Worldwide　urbanization　has　accelerated　expansion　of　urban　built-up　lands　and　resulted　in　substantial　negative　impacts　on　the　global　environments.　Precisely　measuring　the　urban　sprawl　is　becoming　an　increasing　need.　Among　the　satellite-based　earth　observation　systems,　the　Landsat　and　ASTER　data　are　most　suitable　for　mesoscale　measurements　of　urban　changes.　Nevertheless,　to　date　the　difference　in　the　capability　of　mapping　built-up　land　between　the　two　sensors　is　not　clear.　Therefore,　this　study　compared　the　performances　of　the　Landsat-7　ETM+　and　ASTER　sensors　for　built-up　land　mapping　in　the　coastal　areas　of　southeastern　China.　The　comparison　was　implemented　on　three　date-coincident　image　pairs　and　achieved　by　using　three　approaches,　including　per-band-based,　index-based,　and　classification-based　comparisons.　The　index　used　is　the　Index-based　Built-up　Index　(IBI),　while　the　classification　algorithm　employed　is　the　Support　Vector　Machine　(SVM).　Results　show　that　in　the　study　areas,　ETM+　and　ASTER　have　an　overall　similar　performance　in　built-up　land　mapping　but　also　differ　in　several　aspects.　The　IBI　values　determined　from　ASTER　were　consistently　higher　than　from　ETM+　by　up　to　45.54%　according　to　percentage　difference.　The　ASTER　also　estimates　more　built-up　land　area　than　ETM+　by　5.9-6.3%　estimated　with　the　IBI-based　approach　or　3.9-6.1%　with　the　SVM　classification.　The　differences　in　the　spectral　response　functions　and　spatial　resolution　between　relative　spectral　bands　of　the　two　sensors　are　attributed　to　these　different　performances.　©　2013　COSPAR.　Published　by　Elsevier　Ltd.　All　rights　reserved.