Cited by 85Marine Bivalve and Aquaculture StudiesMarine Plankton and BiogeochemistryCoastal and Marine Dynamics

The DOI matches its Crossref record.

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

Open access
Gernez, Pierre, Doxaran, David, Barillé, Laurent·2017·Frontiers in Marine Science

Abstract

The algorithms of Novoa et al. (2017) and Gons et al. (2005) were recalibrated and applied to Sentinel2 data to respectively retrieve suspended particulate matter (SPM) and chlorophyll a (chl a) concentration in the environmentally and economically important intertidal zones. Sentinel2-derived chl a and SPM concentration distributions were analyzed at the scale of an oyster farm over a variety of tidal conditions. Sentinel2 imagery was then coupled with ecophysiological modeling to analyze the influence of tide-driven chl a and SPM dynamics on oyster clearance and chl consumption rates. Within the studied oyster farming site (Bourgneuf Bay along the French Atlantic coast), chl consumption rate mirrored the changes in chl a concentration during neap tides, whereas oyster clearance and chl consumption rates were both negatively impacted by high SPM concentration during spring tides.

Overview

Integrity signals

Registry

Crossref · 10.3389/fmars.2017.00137

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 54

  1. Austin, 1974, Inherent spectral radiance signatures of the ocean surface, 195
  2. Babin, 2003, Variations in the light absorption coefficients of phytoplankton, nonalgal particles, and dissolved organic matter in coastal waters around Europe, J. Geophys. Res. Oceans, 108, 3211, 10.1029/2001jc000882 · DOI
  3. Barillé, 1997, Modélisation de l'écophysiologie de l'huître Crassostrea gigas dans un environnement estuarien, Aquat. Living Res., 10, 31, 10.1051/alr:1997004 · DOI
  4. Beck, 2016, Comparison of satellite reflectance algorithms for estimating chlorophyll-a in a temperate reservoir using coincident hyperspectral aircraft imagery and dense coincident surface observations, Remote Sens. Environ., 178, 15, 10.1016/j.rse.2016.03.002 · DOI
  5. Blondeau-Patissier, 2014, A review of ocean color remote sensing methods and statistical techniques for the detection, mapping and analysis of phytoplankton blooms in coastal and open oceans, Prog. Oceanogr., 123, 123, 10.1016/j.pocean.2013.12.008 · DOI
  6. Bricaud, 1995, Variability in the chlorophyll-specific absorption coefficients of natural phytoplankton: analysis and parameterization, J. Geophys. Res. Oceans, 100, 13321, 10.1029/95JC00463 · DOI
  7. Brito, 2013, Seasonality of microphytobenthos revealed by remote-sensing in a South European estuary, Cont. Shelf Res., 66, 83, 10.1016/j.csr.2013.07.004 · DOI
  8. Carder, 1999, Semianalytic moderate-resolution imaging spectrometer algorithms for chlorophyll a and absorption with bio-optical domains based on nitrate-depletion temperatures, J. Geophys. Res. Oceans, 104, 5403, 10.1029/1998JC900082 · DOI
  9. Choy, 2009, Quantifying the trophic base for benthic secondary production in the Nakdong River estuary of Korea using stable C and N isotopes, J. Exp. Mar. Biol. Ecol., 382, 18, 10.1016/J.JEMBE.2009.10.002 · DOI
  10. Dall'Olmo, 2005, Assessing the potential of SeaWiFS and MODIS for estimating chlorophyll concentration in turbid productive waters using red and near-infrared bands, Remote Sens. Environ., 96, 176, 10.1016/j.rse.2005.02.007 · DOI
  11. Davidson, 2009, A large and prolonged bloom of Karenia mikimotoi in Scottish waters in 2006, Harmful Algae, 8, 349, 10.1016/j.hal.2008.07.007 · DOI
  12. Dessinges, 2012, Ostréiculture en Pays de la Loire
  13. Dutertre, 2009, Temperature and seston quantity and quality effects on field reproduction of farmed oysters, Crassostrea gigas, in Bourgneuf Bay, France, Aquat. Living Res., 22, 319, 10.1051/alr/2009042 · DOI
  14. Gernez, 2014, Remote sensing of suspended particulate matter in turbid oyster-farming ecosystems, J. Geophys. Res. Oceans, 119, 7277, 10.1002/2014JC010055 · DOI
  15. Gitelson, 2008, A simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation, Remote Sens. Environ., 112, 3582, 10.1016/j.rse.2008.04.015 · DOI
  16. Gohin, 2002, A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters, Int. J. Remote Sens., 23, 1639, 10.1080/01431160110071879 · DOI
  17. Gons, 1999, Optical teledetection of chlorophyll a in turbid inland waters, Environ. Sci. Tech., 33, 1127, 10.1021/es9809657 · DOI
  18. Gons, 2002, A chlorophyll-retrieval algorithm for satellite imagery (Medium Resolution Imaging Spectrometer) of inland and coastal waters, J. Plankton Res., 24, 947, 10.1093/plankt/24.9.947 · DOI
  19. Gons, 2005, Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, J. Plankton Res., 27, 125, 10.1093/plankt/fbh151 · DOI
  20. Gower, 1999, Interpretation of the 685 nm peak in water-leaving radiance spectra in terms of fluorescence, absorption and scattering, and its observation by MERIS, Int. J. Remote Sens., 20, 1771, 10.1080/014311699212470 · DOI
  21. Gower, 2005, Detection of intense plankton blooms using the 709 nm band of the MERIS imaging spectrometer, Int. J. Remote Sens., 26, 2005, 10.1080/01431160500075857 · DOI
  22. Herbert, 2016, Ecological impacts of non-native Pacific oysters (Crassostrea gigas) and management measures for protected areas in Europe, Biodivers. Conserv., 25, 2835, 10.1007/s10531-016-1209-4 · DOI
  23. Jesus, 2009, Adaptations of microphytobenthos assemblages to sediment type and tidal position, Cont. Shelf Res., 29, 1624, 10.1016/j.csr.2009.05.006 · DOI
  24. Kang, 2006, Microphytobenthos seasonality determines growth and reproduction in intertidal bivalves, Mar. Ecol. Prog. Ser., 315, 113, 10.3354/meps315113 · DOI
  25. Kazemipour, 2012, Microphytobenthos biomass mapping using the optical model of diatom biofilms: application to hyperspectral images of Bourgneuf Bay, Remote Sens. Environ., 127, 1, 10.1016/j.rse.2012.08.016 · DOI
  26. Koh, 2006, Tidal resuspension of microphytobenthic chlorophyll a in a Nanaura mudflat, Saga, Ariake Sea, Japan: flood–ebb and spring–neap variations, Mar. Ecol. Prog. Ser., 312, 85, 10.3354/meps312085 · DOI
  27. Kurekin, 2014, Satellite discrimination of Karenia mikimotoi and Phaeocystis harmful algal blooms in European coastal waters: Merged classification of ocean colour data, Harmful Algae, 31, 163, 10.1016/j.hal.2013.11.003 · DOI
  28. Le, 2013, Evaluation of chlorophyll-a remote sensing algorithms for an optically complex estuary, Remote Sens. Environ., 129, 75, 10.1016/j.rse.2012.11.001 · DOI
  29. Loisel, 2017, Assessment and analysis of the chlorophyll-a concentration variability over the Vietnamese coastal waters from the MERIS ocean color sensor (2002–2012), Remote Sens. Environ., 190, 217, 10.1016/j.rse.2016.12.016 · DOI
  30. MacIntyre, 1996, Microphytobenthos: the ecological role of the “secret garden” of unvegetated, shallow-water marine habitats. I. Distribution, abundance and primary production, Estuaries, 19, 186, 10.2307/1352224 · DOI
  31. Méléder, 2005, Spatio-temporal changes in microphytobenthos structure analysed by pigment composition in a macrotidal flat (Bourgneuf Bay, France), Mar. Ecol. Prog. Ser., 297, 83, 10.3354/meps297083 · DOI
  32. Méléder, 2003, Microphytobenthos assemblage mapping by spatial visible-infrared remote sensing in a shellfish ecosystem, C. R. Biol., 326, 377, 10.1016/S1631-0691(03)00125-2 · DOI
  33. Montalto, 2016, A mechanistic approach reveals non linear effects of climate warming on mussels throughout the Mediterranean sea, Climatic Change, 139, 293, 10.1007/s10584-016-1780-4 · DOI
  34. Mueller, 2000, Above-water radiance and remote-sensing reflectance measurements and analysis protocols, 171
  35. Neukermans, 2012, Optimization and quality control of suspended particulate matter concentration measurement using turbidity measurements, Limnol. Oceanogr. Meth., 10, 1011, 10.4319/lom.2012.10.1011 · DOI
  36. Novoa, 2017, Atmospheric corrections and multi-conditional algorithm for multi-sensor remote sensing of suspended particulate matter in low-to-high turbidity levels Coastal Waters, Remote Sens., 9, 61, 10.3390/rs9010061 · DOI
  37. Ouellette, 2016, Remote sensing for marine spatial planning and integrated coastal areas management: achievements, challenges, opportunities and future prospects, Remote Sens. Appl. Soc. Environ., 4, 138, 10.1016/j.rsase.2016.07.003 · DOI
  38. Paterson, 1998, Microbiological mediation of spectral reflectance from intertidal cohesive sediments, Limnol. Oceanogr., 43, 1207, 10.4319/lo.1998.43.6.1207 · DOI
  39. Ras, 2008, Spatial variability of phytoplankton pigment distributions in the Subtropical South Pacific Ocean: comparison between in situ and modelled data, Biogeosciences, 5, 353, 10.5194/bg-5-353-2008 · DOI
  40. 2008, R: A Language and Environment for Statistical Computing.
  41. Ruddick, 2000, Atmospheric correction of SeaWiFS imagery for turbid coastal and inland waters, Appl. Opt., 39, 897, 10.1364/AO.39.000897 · DOI
  42. Sourisseau, 2016, Distribution and dynamics of two species of Dinophyceae producing high biomass blooms over the French Atlantic Shelf, Harmful Algae, 53, 53, 10.1016/j.hal.2015.11.016 · DOI
  43. Thomas, 2016, Global change and climate-driven invasion of the Pacific oyster (Crassostrea gigas) along European coasts: a bioenergetics modelling approach, J. Biogeogr., 43, 568, 10.1111/jbi.12665 · DOI
  44. Tilstone, 2017, Which ocean colour algorithm for MERIS in North West European waters?, Remote Sens. Environ., 189, 132, 10.1016/j.rse.2016.11.012 · DOI
  45. Toming, 2016, First experiences in mapping lake water quality parameters with Sentinel-2 MSI imagery, Remote Sens., 8, 640, 10.3390/rs8080640 · DOI
  46. Ubertini, 2012, Spatial variability of benthic-pelagic coupling in an estuary ecosystem: consequences for microphytobenthos resuspension phenomenon, PLoS ONE, 7, e44155, 10.1371/journal.pone.0044155 · DOI
  47. Underwood, 1999, Primary production by phytoplankton and microphytobenthos in Estuaries, Adv. Ecol. Res., 29, 93, 10.1016/S0065-2504(08)60192-0 · DOI
  48. van der Wal, 2010, Spatial synchrony in intertidal benthic algal biomass in temperate coastal and estuarine ecosystems, Ecosystems, 13, 338, 10.1007/s10021-010-9322-9 · DOI
  49. Van der Zande, 2016, Validation of Landsat-8/OLI for ocean colour applications with AERONET-OC sites in Belgian coastal waters, 23
  50. Vanhellemont, 2014, Turbid wakes associated with offshore wind turbines observed with Landsat 8, Remote Sens. Environ., 145, 105, 10.1016/j.rse.2014.01.009 · DOI
  51. Vanhellemont, 2015, Advantages of high quality SWIR bands for ocean colour processing: Examples from Landsat-8, Remote Sens. Environ., 161, 89, 10.1016/j.rse.2015.02.007 · DOI
  52. Vanhellemont, 2016, ACOLITE For Sentinel-2: Aquatic Applications of MSI imagery, 9
  53. Vermote, 2006, Second Simulation of a Satellite Signal in the Solar Spectrum-Vector (6SV)
  54. Woźniak, 2010, Optical variability of seawater in relation to particle concentration, composition, and size distribution in the nearshore marine environment at Imperial Beach, California, J. Geophys. Res. Oceans, 115, C08027, 10.1029/2009jc005554 · DOI

Cited by

Sorted by citations

Metadata

Authors
Gernez, P., Doxaran, D., & Barillé, L.
Affiliations
  • Université de Nantes (FR)
  • Centre National de la Recherche Scientifique (FR)
Corresponding author
Gernez, Pierre
Venue
Frontiers in Marine Science
Published
2017-05-16
Volume
4
ISSN
2296-7745 (ISSN-L)
OpenAlex
W2615064060
Cited by
85
References
0
Citations by year
2026: 8 · 2025: 8 · 2024: 5 · 2023: 12 · 2022: 15 · 2021: 8 · 2020: 13 · 2019: 7 · 2018: 8 · 2017: 1
FWCI
5.50
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Marine Science (publishedVersion, cc-by)
  • HAL (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
Funders
  • Centre National de la Recherche Scientifique (PNTS-2015-07)
  • Agence Nationale de la Recherche (ANR-12-AGRO-0001)
  • Seventh Framework Programme (606797)
  • European Commission (678396)
Topics
  • Life Sciences › Agricultural and Biological Sciences › Aquatic Science › Marine Bivalve and Aquaculture Studies
  • Physical Sciences › Earth and Planetary Sciences › Oceanography › Marine Plankton and Biogeochemistry
  • Physical Sciences › Earth and Planetary Sciences › Earth-Surface Processes › Coastal and Marine Dynamics
Keywords
Sentinel-2, oyster aquaculture, suspended particulate matter, chlorophyll a, turbidity, tidal dynamics, intertidal zone, remote sensing of coastal waters, Bay of Bourgneuf

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review