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UCL Home  /  Geography  /  News & Events  /  News  /  News Archive  /  August 2012  /  Progress in applying Data Assimilation (DA) approaches to interpreting satellite observations of the Earth's vegetation

Progress in applying Data Assimilation (DA) approaches to interpreting satellite observations of the Earth's vegetation

Encouraging results from Philip Lewis and José Gomez-Dans

Progress in applying Data Assimilation (DA) approaches to interpreting satellite observations of the Earth's vegetation

Over the past few years, Professor Philip Lewis and Dr José Gomez-Dans, of UCL Geography, have been trying to understand how to incorporate Data Assimilation (DA) approaches into the interpretation of satellite observations of the Earth's vegetation and the merging of such data into Earth System Models (ESMs). This work has been partly funded by NERC under the National Centre for Earth Observation (NCEO),  because DA is central to NERC strategy.

International organisations such as the European Space Agency (ESA) also have a keen interest in using DA, both for improved monitoring and modelling and also because it provides a basis for the improved use of observations from multiple platforms.

Lewis and Gomez-Dans, with input from NCEO colleagues and researchers at the EU Joint Research Centre (JRC) and FastOpt GMbH, have developed a software tool as a prototype DA system for using Earth Observation (EO) data for monitoring the land surface. This tool, known as EO-LDAS (EO Land DA system) was funded by ESA's Support to Science Element (STSE), and was released on 9th August 2012.

The current version of EO-LDAS is mainly aimed at using (space/time) regularisation constraints to interpret EO data, using a radiative transfer model that describes the interaction of solar radiation with the land surface (soil and vegetation). It implements what is known as a 'variational' DA system and has been used to examine the likely uncertainties in data from the forthcoming ESA Sentinel-2 satellites (`Lewis et al., 2012a: http://www.sciencedirect.com/science/article/pii/S0034425712000788).

It has also been used to compare satellite-derived estimates of crop state with field measurements in Germany (`Lewis et al., 2012b:

http://jgomezdans.github.com/eoldas_release/Data_Assimilation_of_sentinel2_observations_preliminary_results_from_eoldas__Lewis_2012.pdf).

The ground data were collected by colleagues in FSU Jena under the ESA EO-LDAS project.

For full announcement, see:

https://earth.esa.int/web/guest/news/featured-stories/-/asset_publisher/7ipD/content/eo-ldas-article?p_r_p_564233524_assetIdentifier=eo-ldas-article&redirect=%2Fc%2Fportal%2Flayout%3Fp_l_id%3D66969

For the EOLDAS project website, see: http://www.eoldas.info/

For Information on downloading and using the Python software, see: the EOLDAS website or http://jgomezdans.github.com/eoldas_release/

 

See also:

National Centre for Earth Observation: http://www.nceo.ac.uk/

European Space Agency (ESA: http://www.esa.int/esaCP/index.html>`


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