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

• Published in: Remote Sensing
• Main Authors: Hang Zhang, Peng Lu, Miao Yu, Jiaru Zhou, Qingkai Wang, Zhijun Li, Limin Zhang
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2022
• Subjects: Arctic sea ice; melt pond; optical data; remote sensing; Science; Q; Arctic; Sea ice
• Online Access: https://doi.org/10.3390/rs14122831; https://doaj.org/article/ef63b392bd4640519e5e2f322cb69b08

In order to satisfy the demand of key sea ice parameters, including melt pond depth H p and underlying ice thickness H i , in studies of Arctic sea ice change in summer, four algorithms of retrieving H p and H i 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 H p . For the retrieval of H i , 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 H i for Lu18 agreed better with field measurements for thin ice ( H i < 1 m) than that for thick ice, but those results of H p were not satisfactory. The König20 algorithm was only suitable for clear sky conditions, and underestimated H p , while showing a good agreement with H p < 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 H p and H i . Malimka18 also showed the ability to retrieve H i , 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 H p and H i under different conditions, which have the potential to provide necessary data for numerical simulations of Arctic sea ice changes in summer.