INVERSION OF A VEGETATION REFLECTANCE MODEL WITH NOAA AVHRR DATA

We invert a bidirectional reflectance model with NOAA AVHRR data colle cted over a mired grassland during the First ISLSCP Field Experiment ( FIFE). Leaf area index (LAI) and leaf optical properties are accuratel y retrieved in one-parameter inversions. In two-parameter inversions, the accuracy of the retrieved parameters is coupled: LAI is accurately retrieved only when leaf optical properties are accurately retrieved and vice-versa. Since inversion accuracy depends on the sampling geome tries of the reflectance data, we also develop a ''derivative weightin g'' scheme for the merit function. This scheme causes inversion soluti ons to be preferentially determined by data containing the most inform ation about the model parameters. We show this scheme increases the gr adients of the merit function such that more rapid and accurate invers ions are possible. We also use derivative weights to select the most p romising data subsets for inversion. This study confirms the operation al potential of model inversions with inexpensive, widely available sa tellite data. Moreover, our methods can. be used with future remote se nsing systems such as EOS MODIS, MISR, and POLDER. (C) Elsevier Scienc e Inc., 1996.