4.7 Article

Influence of Melt Ponds on the SSMIS-Based Summer Sea Ice Concentrations in the Arctic

Journal

REMOTE SENSING
Volume 13, Issue 19, Pages -

Publisher

MDPI
DOI: 10.3390/rs13193882

Keywords

sea ice concentration; melt pond fraction; SSMIS; arctic

Funding

  1. National Key Research and Development Program of China [2018YFC1407206, 2019YFE0105700-02]

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The study assessed three SSMIS-based sea ice concentration products and found potential underestimation or overestimation of concentration depending on the algorithms used. The presence of melt ponds influenced the concentration biases, with a relationship established between sea ice concentration biases and melt pond fraction observations for improved accuracy in 2D sea ice concentration distributions.
As a long-term, near real-time, and widely used satellite derived product, the summer performance of the Special Sensor Microwave Imager/Sounder (SSMIS)-based sea ice concentration (SIC) is commonly doubted when extensive melt ponds exist on the ice surface. In this study, three SSMIS-based SIC products were assessed using ship-based SIC and melt pond fraction (MPF) observations from 60 Arctic cruises conducted by the Ice Watch Program and the Chinese Icebreaker Xuelong I/II. The results indicate that the product using the NASA Team (SSMIS-NT) algorithm and the product released by the Ocean and Sea Ice Satellite Application Facility (SSMIS-OS) underestimated the SIC by 15% and 7-9%, respectively, which mainly occurred in the high concentration rages, such as 80-100%, while the product using the Bootstrap (SSMIS-BT) algorithm overestimated the SIC by 3-4%, usually misestimating 80% < SIC < 100% as 100%. The MPF affected the SIC biases. For the high MPF case (e.g., 50%), the estimated biases for the three products increased to 20% (SSMIS-NT), 7% (SSMIS-BT), and 20% (SSMIS-OS) due to the influence of MPF. The relationship between the SIC biases and the MPF observations established in this study was demonstrated to greatly improve the accuracy of the 2D SIC distributions, which are useful references for model assimilation, algorithm improvement, and error analysis.

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