Evapotranspiration (ET), including evaporation from soil surface and vegetation transpiration, is an important variable for water and energy balances on the Earth's surface. Quantifying evapotranspiration (ET) from agriculture fields is important for field water planning and management. Also, knowledge of spatio-temporal distribution of evapotranspiration (ET) on large scales, can provide important information on a variety of water resources issues such as water distribution evaluation, water use by different land surfaces and better management of ground and surface water resources. The main method used traditionally to measure ET are subject to individual, field or landscape scales, but regional ET cannot be measure directly or interpolated due to the inherent spatial heterogeneity of the land surface. Due to the development of remote sensing technology, critical land surface variables with spatial distribution can be acquired easily, such as surface albedo, fractional vegetation cover, and land surface temperature. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) is applied to time series of MODerate Resolution Imaging Spectroradiometer (MODIS) Level 3 data of reflectance and surface temperature measurements to estimate monthly evapotranspiration in Iowa. Spatial distribution and seasonal variation of ET were also analyzed on a large scale.