4.7 Article

High spatial resolution spectral mixture analysis of urban reflectance

期刊

REMOTE SENSING OF ENVIRONMENT
卷 88, 期 1-2, 页码 170-186

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2003.04.008

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IKONOS imagery; urban reflectance; spectral mixture analysis

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This study uses IKONOS imagery to quantify the combined spatial and spectral characteristics of urban reflectance in 14 urban areas worldwide. IKONOS 1-m panchromatic imagery provides a detailed measure of spatial variations in albedo while IKONOS 4-m multispectral imagery allows the relative contributions of different materials to the spectrally heterogeneous radiance field to be determined and their abundance to be mapped. Spatial autocorrelation analyses indicate that the characteristic scale of urban reflectance is consistently between 10 and 20 m for the cities in this study. Spectral mixture analysis quantifies the relative contributions of the dominant spectral endmembers to the overall reflectance of the urban mosaic. Spectral mixing spaces defined by the two low-order principal components account for 96% to 99% of image variance and have a consistent triangular structure spanned by high albedo, low albedo and vegetation endmembers. Spectral mixing among these endmembers is predominantly linear although some nonlinear mixing is observed along the gray axis spanning the high and low albedo endmembers. Inversion of a constrained three-component linear mixing model produces stable, consistent estimates of endmember abundance. RMS errors based on the misfit between observed radiance vectors and modeled radiance vectors (derived from fraction estimates and image endmembers) are generally less than 3% of the mean of the observed radiance. Agreement between observed radiance and fraction estimates does not guarantee the accuracy of the a real fraction estimates, but it does indicate that the three-component linear model provides a consistent and widely applicable physical characterization of urban reflectance. Field validated fraction estimates have applications in urban vegetation monitoring and pervious surface mapping. (C) 2003 Published by Elsevier Inc.

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