Satellite remote sensing techniques are widely considered as the most promising way to estimate evapotranspiration (ET) over large geographic extents. In this study, a hybrid dual‐source scheme and trapezoid framework–based evapotranspiration model (HTEM) is developed to map evapotranspiration from satellite imagery. It adopts a theoretically determined vegetation index/land surface temperature trapezoidal space to decompose bulk radiative surface temperature into component temperatures (soil and canopy) and uses a hybrid dual‐source scheme of the layer approach and patch approach to partition net radiation and estimate sensible and latent fluxes separately from the soil and canopy. The proposed model was tested at the Soil Moisture‐Atmosphere Coupling Experiment (SMACEX) site in central Iowa, USA, for 3 days during the campaign in 2002 using Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) data, and at the Weishan flux site in the North China Plain during the main growing season of 2007 with Moderate Resolution Imaging Spectroradiometer Terra images. Results indicate that HTEM is capable of estimating latent heat flux (LE) with mean absolute percentage errors of 6.4% and 11.2% for the SMACEX and the Weishan sites, respectively. In addition, the model was found to be able to give reasonable evaporation and transpiration partitioning at both sites. Compared with other models, HTEM generally produced better sensible and latent flux estimates at the two sites and had comparable abilities in estimating net radiation and ground heat flux. Sensitivity analysis suggests that HTEM is most sensitive to temperature variables and less sensitive to other meteorological observations and parameters.

We developed a new method to estimate terrestrial evapotranspiration (ET) from satellite data without using meteorological inputs. By analyzing observations from 20 eddy covariance tower sites across continental North America, we found a strong relationship between monthly gross primary production (GPP) and ET (R² = 0.72–0.97), implying the potential of using the remotely sensed GPP to invert ET. We therefore adopted the Temperature‐Greenness model which calculates 16 day GPP using MODIS EVI and LST products to estimate GPP and then to calculate ET by dividing GPP with ecosystem water use efficiency (the ratio of GPP to ET). The proposed method estimated 16 day ET very well by comparison with tower‐based measurements (R² = 0.84, p < 0.001, n = 1290) and provided better ET estimates than the MODIS ET product. This suggests that routine estimation of ET from satellite remote sensing without using fine‐resolution meteorological fields is possible and can be very useful for studying water and carbon cycles.