【Objective】As an important component of the global carbon pool, soil organic carbon (SOC) is the largest organic carbon pool in the terrestrial ecosystem and plays an extremely important role in the global carbon cycle and global warming. The SOC pool is subject to the impacts of both natural and human activities and sure closely related to terrain attributes or factors. There are a number of methodsfor calculation of SOC, which can roughly be sorted into three types, that is, empirical, statistical and mechanismones.But none of them can be used to predict or calculate reapidly soil organic carbon pool of a region rapidly.Remote sensing is an efficient technical means for fast acquisition of DTM, from which numerous information can be derived with the aid of GIS, thus making it possible to constitute a model for rapid calculation of SOC.【Method】Based on the Digital Terrain Model (DTM) and the topographic attributesderived thereof, an optimal SOC prediction model was built up, taking into account factor combination and resolution with Cubist, a powerful data mining tool for generating rule-based models. This tool works on condition-specific rules where the output is a set of rules and each rule has a specific multivariate linear model attached. Whenever a situation matches the condition of a rule, the associated model is used to calculate or predictevalues. A total of 8570 soil samplescollected from the 7100km2 study area were divided into two groups randomly, 6362 for training and the other 2208 for model validation, a total of 2514820 models were constructed based on 71 selected resolutions and all possible combinations of no more than 5of the 22 terrains attributes. According to the correlation coefficient (R), terrain factors, varying in number,were selected, to form optimal models with their corresponding resolutions,Based on these models, SOC maps were plotted.【Result】Results show that the relationsships between resolution and single-factor models are diversified, it is not true that the higher the resolution, the better the model. The R value of a single-factor model is not necessarily the factor that determines its importance in a multi-factor model. All the multi-factor modelsexhibit a similar rule of skewed normal distribution. Each factor and its combination has a factor-specific optimal resolution, varying in the range of 60~150m. For models composed of whatever factors, the resolution t be selected should not be lower than 200m. The variable of the optimal single-factor model is RSP, with resolution being 92.8 m, the variables of the optimal two-factor model are RSP and Chnl_base with resolution being 60.8 m, while the variables of the three-factor model are Chnl_alti, Chnl_base and MRVBF, with resolution being 64 m. There are 6 four-factor models, with R being 0.71 and resolution varying in the range of 64 ~ 136 m, and 2 five-factor models with R being 0.78, and resolution being 152meters. Every model consists at least of Chnl_alti, elevation, Chnl_base and MRRTF, and in the 2 five-factor modelsNormalh or Midslppst is added. The R of all the models consisting of any four ro five of the 22 factors was calculated to be 0.78 with two optimal resolutions, i.e. 40 and 64 m. In general, the more the variables, the higher the R of the models. But owing to impact of the noise, models with more than four factors decine in predictive ability.Four to five is the appropriate number of factors in combination, making the models more capable of predicting SOC. Comparative analysis of the SOC maps plotted with the aid of global fan Kriging, global ordinary Kriging, global kriging and ordinary Kriging shows that regardless of the number of factors in the model, this method is better than all the four Kriging interpolation methods in prediction of spatial variation of SOC and prediction accuracy.【Conclusion】Takinginto comprehensive account storage space, amount of calculationm, sophistication of the model,accuracy of prediction and ability of spatial expression, the optimal model for the study region should be the four-factor model, consisting of relative slope position, elevation, normalheight and MRVBF, with resolution being 121.6 m.