This　study　showed　the　potential　for　using　Monte　Carlo　method　for　uncertainty　analyses　based　on　multi-parameter　remote　sensing　data　or　incorporated　models.　The　Monte　Carlo　method,　involves　using　random　numbers　and　probability　to　solve　problems,　is　a　simple　but　useful　tool　for　uncertainty　analyses　with　complex　models.　In　this　study,　the　uncertainties　of　the　inter-annual　and　the　overall　forest　AGB　increments　in　the　QMs　form　2000　to　2012　were　analyzed　based　on　the　Monte　Carlo　method.　The　sensitivity　analysis　showed　that　the　modeled　NPPs　(SEnpp)　contributed　the　most　uncertainty　to　the　inter-annual　forest　AGB　dynamics.　Furthermore,　multi-source　data　and　incorporated　model　fusion　methods　should　be　adopted　to　reduce　model　output　uncertainties.　©　2016　IEEE.