Publication Date:
2014-01-01
Description:
Reference evapotranspiration (ETO) is one of the major parameters affecting hydrological cycle. Use of satellite images can be very helpful to compensate for lack of reliable weather data. This study aimed to determineETOusing land surface temperature (LST) data acquired from MODIS sensor. LST data were considered as inputs of two data-driven models including artificial neural network (ANN) and M5 model tree to estimateETOvalues and their results were compared with calculatedETOby FAO-Penman-Monteith (FAO-PM) equation. Climatic data of five weather stations in Khuzestan province, which is located in the southeastern Iran, were employed in order to calculateETO. LST data extracted from corresponding points of MODIS images were used in training of ANN and M5 model tree. Among study stations, three stations (Amirkabir, Farabi, and Gazali) were selected for creating the models and two stations (Khazaei and Shoeybie) for testing. In Khazaei station, the coefficient of determination (R2) values for comparison between calculatedETOby FAO-PM and estimatedETOby ANN and M5 tree model were 0.79 and 0.80, respectively. In a similar manner,R2values for Shoeybie station were 0.86 and 0.85. In general, the results showed that both models can properly estimateETOby means of LST data derived from MODIS sensor.
Print ISSN:
2356-6361
Electronic ISSN:
2314-6214
Topics:
Energy, Environment Protection, Nuclear Power Engineering
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