NDLI: Improving Land Surface Pixel Level Albedo Characterization Using Sub-Pixel Information Retrieved from Remote Sensing

Content Provider	IEEE Xplore Digital Library
Author	Wenjun Liu Baoxin Hu Shusen Wang
Copyright Year	2008
Description	Author affiliation: Canada Centre for Remote Sensing, Ottawa, ON (Shusen Wang) \|\| Dept. of Earth & Space Sci. & Eng., York Univ., Toronto, ON (Wenjun Liu; Baoxin Hu)
Abstract	Surface albedo plays an important role in climate model simulations. Current climate models usually use simplified approaches to calculate albedo and can not take sub-grid heterogeneity into account. For heterogeneous land areas, retrieving large scale surface albedo by using current albedo characterization schemes can cause considerably spatial scaling bias. The scaling biases in the albedo estimation processes mainly result from overlooking sub-pixel variability of land surface characteristics and non-linear relationships between albedo and related parameters. The objective is to establish a new methodology to further reduce spatial scaling bias of surface albedo at coarse resolution. Contexture-based and texture-based methods were applied to remove spatial scaling bias. In addition, a new method, dealing with spatial variation of between and within land cover type, was proposed and applied. The results indicate that lumped albedo value can be considerably biased from the distributed albedo (about 20% on average). New proposed corrective algorithm is generally effective for heterogeneous boreal forest pixels.
File Size	209888
File Format	PDF
ISBN	9781424428076
DOI	10.1109/IGARSS.2008.4779115
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Land surface Information retrieval Remote sensing Solid modeling Spatial resolution MODIS Large-scale systems Vegetation Surface texture Context modeling Albedo Spatial scaling Sub-pixel information
Content Type	Text
Resource Type	Article

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