Articles | Volume 12, issue 4
The Cryosphere, 12, 1307–1329, 2018
The Cryosphere, 12, 1307–1329, 2018

Research article 12 Apr 2018

Research article | 12 Apr 2018

Open-source algorithm for detecting sea ice surface features in high-resolution optical imagery

Nicholas C. Wright1 and Chris M. Polashenski1,2 Nicholas C. Wright and Chris M. Polashenski
  • 1Thayer School of Engineering, Dartmouth College, Hanover, NH, USA
  • 2U.S. Army Cold Regions Research and Engineering Laboratories, Hanover, NH, USA

Abstract. 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. Increasingly available meter- to decimeter-scale resolution optical imagery captures the evolution of the ice and ocean surface state visually, but methods for quantifying coverage of key surface types from raw imagery are not yet well established. Here we present an open-source system designed to provide a standardized, automated, and reproducible technique for processing optical imagery of sea ice. The method classifies surface coverage into three main categories: snow and bare ice, melt ponds and submerged ice, and open water. The method is demonstrated on imagery from four sensor platforms and on imagery spanning from spring thaw to fall freeze-up. Tests show the classification accuracy of this method typically exceeds 96 %. To facilitate scientific use, we evaluate the minimum observation area required for reporting a representative sample of surface coverage. We provide an open-source distribution of this algorithm and associated training datasets and suggest the community consider this a step towards standardizing optical sea ice imagery processing. We hope to encourage future collaborative efforts to improve the code base and to analyze large datasets of optical sea ice imagery.

Short summary
Satellites, planes, and drones capture thousands of images of the Arctic sea ice cover each year. However, few methods exist to reliably and automatically process these images for scientifically usable information. In this paper, we take the next step towards a community standard for analyzing these images by presenting an open-source platform able to accurately classify sea ice imagery into several important surface types.