【Objective】Soil organic matter(SOM) plays a key role in maintaining soil quality and functions and crop productively and a critical role in global C recycling. Therefore, knowledge and understandings of spatial distribution patterns of SOM are important to sustainable utilization of soil resources, guarantee of food security, and mitigation of the momentum of global climate change.【Method】A total of 413 cropland topsoil samples (0～20 cm) were collected from the southern parts of Jiangsu Province for analysis of SOM contents, and ordinary kriging (OK), regression kriging (RK), and random forest (RF) methods were employed for mapping spatial distribution of SOM contents. Auxiliary data such as soil type, topographic factors derived from DEM, climate, soil temperature and moisture, soil properties, and agricultural management practices (N fertilization rate and C input by crop residues) varying in spatial resolution were first scaled into grids 300 m in resolution using either the kriging interpolation or neighborhood averaging method, and then the auxiliary factors screened by a stepwise-regression process were used in RK and RF predictions of SOM. Moreover, to identify impacts of the correlations between auxiliary factors and SOM content on spatial prediction of SOM contents in accuracy, the root mean square errors (RMSE) derived by RK and RF methods were also compared between the situations of removing and retaining the auxiliary factor with highest correlation coefficient.【Result】The SOM spatial distribution patterns derived with the OK, RK and RF methods were quite similar, that is to say, SOM contents in the eastern parts of the study area are relatively high, whereas those in the western parts are low. But local differences did exist in detail of SOM distribution prediction between the methods can be intuitively observed. The OK method was the lowest in prediction accuracy, with mean RMSE being 6.97 g·kg^-1 and lower than the RK and RF methods, of which the mean RMSE of the RK and RF methods was lowered down to 5.25 and 4.97 g·kg^-1, respectively, when total nitrogen (TN) of the auxiliary factors that were most closely related to SOM was integrated. However, when TN of these auxiliary factors was removed, the RMSE predicted with RK and RF was lower than that with OK, being 6.21 and 6.29 g·kg^-1, respectively, while the average explained variance was decreased to 29% and 28%, respectively. However, the RK and RF methods are still better than the OK method, as the RMSE derived by OK was as high as 6.97 g·kg^-1, and the explained variance of OK was only 9.7%.【Conclusion】RK and RF are both higher than OK in prediction accuracy, however, the difference in prediction accuracy between RK and RF depends on degree of the correlation between the auxiliary factors and SOM. When auxiliary factors most closely related to SOM, such as TN was included in the prediction, RF was better than RK; while those were excluded, , RK was slightly better than RF, indicating that RK is still promising due to the relatively high-cost of TN measurement. In addition, prediction accuracy of RF largely depends on degree of the correlation between the auxiliary data and SOM, when TN was removed from the RF prediction, the predicted RMSE increased significantly, indicating that the current easily attainable and available low-cost auxiliary are still facing many challenges in improving SOM prediction accuracy in plain regions with strong anthropogenic influences. Consequently, developments of new scaling methodology for raw auxiliary data or new higher resolution auxiliary data for quantifying relationships between auxiliary data and SOM are critical for improving accuracy of the prediction of SOM in plain areas with intensifying anthropogenic influences.