Accession Number : ADA449899

Title :   Evaluation of Decision Trees for Cloud Detection from AVHRR Data

Descriptive Note : Conference paper

Corporate Author : NATIONAL AERONAUTICS AND SPACE ADMINISTRATION MOFFETT FIELD CA AMES RESEARCH CENTER

Personal Author(s) : Shiffman, Smadar ; Nemani, Ramakrishna

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Report Date : 2005

Pagination or Media Count : 5

Abstract : 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.

Descriptors :   *METEOROLOGICAL SATELLITES, *CLOUD COVER, *ASTROPHYSICS, ALGORITHMS, STATISTICAL DECISION THEORY, RADIOMETERS, SYMPOSIA

Subject Categories : ATMOSPHERIC PHYSICS
      METEOROLOGY
      STATISTICS AND PROBABILITY
      COMPUTER PROGRAMMING AND SOFTWARE
      CYBERNETICS
      UNMANNED SPACECRAFT

Distribution Statement : APPROVED FOR PUBLIC RELEASE

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