RESEARCH ARTICLE Modelling and Remote Sensing of Land Surface Temperature in Turkey Mehmet Şahin & B. Yiğit Yıldız & Ozan Şenkal & Vedat Peştemalcı Received: 28 September 2010 / Accepted: 15 August 2011 / Published online: 14 September 2011 # Indian Society of Remote Sensing 2011 Abstract This study introduces artificial neural net- works (ANNs) for the estimation of land surface temperature (LST) using meteorological and geo- graphical data in Turkey (26–45°E and 36–42°N). A generalized regression neural network (GRNN) was used in the network. In order to train the neural network, meteorological and geographical data for the period from January 2002 to December 2002 for 10 stations (Adana, Afyon, Ankara, Eskişehir, İstanbul, İzmir, Konya, Malatya, Rize, Sivas) spread over Turkey were used as training (six stations) and testing (four stations) data. Latitude, longitude, elevation and mean air temperature are used in the input layer of the network. Land surface temperature is the output. However, land surface temperature has been estimated as monthly mean by using NOAA-AVHRR satellite data in the thermal range over 10 stations in Turkey. The RMSE between the estimated and ground values for monthly mean with ANN temperature(LST ANN ) and Becker and Li temperature(LST B-L ) method values have been found as 0.077 K and 0.091 K (training stations), 0.045 K and 0.003 K (testing stations), respectively. Keywords Generalized regression neural network . Land surface temperature . Satellite data Introduction Land surface temperature (LST) is an important factor controlling most physical, chemical, and biological processes on Earth. Knowledge of land surface temper- ature is necessary for many environmental studies and management activities of the Earth’ s resources (Li and Becker 1993). In order to monitor macro-scale spatial changes in surface temperature, scanners designed for sensing in the thermal bands are placed onboard platforms for remote sensing of the Earth’ s resources from space (Sabins 1997). The extensive application and significant importance of temperature in environ- mental studies and management is the main force driving the study of LST in remote sensing. With the availability of thermal sensing data, such as channels 4 and 5 of Advanced Very High Resolution Radiometer (AVHRR) data as well as Landsat Thematic Mapper 6 (TM6), the study of LST has become one of the hottest topics in remote sensing during the last two decades (Vogt 1996). Thus, two approaches have been devel- J Indian Soc Remote Sens (September 2012) 40(3):399–409 DOI 10.1007/s12524-011-0158-3 M. Şahin (*) Siirt Vocational School, Siirt University, 56100 Siirt, Turkey e-mail: sahanmehmet2000@yahoo.com B. Y. Yıldız Karaisalı Vocational School, Çukurova University, 01770 Karaisalı, Adana, Turkey O. Şenkal Faculty of Education Department of Computer Education and Instructional Technology, Çukurova University, 01330 Sarıçam, Adana, Turkey V. Peştemalcı Physics Department, Çukurova University, 01330 Sarıçam, Adana, Turkey