Remote Sensing of Algal Photosynthesis

  • Richard J. Geider
  • Bruce A. Osborne

Abstract

Estimates of the rates of photosynthetic energy conversion on regional and global scales are required for predicting the energy available to food chains and for determining the role of algae in geochemical cycles (Sarmiento and Toggweiler, 1984). As all organisms are ultimately dependent on energy input via photosynthesis, and limitations to global carbon dioxide assimilation may constrain the potential productivity of higher trophic levels (Ryther, 1969). Riley (1944) provided the first estimate of global oceanic primary productivity of 860 ± 560 mg C·m−2·d−1. The large standard deviation associated with this estimate arose from uncertainty in extrapolating a small number of observations from a limited range of open and coastal ocean regions to the entire ocean. Despite an additional 45 years of research involving the widespread incorporation of 14C assimilation measurements into biological oceanographic sampling programs, there is continuing uncertainty about the magnitude of oceanic primary productivity (National Academy of Sciences, 1984). Shipboard observers cannot adequately sample the ocean at all spatial and temporal scales necessary to adequately resolve variations in phytoplankton biomass and productivity. In contrast, satellite remote sensing offers the potential to provide a detailed assessment of phytoplankton spatial and temporal variations. The remotely sensed signal is used to infer chlorophyll a concentration from measurements of light reflected from near surface waters. At present, remote sensors do not provide a direct measure of primary productivity; this must be inferred from the relationship between primary productivity and chlorophyll a concentration.

Keywords

Phytoplankton Abundance Incident Irradiance Surface Chlorophyll Algal Photosynthesis Coastal Zone Color Scanner 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 1992

Authors and Affiliations

  • Richard J. Geider
  • Bruce A. Osborne

There are no affiliations available

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