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 (SE_npp) 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.