Satellite; Coastal monitoring; Phytoplankton; Chlorophyll-a; Total suspended matter; Turbidity
Abstract :
[en] Satellite retrievals were derived from MODIS/AQUA, VIIRS/NPP and OLCI-A/Sentinel-3 spectral
reflectance. In situ data were obtained from the coastal phytoplankton networks SOMLIT (CNRS),
REPHY (Ifremer) and associated networks. Satellite and in situ retrievals of the year 2017 were
compared to the historical seasonal cycles and percentiles, 10 and 90, observed in situ. Regarding the
sampling frequency in the Mediterranean Sea, a weekly in situ sampling allowed all major peaks in
Chl-a caught from space to be recorded at sea, and, conversely, all in situ peaks were observed from
space in a frequently cloud-free atmosphere. In waters of the Eastern English Channel, lower levels
of Chl-a were observed, both in situ and from space, compared to the historical averages. However,
despite a good overall agreement for low to moderate biomass, the satellite method, based on blue
and green wavelengths, tends to provide elevated and variable Chl-a in a high biomass environment.
Satellite-derived TSM and Turbidity were quite consistent with in situ measurements. Moreover,
satellite retrievals of the water clarity parameters often showed a lower range of variability than their
in situ counterparts did, being less scattered above and under the seasonal curves of percentiles 10
and 90.
Research center :
FOCUS - Freshwater and OCeanic science Unit of reSearch - ULiège
Disciplines :
Aquatic sciences & oceanology
Author, co-author :
Gohin, Francis
Bryère, Philippe
Lefebvre, Alain
Sauriau, Pierre-Guy
Savoye, Nicolas
Vantrepotte, Vincent
Bozec, Yann
Cariou, Thierry
Conan, Pascal
Coudray, Sylvain
Courtay, Gaelle
Françoise, Sylvaine
Goffart, Anne ; Université de Liège - ULiège > Département de Biologie, Ecologie et Evolution > Océanographie biologique
O’Reilly, J.E.; Maritorena, S.; Mitchell, B.G.; Siegel, D.A.; Carder, K.L.; Garver, S.A.; Kahru, M.; McClain, C. Ocean color chlorophyll algorithm for SeaWiFS. J. Geophys. Res. 1998, 103, 24937–24953. [CrossRef]
Gohin, F.; Saulquin, B.; Oger-Jeanneret, H.; Lozac’h, L.; Lampert, L.; Lefebvre, A.; Riou, P.; Bruchon, F. Towards a better assessment of the ecological status of coastal waters using satellite-derived Chlorophyll-a concentrations. Remote Sens. Environ. 2008, 112, 3329–3340. [CrossRef]
Novoa, S.; Chust, G.; Sagarminaga, Y.; Revilla, M.; Borja, A.; Franco, J. Water Quality Assessment Using Satellite-Derived Chlorophyll-a within the European directives, in the southeastern Bay of Biscay. Mar. Poll. Bull. 2012, 65, 739–750. [CrossRef] [PubMed]
Tilstone, G.; Mallor-Hoya, S.; Gohin, F.; Belo Couto, A.; Sá, C.; Goela, P.; Cristina, S.; Airs, R.; Icely, J.; Zühlke, M.; et al. Which Ocean colour algorithm for MERIS in North West European waters? Remote Sens. Environ. 2017, 189, 132–151. [CrossRef]
Gohin, F.; Van der Zande, D.; Tilstone, G.; Eleveld, M.A.; Lefebvre, A.; Andrieux-Loyer, F.; Blauw, A.N.; Bryère, P.; Devreker, D.; Garnesson, P.; et al. Twenty years of satellite and in situ observations of Chlorophyll-a from the northern Bay of Biscay to the eastern English Channel. Is the water quality improving? Remote Sens. Environ. 2019, 233, 111343. [CrossRef]
Lapucci, C.; Rella, M.A.; Brandini, C.; Ganzin, N.; Gozzini, B.; Maselli, F.; Massi, L.; Nuccio, C.; Ortolani, A.; Trees, C. Evaluation of empirical and semi-analytical chlorophyll algorithms in the Ligurian and North Tyrrhenian Seas. J. Appl. Remote Sens. 2012, 6. [CrossRef]
Gómez-Jakobsen, F.; Mercado, J.M.; Cortés, D.; Ramírez, T.; Salles, S.; Yebra, L. A new regional algorithm for estimating chlorophyll-a in the Alboran Sea (Mediterranean Sea) from MODIS-Aqua satellite imagery. Int. J. Remote Sens. 2016, 37, 1431–1444. [CrossRef]
Mélin, F.; Vantrepotte, V.; Chuprin, A.; Grant, M.G.; Jackson, T.; Sathyendranath, S. Assessing the fitness-for-purpose of satellite multi-mission ocean color climate data records: A protocol applied to OC-CCI Chlorophyll-a data. Remote Sens. Environ. 2017, 203, 139–151. [CrossRef]
Garnesson, P.; Mangin, A.; Fanton d’Andon, O.; Demaria, J.; Bretagnon, M. The CMEMS GlobColour Chlorophyll-a product based on satellite observation: Multi-sensor merging and flagging strategies. Ocean. Sci. 2019, 15, 819–830. [CrossRef]
Gohin, F.; Druon, J.N.; Lampert, L. A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters. Int. J. Remote Sens. 2002, 23, 1639–1661. [CrossRef]
Gohin, F. Annual cycles of chlorophyll-a, non-algal suspended particulate matter, and turbidity observed from space and in situ in coastal waters. Ocean. Sci. 2011, 7, 705–732. [CrossRef]
Loisel, H.; Vantrepotte, V.; Ouillon, S.; Ngoc, D.D.; Herrmann, M.; Tran, V.; Mériaux, X.; Dessailly, D.; Jamet, C.; Duhaut, T.; et al. Assessment and analysis of the chlorophyll-a variability over the Vietnamese coastal waters from the MERIS ocean color sensor (2002−2012). Remote Sens. Environ. 2017, 190, 217–232. [CrossRef]
Lavigne, H.; Van der Zande, D.; Ruddick, K.; Cardoso Dos santos, J.; Gohin, F.; Brotas, V.; Kratzer, S. Quality-control tests for OC4, OC5 and NIR-red satellite chlorophyll-a algorithms applied to coastal waters. Revis. Remote Sens. Environ. 2020, (In Press).
Common Procedure for the Identification of the Eutrophication Status of the OSPAR Maritime Area. Agreement 2013-08, 66p. Available online: https://www.ospar.org/work-areas/hasec/eutrophication/common-procedure (accessed on 25 August 2020).
Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 Establishing a Framework for Community Action in the Field of Water Policy. Off. J. Eur. Commun. 2000, 327, 1–72.
Directive 2008/56/EC of the European Parliament and of the Council of 17 June 2008 establishing a framework for community action in the field of marine environmental policy (Marine Strategy Framework Directive). Off. J. Eur. Commun. 2008, 164, 19–40.
García-García, L.M.; Sivyer, D.; Devlin, M.; Painting, S.; Collingridge, K.; Van der Molen, J. Optimizing monitoring programs: A case study based on the OSPAR eutrophication assessment for UK waters. Front. Mar. Sci. 2019, 5, 503. [CrossRef]
SOMLIT. Service d’Observation en Milieu LITtoral Data. Available online: https://www.somlit.fr/(accessed on 25 August 2020).
REPHY—French Observation and Monitoring program for Phytoplankton and Hydrology in coastal waters. REPHY Dataset—French Observation and Monitoring Program for Phytoplankton and Hydrology in Coastal Waters. 1987–2018 Metropolitan Data; SEANOE. 2019. Available online: https://www.seanoe.org/data/00361/47248/(accessed on 25 August 2020). [CrossRef]
SRN—Regional Observation and Monitoring program for Phytoplankton and Hydrology in the eastern English Channel. SRN Dataset—Regional Observation and Monitoring Program for Phytoplankton and Hydrology in the Eastern English Channel, 1992–2016; SEANOE. 2017. Available online: https://www.seanoe.org/data/00397/50832/(accessed on 25 August 2020). [CrossRef]
Goffart, A.; Hecq, J.H.; Legendre, L. Drivers of the winter–spring phytoplankton bloom in a pristine NW Mediterranean site, the Bay of Calvi (Corsica): A long-term study (1979–2011). Prog. Oceanogr. 2015, 137, 121–139. [CrossRef]
Lienart, C.; Savoye, N.; Bozec, Y.; Breton, E.; Conan, P.; David, V.; Feunteun, E.; Grangere, K.; Kerherve, P.; Lebreton, B.; et al. Dynamics of particulate organic matter composition in coastal systems: A spatio-temporal study at multi-systems scale. Prog. Oceanogr. 2017, 156, 221–239. [CrossRef]
Liénart, C.; Savoye, N.; David, V.; Ramond, P.; Rodriguez, T.P.; Hanquiez, V.; Marieu, V.; Aubert, F.; Aubin, S.; Bichon, S.; et al. Dynamics of particulate organic matter composition in coastal systems: Forcing to the spatio-temporal variability at multi-systems scale. Prog. Oceanogr. 2018, 162, 271–289. [CrossRef]
Polsenaere, P.; Soletchnik, P.; Le Moine, O.; Gohin, F.; Robert, S.; Pépin, H.-F.; Stanisière, J.Y.; Dumas, F.; Béchemin, C.; Goulletquer, P. Potential environmental drivers of a regional blue mussel mass mortality event (winter of 2014, Breton Sound, France). J. Sea Res. 2017, 123, 39–50. [CrossRef]
Zurburg, W.; Smaal, A.; Héral, M.; Dankers, N. Seston dynamics and bivalve feeding in the bay of Marennes-Oléron (France). Neth. J. Aquat. Ecol. 1994, 28, 459–466. [CrossRef]
Yentsch, C.S.; Menzel, D.W. A method for the determination of phytoplankton chlorophyll and phaeophytin by fluorescence. Deep Sea Res. Deep Sea Res. Oceanogr. Abstr. 1963, 10, 221–231. [CrossRef]
Aminot, A.; Kérouel, R. Hydrologie des écosystèmes Marins. Paramètres et Analyses; Ifremer, Méthodes D’analyse en Milieu Marin; Editions Quae: Versailles, France, 2004; 336p, ISBN 2-84433-133-5.
Gohin, F.; Loyer, S.; Lunven, M.; Labry, C.; Froidefond, J.M.; Delmas, D.; Huret, M.; Herbland, A. Satellite-derived parameters for biological modelling in coastal waters: Illustration over the eastern continental shelf of the Bay of Biscay. Remote Sens. Environ. 2005, 95, 29–46. [CrossRef]
Jafar-Sidik, M.; Gohin, F.; Bowers, D.; Howarth, J.; Hull, T. The relationship between Suspended Particulate Matter and Turbidity at a mooring station in a coastal environment: Consequences for satellite-derived products. Oceanologia 2017, 59, 365–378. [CrossRef]
Van Heukelem, L.; Thomas, C.M.; Gilbert, P.M. Sources of Variability in Chlorophyll Analysis by Fluorometry and High Performance Liquid Chromatography in a SIMBIOS Inter-Calibration Exercise; NASA Technical Memorandum 2002-211606; Goddard Space Flight Center: Greenbelt, MD, USA, 2002.
Sathyendranath, S.; Lazzara, L.; Prieur, L. Variations in the spectral values of specific absorption of phytoplankton. Limnol. Oceanogr. 1987, 32, 403–415. [CrossRef]
Li, H.; He, X.; Shanmungam, P.; Bai, Y.; Wang, D.; Huang, H.; Zhu, Q.; Gong, F. Radiometric sensitivity and signal detectability of ocean color satellite sensor under high solar zenith angles. IEEE Trans. Geosci. Remote Sens. 2019, 57, 8492–8505. [CrossRef]
Dall’Olmo, G.; Gitelson, A.A.; Rundquist, D.C.; Leavitt, B.; Barrow, T.; Holz, J.C. Assessing the potential of SeaWiFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red and near-infrared bands. Remote Sens. Environ. 2005, 96, 176–187. [CrossRef]
Smith, M.E.; Robertson Lain, L.; Bernard, S. An optimized chlorophyll a switching algorithm for MERIS and OLCI in phytoplankton-dominated waters. Remote Sens. Environ. 2018, 215, 217–227. [CrossRef]
Gernez, P.; Doxaran, D.; Barillé, L. Shellfish aquaculture from space: Potential of Sentinel2 to monitor tide-driven changes in turbidity, Chlorophyll concentration and oyster physiological response at the scale of an oyster farm. Front. Mar. Sci. 2017, 4, 137. [CrossRef]
Le Traon, P.Y.; Antoine, D.; Bentamy, A.; Bonekamp, H.; Breivik, L.A.; Chapron, B.; Corlett, G.; Dibarboure, G.; DiGiacomo, P.; Donlon, C.; et al. Use of satellite observations for operational oceanography: Recent achievements and future prospects. J. Oper. Oceanogr. 2015, 8, s12–s27. [CrossRef]
Druon, J.N.; Schrimpf, W.; Dobricic, S.; Stips, A. Comparative assessment of large-scale marine eutrophication: North Sea area and Adriatic Seas case studies. Mar. Ecol. Prog. Ser. 2004, 272, 1–23. [CrossRef]
Ferreira, J.G.; Andersen, J.H.; Borja, A.; Bricker, S.B.; Camp, J.; Cardoso da Silva, M.; Garcés, E.; Heiskanen, A.S.; Humborg, C.; Ignatiades, L.; et al. Overview of eutrophication indicators to assess environmental status within the European Marine Strategy Framework Directive. Estuar. Coast. Shelf Sci. 2011, 93, 117–131. [CrossRef]
Ménesguen, A.; Desmit, X.; Dulière, V.; Lacroix, G.; Thouvenin, B.; Thieu, V.; Dussauze, M. How to avoid eutrophication in coastal seas? A new approach to derive river-specific combined nitrate and phosphate maximum concentrations. Sci. Total Environ. 2018, 628–629, 400–414. [CrossRef] [PubMed]
Gohin, F.; Lampert, L.; Guillaud, J.F.; Herbland, A.; Nézan, E. Satellite and in situ observations of a late winter phytoplankton bloom, in the northern Bay of Biscay. Cont. Shelf Res. 2003, 23, 1117–1141. [CrossRef]
Vanhoutte-Brunier, A.; Fernand, L.; Menesguen, A.; Lyons, S.; Gohin, F.; Cugier, P. Modelling the Karenia mikimotoi bloom that occurred in the western English channel during summer 2003. Ecol. Model. 2008, 210, 351–376. [CrossRef]
Hartman, S.E.; Hartman, M.C.; Hydes, D.J.; Smythe-Wright, D.; Gohin, F.; Lazure, P. The role of hydrographic parameters, measured from a ship of opportunity, in bloom formation of Karenia mikimotoi in the English Channel. J. Mar. Syst. 2014, 140, 39–49. [CrossRef]
Gohin, F.; Bryère, P.; Griffiths, J.W. The exceptional surface turbidity of the North-West European shelf seas during the stormy 2013-2014 winter: Consequences for the initiation of the phytoplankton blooms? J. Mar. Syst. 2015, 148, 70–85. [CrossRef]
David, V.; Ryckaert, M.; Karpytchev, M.; Bacher, C.; Arnaudeau, V.; Vidal, N.; Maurer, D.; Niquil, N. Spatial and long-term changes in the functional and structural phytoplankton communities along the French Atlantic coast. Estuar. Coast. Shelf Sci. 2012, 108, 37–51. [CrossRef]
Hernandez-Fariñas, T.; Soudant, D.; Barillé, L.; Belin, C.; Lefebvre, A.; Bacher, C. Temporal changes in the phytoplankton community along the French coast of the eastern English Channel and the southern Bight of the North Sea. ICES J. Mar. Sci. 2013, 71, 821–833. [CrossRef]
Gons, H.J.; Rijkeboer, M.; Ruddick, K.G. A Chlorophyll-retrieval algorithm for satellite imagery (Medium Resolution Imaging Spectrometer) of inland and coastal waters. J. Plankton Res. 2002, 24, 947–951. [CrossRef]
Saulquin, B.; Gohin, F.; Garrello, R. Regional objective analysis for merging high-resolution MERIS, MODIS/Aqua, and seaWiFS Chlorophyll-a data from 1998 to 2008 on the European Atlantic shelf. IEEE Trans. Geosci. Remote Sens. 2011, 49, 143–154. [CrossRef]
Gohin, F. Chlorophylle-a Interpolée (Données Satellite); Ifremer. Available online: https://sextant.ifremer.fr/record/9352f74a-7ecb-485e-8ea3-9aa91001b9a1/(accessed on 25 August 2020). [CrossRef]
Arabi, B.; Salama, M.S.; Pitarch, J.; Verhoef, W. Integration of in-situ and multi-sensor satellite observations for long-term water quality monitoring in coastal areas. Remote Sens. Environ. 2020, 239, 111632. [CrossRef]
Moisan, T.A.; Rufty, K.M.; Moisan, J.R.; Linkswiler, M.A. Satellite observations of phytoplankton functional type spatial distributions, phenology, diversity, and ecotones. Front. Mar. Sci. 2017, 4, 189. [CrossRef]
Brewin, R.J.; Hardman-Mountford, N.J.; Lavender, S.J.; Raitsos, D.E.; Hirata, T.; Uitz, J.; Devred, E.; Bricaud, A.; Ciotti, A.; Gentili, B. An intercomparison of bio-optical techniques for detecting dominant phytoplankton size class from satellite remote sensing. Remote Sens. Environ. 2011, 115, 325–339. [CrossRef]
Alvain, S.; Moulin, C.; Dandonneau, Y.; Loisel, H. Seasonal distribution and succession of dominant phytoplankton groups inthe global ocean: A satellite view. Glob. Biogeochem. Cycles 2008, 22. [CrossRef]
Kurekin, A.; Miller, P.; Van der Woerd, J. Satellite discrimination of Karenia mikimotoi and Phaeocystis harmful algal blooms in European coastal waters: Merged classification of ocean colour data. Harmful Algae 2014, 31, 163–176. [CrossRef]
Sourisseau, M.; Jegou, K.; Lunven, M.; Quere, J.; Gohin, F.; Bryere, P. Distribution and dynamics of two species of Dinophyceae producing high biomass blooms over the French Atlantic Shelf. Harmful Algae 2016, 53, 53–63. [CrossRef]
Balch, W.M.; Gordon, H.R.; Bowler, B.C.; Drapeau, D.T.; Booth, E.S. Calcium carbonate measurements in the surface global ocean based on Moderate-Resolution Imaging Spectroradiometer data. J. Geophys. Res. 2005, 110, C07001. [CrossRef]
Moore, T.S.; Dowell, M.D.; Franz, B.A. Detection of coccolithophore blooms in ocean color satellite imagery: A generalized approach for use with multiple sensors. Remote Sens. Environ. 2012, 117, 249–263. [CrossRef]
Perrot, L.; Gohin, F.; Ruiz-Pino, D.; Lampert, L.; Huret, M.; Dessier, A.; Malestroit, P.; Dupuy, C.; Bourriau, P. Coccolith-derived turbidity and hydrological conditions in May in the Bay of Biscay. Prog. Oceanogr. 2018, 166, 41–53. [CrossRef]
Cocquempot, L.; Delacourt, C.; Paillet, J.; Riou, P.; Aucan, J.; Castelle, B.; Charria, G.; Claudet, J.; Conan, P.; Coppola, L.; et al. Coastal ocean and nearshore observation: A French case study. Front. Mar. Sci. 2019, 6, 324. [CrossRef]
