Articles | Volume 14, issue 11
https://doi.org/10.5194/essd-14-4935-2022
© Author(s) 2022. 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-14-4935-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The HYPERMAQ dataset: bio-optical properties of moderately to extremely turbid waters
Héloïse Lavigne
CORRESPONDING AUTHOR
Royal Belgian Institute of Natural Sciences, Brussels, Belgium
Ana Dogliotti
Instistuto de Astronomía y FísicadelEspacio (IAFE),
CONICET-Universidad de Buenos Aires, Buenos Aires, Argentina
David Doxaran
Laboratoire d'Océanographie de Villefranche, UMR7093 Sorbonne
Université /CNRS, Villefranche-sur-Mer, France
Fang Shen
State Key Laboratory of Estuarine and Coastal Research (SKLEC),
East China Normal University, Shanghai, China
Alexandre Castagna
Laboratory of Protistology and Aquatic Ecology, Ghent University,
Ghent, Belgium
Matthew Beck
Royal Belgian Institute of Natural Sciences, Brussels, Belgium
Quinten Vanhellemont
Royal Belgian Institute of Natural Sciences, Brussels, Belgium
Xuerong Sun
State Key Laboratory of Estuarine and Coastal Research (SKLEC),
East China Normal University, Shanghai, China
Juan Ignacio Gossn
Instistuto de Astronomía y FísicadelEspacio (IAFE),
CONICET-Universidad de Buenos Aires, Buenos Aires, Argentina
European Organisation for the Exploitation of Meteorological
Satellites (EUMETSAT), Darmstadt, Germany
Pannimpullath Remanan Renosh
Laboratoire d'Océanographie de Villefranche, UMR7093 Sorbonne
Université /CNRS, Villefranche-sur-Mer, France
Koen Sabbe
Laboratory of Protistology and Aquatic Ecology, Ghent University,
Ghent, Belgium
Dieter Vansteenwegen
Flanders Marine Institute (VLIZ), Ostend, Belgium
Kevin Ruddick
Royal Belgian Institute of Natural Sciences, Brussels, Belgium
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Cited
7 citations as recorded by crossref.
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- Bio-geo-optical modelling of natural waters S. Bi et al. 10.3389/fmars.2023.1196352
- Chromaticity-Based Discrimination of Algal Bloom from Inland and Coastal Waters Using In Situ Hyperspectral Remote Sensing Reflectance D. Zhao et al. 10.3390/w16162276
- Towards global long-term water transparency products from the Landsat archive D. Maciel et al. 10.1016/j.rse.2023.113889
- An improved hyperspectral sensing approach for the rapid determination of copper ion concentrations in water environment using short-wavelength infrared spectroscopy C. Huang et al. 10.1016/j.envpol.2023.121984
- Mixture density networks for re-constructing historical ocean-color products over inland and coastal waters: demonstration and validation S. Balasubramanian et al. 10.3389/frsen.2025.1488565
- The HYPERMAQ dataset: bio-optical properties of moderately to extremely turbid waters H. Lavigne et al. 10.5194/essd-14-4935-2022
6 citations as recorded by crossref.
- Holistic optical water type classification for ocean, coastal, and inland waters S. Bi & M. Hieronymi 10.1002/lno.12606
- Bio-geo-optical modelling of natural waters S. Bi et al. 10.3389/fmars.2023.1196352
- Chromaticity-Based Discrimination of Algal Bloom from Inland and Coastal Waters Using In Situ Hyperspectral Remote Sensing Reflectance D. Zhao et al. 10.3390/w16162276
- Towards global long-term water transparency products from the Landsat archive D. Maciel et al. 10.1016/j.rse.2023.113889
- An improved hyperspectral sensing approach for the rapid determination of copper ion concentrations in water environment using short-wavelength infrared spectroscopy C. Huang et al. 10.1016/j.envpol.2023.121984
- Mixture density networks for re-constructing historical ocean-color products over inland and coastal waters: demonstration and validation S. Balasubramanian et al. 10.3389/frsen.2025.1488565
1 citations as recorded by crossref.
Latest update: 13 Mar 2025
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.
Because of the large diversity of case 2 waters and the complexity of light transfer, retrieving...
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