title: LAI inversion algorithm based on directional reflectance kernels.
abstract: Leaf area index (LAI) is an important ecological and environmental parameter. A new LAI algorithm is developed using the principles of ground LAI measurements based on canopy gap fraction. First, the relationship between LAI and gap fraction at various zenith angles is derived from the definition of LAI. Then, the directional gap fraction is acquired from a remote sensing bidirectional reflectance distribution function (BRDF) product. This acquisition is obtained by using a kernel driven model and a large-scale directional gap fraction algorithm. The algorithm has been applied to estimate a LAI distribution in China in mid-July 2002. The ground data acquired from two field experiments in Changbai Mountain and Qilian Mountain were used to validate the algorithm. To resolve the scale discrepancy between high resolution ground observations and low resolution remote sensing data, two TM images with a resolution approaching the size of ground plots were used to relate the coarse resolution LAI map to ground measurements. First, an empirical relationship between the measured LAI and a vegetation index was established. Next, a high resolution LAI map was generated using the relationship. The LAI value of a low resolution pixel was calculated from the area-weighted sum of high resolution LAIs composing the low resolution pixel. The results of this comparison showed that the inversion algorithm has an accuracy of 82%. Factors that may influence the accuracy are also discussed in this paper.
authors.affiliation: Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China. tshyjf@yahoo.com.cn
authors.forename: S
authors.initials: S
authors.lastname: Tang
authors.forename: J M
authors.initials: JM
authors.lastname: Chen
authors.forename: Q
authors.initials: Q
authors.lastname: Zhu
authors.forename: X
authors.initials: X
authors.lastname: Li
authors.forename: M
authors.initials: M
authors.lastname: Chen
authors.forename: R
authors.initials: R
authors.lastname: Sun
authors.forename: Y
authors.initials: Y
authors.lastname: Zhou
authors.forename: F
authors.initials: F
authors.lastname: Deng
authors.forename: D
authors.initials: D
authors.lastname: Xie
mesh_terms: D000465:Algorithms; D002681:China; D017753:Ecosystem; D004784:Environmental Monitoring / Q000379:methods*; D005843:Geography; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D017215:Satellite Communications; D013997:Time Factors; D014675:Vegetables / Q000254:growth & development*