Articles | Volume 16, issue 2
https://doi.org/10.5194/os-16-513-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-16-513-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Estimation of phytoplankton pigments from ocean-color satellite observations in the Senegalo–Mauritanian region by using an advanced neural classifier
Khalil Yala
CORRESPONDING AUTHOR
IPSL/LOCEAN, Sorbonne Université (Université Paris 6, CNRS,
IRD, MNHN), 4 Place Jussieu, 75005 Paris, France
N'Dèye Niang
CEDRIC, CNAM, 292 rue Saint Martin, 75003 Paris, France
Julien Brajard
IPSL/LOCEAN, Sorbonne Université (Université Paris 6, CNRS,
IRD, MNHN), 4 Place Jussieu, 75005 Paris, France
Nansen Center, Thormøhlensgate 47, 5006 Bergen, Norway
Carlos Mejia
IPSL/LOCEAN, Sorbonne Université (Université Paris 6, CNRS,
IRD, MNHN), 4 Place Jussieu, 75005 Paris, France
Mory Ouattara
CEDRIC, CNAM, 292 rue Saint Martin, 75003 Paris, France
Roy El Hourany
IPSL/LOCEAN, Sorbonne Université (Université Paris 6, CNRS,
IRD, MNHN), 4 Place Jussieu, 75005 Paris, France
Michel Crépon
IPSL/LOCEAN, Sorbonne Université (Université Paris 6, CNRS,
IRD, MNHN), 4 Place Jussieu, 75005 Paris, France
Sylvie Thiria
IPSL/LOCEAN, Sorbonne Université (Université Paris 6, CNRS,
IRD, MNHN), 4 Place Jussieu, 75005 Paris, France
UVSQ-LATMOS, 78035 Versailles, France
Related authors
No articles found.
Georges Baaklini, Julien Brajard, Leila Issa, Gina Fifani, Laurent Mortier, and Roy El Hourany
Ocean Sci., 20, 1707–1720, https://doi.org/10.5194/os-20-1707-2024, https://doi.org/10.5194/os-20-1707-2024, 2024
Short summary
Short summary
Understanding the flow of the Levantine Sea surface current is not straightforward. We propose a study based on learning techniques to follow interactions between water near the shore and further out at sea. Our results show changes in the coastal currents past 33.8° E, with frequent instances of water breaking away along the Lebanese coast. These events happen quickly and sometimes lead to long-lasting eddies. This study underscores the need for direct observations to improve our knowledge.
Alexander Barth, Julien Brajard, Aida Alvera-Azcárate, Bayoumy Mohamed, Charles Troupin, and Jean-Marie Beckers
Ocean Sci., 20, 1567–1584, https://doi.org/10.5194/os-20-1567-2024, https://doi.org/10.5194/os-20-1567-2024, 2024
Short summary
Short summary
Most satellite observations have gaps, for example, due to clouds. This paper presents a method to reconstruct missing data in satellite observations of the chlorophyll a concentration in the Black Sea. Rather than giving a single possible reconstructed field, the discussed method provides an ensemble of possible reconstructions using a generative neural network. The resulting ensemble is validated using techniques from numerical weather prediction and ocean modelling.
Simon Driscoll, Alberto Carrassi, Julien Brajard, Laurent Bertino, Einar Ólason, Marc Bocquet, and Amos Lawless
EGUsphere, https://doi.org/10.5194/egusphere-2024-2476, https://doi.org/10.5194/egusphere-2024-2476, 2024
Short summary
Short summary
The formation and evolution of sea ice melt ponds (ponds of melted water) are complex, insufficiently understood and represented in models with considerable uncertainty. These uncertain representations are not traditionally included in climate models potentially causing the known underestimation of sea ice loss in climate models. Our work creates the first observationally based machine learning model of melt ponds that is also a ready and viable candidate to be included in climate models.
Stéphane Doléac, Marina Lévy, Roy El Hourany, and Laurent Bopp
EGUsphere, https://doi.org/10.5194/egusphere-2024-1820, https://doi.org/10.5194/egusphere-2024-1820, 2024
Short summary
Short summary
Phytoplankton net primary production (NPP) is influenced by many processes, and their representation varies across Earth-system models. This leads to differing projections for NPP's future under climate change, especially in the North Atlantic. To address this, we identified and assessed the processes controlling NPP in each model. This assessment helped us select the most reliable models, significantly improving NPP projections in the region.
Léo Edel, Jiping Xie, Anton Korosov, Julien Brajard, and Laurent Bertino
EGUsphere, https://doi.org/10.5194/egusphere-2024-1896, https://doi.org/10.5194/egusphere-2024-1896, 2024
Short summary
Short summary
This study developed a new method to estimate Arctic sea ice thickness from 1992 to 2010 using a combination of machine learning and data assimilation. By training a machine learning model on data from 2011–2022, past errors in sea ice thickness can be corrected, leading to improved estimations. This approach provides insights into historical changes on sea ice thickness, showing a notable decline from 1992 to 2022, and offers a valuable resource for future studies.
Cyril Palerme, Thomas Lavergne, Jozef Rusin, Arne Melsom, Julien Brajard, Are Frode Kvanum, Atle Macdonald Sørensen, Laurent Bertino, and Malte Müller
The Cryosphere, 18, 2161–2176, https://doi.org/10.5194/tc-18-2161-2024, https://doi.org/10.5194/tc-18-2161-2024, 2024
Short summary
Short summary
Sea ice forecasts are operationally produced using physically based models, but these forecasts are often not accurate enough for maritime operations. In this study, we developed a statistical correction technique using machine learning in order to improve the skill of short-term (up to 10 d) sea ice concentration forecasts produced by the TOPAZ4 model. This technique allows for the reduction of errors from the TOPAZ4 sea ice concentration forecasts by 41 % on average.
Roy El Hourany, Juan Pierella Karlusich, Lucie Zinger, Hubert Loisel, Marina Levy, and Chris Bowler
Ocean Sci., 20, 217–239, https://doi.org/10.5194/os-20-217-2024, https://doi.org/10.5194/os-20-217-2024, 2024
Short summary
Short summary
Satellite observations offer valuable information on phytoplankton abundance and community structure. Here, we employ satellite observations to infer seven phytoplankton groups at a global scale based on a new molecular method from Tara Oceans. The link has been established using machine learning approaches. The output of this work provides excellent tools to collect essential biodiversity variables and a foundation to monitor the evolution of marine biodiversity.
Georges Baaklini, Roy El Hourany, Milad Fakhri, Julien Brajard, Leila Issa, Gina Fifani, and Laurent Mortier
Ocean Sci., 18, 1491–1505, https://doi.org/10.5194/os-18-1491-2022, https://doi.org/10.5194/os-18-1491-2022, 2022
Short summary
Short summary
We use machine learning to analyze the long-term variation of the surface currents in the Levantine Sea, located in the eastern Mediterranean Sea. We decompose the circulation into groups based on their physical characteristics and analyze their spatial and temporal variability. We show that most structures of the Levantine Sea are becoming more energetic over time, despite those of the western area remaining the most dominant due to their complex bathymetry and strong currents.
Juliette Mignot, Carlos Mejia, Charles Sorror, Adama Sylla, Michel Crépon, and Sylvie Thiria
Geosci. Model Dev., 13, 2723–2742, https://doi.org/10.5194/gmd-13-2723-2020, https://doi.org/10.5194/gmd-13-2723-2020, 2020
Short summary
Short summary
The most robust representation of climate is usually obtained by averaging a large number of simulations, thereby cancelling individual model errors. Here, we work towards an objective way of selecting the least biased models over a certain region, based on physical parameters. This statistical method based on a neural classifier and multi-correspondence analysis is illustrated here for the Senegalo-Mauritanian region, but it could potentially be developed for any other region or process.
Marc Bocquet, Julien Brajard, Alberto Carrassi, and Laurent Bertino
Nonlin. Processes Geophys., 26, 143–162, https://doi.org/10.5194/npg-26-143-2019, https://doi.org/10.5194/npg-26-143-2019, 2019
Short summary
Short summary
This paper describes an innovative way to use data assimilation to infer the dynamics of a physical system from its observation only. The method can operate with noisy and partial observation of the physical system. It acts as a deep learning technique specialised to dynamical models without the need for machine learning tools. The method is successfully tested on chaotic dynamical systems: the Lorenz-63, Lorenz-96, and Kuramoto–Sivashinski models and a two-scale Lorenz model.
Anna Denvil-Sommer, Marion Gehlen, Mathieu Vrac, and Carlos Mejia
Geosci. Model Dev., 12, 2091–2105, https://doi.org/10.5194/gmd-12-2091-2019, https://doi.org/10.5194/gmd-12-2091-2019, 2019
Short summary
Short summary
This work is dedicated to a new model that reconstructs the surface ocean partial pressure of carbon dioxide (pCO2) over the global ocean on a monthly 1°×1° grid. The model is based on a feed-forward neural network and represents the nonlinear relationships between pCO2 and the ocean drivers. Reconstructed pCO2 has a satisfying accuracy compared to independent observational data and shows a good agreement in seasonal and interannual variability with three existing mapping methods.
Julien Brajard, Alberto Carrassi, Marc Bocquet, and Laurent Bertino
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-136, https://doi.org/10.5194/gmd-2019-136, 2019
Revised manuscript not accepted
Short summary
Short summary
We explore the possibility of combining data assimilation with machine learning. We introduce a new hybrid method for a two-fold scope: (i) emulating hidden, possibly chaotic, dynamics and (ii) predicting its future states. Numerical experiments have been carried out using the chaotic Lorenz 96 model, proving both the convergence of the hybrid method and its statistical skills including short-term forecasting and emulation of the long-term dynamics.
Hector Simon Benavides Pinjosovsky, Sylvie Thiria, Catherine Ottlé, Julien Brajard, Fouad Badran, and Pascal Maugis
Geosci. Model Dev., 10, 85–104, https://doi.org/10.5194/gmd-10-85-2017, https://doi.org/10.5194/gmd-10-85-2017, 2017
Short summary
Short summary
The objective of this work is to deliver the adjoint model of SECHIBA obtained with software called YAO, in order to perform 4D-VAR data assimilation. The SECHIBA module of the ORCHIDEE land surface model describes the exchanges of water and energy between the surface and the atmosphere. A distributed version is available when only the land surface temperature is used as an observation, with two examples and documentation.
Related subject area
Approach: Remote Sensing | Depth range: Surface | Geographical range: All Geographic Regions | Phenomena: Biological Processes
Ocean colour opportunities from Meteosat Second and Third Generation geostationary platforms
Carbon-based phytoplankton size classes retrieved via ocean color estimates of the particle size distribution
Assessment of MERIS ocean color data products for European seas
MERIS-based ocean colour classification with the discrete Forel–Ule scale
Improvement to the PhytoDOAS method for identification of coccolithophores using hyper-spectral satellite data
Comparison of global ocean colour data records
Ewa J. Kwiatkowska, Kevin Ruddick, Didier Ramon, Quinten Vanhellemont, Carsten Brockmann, Carole Lebreton, and Hans G. Bonekamp
Ocean Sci., 12, 703–713, https://doi.org/10.5194/os-12-703-2016, https://doi.org/10.5194/os-12-703-2016, 2016
Short summary
Short summary
Copernicus operational services include ocean colour applications from medium-resolution polar-orbiting satellite sensors. The goal is to satisfy EU reporting on the quality of marine, coastal and inland waters, as well as to support climate, fisheries, environmental monitoring, and sediment transport applications. Ocean colour data from polar platforms, however, suffer from fractional coverage. This effort is in developing water turbidity services from Meteosat geostationary instruments.
Tihomir S. Kostadinov, Svetlana Milutinović, Irina Marinov, and Anna Cabré
Ocean Sci., 12, 561–575, https://doi.org/10.5194/os-12-561-2016, https://doi.org/10.5194/os-12-561-2016, 2016
Short summary
Short summary
Recent advances in ocean color remote sensing have allowed the quantification of the particle size distribution (and thus volume) of suspended particles in surface waters, using their backscattering signature. Here, we leverage these developments and use volume-to-carbon allometric relationships to quantify phytoplankton carbon globally using SeaWiFS ocean color data. Phytoplankton carbon concentrations are partitioned among three size classes: picoplankton, nanoplankton and microplankton.
G. Zibordi, F. Mélin, J.-F. Berthon, and E. Canuti
Ocean Sci., 9, 521–533, https://doi.org/10.5194/os-9-521-2013, https://doi.org/10.5194/os-9-521-2013, 2013
M. R. Wernand, A. Hommersom, and H. J. van der Woerd
Ocean Sci., 9, 477–487, https://doi.org/10.5194/os-9-477-2013, https://doi.org/10.5194/os-9-477-2013, 2013
A. Sadeghi, T. Dinter, M. Vountas, B. B. Taylor, M. Altenburg-Soppa, I. Peeken, and A. Bracher
Ocean Sci., 8, 1055–1070, https://doi.org/10.5194/os-8-1055-2012, https://doi.org/10.5194/os-8-1055-2012, 2012
S. Djavidnia, F. Mélin, and N. Hoepffner
Ocean Sci., 6, 61–76, https://doi.org/10.5194/os-6-61-2010, https://doi.org/10.5194/os-6-61-2010, 2010
Cited articles
Aiken, J., Pradhan, Y., Barlow, R., Lavender, S., Poulton, A., and Hardman-Mountford, N. : Phytoplankton pigments and functional types in the Atlantic Ocean: A decadal assessment, 1995–2005, Deep-Sea Res. Pt II, 56, 899–917, https://doi.org/10.1016/J.DSR2.2008.09.017, 2009.
Alvain, S., Moulin, C., Dandonneau, Y., and Breon, F. M.: Remote sensing of
phytoplankton groups in case-1 waters from global SeaWiFS imagery, Deep-Sea
Res. Pt. I, 52, 1989–2004, 2005.
Alvain, S., Loisel, H., and Dessailly, D.: Theoretical analysis of ocean color radiances anomalies and implications for phytoplankton group detection,
Opt. Express, 20, 1070–1083, 2012.
Antoine, D., André, J. M., and Morel, A.: Oceanic primary production :
Estimation at global scale from satellite (Coastal Zone Color Scanner)
chlorophyll, Global Biogeochem. Cy., 10, 57–69, 1996.
Badran, F., Berrada, M., Brajard, J., Crepon, M., Sorror, C., Thiria, S.,
Hermand, J. P., Meyer, M., Perichon, L., and Asch, M.: Inversion of satellite ocean colour imagery and geoacoustic characterization of seabed properties :
Variational data inversion using a semi-automatic adjoint approach, J. Marine
Syst., 69, 126–136, 2008.
Behrenfeld, M. J. and Falkowski, P. G.: Photosynthetic rates derived from
satellite base chlorophyll concentration, Limnol. Oceanogr., 42, 1–20, 1997.
Behrenfeld, M. J., Boss, E., Siegel, D. A., and Shea, D. M.: Carbon-based ocean productivity and phytoplankton physiology from space, Global Biogeochem. Cy., 19, GB1006, https://doi.org/10.1029/2004GB002299, 2005.
Ben Mustapha, Z. S., Alvain, S., Jamet, C., Loisel, H., and Desailly, D.:
Automatic water leaving radiance anomalies from global SeaWiFS imagery:
application to the detection of phytoplankton groups in open waters, Remote
Sens. Environ., 146, 97–112, 2014.
Blasco, D.: Red tide in the upwelling region of Baja California, Limnol.
Oceanogr., 22, 255–263, 1977.
Blasco, D., Estrada, M., and Jones, B.: Relationship between the phytoplankton distribution and composition and the hydrography in the northwest African upwelling region, near Cabo Corbeiro, Deep-Sea Res., 27A, 799–821, 1980.
Brajard, J., Jamet, C., Moulin, C., and Thiria, S.: Atmospheric correction and oceanic constituents retrieval with a neuro-variational method, Neural
Networks, 19, 178–185, 2006a.
Brajard, J., Jamet, C., Moulin, C., and Thiria, S.: Neurovariational inversion of ocean color images, Journal of Atmospheric Space Research, 38, 2169–2175, 2006b.
Brewin, R. J. W., Sathyendranath, S., Hirata, T., Lavender, S. J., Barciela,
R., and Hardman-Montford, N. J.: A three-component model of phytoplankton size class for the Atlantic Ocean, Ecol. Model., 22, 1472–1483, 2010.
Bricaud, A., Mejia, C., Blondeau Patissier, D., Claustre, H., Crepon, M., and
Thiria, S.: Retrieval of pigment concentrations and size structure of algal populations from absorption spectra using multilayered perceptrons, Appl. Optics, 46, 1251–1260, 2006.
Capet, X., Estrade, P., Machu, E., Ndoye, S., Grelet, J., Lazar, A., Marié, L., Dausse, D., and Brehmer, P.: On the Dynamics of the Southern Senegal Upwelling Center: Observed Variability from Synoptic to Superinertial Scales, J. Phys. Oceanogr., 47, 155–180, 2017.
Cavazos, T.: Using Self-Organizing Maps to Investigate Extreme Climate
Events: An Application to Wintertime Precipitation in the Balkans, J.
Climate, 13, 1718–1732, 2000.
Chazotte, A., Crepon, M., Bricaud, A., Ras, J., and Thiria, S.: Statistical
analysis of absorption spectra of phytoplankton and of pigment concentrations observed during three POMME cruises using a neural network clustering method, Appl. Optics, 46, 3790–3799, 2007.
Chazottes, A., Bricaud, A., Crepon, M., and Thiria, S.: Statistical analysis of a data base of absorption spectra of phytoplankton and pigment concentrations using self-organizing maps, Appl. Optics, 45, 8102–8115, 2006.
Ciotti, A. and Bricaud, A.: Retrievals of a size parameter for phytoplankton
and spectral light absorption by colored detrital matter from water-leaving
radiances at SeaWiFS channels in a continental shelf region off Brazil,
Limnol. Oceangr.-Meth., 4, 237–253, 2006.
Demarcq, H. and Faure, V.: Coastal upwelling and associated retention indices
from satellite SST. Application to Octopus vulgaris recruitment, Oceanol. Acta, 23, 391–407, 2000.
Dia, A.: Biomasse et biologie du phytoplancton le long de la petite côte
sénégalaise et relations avec l'hydrologie, Rapport interne
No. 44 du CRODT, Réf: 0C000798, 1981–1982, available at: http://www.sist.sn/gsdl/collect/publi/index/assoc/HASH2127.dir/doc.pdf (last access: 4 March 2020), 1985.
Diouf, D., Niang, A., Brajard, J., Crepon, M., and Thiria, S.: Retrieving aerosol characteristics and sea-surface chlorophyll from satellite ocean color multi-spectral sensors using a neural-variational method, Remote Sens.
Environ., 130, 74–86, https://doi.org/10.1016/j.rse.2012.11.002, 2013.
Farikou, O., Sawadogo, S., Niang, A., Brajard, J., Mejia, C., Crépon, M., and Thiria, S.: Multivariate analysis of the Sénégalo-Mauritanian area by merging satellite remote sensing ocean color and SST observations, Research Journal of Environmental and Earth Sciences, 12, 756–768, 2013.
Farikou, O., Sawadogo, S., Niang, A., Diouf, D., Brajard, J., Mejia, C.,
Dandonneau, Y., Gasc, G., Crepon, M., and Thiria, S.: Inferring the seasonal
evolution of phytoplankton groups in the Senegalo-Mauritanian upwelling region from satellite ocean-color spectral measurements, J.
Geophys. Res.-Oceans, 120, 6581–6601, 2015.
Friedrich, T. and Oschlies, A.: Basin-scale pCO2 maps estimated from ARGO
float data: A model study, J. Geophys. Res., 114, C10012, https://doi.org/10.1029/2009JC005322, 2009.
Gregg, W. W., Casey, N., and McClain, C.: Recent trends in global ocean chlorophyll, Geophys. Res. Lett., 32, L03606, https://doi.org/10.1029/2004GL021808, 2005.
Gross, L., Thiria, S., Frouin, R., and Mitchell, B. G.: Artificial neural networks for modeling transfer function between marine reflectance and phytoplankton pigment concentration, J. Geophys. Res., 105, 3483–3949, 2000.
Gross, L., Frouin, R., Dupouy, C., Andre, J. M., and Thiria, S.: Reducing
biological variability in the retrieval of chlorophyll a concentration from spectral marine reflectance, Appl. Optics, 43, 4041–4054, 2004.
Hewitson, B. C. and Crane, R. G.: Sef organizing maps: application to
synoptic climatology, Clim. Res., 22, 13–26, 2002.
Hirata, T., Aiken, J., Hardman-Mountford, N., Smyth, T. J., and Barlow, R. G.: An absorption model to determine phytoplankton size classes from satellite ocean color, Remote Sens. Environ., 112, 3153–3159, 2008.
Hirata, T., Hardman-Mountford, N. J., Brewin, R. J. W., Aiken, J., Barlow, R., Suzuki, K., Isada, T., Howell, E., Hashioka, T., Noguchi-Aita, M., and Yamanaka, Y.: Synoptic relationships between surface Chlorophyll-a and diagnostic pigments specific to phytoplankton functional types, Biogeosciences, 8, 311–327, https://doi.org/10.5194/bg-8-311-2011, 2011.
IOCCG: Phytoplankton Functional Types from Space, in: Reports of the International Ocean-Colour Coordinating Group, edited by: Sathyendranath, S., IOCCG, Dartmouth, Canada, IOCCG Report No. 15, 156 pp., 2014.
Jamet, C., Thiria, S., Moullin, C., and Crepon, M.: Use of a neural inversion for retrieving Oceanic and Atmospheric constituents for Ocean Color imagery: a feasability study, J. Atmos. Ocean. Tech., 22, 460–475, https://doi.org/10.1175/JTECH1688.1, 2005.
Jeffreys, S. W. and Vesk, M.: Introduction to marine phytoplankton and their pigment signatures, in: Phytoplankton pigments in oceanography: guidelines to modern methods, edited by: Jeffery, S. W., Mantoura, R. F. C., and Wright, S. W., UNESCO, Paris, 33–84, 1997.
Jouini, M., Lévy, M., Crépon, M., and Thiria, S.: Reconstruction of
ocean color images under clouds using a neuronal classification method,
Remote Sens. Environ., 131, 232–246, 2013.
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection, in: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, San Mateo, CA, Morgan Kaufmann Publishers Inc., 2, 1137–1143, 1995.
Kohonen, T.: Self-organizing maps, 3rd edn., Springer, Berlin Heidelberg
New York, 2001.
Kruizinga, S. and Murphy, A.: Use of an analogue procedure to formulate
objective probabilistic temperature forecasts in the Netherlands, Mon. Weather Rev., 111, 2244–2254, 1983.
Lévy, M.: Mesoscale variability of phytoplankton and of new production:
Impact of the large-scale nutrient distribution, J. Geophys. Res., 108, 3358, https://doi.org/10.1029/2002JC001577, 2003.
Lévy, M., Iovino, D., Resplandy, L., Klein, P., Madec, G., Tréguier, A.-M., Masson, S., and Takahashi, K.: Large-scale impacts of submesoscale
dynamics on phytoplankton: Local and remote effects, Ocean Model., 43–44,
77–93, 2012.
Liu, Y. and Weisberg, R. H.: Patterns of ocean current variability on the
West Florida Shelf using the self-organizing map, J. Geophys. Res., 110, C06003, https://doi.org/10.1029/2004JC002786, 2005.
Liu, Y., Weisberg, R. H., and He, R.: Sea surface temperature patterns on the
West Florida Shelf using growing hierarchical self-organizing maps, J.
Atmos. Ocean. Tech., 23, 325–338, 2006.
Longhurst, A. R., Sathyendranath, S., Platt, T., and Caverhill, C.: An estimation of global primary production in the ocean from satellite radiometer data, J. Plankton Res., 17, 1245–1271, 1995.
Lorenz, E. N.: Atmospheric predictability as revealed by naturally occurring
analogs, J. Atmos. Sci., 26, 639–646, 1969.
Morel, A. and Gentili, G.: Diffuse reflectance of oceanic waters. III.
Implication of bidirectionality for the remote-sensing problem, Appl. Optics, 35, 4850–4862, 1996.
Mouw, C. B. and Yoder, J. A.: Optical determination of phytoplankton size
composition from global SeaWiFS imagery, J. Geophys. Res., 115, C12018, https://doi.org/10.1029/2010JC006337, 2010.
Ndoye, S., Capet, X., Estrade, P., Sow, B., Dagorne, D., Lazar, A., Gaye, A., and Brehmer, P.: SST patterns and dynamics of the southern Senegal-Gambia
upwelling center, J. Geophys. Res.-Oceans, 119, 8315–8335, 2014.
Niang, A., Gross, L., Thiria, S., Badran, F., and Moulin, C.: Automatic
neural classification of ocean colour reflectance spectra at the top of
atmosphere with introduction of expert knowledge, Remote Sens. Environ., 86, 257–271, 2003.
Niang, A., Badran, F., Moulin, C., Crépon, M., and Thiria, S.: Retrieval of aerosol type and optical thickness over the Mediterranean from SeaWiFS
images using an automatic neural classification method, Remote Sens. Environ., 100, 82–94, 2006.
O'Reilly, J. E., Maritorena, S., Siegel, D. A., O'Brien, M. C., Toole, D.,
Mitchell, B. G., Kahru, M., Chavez, F. P., Strutton, P., Cota, G. F.,
Hooker, S. B., McClain, C. R., Carder, K. L., Muller-Karger, F., Harding,
L., Magnuson, A., Phinney, D., Moore, G. F., Aiken, J., Arrigo, K. R.,
Letelier, R., and Culver, M.: Ocean color chlorophyll a algorithms for
SeaWiFS, OC2 and OC4: Version 4, in: SeaWiFS postlaunch calibration and validation analyses: Part 3. edited by: Hooker, S. B. and Firestone, E. R., NASA Goddard Space Flight Center, Greenbelt, MD, NASA Tech. Memo. 2000-206892, 11, 9–23, 2001.
Ouattara, M.: Développement et mise en place d'une méthode de classification multi-blocs: application aux données de l'OQAI, PhD thesis, available at: https://www.theses.fr/179489704, last access: 4 March 2020.
Palacz, A. P., John, M. A. St., Brewin, R. J. W., Hirata, T., and Gregg, W. W.: Distribution of phytoplankton functional types in high-nitrate, low-chlorophyll waters in a new diagnostic ecological indicator model, Biogeosciences, 10, 7553–7574, https://doi.org/10.5194/bg-10-7553-2013, 2013.
Raitsos, D. E., Lavender, S. J., Maravelias, C. D., Haralambous, J.,
Richardson, A. J., and Reid, P. C.: Identifying phytoplankton functional
groups from space: an ecological approach, Limnol. Oceanogr., 53, 605–613,
https://doi.org/10.4319/lo.2008.53.2.0605, 2008.
Reusch, D. B., Alley, R. B., and Hewitson, B. C.: North Atlantic climate
variability from a self-organizing map perspective, J. Geophys. Res., 112, D02104, https://doi.org/10.1029/2006JD007460, 2007.
Richardson, A., Risien, C., and Shillington, F.: Using self-organizing maps to identify patterns in satellite imagery, Prog. Oceanogr., 59, 223–239, https://doi.org/10.1016/J.POCEAN.2003.07.006, 2003.
Sathyendranath, S., Watts, L., Devred, E., Platt, T., Caverhill, C. M., and
Maass, H.: Discrimination of diatom from other phytoplankton using
ocean-colour data, Mar. Ecol. Prog. Ser., 272, 59–68, 2004.
Sawadogo, S., Brajard, J., Niang, A., Lathuilière, C., Crepon, M., and Thiria, S.: Analysis of the Senegalo-Mauritanian upwelling by processing satellite remote sensing observations with topological maps, in: 2009 International Joint Conference on Neural Networks (IJCNN), Atlanta, GA, USA, 14–19 June 2009, IEEE, 313–319, 2009.
Sirven, J., Mignot, J., and Crépon, M.: Generation of Rossby waves off the Cape Verde Peninsula: the role of the coastline, Ocean Sci., 15, 1667–1690, https://doi.org/10.5194/os-15-1667-2019, 2019.
Sosik, H. M., Sathyendranath, S., Uitz, J., Bouman, H., and Nair, A.: In situ
methods of measuring phytoplankton functional types, in: Phytoplankton Functional Types from Space, edited by: Sathyendranath, S., IOCCG, Dartmouth, NS, Canada, IOCCG report, No. 15, 21–38, 2014.
Thiria, S., Mejia, C., Badran, F., and Crépon, M.: A neural network approach for modeling nonlinear transfer functions: application for wind retrieval from spaceborne scaterrometer data, J. Geophys. Res., 98, 22827–22841, 2003.
Uitz, J., Claustre, H., Morel, A., and Hooker, S. B.: Vertical distribution of phytoplankton communities in open ocean: an assessment based on surface
chlorophyll, J. Geophys. Res., 111, C08005, https://doi.org/10:1029/2005JC003207, 2006.
Uitz, J., Claustre, H., Gentili, B., and Stramski, D.: Phytoplankton
class-specific primary production in the world's ocean: seasonal and
interannual variability from satellite observations, Global Biogeochem. Cy., 24, GB3016, https://doi.org/10:1029/2009GB003680, 2010.
Van den Dool, H.: Searching for analogs, how long must we wait?, Tellus A, 46, 314–324, 1994.
Varma, S. and Simon, R.: Bias in error estimation when using cross-validation
for model selection, BMC Bioinformatics, 7, 91, https://doi.org/10.1186/1471-2105-7-91,
2006.
Vidussi, F., Claustre, H., Manca, B. B., Luchetta, A., and Marty, J. C.:
Phytoplankton pigment distribution in relation to upper thermocline
circulation in the eastern Mediterranean sea during winter, J. Geophys.
Res., 106, 19939–19956, 2001.
Westberry, T., Behrenfeld, M. J., Siegel, D. A., and Boss, E.: Carbon-based
productivity modeling with vertically resolved photoacclimatation, Global
Biogeochem. Cy., 22, GB2024, https://doi.org/10.1029/2007GB003078, 2008.
Zorita, E. and von Storch, H.: The Analog Method as a Simple Statistical
Downscaling Technique: Comparison with More Complicated Methods, J.
Climate, 12, 2474–2489, 1999.
Short summary
The paper is a contribution to the study of phytoplankton pigment climatology from satellite ocean-color observations in the Senegalo–Mauritanian upwelling, which is a very productive region where in situ observations are lacking. We processed the satellite data with an efficient new neural network classifier. We were able to provide the climatological cycle of diatoms. This study may have an economic impact on fisheries thanks to better knowledge of phytoplankton dynamics.
The paper is a contribution to the study of phytoplankton pigment climatology from satellite...