Forest　canopy　height　plays　an　important　role　in　forest　resource　management　and　conservation.　The　accurate　estimation　of　forest　canopy　height　on　a　large　scale　is　important　for　forest　carbon　stock,　biodiversity,　and　the　carbon　cycle.　With　the　technological　development　of　satellite-based　LiDAR,　it　is　possible　to　determine　forest　canopy　height　over　a　large　area.　However,　the　forest　canopy　height　that　is　acquired　by　this　technology　is　influenced　by　topography　and　climate,　and　the　canopy　height　that　is　acquired　in　complex　subtropical　mountainous　regions　has　large　errors.　In　this　paper,　we　propose　a　method　for　estimating　forest　canopy　height　by　combining　long-time　series　Landsat　images　with　GEDI　satellite-based　LiDAR　data,　with　Fujian,　China,　as　the　study　area.　This　approach　optimizes　the　quality　of　GEDI　canopy　height　data　in　topographically　complex　areas　by　combining　stand　age　and　tree　height,　while　retaining　the　advantage　of　fast　and　effective　forest　canopy　height　measurements　with　satellite-based　LiDAR.　In　this　study,　the　growth　curves　of　the　main　forest　types　in　Fujian　were　first　obtained　by　using　a　large　amount　of　forest　survey　data,　and　the　LandTrendr　algorithm　was　used　to　obtain　the　forest　age　distribution　in　2020.　The　obtained　forest　age　was　then　combined　with　the　growth　curves　of　each　forest　type　in　order　to　determine　the　tree　height　distribution.　Finally,　the　obtained　average　tree　heights　were　merged　with　the　GEDI_V27　canopy　height　product　in　order　to　create　a　modified　forest　canopy　height　model　(MGEDI_V27)　with　a　30　m　spatial　resolution.　The　results　showed　that　the　estimated　forest　canopy　height　had　a　mean　of　15.04　m,　with　a　standard　deviation　of　4.98　m.　In　addition,　we　evaluated　the　accuracy　of　the　GEDI_V27　and　the　MGEDI_V27　using　the　sample　dataset.　The　MGEDI_V27　had　a　higher　accuracy　(R2　=　0.67,　RMSE　=　2.24　m,　MAE　=　1.85　m)　than　the　GEDI_V27　(R2　=　0.39,　RMSE　=　3.35　m,　MAE　=　2.41　m).　R2,　RMSE,　and　MAE　were　improved　by　71.79%,　33.13%,　and　22.53%,　respectively.　We　also　produced　a　forest　age　distribution　map　of　Fujian　for　the　year　2020　and　a　forest　disturbance　map　of　Fujian　for　the　past　32　years.　The　research　results　can　provide　decision　support　for　forest　ecological　protection　and　management　and　for　carbon　sink　analysis　in　Fujian.　©　2023　by　the　authors.