Master of Science Jozef Rusin will Monday October 13th, 2025, at 12:15 hold his Thesis Defense for the PhD degree in Science. The title of the thesis is:
« Towards Higher-Resolution Arctic Sea Ice Concentration: Integrating Passive Microwave Radiometry, Synthetic Aperture Radar, and Data Fusion for Enhanced Routine Sea Ice Monitoring »
Arctic sea ice is a vital component of the Earth's climate system, regulating energy exchange between the ocean and atmosphere and influencing marine ecosystems. Consequently, Sea Ice Concentration (SIC) is a recognised Essential Climate Variable (ECV) that is crucial for monitoring climate trends, driving ocean–ice model forecasts, and supporting maritime operations such as shipping and offshore activities. However, there is an increasing demand for higher-resolution and more accurate SIC products to satisfy the needs of end-users.
While passive microwave radiometers have provided reliable, long-term SIC observations, their coarse spatial resolution (5 to 25 km) limits the detection of fine-scale features, which are crucial for many applications. Conversely, Synthetic Aperture Radar (SAR) offers much higher spatial resolution (≤100 m), enabling detailed mapping of ice conditions, but suffers from speckle noise and overlapping backscatter signatures, which complicate automatic SIC retrieval.
This thesis addresses these limitations through three interlinked studies. The first paper investigates methods to enhance the spatial resolution of SIC derived from the Advanced Microwave Scanning Radiometer 2 (AMSR2), while preserving its low measurement uncertainty. The second applies a deep learning classifier to Sentinel-1 SAR data to generate a robust 2.5 km SIC product, optimised to minimise noise misclassification. The third paper combines the findings from the previous two papers, aiming to leverage the strengths of both sensors by applying a unified deep learning-based fusion framework, which utilises the spatial detail of SAR and the reliability of passive microwave data. Collectively, these contributions form a complementary body of work that advances the spatial resolution, accuracy, and operational utility of SIC products. The thesis provides a robust foundation for next-generation sea ice monitoring, supporting improved forecasting, climate analysis, and maritime decision-making in the Arctic.
1st Opponent: Senior researcher Dr Juha Karvonen, Finnish Meteorological Institute
2nd Opponent: Senior Lecturer and Docent, Dr Céline Heuzé, University of Gothenburg
Internal member and leader of the committee: Associate Professor Dr Malin Johansson, UiT
The defence and trial lecture will be streamed from these following links at Panopto:
Defence (12:15 - 16:00) (Comming Soon)
Trial Lecture (10:15 - 11:15) (Comming Soon)
The thesis is available Here