Tasseled　cap　transformation　(TCT)　is　a　commonly　used　remote-sensing　technique　and　has　been　successfully　used　in　various　remote　sensing-related　applications.　However,　the　TCT　coefficient　set　is　sensor-specific,　and　therefore,　in　this　article,　we　developed　the　TCT　coefficients　specifically　for　Sentinel-2　multi-spectral　instrument　at-sensor　reflectance　data.　A　total　of　ten　synchronous　image　pairs　of　Sentinel-2　and　Landsat-8,　collected　from　the　different　parts　of　the　world,　were　used　for　this　approach.　Instead　of　using　the　traditional　Gram-Schmidt　orthogonalization　(GSO)　method,　we　derived　the　coefficients　using　a　principal　component-based　Procrustes　analysis　(PCP)　method.　This　was　done　by　rotating　principal　component　axes　of　Sentinel-2　data　to　align　to　Landsat-8　Operational　Land　Imager　tasseled　cap　axes　via　the　Procrustes　analysis.　The　results　show　that　the　TCT　coefficients　derived　from　our　PCP　method　can　effectively　enhance　brightness,　greenness,　and　wetness　characteristics　of　the　Sentinel-2　imagery.　The　results　have　also　been　compared　with　those　of　a　previous　study　that　derived　the　Sentinel-2　TCT　data　using　the　GSO　method　instead.　Landsat-8　and　moderate　resolution　imaging　spectroradiometer　(MODIS)-derived　TCT　data　have　been　used　as　references　for　comparison.　The　comparison　shows　that　the　PCP　method　outperforms　the　GSO　method,　as　the　PCP＇s　results　generally　have　lower　root　mean　square　errors　and　higher　correlation　coefficients　(R)　when　compared　with　the　corresponding　TC　components　of　Landsat-8　and　MODIS　data,　respectively.　The　greatest　difference　between　the　PCP　and　GSO　methods　lies　in　the　wetness　component.　The　PCP　method　can　correctly　highlight　vegetation　and　soil　moisture,　as　well　as　water　features　in　the　component.