Akademik Veri Yönetim Sistemi
JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, cilt.40, sa.3, ss.399-409, 2012 (SCI-Expanded)
This study introduces artificial neural networks (ANNs) for the estimation of land surface temperature (LST) using meteorological and geographical data in Turkey (26-45A degrees E and 36-42A degrees 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, EskiAYehir, A degrees stanbul, A degrees zmir, 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(LSTANN) and Becker and Li temperature(LSTB-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.