Articles | Volume 15, issue 12
https://doi.org/10.5194/essd-15-5281-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-5281-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
29 Nov 2023
Data description paper |
| 29 Nov 2023
| 29 Nov 2023
A global daily gap-filled chlorophyll-a dataset in open oceans during 2001–2021 from multisource information using convolutional neural networks
Zhongkun Hong
State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China
Department of Hydraulic Engineering, Institute of Ocean Engineering, Tsinghua University, Beijing 100084, China
State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China
Department of Hydraulic Engineering, Institute of Ocean Engineering, Tsinghua University, Beijing 100084, China
Xingdong Li
State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China
Department of Hydraulic Engineering, Institute of Ocean Engineering, Tsinghua University, Beijing 100084, China
Yiming Wang
State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China
Department of Hydraulic Engineering, Institute of Ocean Engineering, Tsinghua University, Beijing 100084, China
Jianmin Zhang
State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China
Department of Hydraulic Engineering, Institute of Ocean Engineering, Tsinghua University, Beijing 100084, China
Mohamed A. Hamouda
Department of Civil and Environmental Engineering, United Arab Emirates University, Al Ain, United Arab Emirates
National Water and Energy Center, United Arab Emirates University, Al Ain, United Arab Emirates
Mohamed M. Mohamed
Department of Civil and Environmental Engineering, United Arab Emirates University, Al Ain, United Arab Emirates
National Water and Energy Center, United Arab Emirates University, Al Ain, United Arab Emirates
Related authors
No articles found.
Xingdong Li, Di Long, Yanhong Cui, Tingxi Liu, Jing Lu, Mohamed A. Hamouda, and Mohamed M. Mohamed
The Cryosphere, 17, 349–369, https://doi.org/10.5194/tc-17-349-2023, https://doi.org/10.5194/tc-17-349-2023, 2023
Short summary
Short summary
This study blends advantages of altimetry backscattering coefficients and waveforms to estimate ice thickness for lakes without in situ data and provides an improved water level estimation for ice-covered lakes by jointly using different threshold retracking methods. Our results show that a logarithmic regression model is more adaptive in converting altimetry backscattering coefficients into ice thickness, and lake surface snow has differential impacts on different threshold retracking methods.
Safa A. Mohammed, Mohamed A. Hamouda, Mohammed T. Mahmoud, and Mohamed M. Mohamed
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-547, https://doi.org/10.5194/hess-2019-547, 2020
Revised manuscript not accepted
Short summary
Short summary
This study evaluated the accuracy of satellite precipitation estimates across different seasons, rainfall intensities, topographical features, and hydrological regions over the arid country of Saudi Arabia. Results confirmed that the performance of calibrated satellite products surpassed the near-real-time products in terms of consistency and estimated errors. This evaluation could help developers in improving satellite product calibration to achieve better detection accuracy over arid regions.
Xingdong Li, Di Long, Qi Huang, Pengfei Han, Fanyu Zhao, and Yoshihide Wada
Earth Syst. Sci. Data, 11, 1603–1627, https://doi.org/10.5194/essd-11-1603-2019, https://doi.org/10.5194/essd-11-1603-2019, 2019
Short summary
Short summary
Lakes on the Tibetan Plateau experienced rapid changes (mainly expanding) in the past 2 decades. Here we provide a data set of high temporal resolution and accuracy reflecting changes in water level and storage of Tibetan lakes. A novel source of water levels generated from Landsat archives was validated with in situ data and adopted to resolve the inconsistency in existing studies, benefiting monitoring of lake overflow floods, seasonal and interannual variability, and long-term trends.
Xu Shan, Xingdong Li, and Hanbo Yang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-283, https://doi.org/10.5194/hess-2019-283, 2019
Manuscript not accepted for further review
Short summary
Short summary
The Budyko hypothesis has been generally used to quantify how much precipitation transforms into evaporation in one catchment. To approach this hypothesis, previous studies proposed analytical formulas derived based on mathematic reasoning. Differently, this study drew a new derivation for this hypothesis based on fundamental physical principles. It clearly reveals the underlying assumptions in the previous mathematic reasoning and promotes hydrologic understanding on this hypothesis.
Xu Shan, Xindong Li, and Hanbo Yang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2018-598, https://doi.org/10.5194/hess-2018-598, 2018
Manuscript not accepted for further review
Short summary
Short summary
The Budyko hypothesis has been generally used to quantify how much precipitation transforms into evaporation in one catchment. To approach this hypothesis, previous studies proposed analytical formulas derived based on mathematic reasoning. Differently, this study drew a new derivation for this hypothesis based on fundamental physical principles. It clearly reveals the underlying assumptions in the previous mathematic reasoning and promotes hydrologic understanding on this hypothesis.
Hang Zheng, Yang Hong, Di Long, and Hua Jing
Hydrol. Earth Syst. Sci., 21, 949–961, https://doi.org/10.5194/hess-21-949-2017, https://doi.org/10.5194/hess-21-949-2017, 2017
Short summary
Short summary
Do you feel angry if the river in your living place is polluted by industries? Do you want to do something to save your environment? Just log in to http://www.thuhjjc.com and use the Tsinghua Environment Monitoring Platform (TEMP) to photograph the water pollution actives and make your report. This study established a social media platform to monitor and report surface water quality. The effectiveness of the platform was demonstrated by the 324 water quality reports across 30 provinces in China.
Related subject area
Domain: ESSD – Ocean | Subject: Biological oceanography
A new global oceanic multi-model net primary productivity data product
MAREL Carnot data and metadata from the Coriolis data center
Bio-optical properties of the cyanobacterium Nodularia spumigena
An atlas of seabed biodiversity for Aotearoa New Zealand
A synthetic optical database generated by radiative transfer simulations in support of studies in ocean optics and optical remote sensing of the global ocean
The Western Channel Observatory: a century of oceanographic, chemical and biological data compiled from pelagic and benthic habitats in the Western English Channel
The Coastal Surveillance Through Observation of Ocean Color (COASTℓOOC) dataset
HIPPO environmental monitoring: impact of phytoplankton dynamics on water column chemistry and the sclerochronology of the king scallop (Pecten maximus) as a biogenic archive for past primary production reconstructions
AlgaeTraits: a trait database for (European) seaweeds
How to learn more about hydrological conditions and phytoplankton dynamics and diversity in the eastern English Channel and the Southern Bight of the North Sea: the Suivi Régional des Nutriments data set (1992–2021)
Deepwater red shrimp fishery in the eastern–central Mediterranean Sea: AIS-observed monthly fishing effort and frequency over 4 years
Global dataset on seagrass meadow structure, biomass and production
The Green Edge cruise: investigating the marginal ice zone processes during late spring and early summer to understand the fate of the Arctic phytoplankton bloom
A global marine particle size distribution dataset obtained with the Underwater Vision Profiler 5
The COSMUS expedition: seafloor images and acoustic bathymetric data from the PS124 expedition to the southern Weddell Sea, Antarctica
Thomas J. Ryan-Keogh, Sandy J. Thomalla, Nicolette Chang, and Tumelo Moalusi
Earth Syst. Sci. Data, 15, 4829–4848, https://doi.org/10.5194/essd-15-4829-2023, https://doi.org/10.5194/essd-15-4829-2023, 2023
Short summary
Short summary
Oceanic productivity has been highlighted as an important environmental indicator of climate change in comparison to other existing metrics. However, the availability of these data to assess trends and trajectories is plagued with issues, such as application to only a single satellite reducing the time period for assessment. We have applied multiple algorithms to the longest ocean colour record to provide a record for assessing climate-change-driven trends.
Raed Halawi Ghosn, Émilie Poisson-Caillault, Guillaume Charria, Armel Bonnat, Michel Repecaud, Jean-Valery Facq, Loïc Quéméner, Vincent Duquesne, Camille Blondel, and Alain Lefebvre
Earth Syst. Sci. Data, 15, 4205–4218, https://doi.org/10.5194/essd-15-4205-2023, https://doi.org/10.5194/essd-15-4205-2023, 2023
Short summary
Short summary
This article describes a long-term (2004–2022) dataset from an in situ instrumented station located in the eastern English Channel and belonging to the COAST-HF network (ILICO). It provides high temporal resolution (sub-hourly) oceanographic and meteorological measurements. The MAREL Carnot dataset can be used to conduct research in marine ecology, oceanography, and data science. It was utilized to characterize recurrent, rare, and extreme events in the coastal area.
Shungudzemwoyo P. Garaba, Michelle Albinus, Guido Bonthond, Sabine Flöder, Mario L. M. Miranda, Sven Rohde, Joanne Y. L. Yong, and Jochen Wollschläger
Earth Syst. Sci. Data, 15, 4163–4179, https://doi.org/10.5194/essd-15-4163-2023, https://doi.org/10.5194/essd-15-4163-2023, 2023
Short summary
Short summary
These high-quality data document a harmful algal bloom dominated by Nodularia spumigena, a cyanobacterium that has been recurring in waters around the world, using advanced water observation technologies. We also showcase the benefits of experiments of opportunity and the issues with obtaining synoptic spatio-temporal data for monitoring water quality. The dataset can be leveraged to gain more knowledge on related blooms, develop detection algorithms and optimize future monitoring efforts.
Fabrice Stephenson, Tom Brough, Drew Lohrer, Daniel Leduc, Shane Geange, Owen Anderson, David Bowden, Malcolm R. Clark, Niki Davey, Enrique Pardo, Dennis P. Gordon, Brittany Finucci, Michelle Kelly, Diana Macpherson, Lisa McCartain, Sadie Mills, Kate Neill, Wendy Nelson, Rachael Peart, Matthew H. Pinkerton, Geoffrey B. Read, Jodie Robertson, Ashley Rowden, Kareen Schnabel, Andrew Stewart, Carl Struthers, Leigh Tait, Di Tracey, Shaun Weston, and Carolyn Lundquist
Earth Syst. Sci. Data, 15, 3931–3939, https://doi.org/10.5194/essd-15-3931-2023, https://doi.org/10.5194/essd-15-3931-2023, 2023
Short summary
Short summary
Understanding the distribution of species that live at the seafloor is critical to the management of the marine environment but is lacking in many areas. Here, we showcase an atlas of seafloor biodiversity that describes the distribution of approximately 600 organisms throughout New Zealand’s vast marine realm. Each layer in the open-access atlas has been evaluated by leading experts and provides a key resource for the sustainable use of New Zealand's marine environment.
Hubert Loisel, Daniel Schaffer Ferreira Jorge, Rick A. Reynolds, and Dariusz Stramski
Earth Syst. Sci. Data, 15, 3711–3731, https://doi.org/10.5194/essd-15-3711-2023, https://doi.org/10.5194/essd-15-3711-2023, 2023
Short summary
Short summary
Studies of light fields in aquatic environments require data from radiative transfer simulations that are free of measurement errors. In contrast to previously published synthetic optical databases, the present database was created by simulations covering a broad range of seawater optical properties that exhibit probability distributions consistent with a global ocean dominated by open-ocean pelagic environments. This database is intended to support ocean color science and applications.
Andrea J. McEvoy, Angus Atkinson, Ruth L. Airs, Rachel Brittain, Ian Brown, Elaine S. Fileman, Helen S. Findlay, Caroline L. McNeill, Clare Ostle, Tim J. Smyth, Paul J. Somerfield, Karen Tait, Glen A. Tarran, Simon Thomas, Claire E. Widdicombe, E. Malcolm S. Woodward, Amanda Beesley, Dave V. P. Conway, James Fishwick, Hannah Haines, Carolyn Harris, Roger Harris, Pierre Hélaouët, David Johns, Penelope K. Lindeque, Thomas Mesher, Abigail McQuatters-Gollop, Joana Nunes, Frances Perry, Ana M. Queiros, Andrew Rees, Saskia Rühl, David Sims, Ricardo Torres, and Stephen Widdicombe
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-311, https://doi.org/10.5194/essd-2023-311, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
The Western Channel Observatory is an oceanographic time-series and biodiversity reference site within 40 km of Plymouth (UK), sampled since 1903. Differing levels of reporting and formatting hamper the use of the valuable individual datasets. We provide the first summary database as monthly averages where comparisons can be made of the physical, chemical and biological data. We describe the database, illustrate its utility to examine seasonality, longer-term trends and summarize previous work.
Philippe Massicotte, Marcel Babin, Frank Fell, Vincent Fournier-Sicre, and David Doxaran
Earth Syst. Sci. Data, 15, 3529–3545, https://doi.org/10.5194/essd-15-3529-2023, https://doi.org/10.5194/essd-15-3529-2023, 2023
Short summary
Short summary
The COASTlOOC oceanographic expeditions in 1997 and 1998 studied the relationship between seawater properties and biology and chemistry across the European coasts. The team collected data from 379 stations using ships and helicopters to support the development of ocean color remote-sensing algorithms. This unique and consistent dataset is still used today by researchers.
Valentin Siebert, Brivaëla Moriceau, Lukas Fröhlich, Bernd R. Schöne, Erwan Amice, Beatriz Beker, Kevin Bihannic, Isabelle Bihannic, Gaspard Delebecq, Jérémy Devesa, Morgane Gallinari, Yoan Germain, Émilie Grossteffan, Klaus Peter Jochum, Thierry Le Bec, Manon Le Goff, Céline Liorzou, Aude Leynaert, Claudie Marec, Marc Picheral, Peggy Rimmelin-Maury, Marie-Laure Rouget, Matthieu Waeles, and Julien Thébault
Earth Syst. Sci. Data, 15, 3263–3281, https://doi.org/10.5194/essd-15-3263-2023, https://doi.org/10.5194/essd-15-3263-2023, 2023
Short summary
Short summary
This article presents an overview of the results of biological, chemical and physical parameters measured at high temporal resolution (sampling once and twice per week) during environmental monitoring that took place in 2021 in the Bay of Brest. We strongly believe that this dataset could be very useful for other scientists performing sclerochronological investigations, studying biogeochemical cycles or conducting various ecological research projects.
Sofie Vranken, Marine Robuchon, Stefanie Dekeyzer, Ignacio Bárbara, Inka Bartsch, Aurélie Blanfuné, Charles-François Boudouresque, Wim Decock, Christophe Destombe, Bruno de Reviers, Pilar Díaz-Tapia, Anne Herbst, Romain Julliard, Rolf Karez, Priit Kersen, Stacy A. Krueger-Hadfield, Ralph Kuhlenkamp, Akira F. Peters, Viviana Peña, Cristina Piñeiro-Corbeira, Fabio Rindi, Florence Rousseau, Jan Rueness, Hendrik Schubert, Kjersti Sjøtun, Marta Sansón, Dan Smale, Thierry Thibaut, Myriam Valero, Leen Vandepitte, Bart Vanhoorne, Alba Vergés, Marc Verlaque, Christophe Vieira, Line Le Gall, Frederik Leliaert, and Olivier De Clerck
Earth Syst. Sci. Data, 15, 2711–2754, https://doi.org/10.5194/essd-15-2711-2023, https://doi.org/10.5194/essd-15-2711-2023, 2023
Short summary
Short summary
We present AlgaeTraits, a high-quality seaweed trait database. The data are structured within the framework of WoRMS and are supported by an expert editor community. With 45 175 trait records for 21 prioritised biological and ecological traits, and a taxonomic coverage of 1 745 European species, AlgaeTraits significantly advances previous efforts to provide standardised seaweed trait data. AlgaeTraits will serve as a foundation for future research on diversity and evolution of seaweeds.
Alain Lefebvre and David Devreker
Earth Syst. Sci. Data, 15, 1077–1092, https://doi.org/10.5194/essd-15-1077-2023, https://doi.org/10.5194/essd-15-1077-2023, 2023
Short summary
Short summary
The Suivi Regional des Nutriments (SRN) data set includes long-term time series on marine phytoplankton and physicochemical measures in the eastern English Channel and the Southern Bight of the North Sea. These data sets should be useful for comparing contrasted coastal marine ecosystems to further knowledge about the direct and indirect effects of human pressures and environmental changes on ecosystem structure and function, including eutrophication and harmful algal bloom issues.
Jacopo Pulcinella, Enrico Nicola Armelloni, Carmen Ferrà, Giuseppe Scarcella, and Anna Nora Tassetti
Earth Syst. Sci. Data, 15, 809–820, https://doi.org/10.5194/essd-15-809-2023, https://doi.org/10.5194/essd-15-809-2023, 2023
Short summary
Short summary
Deep-sea fishery in the Mediterranean Sea was historically driven by the commercial profitability of deepwater red shrimps. Understanding spatiotemporal dynamics of fishing is key to comprehensively evaluate the status of these resources and prevent stock collapse. The observed monthly fishing effort and frequency dataset released by the automatic identification system (AIS) may help researchers as well as those involved in fishery management and in the update of existing management plans.
Simone Strydom, Roisin McCallum, Anna Lafratta, Chanelle L. Webster, Caitlyn M. O'Dea, Nicole E. Said, Natasha Dunham, Karina Inostroza, Cristian Salinas, Samuel Billinghurst, Charlie M. Phelps, Connor Campbell, Connor Gorham, Rachele Bernasconi, Anna M. Frouws, Axel Werner, Federico Vitelli, Viena Puigcorbé, Alexandra D'Cruz, Kathryn M. McMahon, Jack Robinson, Megan J. Huggett, Sian McNamara, Glenn A. Hyndes, and Oscar Serrano
Earth Syst. Sci. Data, 15, 511–519, https://doi.org/10.5194/essd-15-511-2023, https://doi.org/10.5194/essd-15-511-2023, 2023
Short summary
Short summary
Seagrasses are important underwater plants that provide valuable ecosystem services to humans, including mitigating climate change. Understanding the natural history of seagrass meadows across different types of environments is crucial to conserving seagrasses in the global ocean. This dataset contains data extracted from peer-reviewed publications and highlights which seagrasses have been studied and in which locations and is useful for pointing out which need further investigation.
Flavienne Bruyant, Rémi Amiraux, Marie-Pier Amyot, Philippe Archambault, Lise Artigue, Lucas Barbedo de Freitas, Guislain Bécu, Simon Bélanger, Pascaline Bourgain, Annick Bricaud, Etienne Brouard, Camille Brunet, Tonya Burgers, Danielle Caleb, Katrine Chalut, Hervé Claustre, Véronique Cornet-Barthaux, Pierre Coupel, Marine Cusa, Fanny Cusset, Laeticia Dadaglio, Marty Davelaar, Gabrièle Deslongchamps, Céline Dimier, Julie Dinasquet, Dany Dumont, Brent Else, Igor Eulaers, Joannie Ferland, Gabrielle Filteau, Marie-Hélène Forget, Jérome Fort, Louis Fortier, Martí Galí, Morgane Gallinari, Svend-Erik Garbus, Nicole Garcia, Catherine Gérikas Ribeiro, Colline Gombault, Priscilla Gourvil, Clémence Goyens, Cindy Grant, Pierre-Luc Grondin, Pascal Guillot, Sandrine Hillion, Rachel Hussherr, Fabien Joux, Hannah Joy-Warren, Gabriel Joyal, David Kieber, Augustin Lafond, José Lagunas, Patrick Lajeunesse, Catherine Lalande, Jade Larivière, Florence Le Gall, Karine Leblanc, Mathieu Leblanc, Justine Legras, Keith Lévesque, Kate-M. Lewis, Edouard Leymarie, Aude Leynaert, Thomas Linkowski, Martine Lizotte, Adriana Lopes dos Santos, Claudie Marec, Dominique Marie, Guillaume Massé, Philippe Massicotte, Atsushi Matsuoka, Lisa A. Miller, Sharif Mirshak, Nathalie Morata, Brivaela Moriceau, Philippe-Israël Morin, Simon Morisset, Anders Mosbech, Alfonso Mucci, Gabrielle Nadaï, Christian Nozais, Ingrid Obernosterer, Thimoté Paire, Christos Panagiotopoulos, Marie Parenteau, Noémie Pelletier, Marc Picheral, Bernard Quéguiner, Patrick Raimbault, Joséphine Ras, Eric Rehm, Llúcia Ribot Lacosta, Jean-François Rontani, Blanche Saint-Béat, Julie Sansoulet, Noé Sardet, Catherine Schmechtig, Antoine Sciandra, Richard Sempéré, Caroline Sévigny, Jordan Toullec, Margot Tragin, Jean-Éric Tremblay, Annie-Pier Trottier, Daniel Vaulot, Anda Vladoiu, Lei Xue, Gustavo Yunda-Guarin, and Marcel Babin
Earth Syst. Sci. Data, 14, 4607–4642, https://doi.org/10.5194/essd-14-4607-2022, https://doi.org/10.5194/essd-14-4607-2022, 2022
Short summary
Short summary
This paper presents a dataset acquired during a research cruise held in Baffin Bay in 2016. We observed that the disappearance of sea ice in the Arctic Ocean increases both the length and spatial extent of the phytoplankton growth season. In the future, this will impact the food webs on which the local populations depend for their food supply and fisheries. This dataset will provide insight into quantifying these impacts and help the decision-making process for policymakers.
Rainer Kiko, Marc Picheral, David Antoine, Marcel Babin, Léo Berline, Tristan Biard, Emmanuel Boss, Peter Brandt, Francois Carlotti, Svenja Christiansen, Laurent Coppola, Leandro de la Cruz, Emilie Diamond-Riquier, Xavier Durrieu de Madron, Amanda Elineau, Gabriel Gorsky, Lionel Guidi, Helena Hauss, Jean-Olivier Irisson, Lee Karp-Boss, Johannes Karstensen, Dong-gyun Kim, Rachel M. Lekanoff, Fabien Lombard, Rubens M. Lopes, Claudie Marec, Andrew M. P. McDonnell, Daniela Niemeyer, Margaux Noyon, Stephanie H. O'Daly, Mark D. Ohman, Jessica L. Pretty, Andreas Rogge, Sarah Searson, Masashi Shibata, Yuji Tanaka, Toste Tanhua, Jan Taucher, Emilia Trudnowska, Jessica S. Turner, Anya Waite, and Lars Stemmann
Earth Syst. Sci. Data, 14, 4315–4337, https://doi.org/10.5194/essd-14-4315-2022, https://doi.org/10.5194/essd-14-4315-2022, 2022
Short summary
Short summary
The term
marine particlescomprises detrital aggregates; fecal pellets; bacterioplankton, phytoplankton and zooplankton; and even fish. Here, we present a global dataset that contains 8805 vertical particle size distribution profiles obtained with Underwater Vision Profiler 5 (UVP5) camera systems. These data are valuable to the scientific community, as they can be used to constrain important biogeochemical processes in the ocean, such as the flux of carbon to the deep sea.
Autun Purser, Laura Hehemann, Lilian Boehringer, Ellen Werner, Santiago E. A. Pineda-Metz, Lucie Vignes, Axel Nordhausen, Moritz Holtappels, and Frank Wenzhoefer
Earth Syst. Sci. Data, 14, 3635–3648, https://doi.org/10.5194/essd-14-3635-2022, https://doi.org/10.5194/essd-14-3635-2022, 2022
Short summary
Short summary
Within this paper we present the seafloor images, maps and acoustic camera data collected by a towed underwater research platform deployed in 20 locations across the eastern Weddell Sea, Antarctica, during the PS124 COSMUS expedition with the research icebreaker RV Polarstern in 2021. The 20 deployments highlight the great variability in seafloor structure and faunal communities present. Of key interest was the discovery of the largest fish nesting colony discovered globally to date.
Cited articles
Andersson, T. R., Hosking, J. S., Perez-Ortiz, M., Paige, B., Elliott, A., Russell, C., Law, S., Jones, D. C., Wilkinson, J., Phillips, T., Byrne, J., Tietsche, S., Sarojini, B. B., Blanchard-Wrigglesworth, E., Aksenov, Y., Downie, R., and Shuckburgh, E.: Seasonal Arctic sea ice forecasting with probabilistic deep learning, Nat. Commun., 12, 5124, https://doi.org/10.1038/s41467-021-25257-4, 2021.
Beaulieu, C., Henson, S. A., Sarmiento, J. L., Dunne, J. P., Doney, S. C., Rykaczewski, R. R., and Bopp, L.: Factors challenging our ability to detect long-term trends in ocean chlorophyll, Biogeosciences, 10, 2711–2724, https://doi.org/10.5194/bg-10-2711-2013, 2013.
Behrenfeld, M. J., O'Malley, R. T., Siegel, D. A., McClain, C. R., Sarmiento, J. L., Feldman, G. C., Milligan, A. J., Falkowski, P. G., Letelier, R. M., and Boss, E. S.: Climate-driven trends in contemporary ocean productivity, Nature, 444, 752–755, https://doi.org/10.1038/nature05317, 2006.
Blondeau-Patissier, D., Gower, J. F. R., Dekker, A. G., Phinn, S. R., and Brando, V. E.: 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, 123–144, https://doi.org/10.1016/j.pocean.2013.12.008, 2014.
Cao, Z. G., Ma, R. H., Duan, H. T., Pahlevan, N., Melack, J., Shen, M., and Xue, K.: A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes, Remote Sens. Environ., 248, https://doi.org/10.1016/j.rse.2020.111974, 2020.
Catipovic, L., Matic, F., and Kalinic, H.: Reconstruction Methods in Oceanographic Satellite Data Observation-A Survey, J. Mar. Sci. Eng., 11, 340, https://doi.org/10.3390/jmse11020340, 2023.
Cen, H. B., Jiang, J. H., Han, G. Q., Lin, X. Y., Liu, Y., Jia, X. Y., Ji, Q. Y., and Li, B.: Applying Deep Learning in the Prediction of Chlorophyll-a in the East China Sea, Remote Sens., 14, 5461, https://doi.org/10.3390/rs14215461, 2022.
Chen, S., Hu, C., Barnes, B. B., Xie, Y., Lin, G., and Qiu, Z.: Improving ocean color data coverage through machine learning, Remote Sens. Environ., 222, 286–302, https://doi.org/10.1016/j.rse.2018.12.023, 2019.
Dai, Y., Yang, S., Zhao, D., Hu, C., Xu, W., Anderson, D. M., Li, Y., Song, X.-P., Boyce, D. G., Gibson, L., Zheng, C., and Feng, L.: Coastal phytoplankton blooms expand and intensify in the 21st century, Nature, 615, 280–284, https://doi.org/10.1038/s41586-023-05760-y, 2023.
Dorigo, W. A., Scipal, K., Parinussa, R. M., Liu, Y. Y., Wagner, W., de Jeu, R. A. M., and Naeimi, V.: Error characterisation of global active and passive microwave soil moisture datasets, Hydrol. Earth Syst. Sci., 14, 2605–2616, https://doi.org/10.5194/hess-14-2605-2010, 2010.
Ducklow, H., Cimino, M., Dunton, K. H., Fraser, W. R., Hopcroft, R. R., Ji, R., Miller, A. J., Ohman, M. D., and Sosik, H. M.: Marine Pelagic Ecosystem Responses to Climate Variability and Change, BioScience, 72, 827–850, https://doi.org/10.1093/biosci/biac050, 2022.
Duteil, O. and Park, W.: Future changes in atmospheric synoptic variability slow down ocean circulation and decrease primary productivity in the tropical Pacific Ocean, Npj Climate and Atmospheric Science, 6, 136, https://doi.org/10.1038/s41612-023-00459-3, 2023.
Feng, L. and Hu, C. M.: Comparison of Valid Ocean Observations Between MODIS Terra and Aqua Over the Global Oceans, IEEE T. Geosci. Remote, 54, 1575–1585, https://doi.org/10.1109/tgrs.2015.2483500, 2016.
Feng, L., Dai, Y., Hou, X., Xu, Y., Liu, J., and Zheng, C.: Concerns about phytoplankton bloom trends in global lakes, Nature, 590, E35–E47, https://doi.org/10.1038/s41586-021-03254-3, 2021.
Flynn, K. J.: A mechanistic model for describing dynamic multi-nutrient, light, temperature interactions in phytoplankton, J. Plankton Res., 23, 977–997, https://doi.org/10.1093/plankt/23.9.977, 2001.
Geng, T., Cai, W. J., Wu, L. X., Santoso, A., Wang, G. J., Jing, Z., Gan, B. L., Yang, Y., Li, S. J., Wang, S. P., Chen, Z. H., and McPhaden, M. J.: Emergence of changing Central-Pacific and Eastern-Pacific El Nino-Southern Oscillation in a warming climate, Nat. Commun., 13, 6616, https://doi.org/10.1038/s41467-022-33930-5, 2022.
Gentemann, C. L.: Three way validation of MODIS and AMSR-E sea surface temperatures, J. Geophys. Res.-Oceans, 119, 2583–2598, https://doi.org/10.1002/2013jc009716, 2014.
Groom, S., Sathyendranath, S., Ban, Y., Bernard, S., Brewin, R., Brotas, V., Brockmann, C., Chauhan, P., Choi, J.-k., Chuprin, A., Ciavatta, S., Cipollini, P., Donlon, C., Franz, B., He, X., Hirata, T., Jackson, T., Kampel, M., Krasemann, H., Lavender, S., Pardo-Martinez, S., Mélin, F., Platt, T., Santoleri, R., Skakala, J., Schaeffer, B., Smith, M., Steinmetz, F., Valente, A., and Wang, M.: Satellite Ocean Colour: Current Status and Future Perspective, Front. Mar. Sci., 6, 485, https://doi.org/10.3389/fmars.2019.00485, 2019.
Gruber, N., Boyd, P. W., Frölicher, T. L., and Vogt, M.: Biogeochemical extremes and compound events in the ocean, Nature, 600, 395–407, https://doi.org/10.1038/s41586-021-03981-7, 2021.
Han, Y. and Zhou, Y. T.: Investigating biophysical control of marine phytoplankton dynamics via Bayesian mechanistic modeling, Ecol. Model., 474, 110168, https://doi.org/10.1016/j.ecolmodel.2022.110168, 2022.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horanyi, A., Munoz-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., Holm, E., Janiskova, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thepaut, J. N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hilborn, A. and Costa, M.: Applications of DINEOF to Satellite-Derived Chlorophyll-a from a Productive Coastal Region, Remote Sens., 10, 1449, https://doi.org/10.3390/rs10091449, 2018.
Ho, J. C., Michalak, A. M., and Pahlevan, N.: Widespread global increase in intense lake phytoplankton blooms since the 1980s, Nature, 574, 667–670, https://doi.org/10.1038/s41586-019-1648-7, 2019.
Hong, Z. K., Long, D., Li, X. D., Wang, Y. M., Zhang, J. M., Mohamed, A. H., and Mohamed, M. M.: OCNET global daily Chlorophyll-a products, Zenodo [data set], https://doi.org/10.5281/zenodo.10011908, 2023.
Hu, C., Lee, Z., and Franz, B.: Chlorophyll a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference:a novel ocean chlorophyll a algorithm, J. Geophys. Res.-Oceans, 117, C01011, https://doi.org/10.1029/2011JC007395, 2012.
Hu, C., Feng, L., Lee, Z., Franz, B. A., Bailey, S. W., Werdell, P. J., and Proctor, C. W.: Improving Satellite Global Chlorophyll-a Data Products Through Algorithm Refinement and Data Recovery, J. Geophys. Res.-Oceans, 124, 1524–1543, https://doi.org/10.1029/2019JC014941, 2019a.
Hu, C. M., Feng, L., Lee, Z. P., Franz, B. A., Bailey, S. W., Werdell, P. J., and Proctor, C. W.: Improving Satellite Global Chlorophyll a Data Products Through Algorithm Refinement and Data Recovery, J. Geophys. Res.-Oceans, 124, 1524–1543, https://doi.org/10.1029/2019jc014941, 2019b.
NOAA National Geophysical Data Center: ETOPO1 1 Arc-Minute Global Relief Model, NOAA National Centers for Environmental Information [data set], https://doi.org/10.7289/V5C8276M, 2009.
Jiang, L. D. and Wang, M. H.: Identification of pixels with stray light and cloud shadow contaminations in the satellite ocean color data processing, Appl. Opt., 52, 6757–6770, https://doi.org/10.1364/ao.52.006757, 2013.
Jin, D., Lee, E., Kwon, K., and Kim, T.: A Deep Learning Model Using Satellite Ocean Color and Hydrodynamic Model to Estimate Chlorophyll-a Concentration, Remote Sens., 13, 2003, https://doi.org/10.3390/rs13102003, 2021.
Konik, M., Kowalewski, M., Bradtke, K., and Darecki, M.: The operational method of filling information gaps in satellite imagery using numerical models, Int. J. Appl. Earth Obs., 75, 68–82, https://doi.org/10.1016/j.jag.2018.09.002, 2019.
Krug, L. A., Platt, T., Sathyendranath, S., and Barbosa, A. B.: Ocean surface partitioning strategies using ocean colour remote Sensing: A review, Prog. Oceanogr., 155, 41–53, https://doi.org/10.1016/j.pocean.2017.05.013, 2017.
Laufkötter, C., Vogt, M., Gruber, N., Aumont, O., Bopp, L., Doney, S. C., Dunne, J. P., Hauck, J., John, J. G., Lima, I. D., Seferian, R., and Völker, C.: Projected decreases in future marine export production: the role of the carbon flux through the upper ocean ecosystem, Biogeosciences, 13, 4023–4047, https://doi.org/10.5194/bg-13-4023-2016, 2016.
Le Grix, N., Zscheischler, J., Laufkötter, C., Rousseaux, C. S., and Frölicher, T. L.: Compound high-temperature and low-chlorophyll extremes in the ocean over the satellite period, Biogeosciences, 18, 2119–2137, https://doi.org/10.5194/bg-18-2119-2021, 2021.
Levitus, S., Boyer, T. P., García, H. E., Locarnini, R. A., Zweng, M. M., Mishonov, A. V., Reagan, J. R., Antonov, J. I., Baranova, O. K., Biddle, M., Hamilton, M., Johnson, D. R., Paver, C. R., and Seidov, D.: World Ocean Atlas 2013 (NCEI Accession 0114815), [data set], doi.org/10.7289/v5f769gt, 2014.
Li, H., Xu, F. H., Zhou, W., Wang, D. X., Wright, J. S., Liu, Z. H., and Lin, Y. L.: Development of a global gridded Argo data set with Barnes successive corrections, J. Geophys. Res.-Oceans, 122, 866–889, https://doi.org/10.1002/2016jc012285, 2017.
Li, J., Gao, M., Feng, L., Zhao, H., Shen, Q., Zhang, F., Wang, S., and Zhang, B.: Estimation of Chlorophyll-a Concentrations in a Highly Turbid Eutrophic Lake Using a Classification-Based MODIS Land-Band Algorithm, IEEE J. Sel. Top. Appl., 12, 3769–3783, https://doi.org/10.1109/JSTARS.2019.2936403, 2019.
Li, X., Liu, B., Zheng, G., Ren, Y., Zhang, S., Liu, Y., Gao, L., Liu, Y., Zhang, B., and Wang, F.: Deep-learning-based information mining from ocean remote-sensing imagery, Nat. Sci. Rev., 7, 1584–1605, 10.1093/nsr/nwaa047, 2020.
Lian, F.: Key issues in detecting lacustrine cyanobacterial bloom using satellite remote sensing, J. Lake Sci., 33, 647–652, https://doi.org/10.18307/2021.0301, 2021.
Liu, D., Zhou, C., Keesing, J. K., Serrano, O., Werner, A., Fang, Y., Chen, Y., Masque, P., Kinloch, J., Sadekov, A., and Du, Y.: Wildfires enhance phytoplankton production in tropical oceans, Nat. Commun., 13, 1348, https://doi.org/10.1038/s41467-022-29013-0, 2022.
Liu, X. and Wang, M.: Gap Filling of Missing Data for VIIRS Global Ocean Color Products Using the DINEOF Method, IEEE T. Geosci. Remote, 56, 4464–4476, https://doi.org/10.1109/TGRS.2018.2820423, 2018.
Liu, X. and Wang, M.: Global daily gap-free ocean color products from multi-satellite measurements, Int. J. Appl. Earth Obs., 108, 102714, https://doi.org/10.1016/j.jag.2022.102714, 2022.
McColl, K. A., Vogelzang, J., Konings, A. G., Entekhabi, D., Piles, M., and Stoffelen, A.: Extended triple collocation: Estimating errors and correlation coefficients with respect to an unknown target, Geophys. Res. Lett., 41, 6229–6236, https://doi.org/10.1002/2014gl061322, 2014.
Mikelsons, K. and Wang, M.: Optimal satellite orbit configuration for global ocean color product coverage, Opt. Express, 27, A445, https://doi.org/10.1364/OE.27.00A445, 2019.
Miralles, D. G., Crow, W. T., and Cosh, M. H.: Estimating Spatial Sampling Errors in Coarse-Scale Soil Moisture Estimates Derived from Point-Scale Observations, J. Hydrometeorol., 11, 1423–1429, https://doi.org/10.1175/2010jhm1285.1, 2010.
Moran, N., Stringer, B., Lin, B. C., and Hoque, M. T.: Machine learning model selection for predicting bathymetry, Deep-Sea Res. Pt. I, 185, 103788, https://doi.org/10.1016/j.dsr.2022.103788, 2022.
Mouw, C. B., Barnett, A., McKinley, G. A., Gloege, L., and Pilcher, D.: Phytoplankton size impact on export flux in the global ocean, Global Biogeochem. Cy., 30, 1542–1562, https://doi.org/10.1002/2015gb005355, 2016.
Nelson, N. G., Munoz-Carpena, R., and Phlips, E.: Parameter uncertainty drives important incongruities between simulated chlorophyll-a and phytoplankton functional group dynamics in a mechanistic management model, Environ. Model. Softw., 129, 104708, https://doi.org/10.1016/j.envsoft.2020.104708, 2020.
Nikolaidis, A., Georgiou, G. C., Hadjimitsis, D., and Akylas, E.: Filling in missing sea-surface temperature satellite data over the Eastern Mediterranean Sea using the DINEOF algorithm, Cent. Eur. J. Geosci., 6, 27–41, https://doi.org/10.2478/s13533-012-0148-1, 2014.
Powell, C. F., Baker, A. R., Jickells, T. D., Bange, H. W., Chance, R. J., and Yodle, C.: Estimation of the Atmospheric Flux of Nutrients and Trace Metals to the Eastern Tropical North Atlantic Ocean, J. Atmos. Sci., 72, 4029–4045, https://doi.org/10.1175/jas-d-15-0011.1, 2015.
Reygondeau, G., Longhurst, A., Martinez, E., Beaugrand, G., Antoine, D., and Maury, O.: Dynamic biogeochemical provinces in the global ocean, Global Biogeochem. Cy., 27, 1046–1058, https://doi.org/10.1002/gbc.20089, 2013.
Righetti, D., Vogt, M., Gruber, N., Psomas, A., and Zimmermann, N. E.: Global pattern of phytoplankton diversity driven by temperature and environmental variability, Sci. Adv., 5, eaau6253, https://doi.org/10.1126/sciadv.aau6253, 2019.
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation, 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Munich, Germany, 5–9 October 2015, WOS:000365963800028, 234–241, https://doi.org/10.1007/978-3-319-24574-4_28, 2015.
Roussillon, J., Fablet, R., Gorgues, T., Drumetz, L., Littaye, J., and Martinez, E.: A Multi-Mode Convolutional Neural Network to reconstruct satellite-derived chlorophyll-a time series in the global ocean from physical drivers, Front. Mar. Sci., 10, 1077623, https://doi.org/10.3389/fmars.2023.1077623, 2023.
Salgado-Hernanz, P. M., Racault, M. F., Font-Muñoz, J. S., and Basterretxea, G.: Trends in phytoplankton phenology in the Mediterranean Sea based on ocean-colour remote sensing, Remote Sens. Environ., 221, 50–64, https://doi.org/10.1016/j.rse.2018.10.036, 2019.
Sathyendranath, S., Brewin, R., Brockmann, C., Brotas, V., Calton, B., Chuprin, A., Cipollini, P., Couto, A., Dingle, J., Doerffer, R., Donlon, C., Dowell, M., Farman, A., Grant, M., Groom, S., Horseman, A., Jackson, T., Krasemann, H., Lavender, S., Martinez-Vicente, V., Mazeran, C., Mélin, F., Moore, T., Müller, D., Regner, P., Roy, S., Steele, C., Steinmetz, F., Swinton, J., Taberner, M., Thompson, A., Valente, A., Zühlke, M., Brando, V., Feng, H., Feldman, G., Franz, B., Frouin, R., Gould, R., Hooker, S., Kahru, M., Kratzer, S., Mitchell, B., Muller-Karger, F., Sosik, H., Voss, K., Werdell, J., and Platt, T.: An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI), Sensors, 19, 4285, https://doi.org/10.3390/s19194285, 2019.
Signorini, S. R., Franz, B. A., and McClain, C. R.: Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends, Front. Mar. Sci., 2, 1, https://doi.org/10.3389/fmars.2015.00001, 2015.
Slomp, C. P.: Phosphorus Cycling in the Estuarine and Coastal Zones: Sources, Sinks, and Transformations, Treatise on Estuarine and Coastal Science, Biogeochemistry, 5, 201–229, https://doi.org/10.1016/B978-0-12-374711-2.00506-4, 2011.
Stoffelen, A.: Toward the true near-surface wind speed: Error modeling and calibration using triple collocation, J. Geophys. Res.-Oceans, 103, 7755–7766, https://doi.org/10.1029/97jc03180, 1998.
Stumpf, R. P.: Applications of satellite ocean color sensors for monitoring and predicting harmful algal blooms, Hum. Ecol. Risk Assess., 7, 1363–1368, https://doi.org/10.1080/20018091095050, 2001.
Sun, D., Pan, T., Wang, S., and Hu, C.: Linking phytoplankton absorption to community composition in Chinese marginal seas, Prog. Oceanogr., 192, 102517, https://doi.org/10.1016/j.pocean.2021.102517, 2021.
Takeda, S.: Influence of iron availability on nutrient consumption ratio of diatoms in oceanic waters, Nature, 393, 774–777, https://doi.org/10.1038/31674, 1998.
Urakubo, H., Bullmann, T., Kubota, Y., Oba, S., and Ishii, S.: UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images, Sci. Rep., 9, 19413, https://doi.org/10.1038/s41598-019-55431-0, 2019.
Wagle, N., Acharya, T. D., and Lee, D. H.: Comprehensive Review on Application of Machine Learning Algorithms for Water Quality Parameter Estimation Using Remote Sensing Data, Sensor. Mater., 32, 3879, https://doi.org/10.18494/SAM.2020.2953, 2020.
Wagner, F. H., Sanchez, A., Tarabalka, Y., Lotte, R. G., Ferreira, M. P., Aidar, M. P. M., Gloor, E., Phillips, O. L., and Aragao, L.: Using the U-net convolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images, Remote Sensing in Ecology and Conservation, 5, 360–375, https://doi.org/10.1002/rse2.111, 2019.
Wang, B. L., Liu, C. Q., Maberly, S. C., Wang, F. S., and Hartmann, J.: Coupling of carbon and silicon geochemical cycles in rivers and lakes, Sci. Rep., 6, 35832, https://doi.org/10.1038/srep35832, 2016.
Wang, M., Jiang, L., Mikelsons, K., and Liu, X.: Satellite-derived global chlorophyll-a anomaly products, Int. J. Appl. Earth Obs., 97, 102288, https://doi.org/10.1016/j.jag.2020.102288, 2021.
Wang, Y. Q. and Liu, D. Y.: Reconstruction of satellite chlorophyll-a data using a modified DINEOF method: a case study in the Bohai and Yellow seas, China, Int. J. Remote Sens., 35, 204–217, https://doi.org/10.1080/01431161.2013.866290, 2014.
Xing, X. and Boss, E.: Chlorophyll-Based Model to Estimate Underwater Photosynthetically Available Radiation for Modeling, In-Situ, and Remote-Sensing Applications, Geophys. Res. Lett., 48, e2020GL092189, https://doi.org/10.1029/2020GL092189, 2021.
Yussof, F. N., Maan, N., and Reba, M. N. M.: LSTM Networks to Improve the Prediction of Harmful Algal Blooms in the West Coast of Sabah, Int. J. Environ. Res. Pub. He., 18, 7650, https://doi.org/10.3390/ijerph18147650, 2021.
Zhang, Q., Yuan, Q., Zeng, C., Li, X., and Wei, Y.: Missing Data Reconstruction in Remote Sensing Image With a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Network, IEEE T. Geosci. Remote, 56, 4274–4288, https://doi.org/10.1109/tgrs.2018.2810208, 2018.
Zheng, G. and DiGiacomo, P. M.: Uncertainties and applications of satellite-derived coastal water quality products, Prog. Oceanogr., 159, 45–72, https://doi.org/10.1016/j.pocean.2017.08.007, 2017.
Zuo, H., Balmaseda, M. A., Tietsche, S., Mogensen, K., and Mayer, M.: The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment, Ocean Sci., 15, 779–808, https://doi.org/10.5194/os-15-779-2019, 2019.
Zweng, M. M., Reagan, J. R., Antonov, J. I., Locarnini, R. A., Mishonov, A. V., Boyer, T. P., Garcia, H. E., Baranova, O. K., Johnson, D. R., and D. Seidov, M. M. B.: World Ocean Atlas 2013: Volume 2, edited by: Salinity, S. L. and Mishonov, A., NOAA Atlas NESDIS 74, 39 pp., 2013.
Short summary
Changes in ocean chlorophyll-a (Chl-a) concentration are related to ecosystem balance. Here, we present high-quality gap-filled Chl-a data in open oceans, reflecting the distribution and changes in global Chl-a concentration. Our findings highlight the efficacy of reconstructing missing satellite observations using convolutional neural networks. This dataset and model are valuable for research in ocean color remote sensing, offering data support and methodological references for related studies.
Changes in ocean chlorophyll-a (Chl-a) concentration are related to ecosystem balance. Here, we...
Altmetrics
Final-revised paper
Preprint