Application of a Convolutional Neural Network for the Detection of Sea Ice Leads

Despite accounting for a small fraction of the surface area in the Arctic, long and narrow sea ice fractures, known as “leads”, play a critical role in the energy flux between the ocean and atmosphere. As the volume of sea ice in the Arctic has declined over the past few decades, it is increasingly...

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Bibliographic Details
Published in:Remote Sensing
Main Authors: Jay P. Hoffman, Steven A. Ackerman, Yinghui Liu, Jeffrey R. Key, Iain L. McConnell
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2021
Subjects:
leads
sea ice
MODIS
VIIRS
convolutional neural network
U-Net
Science
Q
Arctic
Sea ice
Online Access:https://doi.org/10.3390/rs13224571
https://doaj.org/article/274f2cebf4bd43319714c2b2b21f6e83
Description
Summary:Despite accounting for a small fraction of the surface area in the Arctic, long and narrow sea ice fractures, known as “leads”, play a critical role in the energy flux between the ocean and atmosphere. As the volume of sea ice in the Arctic has declined over the past few decades, it is increasingly important to monitor the corresponding changes in sea ice leads. A novel approach has been developed using artificial intelligence (AI) to detect sea ice leads using satellite thermal infrared window data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS). In this new approach, a particular type of convolutional neural network, a U-Net, replaces a series of conventional image processing tests from our legacy algorithm. Results show the new approach has a high detection accuracy with F1 Scores on the order of 0.7. Compared to the legacy algorithm, the new algorithm shows improvement, with more true positives, fewer false positives, fewer false negatives, and better agreement between satellite instruments.

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