The importance of climatic data for the society increased in the last years due to the global climate change. But spatial representation of climatic data is also a key point in many applications. The estimation of the spatial distribution of ambient air temperature (AAT) measured 2 m above the ground is a GIS application that is especially problematic in mountainous countries with low autocorrelation among measurements. Kriging (usually optimal interpolation method) is not adapted to inhomogeneous areas, thus a statistical method based on multiple regression is proposed. Within the presented case study, AAT was measured at 20 synoptic stations in Slovenia three times a day (7:00, 14:00 and 21:00) throughout 2005, which means that there are 1095 situations to be interpolated in total. The measurements were linked with the data stored in GIS: firstly, with the DEM and from it derived layers such as relief slope, relief aspect, etc., and secondly with the land cover including derived layers such as distance from the sea. Some of this data has a micro-local and some has a regional influence. Therefore, some explanatory variables were estimated within spatial analyses windows of seven different sizes and only the analysis window, where the attributes were the highest correlated to AAT, was considered in the following procedure. The novelty of the presented study is inclusion of satellite data MODIS is an instrument on board Terra and Aqua satellite that observes the land and sea surface from the polar orbit four times per day. Recorded images are used to derive land surface temperature, surface albedo, normalized difference vegetation index and enhanced vegetation index that were introduced into the case study and used within the multiple regression. Systematic correlations between temperature and explanatory variables introduced into the GIS showed that, next to the relief aspect, especially land surface temperature is a significant and easy obtainable data for AAT interpolation. The standard deviation of all the results improved after MODIS data inclusion from 1.7 to the final 1.5 degrees C.