Phase-Correlation Based Sea Ice Motion Tracking and Classification Using Spaceborne SAR Imagery

Sea ice is subject to continual change. It arises as seawater freezes, and is driven and deformed by winds, currents, ocean waves and temperature fluctuations. To observe the sea ice and its motion on a broad scale, Synthetic Aperture Radar (SAR) satellites are well suited, particularly as they provide images of land and frozen waters though clouds and regardless of solar illumination. In this paper, we present a software processor that is intended for the retrieval of high-resolution sea ice drift vector fields from pairs of subsequent, co-located SAR images. The core of the sea ice drift tracking algorithm is the well-known phase correlation technique executed hierarchically in a multiscale Gaussian pyramid. We explore the capabilities of the algorithm with a SAR image time series taken by the European satellite mission Sentinel-1 over the Arctic Ocean offshore close to Cape Morris Jesup during December 2021, when the ocean showed a closed cover of mainly multiyear ice. The retrieved sea ice drift vectors are validated with buoy data. In addition, we applied sea ice type classification on the SAR image series using the drift tracking algorithm for fusing classification results of the individual, subsequent SAR images. In so doing, we overcome inconsistent classification results and overall increase stability and reliability of the classification results.