The amount of impervious surface area increases with rapid urbanization. Remote sensing indices are used to detect impervious surface areas quickly, cheaply and accurately. This study used Landsat-OLI and Sentiel-2A MSI images in the province of Ankara to compare six impervious surface extraction indices: Normalized Difference Built-up Index (NDBI), Combinational Biophysical Composition Index (CBCI), Normalized Impervious Surface Index (NISI), Urban Index (UI), Index-based Built-up Index (IBI), Enhanced Normalized Difference Impervious Surfaces Index (ENDISI). Spectral discrimination index (SDI) and error matrix were used to evaluate the performance of the indexes. In addition, a visual evaluation of the performance of the indices was made on different surface areas in the study area. UI was the best-performing index in both Landsat-8 OLI and Sentinel-2A MSI data. The UI index achieved 91.67% overall accuracy, 0.824 kappa value and 1.400 SDI values in Landsat-8 OLI data. According to the histogram graphics of the indices, UI was the index that best differentiated between impervious and pervious surfaces. When the results of indexes other than UI were examined, it was determined that although there were no significant differences between the Landsat-8 OLI and Sentinel-2A MSI results, Landsat-8 OLI produced slightly better results.