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

Graban, S; Dall’Olmo, G; Goult, S; 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

[img]
Preview
Text
Graban_etal_2020.pdf - Published Version
Available under License Creative Commons Attribution.

Download (1MB) | Preview
Official URL: http://dx.doi.org/10.1364/OE.397863

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: http://plymsea.ac.uk/id/eprint/9020

Actions (login required)

View Item View Item