Sea Ice Monitoring with Sentinel-1

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Deadline 27 April 2021

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Organisation: RUS Service

During this webinar, you will learn how to create sea ice concentration maps from Sentinel-1 using snappy and Jupyter Notebooks.

Background

Sea ice covers about 7% of the Earth's surface and it has a significant influence on the local, regional and global climate environment and local communities and ecosystems. Sea ice acts like a blanket on the ocean surface, reducing the absorbed solar energy, evaporation and heat exchange between the ocean and atmosphere. When sea ice forms, most of the salt is pushed into the ocean water below the ice, as a result of higher ice concentration the water is denser and sinks to the bottom as well as the local ecosystems. 

There is clear evidence showing that sea ice extent in the Arctic is decreasing as a result of climatic change. With decreasing sea ice, lower albedo and water circulation create a positive feedback loop and contribute to further warming. Accurate ice information is crucial to understand and monitor the changes in the Arctic environment and global climate change. Moreover, the decline in ice extent is creating possibilities for new sea routes and the exploration of natural resources in the Arctic. The Arctic region is estimated to have 22% of the world's oil and natural gas reserves, and a large portion of the natural resources in the Arctic are offshore and unexplored. As a result, more industrial activities are expected in this region in the future and supporting information for safe navigation is necessary. 

The Sentinel-1 SAR provides invaluable information both in bad weather conditions and during polar nights. Typically, SAR images were/are analyzed by ice analysts in operational centers for manual classification of ice types and drawing of ice charts. This procedure, of course requires a significant effort and human power; therefore, a number of automated and semi-automated algorithms have been developed for dual-polarization C-band SAR image segmentation and ice/water classification; for retrieval of ice concentration; for classification of several ice types. 

In this tutorial we will concentrate on the retrieval of sea ice concentration using texture analysis in the form of Gray-Level Co-occurrence Matrices (GLCMs) and supervised classification trained with existing ice chart data.