Open AccessAccurate deep-learning estimation of chlorophyll-a concentration from the spectral particulate beam-attenuation coefficient
Author(s) -
Sebastian Graban,
Giorgio Dall’Olmo,
Stephen Goult,
Raphaëlle Sauzède
Publication year - 2020
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.397863
Subject(s) - attenuation coefficient , attenuation , chlorophyll a , chlorophyll , environmental science , particulates , deep chlorophyll maximum , remote sensing , optics , phytoplankton , materials science , physics , geology , chemistry , nutrient , biochemistry , organic chemistry , photic zone
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 (c p ) 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 c p 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 c p in three red spectral bands, irrespective of time or location and over a wide range of chlorophyll-a concentrations.