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Proceedings Paper

Assessment of soil surface BRDF using an imaging spectrometer
Author(s): Z. Wang; C. A. Coburn; X. Ren; D. Mazumdar; S. Myshak; A. Mullin; P. M. Teillet
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Paper Abstract

Ground reference data are important for understanding and characterizing angular effects on the images acquired by satellite sensors with off-nadir capability. However, very few studies have considered image-based soil reference data for that purpose. Compared to non-imaging instruments, imaging spectrometers can provide detailed information to investigate the influence of spatial components on the bidirectional reflectance distribution function (BRDF) of a mixed target. This research reported in this paper investigated soil spectral reflectance changes as a function of surface roughness, scene components and viewing geometries, as well as wavelength. Soil spectral reflectance is of particular interest because it is an essential factor in interpreting the angular effects on images of vegetation canopies. BRDF data of both rough and smooth soil surfaces were acquired in the laboratory at 30° illumination angle using a Specim V10E imaging spectrometer mounted on the University of Lethbridge Goniometer System version 2.5 (ULGS-2.5). The BRDF results showed that the BRDF of the smooth soil surface was dominated by illuminated pixels, whereas the shaded pixels were a larger component of the BRDF of the rough surface. In the blue, green, red, and near-infrared (NIR), greater BRDF variation was observed for the rough than for the smooth soil surface. For both soil surface roughness categories, the BRDF exhibited a greater range of values in the NIR than in the blue, green, or red. The imaging approach allows the characterization of the impact of spatial components on soil BRDF and leads to an improved understanding of soil reflectance compared to non-imaging BRDF approaches. The imaging spectrometer is an important sensor for BRDF investigations where the effects of individual spatial components need to be identified.

Paper Details

Date Published: 22 October 2010
PDF: 9 pages
Proc. SPIE 7830, Image and Signal Processing for Remote Sensing XVI, 783010 (22 October 2010); doi: 10.1117/12.865099
Show Author Affiliations
Z. Wang, Univ. of Lethbridge (Canada)
C. A. Coburn, Univ. of Lethbridge (Canada)
X. Ren, Univ. of Lethbridge (Canada)
D. Mazumdar, Univ. of Lethbridge (Canada)
S. Myshak, Univ. of Lethbridge (Canada)
A. Mullin, Univ. of Lethbridge (Canada)
P. M. Teillet, Univ. of Lethbridge (Canada)

Published in SPIE Proceedings Vol. 7830:
Image and Signal Processing for Remote Sensing XVI
Lorenzo Bruzzone, Editor(s)

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