Estimation of Arctic sea ice thickness from CryoSat-2 altimetry data. Issue 7 (3rd April 2023)

• Record Type: Journal Article
• Authors: Jiang, Jinghui; Liu, Shanwei; Sun, Qintin; Wan, Jianhua
• Is Part Of: International journal of remote sensing. Volume 44:Issue 7(2023)
• Page Start: 2145
• Page End: 2167
• Publication Date: 2023-04-03
• Subjects: Sea ice thickness -- CryoSat-2 -- Freeboard -- Arctic; Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2023.2195575
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Arctic sea ice change is one of the critical factors affecting the global climate environment; hence, it is crucial to obtain sea ice thickness with high accuracy while studying polar and global climate change. In the process of using altimetry data to estimate sea ice thickness, the mean sea surface height will bring greater uncertainty to the extraction of sea ice freeboard, which will affect the accuracy of sea ice thickness. Also, the influence of snow cover on radar signal penetration will bring greater uncertainty to sea ice thickness. In this paper, we present a processing chain for sea ice thickness estimation. First, we compare the effect of four different MSS models on the freeboard estimation. Then, considering the incomplete penetration of radar signals and the different speeds of radar signals penetrating the snow layer and the vacuum, the traditional sea ice thickness model is optimized to obtain the sea ice thickness. Compared with the Operation IceBridge (OIB) sea ice thickness, the accuracy of sea ice thickness obtained by the optimized model is better than 0.350 m, with a root mean square error of 0.260 m and a mean bias of 0.048 m. The comparison results show that the combination of the latest DTU18 MSS model and sea ice thickness optimization model effectively improve the accuracy of sea ice thickness.