The improved et calculation for semiarid region based on an innovative aerodynamic roughness inversion method using multi-source remote sensing data

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Abstract

The aerodynamic roughness is one of the major parameters in describing the turbulent exchange process between terrestrial and atmosphere. Remote Sensing is recognized as an effective way to inverse this parameter at the regional scale. However, in the long time the inversion method is either dependent on the lookup table for different land covers or the Normalized Difference Vegetation Index (NDVI) factor only, which plays a very limited role in describing the spatial heterogeneity of this parameter and the evapotranspiration (ET) for different land covers. In fact, the aerodynamic roughness is influenced by different factors at the same time, including the roughness unit for hard surfaces, the vegetation dynamic growth and the undulating terrain. Therefore, this paper aims at developing an innovative aerodynamic roughness inversion method based on multi-source remote sensing data in a semiarid region, within the upper and middle reaches of Heihe River Basin. The radar backscattering coefficient was used to inverse the micro-relief of the hard surface. The NDVI was utilized to reflect the dynamic change of vegetated surface. Finally, the slope extracted from SRTM DEM (Shuttle Radar Topography Mission Digital Elevation Model) was used to correct terrain influence. The inversed aerodynamic roughness was imported into ETWatch system to validate the availability. The inversed and tested results show it plays a significant role in improving the spatial heterogeneity of the aerodynamic roughness and related ET for the experimental site.

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Xing, Q., Wu, B., & Zhu, W. (2014). The improved et calculation for semiarid region based on an innovative aerodynamic roughness inversion method using multi-source remote sensing data. In IOP Conference Series: Earth and Environmental Science (Vol. 17). Institute of Physics Publishing. https://doi.org/10.1088/1755-1315/17/1/012146

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