Articles | Volume 18, issue 5
https://doi.org/10.5194/tc-18-2207-2024
https://doi.org/10.5194/tc-18-2207-2024
Research article
 | 
03 May 2024
Research article |  | 03 May 2024

SAR deep learning sea ice retrieval trained with airborne laser scanner measurements from the MOSAiC expedition

Karl Kortum, Suman Singha, Gunnar Spreen, Nils Hutter, Arttu Jutila, and Christian Haas

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Short summary
A dataset of 20 radar satellite acquisitions and near-simultaneous helicopter-based surveys of the ice topography during the MOSAiC expedition is constructed and used to train a variety of deep learning algorithms. The results give realistic insights into the accuracy of retrieval of measured ice classes using modern deep learning models. The models able to learn from the spatial distribution of the measured sea ice classes are shown to have a clear advantage over those that cannot.