Differences in morphology, size, and pigmentation among various phytoplankton taxonomic groups impact their light absorption and scattering properties (e.g., Morel and Bricaud, 1981; Stramski and Kiefer, 1991; IOCCG, 2014), which modifies the color of the ocean. Optical satellite remote sensing enables the detection of backscattered sunlight emanating from the water surface (so-called ocean color). These observations can be exploited to obtain information not only on the overall biomass of phytoplankton, but also on the absence, presence, and dominance, of different phytoplankton groups. The remote-sensing products provide synoptic coverage of surface waters at global scale, and with a spatial coverage impossible from in-situ sampling. In this chapter, we define phytoplankton groups (PG) based on taxonomic criteria and also categorize phytoplankton composition using three phytoplankton size classes (PSC), following the classification of Sieburth et al. (1978). We discuss the characteristics required of satellite ocean-color sensors for PG and PSC detection, the algorithms' principles, we show applications of these satellite products, and discuss the societal impacts of these data sets.

Chapter 7 - Applications of satellite remote sensing technology to the analysis of phytoplankton community structure on large scales

Organelli Emanuele
Ultimo
2021

Abstract

Differences in morphology, size, and pigmentation among various phytoplankton taxonomic groups impact their light absorption and scattering properties (e.g., Morel and Bricaud, 1981; Stramski and Kiefer, 1991; IOCCG, 2014), which modifies the color of the ocean. Optical satellite remote sensing enables the detection of backscattered sunlight emanating from the water surface (so-called ocean color). These observations can be exploited to obtain information not only on the overall biomass of phytoplankton, but also on the absence, presence, and dominance, of different phytoplankton groups. The remote-sensing products provide synoptic coverage of surface waters at global scale, and with a spatial coverage impossible from in-situ sampling. In this chapter, we define phytoplankton groups (PG) based on taxonomic criteria and also categorize phytoplankton composition using three phytoplankton size classes (PSC), following the classification of Sieburth et al. (1978). We discuss the characteristics required of satellite ocean-color sensors for PG and PSC detection, the algorithms' principles, we show applications of these satellite products, and discuss the societal impacts of these data sets.
2021
Istituto di Scienze Marine - ISMAR
Inglese
Bracher A.; Brewin R.J.W; Ciotti A.M.; Clementson L.A.; Hirata T.; Kostadinov T.S.;Mouw C.B; Organelli E.
Clementson, Lesley A.; Eriksen, Ruth S.; Willis, Anusuya
ADVANCES IN PHYTOPLANKTON ECOLOGY Applications of Emerging Technologies
217
244
28
9780128228616
9780128230299
https://www.sciencedirect.com/science/article/abs/pii/B9780128228616000157
Elsevier
Amsterdam
PAESI BASSI
marine bio-optics
marine carbon cycles
marine food web
ocean-color algorithms
phytoplankton functional types
phytoplankton size classes
satellite products
satellite remote sensing of ocean color
In "ADVANCES IN PHYTOPLANKTON ECOLOGY Applications of Emerging Technologies" Clementson, Lesley A.; Eriksen, Ruth S.; Willis, Anusuya (Eds.)
Internazionale
8
02 Contributo in Volume::02.01 Contributo in volume (Capitolo o Saggio)
268
restricted
Bracher, Astrid; Brewin Robert, Jw; Ciotti Aurea, M; Clementson Lesley, A; Hirata, Takafumi; Kostadinov Tihomir, S; Mouw Colleen, B; Organelli, Emanue...espandi
info:eu-repo/semantics/bookPart
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/415613
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