%0 Journal Article
%T Comparative Study of M5 Model Tree and Artificial Neural Network in Estimating Reference Evapotranspiration Using MODIS Products
%A Armin Alipour
%A Jalal Yarahmadi
%A Maryam Mahdavi
%J Journal of Climatology
%D 2014
%R 10.1155/2014/839205
%X Reference evapotranspiration () 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 determine using 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 estimate values and their results were compared with calculated by 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 calculate . 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 () values for comparison between calculated by FAO-PM and estimated by ANN and M5 tree model were 0.79 and 0.80, respectively. In a similar manner, values for Shoeybie station were 0.86 and 0.85. In general, the results showed that both models can properly estimate by means of LST data derived from MODIS sensor. 1. Introduction Decline in availability of water for agriculture is one of the most serious challenges facing human life that has affected agricultural production in some arid and semiarid regions around the world. Determining future demands of water for agriculture section includes computation of several factors such as runoff, groundwater, precipitation, and evapotranspiration (ET) [1]. ET is identified as the combination of two different processes including evaporation from the soil surface and crop transpiration [2]. Accurate and reliable estimates of ET are necessary to determine temporal variations in irrigation requirement, improve allocation of water resources, and evaluate the effect of changes in land use and crop patterns on the water balance [3]. Considering difficulties in direct measurement of ET [4], this parameter is estimated through reference evapotranspiration () and crop coefficient () for a specific crop [5]. Therefore, calculation of (evapotranspiration of the given plant) and subsequently crop water requirement as irrigation water depend on estimates. is defined as the rate of ET from a reference surface in such a way that the surface is assumed to be covered with a hypothetical grass with specific characteristics [2]. A large number of methods have been
%U http://www.hindawi.com/journals/jcli/2014/839205/