Abstract
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 determine ETO 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 ETO values and their results were compared with calculated ETO 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 ETO. 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 calculated ETO by FAO-PM and estimated ETO by ANN and M5 tree model were 0.79 and 0.80, respectively. In a similar manner, R2 values for Shoeybie station were 0.86 and 0.85. In general, the results showed that both models can properly estimate ETO by means of LST data derived from MODIS sensor.
Highlights
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
The main aim of this study is to evaluate the ability of M5 model tree and artificial neural network (ANN) model in estimating daily ETO using land surface temperature (LST) values obtained from MODerate Resolution Imaging Spectroradiometer (MODIS)/Terra sensor
In the first stage of the study, before calculating the ETO, the correlation between LST obtained from MODIS sensor and air temperature was determined
Summary
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]. 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 (ETO) and crop coefficient (Kc) for a specific crop [5]. Calculation of ETc (evapotranspiration of the given plant) and subsequently crop water requirement as irrigation water depend on ETO estimates. ETO is principally calculated by physically based equations (e.g., Penman-Monteith (PM) equation) or by empirical relationships between meteorological variables
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