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‐up and neural network methods are computationally efficient and can be applied on a per pixel basis. Moreover, they can be applied to the most sophisticated models without any simplifications. Finally, they do not require initial guesses to model variables as do the traditional inversion methods. However, traditional inversion and table look‐up methods are inherently designed to handle any arbitrary set of Sun‐view angles. Neural networks have not been generalized, as of yet, to handle arbitrary angles. We believe the most pressing research priority is to perform a rigorous comparison of the various inversion methods in terms of accuracy and stability, computational efficiency, general applicability, and number of variables obtainable. Keywords: Physically‐based modelsVegetationDirectional relectanceInversionTable look‐upNeural networkOptimizationRadiant transferLAIFPARBRDF Notes Corresponding author.