| Summary: | Understanding the spatial and temporal variations of evapotranspiration (<i>ET</i>) is vital for water resources planning and management and drought monitoring. The development of a satellite remote sensing technique is described to provide insight into the estimation of <i>ET</i> at a regional scale. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) was used to calculate the actual <i>ET</i> on a daily scale from Landsat-8 data and daily ground-based meteorological data in the upper reaches of Huaihe River on 20 November 2013, 16 April 2015 and 23 March 2018. In order to evaluate the performance of the SEBAL model, the daily SEBAL <i>ET</i> (<i>ET</i><sub>SEBAL</sub>) was compared against the daily reference <i>ET</i> (<i>ET</i><sub>0</sub>) from four theoretical methods: the Penman-Monteith (P-M), Irmak-Allen (I-A), the Turc, and Jensen-Haise (J-H) method, the <i>ET</i><sub>MOD16</sub> product from the MODerate Resolution Imaging Spectrometer (MOD16) and the <i>ET</i><sub>VIC</sub> from Variable Infiltration Capacity Model (VIC). A linear regression equation and statistical indices were used to model performance evaluation. The results showed that the daily <i>ET</i><sub>SEBAL</sub> correlated very well with the <i>ET</i><sub>0</sub>, <i>ET</i><sub>MOD16</sub>, and <i>ET</i><sub>VIC</sub>, and bias between the <i>ET</i><sub>SEBAL</sub> with them was less than 1.5%. In general, the SEBAL model could provide good estimations in daily <i>ET</i> over the study region. In addition, the spatial-temporal distribution of <i>ET</i><sub>SEBAL</sub> was explored. The variation of <i>ET</i><sub>SEBAL</sub> was significant in seasons with high values during the growth period of vegetation in March and April and low values in November. Spatially, the daily <i>ET</i><sub>SEBAL</sub> values in the mountain area were much higher than those in the plain areas over the study region. The variability of <i>ET</i><sub>SEBAL</sub> in this study area was positively correlated with elevation and negatively correlated with surface reflectance, which implies that elevation and surface reflectance are the important factors for predicting <i>ET</i> in this study area.
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