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8 November 2014 Estimation evapotranspiration over the large landscape by using remote sensing data
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Abstract
Evapotranspiration is the important process of plant physiological and ecological, estimating and monitoring evapotranspiration are very useful for evaluation of the influence on the crop growth situation. Determination evapotranspiration over natural surface, the utilization of satellite remote sensing is indispensable. In this paper, a new method is established based on high resolution remote sensing data(TM/ETM) combination Penman-Monteith regional daily evapotranspiration calculation model. The key of the algorithm is used to calculate the Temperature-Vegetation Coverage Index (TVCI) based on an empirical parameterisation of the relationship between surface temperature (Ts) and vegetation index (NDVI), Ts and NDVI in combination can provide information on vegetation and moisture conditions at the surface. Two methods used to calculate the TVCI. The “Universal triangle” method was used to estimate TVCI according to Carlson et al. (1995). Using a trapezoid (triangle) correlation between surface temperature and fractional vegetation cover, we constructed an improved ‘Actual triangle’ method to estimate TVCI, then coupling the Penman- Monteith equation (1998) to estimate daily ET. Daily ET based on the ‘Actual triangle’ methods was compared well with methods by the ‘soil water lost method’, while daily ET based on the ‘Universal triangle’ methods was underestimated. So, it is suitable to use ‘Actual triangle’ method to estimate TVCI instead of ‘Universal triangle’ method in the North China Plain even if the method was applied under different climate conditions. These results indicate that the method is feasible, and VTCI is a close real-time drought monitoring approach. It is based on satellite derived information and combination with the meteorology data, and the potential for operational application of the method is therefore large.
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Jianmao Guo, Ronghua Liu, Qile Guo, Dunyue Fei, Qian Wang, and Junwei Liu "Estimation evapotranspiration over the large landscape by using remote sensing data", Proc. SPIE 9260, Land Surface Remote Sensing II, 92601G (8 November 2014); https://doi.org/10.1117/12.2068300
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