Accurate deep-learning estimation of chlorophyll-a concentration from the spectral particulate beam-attenuation coefficient

Graban, S, Dall’Olmo, G, Goult, S and Sauzède, R 2020 Accurate deep-learning estimation of chlorophyll-a concentration from the spectral particulate beam-attenuation coefficient. Optics Express, 28 (16). 24214. https://doi.org/10.1364/OE.397863

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Abstract/Summary

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.

Item Type:	Publication - Article
Divisions:	Plymouth Marine Laboratory > National Capability categories > Atlantic Meridional Transect Plymouth Marine Laboratory > National Capability categories > NERC Earth Observation Data Acquisition & Analysis Service (NEODAAS) Plymouth Marine Laboratory > National Capability categories > National Centre for Earth Observation Plymouth Marine Laboratory > National Capability categories > Single Centre NC - CLASS Plymouth Marine Laboratory > Science Areas > Earth Observation Science and Applications
Depositing User:	S Hawkins
Date made live:	03 Aug 2020 10:43
Last Modified:	03 Aug 2020 10:43
URI:	https://plymsea.ac.uk/id/eprint/9020

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