Reconstruction of Antarctic sea ice thickness from sparse satellite laser altimetry data via deep learning
Ziqi Ma, Qinghua Yang, Yue Xu, Wen Shi, Xiaoran Dong, Qian Shi, Hao Luo, Jiping Liu, Petteri Uotila, Yafei Nie · 2025
Read Reconstruction of Antarctic sea ice thickness from sparse satellite laser altimetry data via deep learning by Ziqi Ma, Qinghua Yang, Yue Xu, Wen Shi, Xiaoran Dong, Qian Shi, Hao Luo, Jiping Liu, Petteri Uotila, Yafei Nie (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12828026.txt.
Description
Ziqi Ma and co-authors built a 2025 data set for the thickness of Antarctic sea ice. Full maps of thickness at fine time steps have been missing, which limits work on ice mass and climate links. Satellite laser data give good values but only along narrow tracks.
Using deep learning, the team filled the gaps between tracks from two satellite missions. The result covers the whole Antarctic at five-day steps and fine grid size. It matched sonar measures taken from below the ice better than four other products. Seasonal patterns also matched well. The data set can be updated near real time, which helps tracking and forecasts of ice state.
Catalog record
| CREATOR | Ziqi Ma, Qinghua Yang, Yue Xu, Wen Shi, Xiaoran Dong, Qian Shi, Hao Luo, Jiping Liu, Petteri Uotila, Yafei Nie |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 46 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |