The sea surface temperature climate change initiative: Alternative image classification algorithms for sea-ice affected oceans

• Classification in marginal ice zones is critical for sea surface temperature records.
• We evaluate algorithms for satellite data image classification at high latitudes.
• Clear-cloud-ice classifiers show good water-ice discrimination.
• Combining visible and infrared data enhances ice detection.

We present a Bayesian image classification scheme for discriminating cloud, clear and sea-ice observations at high latitudes to improve identification of areas of clear-sky over ice-free ocean for SST retrieval. We validate the image classification against a manually classified dataset using Advanced Along Track Scanning Radiometer (AATSR) data. A three-way classification scheme using a near-infrared textural feature improves classifier accuracy by 9.9% over the nadir only version of the cloud clearing used in the ATSR Reprocessing for Climate (ARC) project in high latitude regions. The three-way classification gives similar numbers of cloud and ice scenes misclassified as clear but significantly more clear-sky cases are correctly identified (89.9% compared with 65% for ARC). We also demonstrate the potential of a Bayesian image classifier including information from the 0.6 μm channel to be used in sea-ice extent and ice surface temperature retrieval with 77.7% of ice scenes correctly identified and an overall classifier accuracy of 96%.