Author(s): Zhongjing Wang; Ting Xiang
Linked Author(s):
Keywords: Ideal wet limit; I-REDRAW; Algorithm comparison; Remote sensing-based ET model
Abstract: The selection of extreme dry/wet limit adopting in many popular remote sensing -based ET models will increase a significant uncertainty to the model outputs. Some issues still exist in current efforts to address this problem, in terms of the over-simplified, auto scanning the wet and dry points, overlap images and reference dry and wet limits etc. However, all the wet point or limit is based on the reference evapotranspiration (ET0) of Panmanformula or FAO 56 which in either 12mm or 50 mm reference vegetation. In fact, the real ET is sometime exceeded ET0when a crop is actively growing. In this study, amathematical experiment is performed to investigate the convergent property of vegetated wet limit features with the increasing of its vegetation height (hc), thereby to explore the more ideal wet limit. The result shows an important and interesting feature that the surface temperature (Ts), latent heat flux (LE), and *** tend to a stable value when the hc is above 1. 8 m. Therefore, the hc of 1. 8m can be addressed as a new wet limit named ideal reference wet limit. Resembling the well-grown reed without water stress in wet land with the hc of 1. 8 m, is proposed and adopted in the satellite -based energy balance algorithm with ideal reference dry and wet limits (I-REDRAW). The new model is then applied to the humid Cabauw site in the west of The Netherlands and the semi -arid Tongyu site in the northeastern China with MODIS data in 2003. The result demonstrates that I -REDRAW is capable of deriving reliable energy fluxes in both sites with the root mean square deviation (RMSD) from 21. 3 to 52. 7 W m -2 in Cabauw and from 38. 4 to 66. 1 W m-2 in Tongyu. Compared with REDRAW and algorithm taking averaged air temperature (Ta) as wet limit, I-REDRAW determines the lower wet edge, which probably is more likely to be the realistic boundary representing the extreme wet condition, thus performs better LE estimation, especially in relatively arid and sparse vegetation area.
Year: 2013