为了提高地表气温的插值精度，提出了融合多源信息的地表气温插值方法，该方法以地表气温和辅助信息之间显著相关为前提条件，利用多元地统计（拟协同克里金、基于局部变化均值的简单克里金、带外部漂移的克里金）来实现多源信息的融合。对中国720多个气象站2008年8月的月平均地表气温进行了空间插值实验，实验结果表明，综合考虑两种辅助信息的SKlm和KED插值方法最优，其原因在于：1）地表气温和海拔及地表温度显著相关，海拔反映地表气温的总体趋势，而地表温度更侧重反映它的局部趋势，综合考虑它们能更准确地预测地表气温。2）SKlm和KED均是基于非二阶平稳的插值方法，而地表气温的空间分布往往呈非平稳性，因此它们要优于其他方法。

The paper presents an interpolation method for surface air temperature (SAT) based on data fusion of multiple sources.It should check whether there is a significant correlation between primary and secondary variables firstly.Three multivariate geostatistical algorithums which includes collocated cokriging (CCK)，simple kriging with varying local means (SKlm) and kriging with an external drift (KED) were introduced to incorporating ancillary information into the spatial prediction of SAT.The method was illustrated using monthly mean temperature data from more than 720 meteorological stations in China in August 2008，and cross validation was performed to evaluate the performance of the map prediction quality.The results show that:Accounting for both land surface temperature (LST) from remote sensing and digital elevation model (DEM)，used as ancillary spatial information in three algorithms，outperforms accounting for only one ancillary data.Among all different methods，SKlm and KED incorporating LST and DEM have produced the best results，this is because:(1) LST is better to indicator the local trend of SAT.(2) DEM prefers to indicator the global trend of SAT.(3) Both SKlm and KED considering SAT with a non-tationary spatial distribution have better performance than others.