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Photosynthetic rates derived from satellite‐based chlorophyll concentration
Author(s) -
Behrenfeld Michael J.,
Falkowski Paul G.
Publication year - 1997
Publication title -
limnology and oceanography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.7
H-Index - 197
eISSN - 1939-5590
pISSN - 0024-3590
DOI - 10.4319/lo.1997.42.1.0001
Subject(s) - irradiance , carbon fixation , photosynthesis , environmental science , atmospheric sciences , phytoplankton , productivity , spatial variability , chlorophyll a , primary productivity , satellite , mathematics , biology , botany , ecology , statistics , physics , optics , nutrient , macroeconomics , astronomy , economics
We assembled a dataset of 14 C‐based productivity measurements to understand the critical variables required for accurate assessment of daily depth‐integrated phytoplankton carbon fixation ( PP ( PP eu ) u ) from measurements of sea surface pigment concentrations ( C sat )( C sat ). From this dataset, we developed a light‐dependent, depth‐resolved model for carbon fixation (VGPM) that partitions environmental factors affecting primary production into those that influence the relative vertical distribution of primary production ( P z ) z ) and those that control the optimal assimilation efficiency of the productivity profile ( P ( P B opt ). The VGPM accounted for 79% of the observed variability in P z and 86% of the variability in PP eu by using measured values of P B opt . Our results indicate that the accuracy of productivity algorithms in estimating PP eu is dependent primarily upon the ability to accurately represent variability in P bopt . We developed a temperature‐dependent P b opt model that was used in conjunction with monthly climatological images of C sat sea surface temperature, and cloud‐corrected estimates of surface irradiance to calculate a global annual phytoplankton carbon fixation ( PP annu ) rate of 43.5 Pg C yr ‒1 . The geographical distribution of PP annu was distinctly different than results from previous models. Our results illustrate the importance of focusing P b opt model development on temporal and spatial, rather than the vertical, variability.