2004
DOI: 10.5670/oceanog.2004.42
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The Evolution of Optical Water Mass Classification

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Cited by 37 publications
(26 citation statements)
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“…In October 2005, the same trendline was less robust (r=0.6188) and non-linear. The optical characterization of coastal water masses is important for interpreting and optimizing ocean color algorithms (ARNONE et al, 2004;AURIN et al, 2010). Indeed, operational empirical ocean color algorithms were designed only for Case 1 waters and are not expected to work well outside this domain.…”
Section: Bio-optical Water Types and Chl Algorithmmentioning
confidence: 99%
See 1 more Smart Citation
“…In October 2005, the same trendline was less robust (r=0.6188) and non-linear. The optical characterization of coastal water masses is important for interpreting and optimizing ocean color algorithms (ARNONE et al, 2004;AURIN et al, 2010). Indeed, operational empirical ocean color algorithms were designed only for Case 1 waters and are not expected to work well outside this domain.…”
Section: Bio-optical Water Types and Chl Algorithmmentioning
confidence: 99%
“…A number of studies have applied multivariate approaches for the optical water mass classification, in order to contribute to the understanding of biological and chemical processes (e.g. ARNONE et al, 2004). Although a number of semi-analytical models is available (see discussion in , the simpler approach of optical water type classification can optimize ocean color algorithm performances (AURIN et al, 2010), expanding and facilitating the applicability of remote sensing data.…”
Section: Introductionmentioning
confidence: 99%
“…The absorption coefficient by colored detrital matter, adg, provides information about detrital matter, which is mainly driven by colored dissolved organic matter in offshore waters. These three coefficients can be used to classify surface water masses [20], water composition assessment [21][22][23][24][25], oceanic primary production [16,26] and, in consequence, timing of the seasonal biological cycle of fishes (i.e., [27,28]) and potential fishing zones (i.e., [29][30][31]). Further, the derived products of ocean color imagery are a valuable source of data for ecosystem modeling, which provide forecasts of the development of plankton blooms, being useful warnings of harmful algal blooms and eutrophication risk (i.e., [31,32]).…”
Section: Introductionmentioning
confidence: 99%
“…Water color has long been used to define water masses since the introduction of the Forel-Ule color scale in the late 1800s (Hutchinson 1975;Arnone et al 2004). Color No.…”
mentioning
confidence: 99%