To investigate the problem of secondary salinization in agricultural irrigated areas with shallow buried groundwater, regression Kriging (RK) and geographically weighted regression Kriging (GWRK) methods were applied using soil salinity in Da’an, western Jilin province, China. Digital elevation model (DEM), Topographic Wetness Index (TWI) and Groundwater salinity were selected as auxiliary variables based on correlation analysis and stepwise regression analysis. Results showed that the RK and GWRK can both effectively predict the spatial distribution of soil salinity due to the incorporation of auxiliary variables. In addition, the GWRK accuracy is improved by 23.2%, which should be attributed to the consideration of sample spatial non-stationarity. According to qualitative relationship of soil salinity and groundwater, when the groundwater depth was less than 5 m with the similar groundwater salinity, the soil salinity increased with decreasing groundwater depth, while more than 5 m the soil salinity remain unchanged. The relationship between soil salinity, groundwater depth and salinity could be quantitatively expressed using multiple power functions through data fitting. The results provide a scientific basis for regulating groundwater to control soil salinity to prevent soil salinization, and quantitative analysis needs further research.