Normalization of the surface temperature relative to environmental parameters is essential in scientific studies and urban and non-urban areas management. The aim of the current study is to propose a variance-based model for normalization of the surface temperature relative to environmental parameters. For this aim, Landsat 8 satellite bands, MODIS water vapor product, and ASTER digital elevation model were used. In this study, topography parameters, downward radiation on the surface, albedo, environmental lapse rate, vegetation and biophysical characteristics of the surface were considered as environmental parameters. Single channel algorithm was used for surface temperature calculation, and also an improved Coolbaugh model was suggested for modeling the downward solar radiation. Additionally, for modeling of albedo, environmental lapse rate and biophysical characteristics, a combination of Landsat 8 reflective bands, the digital elevation model, and tasseled cap transformation were exploited, respectively. Finally, the least square method was used to calculate the unknown coefficients of each parameter in the proposed normalized model, in order to minimiz the variance of the land surface temperature image. Coefficient correlation indexes and RMSE were used for accuracy assessment between the modeled and observed surface temperature values. Also the variance of normalized surface temperature image was used to estimate the proposed model capability. The results indicate that downward radiation on the surface parameter and both the elevation and greenness parameters, had the highest and the lowest effects on the surface temperature variation. The coefficient correlation and RMSE between the modeled and observed surface temperatures are 0.97 and 1.53, respectively and the variance of normalized surface temperature values is equal to 0.79. Results of the current study implied the high efficiency of the proposed model for normalizing the land surface temperature relative to environmental parameters.