Quantification of Underwater <i>Sargassum</i> Aggregations Based on a Semi-Analytical Approach Applied to Sentinel-3/OLCI (Copernicus) Data in the Tropical Atlantic Ocean
“<i>Sargassum</i>” is a pelagic species of algae that drifts and aggregates in the tropical Atlantic Ocean. The number of <i>Sargassum</i> aggregations increased in the Caribbean Sea during the last decade. The aggregations eventually wash up on shores thus leading to a socio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/20/5230 |
| _version_ | 1797470052069408768 |
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| author | Léa Schamberger Audrey Minghelli Malik Chami |
| author_facet | Léa Schamberger Audrey Minghelli Malik Chami |
| author_sort | Léa Schamberger |
| collection | DOAJ |
| description | “<i>Sargassum</i>” is a pelagic species of algae that drifts and aggregates in the tropical Atlantic Ocean. The number of <i>Sargassum</i> aggregations increased in the Caribbean Sea during the last decade. The aggregations eventually wash up on shores thus leading to a socio-economic issue for the population and the coastal ecosystem. Satellite ocean color data, such as those provided by the Sentinel-3/OLCI satellite sensor (Copernicus), can be used to detect the occurrences of <i>Sargassum</i> and to estimate their abundance per pixel using the Maximum Chlorophyll <i>Index</i> (noted MCI). Such an index is, however, ineffective if the algae are located beneath the sea surface, which frequently happens, considering the rough Caribbean oceanic waters. The objective of this study is to propose a relevant methodology that enables the detection of underwater <i>Sargassum</i> aggregations. The methodology relies on the inversion of the radiative transfer equation in the water column. The inverted model provides the immersion depth of the <i>Sargassum</i> aggregations (per pixel) and their fractional coverage from the above water reflectances. The overall methodology has been applied to Sentinel-3/OLCI data. The comparison with the MCI method, which is solely devoted to the sea surface retrieval of <i>Sargassum</i> aggregations, shows that the proposed methodology allows retrieving about twice more <i>Sargassum</i> aggregation occurrences than the MCI estimates. A relative increase of 31% of the fractional coverage over the entire study area is observed when using the proposed method in comparison to MCI method. For the satellite scenes considered here, the rate of <i>Sargassum</i> aggregations immersed between 2 m and 5 m depth ranges between 30% and 51% over the total amount (i.e., surface + in-water), which clearly demonstrates the importance of considering the retrieval of in-water aggregations to gain understanding on <i>Sargassum</i> spatial variability in the oceanic and coastal ecosystems. |
| first_indexed | 2024-03-09T19:31:19Z |
| format | Article |
| id | doaj.art-0700a481e65f4b4e8204ee2c9b1f690c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T19:31:19Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-0700a481e65f4b4e8204ee2c9b1f690c2023-11-24T02:21:42ZengMDPI AGRemote Sensing2072-42922022-10-011420523010.3390/rs14205230Quantification of Underwater <i>Sargassum</i> Aggregations Based on a Semi-Analytical Approach Applied to Sentinel-3/OLCI (Copernicus) Data in the Tropical Atlantic OceanLéa Schamberger0Audrey Minghelli1Malik Chami2Laboratoire d’Informatique et Système (LIS), Université de Toulon, CNRS UMR 7020, F-83041 Toulon, FranceLaboratoire d’Informatique et Système (LIS), Université de Toulon, CNRS UMR 7020, F-83041 Toulon, FranceLaboratoire Atmosphères Milieux Observations Spatiales (LATMOS), Sorbonne Université, CNRS-INSU, F-06304 Nice, France“<i>Sargassum</i>” is a pelagic species of algae that drifts and aggregates in the tropical Atlantic Ocean. The number of <i>Sargassum</i> aggregations increased in the Caribbean Sea during the last decade. The aggregations eventually wash up on shores thus leading to a socio-economic issue for the population and the coastal ecosystem. Satellite ocean color data, such as those provided by the Sentinel-3/OLCI satellite sensor (Copernicus), can be used to detect the occurrences of <i>Sargassum</i> and to estimate their abundance per pixel using the Maximum Chlorophyll <i>Index</i> (noted MCI). Such an index is, however, ineffective if the algae are located beneath the sea surface, which frequently happens, considering the rough Caribbean oceanic waters. The objective of this study is to propose a relevant methodology that enables the detection of underwater <i>Sargassum</i> aggregations. The methodology relies on the inversion of the radiative transfer equation in the water column. The inverted model provides the immersion depth of the <i>Sargassum</i> aggregations (per pixel) and their fractional coverage from the above water reflectances. The overall methodology has been applied to Sentinel-3/OLCI data. The comparison with the MCI method, which is solely devoted to the sea surface retrieval of <i>Sargassum</i> aggregations, shows that the proposed methodology allows retrieving about twice more <i>Sargassum</i> aggregation occurrences than the MCI estimates. A relative increase of 31% of the fractional coverage over the entire study area is observed when using the proposed method in comparison to MCI method. For the satellite scenes considered here, the rate of <i>Sargassum</i> aggregations immersed between 2 m and 5 m depth ranges between 30% and 51% over the total amount (i.e., surface + in-water), which clearly demonstrates the importance of considering the retrieval of in-water aggregations to gain understanding on <i>Sargassum</i> spatial variability in the oceanic and coastal ecosystems.https://www.mdpi.com/2072-4292/14/20/5230<i>Sargassum</i> aggregationsocean color remote sensingSentinel-3/OLCI satellite sensorradiative transfer modellingtropical Atlantic Ocean |
| spellingShingle | Léa Schamberger Audrey Minghelli Malik Chami Quantification of Underwater <i>Sargassum</i> Aggregations Based on a Semi-Analytical Approach Applied to Sentinel-3/OLCI (Copernicus) Data in the Tropical Atlantic Ocean Remote Sensing <i>Sargassum</i> aggregations ocean color remote sensing Sentinel-3/OLCI satellite sensor radiative transfer modelling tropical Atlantic Ocean |
| title | Quantification of Underwater <i>Sargassum</i> Aggregations Based on a Semi-Analytical Approach Applied to Sentinel-3/OLCI (Copernicus) Data in the Tropical Atlantic Ocean |
| title_full | Quantification of Underwater <i>Sargassum</i> Aggregations Based on a Semi-Analytical Approach Applied to Sentinel-3/OLCI (Copernicus) Data in the Tropical Atlantic Ocean |
| title_fullStr | Quantification of Underwater <i>Sargassum</i> Aggregations Based on a Semi-Analytical Approach Applied to Sentinel-3/OLCI (Copernicus) Data in the Tropical Atlantic Ocean |
| title_full_unstemmed | Quantification of Underwater <i>Sargassum</i> Aggregations Based on a Semi-Analytical Approach Applied to Sentinel-3/OLCI (Copernicus) Data in the Tropical Atlantic Ocean |
| title_short | Quantification of Underwater <i>Sargassum</i> Aggregations Based on a Semi-Analytical Approach Applied to Sentinel-3/OLCI (Copernicus) Data in the Tropical Atlantic Ocean |
| title_sort | quantification of underwater i sargassum i aggregations based on a semi analytical approach applied to sentinel 3 olci copernicus data in the tropical atlantic ocean |
| topic | <i>Sargassum</i> aggregations ocean color remote sensing Sentinel-3/OLCI satellite sensor radiative transfer modelling tropical Atlantic Ocean |
| url | https://www.mdpi.com/2072-4292/14/20/5230 |
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