Compared to non-imaging instruments, imaging spectrometers (ISs) can provide detailed information to investigate the influence of scene components on the bidirectional reflectance distribution function (BRDF) of a mixed target. The research reported in this article investigated soil surface reflectance changes as a function of scene components (i.e. illuminated pixels and shaded pixels), illumination and viewing zenith angles, and wavelength. Image-based BRDF data of both rough and smooth soil surfaces were acquired in a laboratory setting at three different illumination zenith angles and at four different viewing zenith angles over the full 360 degrees azimuth range, at an interval of 20 degrees, using a Specim V10E IS (Specim, Spectral Imaging Ltd., Oulu, Finland) mounted on the University of Lethbridge Goniometer System version 2.5 (ULGS-2.5). 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 soil surface. As the illumination zenith angle was changed from 60 degrees to 45 degrees and then to 30 degrees, the shadowing effect decreased, regardless of the soil surface. Soil surface reflectance was generally higher at the backscattering view zenith angles and decreased continuously to forward scattering view zenith angles in the light principal plane, regardless of the wavelength, due to the Specim V10E IS seeing more illuminated pixels in the backscattering angles than in the forward scattering angles. Higher soil surface reflectance was observed at higher illumination and viewing zenith angle combinations. For both soil surface roughness categories, the BRDF exhibited a greater range of values in the near-infrared than at the visible wavelengths. This research enhances our understanding of soil BRDF for various soil roughness and illumination conditions.

Wang, Z., Coburn, C. A., Ren, X. and Teillet, P. M. 2012. Effect of soil surface roughness and scene components on soil surface BRF. Can. J. Soil Sci. 92: 297–313. Bidirectional Reflectance factor (BRF) data of both rough [surface roughness index (SRI) of 51%] and smooth soil surfaces (SRI of 5%) were acquired in the laboratory under 30° illumination zenith angle using a Specim V10E imaging spectrometer and an Ocean Optics non-imaging spectrometer mounted on the University of Lethbridge Goniometer System version 2.5 (ULGS-2.5) and version 2.0 (ULGS-2.0), respectively. Under controlled laboratory conditions, the rough soil surface exhibited higher spectral reflectance than the smooth surface for most viewing angles. The BRF of the rough surface varied more than the smooth surface as a function of the viewing zenith angle. The shadowing effect was stronger for the rough surface than for the smooth surface and was stronger in the forward-scattering direction than in the backscattering direction. The pattern of the BRF generated with the non-image based data was similar to that generated with the whole region of interest (ROI) of the image-based data, and that of the whole ROI of the image-based data was similar to that of the illuminated scene component. The BRF of the smooth soil surface was dominated by illuminated scene component, i.e., the sunlit pixels, whereas the shaded scene component, i.e., the shaded pixels, was a larger proportion of the BRF of the rough soil surface. The image-based approach allowed the characterization of the contribution of spatial components in the field of view to soil BRF and improved our understanding of soil reflectance.

This paper reports on an investigation of the suitability of rangeland terrain as a terrestrial benchmark site for monitoring the radiometric performance of satellite sensors after launch. The test site considered is in Newell County rangeland in Alberta (NCRA). Seventy-two Landsat and 34 Satellite Pour l'Observation de la Terre (SPOT) images spanning 1985–2008 were used in the retrospective analysis. Coefficient of variation was used to assess the spatial uniformity and temporal stability of the surface radiometry. Mean top-of-atmosphere (TOA) reflectances were also examined. Image statistics were acquired for various window sizes within a region of 13 km × 13 km, the largest area common to almost all images of the NCRA region. Results are presented for a refined area of 3 km × 3 km and the most uniform 1 km × 1 km area within the refined area. In particular, the results indicate that the spatial radiometric uniformity of the 1 km × 1 km NCRA site has been consistently within 5% since 1985 but subject to considerable temporal variation within that 5%. Hence, the NCRA site is not deemed to be a strong candidate as a primary benchmark site for routine calibration monitoring purposes, although it could serve at times as a secondary site in the absence of or in addition to other possibilities.

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.

Vegetation indices based on satellite image data are widely used for change monitoring but, when derived from different satellite sensors, differ as a function of the uncorrectable differences between the analogous spectral bands used to generate them. This is an important issue because multiple satellite sensors in the Landsat class or in the AVHRR and MODIS classes are being used increasingly to monitor vegetation dynamics. This paper reports on an investigation of the impact of spectral band difference effects (SBDEs) on cross-comparisons between vegetation indices (VIs) derived from multiple satellite sensors in the solar-reflective spectral domain. Results from the simulation study, which encompassed three vegetation target types and eight VIs, indicate how large SBDEs can be and for which VI cross-comparisons they are significant. They also indicate that the spectral dependence of atmospheric gas transmittance is the key factor that gives rise to such significant spectral band difference effects. Among the vegetation indices considered, the GEMI proved to be the least sensitive to spectral dissimilarities between sensors, and hence GEMI is worth considering for quantitative monitoring of vegetation using images from multiple sensors. In the context of potential candidates to fill the forthcoming gap in Landsat data continuity, either one of the IRS-P6 sensors or the SPOT-5 HRG is preferable to the CBERS-2 HRCC as a replacement sensor from the standpoint of agreement with Landsat-based vegetation indices.