Vegetation　is　one　of　the　most　important　components　of　the　global　ecosystem.　It　plays　a　vital　role　for　global　energy　balances　and　climate　modulations.　Timely　and　precisely　monitoring　vegetation　changes　has　become　an　increasing　need.　Among　various　satellite-based　earth　observation　systems,　the　Landsat　and　ASTER　sensors　are　most　commonly　used　in　mesoscale　vegetation　observations.　Nevertheless,　the　quantitative　relationship　between　the　two　sensors　in　measuring　vegetation　has　not　been　investigated　in　detail.　Therefore,　this　paper　aims　to　investigate　how　well　ASTER　vegetation　measurements　replicate　ETM+　vegetation　measurements,　and　more　important,　how　much　difference　there　is　in　the　measurements　between　the　two　sensors.　The　study　utilised　three　date-coincident　image　pairs　of　the　two　sensors,　covering　the　subtropical　coastal　areas　of　southeastern　China.　The　approach　was　achieved　by　evaluating　the　consistence　of　the　normalised　difference　vegetation　index　(NDVI)　data　and　the　NDVI-estimated　vegetation　area　between　the　two　sensors.　A　further　inter-type　comparison　among　forest,　paddy　and　grass　has　also　been　carried　out　to　examine　whether　there　is　any　variation　in　the　consistence　between　different　vegetation　types.　Results　suggest　that　the　ASTER　sensor　produces　similar　vegetation　measurements　to　ETM+　in　the　study　areas.　Nevertheless,　differences　have　also　been　observed.　The　study　found　an　overall　lower　spectral　NDVI　measurement　of　ASTER　than　ETM+　based　on　their　differences　in　mean　NDVI　value　and　estimated　vegetation　area.　This　suggests　that　the　ASTER　sensor　is　less　sensitive　to　vegetation　characteristics.　Compared　with　ETM+,　the　ASTER　sensor　produces　lower　NDVI　values　by　up　to　8.1%　and　estimates　less　vegetation　area　by　up　to　4.1%.　Inter-type　analysis　indicates　that　among　the　three　test　vegetation　types,　forest　has　the　highest　degree　of　agreement　in　NDVI　measurements　between　the　two　sensors,　whereas　grass　has　the　greatest　difference　in　mean　NDVI　value　between　both　sensors.　All　these　differences　are　worth　considering　when　the　vegetation　measurement　results　of　ASTER　have　to　be　used　together　with　those　of　ETM+　for　a　synergistic　scientific　application.　This　often　occurs　when　using　the　ASTER　sensor　data　for　Landsat　data　continuity　in　scientific　applications　in　order　to　fill　the　gaps　in　observation.　As　such,　a　data　conversion　between　the　two　sensors　is　recommended.　This　paper　demonstrated　that　the　conversion　using　the　model　equations　obtained　in　this　study　could　reduce　the　root　mean　square　errors　and　improve　data　agreement　between　the　two　sensor　NDVIs.　The　study　shows　that　the　revealed　differences　in　vegetation　measurements　between　both　sensors　are　due　largely　to　the　difference　in　the　relative　spectral　response　functions　of　the　NDVI-related　near-infrared　bands　between　the　two　sensors.　(C)　2012　Elsevier　B.V.　All　rights　reserved.