Synergistic Exploitation of Hyper- and Multi-Spectral Precursor Sentinel Measurements to Determine Phytoplankton Functional Types (SynSenPFT)

Losa, Svetlana N.; Soppa, Mariana Altenburg; Dinter, Tilman; Wolanin, Aleksandra; Brewin, Robert J. W.; Bricaud, Annick; Oelker, Julia; Peeken, Ilka; Gentili, Bernard; Розанов, В. В.; Bracher, Astrid

doi:10.3389/fmars.2017.00203

Cited by 43 publications

(62 citation statements)

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“…For the global evaluation of the two phytoplankton groups in our model, the Synergistic hyper‐ and multispectral satellite PFT (SynSenPFT) product by Losa et al () was used. This is a synergistic product of two satellite retrieval algorithms.…”

Section: Methodsmentioning

confidence: 99%

“…The large‐scale information (0.5° latitude times 0.5° longitude and weekly resolution) on the three phytoplankton‐types diatoms, coccolithophores, and cyanobacteria is derived with the analytical method PhytoDOAS (Bracher et al, ; Bracher, Dinter, et al, ; Sadeghi et al, ), which resolves the spectral imprints of each individual group from the spectrally highly resolved measurements of the sensor SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) mounted on Envisat. This is combined with small‐scale information (4 km by 4 km and daily resolution) on the three groups obtained using the empirical abundance‐based OC‐PFT (Ocean Color‐Phytoplankton Functional Type) method (Hirata et al, ; Soppa et al, , ; supplement in Losa et al, ). The OC‐PFT method uses a statistical model relating a fraction of retrieved PFTs to total chlorophyll.…”

Section: Methodsmentioning

confidence: 99%

“…Because the OC‐PFT estimates depend on OC‐CCI total chlorophyll concentrations, which are assimilated here, the SynSenPFT product (Losa et al, ; products available at Losa et al, ) is not a completely independent data set. However, the partitioning of total chlorophyll among different PFTs in the OC‐PFT algorithm is fully independent from the model dynamics in the MITgcm‐REcoM.…”

Section: Methodsmentioning

confidence: 99%

“…In addition, there is a mismatch between the three PFTs represented in the SynSenPFT data compared to the two PFTs simulated by REcoM2. A detailed discussion of the possible data errors in SynSenPFT is provided by Losa et al (). An indication of the data quality is provided by the comparison to in situ data using scatterplots discussed in section .…”

Section: Methodsmentioning

confidence: 99%

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Assimilation of Global Total Chlorophyll OC‐CCI Data and Its Impact on Individual Phytoplankton Fields

et al. 2019

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The coupled ocean circulation-ecosystem model MITgcm-REcoM2 is used to simulate biogeochemical variables in a global configuration. The ecosystem model REcoM2 simulates two phytoplankton groups, diatoms and small phytoplankton, using a quota formulation with variable carbon, nitrogen, and chlorophyll contents of the cells. To improve the simulation of the phytoplankton variables, chlorophyll-a data from the European Space Agency Ocean-Color Climate Change Initiative (OC-CCI) for 2008 and 2009 are assimilated with an ensemble Kalman filter. Utilizing the multivariate cross covariances estimated by the model ensemble, the assimilation constrains all model variables describing the two phytoplankton groups. Evaluating the assimilation results against the satellite data product SynSenPFT shows an improvement of total chlorophyll and more importantly of individual phytoplankton groups. The assimilation improves both phytoplankton groups in the tropical and midlatitude regions, whereas the assimilation has a mixed response in the high-latitude regions. Diatoms are most improved in the major ocean basins, whereas small phytoplankton show small deteriorations in the Southern Ocean. The improvement of diatoms is larger when the multivariate assimilation is computed using the ensemble-estimated cross covariances between total chlorophyll and the phytoplankton groups than when the groups are updated so that their ratio to total chlorophyll is preserved. The comparison with in situ observations shows that the correlation of the simulated chlorophyll of both phytoplankton groups with these data is increased whereas the bias and error are decreased. Overall, the multivariate assimilation of total chlorophyll modifies the two phytoplankton groups separately, even though the sum of their individual chlorophyll concentrations represents the total chlorophyll.Plain Language Summary Different types of plankton are simulated globally with ocean ecosystem models. To further increase their prediction quality, we combine the model with satellite observations of chlorophyll using modern methods called data assimilation. This method allows us not only to improve the modeled total chlorophyll but also the simulation of the different plankton types. Further, we can fill gaps in the satellite data that results, for example, from clouds. Thus, we are able to better predict the ocean ecosystem, which in turn helps to understand climate change patterns and carbon cycle processes.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Assimilation of Global Total Chlorophyll OC‐CCI Data and Its Impact on Individual Phytoplankton Fields

et al. 2019

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“…Chl-a was calculated as the summed contribution of monovinyl chlorophyll a, chlorophyllide a and divinyl chlorophyll a concentrations. Four phytoplankton groups, namely diatoms, green algae, prymnesiophytes (haptophytes) and prokaryotes, were identified and determined from their biomarker pigments using Diagnostic Pigment Analysis (DPA) [55][56][57][58] that is applied to a large global pigment data set by Losa et al [59] to obtain the specific weights to convert the diagnostic pigment concentrations to Chla contributed by the aforementioned phytoplankton groups.…”

Section: Phytoplankton Pigment Analysismentioning

confidence: 99%

Underway spectrophotometry in the Fram Strait (European Arctic Ocean): a highly resolved chlorophyll a data source for complementing satellite ocean color

et al. 2018

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Satellite remote sensing of chlorophyll a concentration (Chl-a) in the Arctic Ocean is spatially and temporally limited and needs to be supplemented and validated with substantial volumes of in situ observations. Here, we evaluated the capability of obtaining highly resolved in situ surface Chl-a using underway spectrophotometry operated during two summer cruises in 2015 and 2016 in the Fram Strait. Results showed that Chl-a measured using high pressure liquid chromatography (HPLC) was well related (R2 = 0.90) to the collocated particulate absorption line height at 676 nm obtained from the underway spectrophotometry system. This enabled continuous surface Chl-a estimation along the cruise tracks. When used to validate Chl-a operational products as well as to assess the Chl-a algorithms of the aqua moderate resolution imaging spectroradiometer (MODIS-A) and Sentinel-3 Ocean Land Color Imager (OLCI) Level 2 Chl-a operational products, and from OLCI Level 2 products processed with Polymer atmospheric correction algorithm (version 4.1), the underway spectrophotometry based Chl-a data sets proved to be a much more sufficient data source by generating over one order of magnitude more match-ups than those obtained from discrete water samples. Overall, the band ratio (OCI, OC4) Chl-a operational products from MODIS-A and OLCI as well as OLCI C2RCC products showed acceptable results. The OLCI Polymer standard output provided the most reliable Chl-a estimates, and nearly as good results were obtained from the OCI algorithm with Polymer atmospheric correction method. This work confirms the great advantage of the underway spectrophotometry in enlarging in situ Chl-a data sets for the Fram Strait and improving satellite Chl-a validation and Chl-a algorithm assessment over discrete water sample analysis in the laboratory.

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Retrieval of phytoplankton pigments and functional types from underway spectrophotometry in the Fram Strait

Liu¹,

Boss²,

Chase³

et al. 2018

Preprint

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Synergistic Exploitation of Hyper- and Multi-Spectral Precursor Sentinel Measurements to Determine Phytoplankton Functional Types (SynSenPFT)

Cited by 43 publications

References 70 publications

Assimilation of Global Total Chlorophyll OC‐CCI Data and Its Impact on Individual Phytoplankton Fields

Assimilation of Global Total Chlorophyll OC‐CCI Data and Its Impact on Individual Phytoplankton Fields

Underway spectrophotometry in the Fram Strait (European Arctic Ocean): a highly resolved chlorophyll a data source for complementing satellite ocean color

Retrieval of phytoplankton pigments and functional types from underway spectrophotometry in the Fram Strait

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