Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.14/113511
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- Title
- Assimilating canopy reflectance data into an ecosystem model with an Ensemble Kalman Filter
- Related
- Remote sensing of environment, Vol. 112, No. 4 (2008), p.1347-1364
- DOI
- 10.1016/j.rse.2007.05.020
- Publisher
- Elsevier Inc
- Date
- 2008
- FoR/RFCD Code(s)
-
040600 Physical Geography and Environmental Geoscience
090900 Geomatic Engineering
- Author/Creator
- Quaife, Tristan
- Author/Creator
- Lewis, Philip
- Author/Creator
- De Kauwe, Martin
- Author/Creator
- Williams, Mathew
- Author/Creator
- Law, Beverly E
- Author/Creator
- Disney, Mathias
- Author/Creator
- Bowyer, Paul
- Description
- An Ensemble Kalman Filter (EnKF) is used to assimilate canopy reflectance data into an ecosystem model. We demonstrate the use of an augmented state vector approach to enable a canopy reflectance model to be used as a non-linear observation operator. A key feature of data assimilation (DA) schemes, such as the EnKF, is that they incorporate information on uncertainty in both the model and the observations to provide a best estimate of the true state of a system. In addition, estimates of uncertainty in the model outputs (given the observed data) are calculated, which is crucial in assessing the utility of model predictions. Results are compared against eddy-covariance observations of CO₂ fluxes collected over three years at a pine forest site. The assimilation of 500 m spatial resolution MODIS reflectance data significantly improves estimates of Gross Primary Production (GPP) and Net Ecosystem Productivity (NEP) from the model, with clear reduction in the resulting uncertainty of estimated fluxes. However, foliar biomass tends to be over-estimated compared with measurements. Issues regarding this over-estimate, as well as the various assumptions underlying the assimilation of reflectance data are discussed.
- Description
- 18 page(s)
- Subject Keyword
- 040600 Physical Geography and Environmental Geoscience
- Subject Keyword
- 090900 Geomatic Engineering
- Subject Keyword
- carbon dioxide
- Subject Keyword
- ecosystems
- Subject Keyword
- Net Ecosystem Productivity
- Subject Keyword
- Terrestrial Carbon Dynamics
- Subject Keyword
- data acquisition
- Subject Keyword
- canopy reflectance
- Subject Keyword
- carbon cycle
- Subject Keyword
- data assimilation
- Subject Keyword
- ecosystem modeling
- Subject Keyword
- eddy covariance
- Subject Keyword
- estimation method
- Subject Keyword
- Kalman filters
- Subject Keyword
- uncertainty analysis
- Subject Keyword
- canopy reflectance
- Subject Keyword
- data assimilation
- Subject Keyword
- ecosystem modelling
- Subject Keyword
- ensemble Kalman Filter
- Subject Keyword
- gross Primary Production
- Subject Keyword
- MODIS
- Resource Type
- journal article
- Organisation
- Macquarie University. Dept. of Biological Sciences
- Identifier
- http://hdl.handle.net/1959.14/113511
- Identifier
- ISSN:0034-4257
- Identifier
- mq-rm-2010001749
- Language
- eng
- Reviewed
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