In this paper, surface energy balance system (SEBS) was extended into a regional daily evapotranspiration (ET) estimation model based on remote sensing data, and the extended SEBS was applied to estimate the regional daily ET of Huanghe-Huaihe-Haihe rivers region in Northern China Plain by using MODIS/TERRA data. An analysis was made on the estimated daily ET characteristics of different land covers in the study area by using the spatial analysis module of ArcGIS. Since there were no field observations of ET on each land cover, the estimated daily ET of different land covers was compared with each other, taking the data on April 17, 2001 as an example. The results showed that the regional daily ET estimated by SEBS was reasonable. Wetland and cultivated land had the highest daily ET value, followed by forest-, bush- and grassland, and waste land. The characteristics of the daily ET over these land covers were accorded with the existing knowledge of ET over this region, and coincident to the results of previous work in this area. It was interesting that the residential area also had a higher ET value, which was explained as the higher ET of the land use types, e. g., water body, street trees, and grass parcels in the resident areas within the pixel scale. The spatial inhomogeneity of ET among the forest-, bush-, grass- and cultivated land covers were caused by the spatial inhomogeneous soil water content under these land covers, and the spatial inhomogeneity of ET over cultivated land could be a potential indicator of making reasonable and effective irrigation schedule for the farmland. The limitations of using SEBS model in daily ET estimation were discussed, especially the possibility of underestimating the ET over water body and wetland covers due to the unsuitable surface parameterization scheme for these land types in the model.
|Number of pages||9|
|Journal||Chinese Journal of Applied Ecology|
|Publication status||Published - 1 Feb 2007|
- Geographic information system
- Huanghe-Huaihe-Haihe rivers region
- Remote sensing