Application of SEBAL algorithm in estimating maximum daily demand of rain-fed wheat from green water sources using MODIS images (Case study: Ahar county)

Remote sensing techniques have been applied to estimate the spatiotemporal distribution of evapotranspiration as an alternative for field measuring methods. The objective of this study is to estimate the maximum daily demand of rain-fed wheat from green water sources in Ahar county using the SEBAL algorithm and MODIS images. First, the results from the SEBAL algorithm were evaluated by comparing them to the results from the Eagleman-Affholder method in rain-fed wheat fields and to the results from Fao-Penman- Monteith method in irrigated wheat fields and then the wheat maximum daily evapotranspiration values were calculated in the study area. MODIS surface reflectance and land surface temperature images were used to monitor the variation of normalized difference vegetation index during the wheat growth period, to map wheat areas and to estimate wheat evapotranspiration during the wheat booting stage until the wheat yellowing stage in 2010. After evaluating the SEBAL algorithm during the mentioned period, the maximum daily demand of rain-fed wheat from green water sources was estimated on the 17th of July 2019. By comparing the wheat evapotranspiration values from SEBAL and from computational methods, the average absolute error and correlation were calculated as 0.61 mm/day and 0.9 respectively. It was also found that the wheat highest evapotranspiration occurred when the vegetation index curve had reached its peak. In this time, the wheat maximum daily water demand from the green water resources throughout the county in 2019 was estimated almost equal to 0.93 million cubic meters.