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SPARC: New Cloud, Snow, and Cloud Shadow Detection Scheme for Historical 1-km AVHHR Data over Canada
Author(s) -
Konstantin Khlopenkov,
Alexander P. Trishchenko
Publication year - 2007
Publication title -
journal of atmospheric and oceanic technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.774
H-Index - 124
eISSN - 1520-0426
pISSN - 0739-0572
DOI - 10.1175/jtech1987.1
Subject(s) - pixel , advanced very high resolution radiometer , remote sensing , snow , sky , cloud computing , satellite , shadow (psychology) , image resolution , meteorology , environmental science , radiometry , polar orbit , weather satellite , computer science , geology , geography , artificial intelligence , astronomy , physics , psychology , psychotherapist , operating system
The identification of clear-sky and cloudy pixels is a key step in the processing of satellite observations. This is equally important for surface and cloud–atmosphere applications. In this paper, the Separation of Pixels Using Aggregated Rating over Canada (SPARC) algorithm is presented, a new method of pixel identification for image data from the Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA satellites. The SPARC algorithm separates image pixels into clear-sky and cloudy categories based on a specially designed rating scheme. A mask depicting snow/ice and cloud shadows is also generated. The SPARC algorithm has been designed to work year-round (day and night) over the temperate and polar regions of North America, for current and historical AVHRR/NOAA High-Resolution Picture Transmission (HRPT) and Local Area Coverage (LAC) data with original 1-km spatial resolution. The algorithm was tested and applied to data from the AVHRR sensors flown on board NOAA-6 to NOAA-18. The met...

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