The Earths oceans play a vital role in the carbon cycle, making it imperative that we understand marine biological activity enough to predict how our planet will react to the extra 25 000 million tonnes of carbon dioxide humans are pumping into the atmosphere annually.
The colour of oceanic seawater depends largely on the number of microscopic phytoplankton, marine plants that live in the well-lit surface layer. Just like land-based plants, phytoplankton accumulate carbon dioxide during photosynthesis and store it in their tissues, making them potentially important carbon sinks.
While phytoplankton themselves are individually microscopic, the chlorophyll they collectively contain colours the ocean's waters, which provides a means of detecting these tiny organisms from space with dedicated ocean colour sensors.
To support ocean carbon cycle research, ESAs GlobColour project has merged 55 terabytes of data from three state-of-the-art instruments aboard different satellites, including MERIS aboard ESAs Envisat, MODIS aboard NASAs Aqua and SeaWiFS aboard GeoEyes Orbview-2, to produce a 10-year dataset of global ocean colour stretching to 2007.
"I am quite impressed by the work ESA has done so far within GlobColour," said Dr Cyril Moulin of the International Ocean Carbon Coordination Project (IOCCP). "This 10-year dataset is going to be very useful for carbon studies and global modelling."
The ocean colour datasets are freely available to the public via the GlobColour website. A new web interface, Hermes, is available which allows users to select a time period, spatial region and product type. Based on this input, the system extracts the appropriate ocean colour products for users to download.
By combining observations from multiple sensors, GlobColour brings several benefits over existing products, such as better sampling of the daily variability, smaller errors because of the larger amount of data and
'/>"/>
| Contact: Mariangela D'Acunto mariangela.dacunto@esa.int 39-069-418-0856 European Space Agency Source:Eurekalert |