Biological Oceanography by Remote Sensing

Abstract

Biological oceanography may be studied from space using sensors on satellites that determine the color of the ocean. The presence of phytoplankton (microscopic algae) in the upper layers of the ocean changes the color of the water as seen from above. In simplified terms, this is due to the selective absorption of blue light by the phytoplankton pigments (primarily chlorophyll) which changes the appearance of the water from blue to green. These changes in color can be observed using a satellite‐borne spectroradiometer that measures the water‐leaving radiance in a number of bands in the visible part of the electromagnetic spectrum. The limitations of the technique are first, that only information on the phytoplankton in the upper layers of the ocean can be obtained (light does not penetrate very far into the ocean). Second, most of the signal measured by the satellite sensor originates in the atmosphere (due to the molecular and aerosol scattering of photons there), so careful correction for atmospheric effects is necessary if good ocean data are to be obtained. Of course, in the presence of clouds the sensor will not “see” the ocean surface at all, and no data will be obtained. Third, only one component of the ocean ecosystem, namely the phytoplankton, can be studied by this means. Despite these limitations, satellite observations of ocean color have given new insights into biological oceanography on a global scale that could not have been obtained by any other means of observation. Observations of ocean color have contributed to a better understanding of the biophysical interactions that determine the phytoplankton productivity, the seasonal and interannual variations of the phytoplankton biomass on global scales, and the role of phytoplankton in the climate system. They have also contributed to the improved modeling of biogeochemical processes in the ocean.

Seasonal variability of the Black Sea chlorophyll-a concentration

“…The in-situ data are noisy (noise-to-signal ratio up to 60%), sparse and irregular in space and time (Vedernikov and Demidov, 1997). The satellite data (SeaWiFS images) are also noisy with the noise-to-signal ratio near 35% for open water and higher for turbid coastal water such as the Black Sea (Srokosz, 2000;Oguz et al, 2002b).…”

Imaging spectroscopy for coastal biogeochemistry of estuaries and plumes

“…Derivation of geophysical parameters from Case II waters is very complex task (Srokosz, 2000), which demands long duration of field campaigns, together with swift alterations in conditions, constitutes a limit in establishing a global and synoptical view of the state of estuarine ecosystem. It is as well a limit in the design of complete seasonal survey of those highly dynamic systems.…”