Version 7

MODIS sea ice leads detections using a U-Net

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Hoffman, Jay;Ackerman, Steven;Liu, Yinghui;Key, Jeffrey;McConnell, Iain

Description

Sea ice leads are long and narrow sea ice fractures. Despite accounting for a small fraction of the Arctic surface area, 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 recent decades, it is increasingly important to monitor the corresponding changes in sea ice leads. An approach described in Hoffman et al. 2021 uses artificial intelligence (AI) to detect sea ice leads using satellite thermal infrared window data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The AI used to detect sea ice leads in satellite imagery is a particular kind of convolutional neural network, a U-Net. The originally published dataset included only a small case study of results. Here, the dataset is expanded to include the daily detection of leads since 2002 for the season between November through April.

Citations (6)

Mentions (0)

Metrics

Dataset Index

2.2

FAIR Score

77%

Citations

6

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

Dryad

Assigned Domain

Subfield

Atmospheric Science

Field

Earth and Planetary Sciences

Domain

Physical Sciences

Confidence Score

51%

Source

Scholar Data Model

Keywords

FOS: Earth and related environmental sciencesleadsSea iceArcticU-NetConvolutional neural network

Normalization Factors

FT

30.77

CTw

1.00

MTw

1.00