Soil Organic Matter(SOM) is one of the key variables in agronomy and environmental management. It controls soil fertility and has a significant impact on atmospheric CO2 concentration. Carbon sequestration in soil can not only reduce the emissions of the greenhouse gases but also improve the quality and productivity of soils. Therefore, accurate estimation of SOM distribution at large scale is needed for policy making, sustainable soil utilization and management. The aims of this study were to predict the SOM across the Northeast and North Plain in China using a model trees method with a large number of satellite-derived data and soil vis-NIR spectroscopy data. A total of 1078 soil samples were collected to estimate spatial variation of SOM in Northeast and North Plains, China. Remote sensing data, including MODIS, TRMM and STRM, and soil spectroscopy data were used as environmental predictors. 306 soil samples were used as external validation dataset and the others were used to build models. Decision-tree-based M5 algorithm was introduced to construct the prediction models between SOM and the environmental predictors through the modelling tool Cubist. The method converted to a series of rules, each with an associated linear model, that partition the data into regions with similar conditions defined by the characteristics of the predictor variables. Prediction models between SOM and predictors with different number of samples were tested and it was found that the optimal number of training samples is 300. For the evaluation on the validation dataset, the model showed an R2 of 0.69, RMSE of 7.25 g·kg-1 and RPD of 1.53. According to the S=f(s,c,o,r,p,t) function, it was noted in the predicted model that soil spectroscopy and climate predictors were the dominant factors in controlling the spatial variation of SOM, while organism predictors showed less importance and terrain factors had least impact. The predicted map showed a significant increasing trend of SOM content from southwest to northeast. Compared with the map produced by National Soil Survey Office, the predicted map presents similar pattern of the spatial variation of SOM in Northeast and North Plain in China. Nevertheless, the area of high SOM and low SOM decreases in about two decades due to the human activities and tillage in the area.The methodology in this study combines remote sensing with proximal soil spectroscopy using a rule-based soil mapping framework. The result shows that predicting SOM at large area is acceptable through Cubist. The climate factors and soil spectroscopy were the most dominant factors among the environmental factors while terrain factors contributed least. It is found that the spatial pattern of SOM generated by Cubist is consistent with that of the second national soil survey of China produced in early 1980s, meanwhile the area of high SOM and low SOM decreases.