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| Titel |
Remote sensing the sea surface CO2 of the Baltic Sea using the SOMLO methodology |
| VerfasserIn |
G. Parard, A. A. Charantonis, A. Rutgerson |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1726-4170
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| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 12, no. 11 ; Nr. 12, no. 11 (2015-06-04), S.3369-3384 |
| Datensatznummer |
250117966
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| Publikation (Nr.) |
 copernicus.org/bg-12-3369-2015.pdf |
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| Zusammenfassung |
| Studies of coastal seas in Europe have noted the high variability of the
CO2 system. This high variability, generated by the complex mechanisms
driving the CO2 fluxes, complicates the accurate estimation of these
mechanisms. This is particularly pronounced in the Baltic Sea, where the
mechanisms driving the fluxes have not been characterized in as much detail
as in the open oceans. In addition, the joint availability of in situ
measurements of CO2 and of sea-surface satellite data is limited in the
area. In this paper, we used the SOMLO (self-organizing multiple linear output; Sasse et al., 2013) methodology,
which combines two existing methods (i.e. self-organizing maps and multiple
linear regression) to estimate the ocean surface partial pressure of CO2
(pCO2) in the Baltic Sea from the remotely sensed sea surface temperature,
chlorophyll, coloured dissolved organic matter, net primary production, and
mixed-layer depth. The outputs of this research have a horizontal resolution
of 4 km and cover the 1998–2011 period. These outputs give a monthly map of
the Baltic Sea at a very fine spatial resolution. The reconstructed pCO2
values over the validation data set have a correlation of 0.93 with the in
situ measurements and a root mean square error of 36 μatm. Removing any
of the satellite parameters degraded this reconstructed CO2 flux, so we
chose to supply any missing data using statistical imputation. The pCO2
maps produced using this method also provide a confidence level of the
reconstruction at each grid point. The results obtained are encouraging given
the sparsity of available data, and we expect to be able to produce even more
accurate reconstructions in coming years, given the predicted acquisition of
new data. |
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