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.　(C)　2013　COSPAR.　Published　by　Elsevier　Ltd.　All　rights　reserved.