Abstract: Precisely knowing the spatial distribution and variation of soil nutrient is the premise for ecological & environmental protection, rational fertilization and precision agriculture. Ordinary kriging, a method based on regionalized variable theory, was widely used in the research of soil science, such as soil mapping, prediction, etc., but the method heavily depended on quality and number of data and ignored the effect of environmental factors which were very closed to soil nutrient, hence, the method had some defects on prediction of soil properties. Compared with ordinary kriging, regression kriging, which could use many auxiliary environmental variables, was used in the study for interpolating total soil nitrogen and its mapping. The mapping effects and accuracy of interpolation by the two methods were also evaluated. The results showed regression kriging could fully employ easily acquired auxiliary variables, and the prediction accuracy of regression kriging was greatly improved by compared with ordinary kriging. The map interpolated by regression kriging was more realistic and reflected the details of the total soil nitrogen patterns impacted by the environmental factors in nature and the map was more continuous in transition and less affected by extreme sample values. The map interpolated using regression kriging was more accord with soil total nitrogen distribution in nature. Regression kriging method was eligible for prediction and mapping those soil properties which had explicit relation with environmental factors, that was to say, these factors had better have a linear correlation or specific trend with soil properties.