Forest inventory estimates of biomass for large areas are typically calculated by adding model predictions of biomass for individual trees. Quantifying the sources of uncertainty in the individual tree biomass model and analyzing the effects of various uncertainty on the forest biomass estimation can provide a theoretical basis for improving the accuracy of forest biomass estimation. In this study, based on the aboveground biomass data for 52 Cunninghamia lanceolate trees, continuous observation data from the permanent sample plots of Lin'an county, China, and a fitted above-ground tree biomass model of C. lanceolata, the error propagation law was used to quantify the uncertainty in model prediction, including the uncertainty of model residual variability and model parameter uncertainty. The result showed that the mean above-ground biomass of C. lanceolata calculated from the model based on diameter at breast height (DBH) (unary model) amounts to 6.94 Mg/hm² and the uncertainty caused by model residual variability and model parameter uncertainty was approximately 11.1% and 14.4% of the total biomass, respectively. Furthermore, the total uncertainty was 18.18% of the estimation from the model based on DBH. With regard to the model based on DBH and tree height(H) (binary model), the mean above ground tree biomass of C. lanceolata was 7.71 Mg/hm². The uncertainty caused by model residual variability and model parameter uncertainty was approximately 7.0% and 8.53% of the total biomass, respectively, and the total uncertainty was 11.03% of the estimation. The results also showed that the uncertainty of the model parameters decreases with the increase of sample size. For the sample size of 30, 40 and 52, the parameter uncertainty decreased from 20.26% to 16.19% and 14.4%, respectively for unary model. For the binary model, the parameter uncertainty decreased from 13.09% to 9.4% and 8.53%, respectively. In addition, the uncertainty of the residual variability decreased with an increase of the sample size. When the sample size was increased from 30 to 42 and 48, the residual variability uncertainty of the unary model decreased from 15.2% to 12.3% and 11.7%, respectively. As for the binary model, the residual variability uncertainty decreased from 13.3% to 9.4% and 8.3%, respectively. Among the two sources of uncertainty, the model parameter uncertainty has the greatest impact on the estimated results. The total uncertainty of the binary model was lower than that of the unary model. The model uncertainty is related to the modeling sample size, and therefore, can be reduced by increasing modeling samples.