Augmenting Heritage Ocean-Color Aerosol Models for Enhanced Remote Sensing of Inland and Nearshore Coastal Waters
Satellite remote sensing of near-surface water composition in terrestrial and coastal regions is challenging largely due to uncertainties linked to a lack of representative continental aerosols in the atmospheric correction (AC) framework. A comprehensive family of absorbing aerosols is proposed by...
| 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 |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frsen.2022.860816/full |
| _version_ | 1828079920832577536 |
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| author | Martin Montes Martin Montes Nima Pahlevan Nima Pahlevan David M. Giles David M. Giles Jean-Claude Roger Jean-Claude Roger Peng-wang Zhai Brandon Smith Brandon Smith Robert Levy P. Jeremy Werdell Alexander Smirnov Alexander Smirnov |
| author_facet | Martin Montes Martin Montes Nima Pahlevan Nima Pahlevan David M. Giles David M. Giles Jean-Claude Roger Jean-Claude Roger Peng-wang Zhai Brandon Smith Brandon Smith Robert Levy P. Jeremy Werdell Alexander Smirnov Alexander Smirnov |
| author_sort | Martin Montes |
| collection | DOAJ |
| description | Satellite remote sensing of near-surface water composition in terrestrial and coastal regions is challenging largely due to uncertainties linked to a lack of representative continental aerosols in the atmospheric correction (AC) framework. A comprehensive family of absorbing aerosols is proposed by analyzing global AERONET measurements using the Partition Around Medoids (PAM) classifier. The input to the classifier is composed of Version 3, Level 2.0 daily average aerosol properties [i.e., single scattering albedo at λ = 0.44 μm, (SSA(0.44)) and the Angstrom exponents for extinction and absorption AEe(0.44–0.87) and AEa(0.44–0.87), respectively from observations from June 1993 to September 2019. The PAM classification based on low daily aerosol optical depth (AOD(0.44) ≤ 0.4) suggested 27 distinct aerosol clusters encompassing five major absorbing aerosol types (Dust (DU), Marine (MAR), Mixed (MIX), Urban/Industrial (U/I), and Biomass Burning (BB)). Seasonal patterns of dominant PAM-derived clusters at three AERONET sites (GSFC, Kanpur, and Banizoumbou) strongly influenced by U/I, DU, and BB types, respectively, showed a satisfactory agreement with variations of aerosol mixtures reported in the literature. These PAM-derived models augment the National Aeronautics and Space Administration's (NASA's) aerosol models (A2010) applied in its operational AC. To demonstrate the validity and complementary nature of our models, a coupled ocean-atmosphere radiative transfer code is employed to create a simulated dataset for developing two experimental machine-learning AC processors. These two processors differ only in their aerosol models used in training: 1) a processor trained with the A2010 aerosol models (ACI) and 2) a processor trained with both PAM and A2010 aerosol models (ACII). These processors are applied to Landsat-8 Operational Land Imager (OLI) matchups (N = 173) from selected AERONET sites equipped with ocean color radiometers (AERONET-OC). Our assessments showed improvements of up to 30% in retrieving remote sensing reflectance (Rrs) in the blue bands. In general, our empirically derived PAM aerosol models complement A2010 models (designed for regions strongly influenced by marine conditions) over continental and coastal waters where absorbing aerosols are present (e.g., urban environments, areas impacted by dust, or wildfire events). With the expected geographic expansion of in situ aquatic validation networks (e.g., AERONET-OC), the advantages of our models will be accentuated, particularly in the ultraviolet and short blue bands. |
| first_indexed | 2024-04-11T03:09:59Z |
| format | Article |
| id | doaj.art-db7e1e056ca24a9b9ba9ca33f4be3768 |
| institution | Directory Open Access Journal |
| issn | 2673-6187 |
| language | English |
| last_indexed | 2024-04-11T03:09:59Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Remote Sensing |
| spelling | doaj.art-db7e1e056ca24a9b9ba9ca33f4be37682023-01-02T12:12:58ZengFrontiers Media S.A.Frontiers in Remote Sensing2673-61872022-05-01310.3389/frsen.2022.860816860816Augmenting Heritage Ocean-Color Aerosol Models for Enhanced Remote Sensing of Inland and Nearshore Coastal WatersMartin Montes0Martin Montes1Nima Pahlevan2Nima Pahlevan3David M. Giles4David M. Giles5Jean-Claude Roger6Jean-Claude Roger7Peng-wang Zhai8Brandon Smith9Brandon Smith10Robert Levy11P. Jeremy Werdell12Alexander Smirnov13Alexander Smirnov14Science Systems and Applications, Inc., (SSAI), Lanham, MD, United StatesNASA Goddard Space Flight Center, Greenbelt, MD, United StatesScience Systems and Applications, Inc., (SSAI), Lanham, MD, United StatesNASA Goddard Space Flight Center, Greenbelt, MD, United StatesScience Systems and Applications, Inc., (SSAI), Lanham, MD, United StatesNASA Goddard Space Flight Center, Greenbelt, MD, United StatesNASA Goddard Space Flight Center, Greenbelt, MD, United StatesDepartment of Geographical Sciences, University of Maryland, College Park, MD, United StatesDepartment of Physics, University of Maryland Baltimore County, Baltimore, MD, United StatesScience Systems and Applications, Inc., (SSAI), 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 StatesScience Systems and Applications, Inc., (SSAI), Lanham, MD, United StatesNASA Goddard Space Flight Center, Greenbelt, MD, United StatesSatellite remote sensing of near-surface water composition in terrestrial and coastal regions is challenging largely due to uncertainties linked to a lack of representative continental aerosols in the atmospheric correction (AC) framework. A comprehensive family of absorbing aerosols is proposed by analyzing global AERONET measurements using the Partition Around Medoids (PAM) classifier. The input to the classifier is composed of Version 3, Level 2.0 daily average aerosol properties [i.e., single scattering albedo at λ = 0.44 μm, (SSA(0.44)) and the Angstrom exponents for extinction and absorption AEe(0.44–0.87) and AEa(0.44–0.87), respectively from observations from June 1993 to September 2019. The PAM classification based on low daily aerosol optical depth (AOD(0.44) ≤ 0.4) suggested 27 distinct aerosol clusters encompassing five major absorbing aerosol types (Dust (DU), Marine (MAR), Mixed (MIX), Urban/Industrial (U/I), and Biomass Burning (BB)). Seasonal patterns of dominant PAM-derived clusters at three AERONET sites (GSFC, Kanpur, and Banizoumbou) strongly influenced by U/I, DU, and BB types, respectively, showed a satisfactory agreement with variations of aerosol mixtures reported in the literature. These PAM-derived models augment the National Aeronautics and Space Administration's (NASA's) aerosol models (A2010) applied in its operational AC. To demonstrate the validity and complementary nature of our models, a coupled ocean-atmosphere radiative transfer code is employed to create a simulated dataset for developing two experimental machine-learning AC processors. These two processors differ only in their aerosol models used in training: 1) a processor trained with the A2010 aerosol models (ACI) and 2) a processor trained with both PAM and A2010 aerosol models (ACII). These processors are applied to Landsat-8 Operational Land Imager (OLI) matchups (N = 173) from selected AERONET sites equipped with ocean color radiometers (AERONET-OC). Our assessments showed improvements of up to 30% in retrieving remote sensing reflectance (Rrs) in the blue bands. In general, our empirically derived PAM aerosol models complement A2010 models (designed for regions strongly influenced by marine conditions) over continental and coastal waters where absorbing aerosols are present (e.g., urban environments, areas impacted by dust, or wildfire events). With the expected geographic expansion of in situ aquatic validation networks (e.g., AERONET-OC), the advantages of our models will be accentuated, particularly in the ultraviolet and short blue bands.https://www.frontiersin.org/articles/10.3389/frsen.2022.860816/fullabsorbing aerosolsaquatic remote sensingwater qualityatmospheric correctionlakesrivers |
| spellingShingle | Martin Montes Martin Montes Nima Pahlevan Nima Pahlevan David M. Giles David M. Giles Jean-Claude Roger Jean-Claude Roger Peng-wang Zhai Brandon Smith Brandon Smith Robert Levy P. Jeremy Werdell Alexander Smirnov Alexander Smirnov Augmenting Heritage Ocean-Color Aerosol Models for Enhanced Remote Sensing of Inland and Nearshore Coastal Waters Frontiers in Remote Sensing absorbing aerosols aquatic remote sensing water quality atmospheric correction lakes rivers |
| title | Augmenting Heritage Ocean-Color Aerosol Models for Enhanced Remote Sensing of Inland and Nearshore Coastal Waters |
| title_full | Augmenting Heritage Ocean-Color Aerosol Models for Enhanced Remote Sensing of Inland and Nearshore Coastal Waters |
| title_fullStr | Augmenting Heritage Ocean-Color Aerosol Models for Enhanced Remote Sensing of Inland and Nearshore Coastal Waters |
| title_full_unstemmed | Augmenting Heritage Ocean-Color Aerosol Models for Enhanced Remote Sensing of Inland and Nearshore Coastal Waters |
| title_short | Augmenting Heritage Ocean-Color Aerosol Models for Enhanced Remote Sensing of Inland and Nearshore Coastal Waters |
| title_sort | augmenting heritage ocean color aerosol models for enhanced remote sensing of inland and nearshore coastal waters |
| topic | absorbing aerosols aquatic remote sensing water quality atmospheric correction lakes rivers |
| url | https://www.frontiersin.org/articles/10.3389/frsen.2022.860816/full |
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