Refining land product consistency and quality from sensor constellations
09 August 2012
There is no doubt that Earth Observation (EO) data has great potential to contribute to land surface monitoring, providing consistent global coverage with historical archives to document changes. The increasing number of EO missions is opening new opportunities to users to access a larger number of data products derived from different sensors providing estimates of the same geo-physical or climate variables.
Modeling and predicting future changes of the land surface and their interaction with the atmosphere requires quantifying and monitoring surface properties such as, vegetation and soil percentage coverage, area of leaf cover per unit surface area and concentration of photosynthetic active pigments within the canopy. Inconsistencies in the methodologies used to generate these products make it difficult to integrate products from more than one sensor system into modeling assimilation schemes.
The Support to Science Element (STSE) study EO-LDAS (Earth Observation - Land Data Assimilation Scheme) aims to overcome these difficulties by developing a scheme to directly assimilate radiance fields, instead of higher level data products.
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Source: Earthnet Online