http://www.cnr.it/ontology/cnr/individuo/prodotto/ID280050
Seasonality of MODIS LST over Southern Italy and correlation with land cover, topography and solar radiation (Articolo in rivista)
- Type
- Label
- Seasonality of MODIS LST over Southern Italy and correlation with land cover, topography and solar radiation (Articolo in rivista) (literal)
- Anno
- 2014-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.5721/EuJRS20144709 (literal)
- Alternative label
Stroppiana D., M. Antoninetti, P.A. Brivio (2014)
Seasonality of MODIS LST over Southern Italy and correlation with land cover, topography and solar radiation
in European Journal of Remote Sensing; ASSOCIAZIONE ITALIANA TELERILEVAMENTO (AIT), Firenze (Italia)
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- Stroppiana D., M. Antoninetti, P.A. Brivio (literal)
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- http://www.aitjournal.com/articleView.aspx?ID=860 (literal)
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- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- Stroppiana D., M. Antoninetti, P.A. Brivio: CNR-IREA, Milano, Italy (literal)
- Titolo
- Seasonality of MODIS LST over Southern Italy and correlation with land cover, topography and solar radiation (literal)
- Abstract
- Land Surface Temperature (LST) is a key variable in the interactions and energy fluxes between the Earth surface and the atmosphere. Satellite data provide consistent, continuous and spatially distributed information on the Earth's surface conditions among which LST. Ten years of NASA-MODIS day-time and night-time 1 km LST data over Southern Italy have been analyzed to quantify the influence of factors such as topography and the land cover on LST spatio-temporal variations. Results show that topography significantly influence LST variability as a function of the land cover and to a different extent for day-time and night-time data. Moreover, the relation between LST and the influential factors varies with the season during the year. This study contributes to a further understanding of the complex relationship between the spatio-temporal variability of the surface thermal conditions and its driving factors highlighting how these relationships might change within the year (literal)
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