Fast　and　accurate　impervious　surface　extraction　is　important　to　monitor　urban　dynamics　and　ensure　peo-ple＇s　well-being.　In　order　to　improve　the　extraction　accuracy　of　impervious　surface,　a　composite　imper-vious　surface　extraction　index　CBI　based　on　NDBI,　NDVI　and　MNDWI　is　proposed　in　the　paper.　The　impervious　surface　extraction　images　in　different　land　cover　areas　were　compared　with　the　NDBI　and　NDISI,　respectively,　and　were　assessed　by　using　visual　and　statistical　analysis.　The　results　show　that　CBI　has　good　robustness,　less　affected　by　the　type　of　ground　objects　and　sensors.　The　Kappa　coefficients　of　CBI　are　the　highest　in　different　sensors　and　OLI　images　with　different　land　cover　areas,　and　the　overall　accuracy　is　above　91%.　In　the　area　with　high　coverage　of　vegetation　and　water,　the　extraction　accuracy　of　CBI　was　significantly　improved.　CBI　is　more　suitable　for　areas　with　high　coverage　of　vegetation　and　water　and　has　an　improved　Kappa　coefficient　of　0.1116　than　that　of　the　areas　containing　only　a　small　amount　of　water　and　vegetation.　As　the　NDISI　algorithm　uses　the　thermal　infrared　band,　when　the　area　of　the　study　area　is　small,　its　extraction　accuracy　is　low,　indicating　that　the　accuracy　of　the　algorithm　is　affected　by　the　size　of　the　study　area.(c)　2023　National　Authority　of　Remote　Sensing　&　Space　Science.　Published　by　Elsevier　B.V.　This　is　an　open　access　article　under　the　CC　BY　license　(http://creativecommons.org/licenses/by/4.0/).