Quantifying the carbon stocks of forest is critical for understanding the dynamics of carbon fluxes in terrestrial ecosystems and the atmosphere as well as monitoring ecosystem responses to environmental changes. However, due to the lack of methods and data, results of forest carbon estimation from different studies shown large difference, which presents a great uncertainty in the evaluation of forest carbon sink. Different methods can be used to estimate the carbon storage in the same study area, which can be compared with the advantages and disadvantages of each method and provides guidance for forest carbon estimation. On the basis of National Forest Inventory (NFI) data and the Land-sat TM image data collected in Longquan County, Zhejiang Province in 2009, we applied two methods, namely error back-propagation neural network (BPNN) and sequential Gaussian co-simulation (SGCS) to reproduce the distribution of above-ground forest carbon. We randomly divided plots into two sets, a 70-plot set for modeling and a 29-plot set for testing. For the model test, the correlation coefficient of predictive value and the plot data was 0.67 and 0.68 for BPNN and SGCS, respectively. Both of the two methods have the same RRMSE value (0.63). The predictive ability of SGCS was slightly higher than that of BPNN. The estimation results using BPNN showed that the sum of above-ground carbon is 11042990 Mg and the mean carbon density was 36.10 Mg/hm² which was higher than the average from the sample plots with a relative error of 8.82%. The SGCS showed that the sum of above-ground carbon was 11388657 Mg with a mean carbon density 37.23 Mg/hm² which was higher than the average from the sample plots with a relative error of 9.4%. Comparative analysis showed the carbon densities estimated using these two methods are both close to that calculated from the NFI data. However, there were some differences between the two methods with respect to the estimation of the frequency distribution and the carbon distribution in the study area. Predictive value of sample plot obtained using the SGCS method was closer to the plot data value than that obtained using the BPNN. And the correlation between predictive value and the plot data was 0.75, which proved that there were obvious advantages in estimating the spatial distribution of forest carbon. In addition, in terms of carbon density range and frequency distribution, SGCS was more reliable. This study further verifies the effectiveness of the SGSC which could provide effective methods for the estimation of regional forest carbon storage.