Development of a Maximum Specific Photosynthetic Rate Algorithm Based on Remote Sensing Data: a Case Study for the Atlantic Ocean

— 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 gl...

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Bibliographic Details
Published in:Oceanology (Washington. 1965) 2023-12, Vol.63 (Suppl 1), p.S202-S214
Main Authors: Malysheva, A. S., Lobanova, P. V., Tilstone, G. H.
Format: Article
Language:English
Subjects:
Algorithms
Blooms
Chlorophyll
Chlorophyll a
Coefficients
Convection
Earth and Environmental Science
Earth Sciences
Gulf Stream
Irradiance
Marine Biology
Mathematical models
Oceanography
Photosynthesis
Phytoplankton
Plankton
Primary production
Regional development
Remote sensing
Sea surface
Sea surface temperature
Seawater
Surface layers
Surface temperature
Temperature data
Temperature dependence
Water temperature
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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 developed 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 regionally adjusted empirical coefficients correlated best with the in situ data.
ISSN:0001-4370
1531-8508
DOI:10.1134/S000143702307010X