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Evaluation of late summer passive microwave Arctic sea ice retrievals

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2 Author(s)
T. Markus ; Joint Center for Earth Syst. Technol., NASA Goddard Space Flight Center, Greenbelt, MD, USA ; S. T. Dokken

The melt period of the Arctic sea ice cover is of particular interest in studies of climate change due to the albedo feedback mechanisms associated with meltponds and openings in the ice pack. The traditionally used satellite passive microwave sea ice concentration algorithms have deficiencies during the summer months due to the period's highly variable surface properties. A newly developed ice concentration algorithm overcomes some of these deficiencies. It corrects for low ice concentration biases caused by surface effects through the use of 85 GHz data in addition to the commonly used 19 and 37 GHz data and, thus, the definition of an additional ice type representing layering and inhomogeneities in the snow layer. This new algorithm will be the standard algorithm for Arctic sea ice concentration retrievals with the EOS Aqua advanced microwave scanning radiometer (AMSR-E) instrument. In this paper, we evaluate the performance of this algorithm for the summer period of 1996 using data from the special sensor microwave imager (SSM/I) which has frequencies similar to the AMSR instrument. The temporal evolution of summertime passive microwave sea ice signatures are investigated and sea ice concentration retrievals from the standard NASA team and the new algorithm are compared. The results show that the introduction of the additional sea ice type in the new algorithm leads to improved summertime sea ice concentrations. The SSM/I sea ice retrievals are validated using SAR-derived ice concentrations that have been convolved with the SSM/I antenna pattern to ensure an appropriate comparison. For the marginal ice zone, with ice concentrations ranging from 40% to 100%, the correlation coefficient of SAR and SSM/I retrievals is 0.66 with a bias of 5% toward higher SAR ice concentrations. For the central Arctic, where ice concentrations varied between 60% and 100%, the correlation coefficient is 0.87 with a negligible bias

Published in:

IEEE Transactions on Geoscience and Remote Sensing  (Volume:40 ,  Issue: 2 )