Inversion methods for physically‐based models

Abstract

Physically‐based models of vegetation reflectance serve as a basis for extracting vegetation variables using directional and spectral data from modern‐borne sensors (e.g., MODIS, MISR, POLDER, SeaWiFS). Although many models have been inverted, only recently have significant efforts been made to provide operational algorithms. These efforts have exposed a need to significantly improve efficient and accurate methods for inverting these physically‐based models. The characteristics of the traditional inversion, table look‐up, neural network and other methods are discussed as well as the major achievements, advantages/disadvantages, and research issues for each method. The traditional inversion methods using repeated model runs are computationally intensive and are not appropriate for operational application on a per pixel basis for regional and global data. Thus, for larger data sets, simplified (reduced number of variables and/or physical processes) physically‐based models are generally used. The table look‐...