Estimating evapotranspiration by satellite sensors over a heterogeneous landscape

Over the last few decades, there has been a focus on better determining evapotranspiration and its spatial variability, but for many regions routine prediction is not generally available at a spatial resolution appropriate to the underlying surface heterogeneity. Over agricultural regions especially in China, this is particularly critical, since the spatial extent of typical field scales is not regularly resolved within the pixel resolution of satellite sensors. Clearly, for landscapes with significant variability in vegetation cover, type/architecture, and moisture, the spatial resolution of the remote sensing data is crucial for discriminating fluxes for the different land cover types and hence avoiding significant errors due to application of a land surface model to a mixed pixel containing large contrasts in surface temperature and vegetation cover. High resolution remotely sensed data is seemly to discriminate the differences over heterogeneous landscape, but we are inclined to use MODIS data (high temporal resolution, free of charge) to estimate regional ET. At such coarse spatial resolutions, the capability to monitor the impact of land cover change and disturbances on ET or to evaluate ET from different land covers is severely hampered. So, understanding the role of landscape heterogeneity and its influence on the scaling behavior of surface fluxes as observed by satellite sensors with different spatial resolutions is a critical research needed. In this study, we are inclined to use the classified data provided by high-spatial-resolution remotely sensed image to improve regional ET from coarse-spatial-resolution data.