The present study deals with two methods for modeling and estimating the daily and annual variation of soil surface temperature. Soil surface temperature is an important factor for calculating the thermal performance of buildings in direct contact with the soil as well as for predicting the efficiency of earth-to-air heat exchangers. The two estimation methods are a deterministic model and a neural network approach. The two methods are tested and validated against extensive sets of measurements for bare and short-grass covered soil in Athens and Dublin. Finally, the comparison of the two models showed that the proposed intelligent technique is able to adequately estimate the soil surface temperature distribution.