Articles | Volume 12, issue 2
https://doi.org/10.5194/essd-12-1123-2020
© Author(s) 2020. 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-12-1123-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
19 May 2020
Data description paper |
| 19 May 2020
| 19 May 2020
A global compilation of in situ aquatic high spectral resolution inherent and apparent optical property data for remote sensing applications
Kimberly A. Casey
CORRESPONDING AUTHOR
Earth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
U.S. Geological Survey, Reston, VA 20192, USA
Cecile S. Rousseaux
Earth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Global Modeling and Assimilation Office, NASA Goddard Space Flight
Center, Greenbelt, MD 20771, USA
Universities Space Research Association, Columbia, MD 20771, USA
Watson W. Gregg
Earth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Global Modeling and Assimilation Office, NASA Goddard Space Flight
Center, Greenbelt, MD 20771, USA
Emmanuel Boss
School of Marine Sciences, University of Maine, Orono, ME 04469, USA
Alison P. Chase
School of Marine Sciences, University of Maine, Orono, ME 04469, USA
Susanne E. Craig
Universities Space Research Association, Columbia, MD 20771, USA
Ocean Ecology Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Colleen B. Mouw
Graduate School of Oceanography, University of Rhode Island,
Narragansett, RI 02882, USA
Rick A. Reynolds
Marine Physical Laboratory, Scripps Institution of Oceanography,
University of California San Diego, La Jolla, CA 92093, USA
Dariusz Stramski
Marine Physical Laboratory, Scripps Institution of Oceanography,
University of California San Diego, La Jolla, CA 92093, USA
Steven G. Ackleson
Naval Research Laboratory, Washington, DC 20375, USA
Annick Bricaud
CNRS and Sorbonne Université, Laboratoire d'Océanographie de Villefranche (LOV), 06230 Villefranche-sur-mer, France
Blake Schaeffer
Office of Research and Development, U.S. Environmental Protection
Agency, Research Triangle Park, NC 27711, USA
Marlon R. Lewis
Department of Oceanography, Dalhousie University, Halifax, Nova
Scotia, Canada
Stéphane Maritorena
Earth Research Institute, University of California, Santa Barbara,
CA 93106, USA
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Cited
14 citations as recorded by crossref.
- Phytoplankton Light Absorption Impacted by Photoprotective Carotenoids in a Global Ocean Spectrally‐Resolved Biogeochemistry Model E. Álvarez et al. 10.1029/2022MS003126
- Holistic optical water type classification for ocean, coastal, and inland waters S. Bi & M. Hieronymi 10.1002/lno.12606
- Preliminary analysis on the characteristics of light absorption coefficients in typical rivers of different river basins across China Y. Zhang et al. 10.1364/OE.515555
- Satellite Remote Sensing of Herring (Clupea pallasii) Spawning Events: A Case Study in the Strait of Georgia L. Qi et al. 10.1029/2020GL092126
- Living up to the Hype of Hyperspectral Aquatic Remote Sensing: Science, Resources and Outlook H. Dierssen et al. 10.3389/fenvs.2021.649528
- QWIP: A Quantitative Metric for Quality Control of Aquatic Reflectance Spectral Shape Using the Apparent Visible Wavelength H. Dierssen et al. 10.3389/frsen.2022.869611
- Full mission evaluation of EnMAP water leaving reflectance products using three atmospheric correction processors M. Soppa et al. 10.1364/OE.523813
- A three-step semi analytical algorithm (3SAA) for estimating inherent optical properties over oceanic, coastal, and inland waters from remote sensing reflectance D. Jorge et al. 10.1016/j.rse.2021.112537
- Global distribution of the spectral power coefficient of particulate backscattering coefficient obtained by a neural network scheme X. Yu et al. 10.1016/j.rse.2023.113750
- Modeling surface ocean phytoplankton pigments from hyperspectral remote sensing reflectance on global scales S. Kramer et al. 10.1016/j.rse.2021.112879
- A synthetic optical database generated by radiative transfer simulations in support of studies in ocean optics and optical remote sensing of the global ocean H. Loisel et al. 10.5194/essd-15-3711-2023
- The QAA-RGB: A universal three-band absorption and backscattering retrieval algorithm for high resolution satellite sensors. Development and implementation in ACOLITE J. Pitarch & Q. Vanhellemont 10.1016/j.rse.2021.112667
- Retrieval of Phytoplankton Pigment Composition from Their In Vivo Absorption Spectra Y. Zhang et al. 10.3390/rs13245112
- Phytoplankton composition from sPACE: Requirements, opportunities, and challenges I. Cetinić et al. 10.1016/j.rse.2023.113964
14 citations as recorded by crossref.
- Phytoplankton Light Absorption Impacted by Photoprotective Carotenoids in a Global Ocean Spectrally‐Resolved Biogeochemistry Model E. Álvarez et al. 10.1029/2022MS003126
- Holistic optical water type classification for ocean, coastal, and inland waters S. Bi & M. Hieronymi 10.1002/lno.12606
- Preliminary analysis on the characteristics of light absorption coefficients in typical rivers of different river basins across China Y. Zhang et al. 10.1364/OE.515555
- Satellite Remote Sensing of Herring (Clupea pallasii) Spawning Events: A Case Study in the Strait of Georgia L. Qi et al. 10.1029/2020GL092126
- Living up to the Hype of Hyperspectral Aquatic Remote Sensing: Science, Resources and Outlook H. Dierssen et al. 10.3389/fenvs.2021.649528
- QWIP: A Quantitative Metric for Quality Control of Aquatic Reflectance Spectral Shape Using the Apparent Visible Wavelength H. Dierssen et al. 10.3389/frsen.2022.869611
- Full mission evaluation of EnMAP water leaving reflectance products using three atmospheric correction processors M. Soppa et al. 10.1364/OE.523813
- A three-step semi analytical algorithm (3SAA) for estimating inherent optical properties over oceanic, coastal, and inland waters from remote sensing reflectance D. Jorge et al. 10.1016/j.rse.2021.112537
- Global distribution of the spectral power coefficient of particulate backscattering coefficient obtained by a neural network scheme X. Yu et al. 10.1016/j.rse.2023.113750
- Modeling surface ocean phytoplankton pigments from hyperspectral remote sensing reflectance on global scales S. Kramer et al. 10.1016/j.rse.2021.112879
- A synthetic optical database generated by radiative transfer simulations in support of studies in ocean optics and optical remote sensing of the global ocean H. Loisel et al. 10.5194/essd-15-3711-2023
- The QAA-RGB: A universal three-band absorption and backscattering retrieval algorithm for high resolution satellite sensors. Development and implementation in ACOLITE J. Pitarch & Q. Vanhellemont 10.1016/j.rse.2021.112667
- Retrieval of Phytoplankton Pigment Composition from Their In Vivo Absorption Spectra Y. Zhang et al. 10.3390/rs13245112
- Phytoplankton composition from sPACE: Requirements, opportunities, and challenges I. Cetinić et al. 10.1016/j.rse.2023.113964
Latest update: 22 Nov 2024
Short summary
An increase in spectral resolution in forthcoming remote-sensing missions will improve our ability to understand and characterize aquatic ecosystems. We organize and provide a global compilation of high spectral resolution inherent and apparent optical property data from polar, midlatitude, and equatorial open-ocean, estuary, coastal, and inland waters. The data are intended to aid in development of remote-sensing data product algorithms and to perform calibration and validation activities.
An increase in spectral resolution in forthcoming remote-sensing missions will improve our...
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