Assimilating canopy reflectance data into an ecosystem model with an Ensemble Kalman Filter

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• 作者：Tristan Quaife ; Philip Lewis ; Martin De Kauwe ; Mathew Williams ; Beverly E. Law ; Mathias Disney ; Paul Bowyer
• 关键词：Data assimilation ; MODIS ; Ecosystem modelling ; Terrestrial Carbon Dynamics
• 刊名：Remote Sensing of Environment
• 出版年：2008
• 期刊代码：79_00344257
• 类别：ear
• 出版时间：15 April 2008
• 卷：112
• 期：4
• 页码：1347-1364
• 文件大小：2026 K

摘要

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.