Frouin
Robert
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Robert
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ArticleSeasonal forcing of summer dissolved inorganic carbon and chlorophyll a on the western shelf of the Antarctic Peninsula(American Geophysical Union, 2010-03-30) Montes-Hugo, Martin ; Sweeney, Colm ; Doney, Scott C. ; Ducklow, Hugh W. ; Frouin, Robert ; Martinson, Douglas G. ; Stammerjohn, Sharon E. ; Schofield, Oscar M. E.The Southern Ocean is a climatically sensitive region that plays an important role in the regional and global modulation of atmospheric CO2. Based on satellite-derived sea ice data, wind and cloudiness estimates from numerical models (National Centers for Environmental Prediction-National Center for Atmospheric Research reanalysis), and in situ measurements of surface (0–20 m depth) chlorophyll a (ChlSurf) and dissolved inorganic carbon (DICSurf) concentration, we show sea ice concentration from June to November and spring wind patterns between 1979 and 2006 had a significant influence on midsummer (January) primary productivity and carbonate chemistry for the Western Shelf of the Antarctic Peninsula (WAP, 64°–68°S, 63.4°–73.3°W). In general, strong (>3.5 m s−1) and persistent (>2 months) northerly winds during the previous spring were associated with relatively high (monthly mean > 2 mg m−3) ChlSurf and low (monthly mean < 2 mmol kg−1) salinity-corrected DIC (DICSurf*) during midsummer. The greater ChlSurf accumulation and DICSurf* depletion was attributed to an earlier growing season characterized by decreased spring sea ice cover or nearshore accumulation of phytoplankton in association with sea ice. The impact of these wind-driven mechanisms on ChlSurf and DICSurf* depended on the extent of sea ice area (SIA) during winter. Winter SIA affected phytoplankton blooms by changing the upper mixed layer depth (UMLD) during the subsequent spring and summer (December–January–February). Midsummer DICSurf* was not related to DICSurf* concentration during the previous summer, suggesting an annual replenishment of surface DIC during fall/winter and a relatively stable pool of deep (>200 m depth) “winter-like” DIC on the WAP.
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ArticleAn ocean-colour time series for use in climate studies: The experience of the ocean-colour climate change initiative (OC-CCI)(MDPI, 2019-10-03) Sathyendranath, Shubha ; Brewin, Robert J. W. ; Brockmann, Carsten ; Brotas, Vanda ; Calton, Ben ; Chuprin, Andrei ; Cipollini, Paolo ; Couto, André B. ; Dingle, James ; Doerffer, Roland ; Donlon, Craig ; Dowell, Mark ; Farman, Alex ; Grant, Michael ; Groom, Steven ; Horseman, Andrew ; Jackson, Thomas ; Krasemann, Hajo ; Lavender, Samantha ; Martinez-Vicente, Victor ; Mazeran, Constant ; Melin, Frederic ; Moore, Timothy S. ; Müller, Dagmar ; Regner, Peter ; Roy, Shovonlal ; Steele, Chris J. ; Steinmetz, François ; Swinton, John ; Taberner, Malcolm ; Thompson, Adam ; Valente, André ; Zühlke, Marco ; Brando, Vittorio ; Feng, Hui ; Feldman, Gene ; Franz, Bryan A. ; Frouin, Robert ; Gould, Richard ; Hooker, Stanford B. ; Kahru, Mati ; Kratzer, Susanne ; Mitchell, B. Greg ; Muller-Karger, Frank E. ; Sosik, Heidi M. ; Voss, Kenneth ; Werdell, Jeremy ; Platt, TrevorOcean 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 used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.
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ArticleA compilation of global bio-optical in situ data for ocean-colour satellite applications - version two(Copernicus Publications, 2019-07-15) Valente, André ; Sathyendranath, Shubha ; Brotas, Vanda ; Groom, Steven ; Grant, Michael ; Taberner, Malcolm ; Antoine, David ; Arnone, Robert ; Balch, William M. ; Barker, Kathryn ; Barlow, Ray ; Belanger, Simon ; Berthon, Jean-François ; Besiktepe, Sukru ; Borsheim, Yngve ; Bracher, Astrid ; Brando, Vittorio ; Canuti, Elisabetta ; Chavez, Francisco P. ; Cianca, Andrés ; Claustre, Hervé ; Clementson, Lesley ; Crout, Richard ; Frouin, Robert ; García-Soto, Carlos ; Gibb, Stuart W. ; Gould, Richard ; Hooker, Stanford B. ; Kahru, Mati ; Kampel, Milton ; Klein, Holger ; Kratzer, Susanne ; Kudela, Raphael M. ; Ledesma, Jesus ; Loisel, Hubert ; Matrai, Patricia A. ; McKee, David ; Mitchell, Brian G. ; Moisan, Tiffany ; Muller-Karger, Frank E. ; O'Dowd, Leonie ; Ondrusek, Michael ; Platt, Trevor ; Poulton, Alex J. ; Repecaud, Michel ; Schroeder, Thomas ; Smyth, Timothy ; Smythe-Wright, Denise ; Sosik, Heidi M. ; Twardowski, Michael ; Vellucci, Vincenzo ; Voss, Kenneth ; Werdell, Jeremy ; Wernand, Marcel ; Wright, Simon ; Zibordi, GiuseppeA global compilation of in situ data is useful to evaluate the quality of ocean-colour satellite data records. Here we describe the data compiled for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (including, inter alia, MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT and GeP&CO) and span the period from 1997 to 2018. Observations of the following variables were compiled: spectral remote-sensing reflectances, concentrations of chlorophyll a, spectral inherent optical properties, spectral diffuse attenuation coefficients and total suspended matter. The data were from multi-project archives acquired via open internet services or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenization, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The final result is a merged table designed for validation of satellite-derived ocean-colour products and available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) was propagated throughout the work and made available in the final table. By making the metadata available, provenance is better documented, and it is also possible to analyse each set of data separately. This paper also describes the changes that were made to the compilation in relation to the previous version (Valente et al., 2016). The compiled data are available at https://doi.org/10.1594/PANGAEA.898188 (Valente et al., 2019).
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ArticleA compilation of global bio-optical in situ data for ocean-colour satellite applications(Copernicus Publications on behalf of the European Geosciences Union, 2016-06-03) Valente, André ; Sathyendranath, Shubha ; Brotas, Vanda ; Groom, Steven ; Grant, Michael ; Taberner, Malcolm ; Antoine, David ; Arnone, Robert ; Balch, William M. ; Barker, Kathryn ; Barlow, Ray ; Belanger, Simon ; Berthon, Jean-François ; Besiktepe, Sukru ; Brando, Vittorio ; Canuti, Elisabetta ; Chavez, Francisco P. ; Claustre, Hervé ; Crout, Richard ; Frouin, Robert ; García-Soto, Carlos ; Gibb, Stuart W. ; Gould, Richard ; Hooker, Stanford B. ; Kahru, Mati ; Klein, Holger ; Kratzer, Susanne ; Loisel, Hubert ; McKee, David ; Mitchell, Brian G. ; Moisan, Tiffany ; Muller-Karger, Frank E. ; O'Dowd, Leonie ; Ondrusek, Michael ; Poulton, Alex J. ; Repecaud, Michel ; Smyth, Timothy ; Sosik, Heidi M. ; Twardowski, Michael ; Voss, Kenneth ; Werdell, Jeremy ; Wernand, Marcel ; Zibordi, GiuseppeA compiled set of in situ data is important to evaluate the quality of ocean-colour satellite-data records. Here we describe the data compiled for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT, GeP&CO), span between 1997 and 2012, and have a global distribution. Observations of the following variables were compiled: spectral remote-sensing reflectances, concentrations of chlorophyll a, spectral inherent optical properties and spectral diffuse attenuation coefficients. The data were from multi-project archives acquired via the open internet services or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The final result is a merged table designed for validation of satellite-derived ocean-colour products and available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) were preserved throughout the work and made available in the final table. Using all the data in a validation exercise increases the number of matchups and enhances the representativeness of different marine regimes. By making available the metadata, it is also possible to analyse each set of data separately. The compiled data are available at doi:10.1594/PANGAEA.854832 (Valente et al., 2015).
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ArticleSatellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems(John Wiley & Sons, 2018-03-06) Muller-Karger, Frank E. ; Hestir, Erin ; Ade, Christiana ; Turpie, Kevin ; Roberts, Dar A. ; Siegel, David A. ; Miller, Robert J. ; Humm, David ; Izenberg, Noam ; Keller, Mary ; Morgan, Frank ; Frouin, Robert ; Dekker, Arnold G. ; Gardner, Royal ; Goodman, James ; Schaeffer, Blake ; Franz, Bryan A. ; Pahlevan, Nima ; Mannino, Antonio ; Concha, Javier A. ; Ackleson, Steven G. ; Cavanaugh, Kyle C. ; Romanou, Anastasia ; Tzortziou, Maria ; Boss, Emmanuel S. ; Pavlick, Ryan ; Freeman, Anthony ; Rousseaux, Cecile S. ; Dunne, John P. ; Long, Matthew C. ; Salas, Eduardo Klein ; McKinley, Galen A. ; Goes, Joachim I. ; Letelier, Ricardo M. ; Kavanaugh, Maria T. ; Roffer, Mitchell ; Bracher, Astrid ; Arrigo, Kevin R. ; Dierssen, Heidi M. ; Zhang, Xiaodong ; Davis, Frank W. ; Best, Benjamin D. ; Guralnick, Robert P. ; Moisan, John R. ; Sosik, Heidi M. ; Kudela, Raphael M. ; Mouw, Colleen B. ; Barnard, Andrew H. ; Palacios, Sherry ; Roesler, Collin S. ; Drakou, Evangelia G. ; Appeltans, Ward ; Jetz, WalterThe biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite‐based sensors can repeatedly record the visible and near‐infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100‐m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short‐wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14‐bit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3‐d repeat low‐Earth orbit could sample 30‐km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.