To assess the potential of remotely sensed data to estimate GSTs, an empirical model was established based on three AWS in the central TP. The empirical models were established by the correlation between the mean daily ground surface temperature (meteo-GST) of the three AWS observations and the AWS observations at the satellite overpass times, with three different combinations of daytime/nighttime MODIS LST (Terra daytime and nighttime, Aqua daytime and nighttime, and Terra and Aqua daytime and nighttime). Employing both daytime and nighttime from Terra or Aqua MODIS as predictors performed better than using daytime or nighttime alone. Among the three models, the model established by observations of Terra and Aqua daytime and nighttime, MODIS LST, yielded the highest and the lowest MAE and RMSE, but the number of available pixels was substantially reduced. There was no obvious difference in , MAE, and RMSE of three AWS observations, , 2.4°C, and 3°C, respectively. Terra MODIS LST performed better than Aqua MODIS LST to estimate meteo-GST. Models established by the AWS observation values at the MODIS overpass times were better than the models established by the MODIS LST observations. These three AWS are located in the central TP and, therefore, indicate the thermal conditions of permafrost, so the estimation of mean daily GST could be a powerful dataset for the monitoring and modeling of permafrost.