Using Machine Learning for Timely Estimates of Ocean Color Information From Hyperspectral Satellite Measurements in the Presence of Clouds, Aerosols, and Sunglint
Retrievals of ocean color from space are important for better understanding of the ocean ecosystem but can be limited under conditions such as clouds, aerosols, and sunglint. Many ocean color algorithms use a few selected spectral bands to perform an atmospheric correction and then derive the upwell...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Remote Sensing |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frsen.2022.846174/full |
| _version_ | 1797964983844208640 |
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| author | Zachary Fasnacht Zachary Fasnacht Joanna Joiner David Haffner David Haffner Wenhan Qin Wenhan Qin Alexander Vasilkov Alexander Vasilkov Patricia Castellanos Nickolay Krotkov |
| author_facet | Zachary Fasnacht Zachary Fasnacht Joanna Joiner David Haffner David Haffner Wenhan Qin Wenhan Qin Alexander Vasilkov Alexander Vasilkov Patricia Castellanos Nickolay Krotkov |
| author_sort | Zachary Fasnacht |
| collection | DOAJ |
| description | Retrievals of ocean color from space are important for better understanding of the ocean ecosystem but can be limited under conditions such as clouds, aerosols, and sunglint. Many ocean color algorithms use a few selected spectral bands to perform an atmospheric correction and then derive the upwelling radiance from the ocean. The limitations in the atmospheric correction under certain conditions lead to many gaps in daily spatial coverage of ocean color retrievals. To address these limitations, we introduce a new approach that uses machine learning to estimate ocean color from top of atmosphere radiances or reflectance measurements. In this approach, a principal component analysis is used to decompose the hyperspectral measurements into spectral features that describe the scattering and absorption of the atmosphere and the underlying surface. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from the MODIS atmospheric correction algorithm. This machine learning approach is independent of a priori information and does not rely on any radiative transfer modeling. We apply the approach to two hyperspectral UV/VIS instruments, the ozone monitoring instrument (OMI) and the TROPOspheric Monitoring Instrument (TROPOMI), using measurements from 320–500 nm to show that it can be used to reproduce ocean color properties in less-than-ideal conditions. This machine learning approach complements the current atmospheric correction ocean color retrievals by filling in the gaps resulting from cloud, aerosol, and sunglint contamination. This method can be applied to the future hyperspectral Ocean Color Instrument (OCI), which will be onboard NASA’s Plankton, Aerosol Cloud, ocean Ecosystem (PACE) ocean color satellite set to launch in 2024. |
| first_indexed | 2024-04-11T01:53:08Z |
| format | Article |
| id | doaj.art-7785d61ab34a44fca443a513dedf5634 |
| institution | Directory Open Access Journal |
| issn | 2673-6187 |
| language | English |
| last_indexed | 2024-04-11T01:53:08Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Remote Sensing |
| spelling | doaj.art-7785d61ab34a44fca443a513dedf56342023-01-03T05:55:20ZengFrontiers Media S.A.Frontiers in Remote Sensing2673-61872022-05-01310.3389/frsen.2022.846174846174Using Machine Learning for Timely Estimates of Ocean Color Information From Hyperspectral Satellite Measurements in the Presence of Clouds, Aerosols, and SunglintZachary Fasnacht0Zachary Fasnacht1Joanna Joiner2David Haffner3David Haffner4Wenhan Qin5Wenhan Qin6Alexander Vasilkov7Alexander Vasilkov8Patricia Castellanos9Nickolay Krotkov10Science Systems and Applications Inc., Lanham, MD, United StatesNASA Goddard Space Flight Center, Greenbelt, MD, United StatesNASA Goddard Space Flight Center, Greenbelt, MD, United StatesScience Systems and Applications Inc., Lanham, MD, United StatesNASA Goddard Space Flight Center, Greenbelt, MD, United StatesScience Systems and Applications Inc., Lanham, MD, United StatesNASA Goddard Space Flight Center, Greenbelt, MD, United StatesScience Systems and Applications Inc., Lanham, MD, United StatesNASA Goddard Space Flight Center, Greenbelt, MD, United StatesNASA Goddard Space Flight Center, Greenbelt, MD, United StatesNASA Goddard Space Flight Center, Greenbelt, MD, United StatesRetrievals of ocean color from space are important for better understanding of the ocean ecosystem but can be limited under conditions such as clouds, aerosols, and sunglint. Many ocean color algorithms use a few selected spectral bands to perform an atmospheric correction and then derive the upwelling radiance from the ocean. The limitations in the atmospheric correction under certain conditions lead to many gaps in daily spatial coverage of ocean color retrievals. To address these limitations, we introduce a new approach that uses machine learning to estimate ocean color from top of atmosphere radiances or reflectance measurements. In this approach, a principal component analysis is used to decompose the hyperspectral measurements into spectral features that describe the scattering and absorption of the atmosphere and the underlying surface. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from the MODIS atmospheric correction algorithm. This machine learning approach is independent of a priori information and does not rely on any radiative transfer modeling. We apply the approach to two hyperspectral UV/VIS instruments, the ozone monitoring instrument (OMI) and the TROPOspheric Monitoring Instrument (TROPOMI), using measurements from 320–500 nm to show that it can be used to reproduce ocean color properties in less-than-ideal conditions. This machine learning approach complements the current atmospheric correction ocean color retrievals by filling in the gaps resulting from cloud, aerosol, and sunglint contamination. This method can be applied to the future hyperspectral Ocean Color Instrument (OCI), which will be onboard NASA’s Plankton, Aerosol Cloud, ocean Ecosystem (PACE) ocean color satellite set to launch in 2024.https://www.frontiersin.org/articles/10.3389/frsen.2022.846174/fullocean colormachine learningOMITROPOMIPACETEMPO |
| spellingShingle | Zachary Fasnacht Zachary Fasnacht Joanna Joiner David Haffner David Haffner Wenhan Qin Wenhan Qin Alexander Vasilkov Alexander Vasilkov Patricia Castellanos Nickolay Krotkov Using Machine Learning for Timely Estimates of Ocean Color Information From Hyperspectral Satellite Measurements in the Presence of Clouds, Aerosols, and Sunglint Frontiers in Remote Sensing ocean color machine learning OMI TROPOMI PACE TEMPO |
| title | Using Machine Learning for Timely Estimates of Ocean Color Information From Hyperspectral Satellite Measurements in the Presence of Clouds, Aerosols, and Sunglint |
| title_full | Using Machine Learning for Timely Estimates of Ocean Color Information From Hyperspectral Satellite Measurements in the Presence of Clouds, Aerosols, and Sunglint |
| title_fullStr | Using Machine Learning for Timely Estimates of Ocean Color Information From Hyperspectral Satellite Measurements in the Presence of Clouds, Aerosols, and Sunglint |
| title_full_unstemmed | Using Machine Learning for Timely Estimates of Ocean Color Information From Hyperspectral Satellite Measurements in the Presence of Clouds, Aerosols, and Sunglint |
| title_short | Using Machine Learning for Timely Estimates of Ocean Color Information From Hyperspectral Satellite Measurements in the Presence of Clouds, Aerosols, and Sunglint |
| title_sort | using machine learning for timely estimates of ocean color information from hyperspectral satellite measurements in the presence of clouds aerosols and sunglint |
| topic | ocean color machine learning OMI TROPOMI PACE TEMPO |
| url | https://www.frontiersin.org/articles/10.3389/frsen.2022.846174/full |
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