http://www.cnr.it/ontology/cnr/individuo/prodotto/ID59750
Comparison between remote-sensed data and in situ measurements in coastal waters: The Taranto sea case. (Articolo in rivista)
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- Label
- Comparison between remote-sensed data and in situ measurements in coastal waters: The Taranto sea case. (Articolo in rivista) (literal)
- Anno
- 2004-01-01T00:00:00+01:00 (literal)
- Alternative label
R. MATARRESE, V. DE PASQUALE, L. GUERRIERO, A. MOREA, G. PASQUARIELLO, G. UMGIESSER, I. SCROCCARO, G. ALABISO (2004)
Comparison between remote-sensed data and in situ measurements in coastal waters: The Taranto sea case.
in Chemistry in ecology (Print)
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- R. MATARRESE, V. DE PASQUALE, L. GUERRIERO, A. MOREA, G. PASQUARIELLO, G. UMGIESSER, I. SCROCCARO, G. ALABISO (literal)
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- Monitoring and managing small coastal ecosystems requires a considerable understanding of the temporal dynamics of biophysical factors describing the coastal water systems. For this reason, daily observation from space could be a very efficient tool. The objective of the work described in this paper is to evaluate the contribution of remote sensing to the continuous monitoring of coastal areas. It is well known that in coastal areas, the presence of inorganic suspended sediments and coloured dissolved organic matter can make chlorophyll-concentration measurements from remote sensing difficult. To overcome these difficulties, an alternative approach to the SeaWiFS standard
chlorophyll algorithm is presented, based on a semi-analytic model for sea water and on the use of MODIS data as input in a model for atmospheric effects removal. Moreover, land contamination (mixed sea-land pixels) can introduce ambiguities in sea-surface temperature measurements from remote sensing. This paper proposes the use of a hydrodynamic model as a time-space interpolator of in situ campaign data, to extensively validate the temperature values extracted from AVHRR sensor. We validated the proposed approach, using experimental field data collected over a two-year campaign in the Taranto Gulf. The results seem to indicate a good agreement between remote-sensed and in situ data.
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- R. MATARRESEa,?, V. DE PASQUALEa, L. GUERRIEROa, A. MOREAa,
G. PASQUARIELLOb, G. UMGIESSERc, I. SCROCCAROc and G. ALABISOd
aDipartimento di Fisica, Universita` di Bari, via Amendola 173, 70126 Bari, Italy; bCNR-ISSIA,
Via Amendola 122/0, 70126 Bari, Italy; cCNR-ISMAR, S. Polo 1364, 30125, Venezia, Italy;
dCNR-IAMC-Sezione Talassografico 'A. Cerruti', Via Roma 3, 74100, Taranto, Italy (literal)
- Titolo
- Comparison between remote-sensed data and in situ measurements in coastal waters: The Taranto sea case. (literal)
- Abstract
- Monitoring and managing small coastal ecosystems requires a considerable understanding of the temporal dynamics
of biophysical factors describing the coastal water systems. For this reason, daily observation from space could be a
very efficient tool. The objective of the work described in this paper is to evaluate the contribution of remote sensing
to the continuous monitoring of coastal areas. It is well known that in coastal areas, the presence of inorganic
suspended sediments and coloured dissolved organic matter can make chlorophyll-concentration measurements
from remote sensing difficult. To overcome these difficulties, an alternative approach to the SeaWiFS standard
chlorophyll algorithm is presented, based on a semi-analytic model for sea water and on the use of MODIS data
as input in a model for atmospheric effects removal. Moreover, land contamination (mixed sea-land pixels) can
introduce ambiguities in sea-surface temperature measurements from remote sensing. This paper proposes the use
of a hydrodynamic model as a time-space interpolator of in situ campaign data, to extensively validate the
temperature values extracted from AVHRR sensor. We validated the proposed approach, using experimental field
data collected over a two-year campaign in the Taranto Gulf. The results seem to indicate a good agreement
between remote-sensed and in situ data. (literal)
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