Comparison of Pond Depth and Ice Thickness Retrieval Algorithms for Summer Arctic Sea Ice

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 12; p. 2831
• Main Authors: Zhang, Hang, Lu, Peng, Yu, Miao, Zhou, Jiaru, Wang, Qingkai, Li, Zhijun, Zhang, Limin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2022
• Subjects: Algorithms; Arctic sea ice; Cloud cover; Color; Helicopters; Ice; Ice cover; Ice thickness; melt pond; optical data; Optical properties; Ponds; Radiation; Remote sensing; Retrieval; Satellites; Sea ice; Solar energy; Thickness; Unmanned aerial vehicles; Unmanned helicopters; Arctic region
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14122831

In order to satisfy the demand of key sea ice parameters, including melt pond depth Hp and underlying ice thickness Hi, in studies of Arctic sea ice change in summer, four algorithms of retrieving Hp and Hi were compared and validated by using optical data of melt ponds from field observations. The Malinka18 algorithm stood out as the most accurate algorithm for the retrieval of Hp. For the retrieval of Hi, Malinka18 and Zhang21 algorithms could also provide reasonable results and both can be applied under clear and overcast sky conditions, while retrievals under clear sky conditions are more accurate. The retrieval results of Hi for Lu18 agreed better with field measurements for thin ice (Hi < 1 m) than that for thick ice, but those results of Hp were not satisfactory. The König20 algorithm was only suitable for clear sky conditions, and underestimated Hp, while showing a good agreement with Hp < 0.15 m. For Arctic applications, Malinka18 and Zhang21 algorithms provided a basis and reference for the satellite optical data such as WoeldView2 to retrieve Hp and Hi. Malimka18 also showed the ability to retrieve Hi, except for the Lu18 algorithm if pond color captured by helicopters and unmanned aerial vehicles were available. This study identifies the optimal algorithm for retrieval of Hp and Hi under different conditions, which have the potential to provide necessary data for numerical simulations of Arctic sea ice changes in summer.