Articles | Volume 15, issue 16
https://doi.org/10.5194/amt-15-4859-2022
© 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/amt-15-4859-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Aug 2022
Research article |
| 25 Aug 2022
| 25 Aug 2022
Effective uncertainty quantification for multi-angle polarimetric aerosol remote sensing over ocean
NASA Goddard Space Flight Center, Code 616, Greenbelt, MD 20771, USA
Science Systems and Applications, Inc., Greenbelt, MD 20706, USA
Kirk Knobelspiesse
NASA Goddard Space Flight Center, Code 616, Greenbelt, MD 20771, USA
Bryan A. Franz
NASA Goddard Space Flight Center, Code 616, Greenbelt, MD 20771, USA
Peng-Wang Zhai
JCET and Physics Department, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Andrew M. Sayer
NASA Goddard Space Flight Center, Code 616, Greenbelt, MD 20771, USA
Goddard Earth Sciences Technology and Research (GESTAR) II, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Amir Ibrahim
NASA Goddard Space Flight Center, Code 616, Greenbelt, MD 20771, USA
Brian Cairns
NASA Goddard Institute for Space Studies, New York, NY 10025, USA
Otto Hasekamp
Netherlands Institute for Space Research (SRON, NWO-I), Utrecht, the Netherlands
Yongxiang Hu
MS 475, NASA Langley Research Center, Hampton, VA 23681-2199, USA
Vanderlei Martins
JCET and Physics Department, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Goddard Earth Sciences Technology and Research (GESTAR) II, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
P. Jeremy Werdell
NASA Goddard Space Flight Center, Code 616, Greenbelt, MD 20771, USA
Xiaoguang Xu
JCET and Physics Department, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Goddard Earth Sciences Technology and Research (GESTAR) II, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
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Cited
11 citations as recorded by crossref.
- The impact and estimation of uncertainty correlation for multi-angle polarimetric remote sensing of aerosols and ocean color M. Gao et al. 10.5194/amt-16-2067-2023
- Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 2: Local optimization J. Loveridge et al. 10.5194/amt-16-3931-2023
- Simultaneous retrieval of aerosol and ocean properties from PACE HARP2 with uncertainty assessment using cascading neural network radiative transfer models M. Gao et al. 10.5194/amt-16-5863-2023
- Machine learning based aerosol and ocean color joint retrieval algorithm for multiangle polarimeters over coastal waters K. Aryal et al. 10.1364/OE.522794
- Simulating skylight polarization patterns using the backward Markov Chain Monte Carlo method S. Li et al. 10.1016/j.ascom.2023.100772
- Determining the primary sources of uncertainty in the retrieval of marine remote sensing reflectance from satellite ocean color sensors II. Sentinel 3 OLCI sensors A. Gilerson et al. 10.3389/frsen.2023.1146110
- Biomass Burning Plume from Simultaneous Observations of Polarization and Radiance at Different Viewing Directions with SGLI S. Mukai et al. 10.3390/rs15225405
- Retrievals of aerosol optical depth over the western North Atlantic Ocean during ACTIVATE L. Siu et al. 10.5194/amt-17-2739-2024
- Uncertainty of atmospheric scattering functions relevant for Satellite Ocean Colour Radiometry in European Seas F. Zagolski & C. Mazeran 10.1080/01431161.2023.2247531
- Impact of aerosols on the polarization patterns of full-sky background radiation S. Li et al. 10.1364/OE.492041
- Algorithm evaluation for polarimetric remote sensing of atmospheric aerosols O. Hasekamp et al. 10.5194/amt-17-1497-2024
11 citations as recorded by crossref.
- The impact and estimation of uncertainty correlation for multi-angle polarimetric remote sensing of aerosols and ocean color M. Gao et al. 10.5194/amt-16-2067-2023
- Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 2: Local optimization J. Loveridge et al. 10.5194/amt-16-3931-2023
- Simultaneous retrieval of aerosol and ocean properties from PACE HARP2 with uncertainty assessment using cascading neural network radiative transfer models M. Gao et al. 10.5194/amt-16-5863-2023
- Machine learning based aerosol and ocean color joint retrieval algorithm for multiangle polarimeters over coastal waters K. Aryal et al. 10.1364/OE.522794
- Simulating skylight polarization patterns using the backward Markov Chain Monte Carlo method S. Li et al. 10.1016/j.ascom.2023.100772
- Determining the primary sources of uncertainty in the retrieval of marine remote sensing reflectance from satellite ocean color sensors II. Sentinel 3 OLCI sensors A. Gilerson et al. 10.3389/frsen.2023.1146110
- Biomass Burning Plume from Simultaneous Observations of Polarization and Radiance at Different Viewing Directions with SGLI S. Mukai et al. 10.3390/rs15225405
- Retrievals of aerosol optical depth over the western North Atlantic Ocean during ACTIVATE L. Siu et al. 10.5194/amt-17-2739-2024
- Uncertainty of atmospheric scattering functions relevant for Satellite Ocean Colour Radiometry in European Seas F. Zagolski & C. Mazeran 10.1080/01431161.2023.2247531
- Impact of aerosols on the polarization patterns of full-sky background radiation S. Li et al. 10.1364/OE.492041
- Algorithm evaluation for polarimetric remote sensing of atmospheric aerosols O. Hasekamp et al. 10.5194/amt-17-1497-2024
Latest update: 20 Nov 2024
Short summary
In this work, we assessed the pixel-wise retrieval uncertainties on aerosol and ocean color derived from multi-angle polarimetric measurements. Standard error propagation methods are used to compute the uncertainties. A flexible framework is proposed to evaluate how representative these uncertainties are compared with real retrieval errors. Meanwhile, to assist operational data processing, we optimized the computational speed to evaluate the retrieval uncertainties based on neural networks.
In this work, we assessed the pixel-wise retrieval uncertainties on aerosol and ocean color...
