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

Abstract—

New regional empirical algorithms were developed to obtain maximum specific photosynthetic rates of phytoplankton (\(P_{m}^{B}\)) in the surface layer of the Atlantic Ocean. These algorithms were based on the dependence of \(P_{m}^{B}\) 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 \(P_{m}^{B}\), both spatially (from 60° S to 85° N) and seasonally, (2002–2013) was estimated. The highest \(P_{m}^{B}\) 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 \(P_{m}^{B}\) and \(P_{{{\text{opt}}}}^{B}\) algorithms used in primary production models, as well as the \(P_{m}^{B}\) 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 \(P_{m}^{B}\) algorithm developed using seawater temperature data with regionally adjusted empirical coefficients correlated best with the in situ data.