Abstract. We compare field hyperspectral bidirectional reflectance distribution function (BRDF) measurements acquired by a hyperspectral goniometer system known as the goniometer of the Rochester Institute of Technology (GRIT) during an experiment in the Algodones Dunes system in March 2015 with NASA Goddard's light detection and ranging, hyperspectral, and thermal imagery of the site acquired during the experiment. We augment our field spectral data collection with laboratory hyperspectral BRDF measurements of samples brought back from the Algodones Dunes site using GRIT and our second-generation goniometer GRIT-two (GRIT-T).In these laboratory experiments, we vary geophysical parameters such as sediment density and grain size distribution of the sediments that would typically impact observed BRDF with the goal of extending the range of applicability of our resulting BRDF spectral libraries. Geotechnical measurements on site confirm the variability of geophysical parameters such as density and grain size distributions within the dune system, and measurements with GRIT and GRIT-T demonstrate the impact on observed spectral variation. By augmenting field spectral libraries with laboratory BRDF, we show that a greater proportion of the dune system is more faithfully represented in the expanded spectral library. Beyond developing appropriate calibration data for airborne and satellite imagery of the Algodones Dunes, laboratory and field studies also support goals to develop reliable retrieval methods for geophysical quantities such as sediment density directly from spectral imagery. We consider approaches based on the Hapke model. Our approaches use the invariance of the observed functional forms of the single scattering phase function, which must be invariant to differences in the illumination geometry. Fill factor is retrieved and correlates with expected direct measurements of sediment density in a laboratory setting. © The Authors.Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

The increased sensitivity of modern hyperspectral line-scanning systems has led to the development of imaging systems that can acquire each line of hyperspectral pixels at very high data rates (in the 200–400 Hz range). These data acquisition rates present an opportunity to acquire full hyperspectral scenes at rapid rates, enabling the use of traditional push-broom imaging systems as low-rate video hyperspectral imaging systems. This paper provides an overview of the design of an integrated system that produces low-rate video hyperspectral image sequences by merging a hyperspectral line scanner, operating in the visible and near infra-red, with a high-speed pan-tilt system and an integrated IMU-GPS that provides system pointing. The integrated unit is operated from atop a telescopic mast, which also allows imaging of the same surface area or objects from multiple view zenith directions, useful for bi-directional reflectance data acquisition and analysis. The telescopic mast platform also enables stereo hyperspectral image acquisition, and therefore, the ability to construct a digital elevation model of the surface. Imaging near the shoreline in a coastal setting, we provide an example of hyperspectral imagery time series acquired during a field experiment in July 2017 with our integrated system, which produced hyperspectral image sequences with 371 spectral bands, spatial dimensions of 1600 × 212, and 16 bits per pixel, every 0.67 s. A second example times series acquired during a rooftop experiment conducted on the Rochester Institute of Technology campus in August 2017 illustrates a second application, moving vehicle imaging, with 371 spectral bands, 16 bit dynamic range, and 1600 × 300 spatial dimensions every second.

, "Fully automated laboratory and field-portable goniometer used for performing accurate and precise multiangular reflectance measurements," J. Appl. Remote Sens. 11(4), 046014 (2017), doi: 10.1117/1.JRS.11.046014. Abstract. Field-portable goniometers are created for a wide variety of applications. Many of these applications require specific types of instruments and measurement schemes and must operate in challenging environments. Therefore, designs are based on the requirements that are specific to the application. We present a field-portable goniometer that was designed for measuring the hemispherical-conical reflectance factor (HCRF) of various soils and low-growing vegetation in austere coastal and desert environments and biconical reflectance factors in laboratory settings. Unlike some goniometers, this system features a requirement for "target-plane tracking" to ensure that measurements can be collected on sloped surfaces, without compromising angular accuracy. The system also features a second upward-looking spectrometer to measure the spatially dependent incoming illumination, an integrated software package to provide full automation, an automated leveling system to ensure a standard frame of reference, a design that minimizes the obscuration due to self-shading to measure the opposition effect, and the ability to record a digital elevation model of the target region. This fully automated and highly mobile system obtains accurate and precise measurements of HCRF in a wide variety of terrain and in less time than most other systems while not sacrificing consistency or repeatability in laboratory environments.

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Salt marsh vegetation density varies considerably on short spatial scales, complicating attempts to evaluate plant characteristics using airborne remote sensing approaches. In this study, we used a mast-mounted hyperspectral imaging system to obtain cm-scale imagery of a salt marsh chronosequence on Hog Island, VA, where the morphology and biomass of the dominant plant species, Spartina alterniflora, varies widely. The high-resolution hyperspectral imagery allowed the detailed delineation of variations in above-ground biomass, which we retrieved from the imagery using the PROSAIL radiative transfer model. The retrieved biomass estimates correlated well with contemporaneously collected in situ biomass ground truth data ( R 2 = 0.73 ). In this study, we also rescaled our hyperspectral imagery and retrieved PROSAIL salt marsh biomass to determine the applicability of the method across spatial scales. Histograms of retrieved biomass changed considerably in characteristic marsh regions as the spatial scale of the imagery was progressively degraded. This rescaling revealed a loss of spatial detail and a shift in the mean retrieved biomass. This shift is indicative of the loss of accuracy that may occur when scaling up through a simple averaging approach that does not account for the detail found in the landscape at the natural scale of variation of the salt marsh system. This illustrated the importance of developing methodologies to appropriately scale results from very fine scale resolution up to the more coarse-scale resolutions commonly obtained in airborne and satellite remote sensing.

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Spectral data offer a means of estimating the critical parameters of sediments, including sediment composition, moisture content, surface roughness, density, and grain-size distribution. Macroscopic surface roughness in particular has a substantial impact on the structure of the bidirectional reflectance factor (BRF) and the angular distribution of scattered light. In developing the models to invert the properties of the surface beyond just surface composition, roughness must also be accounted for in order to achieve reliable and repeatable results. This paper outlines laboratory studies in which the BRF and surface digital elevation measurements were performed on dry clay sediments. The results were used to explore the suitability of various roughness metrics to account for the radiometric effect of surface roughness. The metrics that are specifically addressed in this paper include random roughness and sill variance. Relative accuracy and tradeoffs between these metrics are described. We find that spectral variability, especially near spectral absorption features, correlates strongly with the quantified measures of surface roughness. We also find that spectral variability is sensitive to the sensor fore-optic size. The results suggest that roughness parameters might be directly determined from the spectrum itself. The relationship between spectral variability and macroscopic surface roughness was particularly strong in some broad spectral ranges of the visible, near infrared, and shortwave infrared, including the near-infrared region between 600 and 850 nm.