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Proceedings Paper

New land surface albedo parameterization based on MODIS data: preliminary result
Author(s): Xin-Zhong Liang; Min Xu; Wei Gao; Kenneth Kunkel; James Slusser; Yongjiu Dai; Qilong Min
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Paper Abstract

A new parameterization of snow-free land surface albedo is developed using the MODerate resolution Imaging Spectroradiometer (MODIS) products of broadband black-sky and white-sky reflectance and vegetation information as well as the North American and Global Land Data Assimilation System (LDAS) outputs of soil moisture during 2000-20003. It represents the predictable albedo dependences on solar zenith angle, surface soil moisture, fractional vegetation cover, and leaf plus stem area index, while including a statistic correction for static effects specific of local surface characteristics. All parameters are estimated by solving optimization problems of a physically based conceptual model for the minimization of the bulk variances between simulations and observations. A preliminary result showed that, for composites of all temporal and spatial samples of a same land cover category over North America, correlation coefficients between the new parameterization with the MODIS data range from 0.6 to 0.9, while relative errors vary within 5-20%. This is a substantial improvement over the existing state-of-the art Common Land Model (CLM) abide scheme, which has correlation coefficients from -0.5 to 0.5 and relative errors of 20-100%.

Paper Details

Date Published: 9 November 2004
PDF: 6 pages
Proc. SPIE 5544, Remote Sensing and Modeling of Ecosystems for Sustainability, (9 November 2004); doi: 10.1117/12.563449
Show Author Affiliations
Xin-Zhong Liang, Univ. of Illinois/Urbana-Champaign (Afghanistan)
Min Xu, Univ. of Illinois/Urbana-Champaign (United States)
Wei Gao, Colorado State Univ. (United States)
Kenneth Kunkel, Univ. of Illinois/Urbana-Champaign (United States)
James Slusser, Colorado State Univ. (United States)
Yongjiu Dai, Beijing Normal Univ. (United States)
Qilong Min, SUNY/Univ. at Albany (United States)

Published in SPIE Proceedings Vol. 5544:
Remote Sensing and Modeling of Ecosystems for Sustainability
Wei Gao; David R. Shaw, Editor(s)

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