Prediction Model of Air Temperature in Relation to Field and Remotely Sensed Data in Baghdad Greater Area

Abstract

Abstract Recently, scientific research paid attention on the air temperature increase of the urban cities. Many technologies were involved in order to identify the relationship among the air temperature and the affective factors. This research was aimed to build a prediction model of air temperature based on the field and remotely sensed data of Landsat 8 OLI. The model building consists of two factors, the first of which was a field measurement of air temperature for specified areas in Baghdad, Iraq. While the second factor was obtained using the remote sensing technology to calculate the normalized difference vegetation index (NDVI), Normalized difference water index (NDWI), Normalized difference built-up index (NDBI), and Land surface temperature (LST). Results of the multiple regression relationship among the measured factors reveals significant values that supports the model. The methodology of this study could be employed to help the environmental researchers to predict the air temperature in Baghdad city based on satellite data. We recommend applying the prediction model based on both seasons for the most significant results of air temperature measures, we also recommend evaluating this model on urban arid and cold climates.