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Malysheva, AS, Lobanova, PV and Tilstone, GH 2024 Development of a Maximum Specific Photosynthetic Rate Algorithm Based on Remote Sensing Data: a Case Study for the Atlantic Ocean. Oceanology, 63 (S1). S202-S214. https://doi.org/10.1134/S000143702307010X
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Malysheva_etal_Oceanology_2023.pdf - Accepted Version Available under License All Rights Reserved. Download (1MB) | Preview |
Abstract/Summary
New regional empirical algorithms were developed to obtain maximum specific photosynthetic rates of phytoplankton ( ) in the surface layer of the Atlantic Ocean. These algorithms were based on the dependence of on seawater temperature. Sea Surface Temperature remote sensing data and the PANGAEA global database of photosynthesis–irradiance parameters were used to test the algorithm. In addition, the variability in , both spatially (from 60° S to 85° N) and seasonally, (2002–2013) was estimated. The highest was obtained in December in areas of deep convection and the interaction between the Labrador Current and the Gulf Stream, while minimum values were observed in the northern and equatorial–tropical parts of the ocean during the time intervals between the phytoplankton blooms (March to September–October). In addition, existing and algorithms used in primary production models, as well as the algorithm devel�oped using temperature and chlorophyll a data from AMT-29, which were then tested using the PANGAEA dataset. The results show that the new algorithm developed using seawater temperature data with region�ally adjusted empirical coefficients correlated best with the in situ data.
| Item Type: | Publication - Article |
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| Additional Keywords: | primary production, chlorophyll a, assimilation number, phytoplankton blooms, Atlantic Ocean |
| Divisions: | Plymouth Marine Laboratory > National Capability categories > Atlantic Meridional Transect Plymouth Marine Laboratory > Science Areas > Earth Observation Science and Applications |
| Depositing User: | S Hawkins |
| Date made live: | 20 Mar 2024 11:03 |
| Last Modified: | 20 Mar 2024 11:12 |
| URI: | https://plymsea.ac.uk/id/eprint/10162 |
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