The climatological dynamics and weather patterns have been studied extensively in the field of remote sensing (RS) and geographic information system (GIS). The meteorological parameters, closely related to the earth surface, play important roles in climatological study. Prediction of these parameters is motivating especially when datasets contain missing and erroneous values. Geostatistical analysis is mandatory for prediction as it facilitates improved modeling of spatial proximities, hence reducing estimation error. However, the interdependencies between the atmospheric and terrestrial contexts play a critical role for proximity estimation. It is challenging to model cross-correlation between these two factors, considering the anisotropy in the terrain. This hybrid approach may facilitate better prediction accuracy for the meteorological parameters. This research work focuses on contextual land-atmospheric interaction modeling of influencing meteorological parameters in the terrain for spatial interpolation. The newly proposed interpolation method is named as semantic kriging (SemK). Theoretical analyses and empirical evidences prove the method to produce better results than most of the existing techniques in literature.