MICROWAVE INVERSION OF ROOT MEAN-SQUARE HEIGHT FROM VEGETATED FIELDS - A DUAL-FREQUENCY TECHNIQUE

An iterative, physically model based inversion algorithm has been used to estimate root mean square (r.m.s.) surface roughness height from r adar data, collected over vegetated areas. The model is based on a dis crete scatterer random media technique, and employs the distorted Born approximation to model the backscatter coefficients for a given scene . In the model, the Fresnel reflectivity (a measure of soil moisture) and surface roughness appear together in the vegetation-ground interac tion term. An approach is followed that utilizes differences in their frequency response to separate the two. Sensitivity analysis shows tha t the change in surface reflectivity owing to the change in frequency from the L- to C-band is dominated by surface r.m.s. height. The Fresn el reflectivity stays almost constant over this frequency interval. Th e inversion algorithm based on these sensitivity differences is applie d to the backscatter model data from a plant canopy of soybean. Calcul ations show that the technique gives accurate results from a model bac kscatter data set that is corrupted with random noise. The inversion a lgorithm is also applied to Synthetic Aperture Radar (SAR) data collec ted over corn fields during the MACHYDRO'90 experiment in Pennsylvania , USA, There is an excellent agreement between the measured and the es timated r.m.s. surface height.