Articles | Volume 15, issue 8
https://doi.org/10.5194/essd-15-3711-2023
© 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-15-3711-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
18 Aug 2023
Data description article |
| 18 Aug 2023
| 18 Aug 2023
A synthetic optical database generated by radiative transfer simulations in support of studies in ocean optics and optical remote sensing of the global ocean
Hubert Loisel
CORRESPONDING AUTHOR
Laboratoire d'Océanologie et de Géosciences, Université du Littoral-Côte-d'Opale, Université Lille, CNRS, IRD, UMR 8187, LOG,
32 avenue Foch, Wimereux, France
Daniel Schaffer Ferreira Jorge
Laboratoire d'Océanologie et de Géosciences, Université du Littoral-Côte-d'Opale, Université Lille, CNRS, IRD, UMR 8187, LOG,
32 avenue Foch, Wimereux, France
Rick A. Reynolds
Marine Physical Laboratory, Scripps Institution of Oceanography,
University of California San Diego, La Jolla, California 92093-0238, USA
Dariusz Stramski
Marine Physical Laboratory, Scripps Institution of Oceanography,
University of California San Diego, La Jolla, California 92093-0238, USA
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Cited
19 citations as recorded by crossref.
- On the fundamental additive modes of ocean color absorption J. Prochaska & P. Gray https://doi.org/10.1002/lno.70098
- Global estimation of phytoplankton functional types using PACE OCI hyperspectral data and a novel deep ensemble learning approach Y. Zhang et al. https://doi.org/10.1016/j.jag.2026.105225
- A hyperspectral approach for retrieving inherent optical properties, phytoplankton pigments, and associated uncertainties from non-water absorption B. Grunert et al. https://doi.org/10.3389/fmars.2025.1549312
- A bio-optical database for the remote sensing of water quality in BRAZil coAstal and inland waters (BRAZA) D. Maciel et al. https://doi.org/10.1038/s41597-025-05609-1
- Modeling underwater photosynthetically available radiation profiles from biogeochemical Argo floats using multi-spectral irradiance measurements J. Tan et al. https://doi.org/10.1364/OE.566083
- Advancing remote sensing of fluorescent dissolved organic matter by removing multi-source interferences in remote sensing reflectance: evidence from the Mackenzie delta R. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.134746
- Analytical modeling and correction of the ocean colour bidirectional reflectance across water types J. Pitarch et al. https://doi.org/10.1016/j.rse.2025.114920
- Integrating In Situ Measurements and Satellite Imagery for Coastal Physical and Biological Analysis in the Cape Fear Coastal Region M. Torkelson et al. https://doi.org/10.3390/rs18101524
- A physics-based framework for remote sensing inversion of fluorescent dissolved organic matter: incorporating fluorescence as an inelastic source term R. Zhang et al. https://doi.org/10.1016/j.watres.2026.125757
- A semi‐analytical Bayesian estimate retrieval algorithm for the inversion of remote‐sensing reflectance in optically deep and shallow waters S. Mukherjee et al. https://doi.org/10.1002/lom3.70004
- Assessment of OLCI absorption coefficients for non-water components across all optical water classes A. Bracher et al. https://doi.org/10.3389/frsen.2025.1545664
- A hyperspectral and multi-angular synthetic dataset for algorithm development in waters of varying trophic levels and optical complexity J. Pitarch & V. Brando https://doi.org/10.5194/essd-17-435-2025
- On the generalization ability of probabilistic neural networks for hyperspectral remote sensing of absorption properties across optically complex waters M. Werther et al. https://doi.org/10.1016/j.rse.2025.114820
- SpectraGeni: A deep learning convolutional conditional variational autoencoder for synthetic hyperspectral data generation from highly imbalanced spectral measurements M. Kaushik et al. https://doi.org/10.1016/j.asoc.2026.115578
- On the challenges of retrieving phytoplankton properties from remote-sensing observations J. Prochaska & R. Frouin https://doi.org/10.5194/bg-22-4705-2025
- Performance Evaluation of Inherent Optical Property Algorithms and Identification of Potential Water Quality Indicators Using GCOM-C Data in Eutrophic Lake Kasumigaura, Japan M. Choto et al. https://doi.org/10.3390/rs17091621
- Machine learning based aerosol and ocean color joint retrieval algorithm for multiangle polarimeters over coastal waters K. Aryal et al. https://doi.org/10.1364/OE.522794
- Remote sensing monitoring of fluorescent dissolved organic matter in Admiralty Bay: fusion of multi-source signal removal and machine learning R. Zhang et al. https://doi.org/10.1016/j.srs.2025.100260
- Gradient Boosting for the Spectral Super-Resolution of Ocean Color Sensor Data B. Slocum et al. https://doi.org/10.3390/s25206389
19 citations as recorded by crossref.
- On the fundamental additive modes of ocean color absorption J. Prochaska & P. Gray https://doi.org/10.1002/lno.70098
- Global estimation of phytoplankton functional types using PACE OCI hyperspectral data and a novel deep ensemble learning approach Y. Zhang et al. https://doi.org/10.1016/j.jag.2026.105225
- A hyperspectral approach for retrieving inherent optical properties, phytoplankton pigments, and associated uncertainties from non-water absorption B. Grunert et al. https://doi.org/10.3389/fmars.2025.1549312
- A bio-optical database for the remote sensing of water quality in BRAZil coAstal and inland waters (BRAZA) D. Maciel et al. https://doi.org/10.1038/s41597-025-05609-1
- Modeling underwater photosynthetically available radiation profiles from biogeochemical Argo floats using multi-spectral irradiance measurements J. Tan et al. https://doi.org/10.1364/OE.566083
- Advancing remote sensing of fluorescent dissolved organic matter by removing multi-source interferences in remote sensing reflectance: evidence from the Mackenzie delta R. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.134746
- Analytical modeling and correction of the ocean colour bidirectional reflectance across water types J. Pitarch et al. https://doi.org/10.1016/j.rse.2025.114920
- Integrating In Situ Measurements and Satellite Imagery for Coastal Physical and Biological Analysis in the Cape Fear Coastal Region M. Torkelson et al. https://doi.org/10.3390/rs18101524
- A physics-based framework for remote sensing inversion of fluorescent dissolved organic matter: incorporating fluorescence as an inelastic source term R. Zhang et al. https://doi.org/10.1016/j.watres.2026.125757
- A semi‐analytical Bayesian estimate retrieval algorithm for the inversion of remote‐sensing reflectance in optically deep and shallow waters S. Mukherjee et al. https://doi.org/10.1002/lom3.70004
- Assessment of OLCI absorption coefficients for non-water components across all optical water classes A. Bracher et al. https://doi.org/10.3389/frsen.2025.1545664
- A hyperspectral and multi-angular synthetic dataset for algorithm development in waters of varying trophic levels and optical complexity J. Pitarch & V. Brando https://doi.org/10.5194/essd-17-435-2025
- On the generalization ability of probabilistic neural networks for hyperspectral remote sensing of absorption properties across optically complex waters M. Werther et al. https://doi.org/10.1016/j.rse.2025.114820
- SpectraGeni: A deep learning convolutional conditional variational autoencoder for synthetic hyperspectral data generation from highly imbalanced spectral measurements M. Kaushik et al. https://doi.org/10.1016/j.asoc.2026.115578
- On the challenges of retrieving phytoplankton properties from remote-sensing observations J. Prochaska & R. Frouin https://doi.org/10.5194/bg-22-4705-2025
- Performance Evaluation of Inherent Optical Property Algorithms and Identification of Potential Water Quality Indicators Using GCOM-C Data in Eutrophic Lake Kasumigaura, Japan M. Choto et al. https://doi.org/10.3390/rs17091621
- Machine learning based aerosol and ocean color joint retrieval algorithm for multiangle polarimeters over coastal waters K. Aryal et al. https://doi.org/10.1364/OE.522794
- Remote sensing monitoring of fluorescent dissolved organic matter in Admiralty Bay: fusion of multi-source signal removal and machine learning R. Zhang et al. https://doi.org/10.1016/j.srs.2025.100260
- Gradient Boosting for the Spectral Super-Resolution of Ocean Color Sensor Data B. Slocum et al. https://doi.org/10.3390/s25206389
Saved (final revised paper)
Latest update: 07 Jun 2026
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.
Studies of light fields in aquatic environments require data from radiative transfer simulations...
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