Publications

Hoffman, JP; Ackerman, SA; Liu, YH; Key, JR (2022). A 20-Year Climatology of Sea Ice Leads Detected in Infrared Satellite Imagery Using a Convolutional Neural Network. REMOTE SENSING, 14(22), 5763.

Abstract
Sea ice leads, or fractures account for a small proportion of the Arctic Ocean surface area, but play a critical role in the energy and moisture exchanges between the ocean and atmosphere. As the sea ice area and volume in the Arctic has declined over the past few decades, changes in sea ice leads have not been studied as extensively. A recently developed approach uses artificial intelligence (AI) and satellite thermal infrared window data to build a twenty-year archive of sea ice lead detects with Moderate Resolution Imaging Spectroradiometer (MODIS) and later, an archive from Visible Infrared Imaging Radiometer Suite (VIIRS). The results are now available and show significant improvement over previously published methods. The AI method results have higher detection rates and a high level detection agreement between MODIS and VIIRS. Analysis over the winter season from 2002-2003 through to the 2021-2022 archive reveals lead detections have a small decreasing trend in lead area that can be attributed to increasing cloud cover in the Arctic. This work reveals that leads are becoming increasingly difficult to detect rather than less likely to occur. Although the trend is small and on the same order of magnitude as the uncertainty, leads are likely increasing at a rate of 3700 km(2) per year with a range of uncertainty of 3500 km(2) after the impact of cloud cover changes are removed.

DOI:
10.3390/rs14225763

ISSN:
2072-4292