Dry-bulb temperature

which can represent the regional characteristics of thermal conditions

is one of the conventional meteorological elements measured over surfaces. Such measurement serves an important function in studying plant physiology

hydrology

the atmosphere

and the environment. Dry-bulb temperatures are usually calculated through linear fitting to original remote sensing data or approximate temperatures retrieved from remote sensing data. These methods are suitable for homogeneous areas with a stable atmospheric stratification and circulation pattern. However

a linear relation does not exist between surface temperature retrieved from remote sensing data and the actual dry temperature because of the limitations of algorithms and the complexity of the underlying surface. Dry-bulb temperatures cannot be calculated accurately with the use of traditional retrieval algorithms. Therefore

a support vector machine（ SVM） model was proposed in this study to calculate dry-bulb temperatures.Nanning City was selected as the research area. First

the temperature retrieved from remote sensing data was compared with in situ data. The relations among brightness temperature

temperature retrieved from remote sensing data

and actual dry-bulb temperature were confirmed. Calculating dry-bulb temperature by remote sensing data was a reasonable approach. Second

the actual dry-bulb temperature in some stations and the corresponding temperatures retrieved from remote sensing data（ at the same time and geographical location） were taken as modeling samples. The SVM prediction model with a strong learning capability and nonlinear processing capability was developed to retrieve dry-bulb temperatures. Finally

the dry-bulb temperature was calculated by using remote sensing brightness temperature and the temperature retrieved from remote sensing data as the input parameters of the SVM model.For the data obtained on May 12 and November 20

2008

the absolute errors of the traditional method（ using Tslinear translation with surface temperature retrieved from remote sensing data） to calculate the dry-bulb temperature are 2. 050112 ℃ and1. 3437564 ℃; the absolute errors of SVM models of brightness temperature are 0. 91915 ℃ and 0. 40294 ℃; and the absolute errors of SVM models of surface temperature are 0. 73802 ℃ and 0. 55002 ℃. The precision of the SVM models is higher than that of traditional methods.The conclusions are as below:（ 1） Actual dry-bulb temperatures

brightness temperatures

and surface temperatures retrieved by remote sensing are well correlated. Using remote sensing data to predict dry-bulb temperatures is a feasible approach. As determinative factors of regional change for dry-bulb temperature are complex

relations among dry-bulb temperatures

brightness temperatures

and surface temperatures are not linear. Using traditional linear methods may result in large biases. The SVM model with nonlinear processing capacity is more suitable than traditional methods for calculating dry-bulb temperature.（ 2） The absolute errors of the SVM model are more reasonable and smaller than those of traditional methods.（ 3） The results of the SVM model

which used brightness temperature and retrieval surface temperature as input parameters

are comparable. The retrieval process for surface temperature is complex; therefore

brightness temperature can be taken as input for SVM directly instead of retrieving surface temperature.（ 4） Given that November is a non-flood season in Guangxi

nonadiabatic heating of heat flux equation is less affected by convection and turbulence. The main factor for dry-bulb temperature is surface thermal radiation; the absolute error for the November data is significantly smaller than that of May even when using the same SVM models.