Estimating daily and monthly air temperature parameters at Kurdistan province using MODIS sensor images

The object of this research is the estimation of spatial distribution of three air temperature parameters at daily and monthly scales including minimum, maximum and mean temperatures at the Kurdistan province using MODIS sensor images setted on Aqua and Terra satellites. For this object, 8 synoptic stations at Kurdistan province were selected and for these 8 stations at 2013 and 2014, daily minimum, maximum and mean air temperature data and also land surface temperature at 4 daily times at these 8 stations for 2013 and 2014 years were extracted by processing on the MODIS sensor images. Afterwards, the multiple linear regression method was used to extract regional regression models for Kurdistan province between these 3 air temperature parameters and land surface temperature and to assesing the errors, cross validation based on the Mean Absolute Error, Mean Bias Error and Nash-Sutcliffe Efficiency coefficient was adopted. The results showed that it is a powerful relation between all of these 3 air temperture and land surface temperatures extracted by sattelite images. The results of cross validation showed an approperiate and reseanable agreement between the measured and estimated values of these 3 parameters at both daily and monthly scales so that the mean absolute error for minimum, maximum and mean temperatures were 2.7, 2.1 and 1.6 °C at daily scale and 1.9, 2. 1 and 1.1 °C at monthly scale, respectively. These results showed that it is possible to estimate these air temperature parameters at the places witout any meteorological stations with an approperiate and acceptable accuracy by extracting land surface temperatures of MODIS sensor for these places and applying the extracted regression models.