Articles | Volume 16, issue 23
https://doi.org/10.5194/amt-16-5863-2023
© 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/amt-16-5863-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Dec 2023
Research article |
| 07 Dec 2023
| 07 Dec 2023
Simultaneous retrieval of aerosol and ocean properties from PACE HARP2 with uncertainty assessment using cascading neural network radiative transfer models
Ocean Ecology Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Science Systems and Applications, Inc., Greenbelt, MD, USA
Bryan A. Franz
Ocean Ecology Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Peng-Wang Zhai
University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Kirk Knobelspiesse
Ocean Ecology Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Andrew M. Sayer
Ocean Ecology Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Xiaoguang Xu
University of Maryland, Baltimore County, Baltimore, MD 21250, USA
J. Vanderlei Martins
University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Brian Cairns
NASA Goddard Institute for Space Studies, New York, NY 10025, USA
Patricia Castellanos
Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Guangliang Fu
Netherlands Institute for Space Research (SRON, NWO-I), Leiden, the Netherlands
Neranga Hannadige
University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Otto Hasekamp
Netherlands Institute for Space Research (SRON, NWO-I), Leiden, the Netherlands
Yongxiang Hu
NASA Langley Research Center, MS 475, Hampton, VA 23681-2199, USA
Amir Ibrahim
Ocean Ecology Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Frederick Patt
Ocean Ecology Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Science Applications International Corp., Greenbelt, MD, USA
Anin Puthukkudy
University of Maryland, Baltimore County, Baltimore, MD 21250, USA
P. Jeremy Werdell
Ocean Ecology Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
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- First Aerosol Retrieval From PACE HARP2 Over Land With Physics-Informed Deep Learning Method W. Man et al. https://doi.org/10.1109/TGRS.2026.3702267
32 citations as recorded by crossref.
- The SMART-P Algorithm for Aerosol and Ocean Properties Part 1: Algorithm Theoretical Basis and Expected Accuracy of PolCube Aerosol Products S. Lee et al. https://doi.org/10.3390/rs18040560
- 星载多角度偏振气溶胶遥感综述（特邀） 陶. Tao Minghui et al. https://doi.org/10.3788/AOS251970
- Satellite remote sensing of aerosol single scattering albedo: Instruments, algorithms, and challenges Y. Dong et al. https://doi.org/10.1016/j.jqsrt.2025.109802
- All-sky direct aerosol radiative effects estimated from integrated A-Train satellite measurements M. Kacenelenbogen et al. https://doi.org/10.5194/acp-25-15875-2025
- An efficient transformer-based aerosol retrieval algorithm for the polarization crossfire (PCF) satellite senor suite: development and validation> H. Gu et al. https://doi.org/10.1016/j.jqsrt.2025.109793
- Advanced simulation and measurement of skylight polarization patterns across distinct aerosol type environments S. Li et al. https://doi.org/10.1016/j.scitotenv.2025.178768
- The Ocean Color Instrument (OCI) on the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Mission: System Design and Prelaunch Radiometric Performance G. Meister et al. https://doi.org/10.1109/TGRS.2024.3383812
- A Capsule Network Model for Aerosol Retrieval from DPC/Gaofen-5(02) Satellite Multi-Angle Polarimetric Observation H. Gu et al. https://doi.org/10.34133/remotesensing.1008
- First lunar-light mapping of nighttime dust season oceanic aerosol optical depth over North Atlantic from space M. Zhou et al. https://doi.org/10.1016/j.rse.2024.114315
- A Review of Machine Learning Applications in Ocean Color Remote Sensing Z. Zhang et al. https://doi.org/10.3390/rs17101776
- 偏振海洋遥感<bold>:</bold> 经典特征分析与新特征构建 子. 张 et al. https://doi.org/10.1360/SSTe-2024-0199
- Ocean color remote sensing: From 2D legacy to the 3D, AI-driven future P. Chen & Z. Zhang https://doi.org/10.1016/j.isprsjprs.2026.04.044
- Collaborative Neural Networks Significantly Improve Global Aerosol Retrievals From Multiangle Polarimeters G. Fu et al. https://doi.org/10.1109/TGRS.2026.3685881
- Impact of the Uncertainties of Polarized Water-Leaving Radiance on the Retrieval of Oceanic Constituents and Inherent Optical Properties in Global Oceans via Multiangle Polarimetric Observations J. Liu et al. https://doi.org/10.3390/rs17071148
- Global retrieval of stokes vector of water-leaving radiance via Unet-based framework J. Liu et al. https://doi.org/10.1016/j.jag.2026.105445
- Snow Parameter Retrieval Algorithm Enhanced by Optimal Estimation (SPR-OE) and Its Validation Over Greenland N. Chen et al. https://doi.org/10.1109/TGRS.2026.3701629
- Monte Carlo-Based Assessment of Polarimetric Accuracy for a Spaceborne Scanning Polarimeter X. Dong et al. https://doi.org/10.1088/1742-6596/3055/1/012036
- 3D cloud masking across a broad swath using multi-angle polarimetry and deep learning S. Foley et al. https://doi.org/10.5194/amt-17-7027-2024
- Global Aerosol Retrieval Over Land Using the Chinese Satellite Polarimeter DPC-2/GF-5(02) Z. Zhang et al. https://doi.org/10.1109/TGRS.2025.3633391
- Warm-phase microphysical evolution in large-eddy simulations of tropical cumulus congestus: evaluating drop size distribution evolution using polarimetry retrievals, in situ measurements, and a thermal-based framework M. Stanford et al. https://doi.org/10.5194/acp-25-11199-2025
- Retrieving oceanic constituents and inherent optical properties in global oceans using multi-angular polarimetric measurements J. Liu et al. https://doi.org/10.1364/OE.550049
- A physics-based AI algorithm integrating radiative transfer and machine learning for joint retrieval of aerosol layer height and optical thickness from hyperspectral satellite observations over the ocean R. Yao et al. https://doi.org/10.1016/j.rse.2026.115379
- Polarimetric ocean remote sensing: Classic feature analysis and novel feature establishment Z. Zhang et al. https://doi.org/10.1007/s11430-024-1599-6
- Polarized reflectance in China from DPC-2/GF-5(02) polarimetric satellite measurements: dataset and model inter-comparison Q. Liu et al. https://doi.org/10.1016/j.jqsrt.2026.109961
- Estimation of Cloud Condensation Nuclei (CCN) using a neural network retrieval algorithm—A synthetic study for SPEXone on the NASA PACE mission N. Hannadige et al. https://doi.org/10.1016/j.jqsrt.2026.109853
- Retrievals of Biomass Burning Aerosol and Liquid Cloud Properties from Polarimetric Observations Using Deep Learning Techniques M. Segal Rozenhaimer et al. https://doi.org/10.3390/rs17101693
- Deriving anisotropic correction for upwelling radiance from PACE's multi-angle polarimetry X. Zhang et al. https://doi.org/10.1016/j.rse.2025.114647
- Detecting the layer height of smoke and dust aerosols over land and ocean using ultraviolet dual-wavelength measurements P. Li et al. https://doi.org/10.1016/j.rse.2025.115001
- Spatial distribution of airborne antibiotic resistance genes over the Pacific ocean: ocean-atmosphere transfer J. Jang et al. https://doi.org/10.1016/j.envpol.2026.128197
- Optimized retrievals of aerosol optical properties from directional polarimetric camera using optimal linear mixture of basis aerosol models supported by the non-negative matrix factorization S. Jin et al. https://doi.org/10.1016/j.rse.2026.115504
- Aerosol and cloud retrieval algorithm of TANSO-3/GOSAT-GW: theoretical basis and validations during the 2024 ASIA-AQ campaign using the TROPOMI observations as a testbed H. Lim et al. https://doi.org/10.1186/s40645-026-00820-z
- First Aerosol Retrieval From PACE HARP2 Over Land With Physics-Informed Deep Learning Method W. Man et al. https://doi.org/10.1109/TGRS.2026.3702267
Saved (final revised paper)
Latest update: 21 Jul 2026
Short summary
This study evaluated the retrievability and uncertainty of aerosol and ocean properties from PACE's HARP2 instrument using enhanced neural network models with the FastMAPOL algorithm. A cascading retrieval method is developed to improve retrieval performance. A global set of simulated HARP2 data is generated and used for uncertainty evaluations. The performance assessment demonstrates that the FastMAPOL algorithm is a viable approach for operational application to HARP2 data after PACE launch.
This study evaluated the retrievability and uncertainty of aerosol and ocean properties from...
