Quantifying river ice movement through a combination of European satellite monitoring services

• Published in: International Journal of Applied Earth Observation and Geoinformation
• Main Authors: Bas Altena, Andreas Kääb
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier 2021
• Subjects: 00-01; 99-00; Physical geography; GB3-5030; Environmental sciences; GE1-350; Arctic; lena river
• Online Access: https://doi.org/10.1016/j.jag.2021.102315; https://doaj.org/article/5b388684bd4b4405a76f2f654edf15d2

Every spring the mechanical river ice break-up and associated ice-runs or flooding pose a threat to communities at Northern latitudes. Monitoring and mitigation efforts along remote Arctic rivers are possible but logistically complex. In recent years, Earth observation programs have emerged based on spaceborne sensors that record large parts of the Earth’s surface at a regular interval and with fast downlink. Most optical satellites have a similar sun-synchronous orbit, and have thus an akin ground track. When different sun-synchronous missions are combined this results in near-simultaneous acquisitions, which make it possible to monitor fast displacements that occur at or near the Earth’s surface over large scales. Hence, it becomes possible to generate a new monitoring system; one of observing river ice movement. In this study we demonstrate the feasibility of a multi-satellite monitoring system by combining data from freely available medium- and coarse-resolution satellites, in this study that is Sentinel-2 and PROBA-V. Velocities of floating river ice during the spring of 2016 are estimated over a more than 700 km long reach of the Lena River in Russia. In order to achieve automatic velocity estimates at such scales, efficient and river-ice specific processing steps are included. Entropy filters are used to detect regions of high contrast and neglects open water or an intact ice cover, and also help the image matching. Post-processing is done through filtering on the general flow direction, stemming from a global river mask dataset. In all, this study shows the potential of extracting river ice movement from a combination of low and medium resolution satellite sensors in sun-synchronous orbit.