Abstract
Standard NASA ocean color algorithm OC4 was developed on the basis of ocean optical data and while appropriate for Case 1 oceanic waters could not be adequately applied for the Black Sea waters due to its different bio-optical properties. OC4 algorithm is shown to overestimate chlorophyll concentration (Chl-a) in summer and underestimate Chl-a during early spring phytoplankton blooms in the Black Sea. For correct conversion of satellite data to Chl-a, primary production and other indicators regional algorithms should be developed taking into account bio-optical properties of the Black Sea waters. Light absorption by phytoplankton pigments- aph(λ) have been measured in open sea and shelf Black Sea waters in different seasons since 1998. It was shown that the first optical depth was located within the upper mixed layer (UML) for most of the year with the exception of the spring when seasonal stratification was developing. As a result spectral features of water leaving radiance were determined by optical properties of the UML. Significant seasonal differences in Chl-a specific light absorption coefficients of phytoplankton within UML have been revealed. These differences were caused by adaptive changes of composition and intracellular pigment concentration due to variable environment conditions-mainly light intensity. Empirical relationships between aph(λ) and Chl-a were derived by least squares fitting to power functions for different seasons. Incorporation of these results will refine the regional ocean color models and provide improved and seasonally adjusted estimates of chlorophyll a concentration, downwelling radiance and primary production in the Black Sea based on satellite data.
Author supplied keywords
Cite
CITATION STYLE
Churilova, T., Suslin, V., Krivenko, O., Efimova, T., Moiseeva, N., Mukhanov, V., & Smirnova, L. (2017). Light absorption by phytoplankton in the upper mixed layer of the Black Sea: Seasonality and parametrization. Frontiers in Marine Science, 4(APR). https://doi.org/10.3389/fmars.2017.00090
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.