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Library Multiscale climatological albedo look-up maps derived from moderate resolution imaging spectroradiometer BRDF/albedo products

Multiscale climatological albedo look-up maps derived from moderate resolution imaging spectroradiometer BRDF/albedo products

Multiscale climatological albedo look-up maps derived from moderate resolution imaging spectroradiometer BRDF/albedo products

Resource information

Date of publication
december 2014
Resource Language
ISBN / Resource ID
AGRIS:US201500006192
Pages
083532-1-083532-15

Surface albedo determines radiative forcing and is a key parameter for driving Earth’s climate. Better characterization of surface albedo for individual land cover types can reduce the uncertainty in estimating changes to Earth’s radiation balance due to land cover change. This paper presents new albedo look-up maps (LUM) using a multi-scale hierarchical approach based on MODIS BRDF/albedo products and Landsat imagery. Ten years (2001-2011) of MODIS BRDF/albedo products were used to generate global albedo climatology. Albedo LUMs of land cover classes defined by the International Geosphere-Biosphere Programme (IGBP) at multiple spatial resolutions were generated. The albedo LUMs included monthly statistics of white-sky (diffuse) and black-sky (direct) albedo of each IGBP class for visible, near infra-red and shortwave broadband under both snow-free and snow-covered conditions. The albedo LUMs were assessed by using the annual MODIS IGBP land cover map and the projected land use scenarios from the Intergovernmental Panel on Climate Change (IPCC) Land-Use Harmonization (LUH) project. The comparisons between the reconstructed albedo and the MODIS albedo data product show good agreements. The LUMs provide high temporal and spatial resolution global albedo statistics without gaps for investigating albedo variations under different land cover scenarios and could be used for land surface modeling.

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Authors and Publishers

Author(s), editor(s), contributor(s)

Gao, Feng
He, Tao
Wang, Zhuosen
Ghimire, Bardan
Shuai, Yanmin
Masek, Jeffrey
Schaaf, Crystal
Williams, Christopher

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