Classification of sea-ice concentration from ship-board S-band radar images using open-source machine learning tools

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

doi:10.5194/gi-15-53-2026

Articles | Volume 15, issue 1

https://doi.org/10.5194/gi-15-53-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gi-15-53-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 1

Research article

|

09 Feb 2026

Research article |

| 09 Feb 2026

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

Data sets

SASSIE Arctic Field Campaign Shipboard S-Band Radar Level 3 Data Fall 2022 Kyla Drushka https://doi.org/10.5067/SASSIE-SBAND4

Sea-ice Category Predictions from a Machine Learning Model for SASSIE Elizabeth Westbrook and Emmett Culhane https://doi.org/10.5067/SASSIE-SBAND-ML

Model code and software

Repository for the SASSIE Sea Ice ML Model Elizabeth Westbrooke https://github.com/NASASASSIE/seaice

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.