Estimation of Soil Temperature Spatial Average at Different Depths Using Meteorological Data in Agricultural Stations of Fars Province

Soil temperature is one of the important parameters in meteorological agriculture and affects many soil biological processes. Unfortunately, due to the lack of soil temperature data in many regions of the country, this issue has always been discussed in agricultural projects. In the present study, practical equations were created to estimate the monthly soil temperature using the correlations between different meteorological parameters and soil temperature in five agricultural stations in different cities of Fars province (Shiraz, Abadeh, Darab, Zarghan, and Jahrom) at depths of 5, 10, 20, 30, 50, and 100 cm. In this regard, multivariate linear regression equations have been used. According to the results, the following parameters affected soil temperature: in Abadeh, meteorological variables of wind speed, rainfall, and maximum temperature; in Darab, relative humidity, wind speed, air pressure, rainfall, maximum temperature, and minimum temperature; in Jahrom, variables of wind speed, air pressure, maximum temperature, and minimum temperature; in Shiraz, the variables of sunshine hours, wind speed, air pressure, rainfall, maximum temperature, and minimum temperature and in Zarghan city, the variables of sunshine hours, wind speed, air pressure, rainfall, maximum temperature, and minimum temperature. In general, fewer parameters are needed in all stations to estimate soil temperature at depths of 5 and 10 cm, and the importance of meteorological parameters and their effective role in estimating soil temperature increases with increasing depth. Also, the effect of meteorological variables on soil temperature is not the same in different climates. According to the coefficients related to the equations, the highest and the lowest effect on soil temperature at different depths, are the maximum temperature and sunshine hours. With increasing depth, regression coefficients decrease as well. In all stations, regression coefficient at depths of 5 and 10 cm has been calculated between 0.98-0.99, and at depth of 100 cm has been calculated between 0.87-0.92, shows decreased about 10% of this coefficient with increasing the depth. According to the results and equations obtained for each station and different climates in Fars Province, this equation can be used to estimate soil temperature in areas without data.