The coastal zone of Lake Malawi contains the greatest diversity of freshwater ichthyofauna in the world. The distribution of habitats along the coast has played an important role in the speciation of fishes but has not been mapped using remote sensing due to cloud cover. The discrimination of rock, sand, and vegetated coasts of tropical Lake Malawi are investigated using synthetic aperture radar (SAR) and optical remote sensing data. The effects of coherent fading, look direction, and incident angle on SAR backscatter are investigated using eight fine-beam RADARSAT SGX images covering 31 km of coast. Adaptive filter trials demonstrate pixels with relatively low backscatter values are identified as image noise most frequently. For each class most of the SAR backscatter averages derived from high and low incident angles within each look direction are similar or within sensor calibration limits. Average rock, sand, and vegetated image tones derived from shoreline segments 150 m in length are statistically separable.Linear discriminant Analyses (LDA) of the RADARSAT and SPOT data are used to attain maximal separation for 33% of rock, sand, and vegetated coastal data and to predict class membership for the 67% of coast for which class membership is known. The RADARSAT and SPOT are treated independently and then combined to use the complementary information available in multi-sensor data sets. LDA of a single extra fine beam RADARSAT image can separate rock, sand, and vegetation using SAR backscatter, coastal slope, and the angle of the shoreline relative to the satellite. Overall classification agreement is 98.5%. LDA of the four multispectral SPOT bands provided classification agreement of 79.3%. Coastal class discrimination is improved with the addition of one or more SAR images to the SPOT data. Classification is not improved above 98.9% when more than one SAR image is added to the data from SPOT. SAR data can be used to map rock, sand, and vegetated coastal zones in areas of persistent cloud cover.