Remote sensing estimation of impervious surface is significant in monitoring urban development and determining the overall environmental health of a watershed, and it has therefore attracted more interest recently in the remote sensing community. The main objective of this paper is to examine and compare the effectiveness of two advanced algorithms for estimating impervious surfaces from medium spatial resolution satellite images, namely, linear spectral mixture analysis (LSMA) and artificial neural network (ANN). Terra's Advanced Spaceborne Thermal Emission and Reflection Radiometer [(ASTER); acquired on June 16,2001] and a Landsat Enhanced Thematic Mapper Plus (ETM+) image (acquired on June 22, 2000) of Indianapolis, IN, were used for the analysis. The LSMA was employed to generate high- and low-albedo, vegetation, and soil fraction images (endmembers), and an image of impervious surfaces was then estimated by adding high- and low-albedo fraction images. Furthermore, an ANN model, specifically the multilayer-perceptron feedforward network with the back-propagation learning algorithm, was employed as a subpixel image classifier to estimate impervious surfaces. Accuracy assessment was performed against a high- resolution digital orthophoto. The results show that ANN was more effective than LSMA in generating impervious surfaces with high statistical accuracy. For the ASTER image, the root-mean-square error (RMSE) of the impervious surface map with the ANN model was 12.3%, and the one that resulted from LSMA was 13.2%. For the ETM+ image, the RMSE with the ANN model was 16.7%, and the one from LSMA was 18.9%. The better performance of ANN over LSMA is mainly attributable to the ANN'S capability of handling the nonlinear mixing of image spectrum. In order to test the seasonal sensitivity of satellite images for estimating impervious surfaces, LSMA was applied to two additional ASTER images of the same area, which are acquired on April 5, 2004, and October 3, 2000, r- - espectively. The results were then compared with the ASTER image acquired in June in terms of RMSE. The June image had the highest accuracy, whereas the October image was better than the one in April. Plant phenology caused changes in the variance partitioning and impacted the mixing-space characterization, leading to a less accurate estimation of impervious surfaces.