Losa, Svetlana N; Soppa, Mariana A; Dinter, Tilman; Wolanin, Aleksandra; Oelker, Julia; Bracher, Astrid (2017): Global chlorophyll a surface concentrations for diatoms, coccolithophores and cyanobacteria as the synergistic SynSenPFT product combined PhytoDOAS and OC-PFT for the period of time August 2002 - April 2012, links to NetCDF files. PANGAEA, https://doi.org/10.1594/PANGAEA.875873, In supplement to: Losa, Svetlana N; Soppa, Mariana A; Dinter, Tilman; Wolanin, Aleksandra; Brewin, Robert J W; Bricaud, Annick; Oelker, Julia; Peeken, Ilka; Gentili, Bernard; Rozanov, Vladimir V; Bracher, Astrid (2017): Synergistic Exploitation of Hyper- and Multi-Spectral Precursor Sentinel Measurements to Determine Phytoplankton Functional Types (SynSenPFT). Frontiers in Marine Science, 4(203), 22 pp, https://doi.org/10.3389/fmars.2017.00203
Always quote citation above when using data! You can download the citation in several formats below.
The SynSenPFT product is presented as chlorophyll "a" concentrations (Chla) for diatoms, coccolithophores and cyanobacteria (some of the phytoplankton functional types, PFT) obtained globally over the World Ocean on a 4 km sinusoidal grid on a daily basis over the period of August 2002 - March 2012. The SynSenPFT is a synergistic combination of the PFT products of initial-input OC-PFT (Hirata et al., 2011, Soppa et al., 2014) applied to
total chlorophyll "a" (TChla) data of Ocean Colour Climate Change Initiative (OC-CCI, Version 2, ESA) and PhytoDOAS (Bracher et al., 2009, Sadeghi et al., 2012) version 3.3 available at doi:10.1594/PANGAEA.870486 with an optimal interpolation (OI). The OI method is applied to OC-PFT and PhytoDOAS Chla products of diatoms, cyanobacteria (called prokarytoes by the OC-PFT method) and haptophytes (for OC-PFT) and coccolithophores (for PhytoDOAS). Note that OC-PFT retrieves haptophytes while PhytoDOAS retrieves coccolithophores, a (often dominating) sub-group of haptophytes. Algorithmically, the SynSenPFT is an update of OC-PFT Chla with PhytoDOAS Chla values weighted in accordance to our degree of belief to both initial-input data products. Within the current version of SynSenPFT algorithm the update is done for every sub-pixel of OC-PFT within a PhytoDOAS pixel. Thus, SynSenPFT in every OC-PFT sub-pixel on average is nudged towards PhytoDOAS values as close as allowed by the prescribed PhytoDOAS and OC-PFT error statistics.
Bracher, Astrid; Vountas, Marco; Dinter, Tilman; Burrows, John Philip; Röttgers, Rüdiger; Peeken, Ilka (2009): Quantitative observation of cyanobacteria and diatoms from space using PhytoDOAS on SCIAMACHY data. Biogeosciences, 6(5), 751-764, https://doi.org/10.5194/bg-6-751-2009
Dinter, Tilman; Rozanov, Vladimir V; Burrows, John Philip; Bracher, Astrid (2015): Retrieving the availability of light in the ocean utilising spectral signatures of vibrational Raman scattering in hyper-spectral satellite measurements. Ocean Science, 11(3), 373-389, https://doi.org/10.5194/os-11-373-2015
European Space Agency: Ocean Colour Climate Change Initiative (OC-CCI) dataset Version 2. http://www.esa-oceancolour-cci.org/
Hirata, Takafumi; Hardman-Mountford, Nicolas J; Brewin, Robert J W; Aiken, James; Barlow, Raymond G; Suzuki, Koji; Isada, Tomonori; Howell, Evan; Hashioka, Taketo; Noguchi-Aita, Maki; Yamanaka, Yasuhiro (2011): Synoptic relationships between surface Chlorophyll-a and diagnostic pigments specific to phytoplankton functional types. Biogeosciences, 8(2), 311-327, https://doi.org/10.5194/bg-8-311-2011
Sadeghi, Alireza; Dinter, Tilman; Vountas, Marco; Taylor, Bettina B; Soppa, Mariana A; Peeken, Ilka; Bracher, Astrid (2012): Improvement to the PhytoDOAS method for identification of coccolithophores using hyper-spectral satellite data. Ocean Science, 8(6), 1055-1070, https://doi.org/10.5194/os-8-1055-2012
Date/Time Start: 2002-08-01T00:00:00 * Date/Time End: 2012-03-31T00:00:00
File names: syn_glb_level3_ YYYYMMDD.nc (organized in a similar to OC-CCI way given the 4 km sinusoidal grid) Variables:- diatom_concentration [mg Chl a/m**3]- coccolithophores_concentration [mg Chl a/m**3]- cyanobacteria_concentration [mg Chl a/m**3]
7030 data points