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https://doi.org/10.5194/egusphere-2025-643
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/egusphere-2025-643
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Preprints
01 Apr 2025
 | 01 Apr 2025
Status: this preprint is open for discussion and under review for Geoscientific Instrumentation, Methods and Data Systems (GI).

Classification of Sea-Ice Concentration from Ship-Board S-Band Radar Images Using Open-Source Machine Learning Tools

Elizabeth Westbrook, Peter Gaube, Emmett Culhane, Frederick Bingham, Astrid Pacini, Carlyn Schmidgall, Julian Schanze, and Kyla Drushka

Abstract. The 2022 NASA Salinity and Stratification at the Sea Ice Edge (SASSIE) expedition measured ocean surface properties and air-sea exchange approximately 400 km north of Alaska and in the Beaufort Sea. The survey lasted 20 days, during which time screen captures from the shipboard S-band radar were collected. Our goal was to analyze these images to determine when the ship was approaching ice, in the ice, or in open water. Here we report on the development of a machine learning method built on the PyTorch software packages to classify the amount of sea ice observed in individual radar images on a scale from L0–L3, with L0 indicating open water and L3 assigned to images taken when the ship was navigating through thick sea ice in the marginal ice zone. The method described here is directly applicable to any radar images of sea ice and allows for the classification and validation of sea ice presence or absence.

How to cite. Westbrook, E., Gaube, P., Culhane, E., Bingham, F., Pacini, A., Schmidgall, C., Schanze, J., and Drushka, K.: Classification of Sea-Ice Concentration from Ship-Board S-Band Radar Images Using Open-Source Machine Learning Tools, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-643, 2025.
Received: 11 Feb 2025 – Discussion started: 01 Apr 2025
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
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Elizabeth Westbrook, Peter Gaube, Emmett Culhane, Frederick Bingham, Astrid Pacini, Carlyn Schmidgall, Julian Schanze, and Kyla Drushka

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Elizabeth Westbrook, Peter Gaube, Emmett Culhane, Frederick Bingham, Astrid Pacini, Carlyn Schmidgall, Julian Schanze, and Kyla Drushka
Elizabeth Westbrook, Peter Gaube, Emmett Culhane, Frederick Bingham, Astrid Pacini, Carlyn Schmidgall, Julian Schanze, and Kyla Drushka

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Short summary
We develop a machine learning methods to detect and classify how much sea ice was present around our research vessel. We used a navigation radar common on many merchant vessels attached to a screen capture device. The captured images were classified using a convolutional neural network and the resulting classification were found to be in good agreement with direct observations and satellite-based products.
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