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Proceedings Paper

Using remote sensing data to estimate evapotranspiration over the inhomogeneous landscape
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Paper Abstract

Estimation evapotranspiration(ET) over large area of inhomogeneous landscape is very important and not an easy problem. Determination evapotranspiration over natural surface, the utilization of satellite remote sensing is indispensable. Using remote sensing data and weather stations data, a parameterization method is described for estimation evapotranspiration over the Tibetan Plateau area. In this paper, the natural surface is classified based on information of remote sensing and relevant information of geography, then the ET can be dealt with by each surface type in different way. Further more, distribution figure of the evapotranspiration is given out. The results indicate: (1) The regional distribution is characteristic by its terrain nature and the regional distribution is obvious and regular. It is seen that the derived regional distributions of the evapotranspiration for the whole mesoscale area is agreed with the land surface status very well. (2) The maximum evapotranspiration is over forest, rivers edge and other area can be irrigated (many flourish grass or crops growing there) are high too, the value of the evapotranspiration over nudation area is low. The derived regional evapotranspiration is contrasted with the value calculated by FAO-PM, and the result can be accepted.

Paper Details

Date Published: 12 August 2010
PDF: 9 pages
Proc. SPIE 7809, Remote Sensing and Modeling of Ecosystems for Sustainability VII, 780912 (12 August 2010); doi: 10.1117/12.860081
Show Author Affiliations
Jianmao Guo, Nanjing Univ. of Information Science & Technology (China)
Institute of Plateau Meteorology (China)
Xujie Li, Nanjing Univ. of Information Science & Technology (China)
Bin Zhu, Nanjing Univ. of Information Science & Technology (China)


Published in SPIE Proceedings Vol. 7809:
Remote Sensing and Modeling of Ecosystems for Sustainability VII
Wei Gao; Thomas J. Jackson; Jinnian Wang, Editor(s)

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