Estimating forest variables from fusion of SAR and TM data and analytical scattering and reflectance models

A method for simultaneous integration of synthetic aperture radar (SAR) and optical remote sensing data in an estimation algorithm is presented which results in estimates of foliage mass over a larger range of values and more accurately than would be possible with either data type alone. The improved estimates are expected to result in more accurate calculation of ecosystem exchange from biogeochemistry models. The solution uses simplified closed-form models of scattering and reflectance derived from more complicated numerical models in a nonlinear estimation algorithm. Results are compared with available field measurements for 25 reference plots. A thorough error analysis is carried out to characterize the statistical accuracy of the estimation results with respect to errors in the microwave scattering and optical reflectance models. The foliage mass data are ultimately to be used to derive leaf area index (LAI), an essential driving variable for forest process models