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NASA NTRS2014

Cris-atms Retrievals Using an AIRS Science Team Version 6-like Retrieval Algorithm

CrIS is the infrared high spectral resolution atmospheric sounder launched on Suomi-NPP in 2011. CrISATMS comprise the IRMW Sounding Suite on Suomi-NPP. CrIS is functionally equivalent to AIRS, the high spectral resolution IR sounder launched on EOS Aqua in 2002 and ATMS is functionally equivalent to AMSU on EOS Aqua. CrIS is an interferometer and AIRS is a grating spectrometer. Spectral coverage, spectral resolution, and channel noise of CrIS is similar to AIRS. CrIS spectral sampling is roughly twice as coarse as AIRSAIRS has 2378 channels between 650 cm-1 and 2665 cm-1. CrIS has 1305 channels between 650 cm-1 and 2550 cm-1. Spatial resolution of CrIS is comparable to AIRS.

AIRS↗

NASA NTRS2017

Hyperspectral Radiative Transfer Modeling to Explore the Combined Retrieval of Biophysical Parameters and Canopy Fluorescence from FLEX - Sentinel-3 Tandem Mission Multi-Sensor Data

The FLuorescence EXplorer (FLEX) satellite mission, selected as ESA's 8th Earth Explorer, has been designed forthe measurement of sun-induced fluorescence (F) spectra emitted by plants. This will be accomplished through amulti-sensor approach by placing it in a common orbit in tandem with the Sentinel-3 (S3) mission, which willhave two optical sensors on board, OLCI (Ocean and Land Colour Instrument) and SLSTR (Sea and Land SurfaceTemperature Radiometer) to complement FLEX. These S3 instruments will be used in combination with theimaging spectrometers on board FLEX to provide data useful for atmospheric correction of FLEX data. However,a fully synergetic approach, i.e. by exploiting the spectral and directional information from all tandem missioninstruments together, is an attractive alternative which is explored in this paper. By employing all combined topof-atmosphere (TOA) spectral radiance data, one can (i) characterize the relevant optical properties of the atmosphere,(ii) retrieve biophysical canopy properties including the associated reflectance anisotropy, and (iii)retrieve a more accurate and consistent canopy F.Regarding retrieval methods, Fraunhofer Line Depth (FLD) and Spectral Fitting (SF) are well-known techniquesapplied to hyperspectral data. Both methods depend on a high spectral resolution and assume aLambertian (isotropic) canopy reflectance. However, most vegetation canopies are non-Lambertian. This impliesthat, in particular when ignoring the anisotropic surface reflection, substantial retrieval errors can occur due tothe interaction between atmospheric absorption bands and surface reflectance anisotropy. In this paper, a novelmethod based on spectral radiative transfer (RT) modeling is proposed, in which coupled RT models are used tosimulate TOA radiance spectra. These are then matched with â€˜measured' spectra in order to retrieve surfacefluorescence, along with a suite of biophysical parameters, by model inversion through optimization. By applyingcoupled RT models of the soil-leaf-canopy and the surface-atmosphere systems, TOA radiance spectra canbe simulated for all optical sensors of this tandem mission. In this way, complex effects due to surface reflectanceanisotropy and the spectral sampling by the various instruments, which are difficult to compensate for in the endproducts, are properly taken into account by their incorporation in the forward modeling. Next, by model inversionof TOA radiance data via optimization, the most accurate F retrievals can be achieved in a consistentmanner, along with important canopy level biophysical parameters that may help interpret the F spectrum, suchas chlorophyll content and leaf area index (LAI). The potential of this approach has been explored in a numericalexperiment, and the results are presented in this paper. We find that, with the assumed well-characterized andplausible FLEX/S3 instrument performances, the simultaneous retrieval of biophysical canopy parameters and Fspectra would be possible with a remarkable accuracy, provided the correct atmospheric characterization isavailable.

Verhoef, Wouter↗

NASA NTRS2019

Differences Between OCO‐2 and GOME‐2 SIF Products From a Model‐Data Fusion Perspective

Space-borne retrievals of solar-induced chlorophyll fluorescence (SIF) over land surfaces have recently become a resource for studying and quantifying the broad scale dynamics of gross carbon uptake (gross primary productivity—GPP) across ecosystems. To prepare for the assimilation of SIF data in terrestrial biosphere models, we examine how differences between SIF products (due to differences in acquisition characteristics and processing chain) may affect the optimization of model parameters and the resultant GPP estimate. We compare recent daily mean SIF products (one from the Orbiting Carbon Observatory-2 [OCO-2] and two from the Global Ozone Monitoring Experiment–2 [GOME-2], GlobFluo [GF] and NASA-v28 [N28], missions), averaged at 0.5° × 0.5° spatial resolution and 16-day temporal resolution, at the biome level. Phase differences between these products are relatively small. A first-order correction of the difference in spectral sampling between the two instruments shows that OCO-2 and N28 are consistent in terms of magnitude and amplitude, while GF is twice as large as the others. Using a bias-blind toy data assimilation framework, we analyze how biases between SIF products, and between model and products, can be partially alleviated by optimizing the slope and intercept parameters of a linear GPP-SIF operator. As observation biases can transfer to biases in other optimized process-based parameters and to modeled carbon fluxes— thereby resulting in unidentified inaccurate parameter values—we argue that potential SIF biases should be treated cautiously in real-world experiments in order to achieve realistic and reliable future simulations.

Gross primary production↗

NASA NTRS2019

Optical design of the Mapping Imaging Spectrometer for Europa (MISE)

e Mapping Imaging Spectrometer for Europa (MISE) is a high-throughput pushbroom imaging spectrometer designed for NASA’s planned flyby mission to Jupiter’s moon Europa. The MISE design utilizes heritage from previously demonstrated instruments on airborne platforms, while advancing the state of the art to operate within Europa’s challenging environment. The instrument operates at F/1.4 and covers a spectral range from 0.8 to 5 microns with 10 nm spectral sampling. Through high resolution mapping, MISE is designed to identify distributions of organics, salts, acid hydrates, water ice phases, altered silicates, radiolytic compounds, and warm thermal anomalies at global, regional, and local scales. Such distribution maps will help study surface and subsurface geologic processes, and assess the habitability of Europa’s ocean. We discuss the optical specifications and baseline performance of the MISE optical design.

Van Gorp, Byron E.↗

NASA NTRS2022

A Deep Learning Approach to Fast Radiative Transfer

Due to the sheer volume of data, leveraging satellite instrument observations effectively in a data assimilation context for numerical weather prediction or for remote sensing requires a radiative transfer model as an observation operator that is both fast and accurate at the same time. Physics-based line-by-line radiative transfer (RT) models fulfil the requirement for accuracy, but are too slow and too costly in computational terms for operational applications. Therefore, fast methods were developed to be able to perform fast RT calculations using techniques such as spectral sampling or pre-computed look-up tables. The operational fast models currently calculate the absorption and scattering coefficients from the pre-computed regression coefficients and atmospheric state and cloud profiles. As a novel solution to this problem, this work investigates a deep learning approach to replace the regression coefficients in the fast RT models. A selection of hidden-layer neural network configurations is trained against atmospheric transmittance profile data computed by an accurate line-by-line model and their performance is evaluated and their advantages and disadvantages are discussed.

Machine learning↗

NASA NTRS2020

Optical Design of the Earth Surface Mineral Dust Source Investigation (EMIT) Imaging Spectrometer

The Earth Surface Mineral Dust Source Investigation (EMIT) instrument is a high fidelity imaging spectrometer developed to characterize surface mineralogy of the Earth’s dust source regions over the spectral range of 380- 2500 nm and spectral sampling of 7.4 nm. EMIT will close the current knowledge gap in dust source mineral composition by collecting over 1 billion high signal-to-noise ratio spectra in this region of our planet. These new measurements will be used in conjunction with state-of-the-art Earth System Models to understand and reduce the uncertainty in the radiative forcing effect of mineral dust aerosols. EMIT will be deployed on the International Space Station that has an orbit that is well suited for measuring the arid land regions of the Earth. The optical design utilizes a Dyson spectrometer to reduce volume and mass for a fast (F/1.8) and wide swath (1240 samples) optical system. An overview of the EMIT optical design, development, and current status are discussed.

Green, Robert O.↗

NASA NTRSDate not supplied

Manufacturing, Characterization and Terrestrial Passive Radiative Cooling Performance of Nanofibrous PTFR-PEO Composite Thermal Coatings

Passive heat management is desirable in many applications since it can reduce the energy expended for cooling, for example, in buildings and automobiles. Specifically, materials with wavelength-dependent reflectance and emittance can manage incident solar fluxes while enabling passive radiative cooling by thermal emission. This study demonstrates the use of polymeric nanofibers, specifically polytetrafluoroethylene (PTFE) and polyethylene oxide (PEO) composite, as thermal coatings for passive temperature control. Employing the electrospinning fabrication process, we engineer nano and micro-scale fibrous structures, studying the influence of fabrication parameters, such as precursor material concentration and rotating collector speed, on fiber geometry and optical properties. To understand the role of material and fiber geometry on optical performance, we characterize various samples' spectral reflectance, absorptance, and transmittance using spectrophotometers interfaced with integrating spheres. The results show the potential of nanofabricated PTFE-PEO coatings as promising candidates for passive thermal control. We highlight their competitive edge by comparing their solar reflectance and emittance values to existing passive radiative thermal control materials. In essence, this study contributes to advancing passive heat management technologies, offering insights into optimizing material composition and fabrication techniques for enhanced performance in various applications.

Chieloka Ibekwe↗

NASA NTRS1992

Studies on spectral analysis of randomly sampled signals: Application to laser velocimetry data

Spectral analysis is very useful in determining the frequency characteristics of many turbulent flows, for example, vortex flows, tail buffeting, and other pulsating flows. It is also used for obtaining turbulence spectra from which the time and length scales associated with the turbulence structure can be estimated. These estimates, in turn, can be helpful for validation of theoretical/numerical flow turbulence models. Laser velocimetry (LV) is being extensively used in the experimental investigation of different types of flows, because of its inherent advantages; nonintrusive probing, high frequency response, no calibration requirements, etc. Typically, the output of an individual realization laser velocimeter is a set of randomly sampled velocity data. Spectral analysis of such data requires special techniques to obtain reliable estimates of correlation and power spectral density functions that describe the flow characteristics. FORTRAN codes for obtaining the autocorrelation and power spectral density estimates using the correlation-based slotting technique were developed. Extensive studies have been conducted on simulated first-order spectrum and sine signals to improve the spectral estimates. A first-order spectrum was chosen because it represents the characteristics of a typical one-dimensional turbulence spectrum. Digital prefiltering techniques, to improve the spectral estimates from randomly sampled data were applied. Studies show that the spectral estimates can be increased up to about five times the mean sampling rate.

Sree, David↗

NASA NTRS1974

Determination of rare-earth elements in Luna 16 regolith sample by chemical spectral method

An analysis was made of regolith from layer A of the Luna 16 sample for rare earth elements, by a chemical spectral method. Chemical and ion exchange concentrations were used to determine the content of 12 elements and Y at the level 0.001 to 0.0001 percent with 10 to 15 percent reproducibility of the emission determination. Results within the limits of reproducibility agree with data obtained by mass spectra, activation, and X-ray fluorescent methods.

Stroganova, N. S.↗

NASA NTRS2002

Martian Analogue Sample Characterization and Spectral Library Development at the Johnson Space Center

An extensive collection of Martian analogue samples housed at the Johnson Space Center is the focus of ongoing research by the JSC Mars soil genesis group and their collaborators. Because the major element composition of Martian meteorites and in situ analyses of Martian soils and rocks indicate that Mars is predominantly an iron-rich basaltic world, the focus of active sample collection and analysis is basaltic materials and their hydrolytic (both aqueous and hydrothermal) and sulfatetic alteration products. Described below are the scope of the JSC Mars analogue sample collection, the characterization process, and plans to incorporate the data into spectral libraries for the Mars 2003 Mars Exploration Rover (MER) and Mars 2005 Mars Reconnaissance Orbiter (MRO) CRISM missions.

Morris, Richard V.↗

NASA NTRS1993

Investigation of spectral analysis techniques for randomly sampled velocimetry data

It is well known that velocimetry (LV) generates individual realization velocity data that are randomly or unevenly sampled in time. Spectral analysis of such data to obtain the turbulence spectra, and hence turbulence scales information, requires special techniques. The 'slotting' technique of Mayo et al, also described by Roberts and Ajmani, and the 'Direct Transform' method of Gaster and Roberts are well known in the LV community. The slotting technique is faster than the direct transform method in computation. There are practical limitations, however, as to how a high frequency and accurate estimate can be made for a given mean sampling rate. These high frequency estimates are important in obtaining the microscale information of turbulence structure. It was found from previous studies that reliable spectral estimates can be made up to about the mean sampling frequency (mean data rate) or less. If the data were evenly samples, the frequency range would be half the sampling frequency (i.e. up to Nyquist frequency); otherwise, aliasing problem would occur. The mean data rate and the sample size (total number of points) basically limit the frequency range. Also, there are large variabilities or errors associated with the high frequency estimates from randomly sampled signals. Roberts and Ajmani proposed certain pre-filtering techniques to reduce these variabilities, but at the cost of low frequency estimates. The prefiltering acts as a high-pass filter. Further, Shapiro and Silverman showed theoretically that, for Poisson sampled signals, it is possible to obtain alias-free spectral estimates far beyond the mean sampling frequency. But the question is, how far? During his tenure under 1993 NASA-ASEE Summer Faculty Fellowship Program, the author investigated from his studies on the spectral analysis techniques for randomly sampled signals that the spectral estimates can be enhanced or improved up to about 4-5 times the mean sampling frequency by using a suitable prefiltering technique. But, this increased bandwidth comes at the cost of the lower frequency estimates. The studies further showed that large data sets of the order of 100,000 points, or more, high data rates, and Poisson sampling are very crucial for obtaining reliable spectral estimates from randomly sampled data, such as LV data. Some of the results of the current study are presented.

Sree, Dave↗

NASA NTRS2017

Spectral Characterization of Analog Samples in Anticipation of OSIRIS-REx's Arrival at Bennu

Donaldson Hanna, K. L.↗

NASA NTRS1993

A tool for manual endmember selection and spectral unmixing

Sampling a continuous radiance spectrum in many narrow contiguous spectral bands results in a high covariance between the bands. Hence, the true dimensionality of imaging spectrometer data is not determined by the number of spectral bands, but by the number of spectrally unique signatures whose mixtures reproduce the spectral variance observed in an image. Methods to unmix high dimensional multispectral data use principal components analysis to reduce the dimensionality. The variance of the spectral data is modeled as a linear combination of a finite set of endmembers in the space of the eigen-vectors that account for most of the variance. The number and characteristics of these endmembers are determined not only by the number and characteristics of the spectrally unique materials on the surface but also by processes (e.g., illumination, atmospheric scattering and absorption) affecting the signal received by the sensor. Selection of endmember spectra has typically been from a library. However, since most libraries are incomplete and do not account for the processes mentioned above, we have devised a computer display that allows researchers to explore interactively the eigenvector space of a representative and mean-corrected subset of the image data in search of extreme spectra to designate as endmembers. This display, which is based on parallel coordinates, is unique in the area of multidimensional visualization in that it includes not only a passive view of higher dimensional data but also the capability to interact and move geometrical objects in higher dimensional spaces.

Bateson, C. Ann↗

NASA NTRS2001

BATSE Observations of the Piccinotti Sample of AGN II: Variability and Spectral Analysis

The Piccinotti sample is the hard x-ray-selected sample of active galactic nuclei (AGNs) best studied at energies below approximately 10 keV. It represents the only unbiased and complete high-energy survey of the sky down to a limiting flux of 3.1 x 10(exp -11) per square erg per second. As such, it has been used to study AGN properties such as X-ray spectral characteristics, log N-log S relation, and luminosity function. The BATSE data provide, for the first time, a systematic coverage of the whole sample at high energies. BATSE data from nearly four years of observations (1993 November - 1997 September) were analyzed using standard BATSE occultation analysis software to extract a signal from sources in the Piccinotti sample. Although the BATSE sensitivity for individual source measurements is relatively poor on short time scales, the near all-sky coverage and long CGRO lifetime allows us to improve our sensitivity considerably by sumrr~ng data over many years. This significantly reduces our statistical errors at the expense of loss of temporal information. However, the systematic errors associated with the summation of data over such a long period are not negligible, and the study of systematic effects was a major part of our effort. We evaluated two types of systematic error: those affecting the overall normalization (which are important for comparison with other instruments) and those affecting the size of the fluctuations (which are relevant for estimating the confidence level of a detection). The former was studied by comparison of BATSE data with other instruments, primarily CGRO/OSSE. The results indicate that our absolute flux values maybe overestimated by as much as 35% for some sources. However, since this does not affect our estimation of the detection confidence, we made no corrections to our flux estimates.

Paciesas, William S.↗

NASA NTRS2017

Spectral Characterization of Analog Samples in Anticipation of OSIRIS-REx's Arrival at Bennu

NASA's Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer (OSIRIS-REx) mission successfully launched on September 8th, 2016. During its rendezvous with near-Earth asteroid (101955) Bennu beginning in 2018, OSIRIS-REx will characterize the asteroid's physical, mineralogical, and chemical properties in an effort to globally map the properties of Bennu, a primitive carbonaceous asteroid, and choose a sampling location [e.g. 1]. In preparation for these observations, we spectrally characterized a suite of analog samples across visible, near- and thermal-infrared wavelengths and used these in initial tests of phase detection and abundance determination software algorithms. Here we present the thermal infrared laboratory measurements of the analog sample suite measured under asteroidlike conditions, which are relevant to the interpretation of spectroscopic observations by the OSIRIS-REx Thermal Emission Spectrometer (OTES) [2, 3]. This suite of laboratory measurements of asteroid analogs under asteroid-like conditions is the first of their kind.

Donaldson Hanna, K. L.↗

NASA NTRS1987

Simultaneous sampling technique for two spectral sources

A technique is described that uses a bundle of fiber optics to simultaneously sample a dye laser and a spectral lamp. By the use of a real-time display with this technique, the two signals can be superimposed, and the effect of any spectral adjustments can be immediately accessed. In the NASA's CARS system used for combustion diagnostics, the dye laser mixes with a simultaneously pulsed Nd:YAG laser at 532 nm to probe the vibrational levels of nitrogen. An illustration of the oscilloscopic display of the system is presented.

Jarrett, Olin, Jr.↗

NASA NTRS1993

Learning class descriptions from a data base of spectral reflectance of soil samples

Consideration is given to a program developed to learn class descriptions from positive and negative training examples of spectral reflectance data of bare soils. It is a combination of 'learning by example' and the generate-and-test paradigm and is designed to provide a robust learning environment that can handle error-prone data. The program was tested by having it learn class descriptions of various categories of organic carbon content, iron oxide content, and particle size distribution in soils. These class descriptions were then used to classify an array of targets. The program found the sequence of relationships between bands that contained the most important information to distinguish the classes. Physical explanations for the class descriptions obtained are presented.

Kimes, D. S.↗

NASA NTRSDate not supplied

Mineralogy of Bennu From Spectral Analysis of the Sample Returned By OSIRIS-REx

Here we report on preliminary tests of hypotheses related to the mineralogy of Bennu that were generated from spectral data collected during the asteroid operations phase of the Origins, Spectral Interpretation, Resource Identification, and SecurityRegolith Explorer (OSIRIS-REx) mission. We focus on whether laboratory spectra confirm the hypotheses that (a) Bennu inorganic materials are dominated by Mg-rich phyllosilicates (with less than 10 vol% anhydrous silicates), contain magnetite, and exhibit signatures of up to three distinct carbonate phases, and (b) the dominant lithologies are comparable in bulk mineralogy to the most aqueously altered carbonaceous chondrites (CCs; petrologic types ≤2.4 in the classification, or types 1 and 2 in the classification). Signatures of organics also were detected but are not discussed here. Analysis of material returned from asteroid Bennu contributes to the broader goals of the OSIRIS-REx mission by allowing us to compare sample data at microscopic scales to meteorites, meter-scale asteroid observations, and disk-integrated ground-based observations. These results allow us to better recognize, and resolve, key gaps in our understanding of primitive solar system bodies.

V. E. Hamilton↗