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United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES Title: Online vegetation parameter estimation using passive microwave remote sensing observations

Authors
item Fitzmaurice, Jean
item Crow, Wade

Submitted to: International Association of Hydrological Science
Publication Type: Abstract Only
Publication Acceptance Date: June 20, 2010
Publication Date: September 30, 2010
Citation: Fitzmaurice, J.A., Crow, W.T. 2010. Online vegetation parameter estimation using passive microwave remote sensing observations [abstract]. International Association of Hydrological Science. p. 26.

Technical Abstract: In adaptive system identification the Kalman filter can be used to identify the coefficient of the observation operator of a linear system. Here the ensemble Kalman filter is tested for adaptive online estimation of the vegetation opacity parameter of a radiative transfer model. A state augmentation approach is used where the vegetation parameter is added to the soil moisture state vector. The filter consists of a two-layer soil hydrology model and the radiative tranfer model and is tested for a 184 day period with daily updates using simulated remote sensing observations. Satisfactory estimation results are obtained for both static and idealized time-varying vegetation parameter cases. Persistent excitation, adding small variance mean zero Gaussian noise, is required in the time-varying case to converge close to the true estimate, consistent with theory. Besides surface soil moisture, vegetation information could be extracted from passive microwave observations using an adaptive system identification filtering approach.

Last Modified: 7/30/2014
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