Parameterization of aerodynamic roughness of China’s land surface vegetation from remote sensing data

Deyong Hu; Liwei Xing; Shengli Huang; Lei Deng; Yingjun Xu

doi:10.1117/1.JRS.8.083528

Abstract

Aerodynamic roughness length (z₀) is one of the important parameters that influence energy exchange at the land–atmosphere interface in numerical models, so it is of significance to accurately parameterize the land surface. To parameterize the z₀ values of China’s land surface vegetation using remote sensing data, we parameterized the vegetation canopy area index using the leaf area index and land cover products of moderate resolution imaging spectroradiometer data. Then we mapped the z₀ values of different land cover types based on canopy area index and vegetation canopy height data. Finally, we analyzed the intra-annual monthly z₀ values. The conclusions are: (1) This approach has been developed to parameterize large scale regional z₀ values from multisource remote sensing data, allowing one to better model the land–atmosphere flux exchange based on this feasible and operational scheme. (2) The variation of z₀ values in the parametric model is affected by the vegetation canopy area index and its threshold had been calculated to quantify different vegetation types. In general, the z₀ value will increase during the growing season. When the threshold in the dense vegetation area or in the growing season is exceeded, the z₀ values will decrease but the zero-plane displacement heights will increase. This technical scheme to parameterize the z₀ can be applied to large-scale regions at a spatial resolution of 1 km, and the dynamic products of z₀ can be used in high resolution land or atmospheric models to provide a useful scheme for land surface parameterization.

Citation Download Citation

Deyong Hu, Deyong Hu, Liwei Xing, Liwei Xing, Shengli Huang, Shengli Huang, Lei Deng, Lei Deng, Yingjun Xu, Yingjun Xu, } "Parameterization of aerodynamic roughness of China’s land surface vegetation from remote sensing data," Journal of Applied Remote Sensing 8(1), 083528 (6 November 2014). https://doi.org/10.1117/1.JRS.8.083528 . Submission:

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