High-Resolution Mapping of Boreal Fluvial Ice Extent Variations from the Perspective of Lake Ice Changes

River ice dynamics significantly impact hydrological processes, ecological systems, and water resource management, particularly in boreal sub-Arctic regions such as Finland. Accurate river ice extent and phenology monitoring is crucial for understanding climate change impacts, improving water management, and mitigating ice-related risks. Field-based river ice monitoring methods face limitations in spatiotemporal coverage and accuracy. This study integrates multi-source remote sensing data and advanced analytical techniques to improve river ice monitoring and prediction capabilities.

Utilizing both optical and synthetic aperture radar (SAR) satellite imagery, combined with in-situ observations from fixed cameras, we develop high-resolution algorithms to detect river ice extent and assess its phenological characteristics. Furthermore, we explore the potential of using lake ice monitoring as a proxy for river ice dynamics, leveraging proximal lake-river systems to predict river ice conditions. Advanced digital twin frameworks will derive critical ice parameters, such as freeze-up, break-up, and ice thickness, enabling real-time monitoring and decision-making.

This research addresses the challenges of monitoring river ice variations by integrating multi-source observational data into predictive models. By enhancing river ice observations' spatial and temporal resolution, this study contributes to sustainable water resource management and supports global adaptation strategies aligned with Sustainable Development Goals (SDGs). The findings highlight the transformative potential of digital solutions in hydrological research and river basin management.