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  • AC3; Arctic Amplification  (2)
  • PANGAEA  (2)
  • Wiley
  • 2015-2019  (2)
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  • PANGAEA  (2)
  • Wiley
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  • 2015-2019  (2)
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  • 1
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    PANGAEA
    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
    Publication Date: 2024-02-14
    Description: 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.
    Keywords: AC3; Arctic Amplification
    Type: Dataset
    Format: application/zip, 3 datasets
    Location Call Number Expected Availability
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  • 2
    Publication Date: 2024-05-11
    Description: This phytoplankton group (PFT) concentration a (Chl a) data are output from the algorithm PhytoDOAS version 3.3 applied to SCIAMACHY data from 2 Aug 2002 to 8 Apr 2012. Data have been gridded monthly on 0.5° latitude to 0.5°. For cyanobacteria (includes all prokaryotic phytoplankton) and diatoms the PhytoDOAS PFT retrieval algorithm by Bracher et al. (2009) and for coccolithophores the algorithm by Sadeghi et al. (2012) have been used. However, these methods have slightly been improved which includes: - Data during SCIAMACHY instrument decontamination are excluded in the analysis. - SCIAMACHY level-1b input data for PhytoDOAS are now version 7.04 data (instead of version 6.0). - The wavelength window for all three phytoplankton groups (PFTs) fit factor starts at 427.5 nm (instead of 429 nm). - Coccolithophores fit factors are retrieved in a retrieval fitting simultaneously diatoms and coccolithophores (instead of a triple fit with also fitting dinoflagellates as in Sadeghi et al. 2012). - Vibrational Raman Scattering (VRS) is now fitted directly in the blue spectrum (450 to 495 nm), following Dinter et al. (2015), (instead of in the UV—A region as in Vountas et al. 2007) except that here the daily solar background spectrum measured by SCIAMACHY and the VRS pseudo absorption spectrum calculated based on a SCIAMACHY solar spectrum following Vountas et al. (2007) was used in order to correct for the variation of instrumental effects over time (this is not achieved when using the RTM simulated background spectrum as done in Dinter et al. 2015). - The PFT Chl a are derived from the ratio of the PFT fit factor to the VRS fit factor multiplied by a LUT (Look Up Table). The LUT is based on radiative transfer model (RTM) SCIATRAN simulations (see Rozanov et al. 2014) accounting also for changing solar zenith angle (SZA).
    Keywords: AC3; Arctic Amplification
    Type: Dataset
    Format: application/zip, 109.9 MBytes
    Location Call Number Expected Availability
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