Observations of Sea Ice in the Arctic Using Multi-Sensor Data From Recent Scientific Cruises in the Arctic Ocean

The Arctic is undergoing the most rapid changes in the climate system worldwide, demonstrated by the thinning and reduction of sea ice, the melting of ice sheets and glaciers, and an increased possibility for extreme weather events. While the source of these Arctic changes remains debated, these interlinked processes are expected to increase the chance of unstable sea ice conditions and increase the occurrence of icebergs. Such rapid and hard to predict changes pose a significant risk for shipping activities in this region, whether it be cargo, fisheries or tourism. Therefore, improved access to new and customized sea ice data products from innovative combinations of in situ measurements and remote sensing imagery and sea ice forecasting models is critical for safe and sustainable Arctic shipping.

Here, we present results from four scientific cruises to the Arctic Ocean; CAATEX 2019, CAATEX 2020, UAK 2021, and CIRFA 2022. During these cruises, which were carried out using the icebreakers KV Svalbard and Kronprins Haakon, numerous ice stations in both fast and drift ice were visited. Remote observations of sea ice were collected using a variety of fixed wing and multirotor unmanned aircraft systems (UAS), carrying both optical and an ultra wideband radar sensor suitable for detecting layers in the snow and ice. In addition, we used a ship based Ku-band interferometric imaging radar, which provides observations of ice drift and ice topography. The optical UAS sensors were used to collect high-resolution imagery, providing information about ice morphology and sea-ice properties. Repeated patterns were carried out to estimate ice drift. In-situ observations including important snow and ice properties were collected.

Results based on a variety of post processing methods will be briefly presented, including machine learning algorithms for automatic segmentation of sea ice classes including estimation of ridges, as well as different methods to estimate ice drift based on remotely sensed data. The collected data presents a unique dataset for validation of satellite products.