Global data sets of Chlorophyll a concentration for diatoms, coccolithophores (haptophytes) and cyanobacteria obtained from in situ observations and satellite retrievals

We derive the chlorophyll a concentration (Chla)for three main phytoplankton functional types (PFTs)-- diatoms, coccolithophores and cyanobacteria- by combining satellite multispectral-based information, being of a high spatial and temporal resolution, with retrievals based on high resolution of PFT...

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Bibliographic Details
Main Authors: 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
Format: Dataset
Language:English
Published: PANGAEA 2017
Subjects:
AC3
Arctic Amplification
Arctic
Climate change
Phytoplankton
Online Access:https://doi.pangaea.de/10.1594/PANGAEA.873210
https://doi.org/10.1594/PANGAEA.873210
Description
Summary:We derive the chlorophyll a concentration (Chla)for three main phytoplankton functional types (PFTs)-- diatoms, coccolithophores and cyanobacteria- by combining satellite multispectral-based information, being of a high spatial and temporal resolution, with retrievals based on high resolution of PFT absorption properties derived from hyperspectral measurements. The multispectral-based PFT Chla retrievals are based on a revised version of the empirical OC-PFT algorithm (Hirata et al. 2011) applied to the Ocean Colour Climate Change Initiative (OC-CCI) total Chla product. The PhytoDOAS analytical algorithm (Bracher et al. 2009, Sadeghi et al. 2012) is used with some modifications to derive PFT Chla from SCIAMACHY hyperspectral measurements. To combine synergistically these two PFT products (OC-PFT and PhytoDOAS), an optimal interpolation is performed for each PFT in every OC-PFT sub-pixel within a PhytoDOAS pixel, given its Chla and its a priori error statistics. The synergistic product (SynSenPFT) is presented for the period of August 2002 ? March 2012 and evaluated against in situ HPLC pigment data and satellite information on phytoplankton size classes (PSC) (Brewin et al. 2010, Brewin et al. 2015) and the size fraction (Sf) by Ciotti and Bricaud (2006. The most challenging aspects of the SynSenPFT algorithm implementation are discussed. Perspectives on SynSenPFT product improvements and prolongation of the time series over the next decades by adaptation to Sentinel multi- and hyperspectral instruments are highlighted.

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