Articles | Volume 16, issue 10
https://doi.org/10.5194/essd-16-4793-2024
© Author(s) 2024. 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-16-4793-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
23 Oct 2024
Data description paper |
| 23 Oct 2024
| 23 Oct 2024
AIGD-PFT: the first AI-driven global daily gap-free 4 km phytoplankton functional type data product from 1998 to 2023
Yuan Zhang
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, China
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, China
Renhu Li
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, China
Mengyu Li
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, China
Zhaoxin Li
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, China
Songyu Chen
State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai, China
Xuerong Sun
Centre for Geography and Environmental Science, Department of Earth and Environmental Sciences, Faculty of Environment, Science and Economy, University of Exeter, Cornwall, United Kingdom
Related authors
No articles found.
Qi Zheng, Johannes J. Viljoen, Xuerong Sun, and Robert J. W. Brewin
EGUsphere, https://doi.org/10.5194/egusphere-2024-3502, https://doi.org/10.5194/egusphere-2024-3502, 2024
Short summary
Short summary
Phytoplankton contribute to half of Earth’s primary production, but not a lot is known about subsurface phytoplankton, living at the base of the sunlit ocean. We develop a two-layered box model to simulate phytoplankton seasonal and interannual variations in different depth layers of the ocean. Our model captures seasonal and long-term trends of the two layers, explaining how they respond to a warming ocean, furthering our understanding of how phytoplankton are responding to climate change.
Héloïse Lavigne, Ana Dogliotti, David Doxaran, Fang Shen, Alexandre Castagna, Matthew Beck, Quinten Vanhellemont, Xuerong Sun, Juan Ignacio Gossn, Pannimpullath Remanan Renosh, Koen Sabbe, Dieter Vansteenwegen, and Kevin Ruddick
Earth Syst. Sci. Data, 14, 4935–4947, https://doi.org/10.5194/essd-14-4935-2022, https://doi.org/10.5194/essd-14-4935-2022, 2022
Short summary
Short summary
Because of the large diversity of case 2 waters and the complexity of light transfer, retrieving main biogeochemical parameters in these waters is still challenging. By providing optical and biogeochemical parameters for 180 sampling stations with turbidity and chlorophyll-a concentration ranging from low to extreme values, the HYPERMAQ dataset will contribute to a better description of marine optics in optically complex water bodies and can help the scientific community to develop algorithms.
Related subject area
Domain: ESSD – Ocean | Subject: Biological oceanography
Microbial plankton occurrence database in the North American Arctic region: synthesis of recent diversity of potentially toxic and/or harmful algae
Bivalve monitoring over French coasts: multi-decadal records of carbon and nitrogen elemental and isotopic ratios (δ13C, δ15N and C:N) as ecological indicators of global change
A Comprehensive Global Mapping of Offshore Lighting
Early-life dispersal traits of coastal fishes: an extensive database combining observations and growth models
An update of data compilation on the biological response to ocean acidification and overview of the OA-ICC data portal
A hyperspectral and multi-angular synthetic dataset for algorithm development in waters of varying trophic levels and optical complexity
A compilation of surface inherent optical properties and phytoplankton pigment concentrations from the Atlantic Meridional Transect
First release of the Pelagic Size Structure database: global datasets of marine size spectra obtained from plankton imaging devices
Global biogeography of N2-fixing microbes: nifH amplicon database and analytics workflow
Metazoan zooplankton in the Bay of Biscay: a 16-year record of individual sizes and abundances obtained using the ZooScan and ZooCAM imaging systems
PANABIO: a point-referenced PAN-Arctic data collection of benthic BIOtas
The Western Channel Observatory: a century of physical, chemical and biological data compiled from pelagic and benthic habitats in the western English Channel
A global daily gap-filled chlorophyll-a dataset in open oceans during 2001–2021 from multisource information using convolutional neural networks
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 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
Nicolas Schiffrine, Fatma Dhifallah, Kaven Dionne, Michel Poulin, Sylvie Lessard, André Rochon, and Michel Gosselin
Earth Syst. Sci. Data, 16, 5681–5701, https://doi.org/10.5194/essd-16-5681-2024, https://doi.org/10.5194/essd-16-5681-2024, 2024
Short summary
Short summary
Growing concern arises in the Arctic Ocean as toxic and harmful phytoplankton emerge due to climate change. The potential surge in these occurrences threatens both human health and the Arctic ecosystem. Our ongoing research yields insights into spatial patterns and biodiversity, challenging the belief that the Arctic is unsuitable for toxic and harmful algal events. This work underscores the need to comprehend and address the ecological impact of these emerging species in the Arctic environment.
Camilla Liénart, Alan Fournioux, Andrius Garbaras, Hugues Blanchet, Nicolas Briant, Stanislas F. Dubois, Aline Gangnery, Anne Grouhel Pellouin, Pauline Le Monier, Arnaud Lheureux, Xavier de Montaudouin, and Nicolas Savoye
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-364, https://doi.org/10.5194/essd-2024-364, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Bivalves such as mussels and oysters reflect the quality of the environment by filtering ambient water. We measured carbon and nitrogen chemical composition in bivalves tissues from 33 sites along French coastlines sampled since the 80’s. Thanks to such time-series, this dataset allow to track how marine species record changing climate, physical-chemical environment and organic matter cycles, and provide precious information on coastal ecosystems response to global change.
Christopher D. Elvidge, Tilottama Ghosh, Namrata Chatterjee, Mikhail Zhizhin, Paul C. Sutton, and Morgan Bazilian
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-342, https://doi.org/10.5194/essd-2024-342, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
We present a comprehensive global map of offshore lighting structures. The data are derived from low light imaging data collected nightly by the NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS) day / night band (DNB). The form of the structures only becomes apparent when data from one or more years are accumulated. Identifiable structures include fishing grounds, platforms, gas flares, anchorages, and transportation routes.
Marine Di Stefano, David Nerini, Itziar Alvarez, Giandomenico Ardizzone, Patrick Astruch, Gotzon Basterretxea, Aurélie Blanfuné, Denis Bonhomme, Antonio Calò, Ignacio Catalan, Carlo Cattano, Adrien Cheminée, Romain Crec'hriou, Amalia Cuadros, Antonio Di Franco, Carlos Diaz-Gil, Tristan Estaque, Robin Faillettaz, Fabiana C. Félix-Hackradt, José Antonio Garcia-Charton, Paolo Guidetti, Loïc Guilloux, Jean-Georges Harmelin, Mireille Harmelin-Vivien, Manuel Hidalgo, Hilmar Hinz, Jean-Olivier Irisson, Gabriele La Mesa, Laurence Le Diréach, Philippe Lenfant, Enrique Macpherson, Sanja Matić-Skoko, Manon Mercader, Marco Milazzo, Tiffany Monfort, Joan Moranta, Manuel Muntoni, Matteo Murenu, Lucie Nunez, M. Pilar Olivar, Jérémy Pastor, Ángel Pérez-Ruzafa, Serge Planes, Nuria Raventos, Justine Richaume, Elodie Rouanet, Erwan Roussel, Sandrine Ruitton, Ana Sabatés, Thierry Thibaut, Daniele Ventura, Laurent Vigliola, Dario Vrdoljak, and Vincent Rossi
Earth Syst. Sci. Data, 16, 3851–3871, https://doi.org/10.5194/essd-16-3851-2024, https://doi.org/10.5194/essd-16-3851-2024, 2024
Short summary
Short summary
We build a compilation of early-life dispersal traits for coastal fish species. The database contains over 110 000 entries collected from 1993 to 2021 in the western Mediterranean. All observations are harmonized to provide information on dates and locations of spawning and settlement, along with pelagic larval durations. When applicable, missing data are reconstructed from dynamic energy budget theory. Statistical analyses reveal sampling biases across taxa, space and time.
Yan Yang, Patrick Brockmann, Carolina Galdino, Uwe Schindler, and Frédéric Gazeau
Earth Syst. Sci. Data, 16, 3771–3780, https://doi.org/10.5194/essd-16-3771-2024, https://doi.org/10.5194/essd-16-3771-2024, 2024
Short summary
Short summary
Studies investigating the effects of ocean acidification on marine organisms and communities have been steadily increasing. To facilitate data comparison, a data compilation hosted by the PANGAEA Data Publisher was initiated in 2008 and is updated on a regular basis. By November 2023, a total of 1501 datasets (~25 million data points) from 1554 papers have been archived. To filter and access relevant biological response data from this compilation, a user-friendly portal was launched in 2018.
Jaime Pitarch and Vittorio Ernesto Brando
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-295, https://doi.org/10.5194/essd-2024-295, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
This research presents a comprehensive synthetic dataset in the optical domain, created thanks to a large mining of available bio-optical data. Utilizing the Hydrolight radiative transfer model, the dataset provides detailed light fields from ultraviolet to visible light, aiding in the development of satellite algorithms. The dataset will significantly enhance research on light behavior in water and supporting future hyperspectral missions. It has been made publicly available on Zenodo.
Thomas M. Jordan, Giorgio Dall'Olmo, Gavin Tilstone, Robert J. W. Brewin, Francesco Nencioli, Ruth Airs, Crystal S. Thomas, and Louise Schlüter
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-267, https://doi.org/10.5194/essd-2024-267, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
We present a compilation of water optical properties and phytoplankton pigments from the surface of the Atlantic Ocean collected during nine cruises between 2009–2019. We derive continuous Chlorophyll a concentrations (a biomass proxy) from water absorption. We then illustrate geographical variations and relationships for water optical properties, Chlorophyll a, and the other pigments. The dataset will be useful to researchers in ocean optics, remote-sensing, ecology, and biogeochemistry.
Mathilde Dugenne, Marco Corrales-Ugalde, Jessica Y. Luo, Rainer Kiko, Todd D. O'Brien, Jean-Olivier Irisson, Fabien Lombard, Lars Stemmann, Charles Stock, Clarissa R. Anderson, Marcel Babin, Nagib Bhairy, Sophie Bonnet, Francois Carlotti, Astrid Cornils, E. Taylor Crockford, Patrick Daniel, Corinne Desnos, Laetitia Drago, Amanda Elineau, Alexis Fischer, Nina Grandrémy, Pierre-Luc Grondin, Lionel Guidi, Cecile Guieu, Helena Hauss, Kendra Hayashi, Jenny A. Huggett, Laetitia Jalabert, Lee Karp-Boss, Kasia M. Kenitz, Raphael M. Kudela, Magali Lescot, Claudie Marec, Andrew McDonnell, Zoe Mériguet, Barbara Niehoff, Margaux Noyon, Thelma Panaïotis, Emily Peacock, Marc Picheral, Emilie Riquier, Collin Roesler, Jean-Baptiste Romagnan, Heidi M. Sosik, Gretchen Spencer, Jan Taucher, Chloé Tilliette, and Marion Vilain
Earth Syst. Sci. Data, 16, 2971–2999, https://doi.org/10.5194/essd-16-2971-2024, https://doi.org/10.5194/essd-16-2971-2024, 2024
Short summary
Short summary
Plankton and particles influence carbon cycling and energy flow in marine ecosystems. We used three types of novel plankton imaging systems to obtain size measurements from a range of plankton and particle sizes and across all major oceans. Data were compiled and cross-calibrated from many thousands of images, showing seasonal and spatial changes in particle size structure in different ocean basins. These datasets form the first release of the Pelagic Size Structure database (PSSdb).
Michael Morando, Jonathan Magasin, Shunyan Cheung, Matthew M. Mills, Jonathan P. Zehr, and Kendra A. Turk-Kubo
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-163, https://doi.org/10.5194/essd-2024-163, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
Nitrogen is crucial in ocean food webs, but only some microbes can fix N2 gas into a bioavailable form. Most are known only by their nifH gene sequence. We created a software workflow for nifH data and ran it on 865 ocean samples, producing a database that captures the global diversity of N2-fixing marine microbes and the environmental factors that influence them. The workflow and DB can standardize analyses on past and future nifH datasets to enable insights into marine microbial communities.
Nina Grandremy, Paul Bourriau, Edwin Daché, Marie-Madeleine Danielou, Mathieu Doray, Christine Dupuy, Bertrand Forest, Laetitia Jalabert, Martin Huret, Sophie Le Mestre, Antoine Nowaczyk, Pierre Petitgas, Philippe Pineau, Justin Rouxel, Morgan Tardivel, and Jean-Baptiste Romagnan
Earth Syst. Sci. Data, 16, 1265–1282, https://doi.org/10.5194/essd-16-1265-2024, https://doi.org/10.5194/essd-16-1265-2024, 2024
Short summary
Short summary
We present two space- and time-resolved zooplankton datasets originating from samples collected in the Bay of Biscay in spring over the 2004–2019 period and imaged with the interoperable imaging systems ZooScan and ZooCAM. These datasets are suited for long-term size-based or combined size- and taxonomy-based ecological studies of zooplankton. The set of sorted images are provided along with a set of morphological descriptors that are useful when machine learning is applied to plankton studies.
Dieter Piepenburg, Thomas Brey, Katharina Teschke, Jennifer Dannheim, Paul Kloss, Marianne Rehage, Miriam L. S. Hansen, and Casper Kraan
Earth Syst. Sci. Data, 16, 1177–1184, https://doi.org/10.5194/essd-16-1177-2024, https://doi.org/10.5194/essd-16-1177-2024, 2024
Short summary
Short summary
Research on ecological footprints of climate change and human impacts in Arctic seas is still hampered by problems in accessing sound data, which is unevenly distributed among regions and faunal groups. To address this issue, we present the PAN-Arctic data collection of benthic BIOtas (PANABIO). It provides open access to valuable biodiversity information by integrating data from various sources and of various formats and offers versatile exploration tools for data filtering and mapping.
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, David 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, 15, 5701–5737, https://doi.org/10.5194/essd-15-5701-2023, https://doi.org/10.5194/essd-15-5701-2023, 2023
Short summary
Short summary
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 and longer-term trends, and summarize previous work.
Zhongkun Hong, Di Long, Xingdong Li, Yiming Wang, Jianmin Zhang, Mohamed A. Hamouda, and Mohamed M. Mohamed
Earth Syst. Sci. Data, 15, 5281–5300, https://doi.org/10.5194/essd-15-5281-2023, https://doi.org/10.5194/essd-15-5281-2023, 2023
Short summary
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.
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.
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
Alvarado, L., Soppa, M., Gege, P., Losa, S., Dröscher, I., Xi, H., and Bracher, A.: Retrievals of the main phytoplankton groups at Lake Constance using OLCI, DESIS, and evaluated with field observations, https://elib.dlr.de/189789 (last access: 21 October 2024), 2022.
Alvera-Azcárate, A., Barth, A., Sirjacobs, D., Lenartz, F., and Beckers, J. M.: Data Interpolating Empirical Orthogonal Functions (DINEOF): a tool for geophysical data analyses, Mediterr. Mar. Sci., 12, 5–11, https://doi.org/10.12681/mms.64, 2011.
Beaugrand, G., Edwards, M., and Legendre, L.: Marine biodiversity, ecosystem functioning, and carbon cycles, P. Natl. Acad. Sci. USA, 107, 10120–10124, https://doi.org/10.1073/pnas.0913855107, 2010.
Bracher, A., Vountas, M., Dinter, T., Burrows, J. P., Röttgers, R., and Peeken, I.: Quantitative observation of cyanobacteria and diatoms from space using PhytoDOAS on SCIAMACHY data, Biogeosciences, 6, 751–764, https://doi.org/10.5194/bg-6-751-2009, 2009.
Bracher, A., Bouman, H. A., Brewin, R. J. W., Bricaud, A., Brotas, V., Ciotti, A. M., Clementson, L., Devred, E., Di Cicco, A., Dutkiewicz, S., Hardman-Mountford, N. J., Hickman, A. E., Hieronymi, M., Hirata, T., Losa, S. N., Mouw, C. B., Organelli, E., Raitsos, D. E., Uitz, J., Vogt, M., and Wolanin, A.: Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development, Front. Mar. Sci., 4, 55, https://doi.org/10.3389/fmars.2017.00055, 2017.
Canadell, J. G., Ciais, P., Gurney, K., Le Quéré, C., Piao, S., Raupach, M. R., and Sabine, C. L.: An international effort to quantify regional carbon fluxes, Eos, Transactions American Geophysical Union, 92, 81–82, https://doi.org/10.1029/2011EO100001, 2011.
Catlett, D., Matson, P. G., Carlson, C. A., Wilbanks, E. G., Siegel, D. A., and Iglesias-Rodriguez, M. D.: Evaluation of accuracy and precision in an amplicon sequencing workflow for marine protist communities, Limnol. Oceanogr.-Meth., 18, 20–40, https://doi.org/10.1002/lom3.10343, 2020.
Chassot, E., Bonhommeau, S., Dulvy, N. K., Mélin, F., Watson, R., Gascuel, D., and Le Pape, O.: Global marine primary production constrains fisheries catches, Ecol. Lett., 13, 495–505, https://doi.org/10.1111/j.1461-0248.2010.01443.x, 2010.
Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air–sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087–1109, https://doi.org/10.5194/bg-19-1087-2022, 2022.
Chauhan, A., Smith, P. A. H., Rodrigues, F., Christensen, A., John, M. S., and Mariani, P.: Distribution and impacts of long-lasting marine heat waves on phytoplankton biomass, Front. Mar. Sci., 10, 1177571, https://doi.org/10.3389/fmars.2023.1177571, 2023.
Copernicus Marine Service: ESA SST CCI and C3S reprocessed sea surface temperature analyses, Copernicus Marine Service [data set], https://doi.org/10.48670/moi-00169, 2023a.
Copernicus Marine Service: Global Ocean OSTIA Sea Surface Temperature and Sea Ice Analysis, Copernicus Marine Service [data set], https://doi.org/10.48670/moi-00165, 2023b.
Copernicus Marine Service: Global Ocean Physics Reanalysis, Copernicus Marine Service [data set], https://doi.org/10.48670/moi-00021, 2023c.
Copernicus Marine Service: Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing), Copernicus Marine Service [data set], https://doi.org/10.48670/moi-00281, 2023d.
Copernicus Marine Service: Global Ocean Biogeochemistry Hindcast, Copernicus Marine Service [data set], https://doi.org/10.48670/moi-00019, 2024.
El Hourany, R., Pierella Karlusich, J., Zinger, L., Loisel, H., Levy, M., and Bowler, C.: Linking satellites to genes with machine learning to estimate phytoplankton community structure from space, Ocean Sci., 20, 217–239, https://doi.org/10.5194/os-20-217-2024, 2024.
Falkowski, P.: OCEAN SCIENCE The power of plankton, Nature, 483, S17–S20, https://doi.org/10.1038/483S17a, 2012.
Field, C. B., Behrenfeld, M. J., Randerson, J. T., and Falkowski, P.: Primary production of the biosphere: Integrating terrestrial and oceanic components, Science, 281, 237–240, https://doi.org/10.1126/science.281.5374.237, 1998.
Fredj, E., Roarty, H., Kohut, J., and Lai, J. W.: Fast Gap Filling of the coastal ocean surface current in the seas around Taiwan, Oceans-Ieee, https://doi.org/10.1109/OCEANSAP.2016.7485427, 2016.
Garcia, D.: Robust smoothing of gridded data in one and higher dimensions with missing values, Comput. Stat. Data An., 54, 1167–1178, https://doi.org/10.1016/j.csda.2009.09.020, 2010.
Garnesson, P., Mangin, A., Fanton d'Andon, O., Demaria, J., and Bretagnon, M.: The CMEMS GlobColour chlorophyll a product based on satellite observation: multi-sensor merging and flagging strategies, Ocean Sci., 15, 819–830, https://doi.org/10.5194/os-15-819-2019, 2019.
Gregg, W. W. and Casey, N. W.: Modeling coccolithophores in the global oceans, Deep-Sea Res. Pt. II, 54, 447–477, https://doi.org/10.1016/j.dsr2.2006.12.007, 2007.
Gruber, N., Clement, D., Carter, B. R., Feely, R. A., van Heuven, S., Hoppema, M., Ishii, M., Key, R. M., Kozyr, A., Lauvset, S. K., Lo Monaco, C., Mathis, J. T., Murata, A., Olsen, A., Perez, F. F., Sabine, C. L., Tanhua, T., and Wanninkhof, R.: The oceanic sink for anthropogenic CO from 1994 to 2007, Science, 363, 1193, https://doi.org/10.1126/science.aau5153, 2019.
Guidi, L., Chaffron, S., Bittner, L., Eveillard, D., Larhlimi, A., Roux, S., Darzi, Y., Audic, S., Berline, L., Brum, J. R., Coelho, L. P., Espinoza, J. C. I., Malviya, S., Sunagawa, S., Dimier, C., Kandels-Lewis, S., Picheral, M., Poulain, J., Searson, S., Stemmann, L., Not, F., Hingamp, P., Speich, S., Follows, M., Karp-Boss, L., Boss, E., Ogata, H., Pesant, S., Weissenbach, J., Wincker, P., Acinas, S. G., Bork, P., de Vargas, C., Iudicone, D., Sullivan, M. B., Raes, J., Karsenti, E., Bowler, C., Gorsky, G., and Coordinator, T. O. C.: Plankton networks driving carbon export in the oligotrophic ocean, Nature, 532, 465, https://doi.org/10.1038/nature16942, 2016.
Gunes, H., Sirisup, S., and Karniadakis, G. E.: Gappy data: To krig or not to krig?, J. Comput. Phys., 212, 358–382, https://doi.org/10.1016/j.jcp.2005.06.023, 2006.
Henson, S. A., Cael, B. B., Allen, S. R., and Dutkiewicz, S.: Future phytoplankton diversity in a changing climate, Nat. Commun., 12, 5372, https://doi.org/10.1038/s41467-021-25699-w, 2021.
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.
Hoareau, N., Portabella, M., Lin, W. M., Ballabrera-Poy, J., and Turiel, A.: Error Characterization of Sea Surface Salinity Products Using Triple Collocation Analysis, IEEE T. Geosci. Remote, 56, 5160–5168, https://doi.org/10.1109/Tgrs.2018.2810442, 2018.
Hoese, D., Raspaud, M., Lahtinen, P., Roberts, W., Lavergne, T., Bot, S., Ghiggi, G., Holl, G., BENR0, Finkensieper, S., Dybbroe, A., Zhang, X., Meraner, A., Itkin, M., Valentino, A. N., Ørum Rasmussen, L., Clementi, L., Valgur, M., Rykov, D., owenlittlejohns, storpipfugl, Savoie, M., Pinault, F., Hawkins, B., Raml, B., Couwenberg, B., and Shadchin, A.: pytroll/pyresample: Version 1.30.0 (v1.30.0), Zenodo [code], https://doi.org/10.5281/zenodo.13387415, 2024.
Karlson, B., Godhe, A., Cusack, C., and Bresnan, E.: Introduction to methods for quantitative phytoplankton analysis, Microscopic and molecular methods for quantitative phytoplankton analysis, 5, 1–20, 2010.
Kim, H., Crow, W., Li, X. J., Wagner, W., Hahn, S., and Lakshmi, V.: True global error maps for SMAP, SMOS, and ASCAT soil moisture data based on machine learning and triple collocation analysis, Remote Sens. Environ., 298, 113776, https://doi.org/10.1016/j.rse.2023.113776, 2023.
Kramer, S. J., Bolanos, L. M., Catlett, D., Chase, A. P., Behrenfeld, M. J., Boss, E. S., Crockford, E. T., Giovannoni, S. J., Graff, J. R., Haentjens, N., Karp-Boss, L., Peacock, E. E., Roesler, C. S., Sosik, H. M., and Siegel, D. A.: Toward a synthesis of phytoplankton communities composition methods for global-scale application, Limnol. Oceanogr.-Meth., https://doi.org/10.1002/lom3.10602, 2024.
Le Quéré, C., Harrison, S. P., Prentice, I. C., Buitenhuis, E. T., Aumont, O., Bopp, L., Claustre, H., Da Cunha, L. C., Geider, R., Giraud, X., Klaas, C., Kohfeld, K. E., Legendre, L., Manizza, M., Platt, T., Rivkin, R. B., Sathyendranath, S., Uitz, J., Watson, A. J., and Wolf-Gladrow, D.: Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models, Global Change Biol., 11, 2016–2040, https://doi.org/10.1111/j.1365-2486.2005.1004.x, 2005.
Li, X. L., Yang, Y., Ishizaka, J., and Li, X. F.: Global estimation of phytoplankton pigment concentrations from satellite data using a deep-learning-based model, Remote Sens. Environ., 294, 113628, https://doi.org/10.1016/j.rse.2023.113628,2023.
Liston, G. E. and Elder, K.: A meteorological distribution system for high-resolution terrestrial modeling (MicroMet), J. Hydrometeorol., 7, 217–234, https://doi.org/10.1175/Jhm486.1, 2006.
Liu, X. M. and Wang, M. H.: 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. M. and Wang, M. H.: Global daily gap-free ocean color products from multi-satellite measurements, Int. J. Appl. Earth Obs., 108, 10271410, https://doi.org/10.1016/j.jag.2022.102714, 2022.
Losa, S. N., Soppa, M. A., Dinter, T., Wolanin, A., Brewin, R. J. W., Bricaud, A., Oelker, J., Peeken, I., Gentili, B., Rozanov, V., and Bracher, A.: Synergistic Exploitation of Hyper- and Multi-Spectral Precursor Sentinel Measurements to Determine Phytoplankton Functional Types (SynSenPFT), Front. Mar. Sci., 4, 203, https://doi.org/10.3389/fmars.2017.00203, 2017a.
Losa, S. N. Soppa, M. A., Dinter, T., Wolanin, A., Oelker, J., and Bracher, A.: Global chlorophyll a surface concentrations for diatoms, coccolithophores and cyanobacteria as the synergistic SynSenPFT product combined PhytoDOAS and OC-PFT for the period of time August 2002–April 2012, links to NetCDF files, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.875873, 2017b.
Mackey, M. D., Mackey, D. J., Higgins, H. W., and Wright, S. W.: CHEMTAX – A program for estimating class abundances from chemical markers: Application to HPLC measurements of phytoplankton, Mar. Ecol. Prog. Ser., 144, 265–283, https://doi.org/10.3354/meps144265, 1996.
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. H.: Optimal satellite orbit configuration for global ocean color product coverage, Opt. Express, 27, A445–A457, https://doi.org/10.1364/Oe.27.00a445, 2019.
Mouw, C. B., Hardman-Mountford, N. J., Alvain, S., Bracher, A., Brewin, R. J. W., Bricaud, A., Ciotti, A. M., Devred, E., Fujiwara, A., Hirata, T., Hirawake, T., Kostadinov, T. S., Roy, S., and Uitz, J.: A Consumer's Guide to Satellite Remote Sensing of Multiple Phytoplankton Groups in the Global Ocean, Front. Mar. Sci., 4, 41, https://doi.org/10.3389/fmars.2017.00041, 2017.
Nair, A., Sathyendranath, S., Platt, T., Morales, J., Stuart, V., Forget, M. H., Devred, E., and Bouman, H.: Remote sensing of phytoplankton functional types, Remote Sens. Environ., 112, 3366–3375, https://doi.org/10.1016/j.rse.2008.01.021, 2008.
Raitsos, D. E., Lavender, S. J., Maravelias, C. D., Haralabous, J., Richardson, A. J., and Reid, P. C.: Identifying four phytoplankton functional types from space: An ecological approach, Limnol. Oceanogr., 53, 605–613, https://doi.org/10.4319/lo.2008.53.2.0605, 2008.
Sadeghi, A., Dinter, T., Vountas, M., Taylor, B., Altenburg-Soppa, M., and Bracher, A.: Remote sensing of coccolithophore blooms in selected oceanic regions using the PhytoDOAS method applied to hyper-spectral satellite data, Biogeosciences, 9, 2127–2143, https://doi.org/10.5194/bg-9-2127-2012, 2012.
Saleh, A. K. and Al-Anzi, B. S.: Statistical Validation of MODIS-Based Sea Surface Temperature in Shallow Semi-Enclosed Marginal Sea: A Comparison between Direct Matchup and Triple Collocation, Water-Sui, 13, 1078, https://doi.org/10.3390/w13081078, 2021.
Sathyendranath, S., Brewin, R. J. W., Brockmann, C., Brotas, V., Calton, B., Chuprin, A., Cipollini, P., Couto, A. B., 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. S., Müller, D., Regner, P., Roy, S., Steele, C. J., Steinmetz, F., Swinton, J., Taberner, M., Thompson, A., Valente, A., Zühlke, M., Brando, V. E., Feng, H., Feldman, G., Franz, B. A., Frouin, R., Gould, R. W., Hooker, S. B., Kahru, M., Kratzer, S., Mitchell, B. G., Muller-Karger, F. E., Sosik, H. M., Voss, K. J., 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-Basel, 19, 4285, https://doi.org/10.3390/s19194285, 2019.
Sun, X. R., Shen, F., Brewin, R. J. W., Li, M. Y., and Zhu, Q.: Light absorption spectra of naturally mixed phytoplankton assemblages for retrieval of phytoplankton group composition in coastal oceans, Limnol. Oceanogr., 67, 946–961, https://doi.org/10.1002/lno.12047, 2022.
Swan, C. M., Vogt, M., Gruber, N., and Laufkoetter, C.: A global seasonal surface ocean climatology of phytoplankton types based on CHEMTAX analysis of HPLC pigments, Deep-Sea Res. Pt. I, 109, 137–156, https://doi.org/10.1016/j.dsr.2015.12.002, 2016.
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.-Oceans, 111, C08005, https://doi.org/10.1029/2005jc003207, 2006.
Veldhuis, M. J. W. and Kraay, G. W.: Application of flow cytometry in marine phytoplankton research: current applications and future perspectives, Sci. Mar., 64, 121–134, https://doi.org/10.3989/scimar.2000.64n2121, 2000.
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.-Oceans, 106, 19939–19956, https://doi.org/10.1029/1999jc000308, 2001.
Wang, G. J., Garcia, D., Liu, Y., de Jeu, R., and Dolman, A. J.: A three-dimensional gap filling method for large geophysical datasets: Application to global satellite soil moisture observations, Environ. Modell. Softw., 30, 139–142, https://doi.org/10.1016/j.envsoft.2011.10.015, 2012.
Wang, T. H., Yu, P., Wu, Z. L., Lu, W. F., Liu, X., Li, Q. P., and Huang, B. Q.: Revisiting the Intraseasonal Variability of Chlorophyll a in the Adjacent Luzon Strait With a New Gap-Filled Remote Sensing Data Set, IEEE T. Geosci. Remote, 60, 4201311, https://doi.org/10.1109/Tgrs.2021.3067646, 2022.
Wei, J., Li, Z. Q., Lyapustin, A., Wang, J., Dubovik, O., Schwartz, J., Sun, L., Li, C., Liu, S., and Zhu, T.: First close insight into global daily gapless 1 km PM pollution, variability, and health impact, Nat. Commun., 14, 8349, https://doi.org/10.1038/s41467-023-43862-3, 2023.
Xi, H., Bretagnon, M., Losa, S. N., Brotas, V., Gomes, M., Peeken, I., Alvarado, L., Mangin, A., and Bracher, A.: Satellite monitoring of surface phytoplankton functional types in the Atlantic Ocean over 20 years (2002–2021), State of the Planet, 1, 1–13, 2023a.
Xi, H., Peeken, I., Gomes, M., Brotas, V., Tilstone, G. H., Brewin, R. J. W., Dall'Olmo, G., Tracana, A., Alvarado, L. M. A., Murawski, S., Wiegmann, S., and Bracher, A.: Phytoplankton pigment concentrations and phytoplankton groups measured on water samples collected from various expeditions in the Atlantic Ocean from 71° S to 84° N, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.954738, 2023b.
Xi, H. Y., Losa, S. N., Mangin, A., Soppa, M. A., Garnesson, P., Demaria, J., Liu, Y. Y., D'Andon, O. H. F., and Bracher, A.: Global retrieval of phytoplankton functional types based on empirical orthogonal functions using CMEMS GlobColour merged products and further extension to OLCI data, Remote Sens. Environ., 240, 111704, https://doi.org/10.1016/j.rse.2020.111704, 2020.
Xi, H. Y., Losa, S. N., Mangin, A., Garnesson, P., Bretagnon, M., Demaria, J., Soppa, M. A., D'Andon, O. H. F., and Bracher, A.: Global Chlorophyll a Concentrations of Phytoplankton Functional Types With Detailed Uncertainty Assessment Using Multisensor Ocean Color and Sea Surface Temperature Satellite Products, J. Geophys. Res.-Oceans, 126, e2020JC017127, https://doi.org/10.1029/2020JC017127, 2021.
Yang, K. X., Luo, Y. M., Li, M. Y., Zhong, S. Y., Liu, Q., and Li, X. H.: Reconstruction of Sentinel-2 Image Time Series Using Google Earth Engine, Remote Sens.-Basel, 14, 4395, https://doi.org/10.3390/rs14174395, 2022.
Yang, N. S., Shi, H. Z., Tang, H., and Yang, X.: Geographical and temporal encoding for improving the estimation of PM concentrations in China using end-to-end gradient boosting, Remote Sens. Environ., 269, 112828, https://doi.org/10.1016/j.rse.2021.112828, 2022.
Zhang, Y.: AIGD-PFT: The first AI-driven Global Daily gap-free 4 km Phytoplankton Functional Type products from 1998 to 2023, TIB AV-Portal [video], https://doi.org/10.5446/67366, 2024.
Zhang, Y. and Shen, F.: Global daily gap-free 4km phytoplankton functional types product from 1998 to 2023. National Tibetan Plateau / Third Pole Environment Data Center, [data set], https://doi.org/10.11888/RemoteSen.tpdc.301164, 2024a.
Zhang, Y. and Shen, F.: AIGD-PFT: The first AI-driven Global Daily gap-free 4 km Phytoplankton Functional Type products from 1998 to 2023, Zenodo [data set], https://doi.org/10.5281/zenodo.10910206, 2024b.
Zhang, Y., Shen, F., Sun, X. R., and Tan, K.: Marine big data-driven ensemble learning for estimating global phytoplankton group composition over two decades (1997–2020), Remote Sens. Environ., 294, 113596, https://doi.org/10.1016/j.rse.2023.113596, 2023.
Short summary
This work describes AIGD-PFT, the first AI-driven global daily gap-free 4 km phytoplankton functional type (PFT) product from 1998 to 2023. AIGD-PFT enhances the accuracy and spatiotemporal coverage quantification of eight major PFTs (i.e. diatoms, dinoflagellates, haptophytes, pelagophytes, cryptophytes, green algae, prokaryotes, and Prochlorococcus).
This work describes AIGD-PFT, the first AI-driven global daily gap-free 4 km phytoplankton...
Altmetrics
Final-revised paper
Preprint