Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloMonitoring of Alpine snow using satellite radiometers and artificial neural networks
Anno di pubblicazione2014
FormatoElettronico
Autore/iSanti E.; Pettinato S.; Paloscia S.; Pampaloni P.; Fontanelli G.; Crepaz A.; Valt M.
Affiliazioni autoriInstitute of Applied Physics, National Research Council, Florence, Italy; CVA Centro Valanghe Arabba, Italy
Autori CNR e affiliazioni
  • PAOLO PAMPALONI
  • GIACOMO FONTANELLI
  • EMANUELE SANTI
  • SIMONE PETTINATO
  • SIMONETTA PALOSCIA
Lingua/e
  • inglese
AbstractThe Alps represent an extremely complex environment in which snow properties suffer dramatic spatial variations that cannot easily be followed by space-borne microwave radiometers, due to their coarse spatial resolution. An improved method for monitoring the Snow Cover Extent (SCE) and the Snow Depth (SD) on alpine areas is presented here. Equivalent brightness Temperature Tbeq at an enhanced spatial resolution, corrected for the effects of orography and forest coverage, were computed from the AMSR-E measurements by using ancillary information on land use, surface temperature, and a digital elevation model (DEM). These equivalent values were used as inputs of an algorithm that merges empirical approaches and Artificial Neural Network (ANN) techniques for estimating snow properties on a global scale. The performances of the algorithm have been tested by using AMSR-E data collected during the winters between 2002 and 2011 on a test area located in the eastern part of the Italian Alps. Index Terms - AMSR-E, Brightness Temperature, Snow Depth, Snow Water Equivalent, Artificial Neural Networks.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da179
Pagine a186
Pagine totali-
RivistaRemote sensing of environment
Attiva dal 1969
Editore: American Elsevier Pub. Co., - New York,
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 0034-4257
Titolo chiave: Remote sensing of environment
Titolo proprio: Remote sensing of environment.
Titolo abbreviato: Remote sens. environ.
Numero volume della rivista144
Fascicolo della rivista-
DOI10.1016/j.rse.2014.01.012
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-84893750296)
Parole chiaveAMSR-E, Artificial neural networks, Brightness temperature, Snow depth, Snow water equivalent
Link (URL, URI)http://www.scopus.com/inward/record.url?eid=2-s2.0-84893750296&partnerID=q2rCbXpz
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Licenza-
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Strutture CNR
  • IFAC — Istituto di fisica applicata "Nello Carrara"
Moduli/Attività/Sottoprogetti CNR-
Progetti Europei-
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