Remote sensing of cloud cover in the Arctic region from AVHRR data during ARTIST experiment (Articolo in rivista)

Type
Label
  • Remote sensing of cloud cover in the Arctic region from AVHRR data during ARTIST experiment (Articolo in rivista) (literal)
Anno
  • 2003-01-01T00:00:00+01:00 (literal)
Alternative label
  • Ananasso, C., R. Santoleri, S. Marullo, F. D?Ortenzio (2003)
    Remote sensing of cloud cover in the Arctic region from AVHRR data during ARTIST experiment
    in International journal of remote sensing (Print)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Ananasso, C., R. Santoleri, S. Marullo, F. D?Ortenzio (literal)
Pagina inizio
  • 437 (literal)
Pagina fine
  • 457 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 10 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto
  • Articolo su rivista ISI (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • ISAC - CNR Roma; ISAC - CNR Roma; ENEA - Casaccia; ISAC - CNR Roma (literal)
Titolo
  • Remote sensing of cloud cover in the Arctic region from AVHRR data during ARTIST experiment (literal)
Abstract
  • In this paper we present a cloud detection algorithm developed for the Arctic region using AVHRR data. Our approach is a simplified version of the Ebert (1987) method to discriminate between clouds, ice and open water in the Arctic Sea. The algorithm is tuned to work on an AVHRR scene typical of the winter to spring transition period. The algorithm has been applied to one month (154 scenes) of NOAA14-AVHRR images (from 16 March to 15 April 1998) covering the region of the Arctic Sea near the Svalbard Islands. The cloud detection results are analyzed using various check procedures. The algorithm’s pixel classification performance was verified by a satellite image expert. The misclassified pixels were digitalized on the image and counted by the expert in order to quantify the algorithm’s accuracy. The cloud classification results are quite accurate: 70% of the images (109) have an error less than 5% and only 11% of the images results to have an error greater than 10%. The method’s performance is also tested against independent cloud and ice observations obtained respectively from the Ny-Ålesund meteorological base and from the Special Sensor Microwave/Imager (SSM/I) data set. The comparison with these independent sources of data confirms the algorithm’s good performance. (literal)
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