[MIS14-P30] Estimation of thin ice thickness and discrimination of ice type from AMSR2 passive microwave data
Keywords:Thin ice thickness, Ice type, AMSR2, Coastal polynya
We use TBs at 36 and 89 GHz from AMSR2 Level 1B data. The footprint sizes are 12 km × 7 km and 5 km × 3 km, respectively. Ice thickness for comparison with AMSR2 PR is derived from clear-sky thermal infrared images by MODIS with a spatial resolution of 1 km. Backscatter images acquired by C-band SAR (C-SAR) on the ESA’s Sentinel-1 satellite with a spatial resolution of 90 m are used to validate two thin ice types. We use these satellite data obtained at Ross Ice Shelf and Cape Darnley polynya areas.
AMSR2 PR-hi relationships for two ice types differs clearly as in Nakata et al. (2019). We estimate ice thickness using lines that best fit into the AMSR-E PR-hi relationships (Nakata et al., 2019) using AMSR2 data. And then, mean bias and root-mean-square error (RMSE) of the data points from the lines are calculated. The errors are slightly larger than the cases of AMSR-E data. When ice thickness is <10 cm, mean bias and RMSE for active frazil are -2.0 and 1.0 cm, respectively, while those for thin solid ice are -6.2 and 9.8 cm, respectively. For ice thickness of 10-20 cm (only for thin solid ice), the bias and RMSE are -5.9 and 9.9 cm, respectively. We also have confirmed that two ice types can be detected using AMSR2 data by a method for AMSR-E data proposed by Nakata et al. (2019). The results of this study indicate that the new thin ice algorithm for AMSR-E data can apply to the AMSR2 data.