of Okhotsk Sea Backscattering characteristics(Frequency, Polarization) Develop a method to extract sea ice physical parameters Our past experience  Field experiments from 1992 to 2011(Lake Saroma)  Single-pol SAR analysis (ERS-1/2,JERS-1,RADARSAT)  Polarimetric SAR analysis (Pi-SAR, PALSAR) Dual-pol. TerraSAR-X data

5. 5 Test sites Southern region of the Sea of Okhotsk » Seasonal Ice Zone : sea ice exists only in wintertime » Most sea ice in this area has the thickness less than 1 m Lake Saroma Lake Saroma ( 150km 2 The third biggest lake in Japan ) » Salt water lake connected to the Sea of Okhotsk with 2 channels » Salinity of lake water is almost the same as that of sea water ( > 30 ppt ) » Lake ice grows till 40 cm thick in winter time and is stable enough to get the ground truth data in winter time » More than 60 sampling points with 500m interval were set in 2010

6. 6 Satellite and Ground Observations Ground truth experiment :2010/02/16-02/26 Satellite observation satellite sensor observation date observation time (UT) polarization incidence angle (scene center) TerraSAR-X2010/02/1808:12HH+VV36.8° ALOS PALSAR 2010/02/2012:37 HH+VV+ HV+VH 24.0° TerraSAR-X2010/02/2408:04HH+VV20.8° List of satellite data used in this analysis

7. 7 31.7km 39.6km 27.4km 54.5km Satellite images (HH-pol) TerraSAR-X(2010.02.18) High incidence angle TerraSAR-X(2010.02.24) Low incidence angle ALOS/PALSAR (2010.02.20) 63.4km 68.9km Lake Saroma -Slant range complex data provided by Pasco and JAXA were used in this research

8. 8 Ground truth sampling points TerraSAR-X(2010.02.18) High incidence angle (62 points) TerraSAR-X(2010.02.24) Low incidence angle (62 points) ALOS/PALSAR (2010.02.20) (36 points) -Sampling points were approximately set 500 m interval on the east part of Lake Saroma

9. 9 Summary of ground truth data 1 Mean of measured data  snow depth ： 8.0 cm  ice thickness ： 31.5 cm  ice surface salinity ： 10.8 ppt -Typical ice on the lake found in mid February

10. 10 Summary of ground truth data 2 Mean of measured surface roughness (Roughness comb measurements)  RMS height: 4.2mm  Correlation length: 35.8mm - At least four measurements were averaged at each sampling point

11. 11 Relation between ice thickness and ice surface salinity comparison of 2010 and previous experiments - Plots in Saroma 2010 showed almost the same characteristics for sea ice of the thickness less than 40 cm.

12. 12 Relation between ice thickness and other parameters - Snow depth and surface roughness were weakly correlated with ice thickness

13. 13 Microwave scattering model analysis Surface-scattering model (Integral Equation Method Model) Surface-scattering model (Integral Equation Method Model) Dielectric constant model Dielectric constant model Ice salinity model Model parameters Frequency Incidence angle Air temperature Water temperature Roughness parameter (RMS height & Cor. length) Ice thickness Snow depth Backscattering coefficients VV to HH backscattering ratio etc.

14. 14 Model simulation results(X-band) - Co-pol backscattering coefficients decrease as ice thickens - Co-pol ratio at higher incidence angle is sensitive to ice thickness

15. 15 Ice thickness vs backscattering coefficient(TerraSAR-X) - Backscattering coefficient at lower incidence angle is correlated with ice thickness, especially at small snow depth area R=0.43R=0.57 (Ts<10cm)

16. 16 Ice thickness vs co-pol ratio (TerraSAR-X) - Co-pol ratio in higher incidence angle at small snow depth has some relation with ice thickness (no strong correlation) R=0.18R=0.50 (Ts<10cm)

17. 17 Snow layer related parameters (TerraSAR-X) - Co-pol correlation and dual-pol entropy in lower incidence angle have weak relations with snow depth

18. 18 Ice thickness vs backscattering coefficient(PALSAR) - PALSAR backscattering coefficient has almost no correlation with ice thickness

19. 19 FD decomposition vs. truth data (PALSAR) - Freeman and Durden three component decomposition gives reasonable relation to RMS height and snow depth

20. 20 Summary of regression analysis at Lake Saroma Relation between TerraSAR-X and ground truth data Ice thickness : relatively higher correlation found in lower incidence angle at small snow depth area Ice surface roughness : no significant relation was found Lower incidence angle observation is better Contribution of snow layer to backscattering coefficient cannot be ignored Relation between PALSAR and ground truth data Ice thickness : no significant relation was found Snow depth and RMS height : 3 component decomposition result shows reasonable relations Scattering decomposition technique is useful to extract information of ice physical data

21. 21 TerraSAR-X and MODIS albedo 2010.02.18 Al=0.3265*B1+0.4364*B3+0.2366*B4 Classification rule based on MODIS albedo Open water ( Al ＜ 0.1 ) New ice ( 0.1 ≦ Al ＜ 0.4 ) Young ice ( 0.4 ≦ Al ＜ 0.6 ) First-year ice ( 0.6 ≦ Al ) where Al: albedo B1,B3 and B4: reflectances observed in Band 1,3,and 4 - MODIS albedo used for sea ice detection is calculated as follows, Reference D.K.Hall, D.J.Cavalieri, T.Markus: Assessment of AMSR-E Antarctic Winter Sea-Ice Concentrations Using Aqua MODIS, IEEE Trans. on Geo-science and Remote Sensing. Vol.48, No.9, pp.3331-3339, 2010. Offshore area

22. 22 PALSAR backscattering and entropy 2010.02.20 Classification rule based on s cattering entropy (H) Open water ( H < 0.15 ) New ice ( 0.4 ≦ H ) Young ice & First-year ice ( 0.15 ≦ H <0.4) Reference H. Wakabayashi, T. Matsuoka, K. Nakamura and F. Nishio: Polarimetric characteristics of sea ice in the Sea of Okhotsk observed by airborne L-band SAR, IEEE Trans. on Geo-science and Remote Sensing, Vol. 42, No.11, pp. 2412-2425, 2004. Offshore area Scattering entropy used for sea ice detection

23. 23 Backscattering characteristics of sea ice in the offshore area TerraSAR-X(2010/02/18)PALSAR(2010/02/20) HH(dB)VV(dB)VV-HH(deg.) MODIS Albedo HH(dB)VV(dB)HV(dB) Scattering entropy MODIS Albedo New ice-16.4-15.06.50.15-21.7-21.0-28.40.730.17 Young ice FY ice -8.6-9.48.00.25-13.3-12.5-25.70.300.26 Open water -19.3-18.10.60.10-9.8-8.6-26.10.130.095

24. 24 Summary of backscattering characteristics of sea ice in the off-shore region New ice area PALSAR : -21 dB(VV) -21.7dB(HH) TerraSAR-X : -15.0dB(VV) -16.4dB(HH) TerraSAR-X : 5 to 6 dB higher than PALSAR Young ice area PALSAR : -12.5 dB(VV) -13.3dB(HH) TerraSAR-X : -9.4dB(VV) -8.6dB(HH) TerraSAR-X : 3 to 5 dB higher than PALSAR Considering TerraSAR-X and PALSAR incidence angles, the difference of backscattering range would be much larger at the same incidence angle TerraSAR-X is better than PALSAR in detecting thin sea ice (e.g. New ice)

25. 25 Summary Ground truth experiment was conducted (Feb. 16 to 26, 2010) In-Situ data at more than 60 sampling points were acquired Backscattering calibration by reflectors was conducted Absolute calibration coefficients were consisted with the provided cal. coefficients. Phase difference between HH and VV should be corrected at lower incidence angle. TerraSAR-X and PALSAR regression analysis on Lake Saroma TerraSAR-X Lower incidence angle observation is preferable for ice physical data extraction. Contribution of snow layer to backscattering coefficient cannot be ignored. PALSAR Scattering decomposition is useful to extract information of ice physical data. Backscattering characteristics of sea ice in the offshore area Backscattering coefficients for new ice and young ice were higher in X-band than that in L-band. X-band sea ice detection is better than L-band in thin sea ice area.

26. 26 Acknowledgement This research was supported by Grant-in-Aid for Exploratory Research of MEXT (No. 20651004). The PALSAR data were distributed under the agreement of JAXA Research Announcement. The research was titled "Sea ice study and its application using PALSAR polarimetric data in the Sea of Okhotsk (JAXA-PI: 205)". TerraSAR-X data were distributed under the support of SAR technical application research committee organized by Pasco cooperation. Future work Develop a backscattering model of sea ice in X-band to include snow layer on the ice. Investigate an inversion technique to extract ice physical data, such as snow depth on ice, ice surface roughness and ice thickness.

27. 27 Weather data(AMEDAS at Tokoro)

28. 28 CR deployment 500 m 50 cm Trihedral CR 70 cm Trihedral CR 50 cm Trihedral CR 70 cm Trihedral CR 500 m N

29. 29 Image of corner reflectors TerraSAR-X(2010.02.18) High incidence angle TerraSAR-X(2010.02.24) Low incidence angle Range Azimuth

30. 30 Result of radiometric calibration 2/182/24 HH(dB)VV(dB)VV-HH(deg.)HH(dB)VV(dB)VV-HH(deg.) CR#149.38749.336-1.147.28847.05081.2 CR#249.32449.261-1.546.98847.14082.1 CR#348.97148.620-1.846.88946.89580.9 CR#449.70149.367-1.247.34147.45282.0 Average49.34649.146-1.447.12747.13481.6 Cal. data Provided 49.58347.308 CR#1CR#2CR#3CR#4CR#1CR#2CR#3CR#4

31. 31 Correlation matrix(high incidence angle) σ 0 HH σ 0 VV σ 0 VV /σ 0 HH ρ HHVV TsTs TiTi σHσH l σ 0 HH 1 σ 0 VV 0.9661 σ 0 VV /σ 0 HH 0.1750.4231 ρ HHVV 0.7730.8030.3531 TsTs -0.167-0.123-0.203-0.2391 TiTi -0.066-0.110-0.187-0.1180.6571 σHσH -0.039-0.0330.013 0.3180.4201 l 0.2000.2520.2610.340-0.1900.1030.2011

32. 32 Correlation matrix(low incidence angle) σ 0 HH σ 0 VV σ 0 VV /σ 0 HH ρ HHVV TsTs TiTi σHσH l σ 0 HH 1 σ 0 VV 0.9911 σ 0 VV /σ 0 HH 0.0390.1701 ρ HHVV 0.8420.8780.3411 TsTs -0.547-0.5320.056-0.5101 TiTi -0.430-0.4250.026-0.3490.6571 σHσH -0.116-0.0960.143-0.0330.3180.4201 l 0.3670.3680.0380.353-0.1900.1030.2011

33. 33 Correlation matrix(combined high and low incidence angle) σ 0 HH (θ L )-σ 0 HH (θ H )σ 0 VV (θ L )-σ 0 VV (θ H )TsTs TiTi σHσH l σ 0 HH (θ L )-σ 0 HH (θ H )1 σ 0 VV (θ L )-σ 0 VV (θ H )0.9721 TsTs -0.628-0.6201 TiTi -0.513-0.5140.6571 σHσH -0.125-0.1100.3180.4201 l 0.1930.190-0.1900.1030.2011