Articles | Volume 22, issue 15
https://doi.org/10.5194/bg-22-3769-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-22-3769-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Aug 2025
Research article |
| 05 Aug 2025
| 05 Aug 2025
Improved understanding of nitrate trends, eutrophication indicators, and risk areas using machine learning
Deep S. Banerjee
CORRESPONDING AUTHOR
Plymouth Marine Laboratory, PL1 3DH Plymouth, UK
National Centre for Earth Observation, PL1 3DH Plymouth, UK
Jozef Skákala
Plymouth Marine Laboratory, PL1 3DH Plymouth, UK
National Centre for Earth Observation, PL1 3DH Plymouth, UK
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Cited articles
Anderson, D. M., Cembella, A. D., and Hallegraeff, G. M.: Progress in understanding harmful algal blooms: paradigm shifts and new technologies for research, monitoring, and management, Annu. Rev. Mar. Sci., 4, 143–176, 2012. a
Arrigo, R., Baker, A. R., Capone, D. G., Cornell, S., Dentener, F., Galloway, J., Ganeshram, R. S., Geider, R. J., Jickells, T., Kuypers, M. M., Langlois, R., Liss, P. S., Liu, S. M., Middelburg, J. J., Moore, C. M., Nickovic, S., Oschlies, A., Pedersen, T., Prospero, J., Schlitzer, R., Seitzinger, S., Sorensen, L. L., Uematsu, M., Ulloa, O., Voss, M., Ward, B., and Zamora, L.: Impacts of atmospheric anthropogenic nitrogen on the open ocean, Science, 320, 893–897, 2008. a
Banerjee, D S. : Neural Network Model code, GitHub [code], https://github.com/dsbanerjee90/neccton_algo_bgcnn, last access: 20 June 2025. a
Baretta, J., Ebenhöh, W., and Ruardij, P.: The European regional seas ecosystem model, a complex marine ecosystem model, Neth. J. Sea Res., 33, 233–246, 1995. a
Behrenfeld, M. J. and Boss, E. S.: Resurrecting the ecological underpinnings of ocean plankton blooms, Annu. Rev. Mar. Sci., 6, 167–194, 2014. a
Beman, J. M., Popp, B. N., and Francis, C. A.: Molecular and biogeochemical evidence for ammonia oxidation by marine Crenarchaeota in the Gulf of California, ISME J., 2, 429–441, 2008. a
Board, O. S. and Council, N. R.: Clean coastal waters: understanding and reducing the effects of nutrient pollution, National Academies Press, ISBN 9780309069489, 2000. a
Borges, A., Schiettecatte, L.-S., Abril, G., Delille, B., and Gazeau, F.: Carbon dioxide in European coastal waters, Estuar. Coast. Shelf S., 70, 375–387, 2006. a
Bresnan, E., Cook, K., Hindson, J., Hughes, S., Lacaze, J., Walsham, P., Webster, L., and Turrell, W.: The Scottish coastal observatory 1997–2013. Part 2-description of Scotland's coastal waters, Scottish Marine and Freshwater Science, 7, https://marine.gov.scot/sma/content/scottish-coastal-observatory-1997-2013-part-2-description-scotlands-coastal-waters (last access: 20 June 2025), 2016. a
Brewin, R. J., Sathyendranath, S., Hirata, T., Lavender, S. J., Barciela, R. M., and Hardman-Mountford, N. J.: A three-component model of phytoplankton size class for the Atlantic Ocean, Ecol. Model., 221, 1472–1483, 2010. a
Brewin, R. J., Ciavatta, S., Sathyendranath, S., Jackson, T., Tilstone, G., Curran, K., Airs, R. L., Cummings, D., Brotas, V., Organelli, E., Dall'Olmo, G., and Dionysios, R. E.: Uncertainty in ocean-color estimates of chlorophyll for phytoplankton groups, Frontiers in Marine Science, 4, 104, https://doi.org/10.3389/fmars.2017.00104, 2017. a
Brockmann, U. and Eberlein, K.: River input of nutrients into the German Bight, in: The Role of Freshwater Outflow in Coastal Marine Ecosystems, Springer, 231–240, https://doi.org/10.1007/978-3-642-70886-2_15, 1986. a
Brockmann, U., Topcu, D., Schütt, M., and Leujak, W.: Eutrophication assessment in the transit area German Bight (North Sea) 2006–2014–stagnation and limitations, Mar. Pollut. Bull., 136, 68–78, 2018. a
Bruggeman, J. and Bolding, K.: A general framework for aquatic biogeochemical models, Environ. Modell. Softw., 61, 249–265, 2014. a
Burson, A., Stomp, M., Akil, L., Brussaard, C. P., and Huisman, J.: Unbalanced reduction of nutrient loads has created an offshore gradient from phosphorus to nitrogen limitation in the North Sea, Limnol. Oceanogr., 61, 869–888, https://doi.org/10.1002/LNO.10257, 2016. a, b
Butenschön, M., Clark, J., Aldridge, J. N., Allen, J. I., Artioli, Y., Blackford, J., Bruggeman, J., Cazenave, P., Ciavatta, S., Kay, S., Lessin, G., van Leeuwen, S., van der Molen, J., de Mora, L., Polimene, L., Sailley, S., Stephens, N., and Torres, R.: ERSEM 15.06: a generic model for marine biogeochemistry and the ecosystem dynamics of the lower trophic levels, Geosci. Model Dev., 9, 1293–1339, https://doi.org/10.5194/gmd-9-1293-2016, 2016. a
Chen, S., Meng, Y., Lin, S., Yu, Y., and Xi, J.: Estimation of sea surface nitrate from space: current status and future potential, Sci. Total Environ., 899, 165690, https://doi.org/10.1016/j.scitotenv.2023.165690, 2023. a, b
Ciavatta, S., Brewin, R. J. W., Skákala, J., Polimene, L., de Mora, L., Artioli, Y., and Allen, J. I.: Assimilation of ocean‐color plankton functional types to improve marine ecosystem simulations, J. Geophys. Res.-Oceans, 123, 834–854, https://doi.org/10.1002/2017JC013490, 2018 (data available at: https://doi.org/10.48670/moi-00058). a
Devlin, M. J., Prins, T. C., Enserink, L., Leujak, W., Heyden, B., Axe, P. G., Ruiter, H., Blauw, A., Bresnan, E., Collingridge, K., Devreker, D., Fernand, L., Jakobsen, Gomez J, F., Graves, C., Lefebvre, A., Lenhart, H., Markager, S., Nogueria, M., O'Donnell, G., Parner, H., Skarbovik, E., Skogen, D. Morten, S. L., Van Leeuwen, S. M., Wilkes, R., Dening, E., and Iglesias-Campos, A.: A first ecological coherent assessment of eutrophication across the North-East Atlantic waters (2015–2020), Frontiers in Ocean Sustainability, 1, 1253923, https://doi.org/10.3389/focsu.2023.1253923, 2023. a, b, c, d, e
Diaz, R. J. and Rosenberg, R.: Spreading dead zones and consequences for marine ecosystems, Science, 321, 926–929, 2008. a
Doney, S. C., Fabry, V. J., Feely, R. A., and Kleypas, J. A.: Ocean acidification: the other CO2 problem, Annu. Rev. Mar. Sci., 1, 169–192, 2009. a
Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and Wimmer, W.: The operational sea surface temperature and sea ice analysis (OSTIA) system, Remote Sens. Environ., 116, 140–158, 2012. a
Duarte, C. M.: Coastal eutrophication research: a new awareness, in: Eutrophication in Coastal Ecosystems: Towards better understanding and management strategies Selected Papers from the Second International Symposium on Research and Management of Eutrophication in Coastal Ecosystems, 20–23 June 2006, Nyborg, Denmark, Springer, 263–269, https://doi.org/10.1007/s10750-009-9795-8, 2009. a
Durairaj, P., Sarangi, R. K., Ramalingam, S., Thirunavukarassu, T., and Chauhan, P.: Seasonal nitrate algorithms for nitrate retrieval using OCEANSAT-2 and MODIS-AQUA satellite data, Environ. Monit. Assess., 187, 1–15, 2015. a
Dutkiewicz, S., Follows, M., Marshall, J., and Gregg, W. W.: Interannual variability of phytoplankton abundances in the North Atlantic, Deep-Sea Res. Pt. II, 48, 2323–2344, 2001. a
Follows, M. and Dutkiewicz, S.: Meteorological modulation of the North Atlantic spring bloom, Deep-Sea Res. Pt. II, 49, 321–344, 2001. a
Garcia, H., Weathers, K., Paver, C., Smolyar, I., Boyer, T., Locarnini, M., Zweng, M., Mishonov, A., Baranova, O., Seidov, D., Reagan, J., 375 Paver, C., Smolyar, I., Boyer, T., Locarnini, M., Zweng, M., Mishonov, A., Baranova, O., Seidov, D., and Reagan, J.: World Ocean Atlas 2018, vol. 4, Dissolved inorganic nutrients (phosphate, nitrate and nitrate+nitrite, silicate), https://archimer.ifremer.fr/doc/00651/76336/ (last access: 20 June 2025), 2019. a, b
Good, S., Fiedler, E., Mao, C., Martin, M. J., Maycock, A., Reid, R., Roberts-Jones, J., Searle, T., Waters, J., While, J., and Worsfold, M.: The current configuration of the OSTIA system for operational production of foundation sea surface temperature and ice concentration analyses, Remote Sens.-Basel, 12, 720, https://doi.org/10.3390/rs12040720, 2020. a
Greenwood, N., Devlin, M. J., Best, M., Fronkova, L., Graves, C. A., Milligan, A., Barry, J., and Van Leeuwen, S. M.: Utilizing eutrophication assessment directives from transitional to marine systems in the Thames Estuary and Liverpool Bay, UK, Frontiers in Marine Science, 6, 116, https://doi.org/10.3389/fmars.2019.00116, 2019. a
Grosse, J., van Breugel, P., Brussaard, C. P., and Boschker, H. T.: A biosynthesis view on nutrient stress in coastal phytoplankton, Limnol. Oceanogr., 62, 490–506, 2017. a
Harris, R.: The L4 time-series: the first 20 years, J. Plankton Res., 32, 577–583, 2010. a
He, R., Chen, K., Fennel, K., Gawarkiewicz, G. G., and McGillicuddy Jr, D. J.: Seasonal and interannual variability of physical and biological dynamics at the shelfbreak front of the Middle Atlantic Bight: nutrient supply mechanisms, Biogeosciences, 8, 2935–2946, https://doi.org/10.5194/bg-8-2935-2011, 2011. a
Henson, S. A., Robinson, I., Allen, J. T., and Waniek, J. J.: Effect of meteorological conditions on interannual variability in timing and magnitude of the spring bloom in the Irminger Basin, North Atlantic, Deep-Sea Res. Pt. I, 53, 1601–1615, 2006. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R.J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: Complete ERA5 from 1940: Fifth generation of ECMWF atmospheric reanalyses of the global climate, Copernicus Climate Change Service (C3S) Data Store (CDS) [data set], https://doi.org/10.24381/cds.143582cf, 2017. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G. D., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/QJ.3803, 2020. a
Hill, R., Rinker, R., and Wilson, H. D.: Atmospheric nitrogen fixation by lightning, J. Atmos. Sci., 37, 179–192, 1980. a
Hindson, J., Berx, B., Hughes, S., Walsham, P., Machairpoulou, M., Bresnan, E., and Turrell, B.: The Scottish Coastal Observatory, Bollettino di Geofisica, 333, https://www.vliz.be/imisdocs/publications/321529.pdf#page=335 (last access: 20 June 2025), 2018. a
Hinrichs, I., Gouretski, V., Pätch, J., Emeis, K., and Stammer, D.: North sea biogeochemical climatology, https://pure.mpg.de/rest/items/item_2478691/component/file_3185079/content (last access: 20 June 2025), 2017. a
Huthnance, J. M., Holt, J. T., and Wakelin, S. L.: Deep ocean exchange with west-European shelf seas, Ocean Sci., 5, 621–634, 2009. a
Jahnke, R. A.: Global synthesis, in: Carbon and nutrient fluxes in continental margins: A global synthesis, Springer, 597–615, ISBN 978-3-662-51787-1, https://doi.org/10.1007/978-3-540-92735-8_16, 2010. a
Jin, H., Song, Q., and Hu, X.: Auto-Keras: An Efficient Neural Architecture Search System, in: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD '19, Association for Computing Machinery, 1946–1956, ISBN 9781450362016, https://doi.org/10.1145/3292500.3330648, 2019. a
Lenhart, H.-J. and Große, F.: Assessing the effects of WFD nutrient reductions within an OSPAR frame using trans-boundary nutrient modeling, Frontiers in Marine Science, 5, 447, https://doi.org/10.3389/fmars.2018.00447, 2018. a, b, c
Lenhart, H.-J., Mills, D. K., Baretta-Bekker, H., van Leeuwen, S. M., van der Molen, J., Baretta, J. W., Blaas, M., Desmit, X., Kühn, W., Lacroix, G., Los, H. J., Ménesguen, A., Neves, R., Proctor, R., Ruardij, P., Skogen, M. D., Vanhoutte-Brunier, A., Villars, M. T., and Wakelin, S. L.: Predicting the consequences of nutrient reduction on the eutrophication status of the North Sea, J. Marine Syst., 81, 148–170, https://doi.org/10.1016/j.jmarsys.2009.12.014, 2010. a
Loebl, M., Colijn, F., van Beusekom, J. E., Baretta-Bekker, J. G., Lancelot, C., Philippart, C. J., Rousseau, V., and Wiltshire, K. H.: Recent patterns in potential phytoplankton limitation along the Northwest European continental coast, J. Sea Res., 61, 34–43, 2009. a
Madec, G., Bourdallé-Badie, R., Bouttier, P.-A., Bricaud, C., Bruciaferri, D., Calvert, D., Chanut, J., Clementi, E., Coward, A., and Delrosso, D.: NEMO Ocean Engine, Zenodo [data set], https://doi.org/10.5281/zenodo.3248739, 2017. a
Nazari-Sharabian, M., Ahmad, S., and Karakouzian, M.: Climate change and eutrophication: a short review, Engineering, Technology and Applied Science Research, 8, 3668, https://digitalscholarship.unlv.edu/fac_articles/562 (last access: 20 June 2025), 2018. a
Noxon, J.: Atmospheric nitrogen fixation by lightning, Geophys. Res. Lett., 3, 463–465, 1976. a
OSPAR Commission: Common Procedure for the Identification of the Eutrophication Status of the OSPAR Maritime Area, OSPAR Commission, 3, https://oap.ospar.org/en/ospar-assessments/intermediate-assessment-2017/pressures-human-activities/eutrophication/third-comp-summary-eutrophication/ (last access: 20 June 2025), 2005. a
Painting, S., Van der Molen, J., Parker, E., Coughlan, C., Birchenough, S., Bolam, S., Aldridge, J., Forster, R., and Greenwood, N.: Development of indicators of ecosystem functioning in a temperate shelf sea: a combined fieldwork and modelling approach, Biogeochemistry, 113, 237–257, 2013. a
Pauly, D., Christensen, V., Guénette, S., Pitcher, T. J., Sumaila, U. R., Walters, C. J., Watson, R., and Zeller, D.: Towards sustainability in world fisheries, Nature, 418, 689–695, 2002. a
Philippart, C. J., Beukema, J. J., Cadée, G. C., Dekker, R., Goedhart, P. W., van Iperen, J. M., Leopold, M. F., and Herman, P. M.: Impacts of nutrient reduction on coastal communities, Ecosystems, 10, 96–119, 2007. a
Postgate, J. R.: Nitrogen Fixation, Cambridge University Press, ISBN 9780521648530, 1998. a
Rabalais, N. N., Turner, R. E., and Wiseman Jr., W. J.: Gulf of Mexico hypoxia, aka “The dead zone”, Annu. Rev. Ecol. Syst., 33, 235–263, 2002. a
Rabalais, N. N., Turner, R. E., Díaz, R. J., and Justić, D.: Global change and eutrophication of coastal waters, ICES J. Mar. Sci., 66, 1528–1537, 2009. a
Radach, G.: Ecosystem functioning in the German Bight under continental nutrient inputs by rivers, Estuaries, 15, 477–496, 1992. a
Radach, G. and Pätsch, J.: Variability of continental riverine freshwater and nutrient inputs into the North Sea for the years 1977–2000 and its consequences for the assessment of eutrophication, Estuar. Coast., 30, 66–81, 2007. a
Renshaw, R., Wakelin, S., Golbeck, I., and O'Dea, E.: North West European Shelf Production Centre NWSHELF_MULTIYEAR_PHY_004_009, https://documentation.marine.copernicus.eu/QUID/CMEMS-NWS-QUID-004-009.pdf (last access: 1 August 2025), 2016 (data available at: https://doi.org/10.48670/moi-00059). a
Ryther, J. H. and Dunstan, W. M.: Nitrogen, phosphorus, and eutrophication in the coastal marine environment, Science, 171, 1008–1013, 1971. a
Scottish Coastal Observatory: Dataset – 12138-1: Scottish Coastal Observatory Dataset – 12138-1, Scottish Coastal Observatory [data set] https://doi.org/10.7489/12138-1, 2018a. a
Scottish Coastal Observatory: Dataset – 610-1: Scottish Coastal Observatory Dataset – 610-1, Scottish Coastal Observatory [data set], https://doi.org/10.7489/610-1, 2018b. a
Scottish Coastal Observatory: Dataset – 948-1: Scottish Coastal Observatory Dataset – 948-1, Scottish Coastal Observatory [data set], https://doi.org/10.7489/948-1, 2018c. a
Scottish Coastal Observatory: Dataset – 952-1: Scottish Coastal Observatory Dataset – 952-1, Scottish Coastal Observatory [data set], https://doi.org/10.7489/952-1, 2018d. a
Scottish Coastal Observatory: Dataset – 953-1: Scottish Coastal Observatory Dataset – 953-1, Scottish Coastal Observatory [data set], https://doi.org/10.7489/953-1, 2018e. a
Skákala, J., Ford, D., Bruggeman, J., Hull, T., Kaiser, J., King, R. R., Loveday, B., Palmer, M. R., Smyth, T., Williams, C. A., and Ciavatta, S.: Towards a multi-platform assimilative system for North Sea biogeochemistry, J. Geophys. Res.-Oceans, 126, e2020JC016649, https://doi.org/10.1029/2020JC016649, 2021. a
Skákala, J., Bruggeman, J., Ford, D., Wakelin, S., Akpınar, A., Hull, T., Kaiser, J., Loveday, B. R., O'Dea, E., Williams, C. A., and Ciavatta, S.: The impact of ocean biogeochemistry on physics and its consequences for modelling shelf seas, Ocean Model., 172, 101976, https://doi.org/10.1029/2018JC014153, 2022. a, b, c, d
Skogen, M. D., Søiland, H., and Svendsen, E.: Effects of changing nutrient loads to the North Sea, J. Marine Syst., 46, 23–38, 2004. a
Soetaert, K., Middelburg, J. J., Heip, C., Meire, P., Van Damme, S., and Maris, T.: Long-term change in dissolved inorganic nutrients in the heterotrophic Scheldt estuary (Belgium, The Netherlands), Limnol. Oceanogr., 51, 409–423, 2006. a
Sonesten, L., Axe, P., Bellert, B., Burtschell, L., Eumont, D., Fairbank, V., Farkas, C., Graves, C., Martínez García-Denche, L., McDermott, G., Moeslund Svendsen, L., Mönnich, J., Nunes, S., Pohl, M., Posen, P., Sánchez Fernández, B., Skarbøvik, E., Thiesse, E., Vannevel, R., and Wilkes, R.: Waterborne and Atmospheric Inputs of Nutrients and Metals to the Sea, in: OSPAR, 2023: The 2023 Quality Status Report for the Northeast Atlantic, OSPAR Commission, London, https://oap.ospar.org/en/ospar-assessments/quality-status-reports/qsr-2023/other-assessments/inputs-nutrients-and-metals (last access: 20 June 2025), 2022. a, b
Sverdrup, H. U.: On conditions for the vernal blooming of phytoplankton, J. Cons. Int. Explor. Mer, 18, 287–295, 1953. a
Tett, P., Droop, M. R., and Heaney, S. I.: The redfield ratio and phytoplankton growth rate, J. Mar. Biol. Assoc. UK, 65, 487–504, https://doi.org/10.1017/S0025315400050566, 1985. a
Topcu, D. and Brockmann, U.: Consistency of thresholds for eutrophication assessments, examples and recommendations, Environ. Monit. Assess., 193, 1–15, 2021. a
Ueyama, R. and Monger, B. C.: Wind-induced modulation of seasonal phytoplankton blooms in the North Atlantic derived from satellite observations, Limnol. Oceanogr., 50, 1820–1829, 2005. a
van Leeuwen, S. and Lenhart, H.: OSPAR ICG-EMO riverine database 2020-05-01 used in 2020 workshop, NIOZ, the Royal Netherlands Institute for Sea Research [data set], https://doi.org/10.25850/nioz/7b.b.vc, 2021. a
Van Leeuwen, S. M., Lenhart, H.-J., Prins, T. C., Blauw, A., Desmit, X., Fernand, L., Friedland, R., Kerimoglu, O., Lacroix, G., Van Der Linden, A., Lefebvre, A., Molen, J. v. d., Plus, M., Baroni, I. R., Silva, T., Stegert, C., Troost, T. A., and Vilmin, L.: Deriving pre-eutrophic conditions from an ensemble model approach for the North-West European seas, Frontiers in Marine Science, 10, 1129951, https://doi.org/10.3389/fmars.2023.1129951, 2023. a, b, c
Voss, M., Bange, H. W., Dippner, J. W., Middelburg, J. J., Montoya, J. P., and Ward, B.: The marine nitrogen cycle: recent discoveries, uncertainties and the potential relevance of climate change, Philos. T. Roy. Soc. B, 368, 20130121, https://doi.org/10.1098/rstb.2013.0121, 2013. a
Western Channel Observatory: L4 Nutrients – Time Series, https://www.westernchannelobservatory.org.uk/l4_nutrients.php, last access: 20 June 2025. a
Withers, P. J., Neal, C., Jarvie, H. P., and Doody, D. G.: Agriculture and eutrophication: where do we go from here?, Sustainability, 6, 5853–5875, 2014. a
Young, E. and Holt, J.: Prediction and analysis of long-term variability of temperature and salinity in the Irish Sea, J. Geophys. Res.-Oceans, 112, C01008, https://doi.org/10.1029/2005JC003386, 2007. a, b
Yu, X., Chen, S., and Chai, F.: Remote estimation of sea surface nitrate in the California current system from satellite ocean color measurements, IEEE T. Geosci. Remote, 60, 1–17, 2021. a
Zhai, L., Platt, T., Tang, C., Sathyendranath, S., and Walne, A.: The response of phytoplankton to climate variability associated with the North Atlantic Oscillation, Deep-Sea Res. Pt. II, 93, 159–168, 2013. a
Short summary
Nitrate is a crucial nutrient in oceans, whose excess can trigger uncontrolled algae growth that damages marine ecosystems. We used machine learning to generate skilled, gap-free, bi-decadal surface nitrate data from sparse observations, revealing areas on the North-West European Shelf that are more vulnerable to excess algae growth if nutrient pollution occurs. We also looked at bi-decadal trends in coastal nitrate and the impact of winter nitrate on spring phytoplankton blooms.
Nitrate is a crucial nutrient in oceans, whose excess can trigger uncontrolled algae growth that...
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