Open Source Sea-ice Processing Algorithm v2.1.0

• Main Authors: Nicholas Wright, Chris Polashenski
• Format: Dataset
• Language: unknown
• Published: Arctic Data Center 2018
• Subjects: EARTH SCIENCE > CRYOSPHERE; EARTH SCIENCE > CRYOSPHERE > SEA ICE; EARTH SCIENCE > CRYOSPHERE > SEA ICE > ICE FLOES; EARTH SCIENCE > CRYOSPHERE > SEA ICE > ICE GROWTH/MELT; EARTH SCIENCE > CRYOSPHERE > SEA ICE > ICE TYPES; EARTH SCIENCE > CRYOSPHERE > SEA ICE > REFLECTANCE; EARTH SCIENCE > CRYOSPHERE > SEA ICE > SEA ICE CONCENTRATION; EARTH SCIENCE > CRYOSPHERE > SNOW/ICE > ALBEDO; EARTH SCIENCE SERVICES > DATA ANALYSIS AND VISUALIZATION > VISUALIZATION/IMAGE PROCESSING; Arctic; Arctic Ocean; albedo; Sea ice
• Online Access: https://search.dataone.org/view/urn:uuid:ae774a6b-f527-4684-9304-694d02165ca5

Snow, ice, and melt ponds cover the surface of the Arctic Ocean in fractions that change throughout the seasons. These surfaces control albedo and exert tremendous influence over the energy balance in the Arctic. Meter scale resolution WorldView optical satellite imagery is able to capture the evolution of the ice and ocean surface state directly. This dataset contains the python code used to process high resolution optical imagery of sea ice designed to provide a standardized, automated, and reproducible technique. This method classifies surface coverage into three main categories: (1) Snow and bare ice, (2) melt ponds and submerged ice, and (3) open water. Tests show the classification accuracy of this method typically exceeds 96%. This code is based on the work presented in: https://www.the-cryosphere.net/12/1307/2018/tc-12-1307-2018.html