In this research, a physically-based soil surface energy balance model was adopted to estimating daily soil surface temperature of a bare soil. For this purpose, daily meteorological data including mean air temperature, precipitation, wind speed, relative humidity and sunshine along with the maximum and minimum soil surface temperature data of an Agro-meteorological station located at West of Iran were considered in the 1996-2010 period. The agreement between the simulated and measured daily mean soil surface temperatures was evaluated using Mean Absolute Error (MAE) and Nash-Sutcliffe Efficiency (NSE) indices. The results showed an acceptable agreement between the simulated and measured data with the 2.3 °C and 0.92 values for the MAE and NSE indices, respectively. By considering the NSE index, the lowest and highest model performance was obtained at the winter and autumn seasons, respectively. With respect to having some extra uncertainty sources regarding to the snow modeling during winter and its effect on the soil surface temperature, a more complicated simulating of the soil surface temperature under the snow pack could be considered as the reason of the lower model performance during the winter. To obtain the better results from the model, a calibration process is suggested to adjust the main and effective model parameter values by measuring more data regarding to the different energy balance components.
