The object of this study is to determine whether elevation could be used to enhance the accuracy of prediction of soil organic matter spatial patterns and the accuracy of modeling the prediction uncertainty. The study was conducted on a area in Pinggu district of Beijing. Soil organic matter was used as target variable. The sequential Gaussian simulation accounting for single attribute and the sequential Gaussian co-simulation accounting for elevation secondary information were used for simulation of the soil organic matter. Thus, the results of the two methods were compared. The results showed that the accuracy of the spatial prediction, the accuracy of modeling the local uncertainty as well as the spatial uncertainty were increased by using the method of condition simulation accounting for the intensive elevation information. The findings are tremendously significant for sustainable agricultural production and global carbon change modeling.