Digital SEASAT synthetic aperture radar (SAR) and LANDSAT multispectral scanner (MSS) data were evaluated to determine their utility to discriminate suburban and regional cover in the eastern fringe area of the Denver, Colorado, metropolitan area. The primary emphasis of the study was land-cover discrimination performance of MSS versus SAR and SAR/MSS combined. In addition, both a median-filtering and a data-smoothing procedure were tested in an attempt to increase the spectral separability between land-use/land-cover classes for SAR data. The results indicated that analysis of LANDSAT MSS data alone provided a significantly (α = 0·05) higher overall classification accuracy or improved spectral class separation than the best SEASAT SAR classification. However, when using LANDSAT MSS and SEASAT SAR data simultaneously, a significant increase in classification accuracy was obtained. Analysis indicated that SEASAT SAR data provided a measure of surface geometry that complemented the reflective characteristics of LANDSAT MSS visible and near-infrared data. Smoothing and median filtering provided significant improvement in classification accuracy over non-filtered SAR data.