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dc.contributor.authorLEWIS P.en_GB
dc.contributor.authorGÓMEZ-DANS J.en_GB
dc.contributor.authorKAMINSKI Thomasen_GB
dc.contributor.authorSETTLE J.en_GB
dc.contributor.authorQUAIFE Tristanen_GB
dc.contributor.authorGOBRON Nadineen_GB
dc.contributor.authorSTYLES J.en_GB
dc.contributor.authorBERGER M.en_GB
dc.date.accessioned2012-12-05T01:02:21Z-
dc.date.available2012-12-04en_GB
dc.date.available2012-12-05T01:02:21Z-
dc.date.created2012-09-20en_GB
dc.date.issued2012en_GB
dc.date.submitted2012-04-19en_GB
dc.identifier.citationREMOTE SENSING OF ENVIRONMENT vol. 120 p. 219-235en_GB
dc.identifier.issn0034-4257en_GB
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S0034425712000788en_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC70156-
dc.description.abstractCurrent methods for estimating vegetation parameters are generally sub-optimal in the way they exploit in formation and do not generally consider uncertainties. We look forward to a future where operational data assimilation schemes improve estimates by tracking land surface processes and exploiting multiple types of observations. Data assimilation schemes seek to combine observations and models in a statistically optimal way taking into account uncertainty in both, but have not yet been much exploited in this area. The EO-LDAS scheme and prototype, developed under ESA funding, is designed to exploit the anticipated wealth of data that will be available under GMES missions, such as the Sentinel family of satellites, to provide improved mapping of land surface biophysical parameters. This paper describes the EO-LDAS implementation, and explores some of its core functionality. EO-LDAS is a weak constraint variational data assimilation system. The prototype provides a mechanism for constraint based on a prior estimate of the state vector, a linear dynamic model, and Earth Observation data (top-of-canopy reflectance here). The observation operator is a non-linear optical radiative transfer model for a vegetation canopy with a soil lower boundary, operating over the range 400 to 2500 nm. Adjoint codes for all model and operator components are provided in the prototype by automatic differentiation of the computer codes. In this paper, EO-LDAS is applied to the problem of daily estimation of six of the parameters controlling the radiative transfer operator over the course of a year (>2000 state vector elements). Zero and first order process model constraints are implemented and explored as the dynamic model. The assimilation estimates all state vector elements simultaneously. This is performed in the context of a typical Sentinel-2 MSI operating scenario, using synthetic MSI observations simulated with the observation operator, with uncertainties typical of those achieved by optical sensors supposed for the data. The experiments consider a baseline state vector estimation case where dynamic constraints are applied, and assess the impact of dynamic constraints on the a posteriori uncertainties. The results demonstrate that reductions in uncertainty by a factor of up to two might be obtained by applying the sorts of dynamic constraints used here. The hyperparameter (dynamic model uncertainty) required to control the assimilation are estimated by a cross-validation exercise. The result of the assimilation is seen to be robust to missing observations with quite large data gaps.en_GB
dc.description.sponsorshipJRC.H.7-Climate Risk Managementen_GB
dc.format.mediumPrinteden_GB
dc.languageENGen_GB
dc.publisherELSEVIER SCIENCE INCen_GB
dc.relation.ispartofseriesJRC70156en_GB
dc.titleAn Earth Observation Land Data Assimilation System (EO-LDAS)en_GB
dc.typeArticles in periodicals and booksen_GB
dc.identifier.doi10.1016/j.rse.2011.12.027en_GB
JRC Directorate:Sustainable Resources

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