Articles | Volume 10, issue 11
https://doi.org/10.5194/amt-10-4235-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-10-4235-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
08 Nov 2017
Research article |
| 08 Nov 2017
Combined neural network/Phillips–Tikhonov approach to aerosol retrievals over land from the NASA Research Scanning Polarimeter
SRON Netherlands Institute for Space Research, Sorbonnelaan 2, 3584CA Utrecht, the Netherlands
Otto P. Hasekamp
SRON Netherlands Institute for Space Research, Sorbonnelaan 2, 3584CA Utrecht, the Netherlands
Lianghai Wu
SRON Netherlands Institute for Space Research, Sorbonnelaan 2, 3584CA Utrecht, the Netherlands
Bastiaan van Diedenhoven
Columbia University, Center for Climate Systems Research, 2910 Broadway, New York, NY 10025, USA
NASA Goddard Institute for Space Studies, 2880 Broadway, New York, NY 10025, USA
Brian Cairns
NASA Goddard Institute for Space Studies, 2880 Broadway, New York, NY 10025, USA
John E. Yorks
NASA Goddard Space Flight Center, 8800 Greenbelt Rd, Greenbelt, MD 20771, USA
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- Polarimetric remote sensing of atmospheric aerosols: Instruments, methodologies, results, and perspectives O. Dubovik et al. 10.1016/j.jqsrt.2018.11.024
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- Identification of new particle formation events with deep learning J. Joutsensaari et al. 10.5194/acp-18-9597-2018
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- A review of advances in the retrieval of aerosol properties by remote sensing multi-angle technology Y. Si et al. 10.1016/j.atmosenv.2020.117928
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- Development of neural network retrievals of liquid cloud properties from multi-angle polarimetric observations M. Segal-Rozenhaimer et al. 10.1016/j.jqsrt.2018.08.030
- Intercomparison of airborne multi-angle polarimeter observations from the Polarimeter Definition Experiment K. Knobelspiesse et al. 10.1364/AO.58.000650
- Deep-learning-based post-process correction of the aerosol parameters in the high-resolution Sentinel-3 Level-2 Synergy product A. Lipponen et al. 10.5194/amt-15-895-2022
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29 citations as recorded by crossref.
- Algorithm evaluation for polarimetric remote sensing of atmospheric aerosols O. Hasekamp et al. 10.5194/amt-17-1497-2024
- Model-enforced post-process correction of satellite aerosol retrievals A. Lipponen et al. 10.5194/amt-14-2981-2021
- Improving Aerosol Retrieval From MISR With a Physics-Informed Deep Learning Method W. Man et al. 10.1109/TGRS.2024.3376598
- Retrieving Aerosol Characteristics From the PACE Mission, Part 2: Multi-Angle and Polarimetry L. Remer et al. 10.3389/fenvs.2019.00094
- An algorithm for hyperspectral remote sensing of aerosols: 3. Application to the GEO-TASO data in KORUS-AQ field campaign W. Hou et al. 10.1016/j.jqsrt.2020.107161
- Aerosol retrieval study from multiangle polarimetric satellite data based on optimal estimation method F. Zheng et al. 10.1117/1.JRS.14.014516
- A neural network approach to the estimation of in-water attenuation to absorption ratios from PACE mission measurements J. Agagliate et al. 10.3389/frsen.2023.1060908
- An Overview of Neural Network Methods for Predicting Uncertainty in Atmospheric Remote Sensing A. Doicu et al. 10.3390/rs13245061
- First lunar-light mapping of nighttime dust season oceanic aerosol optical depth over North Atlantic from space M. Zhou et al. 10.1016/j.rse.2024.114315
- Polarimetric remote sensing of atmospheric aerosols: Instruments, methodologies, results, and perspectives O. Dubovik et al. 10.1016/j.jqsrt.2018.11.024
- Efficient multi-angle polarimetric inversion of aerosols and ocean color powered by a deep neural network forward model M. Gao et al. 10.5194/amt-14-4083-2021
- Retrieval of aerosol properties from in situ, multi-angle light scattering measurements using invertible neural networks R. Boiger et al. 10.1016/j.jaerosci.2022.105977
- Identification of new particle formation events with deep learning J. Joutsensaari et al. 10.5194/acp-18-9597-2018
- Neural network for aerosol retrieval from hyperspectral imagery S. Mauceri et al. 10.5194/amt-12-6017-2019
- Aerosol Parameters Retrieval From TROPOMI/S5P Using Physics-Based Neural Networks L. Rao et al. 10.1109/JSTARS.2022.3196843
- Retrieval of aerosol microphysical and optical properties over land using a multimode approach G. Fu & O. Hasekamp 10.5194/amt-11-6627-2018
- Aerosol retrievals from different polarimeters during the ACEPOL campaign using a common retrieval algorithm G. Fu et al. 10.5194/amt-13-553-2020
- Satellite remote sensing of atmospheric particulate matter mass concentration: Advances, challenges, and perspectives Y. Zhang et al. 10.1016/j.fmre.2021.04.007
- Retrievals of aerosol optical depth over the western North Atlantic Ocean during ACTIVATE L. Siu et al. 10.5194/amt-17-2739-2024
- Polarization Lidar: Principles and Applications X. Liu et al. 10.3390/photonics10101118
- Advantages of Measuring the Q Stokes Parameter in Addition to the Total Radiance I in the Detection of Absorbing Aerosols S. Stamnes et al. 10.3389/feart.2018.00034
- Retrieval of aerosol optical thickness and surface parameters based on multi-spectral and multi-viewing space-borne measurements M. Vountas et al. 10.1016/j.jqsrt.2020.107311
- A review of advances in the retrieval of aerosol properties by remote sensing multi-angle technology Y. Si et al. 10.1016/j.atmosenv.2020.117928
- Retrieval of liquid water cloud properties from POLDER-3 measurements using a neural network ensemble approach A. Di Noia et al. 10.5194/amt-12-1697-2019
- Cloud detection from multi-angular polarimetric satellite measurements using a neural network ensemble approach Z. Yuan et al. 10.5194/amt-17-2595-2024
- Development of neural network retrievals of liquid cloud properties from multi-angle polarimetric observations M. Segal-Rozenhaimer et al. 10.1016/j.jqsrt.2018.08.030
- Intercomparison of airborne multi-angle polarimeter observations from the Polarimeter Definition Experiment K. Knobelspiesse et al. 10.1364/AO.58.000650
- Deep-learning-based post-process correction of the aerosol parameters in the high-resolution Sentinel-3 Level-2 Synergy product A. Lipponen et al. 10.5194/amt-15-895-2022
- The retrieval of aerosol optical properties based on a random forest machine learning approach: Exploration of geostationary satellite images F. Bao et al. 10.1016/j.rse.2022.113426
Saved (final revised paper)
Latest update: 05 Nov 2024
Short summary
In this paper an algorithm for the retrieval of aerosol properties from NASA Research Scanning Polarimeter (RSP) data is presented. An artificial neural network is used to produce a first estimate of the aerosol properties, which is then improved using an iterative retrieval scheme based on Phillips–Tikhonov regularization. Using the neural network retrievals as a first guess for the Phillips–Tikhonov improved the retrieval convergence, confirming results previously found on ground-based data.
In this paper an algorithm for the retrieval of aerosol properties from NASA Research Scanning...
