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Identification and exclusion of clouds from satellite-based infrared fields is critical to achieve accurate retrievals of sea surface temperature (SST). Historically, identification of clouds has been driven primarily by a few uniformity tests involving a small number of pixels, brightness temperature range tests, and comparisons to low-resolution gap-free reference fields. Collectively these tests are adequate at identifying large, upper-level, very cold cumulus clouds, and uniformity tests identify moderately sized patchy cumulus clouds. But the efficacy of cloud identification often decreases at cloud edges, for small or thin cirrus clouds, and for the lower, more uniform stratus clouds, for which cloud-top temperature can be comparable to that of the sea surface, particularly at high latitudes. The heavy reliance on stringent uniformity thresholds often also has the unintended consequence of eliminating strong SST frontal regions from the pool of best-quality retrievals. This paper presents results for an ensemble cloud classifier based on a machine-learning approach, boosted alternating decision trees (ADtrees), applied to NASA MODIS and VIIRS SST imagery. The ADtree algorithm relies on the use of a majority vote from a collection of both “weak” and “strong” classifiers. This approach offers the potential to identify more cloud types and improve the retention of SST gradients in best-quality SST retrievals and also provides a per pixel confidence estimate in the classification.

Alternating Decision Trees for Cloud Masking in MODIS and VIIRS NASA Sea Surface Temperature Products