The Ocean Colour Climate Change Initiative: II. Spatial and temporal homogeneity of satellite data retrieval due to systematic effects in atmospheric correction processors

Müller, Dagmar; Krasemann, Hajo; Brewin, Robert J. W.; Brockmann, Carsten; Deschamps, Pierre-Yves; Fomferra, Norman; Franz, Bryan A.; Grant, Michael; Groom, Steve; Mélin, Frédéric; Platt, Trevor; Regner, Peter; Sathyendranath, Shubha; Steinmetz, François; Swinton, John R.

doi:10.1016/j.rse.2015.01.033

Cited by 27 publications

(23 citation statements)

References 11 publications

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“…A comprehensive set of criteria was established for selection of atmospheric-correction processors and of in-water bio-optical algorithms, as described in Brewin et al [33], Müller et al [34], and Müller et al [35]. The criteria included a quantitative assessment and a qualitative assessment of the suitability of algorithms for climate studies [21].…”

Section: Algorithm Selection Criteriamentioning

confidence: 99%

An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)

Sathyendranath

Brewin

Brockmann

et al. 2019

Sensors

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317

226

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Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were u...

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Section: Algorithm Selection Criteriamentioning

confidence: 99%

An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)

Sathyendranath

Brewin

Brockmann

et al. 2019

Sensors

Self Cite

317

226

View full text Add to dashboard Cite

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“…However, some AC processors incorporate sun glint contributions in their reflectance models and derive normalized R rs in this condition. These AC yield much larger coverage of data (Müller et al, 2015b), but areas with high glint should nevertheless be considered cautiously. One of the AC processors is Polymer (Steinmetz et al, 2011), which reveals good performance in comparison with MERIS matchups (Müller et al, 2015a).…”

Section: Retrieval Accuracymentioning

confidence: 99%

The OLCI Neural Network Swarm (ONNS): A Bio-Geo-Optical Algorithm for Open Ocean and Coastal Waters

2017

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The processing scheme of a novel in-water algorithm for the retrieval of ocean color products from Sentinel-3 OLCI is introduced. The algorithm consists of several blended neural networks that are specialized for 13 different optical water classes. These comprise clearest natural waters but also waters reaching the frontiers of marine optical remote sensing, namely extreme absorbing, or scattering waters. Considered chlorophyll concentrations reach up to 200 mg m −3 , non-algae particle concentrations up to 1,500 g m −3 , and the absorption coefficient of colored dissolved organic matter at 440 nm is up to 20 m −1 . The algorithm generates different concentrations of water constituents, inherent and apparent optical properties, and a color index. In addition, all products are delivered with an uncertainty estimate. A baseline validation of the products is provided for various water types. We conclude that the algorithm is suitable for the remote sensing estimation of water properties and constituents of most natural waters.

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“…OLCI Polymer-Standard, OCI-and OC4-Polymer algorithms) and OLCI C2RCC algorithm are two to three times greater than those by the operational MODIS-A OCI and OLCI OC4 algorithms. This is because OLCI C2RCC and Polymer are the atmospheric correction processors that incorporate the contributions of sun glint and thin clouds in their reflectance models to derive atmospheric corrected remote sensing reflectance, allowing for much larger coverage of data [72,73]. MERIS Polymer products have shown to improve the spatial coverage by almost a factor of two [74] and have proven successful for retrieving (MERIS) ocean color products: Polymer was selected as the MERIS processor for atmospheric correction for the Ocean Color Climate Change Initiative (OC-CCI) after an extensive validation and intercomparison with other atmospheric correction algorithms in which each algorithm's uncertainty was assessed [73].…”

Section: Satellite Chl-a Validationmentioning

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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The Ocean Colour Climate Change Initiative: II. Spatial and temporal homogeneity of satellite data retrieval due to systematic effects in atmospheric correction processors

Cited by 27 publications

References 11 publications

An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)

An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)

The OLCI Neural Network Swarm (ONNS): A Bio-Geo-Optical Algorithm for Open Ocean and Coastal Waters

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

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