Average seasonal changes in chlorophyll a in Icelandic waters

Kristinn Gudmundsson, Mike R. Heath, Elizabeth D. Clarke, Marine Laboratory, Aberdeen, Scotland (Funder), Marine Research Institute, Reykjavik, Iceland (Funder)

Research output: Contribution to journalArticle

3 Citations (Scopus)
32 Downloads (Pure)

Abstract

The standard algorithms used to derive sea surface chlorophyll a concentration from remotely sensed ocean colour data are based almost entirely on the measurements of surface water samples collected in open sea (case 1) waters which cover ~60% of the worlds oceans, where strong correlations between reflectance and chlorophyll concentration have been found. However, satellite chlorophyll data for waters outside the defined case 1 areas, but derived using standard calibrations, are frequently used without reference to local in situ measurements and despite well-known factors likely to lead to inaccuracy. In Icelandic waters, multiannual averages of 8-d composites of SeaWiFS chlorophyll concentration accounted for just 20% of the variance in a multiannual dataset of in situ chlorophyll a measurements. Nevertheless, applying penalized regression spline methodology to model the spatial and temporal patterns of in situ measurements, using satellite chlorophyll as one of the predictor variables, improved the correlation considerably. Day number, representing seasonal variation, accounted for substantial deviation between SeaWiFS and in situ estimates of surface chlorophyll. The final model, using bottom depth and bearing to the sampling location as well as the two variables mentioned above, explained 49% of the variance in the fitting dataset.
Original languageEnglish
Pages (from-to)2133-2140
Number of pages8
JournalICES Journal of Marine Science
Volume66
Issue number10
DOIs
Publication statusPublished - Aug 2009

Fingerprint

chlorophyll a
chlorophyll
SeaWiFS
in situ measurement
water
ocean color
oceans
sea surface
reflectance
seasonal variation
calibration
surface water
methodology
sampling
ocean
color
in situ

Keywords

  • chlorophyll a
  • modelling
  • north atlantic
  • remote sensing
  • seasonal
  • subarctic
  • mathematics
  • marine science

Cite this

Gudmundsson, K., Heath, M. R., Clarke, E. D., Marine Laboratory, Aberdeen, Scotland (Funder), & Marine Research Institute, Reykjavik, Iceland (Funder) (2009). Average seasonal changes in chlorophyll a in Icelandic waters. ICES Journal of Marine Science, 66(10), 2133-2140. https://doi.org/10.1093/icesjms/fsp208
Gudmundsson, Kristinn ; Heath, Mike R. ; Clarke, Elizabeth D. ; Marine Laboratory, Aberdeen, Scotland (Funder) ; Marine Research Institute, Reykjavik, Iceland (Funder). / Average seasonal changes in chlorophyll a in Icelandic waters. In: ICES Journal of Marine Science. 2009 ; Vol. 66, No. 10. pp. 2133-2140.
@article{33aa99e0458e4ac0bcddfe42d92ecc37,
title = "Average seasonal changes in chlorophyll a in Icelandic waters",
abstract = "The standard algorithms used to derive sea surface chlorophyll a concentration from remotely sensed ocean colour data are based almost entirely on the measurements of surface water samples collected in open sea (case 1) waters which cover ~60{\%} of the worlds oceans, where strong correlations between reflectance and chlorophyll concentration have been found. However, satellite chlorophyll data for waters outside the defined case 1 areas, but derived using standard calibrations, are frequently used without reference to local in situ measurements and despite well-known factors likely to lead to inaccuracy. In Icelandic waters, multiannual averages of 8-d composites of SeaWiFS chlorophyll concentration accounted for just 20{\%} of the variance in a multiannual dataset of in situ chlorophyll a measurements. Nevertheless, applying penalized regression spline methodology to model the spatial and temporal patterns of in situ measurements, using satellite chlorophyll as one of the predictor variables, improved the correlation considerably. Day number, representing seasonal variation, accounted for substantial deviation between SeaWiFS and in situ estimates of surface chlorophyll. The final model, using bottom depth and bearing to the sampling location as well as the two variables mentioned above, explained 49{\%} of the variance in the fitting dataset.",
keywords = "chlorophyll a, modelling, north atlantic, remote sensing, seasonal, subarctic, mathematics, marine science",
author = "Kristinn Gudmundsson and Heath, {Mike R.} and Clarke, {Elizabeth D.} and {Marine Laboratory, Aberdeen, Scotland (Funder)} and {Marine Research Institute, Reykjavik, Iceland (Funder)}",
year = "2009",
month = "8",
doi = "10.1093/icesjms/fsp208",
language = "English",
volume = "66",
pages = "2133--2140",
journal = "ICES Journal of Marine Science",
issn = "1054-3139",
number = "10",

}

Gudmundsson, K, Heath, MR, Clarke, ED, Marine Laboratory, Aberdeen, Scotland (Funder) & Marine Research Institute, Reykjavik, Iceland (Funder) 2009, 'Average seasonal changes in chlorophyll a in Icelandic waters', ICES Journal of Marine Science, vol. 66, no. 10, pp. 2133-2140. https://doi.org/10.1093/icesjms/fsp208

Average seasonal changes in chlorophyll a in Icelandic waters. / Gudmundsson, Kristinn; Heath, Mike R.; Clarke, Elizabeth D.; Marine Laboratory, Aberdeen, Scotland (Funder); Marine Research Institute, Reykjavik, Iceland (Funder).

In: ICES Journal of Marine Science, Vol. 66, No. 10, 08.2009, p. 2133-2140.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Average seasonal changes in chlorophyll a in Icelandic waters

AU - Gudmundsson, Kristinn

AU - Heath, Mike R.

AU - Clarke, Elizabeth D.

AU - Marine Laboratory, Aberdeen, Scotland (Funder)

AU - Marine Research Institute, Reykjavik, Iceland (Funder)

PY - 2009/8

Y1 - 2009/8

N2 - The standard algorithms used to derive sea surface chlorophyll a concentration from remotely sensed ocean colour data are based almost entirely on the measurements of surface water samples collected in open sea (case 1) waters which cover ~60% of the worlds oceans, where strong correlations between reflectance and chlorophyll concentration have been found. However, satellite chlorophyll data for waters outside the defined case 1 areas, but derived using standard calibrations, are frequently used without reference to local in situ measurements and despite well-known factors likely to lead to inaccuracy. In Icelandic waters, multiannual averages of 8-d composites of SeaWiFS chlorophyll concentration accounted for just 20% of the variance in a multiannual dataset of in situ chlorophyll a measurements. Nevertheless, applying penalized regression spline methodology to model the spatial and temporal patterns of in situ measurements, using satellite chlorophyll as one of the predictor variables, improved the correlation considerably. Day number, representing seasonal variation, accounted for substantial deviation between SeaWiFS and in situ estimates of surface chlorophyll. The final model, using bottom depth and bearing to the sampling location as well as the two variables mentioned above, explained 49% of the variance in the fitting dataset.

AB - The standard algorithms used to derive sea surface chlorophyll a concentration from remotely sensed ocean colour data are based almost entirely on the measurements of surface water samples collected in open sea (case 1) waters which cover ~60% of the worlds oceans, where strong correlations between reflectance and chlorophyll concentration have been found. However, satellite chlorophyll data for waters outside the defined case 1 areas, but derived using standard calibrations, are frequently used without reference to local in situ measurements and despite well-known factors likely to lead to inaccuracy. In Icelandic waters, multiannual averages of 8-d composites of SeaWiFS chlorophyll concentration accounted for just 20% of the variance in a multiannual dataset of in situ chlorophyll a measurements. Nevertheless, applying penalized regression spline methodology to model the spatial and temporal patterns of in situ measurements, using satellite chlorophyll as one of the predictor variables, improved the correlation considerably. Day number, representing seasonal variation, accounted for substantial deviation between SeaWiFS and in situ estimates of surface chlorophyll. The final model, using bottom depth and bearing to the sampling location as well as the two variables mentioned above, explained 49% of the variance in the fitting dataset.

KW - chlorophyll a

KW - modelling

KW - north atlantic

KW - remote sensing

KW - seasonal

KW - subarctic

KW - mathematics

KW - marine science

U2 - 10.1093/icesjms/fsp208

DO - 10.1093/icesjms/fsp208

M3 - Article

VL - 66

SP - 2133

EP - 2140

JO - ICES Journal of Marine Science

JF - ICES Journal of Marine Science

SN - 1054-3139

IS - 10

ER -

Gudmundsson K, Heath MR, Clarke ED, Marine Laboratory, Aberdeen, Scotland (Funder), Marine Research Institute, Reykjavik, Iceland (Funder). Average seasonal changes in chlorophyll a in Icelandic waters. ICES Journal of Marine Science. 2009 Aug;66(10):2133-2140. https://doi.org/10.1093/icesjms/fsp208