Abstract
The physical properties of a medium such as density, grain size and surface roughness all influence the angular dependence of spectral signatures. Radiative transfer models, such as the one developed by Hapke, can relate the angular dependence of the reflectance to these geophysical variables. This paper focuses on extracting geophysical parameters, fill factor (decreasing porosity) and the single scattering albedo (SSA), through the inversion of a modified version of the Hapke model of airborne and space-borne imagery. The inversion methodology was validated through controlled experiments within a laboratory setting, where a good correlation (R 2 = 0.72) between the retrieved fill factor and the measured density was obtained. Using the same approach, we also retrieved the sediment fill factor and SSA from airborne data collected by the NASA G-LiHT system, and space-borne data observed by the NOAA GOES imager. The results from these studies provide a mechanism to understand geophysical characteristics of the terrain and may potentially be used for long-term monitoring of the dynamic dunes system.
Highlights
Remote sensing techniques are continuously being developed to extract physical information about the Earth’s surface
We demonstrate that fill factor can be retrieved from airborne imagery from the National Aeronautics and Space Administration (NASA) G-LiHT system collected during the 2015 campaign and from imagery collected by the the Advanced Baseline Imager (ABI) on the Geostationary Operational Environmental Satellite (GOES) series [42]
This paper focused on extracting geophysical parameters, fill factor and the single scattering albedo, through the inversion of a modified Hapke model of airborne and space-borne imagery
Summary
Remote sensing techniques are continuously being developed to extract physical information about the Earth’s surface. Space-borne and airborne sensors have been used for the characterization of surface sediments [1–3]. Spectral observations of sediments can be used to effectively identify the physical characteristics of the surface ranging from its texture, roughness, grain size, to its density [1,3–7]. The mapping of soil compaction plays a significant role in agriculture, in improving crop production [8,10] and in other environmental and geophysical applications [9,11–14]. Soil compaction can be quantified by measuring the bulk density or porosity [15]. Ascertaining bulk density at large scales is a difficult task due to variability of the sediment surface [8]. Remote sensing techniques can provide the necessary means of characterizing sediment surfaces over regional and global scales [8]
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