ARCSTONE: Calibration of Lunar Spectral Reflectance from Space
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Radiometric calibration in reflected solar spectra↗
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A Relationship Between Blue and Near‐IR Global Spectral Reflectance and the Response of Global Average Reflectance to Change in Cloud Cover Observed From EPIC
We performed a detailed analysis of Earth Polychromatic Imaging Camera (EPIC) spectral data. We found that the vector composed of blue and near-infrared (NIR) reflectance follows a counterclockwise closed-loop trajectory from 0 to 24 UTC as Earth rotates. This non-linear relationship was not observed by any other satellites due to limited spatial or temporal coverage of either low earth orbit (LEO) or geostationary (GEO) satellites. We found that clouds play an important role in determining the non-linear relationship in addition to the well-known cloud free land-ocean reflectance contrast in the two bands. The non-linear relationship is the result of three factors: (1) a much larger cloud free land-ocean contrast in the NIR band compared to the blue band; (2) significantly larger difference between cloudy-land and cloudy-ocean reflectance in theNIR band compared to the blue band; (3) the periodic variation of fractions of clear land, clear ocean, cloudy land, and cloudy ocean in the sunlit hemisphere as Earth rotates. We found that the green vegetation contributes significantly to the NIR global average reflectance when the South and North Americas appear and disappear in the EPIC's field-of-view. The blue and NIR relationship can be useful for exoplanet research. Clouds impose a strong impact on global spectral reflectance and the reflectance response to a change in cloud cover depends on whether the change is over land or over the ocean. On average, an increase of 0.1 in cloud coverage will lead to a 7%increase in spectrally integrated global average reflectance.
EPIC↗
Spectral reflectance of SNC meteorites: Relationships to Martian surface composition
The spectral signatures of each of the Shergottite-Nakhlite-Chassignite (SNC) meteorite types measured to date are unique among extraterrestrial materials. Reflectance spectra of dark regions of Mars show evidence of basaltic composition. Analytic analysis of absorption band positions and widths in reflectance spectra of SNC meteorites will permit comparisons with spectra from approximately 600 km sized regions for which high-quality, near-IR spectra are available. Multi-spectral mapping data from orbital spacecraft is expected to provide the necessary spectra to determine basaltic compositions of smaller regions on Mars provided fresh, unaltered basalts can be observed or the effects of Martian weathering can be understood and removed from the spectra. With modeling of spectral weathering and mixing of SNC meteoritic assemblages it should be possible with the Mars Observer data to test for the presence of SNC analogs on the Martian surface. Before the relationship between the basaltic composition of units on Mars and the SNC meteorites can be addressed, it is necessary to analyze the absorption band parameters of the SNC reflectance spectra and to acquire high resolution spectral data on smaller regions of the Martian surface.
Mcfadden, L. A.↗
Low-reflectivity spectrally selective coating
Mirror, with area of low reflectivity, replaces neutral-density transmission filter in star-tracking system, increasing reliability. It may have applications in other optical systems.
Zaniewski, J. J.↗
Disk-resolved spectral reflectance properties of Phobos from 0.3-3.2 micron: Preliminary integrated results from Phobos 2
The Phobos 2 mission provided multispectral observations of Phobos over a large wavelength range and with relatively high spectral resolution. Here, researchers integrate results from three multispectral detectors by determining the ultraviolet-visible near infrared spectral properties of color and brightness features recognized in VSK TV images. Researchers present evidence that there are two fundamental spectral units within the region of overlapping coverage by the detectors. They describe the units' spectral and reflectance properties and discuss the implications of these results for the composition of Phobos.
Murchie, Scott L.↗
Spectral reflectance and emittance of Apollo 11 and 12 lunar material.
The thermal radiation properties of Apollo 11 and 12 fines (soils) are reviewed and presented as a function of wavelength, angle of illumination, and bulk density. The spectral directional reflectance is presented for wavelengths from 0.6 to 2 microns and for angles of illumination of 10, 20, 30, 45, and 60 deg. The normal emittance is presented for wavelengths from 2.5 to 14.5 microns. The solar albedo and total normal emittance as a function of temperature were calculated from the spectral values. The solar albedo for Apollo 11 fines is 0.099 whereas for Apollo 12 fines is approximately 0.119 for angles of illumination of 10 deg and a density of 1600 kg cu m.
Birkebak, R. C.↗
Application of a two-stream radiative transfer model for leaf lignin and cellulose concentrations from spectral reflectance measurements, part 1
Lignin and nitrogen contents of leaves constitute the primary rate-limiting parameters for the decomposition of forest litter, and are determinants of nutrient- and carbon-cyclic rates in forest ecosystems (Melillo et al., 1982). Wessman et al. (1988a) developed empirical multivariate relationships between forest canopy lignin amount and the (first-difference) AIS spectral response in three bands spread over the wavelength interval 1256-1555 nm. Wessman et al. (1988b) and McLellan et al. (1991) developed similar regression relationships from laboratory reflectance measurements on dried samples prepared in a standard fashion. They used four to six infrared bands for analysis of nitrogen, lignin and cellulose content of foliage in forest and prairie species. In the present article (Parts 1 and 2) the feasibility of compositional determinations is explored using positions of composite absorption bands that originate from mixtures of lignin, cellulose, and possibly other chemical constituents in the spectral reflectance of green leaves. To carry out this program, we employ full-spectral-resolution single-leaf diffuse reflectance measurements made with a laboratory spectrometer and integrating sphere. The leaf and other chemical reflectance data compiled by Elvidge (1990) have also been utilized extensively.
Conel, James E.↗
ARCSTONE: Calibration of Lunar Spectral Reflectance from Space
Explore the source record for details and available documents.
lunar spectral reflectance calibration radiometry↗
Full spectrum (350-2500 nm) canopy spectral reflectance, Seward Peninsula, Alaska, 2018
Full-range (350-2500 nm) canopy reflectance spectra of various Arctic tundra ecosystem endmembers, including canopy-scale species endmember spectra, plot-scale spectra, and transect spectra as well as non-vegetated surface (NVS) spectra. The datasets were collected at the three core NGEE-Arctic watersheds, Kougarok, Teller, and Council, as well as the supplemental Kougarok Mile 80 site within the larger Seward Peninsula, Alaska region. The data were collected in July 2018 using a full-range Spectra Vista Corporation (SVC) HR-1024i spectroradiometer. Canopy-scale reflectance was collected with an 8-degree field-of-view (FOV) foreoptic lens. All spectral measurements were collected as calibrated surface radiance and converted to surface reflectance using a 99.99% reflective Spectralon white reference standard. For those canopy spectra collected with associated functional trait data, the FOV of the instrument was positioned to include the same leaves harvested for functional trait measurements, including leaf mass per area (LMA) and foliar carbon and nitrogen content (see associated dataset). Species included in the spectra and functional trait dataset include Alnus viridis, Betula nana, Betula glandulosa, Arctous alpina, and Salix pulchra. This data package includes 35 files in a variety of formats including processed canopy spectra (*.csv), processed dGPS locations (*.csv and *.kmz), digital photographs of spectral targets (*.jpg) and raw data from spectroradiometer and dGPS instruments (compressed as tar.gz). Metadata files include data descriptions (_dd.csv) for tabular data and a key to species symbols used in data files. All included files are listed and described in NGA208_flmd.csv.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
54 ENVIRONMENTAL SCIENCES↗
Spectral absorption coefficients of argon and silicon and spectral reflectivity of aluminum
A theoretical investigation was conducted to estimate the spectral properties of argon as a function of pressure, temperature, and wave number. The spectral characteristics of the argon buffer gas exert a strong influence on radiative energy transfer in the in-reactor test configuration of the nuclear light bulb engine. An existing computer program was modified and used to calculate the spectral absorption coefficients of argon at total pressures of 50, 100, 250, 500, 750 and 1000 atm in the temperature interval between 1000 and 30,000 K. At each pressure and temperature, spectral properties were calculated for forty-seven wave numbers in the interval between 1000 and 1,000,000 cm/1. Estimates of the spectral absorption coefficients of silicon were made as part of an evaluation of silicon vapor as a possible buffer-gas seeding agent for the reference nuclear light bulb engine. Existing cross-section data were used to calculate the spectral characteristics of silicon at twenty-four temperatures in the interval between 2000 and 10,000 K.
Krascella, N. L.↗
QWIP: A Quantitative Metric for Quality Control of Aquatic Reflectance Spectral Shape using the Apparent Visible Wavelength
The colors of the ocean and inland waters span clear blue to turbid brown, and the corresponding spectral shapes of the waterleaving signal are diverse depending on the various types and concentrations of phytoplankton, sediment, detritus and colored dissolved organic matter. Here we present a simple metric developed from a global dataset spanning blue, green and brown water types to assess the quality of a measured or derived aquatic spectrum. The Quality Water Index Polynomial (QWIP) is founded on the Apparent Visible Wavelength (AVW), a one-dimensional geophysical metric of color that is inherently correlated to spectral shape calculated as a weighted harmonic mean across visible wavelengths. The QWIP represents a polynomial relationship between the hyperspectral AVW and a Normalized Difference Index (NDI) using red and green wavelengths. The QWIP score represents the difference between a spectrum’s AVW and NDI and the QWIP polynomial. The approach is tested extensively with both raw and quality controlled field data to identify spectra that fall outside the general trends observed in aquatic optics. For example, QWIP scores less than or greater than 0.2 would fail an initial screening and be subject to additional quality control. Common outliers tend to have spectral features related to: 1) incorrect removal of surface reflected skylight or 2) optically shallow water. The approach was applied to hyperspectral imagery from the Hyperspectral Imager for the Coastal Ocean (HICO), as well as to multispectral imagery from the Visual Infrared Imaging Radiometer Suite (VIIRS) using sensor-specific extrapolations to approximate AVW. This simple approach can be rapidly implemented in ocean color processing chains to provide a level of uncertainty about a measured or retrieved spectrum and flag questionable or unusual spectra for further analysis.
remote sensing reflectance↗
Learning class descriptions from a data base of spectral reflectance with multiple view angles
A learning program has been developed which combines 'learning by example' with the generate-and-test paradigm to furnish a robust learning environment capable of handling error-prone data. The problem is shown to be capable of learning class descriptions from positive and negative training examples of spectral and directional reflectance data taken from soil and vegetation. The program, which used AI techniques to automate very tedious processes, found the sequence of relationships that contained the most important information which could distinguish the classes.
Kimes, Daniel S.↗
Environmental Processes and Spectral Reflectance Characteristics Associated with Soil Erosion in Desert Fringe Regions
Results of analysis of spectral variation of sand dunes in El Ghorabi, Bahariya, Egypt; Tombouctou/Azaouad, Mali; and Tsodilo Hills, western Botswana are presented. Seasonal variations in dune extent and location of dune crests and their relationship to such factors as wind and weather variations are emphasized.
Jacobberger, P. A.↗
Spectral reflectance measurements of a virus host model
A technique has been developed to detect the characteristic spectral signatures of healthy and infected St. Augustine grass. It is possible to predict the coverage of the infected area provided ground truth coverage shows positive St. Augustine grass turf. Qualitative measurements from photographs of plants in the blue and red regions with polarization show that light reflected from healthy plants is more strongly polarized than that from diseased plants. Photographs taken through the blue Wratten 47 filter in conjuction with a polarizer show an excellent differentiation. A large photographic difference also appears in the red region. Much smaller differences were noted in the 540 to 550 nm region. Although the intensity in the near-IR region is much higher than the visible region of the spectrum, differences in the healthy and diseased plants' reflectance were quite small.
Toler, R. W.↗
Full Spectrum, 350 - 2500 nm, Leaf and Canopy Spectral Reflectance, Seward Peninsula, Alaska, 2017
Full-range (350 - 2500 nm) leaf and canopy reflectance spectra of various Arctic tundra ecosystem endmembers, including species-level leaf reflectance, canopy-scale species endmember spectra, plot-scale spectra, and transect spectra as well as non-vegetated surface (NVS) spectra. The datasets were collected at the three core NGEE-Arctic watersheds, Kougarok, Teller, and Council within the larger Seward Peninsula, Alaska region. The data were collected in the months of July and August of 2017 using a full-range Spectra Vista Corporation (SVC) HR-1024i spectroradiometer. Leaf-level spectra were collected with the original SVC leaf clip/plant probe connected to the spectrometer through a 1.15 meter long fiber optic cable, while canopy-scale reflectance was collected with an 8-degree field-of-view (FOV) foreoptic lens. All spectral measurements were collected as calibrated surface radiance and converted to surface reflectance using a 99.99% reflective Spectralon white reference standard. For those canopy spectra collected with associated functional trait data, the FOV of the instrument was positioned to include the same leaves harvested for functional trait measurements, including leaf mass per area (LMA) and foliar carbon and nitrogen content (see associated dataset). This data package includes 26 files in a variety of formats including processed canopy and leaf spectra (*.csv), processed dGPS locations (*.csv and *.kmz), digital photographs of spectral targets (*.jpg) and raw data from spectroradiometer and dGPS instruments (compressed as tar.gz). Metadata files include data descriptions (_dd.csv) for tabular data and a key to species symbols used in data files. All included files are listed and described in NGA110_flmd.csv.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
54 ENVIRONMENTAL SCIENCES↗
Full spectrum (350-2500 nm) leaf and canopy spectral reflectance, Seward Peninsula, Alaska, 2019
Full-range (350 - 2500 nm) leaf and canopy reflectance spectra of various Arctic tundra ecosystem endmembers, including species-level leaf reflectance, canopy-scale species endmember spectra, plot-scale spectra, and transect spectra. These data were collected in support of ongoing NASA ABoVE AVIRIS data synthesis work. The datasets were collected at the NGEE-Arctic watershed sites, Kougarok MM64 and MM80, and Teller MM27 on the Seward Peninsula, Alaska. The data were collected in July 2019 using a full-range Spectra Vista Corporation (SVC) HR-1024i spectroradiometer. Leaf-level spectra were collected with the original SVC leaf clip/plant probe connected to the spectrometer through a 1.15 meter long fiber optic cable, while canopy-scale reflectance was collected with an 8-degree field-of-view (FOV) foreoptic lens. All spectral measurements were collected as calibrated surface radiance and converted to surface reflectance using a 99.99% reflective Spectralon white reference standard. For those canopy spectra collected with associated functional trait data, the FOV of the instrument was positioned to include the same leaves harvested for functional trait measurements, including leaf mass per area (LMA) and foliar carbon and nitrogen content (see leaf trait data package for these data). Canopy spectra were obtained for 23 different species, and leaf spectra for 8 species. This data package includes processed canopy and leaf spectra (*.csv), processed dGPS locations (*.csv and *.kmz), digital photographs of spectral targets (*.jpg) and raw data from spectroradiometer and dGPS instruments (compressed as tar.gz). Metadata files include data descriptions (_dd.csv) for tabular data and a key to species symbols used in data files. All included files are listed and described in NGA212_flmd.csv.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
54 ENVIRONMENTAL SCIENCES↗
Spectral reflectance (0.4 - 5.0 microns) of sulfur related compounds and mixtures
Studied and quantified were: (1) the spectral properties of sulfur related compounds and materials; (2) the effects of thermal history, temperature and mixing ratios on the spectral properties of these materials; and (3) the application of these results to the Jovian system, in particular, the surfaces of Io, Europa, and Amalthea, and the chromophores in the Jovian atmosphere. Major results of the study include: (1) a study of how sulfur deposition affects the band strengths in various silicates; (2) a study of how the ultraviolet absorption band of sulfur is affected by dilution in ice; (3) the application of these two sets of experimental studies to Io, in the case of silicate band strengths, and Europa, as in the case of sulfur dilution in ices.
Gradie, Jonathan C.↗
Measurements of thermal infrared spectral reflectance of frost, snow, and ice
Because much of Earth's surface is covered by frost, snow, and ice, the spectral emissivities of these materials are a significant input to radiation balance calculations in global atmospheric circulation and climate change models. Until now, however, spectral emissivities of frost and snow have been calculated from the optical constants of ice. We have measured directional hemispherical reflectance spectra of frost, snow, and ice from which emissivities can be predicted using Kirchhoff's law (e = 1-R). These measured spectra show that contrary to conclusions about the emissivity of snow drawn from previously calculated spectra, snow emissivity departs significantly from blackbody behavior in the 8-14 micrometer region of the spectrum; snow emissivity decreases with both increasing particle size and increasing density due to packing or grain welding; while snow emissivity increases due to the presence of meltwater.
Salisbury, John W.↗