This　study　improved　simulation　of　forest　carbon　fluxes　in　the　Changbai　Mountains　with　a　process-based　model　(Biome-BGC)　using　incorporation　and　data　assimilation.　Firstly,　the　original　remote　sensing-based　MODIS　MOD_17　GPP　(MOD_17)　model　was　optimized　using　refined　input　data　and　biome-specific　parameters.　The　key　ecophysiological　parameters　of　the　Biome-BGC　model　were　determined　through　the　Extended　Fourier　Amplitude　Sensitivity　Test　(EFAST)　sensitivity　analysis.　Then　the　optimized　MOD_17　model　was　used　to　calibrate　the　Biome-BGC　model　by　adjusting　the　sensitive　ecophysiological　parameters.　Once　the　best　match　was　found　for　the　10　selected　forest　plots　for　the　8-day　GPP　estimates　from　the　optimized　MOD_17　and　from　the　Biome-BGC,　the　values　of　sensitive　ecophysiological　parameters　were　determined.　The　calibrated　Biome-BGC　model　agreed　better　with　the　eddy　covariance　(EC)　measurements　(R-2　=　0.87,　RMSE　=　1.583　gC.m(-2).d(-1))　than　the　original　model　did　(R-2　=　0.72,　RMSE　=　2.419　gC.m(-2).d(-1)).　To　provide　a　best　estimate　of　the　true　state　of　the　model,　the　Ensemble　Kalman　Filter　(EnKF)　was　used　to　assimilate　five　years　(of　eight-day　periods　between　2003　and　2007)　of　Global　LAnd　Surface　Satellite　(GLASS)　LAI　products　into　the　calibrated　Biome-BGC　model.　The　results　indicated　that　LAI　simulated　through　the　assimilated　Biome-BGC　agreed　well　with　GLASS　LAI.　GPP　performances　obtained　from　the　assimilated　Biome-BGC　were　further　improved　and　verified　by　EC　measurements　at　the　Changbai　Mountains　forest　flux　site　(R-2　=　0.92,　RMSE　=　1.261　gC.m(-2).d(-1)).