Improved machine-learning based open-water/sea-ice/cloud discrimination over wintertime Antarctic sea ice using MODIS thermal-infrared imagery

• Main Authors: Paul, Stephan, Huntemann, Marcus
• Format: Text
• Language: English
• Published: 2020
• Subjects: Antarctic; Antarc*; Sea ice
• Online Access: https://doi.org/10.5194/tc-2020-159; https://tc.copernicus.org/preprints/tc-2020-159/

The frequent presence of cloud cover in polar regions limits the use of the Moderate-Resolution Imageing Spectroradiometer (MODIS) and similar instruments for the investigation and monitoring of sea-ice polynyas compared to passive-microwave-based sensors. The very low thermal contrast between present clouds and the sea-ice surface in combination with the lack of available visible and near-infrared channels during polar nighttime results in deficiencies in the MODIS cloud mask and dependent MODIS data products. This leads to frequent misclassifications of i) present clouds as sea ice and ii) open-water/thin-ice areas as clouds, which results in an underestimation of polynya area and subsequently derived information. Here, we present a novel machine-learning based approach using a deep neural network that is able to reliably discriminate between clouds, sea-ice, and open-water/thin-ice areas in a given swath solely from thermal-infrared MODIS channels and additionally derived information. Compared to the reference MODIS sea-ice product, our data results in an overall increase of 31 % in annual swath-based coverage, attributed to an improved cloud-cover discrimination. Overall, higher spatial coverage results in a better sub-daily representation of thin-ice conditions that cannot be reconstructed with current state-of-the-art cloud-cover compensation methods.