Lack of good quality satellite images because of cloud contamination or long revisit time severely degrades predictions of evapotranspiration (ET) time series at watershed/regional scales from satellite-based surface flux models. We integrate the feedback model developed by Granger and Gray (the GG model) with the Surface Energy Balance Algorithm for Land (SEBAL), with the objective to generate ET time series of high spatial resolution and reliable temporal distribution at watershed scales. First, SEBAL is employed to yield estimates of ET for the Baiyangdian watershed in a semihumid climatic zone in north China on cloud-free days, where there exists the complementary relationship (CR) between actual ET and pan ET. These estimates constitute input to the GG model to inversely derive the relationship between the relative evaporation and the relative drying power of the air. Second, the modified GG model is used to yield ET time series on a daily basis simply by using routine meteorological data and Moderate Resolution Imaging Spectroradiometer (MODIS) albedo and leaf area index products. Results suggest that the modified GG model that has incorporated remotely sensed ET can effectively extend remote sensing based ET to days without images and improve spatial representation of ET at watershed scales. Utility of the evaporative fraction method and the crop coefficients approaches to extrapolate ET time series depends largely on the number and interval of good quality satellite images. Comparison of ET time series from the two techniques and the proposed integration method for days with daily net radiation larger than 100 W m−2 and corresponding pan ET clearly shows that only the integration method can exhibit an asymmetric CR at the watershed scale and daily time scale. Validation performed using hydrologic budget calculations indicate that the proposed method has the highest accuracy in terms of annual estimates of ET for both watersheds in north China.