Derivation of regional aerodynamic roughness length by combining optical remote sensing and ground measurements over agricultural land in Heihe River Basin

Qiting Chen; Li Jia; Ronald Hutjes

doi:10.1117/12.2068995

18 November 2014 Derivation of regional aerodynamic roughness length by combining optical remote sensing and ground measurements over agricultural land in Heihe River Basin

Qiting Chen, Li Jia, Ronald Hutjes

Author Affiliations +

Proceedings Volume 9265, Remote Sensing and Modeling of the Atmosphere, Oceans, and Interactions V; 926504 (2014) https://doi.org/10.1117/12.2068995
Event: SPIE Asia-Pacific Remote Sensing, 2014, Beijing, China

Abstract

Information of temporal and spatial variation of aerodynamic roughness length is required in most land surface models. The current research presents a practical approach for determining spatially distributed vegetation aerodynamic roughness length with fine temporal and spatial resolution by combining remote sensing and ground measurements. The basic framework of Raupach (1992), with the bulk surface parameters revised by Jasinski et al. (2005) has been applied to optical remote sensing data of HJ-1A/1B missions. In addition, a method for estimating regional scale vegetation height was introduced, so the aerodynamic roughness length, which is more preferred by users than the height normalized form has been developed. Direct validation on different vegetation classes have finally been performed taking advantage of the data-dense field experiments of Heihe Watershed Allied Telemetry Experimental Research (HiWATER). The roughness model had an overall good performance on most of Eddy Covariance sites of HiWATER. However, deviations still existed on different sites, and these have been further analyzed.

Citation Download Citation

Qiting Chen, Li Jia, and Ronald Hutjes "Derivation of regional aerodynamic roughness length by combining optical remote sensing and ground measurements over agricultural land in Heihe River Basin", Proc. SPIE 9265, Remote Sensing and Modeling of the Atmosphere, Oceans, and Interactions V, 926504 (18 November 2014); https://doi.org/10.1117/12.2068995

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KEYWORDS

Vegetation

Aerodynamics

Remote sensing

Agriculture

Atmospheric modeling

Chromium

Spatial resolution

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