HR: 0800h
AN: B21A-0040    [Abstracts]
TI: Seasonally Varying Leaf Area for Climate and Carbon Models from Assimilation of Satellite Reflectance data into a Dynamical Leaf Model
AU: * Liu, Q
EM: qing.liu@eas.gatech.edu
AF: School of Earth and Atmospehric Sciences, Georgia Institute of Technology, 311 Ferst Dr., Atlanta, GA 30332-0340, United States
AU: Gu, L
EM: lianhong-gu@ornl.gov
AF: Environmental Sciences Division, Oak Ridge National Laboratary, Bldg 1509, ORNL, Oak Ridge, TN 37831, United States
AU: Dickinson, R E
EM: robted@eas.gatech.edu
AF: School of Earth and Atmospehric Sciences, Georgia Institute of Technology, 311 Ferst Dr., Atlanta, GA 30332-0340, United States
AU: Tian, Y
EM: yuhong.tian@noaa.gov
AF: School of Earth and Atmospehric Sciences, Georgia Institute of Technology, 311 Ferst Dr., Atlanta, GA 30332-0340, United States
AU: Zhou, L
EM: liming.zhou@noaa.gov
AF: School of Earth and Atmospehric Sciences, Georgia Institute of Technology, 311 Ferst Dr., Atlanta, GA 30332-0340, United States
AU: Post, W M
EM: wmp@ornl.gov
AF: Environmental Sciences Division, Oak Ridge National Laboratary, Bldg 1509, ORNL, Oak Ridge, TN 37831, United States
AB: Leaf area index is an important land surface parameter and is a necessary input for climate and carbon models. The widely-used leaf area products derived from satellite observed surface reflectances contain substantial erratic fluctuations in time due to incomplete atmospheric corrections and observational and retrieval uncertainties, which are inconsistent with the seasonal dynamics of leaf area that are generally gradual in nature. We propose a data assimilation approach to combine the satellite observations with a dynamical leaf model so that the seasonal cycle of the directly retrieved leaf areas can be also constrained by the dynamical model simulations. The data assimilation allows automatic adjustment of the dynamical model parameters, such that the optimal compromise between the estimated surface reflectances based on the modeled leaf area and that of satellite observations can be reached. Testing results at three United State deciduous forests with relatively homogenous landscapes have shown that the data assimilation significantly smoothens the seasonal cycle of the estimated leaf areas compared to that without the dynamical leaf model constraining. Meanwhile, it does not deteriorate the agreement between the modeled and satellite observed surface reflectances.
DE: 0428 Carbon cycling (4806)
DE: 0439 Ecosystems, structure and dynamics (4815)
DE: 0480 Remote sensing
DE: 3315 Data assimilation
DE: 3322 Land/atmosphere interactions (1218, 1631, 1843)
SC: Biogeosciences [B]
MN: 2007 Fall Meeting