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MODIS Sea ice leads detections using a U-Net

Citation

Hoffman, Jay et al. (2022), MODIS Sea ice leads detections using a U-Net, Dryad, Dataset, https://doi.org/10.5061/dryad.79cnp5hz2

Abstract

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.

Methods

AI is used to identify sea ice leads in thermal imagery from the 11 µm from MODIS (band 31, AQUA and TERRA imagery). A U-Net detection model is run for each satellite overpass and reported as daily aggrigated results. The lead detection results are projected into a standard 1 km resolution EASE-Grid 2.0 projection. The included data arrays are the daily number satellite overpasses, number of overpasses a lead is identified, the maximum lead detection score from the U-Net, and a lead mask for each EASE-Grid 2.0 pixel.  Daily files are compressed inside November through April seasonal tar files.

Usage Notes

The daily results are recorded as hdf5 format files. For each season, the daily results from November through April for each season are combined into a new tar file with gzip compression.

Funding

NASA Headquarters, Award: 80NSSC18K0786