Currently, applications of remote sensing evapotranspiration?(ET) products are limited by the coarse resolution of satellite remote sensing data caused by land surface heterogeneities and the temporal-scale extrapolation of the instantaneous latent heat flux?(LE) based on satellite overpass time. This study proposes a simple but efficient model?(EFAF) for estimating the daily ET of remotely sensed mixed pixels using a model of the evaporative fraction?(EF) and area fraction?(AF) to increase the accuracy of ET estimate over heterogeneous land surfaces. To accomplish this goal, we derive an equation for calculating the EF of mixed pixels based on two key hypotheses. Hypothesis?1 states that the available energy?(AE) of each sub-pixel is approximately equal to that of any other sub-pixels in the same mixed pixel within an acceptable margin of error and is equivalent to the AE of the mixed pixel. This approach simplifies the equation, and uncertainties and errors related to the estimated ET values are minor. Hypothesis?2 states that the EF of each sub-pixel is equal to that of the nearest pure pixel(s) of the same land cover type. This equation is designed to correct spatial-scale errors for the EF of mixed pixels; it can be used to calculate daily ET from daily AE data. The model was applied to an artificial oasis located in the midstream area of the Heihe River using HJ-1B satellite data with a 300m resolution. The results generated before and after making corrections were compared and validated using site data from eddy covariance systems. The results show that the new model can significantly improve the accuracy of daily ET estimates relative to the lumped method; the coefficient of determination?(R2) increased to?0.82 from?0.62, the root mean square error?(RMSE) decreased to 1.60?from 2.47MJm?2(decreased approximately to 0.64?from 0.99mm) and the mean bias error?(MBE) decreased from 1.92?to 1.18MJm?2 (decreased from approximately 0.77?to 0.47mm). It is concluded that EFAF can reproduce daily ET with reasonable accuracy; can be used to produce the ET product; and can be applied to hydrology research, precision agricultural management and monitoring natural ecosystems in the future.
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