Summary
Soil temperature is a very important variable in agricultural meteorology and strongly influences agricultural activities and planning (e.g. the date and depth of sowing crops, frost protection). There are many physically based studies in the literature which model soil temperature, but few are easily applicable for use in the field. Simple and precise short-term forecasting of soil temperature with minimum data requirements is the main goal of this study. The soil temperature at 0300, 0900 and 1500 GMT was forecast based only on surface air temperatures using artificial neural network (ANN) and wavelet transform artificial neural network (WANN) models. The hourly data were collected from the Mashhad synoptic station in Khorasan Razavi province in Iran between 2010 and 2013. The results of
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