探讨了使用中高分辨率卫星数据提供的地表分类以及植被指数信息与中低分辨率卫星数据相结合,在混合像元内部进行亚像元处理,以纠正混合像元造成的通量估算误差的方法。其意义在于利用中低分辨率卫星数据进行长期大面积蒸散监测时,只需要少量的中高分辨率数据支持,就可以在一定程度上改善监测结果,具有很好的可操作性。

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.