Adaptive Data Screening for Multi-Angle Polarimetric Aerosol and Ocean Color Remote Sensing Accelerated by Deep Learning
Remote sensing measurements from multi-angle polarimeters (MAPs) contain rich aerosol microphysical property information, and these sensors have been used to perform retrievals in optically complex atmosphere and ocean systems. Previous studies have concluded that, generally, five moderately separat...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-12-01
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| Series: | Frontiers in Remote Sensing |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frsen.2021.757832/full |
| _version_ | 1797969139022692352 |
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| author | Meng Gao Meng Gao Kirk Knobelspiesse Bryan A. Franz Peng-Wang Zhai Vanderlei Martins Sharon P. Burton Brian Cairns Richard Ferrare Marta A. Fenn Marta A. Fenn Otto Hasekamp Yongxiang Hu Amir Ibrahim Andrew M. Sayer Andrew M. Sayer P. Jeremy Werdell Xiaoguang Xu |
| author_facet | Meng Gao Meng Gao Kirk Knobelspiesse Bryan A. Franz Peng-Wang Zhai Vanderlei Martins Sharon P. Burton Brian Cairns Richard Ferrare Marta A. Fenn Marta A. Fenn Otto Hasekamp Yongxiang Hu Amir Ibrahim Andrew M. Sayer Andrew M. Sayer P. Jeremy Werdell Xiaoguang Xu |
| author_sort | Meng Gao |
| collection | DOAJ |
| description | Remote sensing measurements from multi-angle polarimeters (MAPs) contain rich aerosol microphysical property information, and these sensors have been used to perform retrievals in optically complex atmosphere and ocean systems. Previous studies have concluded that, generally, five moderately separated viewing angles in each spectral band provide sufficient accuracy for aerosol property retrievals, with performance gradually saturating as angles are added above that threshold. The Hyper-Angular Rainbow Polarimeter (HARP) instruments provide high angular sampling with a total of 90–120 unique angles across four bands, a capability developed mainly for liquid cloud retrievals. In practice, not all view angles are optimal for aerosol retrievals due to impacts of clouds, sunglint, and other impediments. The many viewing angles of HARP can provide resilience to these effects, if the impacted views are screened from the dataset, as the remaining views may be sufficient for successful analysis. In this study, we discuss how the number of available viewing angles impacts aerosol and ocean color retrieval uncertainties, as applied to two versions of the HARP instrument. AirHARP is an airborne prototype that was deployed in the ACEPOL field campaign, while HARP2 is an instrument in development for the upcoming NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. Based on synthetic data, we find that a total of 20–30 angles across all bands (i.e., five to eight viewing angles per band) are sufficient to achieve good retrieval performance. Following from this result, we develop an adaptive multi-angle polarimetric data screening (MAPDS) approach to evaluate data quality by comparing measurements with their best-fitted forward model. The FastMAPOL retrieval algorithm is used to retrieve scene geophysical values, by matching an efficient, deep learning-based, radiative transfer emulator to observations. The data screening method effectively identifies and removes viewing angles affected by thin cirrus clouds and other anomalies, improving retrieval performance. This was tested with AirHARP data, and we found agreement with the High Spectral Resolution Lidar-2 (HSRL-2) aerosol data. The data screening approach can be applied to modern satellite remote sensing missions, such as PACE, where a large amount of multi-angle, hyperspectral, polarimetric measurements will be collected. |
| first_indexed | 2024-04-11T02:57:24Z |
| format | Article |
| id | doaj.art-7ea400914e3b4ed3bbb814602667132a |
| institution | Directory Open Access Journal |
| issn | 2673-6187 |
| language | English |
| last_indexed | 2024-04-11T02:57:24Z |
| publishDate | 2021-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Remote Sensing |
| spelling | doaj.art-7ea400914e3b4ed3bbb814602667132a2023-01-02T14:50:08ZengFrontiers Media S.A.Frontiers in Remote Sensing2673-61872021-12-01210.3389/frsen.2021.757832757832Adaptive Data Screening for Multi-Angle Polarimetric Aerosol and Ocean Color Remote Sensing Accelerated by Deep LearningMeng Gao0Meng Gao1Kirk Knobelspiesse2Bryan A. Franz3Peng-Wang Zhai4Vanderlei Martins5Sharon P. Burton6Brian Cairns7Richard Ferrare8Marta A. Fenn9Marta A. Fenn10Otto Hasekamp11Yongxiang Hu12Amir Ibrahim13Andrew M. Sayer14Andrew M. Sayer15P. Jeremy Werdell16Xiaoguang Xu17Ocean Ecology Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, United StatesScience Systems and Applications, Inc., Lanham, MD, United StatesOcean Ecology Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, United StatesOcean Ecology Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, United StatesJCET and Physics Department, University of Maryland, Baltimore, MD, United StatesJCET and Physics Department, University of Maryland, Baltimore, MD, United StatesNASA Langley Research Center, Hampton, VA, United StatesNASA Goddard Institute for Space Studies, New York, NY, United StatesNASA Langley Research Center, Hampton, VA, United StatesScience Systems and Applications, Inc., Lanham, MD, United StatesNASA Langley Research Center, Hampton, VA, United StatesNetherlands Institute for Space Research (SRON, NWO-I), Utrecht, NetherlandsNASA Langley Research Center, Hampton, VA, United StatesOcean Ecology Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, United StatesOcean Ecology Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, United StatesGESTAR II, University of Maryland Baltimore County, Baltimore, MD, United StatesOcean Ecology Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, United StatesJCET and Physics Department, University of Maryland, Baltimore, MD, United StatesRemote sensing measurements from multi-angle polarimeters (MAPs) contain rich aerosol microphysical property information, and these sensors have been used to perform retrievals in optically complex atmosphere and ocean systems. Previous studies have concluded that, generally, five moderately separated viewing angles in each spectral band provide sufficient accuracy for aerosol property retrievals, with performance gradually saturating as angles are added above that threshold. The Hyper-Angular Rainbow Polarimeter (HARP) instruments provide high angular sampling with a total of 90–120 unique angles across four bands, a capability developed mainly for liquid cloud retrievals. In practice, not all view angles are optimal for aerosol retrievals due to impacts of clouds, sunglint, and other impediments. The many viewing angles of HARP can provide resilience to these effects, if the impacted views are screened from the dataset, as the remaining views may be sufficient for successful analysis. In this study, we discuss how the number of available viewing angles impacts aerosol and ocean color retrieval uncertainties, as applied to two versions of the HARP instrument. AirHARP is an airborne prototype that was deployed in the ACEPOL field campaign, while HARP2 is an instrument in development for the upcoming NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. Based on synthetic data, we find that a total of 20–30 angles across all bands (i.e., five to eight viewing angles per band) are sufficient to achieve good retrieval performance. Following from this result, we develop an adaptive multi-angle polarimetric data screening (MAPDS) approach to evaluate data quality by comparing measurements with their best-fitted forward model. The FastMAPOL retrieval algorithm is used to retrieve scene geophysical values, by matching an efficient, deep learning-based, radiative transfer emulator to observations. The data screening method effectively identifies and removes viewing angles affected by thin cirrus clouds and other anomalies, improving retrieval performance. This was tested with AirHARP data, and we found agreement with the High Spectral Resolution Lidar-2 (HSRL-2) aerosol data. The data screening approach can be applied to modern satellite remote sensing missions, such as PACE, where a large amount of multi-angle, hyperspectral, polarimetric measurements will be collected.https://www.frontiersin.org/articles/10.3389/frsen.2021.757832/fullPACEmulti-angle polarimeteraerosol remote sensingocean colordata screeningcloud masking |
| spellingShingle | Meng Gao Meng Gao Kirk Knobelspiesse Bryan A. Franz Peng-Wang Zhai Vanderlei Martins Sharon P. Burton Brian Cairns Richard Ferrare Marta A. Fenn Marta A. Fenn Otto Hasekamp Yongxiang Hu Amir Ibrahim Andrew M. Sayer Andrew M. Sayer P. Jeremy Werdell Xiaoguang Xu Adaptive Data Screening for Multi-Angle Polarimetric Aerosol and Ocean Color Remote Sensing Accelerated by Deep Learning Frontiers in Remote Sensing PACE multi-angle polarimeter aerosol remote sensing ocean color data screening cloud masking |
| title | Adaptive Data Screening for Multi-Angle Polarimetric Aerosol and Ocean Color Remote Sensing Accelerated by Deep Learning |
| title_full | Adaptive Data Screening for Multi-Angle Polarimetric Aerosol and Ocean Color Remote Sensing Accelerated by Deep Learning |
| title_fullStr | Adaptive Data Screening for Multi-Angle Polarimetric Aerosol and Ocean Color Remote Sensing Accelerated by Deep Learning |
| title_full_unstemmed | Adaptive Data Screening for Multi-Angle Polarimetric Aerosol and Ocean Color Remote Sensing Accelerated by Deep Learning |
| title_short | Adaptive Data Screening for Multi-Angle Polarimetric Aerosol and Ocean Color Remote Sensing Accelerated by Deep Learning |
| title_sort | adaptive data screening for multi angle polarimetric aerosol and ocean color remote sensing accelerated by deep learning |
| topic | PACE multi-angle polarimeter aerosol remote sensing ocean color data screening cloud masking |
| url | https://www.frontiersin.org/articles/10.3389/frsen.2021.757832/full |
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