Evaluation of Decision Trees for Cloud Detection from AVHRR Data

Shiffman, Smadar; Nemani, Ramakrishna

Automated cloud detection and tracking is an important step in assessing changes in radiation budgets associated with global climate change via remote sensing. Data products based on satellite imagery are available to the scientific community for studying trends in the Earth's atmosphere. The data products include pixel-based cloud masks that assign cloud-cover classifications to pixels. Many cloud-mask algorithms have the form of decision trees. The decision trees employ sequential tests that scientists designed based on empirical astrophysics studies and simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In a previous study we compared automatically learned decision trees to cloud masks included in Advanced Very High Resolution Radiometer (AVHRR) data products from the year 2000. In this paper we report the replication of the study for five-year data, and for a gold standard based on surface observations performed by scientists at weather stations in the British Islands. For our sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks p < 0.001.

Document ID

20060028084

Acquisition Source

Ames Research Center

Document Type

Conference Paper

External Source(s)

hdl:http://handle.dtic.mil/100.2/ADA449899

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Date Acquired

September 7, 2013

Publication Date

January 1, 2005

Publication Information

ISBN: 0-7803-9051

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Report/Patent Number

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Public

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