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  • 1
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    Database (DSM) of in situ POC and SPM and satellite Rrs (matchup) collected between 2002 and 2018 (2023)
    PANGAEA
    Details
    Publication Date: 2023-11-01
    Description: This data release includes in situ measurements collected between 2002 and 2012 during different study sites (Full description of the methods adopted for each measurements in the references). DSM measurements include 101 coincident in situ concentrations of Particulate Organic Carbon (POC) (μgL-1) and Suspended Particulate Matter (SPM) (mg/l) and remote-sensing reflectances (Rrs, sr-1) matchups for the sensor Meris. It addressed the objective: 1) Validate the POC/SPM ratio using satellite Rrs (matchup)
    Keywords: According to source references; bbp; Carbon, organic, particulate; cp; Cruise/expedition; DATE/TIME; Event label; Grande_campagne_Belgica2010; LATITUDE; LONGITUDE; Phabop_1; Phabop_2; Phabop_3; Phabop_4; Phabop_5; Phabop_6; Phabop_7; POC; Remote sensing reflectance at 412 nm; Remote sensing reflectance at 443 nm; Remote sensing reflectance at 490 nm; Remote sensing reflectance at 510 nm; Remote sensing reflectance at 560 nm; Remote sensing reflectance at 665 nm; Rrs; Somlit_Id:1_1; Somlit_Id:1_10; Somlit_Id:1_11; Somlit_Id:1_12; Somlit_Id:1_13; Somlit_Id:1_14; Somlit_Id:1_15; Somlit_Id:1_16; Somlit_Id:1_17; Somlit_Id:1_18; Somlit_Id:1_19; Somlit_Id:1_2; Somlit_Id:1_20; Somlit_Id:1_21; Somlit_Id:1_22; Somlit_Id:1_3; Somlit_Id:1_4; Somlit_Id:1_5; Somlit_Id:1_6; Somlit_Id:1_7; Somlit_Id:1_8; Somlit_Id:1_9; Somlit_Id:12_1; Somlit_Id:17_1; Somlit_Id:17_2; Somlit_Id:17_3; Somlit_Id:17_4; Somlit_Id:17_5; Somlit_Id:17_6; Somlit_Id:17_7; Somlit_Id:2_1; Somlit_Id:2_10; Somlit_Id:2_11; Somlit_Id:2_12; Somlit_Id:2_13; Somlit_Id:2_14; Somlit_Id:2_15; Somlit_Id:2_16; Somlit_Id:2_17; Somlit_Id:2_18; Somlit_Id:2_19; Somlit_Id:2_2; Somlit_Id:2_20; Somlit_Id:2_21; Somlit_Id:2_22; Somlit_Id:2_23; Somlit_Id:2_24; Somlit_Id:2_25; Somlit_Id:2_26; Somlit_Id:2_27; Somlit_Id:2_28; Somlit_Id:2_29; Somlit_Id:2_3; Somlit_Id:2_30; Somlit_Id:2_31; Somlit_Id:2_32; Somlit_Id:2_4; Somlit_Id:2_5; Somlit_Id:2_6; Somlit_Id:2_7; Somlit_Id:2_8; Somlit_Id:2_9; Somlit_Id:3_1; Somlit_Id:3_10; Somlit_Id:3_11; Somlit_Id:3_12; Somlit_Id:3_13; Somlit_Id:3_14; Somlit_Id:3_15; Somlit_Id:3_16; Somlit_Id:3_17; Somlit_Id:3_18; Somlit_Id:3_19; Somlit_Id:3_2; Somlit_Id:3_20; Somlit_Id:3_21; Somlit_Id:3_22; Somlit_Id:3_23; Somlit_Id:3_24; Somlit_Id:3_25; Somlit_Id:3_26; Somlit_Id:3_27; Somlit_Id:3_28; Somlit_Id:3_29; Somlit_Id:3_3; Somlit_Id:3_4; Somlit_Id:3_5; Somlit_Id:3_6; Somlit_Id:3_7; Somlit_Id:3_8; Somlit_Id:3_9; SPM; Suspended particulate matter; VITEL2011_1; VITEL2011_2
    Type: Dataset
    Format: text/tab-separated-values, 908 data points
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 2
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    Database (DSM) of in situPOC, SPM and Rrs collected between 1997 and 2018 (2023)
    PANGAEA
    Details
    Publication Date: 2023-11-01
    Description: This data release includes in situ measurements collected between 2010 and 2014 during different cruise missions and study sites (Full description of the methods adopted for each measurements in the references). Measurements include concentrations of Particulate Organic Carbon (POC) (μgL-1) and Suspended Particulate Matter (SPM) (mg/l), remote-sensing reflectances (Rrs, sr-1), the particulate backscattering coefficient (bbp), and the particulate attenuation coefficient (cp) (m-1) at 650 nm. The first in situ database, named DS0, includes 300 coincident in situ POC, SPM, bbp, and cp measurements. DS1 includes 325 coincident in situ POC, SPM and Rrs. It addressed two main objectives: 1) Check the relationship between POC/SPM and bbp/cp 2) Develop a POC/SPM algorithm based on Rrs
    Keywords: Attenuation coefficient of particles, 650 nm; Backscattering coefficient/attenuation coefficient of particle ratio; Backscattering coefficient of particles, 650 nm; bbp; Belcolor_250; Belcolor_702N; Belcolor_MH3; Belcolor_MH4; Belcolor_MH5; Belcolor_MH5-B_profil; Belcolor_MH5-C_profil; Belcolor_MH5-F_profil; Belcolor_MH5-G_profil; Belcolor_MH5-H_profil; Belcolor_MH5-I_profil; Belcolor_MH5-J_profil; Belcolor_MH6; Belcolor_MOD-A_profil; Belcolor_MOD-B_profil; Belcolor_MOW-G; Belcolor_MOW-J; Belcolor_MOW-M; Belcolor_S01; Belcolor_W02; Belcolor_W03; Belcolor_W04; Belcolor_W04_profil; Belcolor_W05; Belcolor_W06_profil; Belcolor_W07; Belcolor_W08_profil; Belcolor_W10; Black_Carbon2012_BC2012_HL01; Black_Carbon2012_BC2012_HL02; Black_Carbon2012_BC2012_HL02-01; Black_Carbon2012_BC2012_HL02-02; Black_Carbon2012_BC2012_HL02-03; Black_Carbon2012_BC2012_HL02-04; Black_Carbon2012_BC2012_HL02-05; Black_Carbon2012_BC2012_HL02-06; Black_Carbon2012_BC2012_HL03; Black_Carbon2012_BC2012_HL04; Black_Carbon2012_BC2012_HL05; Black_Carbon2012_BC2012_HL06; Black_Carbon2012_BC2012_HL07; Black_Carbon2012_BC2012_HL08; Black_Carbon2012_BC2012_HL09; Black_Carbon2012_BC2012_HL10; Black_Carbon2012_BC2012_HL11; Black_Carbon2012_BC2012_HL12; Black_Carbon2012_BC2012_HL13; Black_Carbon2012_BC2012_HL13-01; Black_Carbon2012_BC2012_HL13-03; Black_Carbon2012_BC2012_HL13-04; Black_Carbon2012_BC2012_HL13-05; Black_Carbon2012_BC2012_HL13-06; Black_Carbon2012_BC2012_HL14; Black_Carbon2012_BC2012_HL14-01; Black_Carbon2012_BC2012_HL14-02; Black_Carbon2012_BC2012_HL14-03; Black_Carbon2012_BC2012_HL14-04; Black_Carbon2012_BC2012_HL14-05; Black_Carbon2012_BC2012_HL14-06; Black_Carbon2012_BC2012_HL15; Black_Carbon2012_BC2012_HL16; Black_Carbon2013_BC2013_HL02-01; Black_Carbon2013_BC2013_HL02-02; Black_Carbon2013_BC2013_HL02-03; Black_Carbon2013_BC2013_HL02-04; Black_Carbon2013_BC2013_HL02-05; Black_Carbon2013_BC2013_HL02-06; Black_Carbon2013_BC2013_HL13-01; Black_Carbon2013_BC2013_HL13-02; Black_Carbon2013_BC2013_HL13-03; Black_Carbon2013_BC2013_HL13-04; Black_Carbon2013_BC2013_HL13-05; Black_Carbon2013_BC2013_HL13-06; Black_Carbon2013_BC2013_HL14-01; Black_Carbon2013_BC2013_HL14-02; Black_Carbon2013_BC2013_HL14-04; Black_Carbon2013_BC2013_HL14-05; Black_Carbon2013_BC2013_HL14-06; Calculated; Carbon, organic, particulate; Carbon, organic, particulate/suspended particulate matter ratio; cp; Cruise/expedition; DATE/TIME; Dyphyma_DPM01; Dyphyma_DPM06; Dyphyma_DPM07; Dyphyma_DPM09; Dyphyma_DPM10; Dyphyma_DPM11; Dyphyma_DPM12; Dyphyma_DPM13; Dyphyma_DPM15; Dyphyma_DPM38; Dyphyma_DPM40; Dyphyma_DPM41; Dyphyma_DPM42; Dyphyma_DPM43; Dyphyma_DPM44; Dyphyma_DPM45; Dyphyma_DPM48; Dyphyma_DPM49; Dyphyma_DPM51; Dyphyma_DPM52; Dyphyma_DPM53; Event label; Grande_campagne_Belgica2010_ZBLR04; Grande_campagne_Belgica2010_ZBLR05; Grande_campagne_Belgica2010_ZBLR06; Grande_campagne_Belgica2010_ZBLR07; Grande_campagne_Belgica2010_ZBLR10; Grande_campagne_Belgica2010_ZBLR11; Grande_campagne_Belgica2010_ZBLR17; Grande_campagne_Belgica2010_ZBLR18; Grande_campagne_Belgica2010_ZBLR19; Grande_campagne_Belgica2010_ZBLR20; Grande_campagne_Belgica2010_ZBLR22; Grande_campagne_Belgica2010_ZBLR23; Grande_campagne_Belgica2010_ZBLR24; Grande_campagne_Belgica2010_ZBLR26; Grande_campagne_Belgica2010_ZBLR28; Grande_campagne_Belgica2010_ZBLR29; Grande_campagne_Belgica2010_ZBLR31; Grande_campagne_Belgica2010_ZBLR32; Grande_campagne_Belgica2010_ZBLR33; Grande_campagne_Belgica2010_ZBLR34; Grande_campagne_Belgica2010_ZBLR35; Guyane2012_G2012_A1; Guyane2012_G2012_B4; Guyane2012_G2012_S113; Guyane2012_G2012_S13; Guyane2012_G2012_S161b; Guyane2012_G2012_S166; Guyane2012_G2012_S167; Guyane2012_G2012_S173; Guyane2012_G2012_S174; Guyane2012_G2012_S29b; Guyane2012_G2012_S31b; Guyane2012_G2012_S8; In situ Instrument; ISI; LATITUDE; LONGITUDE; POC; Rrs; SPM; Station label; Suspended particulate matter; VITEL2011_A01; VITEL2011_A02; VITEL2011_A03; VITEL2011_A04; VITEL2011_A05; VITEL2011_A06; VITEL2011_A07; VITEL2011_A08; VITEL2011_A09; VITEL2011_A10; VITEL2011_A12; VITEL2011_A13; VITEL2011_A14; VITEL2011_A15; VITEL2011_A16; VITEL2011_A17; VITEL2011_A18; VITEL2011_A19; VITEL2011_A20; VITEL2011_A21; VITEL2011_A22; VITEL2011_A23; VITEL2011_D01; VITEL2011_D02; VITEL2011_D03; VITEL2011_D04; VITEL2011_D05; VITEL2011_D06; VITEL2011_D07; VITEL2011_DV01; VITEL2011_DV02; VITEL2011_DV03; VITEL2011_DV04; VITEL2011_DV09; VITEL2011_DV11; VITEL2011_DV12; VITEL2011_DV13; VITEL2011_DV14; VITEL2011_H06; VITEL2011_H10; VITEL2011_H11; VITEL2011_H13; VITEL2011_H15; VITEL2011_H23; VITEL2011_H28; VITEL2011_H32; VITEL2011_H33; VITEL2014_N01; VITEL2014_N02; VITEL2014_N03; VITEL2014_N04; VITEL2014_N05; VITEL2014_N11; VITEL2014_N12; VITEL2014_N13; VITEL2014_N14; VITEL2014_N15; VITEL2014_N21; VITEL2014_N22; VITEL2014_N23; VITEL2014_N24; VITEL2014_N25; VITEL2014_N31; VITEL2014_N32; VITEL2014_N33; VITEL2014_N42; VITEL2014_N51; VITEL2014_N52; VITEL2014_N53; VITEL2014_N54; VITEL2014_N62; VITEL2014_N63; VITEL2014_N71; VITEL2014_N72
    Type: Dataset
    Format: text/tab-separated-values, 2400 data points
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 3
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    A data set of collocated satellite remote sensing reflectance from GlobColour merged products, chlorophyll a concentration of phytoplankton functional types derived from in situ pigment data, and CMEMS sea surface temperature from 2002 to 2012 (2021)
    PANGAEA
    Details
    Publication Date: 2024-02-14
    Description: This data set provides the collocated data of remote sensing reflectance (Rrs) at 9 bands extracted from the merged ocean color products from GlobColour archive (https://www.globcolour.info/), satellite sea surface temperature from CMEMS (https://marine.copernicus.eu/), and chlorophyll a concentrations (Chl-a) derived from a global database of in situ HPLC pigment data collected from 2002 to 2012. The total Chl-a, Chl-a of six phytoplankton functional types (PFTs) that are diatoms, dinoflagellates, haptophytes, green algae, prokaryotes and Prochlorococcus, and two fractions of prokaryotes and Prochlorococcus are included in this data set. PFT Chl-a and fractions are derived using an updated diagnostic pigment analysis (DPA) method (Soppa et al., 2014; Losa et al., 2017), that was originally developed by Vidussi et al. (2001), adapted in Uitz et al. (2006) and further refined by Hirata et al. (2011) and Brewin et al. (2015). Matchups of satellite Rrs to in situ PFT data (which were also matchups to SST) were extracted from global 4-km daily merged products. Extraction and averaging protocol including quality control were described in detail in Xi et al. (2020).
    Keywords: Chlorophyll a; Chlorophyll a, Diatoms; Chlorophyll a, Dinoflagellata; Chlorophyll a, Green algae; Chlorophyll a, Haptophyta; Chlorophyll a, Prochlorococcus; Chlorophyll a, Prokaryotes; Chlorophyll a, total; DATE/TIME; DEPTH, water; GlobColour; LATITUDE; LONGITUDE; ocean color; OLCI-PFT; ORDINAL NUMBER; particulate matter; PFT; Prochlorococcus, fractional; Prokaryotes, fractional; Remote sensing reflectance at 412 nm; Remote sensing reflectance at 443 nm; Remote sensing reflectance at 490 nm; Remote sensing reflectance at 510 nm; Remote sensing reflectance at 531 nm; Remote sensing reflectance at 547 nm; Remote sensing reflectance at 555 nm; Remote sensing reflectance at 670 nm; Remote sensing reflectance at 678 nm; Rrs; Sea surface temperature; SST
    Type: Dataset
    Format: text/tab-separated-values, 9015 data points
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 4
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    Modulation of the vertical particle transfer efficiency in the oxygen minimum zone off Peru (2018)
    Copernicus Publications (EGU)
    In:  Biogeosciences (BG), 15 (16). pp. 5093-5111.
    Details
    Publication Date: 2021-02-08
    Description: The fate of the organic matter (OM) produced by marine life controls the major biogeochemical cycles of the Earth's system. The OM produced through photosynthesis is either preserved, exported towards sediments or degraded through remineralisation in the water column. The productive eastern boundary upwelling systems (EBUSs) associated with oxygen minimum zones (OMZs) would be expected to foster OM preservation due to low O2 conditions. But their intense and diverse microbial activity should enhance OM degradation. To investigate this contradiction, sediment traps were deployed near the oxycline and in the OMZ core on an instrumented moored line off Peru. Data provided high-temporal-resolution O2 series characterising two seasonal steady states at the upper trap: suboxic ([O2] 〈 25µmolkg−1) and hypoxic–oxic (15 〈 [O2] 〈 160µmolkg−1) in austral summer and winter–spring, respectively. The OMZ vertical transfer efficiency of particulate organic carbon (POC) between traps (Teff) can be classified into three main ranges (high, intermediate, low). These different Teff ranges suggest that both predominant preservation (high Teff 〉 50%) and remineralisation (intermediate Teff 20 〈 50% or low Teff 〈 6%) configurations can occur. An efficient OMZ vertical transfer (Teff 〉 50%) has been reported in summer and winter associated with extreme limitation in O2 concentrations or OM quantity for OM degradation. However, higher levels of O2 or OM, or less refractory OM, at the oxycline, even in a co-limitation context, can decrease the OMZ transfer efficiency to below 50%. This is especially true in summer during intraseasonal wind-driven oxygenation events. In late winter and early spring, high oxygenation conditions together with high fluxes of sinking particles trigger a shutdown of the OMZ transfer (Teff 〈 6%). Transfer efficiency of chemical elements composing the majority of the flux (nitrogen, phosphorus, silica, calcium carbonate) follows the same trend as for carbon, with the lowest transfer level being in late winter and early spring. Regarding particulate isotopes, vertical transfer of δ15N suggests a complex pattern of 15N impoverishment or enrichment according to Teff modulation. This sensitivity of OM to O2 fluctuations and particle concentration calls for further investigation into OM and O2-driven remineralisation processes. This should include consideration of the intermittent behaviour of OMZ towards OM demonstrated in past studies and climate projections.
    Type: Article , PeerReviewed
    Format: text
    Format: text
    DOI: 10.5194/bg-15-5093-2018
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    OceanRep: Article in a Scientific Journal - peer-reviewed
  • 5
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    Synthesis of satellite validation results (2022)
    EuroSea
    Details
    Publication Date: 2022-11-15
    Description: This report presents the results of Task 4.4: Improving the use of in situ observations for the long-term validation of satellite observations
    Type: Report , NonPeerReviewed , info:eu-repo/semantics/book
    Format: text
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    OceanRep: Report - other report
  • 6
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    Global chlorophyll a concentrations of phytoplankton functional types with detailed uncertainty assessment using multi‐sensor ocean color and sea surface temperature satellite products (2021)
    AGU
    In:  EPIC3Journal of Geophysical Research: Oceans, AGU, 126(5), ISSN: 2169-9275
    Details
    Publication Date: 2021-05-06
    Description: First, we retune an algorithm based on empirical orthogonal functions (EOFs) for globally retrieving the chlorophyll a concentration (Chl‐a) of phytoplankton functional types (PFTs) from multisensor merged ocean color (OC) products. The retuned algorithm, referred to as EOF‐SST hybrid algorithm, is improved by: (i) using 23% more matchups between the updated global in situ pigment database and satellite remote sensing reflectance (Rrs) products, and (ii) including sea surface temperature (SST) as an additional input parameter. In addition to the Chl‐a of the six PFTs (diatoms, haptophytes, dinoflagellates, green algae, prokaryotes, and Prochlorococcus), the fractions of prokaryote and Prochlorococcus Chl‐a to total Chl‐a (TChl‐a), are also retrieved by the EOF‐SST hybrid algorithm. Matchup data are separated for low and high‐temperature regimes based on different PFT dependences on SST, to establish SST‐separated hybrid algorithms which demonstrate further improvements in performance as compared to the EOF‐SST hybrid algorithm. The per‐pixel uncertainty of the retrieved TChl‐a and PFT products is estimated by taking into account the uncertainties from both input data and model parameters through Monte Carlo simulations and analytical error propagation. The algorithm and its method to determine uncertainties can be transferred to similar OC products until today, enabling long‐term continuous satellite observations of global PFT products. Satellite PFT uncertainty is essential to evaluate and improve coupled ecosystem‐ocean models which simulate PFTs, and furthermore can be used to directly improve these models via data assimilation.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
    Format: application/pdf
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  • 7
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    Intercomparison of Phytoplankton functional types dynamics from satellite observations (2021)
    In:  EPIC3EGU General Assembly 2021, 2021-04-19-2021-04-30Intercomparison of Phytoplankton functional types dynamics from satellite observations
    Details
    Publication Date: 2022-01-10
    Description: Copernicus marine environment monitoring service (CMEMS) gives users access to a wide range of ocean descriptors. Both physics and biogeochemistry of the marine environment can be studied with complementary source of data, such as in situ data, modelling output and satellite observations at global scale and/or for European marginal seas. Among the ocean descriptors supplied as part of CMEMS, phytoplankton functional types (PFTs) describe the phytoplanktonic composition at global level or over European marginal seas. Studied phytoplankton assemblage is particularly important as it is the basis of the marine food-web. Composition of the first trophic level is a valuable indicator to infer the structure of the ecosystem and its health. Over the last decades, ocean colour remote sensing has been used to estimate the phytoplanktonic composition. The algorithms developed to estimate PFTs composition based on ocean colour observation can be classified in three categories: the spectral approaches, the abundance-based approaches (derived from the chlorophyll concentration) and the ecological approaches. The three approaches can lead to differences or, conversely, to similar patterns. Difference and similarity in PFTs estimation from remote sensing is a useful information for data assimilation or model simulation, as it provides indications on the uncertainties/variability associated to the PFT estimates. Indeed, PFT estimates from satellite observations are increasingly assimilated into ecological models to improve biogeochemical simulations, what highlights the importance to get an index or at least information describing the validity range of such PFTs estimates. In this study, four algorithms (two abundance-based, and two spectral approaches) are compared. The aim of this study is to compare the related PFT products spatially and temporally, and to study the agreement of their derived PFT phenology. This study proposes also to compare PFT algorithms developed for the global ocean with those developed for specific regions in order to assess the potential strength and weakness of the different approaches. Once similarities and discrepancies between the different approaches are assessed, this information could be used by model to give an interval of confidence in model simulation.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
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  • 8
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    Global chlorophyll a concentrations of phytoplankton functional types with detailed uncertainty assessment using multi-sensor ocean color and sea surface temperature products (2021)
    In:  EPIC3EGU General Assembly 2021, online, 2021-04-19-2021-04-30Global chlorophyll a concentrations of phytoplankton functional types with detailed uncertainty assessment using multi-sensor ocean color and sea surface temperature products
    Details
    Publication Date: 2022-01-04
    Description: With the extensive use of ocean color (OC) satellite products, diverse algorithms have been developed in the past decades to observe the phytoplankton community structure in terms of functional types, taxonomic groups and size classes. There is a need to combine satellite observations and biogeochemical modelling to enable comprehensive phytoplankton groups time series data and predictions under the changing climate. A prerequisite for this is continuous long-term satellite observations from past and current OC sensors with quantified uncertainties are essential to ensure their application. Previously we have configured an approach, namely OLCI-PFT (v1), to globally retrieve total chlorophyll a concentration (TChl-a), and chlorophyll a concentration (Chl-a) of multiple phytoplankton functional types (PFTs). This algorithm is developed based on empirical orthogonal functions (EOF) using satellite remote sensing reflectance (Rrs) products from the GlobColour archive (https://www.globcolour.info/). The algorithm can be applied to both, merged OC products and Sentinel 3A OLCI data. Global PFT Chl-a products of OLCI-PFT v1 are available on CMEMS under Ocean Products since July 2020. Lately we have updated the approach and established the OLCI-PFT v2 by including sea surface temperature (SST) as input data. The updated version delivers improved global products for the aforementioned PFT quantities. The per-pixel uncertainty of the retrieved TChl-a and PFT Chl-a products is estimated and validated by taking into account the uncertainties from both input data (satellite Rrs and SST) and model parameters through Monte Carlo simulations and analytical error propagation. The uncertainty of the OLCI-PFT products v2 was assessed on a global scale. For PFT Chl-a products this has been done for the first. The uncertainty of OLCI-PFT v2 TChl-a product is in general much lower than that of the TChl-a product generated in the frame of the ESA Ocean Colour Climate Change Initiative project (OC-CCI). The OLCI-PFT algorithm v1 and v2 have also been further adapted to use a merged MODIS-VIRRS input. Good consistency has been found between the OLCI-PFT products derived from using input data from the different OC sensors. This sets the ground to realize long-term continuous satellite global PFT products from OLCI-PFT. Satellite PFT uncertainty, as provided for our products, is essential to evaluate and improve coupled ecosystem-ocean models which simulate PFTs, and furthermore can be used to improve these models directly via data assimilation.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
    Format: application/pdf
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  • 9
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    Phytoplankton functional types observation from space in the Fram Strait (2002-2020) (2022)
    In:  EPIC3Ocean Carbon From Space Workshop, Online, 2022-02-14-2022-02-18
    Details
    Publication Date: 2023-06-21
    Description: Phytoplankton in the sunlit layer of the ocean act as the base of the marine food web fueling fisheries, and also regulate key biogeochemical processes. Phytoplankton composition structure varies in ocean biomes and different phytoplankton groups drive differently the marine ecosystem and biogeochemical processes. Because of this, variations in phytoplankton composition influence the entire ocean environment, specifically the ocean energy transfer and the export of organic carbon to the deep ocean. As one of the algorithms deriving phytoplankton composition from space borne data, within the framework of the EU Copernicus Marine Service (CMEMS), EOF-PFT algorithm was developed using multi-spectral satellite data collocated to an extensive in-situ PFT data set based on HPLC pigments and sea surface temperature data (Xi et al. 2020, 2021; https://marine.copernicus.eu/). By using multi-sensor merged products and Sentinel-3 OLCI data, the algorithm provides global chlorophyll a data with per-pixel uncertainty for diatoms, haptophytes, dinoflagellates, chlorophytes and prokaryotic phytoplankton spanning the period from 2002 until today. Due to different lifespans and radiometric characteristics of the ocean color sensors, the consistency of the PFTs is evaluated to provide quality-assured data for a consistent long-term monitoring of the phytoplankton community structure. As current commonly used phytoplankton carbon estimation methods rely mostly on the backscattering property of phytoplankton, which could vary dramatically for different phytoplankton taxa, as a perspective of this study, phytoplankton carbon may be better estimated in a way that accounts for phytoplankton taxonomy.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
    Format: application/pdf
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  • 10
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    Phytoplankton functional types from multi-sensor satellite observations – towards a long-term monitoring (2002-2020) (2022)
    In:  EPIC3Ocean Science Meeting 2022, 2022-02-24-2022-03-04
    Details
    Publication Date: 2023-06-21
    Description: Phytoplankton in the sunlit layer of the ocean act as the base of the marine food web fueling fisheries, and also regulate key biogeochemical processes such as exporting carbon to the deep ocean. Phytoplankton composition structure varies in ocean biomes and different phytoplankton groups drive differently the marine ecosystem. As one of the algorithms deriving phytoplankton composition from space borne data, within the framework of the EU Copernicus Marine Service (CMEMS), OLCI-PFT algorithm was developed using multi-spectral satellite data collocated to an extensive in-situ PFT data set based on HPLC pigments and sea surface temperature data (Xi et al. 2020, 2021). It provides global PFT retrievals including chlorophyll a estimations of diatoms, haptophytes, dinoflagellates, chlorophytes and prokaryotic phytoplankton spanning the period from 2002 until today, by using multi-sensor merged products and OLCI data. These PFT products with per-pixel uncertainty are publicly available on the CMEMS. Due to different lifespans and radiometric characteristics of the ocean color sensors, it is crucial to evaluate the CMEMS PFT products to provide quality-assured data for a consistent long-term monitoring of the phytoplankton community structure. In this study, using in-situ phytoplankton data (HPLC pigment data further evaluated with microscopic, flow cytometry, molecular and hyperspectral optical data) collected from expeditions since 2009 in the tropical, temperate and polar (mainly Fram Strait within the PEBCAO network) regions, we aim to 1) validate the CMEMS PFT products and investigate the continuity of the PFTs data derived from different satellites, and 2) deliver two-decade consistent PFT products for times series analysis. For the latter we determine inter-annual trends and variation of the surface phytoplankton community structure targeting some key sub-regions (e.g.,east Fram Strait) that have been observed being influenced by the changing marine environment.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev , info:eu-repo/semantics/conferenceObject
    Format: application/pdf
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