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Laine, M, Kulk, G, Jönsson, BF and Sathyendranath, S 2024 A machine learning model-based satellite data record of dissolved organic carbon concentration in surface waters of the global open ocean. Frontiers in Marine Science, 11. https://doi.org/10.3389/fmars.2024.1305050
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Laine M et al (2024).pdf - Published Version Available under License Creative Commons Attribution. Download (12MB) | Preview |
Abstract/Summary
Dissolved Organic Carbon (DOC) is the largest organic carbon pool in the ocean. Considering the biotic and abiotic factors controlling DOC processes, indirect satellite methods for open ocean DOC estimation can be developed, using conceptual, empirical or statistical models, driven by multiple satellite products. In this study, we infer a time series of global DOC from data of the European Space Agency’s (ESA) Ocean Colour Climate Change Initiative (OC-CCI) in combination with a global database of in situ DOC observations. We tested empirical machine learning modelling approaches in which the available in situ data are used to train the models and to find empirical relationships between DOC and variables available from remote sensing. Of the tested methods, a random forest regression showed the best results, and the details of this model are further reported here. We present a time series of global open ocean DOC concentrations between 2010–2018 that is made freely available through the archive of the UK Centre for Environmental Data Analysis (CEDA)
| Item Type: | Publication - Article |
|---|---|
| Additional Keywords: | ocean carbon cycle, dissolved organic carbon, ocean colour, satellite observations, machine learning, random forest |
| Divisions: | Plymouth Marine Laboratory > National Capability categories > National Centre for Earth Observation Plymouth Marine Laboratory > Science Areas > Earth Observation Science and Applications |
| Depositing User: | S Hawkins |
| Date made live: | 10 Dec 2024 13:32 |
| Last Modified: | 10 Dec 2024 13:32 |
| URI: | https://plymsea.ac.uk/id/eprint/10349 |
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