We reiterate the fact that the apparent emissivity of a solid surface can depend on not only the composition and
particle size distribution comprising the surface but also on view-angle relative to the bulk surface geometry. We
report on experiments measuring the longwave infrared emissivity of quartz sand samples sorted by particle size
and observed at a series of view angles up to 60° away from the normal to the bulk sample surface. We show that
particle-size and view-angle effects on the apparent emissivity of our quartz samples can mimic each other. In
circumstances where significantly off-normal view-angles are unavoidable and characterizing surface qualities are
desired, these two effects could be confused. We discuss the existing explanations for these effects. We argue that
the view-angle emissivity dependence is intrinsically a bulk geometric effect, not due to a change in apparent
particle-size (via coarse grains obscuring finer ones at larger view-angles). We summarize the predominant
qualitative explanations of the particle-size effects and review the literature comparing quantitative models and
observations. We argue that the existing quantitative models are inadequate for explaining the observations,
pointing to a need for further work in this area.
We conducted an investigation of accuracy and applicability of linear and non-linear mixture models to analyze
the spectra of binary mixtures of particulates as they apply to hyperspectral remote sensing. The goal of the
spectral analysis is to estimate the abundance of each constituent in the binary mixture in terms of the mass
of each constituent. All of the data analyzed for this were collected under controlled laboratory conditions
using particulate materials that were carefully sifted to limit the particle size distributions. Quantification of
intimate mixtures may not be practical in remote sensing due to the requirement that one needs to have detailed
knowledge of the materials being observed and the manner in which they are mixed. However, when the particle
sizes and mass densities are similar to one another, one can get a reasonable estimate of the mass fractions of a
mixture using a simple linear mixture model.
Fusion of Light Detection and Ranging (LiDAR) and Hyperspectral Imagery (HSI) products is useful for geological
analysis, particularly for visualization of geomorphology and hydrology. In early 2007, coincident hyperspectral
imagery and LiDAR were acquired over Cuprite, Nevada. The data were analyzed with ENVI and the ENVI LiDAR
Toolkit. Results of the analysis of these data suggest, for some surfaces, a correlation between mineral content and
surface roughness. However, the LiDAR resolution (~1 meter ground sampling distance) is likely too coarse to extract
surface texture properties of clay minerals in some of the alluvial fans captured in the imagery. Though not
demonstrated in this particular experiment (but a goal of the research), the relation between surface roughness and
mineral composition may provide valuable information about the mechanical properties of the surface cover-in
addition to generating another variable useful for material characterization, image classification, and scene segmentation.
Future mission planning should include consideration of determining optimal ground sampling to be used by LiDAR and
HSI systems. The fusion of LiDAR elevation data and multi- and hyperspectral classification results is, in and of itself, a
valuable tool for imagery analysis and should be explored further.