Accurate delineating of the spatial distribution of soil heavy metal content is essential for pollution assessment and remediation. The objective of this paper is to evaluate various spatial interpolation methods, including ordinary Kriging (OK), simple Kriging (SK), lognormal Kriging (LNK), universal Kriging (UK), disjunctive Kriging (DK) and inverse distance weighting interpolation (IDW) for estimating soil surface Hg content with lognormal distribution, the linear and second-order polynomial trend, and to determine the optimal interpolation method. The predicted errors, statistical feature values and prediction maps obtained by different interpolation methods were compared. The result indicated that first-order trend OK method performed better than both zero and second-order OK methods. Within the method of first-order trend OK, Gaussian semi-variogram model performed better than both the spherical and exponential models. The method using transformed data performed worse than the methods without data transformation because of the 'distortion' effect arising from log transformation. Those with trend effect were better than those without trend effect. First-order trend UK method is the best method among the six methods studied, while the IDW method is the least.