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NASA NTRS2012

Seismic Imager Space Telescope

A concept has been developed for a geostationary seismic imager (GSI), a space telescope in geostationary orbit above the Pacific coast of the Americas that would provide movies of many large earthquakes occurring in the area from Southern Chile to Southern Alaska. The GSI movies would cover a field of view as long as 300 km, at a spatial resolution of 3 to 15 m and a temporal resolution of 1 to 2 Hz, which is sufficient for accurate measurement of surface displacements and photometric changes induced by seismic waves. Computer processing of the movie images would exploit these dynamic changes to accurately measure the rapidly evolving surface waves and surface ruptures as they happen. These measurements would provide key information to advance the understanding of the mechanisms governing earthquake ruptures, and the propagation and arrest of damaging seismic waves. GSI operational strategy is to react to earthquakes detected by ground seismometers, slewing the satellite to point at the epicenters of earthquakes above a certain magnitude. Some of these earthquakes will be foreshocks of larger earthquakes; these will be observed, as the spacecraft would have been pointed in the right direction. This strategy was tested against the historical record for the Pacific coast of the Americas, from 1973 until the present. Based on the seismicity recorded during this time period, a GSI mission with a lifetime of 10 years could have been in position to observe at least 13 (22 on average) earthquakes of magnitude larger than 6, and at least one (2 on average) earthquake of magnitude larger than 7. A GSI would provide data unprecedented in its extent and temporal and spatial resolution. It would provide this data for some of the world's most seismically active regions, and do so better and at a lower cost than could be done with ground-based instrumentation. A GSI would revolutionize the understanding of earthquake dynamics, perhaps leading ultimately to effective warning capabilities, to improved management of earthquake risk, and to improved public safety policies. The position of the spacecraft, its high optical quality, large field of view, and large field of regard will make it an ideal platform for other scientific studies. The same data could be simply reused for other studies. If different data, such as multi-spectral data, is required, additional instruments could share the telescope.

Sidick, Erkin↗

NASA NTRS2016

Preliminary On-Orbit Neutron Dose Equivalent and Energy Spectrum Results from the ISS-RAD Fast Neutron Detector (FND)

The ISS-RAD instrument was activated on ISS on February 1st, 2016. Integrated in ISS-RAD, the Fast Neutron Detector (FND) performs, for the first time on ISS, routine and precise direct neutron measurements between 0.5 and 8 MeV. Preliminary results for neutron dose equivalent and neutron flux energy distributions from online/on-board algorithms and offline ground analyses will be shown, along with comparisons to simulated data and previously measured neutron spectral data. On-orbit data quality and pre-launch analysis validation results will be discussed as well.

Semones, Edward↗

NASA NTRS2016

Providing Comprehensive and Consistent Access to Astronomical Observatory Archive Data: The NASA Archive Model

Since the turn of the millennium a constant concern of astronomical archives have begun providing data to the public through standardized protocols unifying data from disparate physical sources and wavebands across the electromagnetic spectrum into an astronomical virtual observatory (VO). In October 2014, NASA began support for the NASA Astronomical Virtual Observatories (NAVO) program to coordinate the efforts of NASA astronomy archives in providing data to users through implementation of protocols agreed within the International Virtual Observatory Alliance (IVOA). A major goal of the NAVO collaboration has been to step back from a piecemeal implementation of IVOA standards and define what the appropriate presence for the US and NASA astronomy archives in the VO should be. This includes evaluating what optional capabilities in the standards need to be supported, the specific versions of standards that should be used, and returning feedback to the IVOA, to support modifications as needed. We discuss a standard archive model developed by the NAVO for data archive presence in the virtual observatory built upon a consistent framework of standards defined by the IVOA. Our standard model provides for discovery of resources through the VO registries, access to observation and object data, downloads of image and spectral data and general access to archival datasets. It defines specific protocol versions, minimum capabilities, and all dependencies. The model will evolve as the capabilities of the virtual observatory and needs of the community change.

Virtual observatory; data archives; standards; IVO↗

DOE OSTI2019

Application of the PHDS Germanium Gamma-ray Imager (GeGI) in Facility Decontamination Assessment - 19471

As part of a site wide investigation, U.S. EPA Region 5 had to assess the nature and extent of contamination in a former radioactive source manufacturing facility. This work involved verification of the radioisotope expected, ensuring no other radioisotopes were present, and quantification of the activity and areal extent of contamination throughout the facility. The device used to support this assessment work was the Germanium Gamma-ray Imager (GeGI) manufactured by PHDS Co. of Knoxville, Tennessee. GeGI is a germanium-semiconductor imaging detector system that provides identification, location, and quantification of radioactive materials within its field of view. GeGI provides the highest fidelity radiological assay information including the best gamma-ray energy resolution and spatial localization of any existing technology, including gamma-ray energy resolution of ∼0.3% full-width half-max (FWHM) at 662 keV and angular location resolution ∼ +/-1 degree. The GeGI is a mechanically cooled, portable system well-suited to field operations. PHDS Co. also provided on-site support during assessment operations and performed subsequent data analysis. Gamma-ray spectral data and associated images were collected with GeGI in six primary areas of the facility having significant residual contamination. A total of 61 gamma-ray spectrum/image sets were collected over the course of three working days (22 Compton images and 39 Pinhole/Compton images). During data collection, Compton imaging was used as the means by which to identify and prioritize hotspots. After contaminated regions were identified via Compton imaging, short-distance Pinhole spectrum/image sets were collected for additional analysis. All of the spectrum/image sets were analyzed to determine the radioisotope distribution within the GeGI field of view, as well as to provide an estimate of the activity associated with each hotspot. It was determined that the nature of residual contamination present was distributed across specific areas of elevated activity, where in some areas there was significant accumulation of contaminated material. It was also determined that no discrete sources of high-activity were present, as would be expected in the case of radioactive material product intended for sealed source manufacture. Results included the activity of residual contamination for each of the areas assessed, and an estimate of the physical area over which that contamination was distributed. It was anticipated by EPA that if decontamination were to be performed that it would involve some type of concrete scabbling or other means of physical removal of contaminated material. The GeGI data could be used to estimate the areal extent (square footage) of concrete removal that would be required for decontamination. Using the activity estimate data from the GeGI along with an estimated depth of concrete removal, planning could be performed for decontamination work (equipment and personnel), waste collection, packaging, transportation, and disposal, along with an associated cost estimate. With regard to health and safety, use of the GeGI reduced the radiation dose to the EPA R5 staff performing the assessment work. The GeGI is a non-destructive device that supports assessment of radioactively contaminated areas and sources from a distance, reducing the need for close contact to radiation, and the time necessary for personnel to operate near such hazards. Use of the GeGI in this facility assessment was our first attempt to apply this technology in what proved to be a quick and efficient decontamination assessment method. The GeGI can assess the nature and extent of residual contamination in a facility, first by quickly identifying the areas of elevated activity, followed by refined measurement and assessment of those elevated areas. The GeGI data can then be used to develop planning and a cost estimate of required decontamination and waste management activities. Since it's a non-destructive gamma identification and imaging device, it can reduce the distance of workers from radioactive sources and the dose incurred from assessment work. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS1988

Assessment of AVIRIS data from vegetated sites in the Owens Valley, California

Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were acquired from the Bishop, CA area, located at the northern end of the Owens Valley, on July 30, 1987. Radiometrically-corrected AVIRIS data were flat-field corrected, and spectral curves produced and analyzed for pixels taken from both native and cultivated vegetation sites, using the JPS SPAM software program and PC-based spreadsheet programs. Analyses focussed on the chlorophyll well and red edge portions of the spectral curves. Results include the following: AVIRIS spectral data are acquired at sufficient spectral resolution to allow detection of blue shifts of both the chlorophyll well and red edge in moisture-stressed vegetation when compared with non-stressed vegetation; a normalization of selected parameters (chlorophyll well and near infrared shoulder) may be used to emphasize the shift in red edge position; and the presence of the red edge in AVIRIS spectral curves may be useful in detecting small amounts (20 to 30 pct cover) of semi-arid and arid vegetation ground cover. A discussion of possible causes of AVIRIS red edge shifts in respsonse to stress is presented.

Rock, B. N.↗

DOE OSTI2016

Quantifying the influences of spectral resolution on uncertainty in leaf trait estimates through a Bayesian approach to RTM inversion

The remote monitoring of plant canopies is critically needed for understanding of terrestrial ecosystem mechanics and biodiversity as well as capturing the short- to long-term responses of vegetation to disturbance and climate change. A variety of orbital, sub-orbital, and field instruments have been used to retrieve optical spectral signals and to study different vegetation properties such as plant biochemistry, nutrient cycling, physiology, water status, and stress. Radiative transfer models (RTMs) provide a mechanistic link between vegetation properties and observed spectral features, and RTM spectral inversion is a useful framework for estimating these properties from spectral data. However, existing approaches to RTM spectral inversion are typically limited by the inability to characterize uncertainty in parameter estimates. Here, we introduce a Bayesian algorithm for the spectral inversion of the PROSPECT 5 leaf RTM that is distinct from past approaches in two important ways: First, the algorithm only uses reflectance and does not require transmittance observations, which have been plagued by a variety of measurement and equipment challenges. Second, the output is not a point estimate for each parameter but rather the joint probability distribution that includes estimates of parameter uncertainties and covariance structure. We validated our inversion approach using a database of leaf spectra together with measurements of equivalent water thickness (EWT) and leaf dry mass per unit area (LMA). The parameters estimated by our inversion were able to accurately reproduce the observed reflectance (RMSE VIS = 0.0063, RMSE NIR-SWIR = 0.0098) and transmittance (RMSE VIS = 0.0404, RMSE NIR-SWIR = 0.0551) for both broadleaved and conifer species. Inversion estimates of EWT and LMA for broadleaved species agreed well with direct measurements (CV EWT = 18.8%, CV LMA = 24.5%), while estimates for conifer species were less accurate (CV EWT = 53.2%, CV LMA = 63.3%). To examine the influence of spectral resolution on parameter uncertainty, we simulated leaf reflectance as observed by ten common remote sensing platforms with varying spectral configurations and performed a Bayesian inversion on the resulting spectra. We found that full-range hyperspectral platforms were able to retrieve all parameters accurately and precisely, while the parameter estimates of multispectral platforms were much less precise and prone to bias at high and low values. We also observed that variations in the width and location of spectral bands influenced the shape of the covariance structure of parameter estimates. Lastly, our Bayesian spectral inversion provides a powerful and versatile framework for future RTM development and single- and multi-instrumental remote sensing of vegetation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Cross-scale sensing of field-level crop residue cover: Integrating field photos, airborne hyperspectral imaging, and satellite data

Conservation tillage practices can bring benefits to agricultural sustainability. Accurate spatial and temporal resolved information of field-scale crop residue cover, which reflects tillage intensity, is highly valuable for evaluating the outcomes of government conservation programs and voluntary ecosystem service markets, as well as facilitating agroecosystem modeling to quantify cropland biogeochemical processes. Remote sensing has the potential to cost-effectively detect crop residue cover, however, existing regional-scale studies were limited by insufficient ground truth data, scale mismatch between coarse satellite pixels and ground data, and the lack of key spectral data for detecting crop residues. Therefore, this study developed an innovative cross-sensing framework to integrate proximal sensing, airborne hyperspectral imaging, and satellite Earth Observation through deep learning to quantify field-level crop residue cover fractions at the regional scale. Specifically, we have collected intensive ground orthographic photos and conducted airborne hyperspectral surveys at corn and soybean fields of Champaign and nearby counties in Illinois, the heartland of the U.S. Corn Belt. Through semi-automatic labeling aided by ResNet-50 and superpixel image segmentation, we obtained 6719 records of ground residue fractions. With these ground data, we developed the 1-dimensional convolution neural network (CNN) model using airborne hyperspectral reflectance, which has 0.5m spatial resolution and 3–5 nm spectral resolution from 400 to 2400 nm, to predict residue fractions. By applying the CNN model to airborne pixels, we augmented “ground truth” data of crop residues and further combined them with Harmonized Landsat and Sentinel-2 (HLS) satellite data to quantify regional residue fractions at 30 m resolution. Results show that airborne hyperspectral imagery with CNN can accurately detect residue fractions (R 2 = 0.82, relative RMSE = 11.73%) to effectively generate quasi “ground truth” data to support satellite upscaling to all fields. With independent ground data for testing, we found that the ground-airborne-satellite integrative framework achieved better predictions in estimating crop residue cover (R 2 = 0.67, relative RMSE = 17.53%) than the conventional ground-satellite upscaling (R 2 = 0.22, relative RMSE = 32.09%). Here we also found that the shortwave infrared wavelengths, particularly 2100–2300 nm, are vital for predicting crop residue cover. Sentinel-2 and Landsat-8 data have a comparable capability to track residue fractions due to similar shortwave infrared wavelengths. This study highlights the high accuracy of hyperspectral imaging to detect agroecosystem tillage management practices and the advantages of cross-scale sensing to cost-effectively integrate multi-source data to quantify field-level agroecosystem variables across scales.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1993

High-resolution X-ray spectroscopy of the supernova remnant N132D

A joint nonequilibrium ionization analysis of spectral data from the Einstein Observatory of the SNR N132D in the LMC is presented on the basis of data from the Focal Plane Crystal Spectrometer (FPCS) and the Solid State Spectrometer (SSS), and lower spectral resolution data from the IPC and the Monitor Proportional Counter (MPC). The FPCS detected individual emission lines of O VII, O VIII, Ne IX, Ne X, Fe XVII, and possibly Fe XX. Measured line widths for the oxygen lines suggest Doppler broadening that is roughly consistent with optically measured expansion velocities of 2250 km/s. At the SSS/IPC temperature, FPCS flux ratios constrain the O/Fe abundance to be at least 1.9 times its solar value and the O/Ne abundance to be 0.2-1.0 times its solar value. Models for remnants with progenitor masses of 20 and 25 solar masses are completely consistent with the data, while remnants with progenitor masses of 13 and 15 solar masses can be made consistent if the progenitors are required to eject a large fraction of their iron cores.

Hwang, Una↗

NASA NTRS1983

The role of spatial, spectral and radiometric resolution on information content

The results of a factorial experiment to evaluate the effects of spatial, spectral, and radiometric resolution on training-data spectral separability and classification accuracy are reported. Aircraft scanner data from five flightlines at 19.8 km over California including croplands, rangeland, forest, water, and urban areas were systematically degraded over a range approximately from Landsat MSS to Thematic Mapper specifications. Reference data were collected on the ground and from aerial photography. The degradations, training-site delineation, data-analysis procedures, and accuracy-assessment techniques are described; the results are presented in tables and graphs and discussed. It is found that while accuracy was increased by higher spectral resolution in 70 percent of the cases and uniformly by increased radiometric resolution, it was decreased by higher spatial resolution. This phenomenon is attributed to classification methods.

Buis, J. S.↗

NASA NTRS1991

Retrievals of surface reflectances from AVIRIS data

A method for retrieving surface reflectances from spectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) is being developed. In this method, the integrated water vapor amount on a pixel by pixel basis is derived from the 0.94- and 1.14-micron water vapor features. The water vapor, carbon dioxide (CO2), oxygen (O2) and methane (CH4) transmission spectrum in the 0.4-2.5 micron region is calculated. The derived water vapor value and the solar and observational geometry are used in the calculation. The AVIRIS spectrum is ratioed against the transmission spectrum to obtain the surface reflectance spectrum. Major mineral absorption features near 2.2 microns in retrieved reflectance spectra can be identified. Different vegetation absorption characteristics are observed. At present, the method is most useful for deriving surface reflectances from AVIRIS data measured on clear days with high visibilities.

Gao, Bo-Cai↗

NASA NTRS1974

Estimation of sea surface temperature from remote sensing in the 11-13 micron window region

The Nimbus 3 and 4 IRIS spectral data in the 11-13 micron water vapor window region are analyzed to determine the sea surface temperature (SST). The high spectral resolution data of IRIS are averaged over approximately 1 micron wide intervals to simulate channels of a radiometer to measure the SST. Three channels are utilized to measure SST over cloud-free oceans. However, two of these channels are sufficient in routine SST determination. The differential absorption properties of water vapor in the two channels enable one to determine the water vapor absorption correction without detailed knowledge of the vertical profiles of temperature and water vapor. The feasibility of determining the SST is demonstrated globally with Nimbus 3 data where cloud-free areas can be selected with the help of albedo data from the MRIR experiment on board the same satellite.

Prabhakara, C.↗

NASA NTRS2005

Radiative Studies of Planetary Atmospheres

Retrieval algorithms and associated software for application to CIRS infrared spectral data have been developed and coded. A general forward radiative transfer code has been written that runs efficiently on a Macintosh, even at high spectral resolution (0.5 per centimeter). It makes use of the correlated-k approach for representation of the gaseous absorption and can include those gases listed in the HITRAN and GEISA atlases, along with collision-induced absorption. Cloud effects are included as spectrally dependent absorbers. Provision has been made for future extension to include particle scattering in an n-stream approximation. The primary purpose of the code is to produce synthetic data and to serve as the forward calculating element in gas and cloud retrieval programs developed for the Mac as well as other platforms. Initial development of algorithms and production software suitable for application to CIRS data to be obtained from Jupiter, Saturn and Titan has been completed, and production versions of the software for application to the spectral data are in place. This includes temperature, gaseous constituent, and cloud opacity retrieval, algorithms that can be applied to both nadir and limb data. This work has been done as a cooperative effort between Conrath and Matcheva (Cornell), Achterberg (GSFWSSAI), and Flasar (GSFC).

Conrath, Barney J.↗

DOE OSTI2016

SICK: THE SPECTROSCOPIC INFERENCE CRANK

There exists an inordinate amount of spectral data in both public and private astronomical archives that remain severely under-utilized. The lack of reliable open-source tools for analyzing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick is agnostic to the wavelength coverage, resolving power, or general data format, allowing any user to easily construct a generative model for their data, regardless of its source. sick can be used to provide a nearest-neighbor estimate of model parameters, a numerically optimized point estimate, or full Markov Chain Monte Carlo sampling of the posterior probability distributions. This generality empowers any astronomer to capitalize on the plethora of published synthetic and observed spectra, and make precise inferences for a host of astrophysical (and nuisance) quantities. Model intensities can be reliably approximated from existing grids of synthetic or observed spectra using linear multi-dimensional interpolation, or a Cannon-based model. Additional phenomena that transform the data (e.g., redshift, rotational broadening, continuum, spectral resolution) are incorporated as free parameters and can be marginalized away. Outlier pixels (e.g., cosmic rays or poorly modeled regimes) can be treated with a Gaussian mixture model, and a noise model is included to account for systematically underestimated variance. Combining these phenomena into a scalar-justified, quantitative model permits precise inferences with credible uncertainties on noisy data. I describe the common model features, the implementation details, and the default behavior, which is balanced to be suitable for most astronomical applications. Using a forward model on low-resolution, high signal-to-noise ratio spectra of M67 stars reveals atomic diffusion processes on the order of 0.05 dex, previously only measurable with differential analysis techniques in high-resolution spectra. sick is easy to use, well-tested, and freely available online through GitHub under the MIT license.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1974

Estimation of sea surface temperature from remote sensing in the 11to 13-micron window region

The Nimbus 3 and 4 Iris spectral data in the 11- to 13-micron water vapor window region are analyzed to determine the sea surface temperature (SST). The high spectral resolution data of Iris are averaged over approximately 1-micron-wide intervals to simulate channels of a radiometer to measure the SST. In the present exploratory study, three such channels in the 775- to 960-per cm (12.9-10.5 micron) region are utilized to measure the SST over cloud-free oceans. However, two of these channels are sufficient in routine SST determination. The differential absorption properties of water vapor in the two channels make it possible to determine the water vapor absorption correction without detailed knowledge of the vertical profiles of temperature and water vapor. The feasibility of determining the SST is demonstrated globally with Nimbus 3 data, where cloud-free areas can be selected with the help of albedo data from the medium-resolution infrared radiometer experiment on board the same satellite. The SST derived from this technique agrees with the measurements made by ships to about 1 C.-

Prabhakara, C.↗

NASA NTRS1983

Evaluation of spatial, radiometric and spectral Thematic Mapper performance for coastal studies

A series of experiments were initiated to determine the feasibility of using thematic mapper spectral data to estimate wetlands biomass. The experiments were conducted using hand-held radiometers simulating thematic mapper wavebands 3, 4 and 5. Spectral radiance data were collected from the ground and from a low altitude aircraft in an attempt to gain some insight into the potential utility of actual thematic mapper data for biomass estimation in wetland plant communities. In addition, radiative transfer models describing volume reflectance of eight water column containing submerged aquatic vegetation were refined.

Klemas, V.↗

DOE OSTI2019

Doppler-free, Stark broadened profiles at low plasma densities in helium

This paper utilizes Doppler-free saturation spectroscopy to measure Doppler-free, Stark broadened spectral profi les for the π-polarization of the HeI 2¹P → 6¹D transition in a low density (10¹⁰ ₋ 10¹² cm⁻³) helium plasma. The measurements were performed in an electron-cyclotron resonance discharge at 23 mTorr with electron density being diagnosed using a combination of Langmuir probe and microwave interferometry techniques. The Doppler-free pro files were observed to be nearly symmetric at n e < 10¹⁰ cm⁻³ but markedly asymmetric above this transition point. Electron density is extracted from the spectral data via fi tting to a spectral model based on quasi-static Stark broadening and compared to the measured n e . The quasi-static model is shown to be valid in the low-density regime for 2¹P → 6¹D helium transition.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Fungal elemental profiling unleashed through rapid laser-induced breakdown spectroscopy (LIBS)

Elemental profiling of fungal species as a phenotyping tool is an understudied topic and is typically performed to examine plant tissue or non-biological materials. Traditional analytical techniques such as inductively coupled plasma–optical emission spectroscopy (ICP-OES) and inductively coupled plasma–mass spectrometry (ICP-MS) have been used to identify elemental profiles of fungi; however, these techniques can be cumbersome due to the difficulty of preparing samples. Additionally, the instruments used for these techniques can be expensive to procure and operate. Laser-induced breakdown spectroscopy (LIBS) is an alternative elemental analytical technique—one that is sensitive across the periodic table, easy to use on various sample types, and is cost-effective in both procurement and operation. LIBS has not been used on axenic filamentous fungal isolates grown in substrate media. In this work, as a proof of concept, we used LIBS on two genetically distinct fungal species grown on a nutrient-rich and nutrient-poor substrate media to determine whether robust elemental profiles can be detected and whether differences between the fungal isolates can be identified. This data set contains the raw LIBS spectral data for the summarized results described inRush, et. al. 2024

elemental profiling↗

NASA NTRS1976

Pioneer 10 and 11 and ground-based infrared data on Jupiter - The thermal structure and He-H2 ratio

Temperature profiles for four midlatitude regions on Jupiter and an improved value of the H2 molar mixing ratio are obtained from Pioneer 11 IR radiometer data and ground-based IR spectral data. The investigated regions include two in the Southern Hemisphere and two in the Northern Hemisphere, all of which consist of an adjacent belt and zone. The temperature profiles for the pressure regime between 0.1 and 1.0 bar are plotted along with the emitted net flux, flux divergence, and cooling rate for a temperature profile that is an average of those derived for the South Equatorial Belt and a 'cloudy' model of the South Tropical Zone. Averaging of the results obtained with clear and cloudy zone models yields a weighted mean value of 0.88 for the H2 molar mixing ratio, which corresponds to a He/H2 ratio of 0.14. It is emphasized that this value depends on perfect validity of the adopted opacity model, the absence of systematic errors in the data, and other factors. Local effective temperatures for each region are found to range from 124.2 to 127.6 K, which are close to the model-independent global average of 125 K.

Orton, G. S.↗