Evaluating Atmospheric Correction Algorithms Applied to OLCI Sentinel-3 Data of Chesapeake Bay Waters
Satellite remote sensing permits large-scale monitoring of coastal waters through synoptic measurements of water-leaving radiance that can be scaled to relevant water quality metrics and in turn help inform local and regional responses to a variety of stressors. As both the incident and water-leavin...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/8/1881 |
| _version_ | 1827626918954926080 |
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| author | Anna E. Windle Hayley Evers-King Benjamin R. Loveday Michael Ondrusek Greg M. Silsbe |
| author_facet | Anna E. Windle Hayley Evers-King Benjamin R. Loveday Michael Ondrusek Greg M. Silsbe |
| author_sort | Anna E. Windle |
| collection | DOAJ |
| description | Satellite remote sensing permits large-scale monitoring of coastal waters through synoptic measurements of water-leaving radiance that can be scaled to relevant water quality metrics and in turn help inform local and regional responses to a variety of stressors. As both the incident and water-leaving radiance are affected by interactions with the intervening atmosphere, the efficacy of atmospheric correction algorithms is essential to derive accurate water-leaving radiometry. Modern ocean color satellite sensors such as the Ocean and Land Colour Instrument (OLCI) onboard the Copernicus Sentinel-3A and -3B satellites are providing unprecedented operational data at the higher spatial, spectral, and temporal resolution that is necessary to resolve optically complex coastal water quality. Validating these satellite-based radiance measurements with vicarious in situ radiometry, especially in optically complex coastal waters, is a critical step in not only evaluating atmospheric correction algorithm performance but ultimately providing accurate water quality metrics for stakeholders. In this study, a regional in situ dataset from the Chesapeake Bay was used to evaluate the performance of four atmospheric correction algorithms applied to OLCI Level-1 data. Images of the Chesapeake Bay are processed through a neural-net based algorithm (C2RCC), a spectral optimization-based algorithm (POLYMER), an iterative two-band bio-optical-based algorithm (L2gen), and compared to the standard Level-2 OLCI data (BAC). Performance was evaluated through a matchup analysis to in situ remote sensing reflectance data. Statistical metrics demonstrated that C2RCC had the best performance, particularly in the longer wavelengths (>560 nm) and POLYMER contained the most clear day coverage (fewest flagged data). This study provides a framework with associated uncertainties and recommendations to utilize OLCI ocean color data to monitor the water quality and biogeochemical dynamics in Chesapeake Bay. |
| first_indexed | 2024-03-09T13:04:17Z |
| format | Article |
| id | doaj.art-f4b64973bfdf4db38720c7eda8e70e3f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T13:04:17Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-f4b64973bfdf4db38720c7eda8e70e3f2023-11-30T21:51:02ZengMDPI AGRemote Sensing2072-42922022-04-01148188110.3390/rs14081881Evaluating Atmospheric Correction Algorithms Applied to OLCI Sentinel-3 Data of Chesapeake Bay WatersAnna E. Windle0Hayley Evers-King1Benjamin R. Loveday2Michael Ondrusek3Greg M. Silsbe4Horn Point Laboratory, University of Maryland Center for Environmental Science, Cambridge, MD 21613, USAEUMETSAT (European Organisation for the Exploitation of Meteorological Satellites), 64295 Darmstadt, GermanyInnoflair UG, 64287 Darmstadt, GermanyNOAA/NESDIS/STAR, College Park, MD 20740, USAHorn Point Laboratory, University of Maryland Center for Environmental Science, Cambridge, MD 21613, USASatellite remote sensing permits large-scale monitoring of coastal waters through synoptic measurements of water-leaving radiance that can be scaled to relevant water quality metrics and in turn help inform local and regional responses to a variety of stressors. As both the incident and water-leaving radiance are affected by interactions with the intervening atmosphere, the efficacy of atmospheric correction algorithms is essential to derive accurate water-leaving radiometry. Modern ocean color satellite sensors such as the Ocean and Land Colour Instrument (OLCI) onboard the Copernicus Sentinel-3A and -3B satellites are providing unprecedented operational data at the higher spatial, spectral, and temporal resolution that is necessary to resolve optically complex coastal water quality. Validating these satellite-based radiance measurements with vicarious in situ radiometry, especially in optically complex coastal waters, is a critical step in not only evaluating atmospheric correction algorithm performance but ultimately providing accurate water quality metrics for stakeholders. In this study, a regional in situ dataset from the Chesapeake Bay was used to evaluate the performance of four atmospheric correction algorithms applied to OLCI Level-1 data. Images of the Chesapeake Bay are processed through a neural-net based algorithm (C2RCC), a spectral optimization-based algorithm (POLYMER), an iterative two-band bio-optical-based algorithm (L2gen), and compared to the standard Level-2 OLCI data (BAC). Performance was evaluated through a matchup analysis to in situ remote sensing reflectance data. Statistical metrics demonstrated that C2RCC had the best performance, particularly in the longer wavelengths (>560 nm) and POLYMER contained the most clear day coverage (fewest flagged data). This study provides a framework with associated uncertainties and recommendations to utilize OLCI ocean color data to monitor the water quality and biogeochemical dynamics in Chesapeake Bay.https://www.mdpi.com/2072-4292/14/8/1881ocean colorOLCI Sentinel-3atmospheric correctionChesapeake Bayremote sensing of coastal waters |
| spellingShingle | Anna E. Windle Hayley Evers-King Benjamin R. Loveday Michael Ondrusek Greg M. Silsbe Evaluating Atmospheric Correction Algorithms Applied to OLCI Sentinel-3 Data of Chesapeake Bay Waters Remote Sensing ocean color OLCI Sentinel-3 atmospheric correction Chesapeake Bay remote sensing of coastal waters |
| title | Evaluating Atmospheric Correction Algorithms Applied to OLCI Sentinel-3 Data of Chesapeake Bay Waters |
| title_full | Evaluating Atmospheric Correction Algorithms Applied to OLCI Sentinel-3 Data of Chesapeake Bay Waters |
| title_fullStr | Evaluating Atmospheric Correction Algorithms Applied to OLCI Sentinel-3 Data of Chesapeake Bay Waters |
| title_full_unstemmed | Evaluating Atmospheric Correction Algorithms Applied to OLCI Sentinel-3 Data of Chesapeake Bay Waters |
| title_short | Evaluating Atmospheric Correction Algorithms Applied to OLCI Sentinel-3 Data of Chesapeake Bay Waters |
| title_sort | evaluating atmospheric correction algorithms applied to olci sentinel 3 data of chesapeake bay waters |
| topic | ocean color OLCI Sentinel-3 atmospheric correction Chesapeake Bay remote sensing of coastal waters |
| url | https://www.mdpi.com/2072-4292/14/8/1881 |
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