Large numbers of important researches have been done to estimate regional surface heat fluxes using remote sensingdata over the past few decades. Due to the spatial heterogeneity of the land surface on a regional scale, many problems stillneed to be explored. Clearly, for landscapes with significant variability in vegetation cover, type, architecture, and moisture, dueto the large contrasts in surface temperature, vegetation cover, surface roughness length and zero plane displacement height, theapplication of a land surface model to a mixed pixel causes significant errors. In this paper, we discussed the method of combiningthe land cover information and remotely sensed vegetation index provided by Landsat data and Moderate Resolution ImagingSpectroradiometer (MODIS) data to correct spatial-scale errors. It makes full use of the advantages of the temporal resolutions ofMODIS data and spatial resolutions of Landsat data to construct a regional evapotranspiration model, which meets the requirementsof spatial heterogeneity scale and makes the higher frequency of large area flux monitoring more operational.