Accurate prediction of three-dimensional (3D) spatial distribution of soil salinity can provide a scientific basis for land use planning and improvement of salt-affected soil. An area of about 70 hm 2 of saline and alkaline land in Xinjiang was taken as the study area, and a total of 1 386 data of soil salt content was obtained from different soil layers (0~200 cm) at 126 sampling sites by the method combining the electromagnetic induction technique with soil sampler. The spatial interpolation of soil salinity was made with 3D-inverse distance weighting (3D-IDW) method and the effects of vertical expanding multiples and searching points amount on the interpolation results were explored. The results indicated that it had higher average salt content and larger variations in soil layer of 0~140 cm depth than those in 140~200 cm soil depth. The average soil salt content in 0~140 cm soil layer was in the range of 1.84~2.11 g/kg, while it was 1.74~1.79 g/kg in 140~200 cm soil layer. The statistical characteristics (mean, standard deviation and coefficient of variation, etc.) of soil salinity decreased as soil depths increased. The root mean square error (RMSE) decreased with the increase of vertical expanding multiples, but it increased with the increasing amount of searching points, and the RMSE varied from 0.1 g/kg to 0.4 g/kg. When the vertical size was expanded by 300 folds and searching point was six, the optimal 3D spatial distribution map of soil salinity was obtained by the 3D-IDW method, and the results showed that the spatial distribution of soil salinity agreed well with the observed values. The soil salt content in most of the study area was less than 2.5 g/kg, and the areas close to the north and south boundaries belonged to non-salinized soil with relatively low soil salt content, while the heavy-salinized soil was mainly distributed in the central and south of the study area with soil salt content over 4 g/kg. About 80% of the study area belonged to non- and light-salinized soils, and only 20% of the study area belonged to moderate and heavy salinity soils. The main factors influencing the distribution of soil salinity were irrigation, local terrain, clay layers depths, groundwater depth and its degree of mineralization. When the difference of sampling interval in different directions was very large, it would be crucial to choose suitable expanding multiples and amount of searching points to improve the prediction accuracy of the 3D-IDW method.