The Seasat-A scatterometer (SASS) was designed to measure the near-surface wind field over the ocean by inferring the wind from measurements of the surface radar backscatter. While backscatter measurements were also made over land, they have been primarily used for the calibration of the instrument. This has been due in part to the low resolution of the scatterometer measurements (nominally 50 km). In a separate paper the present authors introduced a new method for generating enhanced resolution radar measurements of the Earth's surface using spaceborne scatterometry. In the present paper, the method is used with SASS data to study vegetation classification over the extended Amazon basin using the resulting medium-scale radar images. The remarkable correlation between the Ku-band radar images and vegetation formations is explored, and the results of several successful experiments to classify the general vegetation classes using the image data are presented. The results demonstrate the utility of medium-scale radar imagery in the study of tropical vegetation and permit utilization of both historic and contemporary scatterometer data for studies of global change. Because the scatterometer provides frequent, wide-area coverage at a variety of incidence angles, it can supplement higher resolution instruments which often have narrow swaths with limited coverage and incidence angle diversity.
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