Generation of soil moisture maps from ENVISAT/ASAR images in mountainous areas: a case study (Articolo in rivista)

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Label
  • Generation of soil moisture maps from ENVISAT/ASAR images in mountainous areas: a case study (Articolo in rivista) (literal)
Anno
  • 2010-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1080/01431160902953891 (literal)
Alternative label
  • S. Paloscia, P. Pampaloni, S. Pettinato, E. Santi (2010)
    Generation of soil moisture maps from ENVISAT/ASAR images in mountainous areas: a case study
    in International journal of remote sensing (Print); Taylor & Francis Ltd., Abingdon (Regno Unito)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • S. Paloscia, P. Pampaloni, S. Pettinato, E. Santi (literal)
Pagina inizio
  • 2265 (literal)
Pagina fine
  • 2276 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Questo articolo e' stato pubblicato nel 2010, ma e' stato accettato il 6 Aprile 2009 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 31 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 9 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto
  • Generazione di mappe di umidità del terreno in ambiente montano da dati satellitari ENIVISAT/ASAR (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-IFAC (literal)
Titolo
  • Generation of soil moisture maps from ENVISAT/ASAR images in mountainous areas: a case study (literal)
Abstract
  • A method for producing soil moisture maps in mountainous areas by using ENVISAT/ASAR images at C-band has been investigated in this paper. For this purpose, some experimental campaigns were carried out in 2004 in the Cordevole watershed in Italy during the ENVISAT passes. Ground truth measurements of soil and vegetation parameters were carried out simultaneously using satellite surveys. A preliminary classification of the area was carried out in order to mask those zones in which soil moisture measurement was unobtainable. The performance of an inversion algorithm, based on Artificial Neural Networks (ANN) in retrieving soil moisture content from the collected images, was then tested and compared with ground measurements. The results obtained on a restricted portion of the watershed showed a reasonable agreement of backscattering with ground truth data and meteorological conditions, thus making it possible to extend the algorithm to the entire test area. Afterwards, the contribution of vegetation cover was simulated by using a discrete elements model based on the radiative transfer theory. Three pixel- by-pixel soil moisture maps of the test site, with four levels of soil moisture, were generated from the available images by using a new ANN that takes into account the effects of vegetation. (literal)
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