Articles | Volume 14, issue 6
https://doi.org/10.5194/amt-14-4083-2021
© Author(s) 2021. 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-14-4083-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Jun 2021
Research article |
| 04 Jun 2021
| 04 Jun 2021
Efficient multi-angle polarimetric inversion of aerosols and ocean color powered by a deep neural network forward model
Ocean Ecology Laboratory – Code 616, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA
Science Systems and Applications, Inc., Greenbelt, MD, USA
Bryan A. Franz
Ocean Ecology Laboratory – Code 616, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA
Kirk Knobelspiesse
Ocean Ecology Laboratory – Code 616, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA
Peng-Wang Zhai
JCET and Physics Department, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Vanderlei Martins
JCET and Physics Department, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Sharon Burton
MS 475, NASA Langley Research Center, Hampton, VA 23681-2199, USA
Brian Cairns
NASA Goddard Institute for Space Studies, New York, NY 10025, USA
Richard Ferrare
MS 475, NASA Langley Research Center, Hampton, VA 23681-2199, USA
Joel Gales
Ocean Ecology Laboratory – Code 616, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA
Science Applications International Corp., Greenbelt, MD, 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
Amir Ibrahim
Ocean Ecology Laboratory – Code 616, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA
Science Systems and Applications, Inc., Greenbelt, MD, USA
Brent McBride
JCET and Physics Department, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Science Systems and Applications, Inc., Greenbelt, MD, USA
Anin Puthukkudy
JCET and Physics Department, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
P. Jeremy Werdell
Ocean Ecology Laboratory – Code 616, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA
Xiaoguang Xu
JCET and Physics Department, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
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- Assessment of vegetation conservation status in plateau areas based on multi‐view and difference identification X. Song et al. 10.1002/cpe.7223
- 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
- Effective uncertainty quantification for multi-angle polarimetric aerosol remote sensing over ocean M. Gao et al. 10.5194/amt-15-4859-2022
- A deep learning approach to fast radiative transfer P. Stegmann et al. 10.1016/j.jqsrt.2022.108088
- Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 1: Model description and Jacobian calculation J. Loveridge et al. 10.5194/amt-16-1803-2023
- Long‐Term Trends in Chesapeake Bay Remote Sensing Reflectance: Implications for Water Clarity J. Turner et al. 10.1029/2021JC017959
- Algorithm evaluation for polarimetric remote sensing of atmospheric aerosols O. Hasekamp et al. 10.5194/amt-17-1497-2024
- The PACE-MAPP algorithm: Simultaneous aerosol and ocean polarimeter products using coupled atmosphere-ocean vector radiative transfer S. Stamnes et al. 10.3389/frsen.2023.1174672
- 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
- Adaptive Data Screening for Multi-Angle Polarimetric Aerosol and Ocean Color Remote Sensing Accelerated by Deep Learning M. Gao et al. 10.3389/frsen.2021.757832
- Jacobians of single-scattering optical properties of super-spheroids computed using neural networks J. Yu et al. 10.1364/OE.471821
- Intelligent Atmospheric Correction Algorithm for Polarization Ocean Color Satellite Measurements Over the Open Ocean X. He et al. 10.1109/TGRS.2023.3348159
- UVBoost: An erythemal weighted ultraviolet radiation estimator based on a machine learning gradient boosting algorithm M. Corrêa 10.1016/j.jqsrt.2023.108490
- Optimizing retrieval spaces of bio-optical models for remote sensing of ocean color N. Hannadige et al. 10.1364/AO.484082
- Opinion: Aerosol remote sensing over the next 20 years L. Remer et al. 10.5194/acp-24-2113-2024
- Performance evaluation of three bio-optical models in aerosol and ocean color joint retrievals N. Hannadige et al. 10.5194/amt-16-5749-2023
- An Overview of Neural Network Methods for Predicting Uncertainty in Atmospheric Remote Sensing A. Doicu et al. 10.3390/rs13245061
- An Artificial Neural Network Algorithm to Retrieve Chlorophyll a for Northwest European Shelf Seas from Top of Atmosphere Ocean Colour Reflectance M. Hadjal et al. 10.3390/rs14143353
- Radiative Transfer Speed-Up Combining Optimal Spectral Sampling With a Machine Learning Approach S. Mauceri et al. 10.3389/frsen.2022.932548
- Instantaneous photosynthetically available radiation models for ocean waters using neural networks K. Aryal et al. 10.1364/AO.474914
- Optimal estimation framework for ocean color atmospheric correction and pixel-level uncertainty quantification A. Ibrahim et al. 10.1364/AO.461861
- Improving Aerosol Retrieval From MISR With a Physics-Informed Deep Learning Method W. Man et al. 10.1109/TGRS.2024.3376598
- 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
- Retrieval of Aerosol Optical Depth and FMF over East Asia from Directional Intensity and Polarization Measurements of PARASOL S. Wang et al. 10.3390/atmos15010006
- A robust and flexible satellite aerosol retrieval algorithm for multi-angle polarimetric measurements with physics-informed deep learning method M. Tao et al. 10.1016/j.rse.2023.113763
- 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
28 citations as recorded by crossref.
- Unifying radiative transfer models in computer graphics and remote sensing, Part I: A survey K. Salesin et al. 10.1016/j.jqsrt.2023.108847
- Aerosol Parameters Retrieval From TROPOMI/S5P Using Physics-Based Neural Networks L. Rao et al. 10.1109/JSTARS.2022.3196843
- Assessment of vegetation conservation status in plateau areas based on multi‐view and difference identification X. Song et al. 10.1002/cpe.7223
- 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
- Effective uncertainty quantification for multi-angle polarimetric aerosol remote sensing over ocean M. Gao et al. 10.5194/amt-15-4859-2022
- A deep learning approach to fast radiative transfer P. Stegmann et al. 10.1016/j.jqsrt.2022.108088
- Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 1: Model description and Jacobian calculation J. Loveridge et al. 10.5194/amt-16-1803-2023
- Long‐Term Trends in Chesapeake Bay Remote Sensing Reflectance: Implications for Water Clarity J. Turner et al. 10.1029/2021JC017959
- Algorithm evaluation for polarimetric remote sensing of atmospheric aerosols O. Hasekamp et al. 10.5194/amt-17-1497-2024
- The PACE-MAPP algorithm: Simultaneous aerosol and ocean polarimeter products using coupled atmosphere-ocean vector radiative transfer S. Stamnes et al. 10.3389/frsen.2023.1174672
- 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
- Adaptive Data Screening for Multi-Angle Polarimetric Aerosol and Ocean Color Remote Sensing Accelerated by Deep Learning M. Gao et al. 10.3389/frsen.2021.757832
- Jacobians of single-scattering optical properties of super-spheroids computed using neural networks J. Yu et al. 10.1364/OE.471821
- Intelligent Atmospheric Correction Algorithm for Polarization Ocean Color Satellite Measurements Over the Open Ocean X. He et al. 10.1109/TGRS.2023.3348159
- UVBoost: An erythemal weighted ultraviolet radiation estimator based on a machine learning gradient boosting algorithm M. Corrêa 10.1016/j.jqsrt.2023.108490
- Optimizing retrieval spaces of bio-optical models for remote sensing of ocean color N. Hannadige et al. 10.1364/AO.484082
- Opinion: Aerosol remote sensing over the next 20 years L. Remer et al. 10.5194/acp-24-2113-2024
- Performance evaluation of three bio-optical models in aerosol and ocean color joint retrievals N. Hannadige et al. 10.5194/amt-16-5749-2023
- An Overview of Neural Network Methods for Predicting Uncertainty in Atmospheric Remote Sensing A. Doicu et al. 10.3390/rs13245061
- An Artificial Neural Network Algorithm to Retrieve Chlorophyll a for Northwest European Shelf Seas from Top of Atmosphere Ocean Colour Reflectance M. Hadjal et al. 10.3390/rs14143353
- Radiative Transfer Speed-Up Combining Optimal Spectral Sampling With a Machine Learning Approach S. Mauceri et al. 10.3389/frsen.2022.932548
- Instantaneous photosynthetically available radiation models for ocean waters using neural networks K. Aryal et al. 10.1364/AO.474914
- Optimal estimation framework for ocean color atmospheric correction and pixel-level uncertainty quantification A. Ibrahim et al. 10.1364/AO.461861
- Improving Aerosol Retrieval From MISR With a Physics-Informed Deep Learning Method W. Man et al. 10.1109/TGRS.2024.3376598
- 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
- Retrieval of Aerosol Optical Depth and FMF over East Asia from Directional Intensity and Polarization Measurements of PARASOL S. Wang et al. 10.3390/atmos15010006
- A robust and flexible satellite aerosol retrieval algorithm for multi-angle polarimetric measurements with physics-informed deep learning method M. Tao et al. 10.1016/j.rse.2023.113763
- 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
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Short summary
Multi-angle polarimetric measurements can retrieve accurate aerosol properties over complex atmosphere and ocean systems; however, most retrieval algorithms require high computational costs. We propose a deep neural network (NN) forward model to represent the radiative transfer simulation of coupled atmosphere and ocean systems and then conduct simultaneous aerosol and ocean color retrievals on AirHARP measurements. The computational acceleration is 103 times with CPU or 104 times with GPU.
Multi-angle polarimetric measurements can retrieve accurate aerosol properties over complex...
