Articles | Volume 15, issue 1
https://doi.org/10.5194/os-15-127-2019
© Author(s) 2019. 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-15-127-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Feb 2019
Research article |
| 12 Feb 2019
| 12 Feb 2019
Mediterranean ocean colour Level 3 operational multi-sensor processing
Gianluca Volpe
CORRESPONDING AUTHOR
Istituto di Scienze Marine, Via Fosso del Cavaliere 100, 00133, Rome, Italy
Simone Colella
Istituto di Scienze Marine, Via Fosso del Cavaliere 100, 00133, Rome, Italy
Vittorio E. Brando
Istituto di Scienze Marine, Via Fosso del Cavaliere 100, 00133, Rome, Italy
Vega Forneris
Istituto di Scienze Marine, Via Fosso del Cavaliere 100, 00133, Rome, Italy
Flavio La Padula
Istituto di Scienze Marine, Via Fosso del Cavaliere 100, 00133, Rome, Italy
Annalisa Di Cicco
Istituto di Scienze Marine, Via Fosso del Cavaliere 100, 00133, Rome, Italy
Michela Sammartino
Istituto di Scienze Marine, Via Fosso del Cavaliere 100, 00133, Rome, Italy
Marco Bracaglia
Istituto di Scienze Marine, Via Fosso del Cavaliere 100, 00133, Rome, Italy
Università degli Studi di Napoli Parthenope, Via Amm. F. Acton 38, 80133, Naples, Italy
Florinda Artuso
Agenzia nazionale per le nuove tecnologie, l'energia e lo sviluppo
economico sostenibile, Dipartimento Ambiente, Centro Ricerche Frascati, Frascati, Italy
Rosalia Santoleri
Istituto di Scienze Marine, Via Fosso del Cavaliere 100, 00133, Rome, Italy
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A noticeable cold spell occurred in eastern Europe at the beginning of 2022 and was the main driver of intense deep water formation and associated transport of nutrient to the surface. Southeast of Crete the availability of both light and nutrient in the surface layer stimulated an anomalous phytoplankton bloom. In the area, chlorophyll concentration (a proxy for bloom intensity) and primary production were considerably higher than usual suggesting possible impacts on fish catches.
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Several operational satellite chlorophyll a (CHL) in the Baltic Sea were tested at a regional scale. Comparison to an extensive in situ CHL dataset showed low linearity. Bias-corrected CHL annual cycles were computed. The Gulf of Bothnia displays a single CHL peak during spring. In Skagerrak and Kattegat, there is a small bloom in spring and a minimum in summer. In the central Baltic, CHL follows a dynamic of a mild spring bloom followed by a much stronger bloom in summer.
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We describe the seasonal and year-to-year variability of the spatial distribution of the phytoplankton size classes (PSCs) in the Mediterranean Sea using the time series of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) observations (1998 to 2010). We used a chlorophyll-a-based model to estimate the phytoplankton composition. Our results, based on ocean colour data, confirm the seasonal and inter-annual pattern of the phytoplankton community observed from in situ data and in previous studies.
Related subject area
Approach: Remote Sensing | Depth range: Surface | Geographical range: Mediterranean Sea | Phenomena: Biological Processes
Spatio-temporal variability of micro-, nano- and pico-phytoplankton in the Mediterranean Sea from satellite ocean colour data of SeaWiFS
The Mediterranean Ocean Colour Observing System – system development and product validation
Comparison of SeaWiFS and MODIS time series of inherent optical properties for the Adriatic Sea
M. Sammartino, A. Di Cicco, S. Marullo, and R. Santoleri
Ocean Sci., 11, 759–778, https://doi.org/10.5194/os-11-759-2015, https://doi.org/10.5194/os-11-759-2015, 2015
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We describe the seasonal and year-to-year variability of the spatial distribution of the phytoplankton size classes (PSCs) in the Mediterranean Sea using the time series of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) observations (1998 to 2010). We used a chlorophyll-a-based model to estimate the phytoplankton composition. Our results, based on ocean colour data, confirm the seasonal and inter-annual pattern of the phytoplankton community observed from in situ data and in previous studies.
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Ocean Sci., 8, 869–883, https://doi.org/10.5194/os-8-869-2012, https://doi.org/10.5194/os-8-869-2012, 2012
F. Mélin
Ocean Sci., 7, 351–361, https://doi.org/10.5194/os-7-351-2011, https://doi.org/10.5194/os-7-351-2011, 2011
Cited articles
Antoine, D., Guevel, P., Desté, J. F., Bécu, G., Louis, F., Scott, A.
J., and Bardey, P.: The “BOUSSOLE” Buoy – A new transparent-to-swell taut
mooring dedicated to marine optics: Design, tests, and performance at sea,
J. Atmos. Ocean. Tech., 25, 968–989, https://doi.org/10.1175/2007JTECHO563.1, 2008.
Bailey, S. W. and Werdell, P. J.: A multi-sensor approach for the on-orbit
validation of ocean color satellite data products, Remote Sens. Environ.,
102, 12–23, https://doi.org/10.1016/j.rse.2006.01.015, 2006.
Barnes, B. B., Cannizzaro, J. P., English, D. C., and Hu, C.: Validation of
VIIRS and MODIS reflectance data in coastal and oceanic waters: An assessment
of methods, Remote Sens. Environ., 220, 110–123,
https://doi.org/10.1016/j.rse.2018.10.034, 2019.
Berthon, J. F., Zibordi, G., Doyle, J. P., Grossi, S., der Linde, D., and
Targa, C.: Coastal atmosphere and sea time series (CoASTS), Part 2: Data
Analysis, NASA Tech. Memo, 206892, vol. 20, edited by: Hooker, S. B. and
Firestone, E. R., NASA Goddard Space Flight Center, Greenbelt, Maryland, 25 pp., 2002.
Bouali, M. and Ignatov, A.: Adaptive reduction of striping for improved sea
surface temperature imagery from Suomi National Polar-Orbiting Partnership
(S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS), J. Atmos. Ocean.
Techn., 31, 150–163, https://doi.org/10.1175/JTECH-D-13-00035.1, 2014.
Bricaud, A., Bosc, E., and Antoine, D.: Algal biomass and sea surface
temperature in the Mediterranean Basin Intercomparison of data from various
satellite sensors, and implications for primary production estimates, Remote
Sens. Environ., 81, 163–178, https://doi.org/10.1016/S0034-4257(01)00335-2, 2002.
Brown, C. A., Huot, Y., Werdell, P. J., Gentili, B., and Claustre, H.: The
origin and global distribution of second order variability in satellite ocean
color and its potential applications to algorithm development, Remote Sens.
Environ., 112, 4186–4203, https://doi.org/10.1016/j.rse.2008.06.008, 2008.
CCI: Ocean Colour Climate Change Initiative Product User Guide version 3.1,
47 pp., available at: http://www.esa-oceancolour-cci.org/ (last access:
7 February 2019), 2016a.
CCI: Ocean colour data bias correction and merging. Ocean colour-Climate
Change Initiative, algorithm theoretical basis document, version 3.7, 36 pp.,
available at: http://www.esa-oceancolour-cci.org/ (last access:
7 February 2019), 2016b.
CCI: Ocean Colour Climate Change Initiative (Phase Two), Climate Assessment
Report, AO-1/6207/09/I-LG, 125 pp., available at:
http://esa-oceancolour-cci.org/?q=webfm_send/702 (last access:
7 February 2019), 2017.
Colella, S., Falcini, F., Rinaldi, E., Sammartino, M., and Santoleri, R.:
Mediterranean ocean colour chlorophyll trends, PLoS One, 11, 1–16,
https://doi.org/10.1371/journal.pone.0155756, 2016.
D'Alimonte, D. and Zibordi, G.: Phytoplankton determination in an optically
complex coastal region using a multilayer perceptron neural network, IEEE
T. Geosci. Remote, 41, 2861–2868, https://doi.org/10.1109/TGRS.2003.817682, 2003.
D'Alimonte, D., Mélin, F., Zibordi, G., and Berthon, J. F.: Use of the
novelty detection technique to identify the range of applicability of
empirical ocean color algorithms, IEEE T. Geosci. Remote, 41, 2833–2843,
https://doi.org/10.1109/TGRS.2003.818020, 2003.
D'Alimonte, D., Zibordi, G., Kajiyama, T., and Cunha, J. C.: Monte Carlo code
for high spatial resolution ocean color simulations, Appl. Optics, 49, 4936,
https://doi.org/10.1364/AO.49.004936, 2010.
Dierssen, H. M.: Perspectives on empirical approaches for ocean color remote
sensing of chlorophyll in a changing climate, P. Natl. Acad. Sci. USA, 107,
17073–17078, https://doi.org/10.1073/pnas.0913800107, 2010.
D'Ortenzio, F., Marullo, S., Ragni, M., D'Alcalà, M. R., and Santoleri,
R.: Validation of empirical SeaWiFS algorithms for chlorophyll-a retrieval in
the Mediterranean Sea: A case study for oligotrophic seas, Remote Sens.
Environ., 82, 79–94, https://doi.org/10.1016/S0034-4257(02)00026-3, 2002.
Laws, E. A. and Archie, J. W.: Appropriate use of regression analysis in
Marine Biology, Mar. Biol., 65, 13–16, 1981.
Lee, Z., Carder, K. L., and Arnone, R. A.: Deriving inherent optical
properties from water color: a multiband quasi-analytical algorithm for
optically deep waters, Appl. Optics, 41, 5755–5772, 2002.
Le Traon, P.-Y., Antoine, D., Bentamy, A., Bonekamp, H., Breivik, L. A.,
Chapron, B., Corlett, G., Dibarboure, G., DiGiacomo, P., Donlon, C.,
Faugère, Y., Font, J., Girard-Ardhuin, F., Gohin, F., Johannessen, J. A.,
Kamachi, M., Lagerloef, G., Lambin, J., Larnicol, G., Le Borgne, P.,
Leuliette, E., Lindstrom, E., Martin, M. J., Maturi, E., Miller, L., Mingsen,
L., Morrow, R., Reul, N., Rio, M. H., Roquet, H., Santoleri, R., and Wilkin,
J.: Use of satellite observations for operational oceanography: recent
achievements and future prospects, J. Oper. Oceanogr., 8, s12–s27,
https://doi.org/10.1080/1755876X.2015.1022050, 2015.
Loew, A., Bell, W., Brocca, L., Bulgin, C. E., Burdanowitz, J., Calbet, X.,
Donner, R. V., Ghent, D., Gruber, A., Kaminski, T., Kinzel, J., Klepp, C.,
Lambert, J. C., Schaepman-Strub, G., Schröder, M., and Verhoelst, T.:
Validation practices for satellite-based Earth observation data across
communities, Rev. Geophys., 55, 779–817, https://doi.org/10.1002/2017RG000562, 2017.
Manzella, G. M. R., Hopkins, T. S., Minnett, P. J., and Nacini, E.: Atlantic
water in the Strait of Sicily, J. Geophys. Res., 95, 1569–1575, 1990.
Maritorena, S. and Siegel, D. A.: Consistent merging of satellite ocean color
data sets using a bio-optical model, Remote Sens. Environ., 94, 429–440,
https://doi.org/10.1016/j.rse.2004.08.014, 2005.
Mélin, F. and Sclep, G.: Band shifting for ocean color multi-spectral
reflectance data, Opt. Express, 23, 2262, https://doi.org/10.1364/OE.23.002262, 2015.
Mélin, F., Vantrepotte, V., Clerici, M., D'Alimonte, D., Zibordi, G.,
Berthon, J. F., and Canuti, E.: Multi-sensor satellite time series of optical
properties and chlorophyll-a concentration in the Adriatic Sea, Prog.
Oceanogr., 91, 229–244, https://doi.org/10.1016/j.pocean.2010.12.001, 2011.
Mikelsons, K., Wang, M., Jiang, L., and Bouali, M.: Destriping algorithm for
improved satellite-derived ocean color product imagery, Opt. Express, 22,
28058, https://doi.org/10.1364/OE.22.028058, 2014.
Mobley, C. D., Werdell, J., Franz, B., Ahmad, Z., and Bailey, S.: Atmospheric
Correction for Satellite Ocean Color Radiometry, NASA Tech. Memo.,
(NASA/TM–2016-217551), 73 pp., 2016.
Moore, T. S., Dowell, M. D., Bradt, S., and Ruiz Verdu, A.: An optical water
type framework for selecting and blending retrievals from bio-optical
algorithms in lakes and coastal waters, Remote Sens. Environ., 143, 97–111,
https://doi.org/10.1016/j.rse.2013.11.021, 2014.
Morel, A. and Prieur, L.: Analysis of variations in ocean color, Limnol.
Oceanogr., 22, 709–722, https://doi.org/10.4319/lo.1977.22.4.0709, 1977.
Mueller, J. L.: SeaWiFS Algorithm for the Diffuse Attenuation Coefficient,
K(490), Using Water-Leaving Radiances at 490 and 555 nm, in: SeaWiFS
postlaunch calibration Valid. Anal. Part 3, edited by: Hooker, S. B. and
Firestone, E. R., Goddard Space Flight Center, Greenbelt, NASA Tech. Memo.
2000-206892, vol. 11, 24–27, 2000.
Mueller, J. L. and Austin, R. W.: Ocean Optics Protocols for SeaWiFS Validation,
Revision 1, in: SeaWiFS Technical Report Series, NASA Technical Memorandum 104566,
Vol. 25, edited by: Hooker, S. B., Firestone, E. R., and Acker, J. G., Goddard
Space Flight Center, Greenbelt, 1995.
O'Reilly, J. E., Maritorena, S., Siegel, D. A., O'Brien, M. C., Toole, D.,
Mitchell, B. G., Kahru, M., Chaves, 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 Tech. Rep. Ser. vol. 11, SeaWiFS
postlaunch calibration Valid. Anal. Part 3, edited by: Hooker, S. B. and
Firestone, E. R., Goddard Space Flight Center, Greenbelt, 9–23,
https://doi.org/10.1103/PhysRevLett.104.255302, 2000.
Organelli, E., Barbieux, M., Claustre, H., Schmechtig, C., Poteau, A.,
Bricaud, A., Uitz, J., D'Ortenzio, F., and Dall'Olmo, G.: A global biooptical
database derived from Biogeochemical Argo float measurements within the layer
of interest for field and remote ocean color applications, SEANOE,
https://doi.org/10.17882/47142, 2016.
Organelli, E., Barbieux, M., Claustre, H., Schmechtig, C., Poteau, A.,
Bricaud, A., Boss, E., Briggs, N., Dall'Olmo, G., D'Ortenzio, F., Leymarie,
E., Mangin, A., Obolensky, G., Penkerc'h, C., Prieur, L., Roesler, C., Serra,
R., Uitz, J., and Xing, X.: Two databases derived from BGC-Argo float
measurements for marine biogeochemical and bio-optical applications, Earth
Syst. Sci. Data, 9, 861–880,https://doi.org/10.5194/essd-9-861-2017, 2017.
Pitarch, J., Bellacicco, M., Volpe, G., Colella, S., and Santoleri, R.: Use
of the quasi-analytical algorithm to retrieve backscattering from in-situ
data in the Mediterranean Sea, Remote Sens. Lett., 7, 591–600,
https://doi.org/10.1080/2150704X.2016.1171922, 2016.
Santoleri, R., Volpe, G., Marullo, S., and Nardelli, B. B.: Open waters
optical remote sensing of the Mediterranean Sea, in: Remote Sensing of the
European Seas, edited by: Barale, V. and Gade, M., Springer, Dordrecht, 103–116,
https://doi.org/10.1007/978-1-4020-6772-3_8, 2008.
Sathyendranath, S. User requirements document, Technical Report ESA/ESRIN,
available at: http://www.esa-oceancolour-cci.org/ (last access:
7 February 2019), 2011.
Sathyendranath, S., Brewin, R. J. W., Jackson, T., Mélin, F., and Platt,
T.: Ocean-colour products for climate-change studies: What are their ideal
characteristics?, Remote Sens. Environ., 203, 125–138,
https://doi.org/10.1016/j.rse.2017.04.017, 2017.
Steinmetz, F., Deschamps, P.-Y., and Ramon, D.: Atmospheric correction in
presence of sun glint: application to MERIS, Opt. Express, 19, 9783,
https://doi.org/10.1364/OE.19.009783, 2011.
Szeto, M., Werdell, P. J., Moore, T. S., and Campbell, J. W.: Are the world's
oceans optically different?, J. Geophys. Res., 116, C00H04,
https://doi.org/10.1029/2011JC007230, 2011.
Teruzzi, A., Dobricic, S., Solidoro, C., and Cossarini, G.: A 3-D variational
assimilation scheme in coupled transport-biogeochemical models: Forecast of
Mediterranean biogeochemical properties, J. Geophys. Res.-Oceans, 119,
200–217, https://doi.org/10.1002/2013JC009277, 2014.
Thuillier, G., Labs, D., Foujols, T., Peetermans, W., Gillotay, D., Simon, P.
C., and Mandel, H.: The solar spectral irradiance from 200 to 2400 nm as
measured by the SOLSPEC spectrometer from the ATLAS and EURECA missions, Sol.
Phys., 214, 1–22, https://doi.org/10.1023/A:1024048429145, 2003.
Valente, A., Sathyendranath, S., Brotas, V., Groom, S., Grant, M., Taberner,
M., Antoine, D., Arnone, R., Balch, W. M., Barker, K., Barlow, R.,
Bélanger, S., Berthon, J.-F., Besiktepe, S., Brando, V., Canuti, E.,
Chavez, F., Claustre, H., Crout, R., Frouin, R., García-Soto, C., Gibb,
S. W., Gould, R., Hooker, S., Kahru, M., Klein, H., Kratzer, S., Loisel, H.,
McKee, D., Mitchell, B. G., Moisan, T., Muller-Karger, F., O'Dowd, L.,
Ondrusek, M., Poulton, A. J., Repecaud, M., Smyth, T., Sosik, H. M.,
Twardowski, M., Voss, K., Werdell, J., Wernand, M., and Zibordi, G.: A
compilation of global bio-optical in situ data for ocean-colour satellite
applications, Earth Syst. Sci. Data, 8, 235–252,
https://doi.org/10.5194/essd-8-235-2016, 2016.
Volpe, G., Santoleri, R., Vellucci, V., Ribera d'Alcalà, M., Marullo, S.,
and D'Ortenzio, F.: The colour of the Mediterranean Sea: Global versus
regional bio-optical algorithms evaluation and implication for satellite
chlorophyll estimates, Remote Sens. Environ., 107, 625–638,
https://doi.org/10.1016/j.rse.2006.10.017, 2007.
Volpe, G., Colella, S., Forneris, V., Tronconi, C., and Santoleri, R.: The
Mediterranean Ocean Colour Observing System – system development and product
validation, Ocean Sci., 8, 869–883, https://doi.org/10.5194/os-8-869-2012, 2012.
Volpe, G., Buongiorno Nardelli, B., Colella, S., Pisano, A., and Santoleri,
R.: An Operational Interpolated Ocean Colour Product in the Mediterranean
Sea, in: New Frontiers in Operational Oceanography, edited by: Chassignet, E.
P., Pascual, A., Tintorè, J., and Verron, J., GODAE OceanView, 227–244, 2018.
von Schuckmann, K., Le Traon, P. Y., Alvarez-Fanjul, E., Axell, L.,
Balmaseda, M., Breivik, L. A., Brewin, R. J. W., Bricaud, C., Drevillon, M.,
Drillet, Y., Dubois, C., Embury, O., Etienne, H., Sotillo, M. G., Garric, G.,
Gasparin, F., Gutknecht, E., Guinehut, S., Hernandez, F., Juza, M., Karlson,
B., Korres, G., Legeais, J. F., Levier, B., Lien, V. S., Morrow, R.,
Notarstefano, G., Parent, L., Pascual, Á., Pérez-Gómez, B.,
Perruche, C., Pinardi, N., Pisano, A., Poulain, P. M., Pujol, I. M., Raj, R.
P., Raudsepp, U., Roquet, H., Samuelsen, A., Sathyendranath, S., She, J.,
Simoncelli, S., Solidoro, C., Tinker, J., Tintoré, J., Viktorsson, L.,
Ablain, M., Almroth-Rosell, E., Bonaduce, A., Clementi, E., Cossarini, G.,
Dagneaux, Q., Desportes, C., Dye, S., Fratianni, C., Good, S., Greiner, E.,
Gourrion, J., Hamon, M., Holt, J., Hyder, P., Kennedy, J., Manzano-Muñoz,
F., Melet, A., Meyssignac, B., Mulet, S., Buongiorno Nardelli, B., O'Dea, E.,
Olason, E., Paulmier, A., Pérez-González, I., Reid, R., Racault, M.
F., Raitsos, D. E., Ramos, A., Sykes, P., Szekely, T., and Verbrugge, N.: The
Copernicus Marine Environment Monitoring Service Ocean State Report, J. Oper.
Oceanogr., 9, s235–s320, https://doi.org/10.1080/1755876X.2016.1273446, 2016.
Werdell, P. J. and Bailey, S. W.: An improved in-situ bio-optical data set
for ocean color algorithm development and satellite data product validation,
Remote Sens. Environ., 98, 122–140, https://doi.org/10.1016/j.rse.2005.07.001, 2005.
Zibordi, G. and Ferrari, G. M.: Instrument self-shading in underwater optical
measurements: experimental data, Appl. Optics, 34, 2750–2754,
https://doi.org/10.1364/AO.34.002750, 1995.
Zibordi, G., Hooker, S. B., Berthon, J. F., and D'Alimonte, D.: Autonomous
above-water radiance measurements from an offshore platform: A field
assessment experiments, J. Atmos. Ocean. Tech., 19, 808–819,
https://doi.org/10.1175/1520-0426(2002)019<0808:AAWRMF>2.0.CO;2,
2002.
Zibordi, G., D'Alimonte, D., and Berthon, J. F.: An evaluation of depth
resolution requirements for optical profiling in coastal waters, J. Atmos.
Ocean. Tech., 21, 1059–1073,
https://doi.org/10.1175/1520-0426(2004)021<1059:AEODRR>2.0.CO;2,
2004.
Zibordi, G., Holben, B., Slutsker, I., Giles, D., D'alimonte, D., Mélin,
F., Berthon, J. F., Vandemark, D., Feng, H., Schuster, G., Fabbri, B. E.,
Kaitala, S., and Seppälä, J.: AERONET-OC: A network for the
validation of ocean color primary products, J. Atmos. Ocean. Tech., 26,
1634–1651, https://doi.org/10.1175/2009JTECHO654.1, 2009.
Zibordi, G., Berthon, J. F., Mélin, F., and D'Alimonte, D.: Cross-site
consistent in situ measurements for satellite ocean color applications: The
BiOMaP radiometric dataset, Remote Sens. Environ., 115, 2104–2115,
https://doi.org/10.1016/j.rse.2011.04.013, 2011.
Zibordi, G., Ruddick, K., Ansko, I., Moore, G., Kratzer, S., Icely, J., and
Reinart, A.: In situ determination of the remote sensing reflectance: an
inter-comparison, Ocean Sci., 8, 567–586, https://doi.org/10.5194/os-8-567-2012, 2012.
Short summary
This work fully describes all the technical steps that are currently put in place in the context of the European Copernicus Marine Environment and Monitoring Service to make ocean colour data freely available to the general public. These data are useful for mapping phytoplankton dynamics on a daily and basin scale. The multi-sensor output compares well to data collected during dedicated field cruises, proving that the operational product can be successfully used for environmental monitoring.
This work fully describes all the technical steps that are currently put in place in the context...
