Assessing Effects of Azimuthally Oriented Roughness on Directional Reflectance of Sand

Badura, Gregory; Bachmann, Charles M.

doi:10.1109/jstars.2019.2896592

Cited by 13 publications

(10 citation statements)

References 29 publications

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“…Since our study site, the salt panne, was a relatively flat and smooth surface, we chose not to include Hapke's correction for surface roughness [12] in our model. In addition, limitations of the Hapke surface roughness correction have been identified in recent studies by us and others [26,49]. In [26], for example, we showed that the Hapke model does not faithfully reproduce the effects of sub-centimeter scale roughness, especially in the forward scattering direction.…”

Section: Methods: Sources Of Uncertaintymentioning

confidence: 61%

“…In addition, limitations of the Hapke surface roughness correction have been identified in recent studies by us and others [26,49]. In [26], for example, we showed that the Hapke model does not faithfully reproduce the effects of sub-centimeter scale roughness, especially in the forward scattering direction. For the salt panne, including Hapke's correction for macroscopic roughness, therefore, might introduce additional systematic error.…”

Section: Methods: Sources Of Uncertaintymentioning

confidence: 61%

“…Inversion of surface properties has served as a central theme in application of the Hapke model and variants. Hapke's IMSA solution to the radiative transfer equation accounts for influences such as macroscopic surface roughness [12,26], sediment filling factor (the fraction of volume occupied by particles) [1,10], and grain size distribution [1,10,27], as well as intrinsic properties of materials such as single scattering albedo [1]. Extensions of the Hapke model also have considered methods to include the influence of pore water in sediment [28].…”

Section: Introductionmentioning

confidence: 99%

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Retrieval of Sediment Filling Factor in a Salt Panne from Multi-View Hyperspectral Imagery

et al. 2020

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This work describes a study using multi-view hyperspectral imagery to retrieve sediment filling factor through inversion of a modified version of the Hapke radiative transfer model. We collected multi-view hyperspectral imagery from a hyperspectral imaging system mounted atop a telescopic mast from multiple locations and viewing angles of a salt panne on a barrier island at the Virginia Coast Reserve Long-Term Ecological Research site. We also collected ground truth data, including sediment bulk density and moisture content, within the common field of view of the collected hyperspectral imagery. For samples below a density threshold for coherent effects, originally predicted by Hapke, the retrieved sediment filling factor correlates well with directly measured sediment bulk density ( R 2 = 0.85 ). The majority of collected samples satisfied this condition. The onset of the threshold occurs at significantly higher filling factors than Hapke’s predictions for dry sediments because the salt panne sediment has significant moisture content. We applied our validated inversion model to successfully map sediment filling factor across the common region of overlap of the multi-view hyperspectral imagery of the salt panne.

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Section: Methods: Sources Of Uncertaintymentioning

confidence: 61%

Section: Methods: Sources Of Uncertaintymentioning

confidence: 61%

Section: Introductionmentioning

confidence: 99%

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Retrieval of Sediment Filling Factor in a Salt Panne from Multi-View Hyperspectral Imagery

et al. 2020

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“…Relationships such as these should be further explored in future studies. While there are some radiative transfer models such as those due to Hapke 36,37 that explicitly attempt to account for macroscopic roughness, some of www.nature.com/scientificreports/ our past studies 38 and those of other authors 39 have found significant discrepancies, especially in the forward scatter direction 38 . These are areas that require active continued research.…”

Section: Discussionmentioning

confidence: 94%

Mapping barrier island soil moisture using a radiative transfer model of hyperspectral imagery from an unmanned aerial system

Eon

Bachmann

2021

Sci Rep

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The advent of remote sensing from unmanned aerial systems (UAS) has opened the door to more affordable and effective methods of imaging and mapping of surface geophysical properties with many important applications in areas such as coastal zone management, ecology, agriculture, and defense. We describe a study to validate and improve soil moisture content retrieval and mapping from hyperspectral imagery collected by a UAS system. Our approach uses a recently developed model known as the multilayer radiative transfer model of soil reflectance (MARMIT). MARMIT partitions contributions due to water and the sediment surface into equivalent but separate layers and describes these layers using an equivalent slab model formalism. The model water layer thickness along with the fraction of wet surface become parameters that must be optimized in a calibration step, with extinction due to water absorption being applied in the model based on equivalent water layer thickness, while transmission and reflection coefficients follow the Fresnel formalism. In this work, we evaluate the model in both field settings, using UAS hyperspectral imagery, and laboratory settings, using hyperspectral spectra obtained with a goniometer. Sediment samples obtained from four different field sites representing disparate environmental settings comprised the laboratory analysis while field validation used hyperspectral UAS imagery and coordinated ground truth obtained on a barrier island shore during field campaigns in 2018 and 2019. Analysis of the most significant wavelengths for retrieval indicate a number of different wavelengths in the short-wave infra-red (SWIR) that provide accurate fits to measured soil moisture content in the laboratory with normalized root mean square error (NRMSE)< 0.145, while independent evaluation from sequestered test data from the hyperspectral UAS imagery obtained during the field campaign obtained an average NRMSE = 0.169 and median NRMSE = 0.152 in a bootstrap analysis.

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“…The onboard sensors are Analytical Spectral Devices (ASD), Inc., FieldSpec 4 Hi-Res spectroradiometers, capturing the spectral range between 350 and 2500 nm, with 2151 spectral channels in 1-nm intervals (resampled from 3-nm resolution in the VIS/NIR and 8-nm resolution in the SWIR). GRIT-T has been extensively used to capture a variety of BRDF data both in the field and in the laboratory [18]- [21]. Complete details of the system are outlined in Harms et al [17].…”

Section: Measurement Methodsmentioning

confidence: 99%

On Leaf BRDF Estimates and Their Fit to Microfacet Models

Roth

Saunders

Bachmann

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Remote sensing provides high accuracy/precision for quantifying forest biophysical parameters needed for ecological management. Although the significant impact of bidirectional scattering distribution functions (BSDFs) on remote sensing of vegetation is well known, current forest metrics derived from sensor data seldom take leaf BSDF into account, and despite the importance of BSDF effects, leaf directional scattering measurements are almost nonexistent. Previous studies have been limited in the spectral coverage and resolution of observed electromagnetic radiation and lacked models to interpolate all source-sensor angles beyond measurements. This study captured deciduous broadleaf bidirectional reflectance distribution functions (BRDFs) from the visible through shortwave infrared spectral regions (350-2500 nm) and accurately modeled the BRDF for extension to any illumination angle, viewing zenith, or azimuthal angle. We measured biconical directional reflectance factor of leaves from three species of large trees, Norway maple (Acer platanoides), American sweetgum (Liquidambar styraciflua), and northern red oak (Quercus rubra). We then fit the data through nonlinear regression to physical, microfacet BRDF models, resulting in normalized root-mean-square errors of less than 8%, averaged across all wavelengths (excluding low signalto-noise spectral regions). We extracted leaf physical parameters, including the index of refraction and a relative physical roughness from the microfacet models delineating the three species. The implications for forestry remote sensing are important, as rigorous models to represent leaves allow for the creation of more accurate forest scenes for radiative transfer modeling. Such accuracy enables higher fidelity sensor evaluations and data processing algorithms.

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Assessing Effects of Azimuthally Oriented Roughness on Directional Reflectance of Sand

Cited by 13 publications

References 29 publications

Retrieval of Sediment Filling Factor in a Salt Panne from Multi-View Hyperspectral Imagery

Retrieval of Sediment Filling Factor in a Salt Panne from Multi-View Hyperspectral Imagery

Mapping barrier island soil moisture using a radiative transfer model of hyperspectral imagery from an unmanned aerial system

On Leaf BRDF Estimates and Their Fit to Microfacet Models

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