Monte Carlo simulation model for electromagnetic scattering from vegetation and inversion of vegetation parameters

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 171-185).In this thesis research, a coherent scattering model for microwave remote sensing of vegetation canopy is developed on the basis of Monte Carlo simulations. An accurate model of vegetation structure is essential for the calculation of scattering from vegetations, especially those with closely spaced elements in clusters. The Monte Carlo approach has an advantage over the conventional wave theory in dealing with complex vegetation structures because it is not necessary to find the probability density functions and the pair-distribution functions required in the analytic formulation and usually difficult to obtain for natural vegetation. To achieve a realistic description of the vegetation structure under consideration, two methods may be employed. One method requires the specification of the number of each type of component and the relative orientations of the components. In a structural model which incorporates this method, the detailed features can be preserved to the desired level of accuracy. This structural model is applied to two types of vegetation- --rice crops and sunflowers.(cont.) The developed structural model for rice crops takes into account the coherent wave interactions made prominent by the clustered and closely spaced structure of rice crops, and is validated with the ERS-1 and RADARSAT data. It is utilized to interpret the experimental observations from the JERS-1 data, such as the effects of the structure of rice fields, and to predict the temporal response of rice growth. The structural model developed for sunflowers is validated using the airborne Remote Sensing Campaign Mac-Europe 91 multi-frequency and multi-polarization data acquired for sunflower fields at the Montespertoli test site in Italy. Another method to characterize vegetation structure uses growth rules. This is especially useful in modeling trees, which are structurally more complex. The Lindenmayer systems (L-systems) are utilized to fully capture the architecture of trees and describe their growth. Monte Carlo simulation results of the scattering returns from trees with different structures and at different growth stages are calculated and analyzed. The concept of the "structure factor" which extracts the structural information of a tree and and provides a measure of the spatial distribution of branches is defined, and computed for trees with different architectures.(cont.) After study of the forward scattering problem in which the scattering coefficients are determined on the basis of known physical characteristics of the scattering objects or medium, the inverse scattering problem is considered in which the characteristics of the scattering objects or medium are to be calculated from the scattering data. In this thesis research, neural networks are applied to the inversion of geophysical parameters including soil moisture and surface parameters, sunflower biomass, as well as forest age (or equivalently, forest biomass). They are found to be especially useful for multi-dimensional inputs such as multi-frequency polarimetric scattering data. For the inversion of soil moisture and surface parameters, neural networks are trained with theoretical surface scattering models. To retrieve the sunflower biomass, neural networks are trained with the scattering returns obtained from the developed vegetation scattering model based on the Monte Carlo approach. To assess the performance of the use of experimental data to train the neural networks, the polarimetric radar data acquired by the Spaceborne Imaging Radar-C (SIR-C) over the Landes Forest in France are utilized as the training data to retrieve the forest age. Different combinations of backscattering data are used as input to the neural net in order to determine the combination which yields the best inversion result.by L-i-Fang Wang.Ph.D