Given the importance of penetration of light in the soil for seed germination, soil warming, and the photolytic
degradation of pesticides, directional transmission of thin sand samples are studied in this paper under both dry and
saturated conditions. The detector views upward through a glass-bottom sample holder, filled to 3 or 4 mm with a
coarse, translucent, quartz sand sample. Transmission through the samples was measured as the illumination zenith angle
moved from 0 to 70° in 5° intervals. In the most cases, transmission decreased monotonically, but slowly with increasing
illumination angle at all wavelengths. A peak in transmission only appeared at 0° illumination for the low bulk density,
dry sample at 3 mm depth. The 0° peak disappeared when the sample was wetted, when the bulk density increased, or
when the depth of the sample increased, which indicates that the radiation transmitting through a sand layer can be
diffused thoroughly with a millimeters-thin sand layer. For the saturated samples, water influences light transmission in
contrasting ways in shorter and longer wavelength. Transmission increased in the VNIR when saturated relative to dry,
while transmission decreased sharply after 1300 nm, with spectral absorption features characteristic of water absorption.
In VNIR region, water absorption is low and the low relative index of refraction enhanced transmission through sand
sample. In contrast, water absorption became dominant at longer wavelengths region leading to the strongly reduced
Direct observations of transmission through a thin layer of quartz sand indicate that the transmitted radiation – from the
visible through the shortwave infrared – is essentially diffuse after little more than one attenuation length. Except for an
anomalously high transmission in a dry, 3-mm deep quartz sample when the detector was directly aligned with the light
source, no complex forward scattering features were apparent. A simple model designed to describe the observations is
explored for insight into the angular dependence and the spectral distribution of the transmitted radiation. The model
suggests that the observed variation of the transmittance with illumination angle can be attributed to surface effects
(including absorption), that much of the transmitted light has passed through the sand particles, and that a wavelengthdependence
of the attenuation in the visible is consistent with scattering within the sand particles.
This paper describes a portable hyperspectral goniometer system for measurement of hemispherical conical reflectance factor (HCRF) data for terrestrial applications, especially in the coastal zone. This system, the Goniometer for Portable Hyperspectral Earth Reflectance (GOPHER), consists of a computer-controlled Spectra Vista Corporation HR-1024 full-range spectrometer mounted on a rotating arc and track assembly, allowing complete coverage in zenith and azimuth of a full hemisphere for recording HCRF. The control software allows customized scan patterns to be quickly modified in the field, providing for flexibility in recording HCRF and the opposition effect with varying grid sizes and scan ranges in both azimuth and zenith directions. The spectrometer track can be raised and lowered on a mast to accommodate variations in terrain and land cover. To minimize the effect of variations in illumination during GOPHER scan cycles, a dual-spectrometer approach has been adapted to link records of irradiance recorded by a second spectrometer during the GOPHER HCRF scan cycle. Examples of field data illustrate the utility of the instrument for coastal studies.
The spectral reflectance of a sample of quartz sand was monitored as the sample progressed from air-dry to fully saturated, and then back to air-dry. Wetting was accomplished by spraying small amounts of water on the surface of the sample, and collecting spectra whenever change occurred. Drying was passive, driven by evaporation from the sand surface, with spectra collected every 5 minutes until the sample was air dry. Water content was determined by monitoring the weight of the sample through both wetting and drying. There was a pronounced difference in the pattern of change in reflectance during wetting and drying, with the differences being apparent both in spectral details (i.e., the depth of absorption bands) and in the magnitude of the reflectance for a particular water content. The differences are attributable to the disposition of water in the sample. During wetting, water initially occurred only on the surface, primarily as water adsorbed onto sand particles. With increased wetting the water infiltrated deeper into the sample, gradually covering all particles and filling the pore spaces. During drying, water and air were distributed throughout the sample for most of the drying period. The differences in water distribution are assumed to be the cause of the differences in reflectance and to the differences in the depths of four strong water absorption bands.
In past work, we have shown that density effects in hyperspectral bi-directional reflectance function (BRDF) data are consistent in laboratory goniometer data, field goniometer measurements with the NRL Goniometer for Portable Hyperspectral Earth Reflectance (GOPHER), and airborne CASI-1500 hyperspectral imagery. Density effects in granular materials have been described in radiative transfer models and are known, for example, to influence both the overall level of reflectance as well as the size of specific characteristics such as the width of the opposition effect in the BRDF. However, in mineralogically complex sands, such as coastal sands, the relative change in reflectance with density depends on the composite nature of the sand. This paper examines the use of laboratory and field hyperspectral goniometer data and their utility for retrieving sand density from airborne hyperspectral imagery. We focus on limitations of current models to describe density effects in BRDF data acquired in the field, laboratory setting, and from airborne systems.
In June 2011, a multi-sensor airborne remote sensing campaign was flown at the Virginia Coast Reserve Long Term
Ecological Research site with coordinated ground and water calibration and validation (cal/val) measurements.
Remote sensing imagery acquired during the ten day exercise included hyperspectral imagery (CASI-1500),
topographic LiDAR, and thermal infra-red imagery, all simultaneously from the same aircraft. Airborne synthetic
aperture radar (SAR) data acquisition for a smaller subset of sites occurred in September 2011 (VCR'11). Focus
areas for VCR'11 were properties of beaches and tidal flats and barrier island vegetation and, in the water column,
shallow water bathymetry. On land, cal/val emphasized tidal flat and beach grain size distributions, density,
moisture content, and other geotechnical properties such as shear and bearing strength (dynamic deflection
modulus), which were related to hyperspectral BRDF measurements taken with the new NRL Goniometer for
Outdoor Portable Hyperspectral Earth Reflectance (GOPHER). This builds on our earlier work at this site in 2007
related to beach properties and shallow water bathymetry. A priority for VCR'11 was to collect and model
relationships between hyperspectral imagery, acquired from the aircraft at a variety of different phase angles, and
geotechnical properties of beaches and tidal flats. One aspect of this effort was a demonstration that sand density
differences are observable and consistent in reflectance spectra from GOPHER data, in CASI hyperspectral imagery,
as well as in hyperspectral goniometer measurements conducted in our laboratory after VCR'11.
This work describes the water collection experiment component of the Megacollect 2004 campaign. Megacollect was a collaborative campaign coordinated by RIT with several institutions to spectrally measure various target/background scenarios with airborne sensors and ground instruments. An extension to the terrestrial campaign was an effort
to simultaneously measure water optical properties in different bodies of water in the Rochester Embayment. This collection updates a previous effort in which water surface measurements were made during an AVIRIS mission over the Rochester Embayment (May 1999).
Megacollect 2004 builds on this through an expanded campaign that increased the number of stations sampled, extended the spectral range of measurements, and improved the spatial resolution of the imagery through the use of multiple sensors (COMPASS, SEBASS, MISI, WASP). A larger set of in-water instruments were deployed on several vessels to sample and measure water optical properties near the shores of Lake Ontario, the northern portions of Irondequoit Bay, and several smaller ponds and bays in the Rochester Embayment. This paper describes the different in-water instruments deployed, the measurements obtained and how they will be used for future modeling efforts and development of hyperspectral algorithms.
Passive, hyperspectral image data and bathymetric lidar data are complimentary data types that can be used effectively in tandem. Hyperspectral data contain information related to water quality, depth, and bottom type; and bathymetric lidar data contain precise information about the depth of the water and qualitative information about water quality and bottom reflectance. The two systems together provide constraints on each other. For example, lidar-derived depths can be used to constrain spectral radiative transfer models for hyperspectral data, which allows for the estimation of bottom reflectance for each pixel. Similarly, depths can be used to calibrate models, which permit the estimation of depths from the hyperspectral data cube on the raster defined by the spectral imagery. We demonstrate these capabilities by fusing hyperspectral data from the LASH and AVIRIS spectrometers with depth data from the SHOALS bathymetric laser to achieve bottom classification and increase the density of depth measurements in Kaneohe Bay, Hawaii. These capabilities are envisioned as operating modes of the next-generation SHOALS system, CHARTS, which will deploy a bathymetric laser and spectrometer on the same platform.
With the goal of applying derivative spectral analysis to analyze high resolution, spectrally continuous remote sensing data, several smoothing and derivative computation algorithms have been reviewed and modified to develop a set of cross-platform spectral analysis tools. Emphasis was placed on exploring different smoothing and derivative algorithms to extract subtle spectral features from any continuous spectral data sets. With interactive selection of bandwidth and sampling interval (band separation), the algorithm can optimize noise reduction and better match the scale of spectral features of interest. Laboratory spectral data were used to test the performance of the implemented derivative analysis modules. An algorithm for detecting the absorption band positions was executed on synthetic spectra and a soybean fluorescence spectrum to demonstrate the usage of the implemented modules in extracting spectral features. Upon examination of the developed modules, issues related to the smoothing and the spectral deviation caused by the smoothing or derivative computation algorithms were also observed and discussed. The scaling effect resulting from the migration of band separations when using the finite approximation derivative algorithm was thoroughly inspected to understand the relationship between the scaling effect and noise removal.
The usual goal of aircraft and satellite remote sensing is to extract information which is directly related to ground targets in spite of atmospheric degradation which often complicates target identification and classification. Empirical algorithms which attempt to characterize targets by their spectral shape (slope, curvature, etc.) have been successful under special conditions, but fail when spectral variations in the solar or atmospheric parameters overwhelm those of the target reflectance. It is possible to derive an algorithm based on derivatives of the radiative transfer equation. This makes it possible to define the conditions under which a derivative algorithm will be insensitive to atmospheric effects and allows estimation of expected errors. This paper describes the development of the "derivative ratio algorithm," based on derivatives of a simple radiative transfer equation. The limiting conditions of the algorithm are derived and demonstrated using examples of reflectance spectra of turbid water and an ash leaf.