Tasseled　Cap　Transformation　(TCT)　is　a　commonly　used　remote　sensing　technique　that　has　been　successfully　applied　in　various　remote　sensing　fields.　However,　for　high-resolution　satellite　sensors　that　usually　have　only　four　visible　near-infrared　bands　but　lack　a　mid-infrared　band,　the　retrieval　of　the　TCT　wetness　component　has　not　always　been　successful　with　the　traditional　Gram-Schmidt　(GS)　orthogonalization　method.　Moreover,　although　a　few　studies　have　developed　the　wet　component　for　such　four-band　sensor　data,　the　derived　results　are　somewhat　unreasonable.　Therefore,　this　study　proposes　a　new　method　to　derive　the　coefficients　of　the　TCT　wetness　component　for　the　four-band　sensor　data.　In　particular,　the　new　method　is　used　to　derive　the　TCT　coefficients　of　the　ZiYuan-3　(ZY-3)　MUX　sensor　data　of　China.　Eleven　ZY-3　MUX　images　and　six　synchronous/near　synchronous　Landsat　8　Operational　Land　Imager　(OLI)　images　from　different　regions　across　China　were　used　as　test　and　validation　images.　From　these　image　sets,　seven　ZY-3　MUX　images　and　three　Landsat　8　OLI　images　served　as　test　images,　while　the　other　four　ZY-3　MUX　images　and　three　Landsat　8　OLI　image　served　as　validation　images.　A　large　number　of　samples　representing　different　land-cover　types,　such　as　dry　and　wet　soil,　dense　vegetation,　and　water,　were　randomly　selected　from　the　test　images.　The　new　method　proposed　in　this　study　for　deriving　the　TCT　coefficients　is　a　back　derivation　(BD),　in　which　the　TCT　wetness　component　rather　than　the　brightness　component　was　first　retrieved,　as　previously　performed　in　the　traditional　Gram-Schmidt　method.　Three　synchronous/near　synchronous　image　pairs　of　ZY-3　MUX　and　Landsat　8　OLI　were　used　to　derive　the　wetness　component　coefficient　of　ZY-3　MUX,　particularly　by　relating　the　ZY-3　MUX　data　with　the　Landsat　8　wetness　component　based　on　the　selected　735297　pixel　samples.　Then,　the　brightness　and　greenness　components　of　the　ZY-3　data　were　derived　by　implementing　the　traditional　methods.　Finally,　the　new　BD　method　and　the　traditional　method　were　compared　to　verify　the　feasibility　of　the　new　method.　The　experimental　results　indicate　the　following:　(1)　the　TCT　wetness　component　of　ZY-3　MUX　retrieved　by　the　BD　method　can　effectively　solve　the　spectral　distortion　problem　that　exists　with　the　wetness　component　of　the　four-band　sensor　data　derived　by　the　traditional　method;　(2)　the　scatters　of　the　three　components　(brightness,　greenness,　and　wetness)　derived　by　the　new　method　have　typical　tasselled-cap-like　shapes　in　3D　feature　space,　and　they　are　composed　of　the　three　components.　Compared　with　the　traditional　GS　method,　the　scatters　of　water,　vegetation,　and　built　land　or　bare　soil　retrieved　by　the　BD　method　are　clearly　separated　in　3D　feature　space,　whereas　the　scatters　vaguely　overlap　in　the　traditional　GS　method;　(3)　the　accuracy　of　the　TCT　coefficients　derived　by　the　new　method　is　higher　than　that　derived　by　the　traditional　GS　method,　considering　that　the　new　method　has　a　higher　correlation　coefficient　(R)　and　a　lower　root　mean　square　error　when　validated　with　the　corresponding　TCT　components　of　the　Landsat　8　data.　This　finding　is　due　largely　to　the　improved　accuracy　of　the　wetness　component　derived　by　the　new　method.　This　study　provided　a　set　of　TCT　coefficients　for　ZY-3　MUX　sensor　data,　and　it　presented　a　new　method　for　deriving　TCT　coefficients　for　high-resolution　spatial　remote　sensing　imageries　with　only　four　visible　near-infrared　bands　but　lack　mid-infrared　bands.　The　new　method　effectively　solves　the　retrieval　problem　of　the　wetness　component　of　the　four-band　sensor　data　existing　in　the　traditional　GS　method.　©　2019,　Science　Press.　All　right　reserved.