Accurate deep-learning estimation of chlorophyll-a concentration from the spectral particulate beam-attenuation coefficient
| Type | : | ACL |
|---|---|---|
| Nature | : | Production scientifique |
| Au bénéfice du Laboratoire | : | Oui |
| Statut de publication | : | Publié |
| Année de publication | : | 2020 |
| Auteurs (4) | : | GRABAN Sebastian DALL’OLMO Giorgio GOULT Stephen SAUZEDE Raphaelle |
| Revue scientifique | : | Optics Express |
| Volume | : | 28 |
| Fascicule | : | 16 |
| Pages | : | 24214-24228 |
| DOI | : | 10.1364/oe.397863 |
| URL | : | http://www.opticsexpress.org/abstract.cfm?uri=oe-28-16-24214 |
| Abstract | : | Different techniques exist for determining chlorophyll-a concentration as a proxy of phytoplankton abundance. In this study, a novel method based on the spectral particulate beam-attenuation coefficient (cp) was developed to estimate chlorophyll-a concentrations in oceanic waters. A multi-layer perceptron deep neural network was trained to exploit the spectral features present in cp around the chlorophyll-a absorption peak in the red spectral region. Results show that the model was successful at accurately retrieving chlorophyll-a concentrations using cp in three red spectral bands, irrespective of time or location and over a wide range of chlorophyll-a concentrations. |
| Mots-clés | : | - |
| Commentaire | : | - |
| Tags | : | - |
| Fichier attaché | : | - |
| Citation | : |
Graban S, Dall’Olmo G, Goult S, Sauzede R (2020) Accurate deep-learning estimation of chlorophyll-a concentration from the spectral particulate beam-attenuation coefficient. Opt Express 28: 24214-24228 | doi: 10.1364/oe.397863
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