Comparison of Passive Microwave Data with Shipborne Photographic Observations of Summer Sea Ice Concentration along an Arctic Cruise Path

• Main Authors: Qingkai Wang, Peng Lu, Yongheng Zu, Zhijun Li, Matti Leppäranta, Guiyong Zhang
• Format: Article
• Language: English
• Published: MDPI AG 2019-08-01
• Series: Remote Sensing
• Subjects: sea ice concentration; passive microwave; shipborne observation; Arctic navigation
• Online Access: https://www.mdpi.com/2072-4292/11/17/2009

Arctic sea ice concentration (SIC) has been studied extensively using passive microwave (PM) remote sensing. This technology could be used to improve navigation along vessel cruise paths; however, investigations on this topic have been limited. In this study, shipborne photographic observation (P-OBS) of sea ice was conducted using oblique-oriented cameras during the Chinese National Arctic Research Expedition in the summer of 2016. SIC and the areal fractions of open water, melt ponds, and sea ice (<i>A</i>_w, <i>A</i>_p, and <i>A</i>_i, respectively) were determined along the cruise path. The distribution of SIC along the cruise path was U-shaped, and open water accounted for a large proportion of the path. The SIC derived from the commonly used PM algorithms was compared with the moving average (MA) P-OBS SIC, including Bootstrap and NASA Team (NT) algorithms based on Special Sensor Microwave Imager/Sounder (SSMIS) data; and ARTIST sea ice, Bootstrap, Sea Ice Climate Change Initiative, and NASA Team 2 (NT2) algorithms based on Advanced Microwave Scanning Radiometer 2 (AMSR2) data. P-OBS performed better than PM remote sensing at detecting low SIC (&lt; 10%). Our results indicate that PM SIC overestimates MA P-OBS SIC at low SIC, but underestimates it when SIC exceeds a turnover point (TP). The presence of melt ponds affected the accuracy of the PM SIC; the PM SIC shifted from an overestimate to an underestimate with increasing <i>A</i>_p, compared with MA P-OBS SIC below the TP, while the underestimation increased above the TP. The PM algorithms were then ranked; SSMIS-NT and AMSR2-NT2 are the best and worst choices for Arctic navigation, respectively.