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 Visible Infrared Imaging Radiometer Suite (VIIRS). 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 2011 for the season between November through April.

AI is used to identify sea ice leads in thermal imagery from the 11 µm from VIIRS (band I-5, SNPP and NOAA-20 imagery). A U-Net detection model is run for each satellite overpass and reported as daily aggregated 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.

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.