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UCL Home  /  Geography  /  News & Events  /  News  /  News Archive  /  July 2008  /  Top Downloaded Paper

Top Downloaded Paper

A paper by UCL researchers and collaborators in Edinburgh and Oregon was the most downloaded paper in the journal 'Remote Sensing of Environment' over the period January to March 2008.

Top Downloaded Paper

'Assimilating Canopy Reflectance data into an Ecosystem Model with an Ensemble Kalman Filter' was the title of the most  downloaded paper in the interdisciplinary journal Remote Sensing of Environment over the period January to March 2008.  Dr Tristan Quaife, Professor Philip Lewis and Dr Mathias Disney from UCL Geography contributed to the paper.

Remote sensing of the land surface from satellite sensors has the potential to help improve our ability to monitoring the dynamics of terrestrial ecosystems and important quantities such as carbon fluxes (how much carbon is taken up or released by vegetation). However, such instruments measure the amount of scattered radiation at particular wavelengths rather than quantities of direct ecological interest. One way to translate the remote measurements into such data is by combining them with a model of ecological process. This can be achieved in various ways, but many such methods end up with inconsistencies in what we assume in the 'ecological' and the 'remote sensing' models.

Data assimilation optimally combines models and measurements. It has been widely applied using atmospheric and oceanic EO data, but is still new in the land-surface community. This paper describes novel work in the NERC Centre for Terrestrial Carbon Dynamics (CTCD) and collaborators at Oregon State University to integrate low-level EO data with an ecological model to predict Carbon fluxes with associated uncertainties. The way in which we do this provides a route for reducing inconsistencies between the models and providing for improved monitoring of carbon fluxes from remote sensing.

For further information contact:

Dr Tristan Quaife (tquaife@geog.ucl.ac.uk)
Professor Philip Lewis (plewis@geog.ucl.ac.uk)
Dr Mathias Disney (mdisney@geog.ucl.ac.uk)


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