The OLCI Neural Network Swarm (ONNS): A Bio-Geo-Optical Algorithm for Open Ocean and Coastal Waters
The processing scheme of a novel in-water algorithm for the retrieval of ocean color products from Sentinel-3 OLCI is introduced. The algorithm consists of several blended neural networks that are specialized for 13 different optical water classes. These comprise clearest natural waters but also wat...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2017-05-01
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| Series: | Frontiers in Marine Science |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/article/10.3389/fmars.2017.00140/full |
| _version_ | 1818286891115479040 |
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| author | Martin Hieronymi Dagmar Müller Roland Doerffer Roland Doerffer |
| author_facet | Martin Hieronymi Dagmar Müller Roland Doerffer Roland Doerffer |
| author_sort | Martin Hieronymi |
| collection | DOAJ |
| description | The processing scheme of a novel in-water algorithm for the retrieval of ocean color products from Sentinel-3 OLCI is introduced. The algorithm consists of several blended neural networks that are specialized for 13 different optical water classes. These comprise clearest natural waters but also waters reaching the frontiers of marine optical remote sensing, namely extreme absorbing, or scattering waters. Considered chlorophyll concentrations reach up to 200 mg m−3, non-algae particle concentrations up to 1,500 g m−3, and the absorption coefficient of colored dissolved organic matter at 440 nm is up to 20 m−1. The algorithm generates different concentrations of water constituents, inherent and apparent optical properties, and a color index. In addition, all products are delivered with an uncertainty estimate. A baseline validation of the products is provided for various water types. We conclude that the algorithm is suitable for the remote sensing estimation of water properties and constituents of most natural waters. |
| first_indexed | 2024-12-13T01:31:47Z |
| format | Article |
| id | doaj.art-61403041676145a2b057bf31bb0d52f3 |
| institution | Directory Open Access Journal |
| issn | 2296-7745 |
| language | English |
| last_indexed | 2024-12-13T01:31:47Z |
| publishDate | 2017-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj.art-61403041676145a2b057bf31bb0d52f32022-12-22T00:03:59ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452017-05-01410.3389/fmars.2017.00140248858The OLCI Neural Network Swarm (ONNS): A Bio-Geo-Optical Algorithm for Open Ocean and Coastal WatersMartin Hieronymi0Dagmar Müller1Roland Doerffer2Roland Doerffer3Department of Remote Sensing, Institute of Coastal Research, Helmholtz-Zentrum GeesthachtGeesthacht, GermanyDepartment of Remote Sensing, Institute of Coastal Research, Helmholtz-Zentrum GeesthachtGeesthacht, GermanyDepartment of Remote Sensing, Institute of Coastal Research, Helmholtz-Zentrum GeesthachtGeesthacht, GermanyBrockmann Consult GmbHGeesthacht, GermanyThe processing scheme of a novel in-water algorithm for the retrieval of ocean color products from Sentinel-3 OLCI is introduced. The algorithm consists of several blended neural networks that are specialized for 13 different optical water classes. These comprise clearest natural waters but also waters reaching the frontiers of marine optical remote sensing, namely extreme absorbing, or scattering waters. Considered chlorophyll concentrations reach up to 200 mg m−3, non-algae particle concentrations up to 1,500 g m−3, and the absorption coefficient of colored dissolved organic matter at 440 nm is up to 20 m−1. The algorithm generates different concentrations of water constituents, inherent and apparent optical properties, and a color index. In addition, all products are delivered with an uncertainty estimate. A baseline validation of the products is provided for various water types. We conclude that the algorithm is suitable for the remote sensing estimation of water properties and constituents of most natural waters.http://journal.frontiersin.org/article/10.3389/fmars.2017.00140/fullocean colorremote sensingSentinel-3OLCIextreme Case-2 watersneural network |
| spellingShingle | Martin Hieronymi Dagmar Müller Roland Doerffer Roland Doerffer The OLCI Neural Network Swarm (ONNS): A Bio-Geo-Optical Algorithm for Open Ocean and Coastal Waters Frontiers in Marine Science ocean color remote sensing Sentinel-3 OLCI extreme Case-2 waters neural network |
| title | The OLCI Neural Network Swarm (ONNS): A Bio-Geo-Optical Algorithm for Open Ocean and Coastal Waters |
| title_full | The OLCI Neural Network Swarm (ONNS): A Bio-Geo-Optical Algorithm for Open Ocean and Coastal Waters |
| title_fullStr | The OLCI Neural Network Swarm (ONNS): A Bio-Geo-Optical Algorithm for Open Ocean and Coastal Waters |
| title_full_unstemmed | The OLCI Neural Network Swarm (ONNS): A Bio-Geo-Optical Algorithm for Open Ocean and Coastal Waters |
| title_short | The OLCI Neural Network Swarm (ONNS): A Bio-Geo-Optical Algorithm for Open Ocean and Coastal Waters |
| title_sort | olci neural network swarm onns a bio geo optical algorithm for open ocean and coastal waters |
| topic | ocean color remote sensing Sentinel-3 OLCI extreme Case-2 waters neural network |
| url | http://journal.frontiersin.org/article/10.3389/fmars.2017.00140/full |
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