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NASA NTRSDate not supplied

Unreal Engine Testbed for Computer Vision of Tall Lunar Tower Assembly

The Tall Lunar Tower project at the NASA Langley Research Center is focused on the design, modeling, fabrication, and testing of a supervised autonomously assembled engineering development unit for tall lunar towers. The lunar south pole environment poses many challenges for robotic assembly of the tall tower, particularly to computer vision camera systems due to a high-contrast lighting environment. This paper will present an Unreal Engine 5 video game engine Lunar South Pole Lighting Testbed to simulate realistic lunar lighting conditions for synthetic image generation. The fidelity of the simulation environment is investigated by comparing the accuracy of computer vision models trained using synthetic image data and trained from real image data collected in a lunar analog environment.

Unreal Engine↗

NASA NTRS2023

Unreal Engine Testbed for Computer Vision of Tall Lunar Tower Assembly

The Tall Lunar Tower project at the NASA Langley Research Center is focused on the design, modeling, fabrication, and testing of a supervised autonomously assembly engineering development unit for tall lunar towers. The lunar south pole environment poses many challenges for robotic assembly of the tall tower, particularly to computer vision camera systems due to a high-contrast lighting environment. This paper will present an Unreal Engine 5 video game engine Lunar South Pole Lighting Testbed to simulate realistic lunar lighting conditions for synthetic image generation. The fidelity of the simulation environment is investigated by comparing the accuracy of computer vision models trained using synthetic image data and trained from real image data collected in a lunar analog environment.

Unreal Engine↗

NASA NTRS2012

Modeling Radar Scatter from Icy and Young Rough Lunar Craters

For lunar orbital synthetic aperture radars, such as the Chandrayaan Mini-RF operating at S- band (13-cm) wavelength and the Lunar Reconnaissance Orbiter Mini-RF operating at S- band and X-band (3-cm) wavelengths, it is important to understand the radar backscattering characteristics of the icy and young, rough craters. Assuming a mixing model consisting of diffuse and quasi-specular scattering components, we have modeled the opposite-sense circular (OC) and same-sense circular (SC) backscattering characteristics. The specular component, consisting of only OC echoes, represents the echoes from the surface and subsurface layers that are oriented perpendicular to the radar's line-of-sight. The diffuse component, consisting of both SC and OC echoes, represents the echoes associated with either rocks or ice. Also, diffuse echoes have backscatter that is proportional to the cosine of the incidence angle. We modeled how these two (specular and diffuse) radar scattering components could be modulated by factors such as surface roughness associated with young craters. We also modeled how ice radar scattering components could be modulated by a thin regolith covering, and/or by the situation where ice occupies small patches within a larger radar pixel. We tested this modeling by examining 4 nonpolar craters and 12 polar craters using LRO Mini-RF data. Results indicate that icy and young rough craters can be distinguished based upon their SC enhancements (Alpha) and OC enhancements (Gamma). In addition, we also examined the craters that have unusual circular polarization ratios (CPRs) that likely result from a double bounce mode of scattering. Blocky fresh craters, icy craters, and craters exhibiting double bounce scattering can be separated based on the values of Alpha, Gamma, the ratio of Alpha/Gamma and the weighted sum of Alpha and Gamma.

lunar craters↗

NASA NTRS1977

Models of radar imaging of the ocean surface waves

A number of models which would explain ocean wave imagery taken with a synthetic aperture imaging radar are analyzed analytically and numerically. Actual radar imagery is used to support some conclusions. The models considered correspond to three sources of radar backscatter cross section modulation: tilt modulation, roughness variation, and the wave orbital velocity. The effect of the temporal changes of the surface structure, parametric interactions, and the resulting distortions are discussed.

Elachi, C.↗

NASA NTRS2004

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry: Method and Uncertainties - Part 1

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating/drying profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and non-convective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud resolving model simulations, and from the Bayesian formulation itself. Synthetic rain rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in instantaneous rain rate estimates at 0.5 deg resolution range from approximately 50% at 1 mm/h to 20% at 14 mm/h. These errors represent about 70-90% of the mean random deviation between collocated passive microwave and spaceborne radar rain rate estimates. The cumulative algorithm error in TMI estimates at monthly, 2.5 deg resolution is relatively small (less than 6% at 5 mm/day) compared to the random error due to infrequent satellite temporal sampling (8-35% at the same rain rate).

Olson, William S.↗

NASA NTRS2022

Hybrid data-driven and model-informed online tool wear detection in milling machines

Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.

Online anomaly detection↗

NASA NTRS1990

Remote sensing of Earth terrain

Remote sensing of earth terrain is examined. The layered random medium model is used to investigate the fully polarimetric scattering of electromagnetic waves from vegetation. The model is used to interpret the measured data for vegetation fields such as rice, wheat, or soybean over water or soil. Accurate calibration of polarimetric radar systems is essential for the polarimetric remote sensing of earth terrain. A polarimetric calibration algorithm using three arbitrary in-scene reflectors is developed. In the interpretation of active and passive microwave remote sensing data from the earth terrain, the random medium model was shown to be quite successful. A multivariate K-distribution is proposed to model the statistics of fully polarimetric radar returns from earth terrain. In the terrain cover classification using the synthetic aperture radar (SAR) images, the applications of the K-distribution model will provide better performance than the conventional Gaussian classifiers. The layered random medium model is used to study the polarimetric response of sea ice. Supervised and unsupervised classification procedures are also developed and applied to synthetic aperture radar polarimetric images in order to identify their various earth terrain components for more than two classes. These classification procedures were applied to San Francisco Bay and Traverse City SAR images.

Kong, Jin AU↗

NASA NTRS1990

Modeling the ultraviolet photospheric spectrum of cool giant stars. I - Arcturus. II - Gamma Crucis

The UV photospheric spectra of cool giants are modeled analytically to facilitate the identification of UV spectral features and aid in the measurement of actual chromospheric flux at different wavelengths. Atomic line data derived experimentally and semiempirically are used with LTE model atmospheres by means of the SYNTHE FORTRAN code to generate the synthetic spectra. Surface flux spectra are determined and fitted to the absorption features of observed IUE high resolution spectra, and the modeled results are compared to the observed spectra of the cool giants Arcturus and the cooler Gamma Crucis. It is shown that the synthetic spectra can be produced for the stars, and chromospheric emission lines are thought to account for significant discrepancies between observed and calculated values. Two new fluorescence mechanisms are identified by modeling the ultraviolet photospheric spectrum of Gamma Crucis.

Carpenter, Kenneth G.↗

NASA NTRS2007

Modeling of Radiative Transfer in Protostellar Disks

This program implements a spectral line, radiative transfer tool for interpreting Spitzer Space Telescope observations by matching them with models of protostellar disks for improved understanding of planet and star formation. The Spitzer Space Telescope detects gas phase molecules in the infrared spectra of protostellar disks, with spectral lines carrying information on the chemical composition of the material from which planets form. Input to the software includes chemical models developed at JPL. The products are synthetic images and spectra for comparison with Spitzer measurements. Radiative transfer in a protostellar disk is primarily affected by absorption and emission processes in the dust and in molecular gases such as H2, CO, and HCO. The magnitude of the optical absorption and emission is determined by the population of the electronic, vibrational, and rotational energy levels. The population of the molecular level is in turn determined by the intensity of the radiation field. Therefore, the intensity of the radiation field and the population of the molecular levels are inter-dependent quantities. To meet the computational challenges of solving for the coupled radiation field and electronic level populations in disks having wide ranges of optical depths and spatial scales, the tool runs in parallel on the JPL Dell Cluster supercomputer with C++ and Fortran compiler with a Message Passing Interface. Because this software has been developed on a distributed computing platform, the modeling of systems previously beyond the reach of available computational resources is possible.

VonAllmen, Paul↗

NASA NTRS1993

Active tectonics in southern Xinjiang, China: Analysis of terrace riser and normal fault scarp degradation along the Hotan-Qira fault system

The northern piedmont of the western Kunlun mountains (Xinjiang, China) is marked at its easternmost extremity, south of the Hotan-Qira oases, by a set of normal faults trending N50E for nearly 70 km. Conspicuous on Landsat and SPOT images, these faults follow the southeastern border of a deep flexural basin and may be related to the subsidence of the Tarim platform loaded by the western Kunlun northward overthrust. The Hotan-Qira normal fault system vertically offsets the piedmont slope by 70 m. Highest fault scarps reach 20 m and often display evidence for recent reactivations about 2 m high. Successive stream entrenchments in uplifted footwallls have formed inset terraces. We have leveled topographic profiles across fault scarps and transverse abandoned terrace risers. The state of degradation of each terrace edge has been characterized by a degradation coefficient tau, derived by comparison with analytical erosion models. Edges of highest abandoned terraces yield a degradation coefficient of 33 +/- 4 sq.m. Profiles of cumulative fault scarps have been analyzed in a similar way using synthetic profiles generated with a simple incremental fault scarp model.

Avouac, Jean-Philippe↗

NASA NTRS2000

Estimating Forest Vertical Structure from Multialtitude, Fixed-Baseline Radar Interferometric and Polarimetric Data

Parameters describing the vertical structure of forests, for example tree height, height-to-base-of-live-crown, underlying topography, and leaf area density, bear on land-surface, biogeochemical, and climate modeling efforts. Single, fixed-baseline interferometric synthetic aperture radar (INSAR) normalized cross-correlations constitute two observations from which to estimate forest vertical structure parameters: Cross-correlation amplitude and phase. Multialtitude INSAR observations increase the effective number of baselines potentially enabling the estimation of a larger set of vertical-structure parameters. Polarimetry and polarimetric interferometry can further extend the observation set. This paper describes the first acquisition of multialtitude INSAR for the purpose of estimating the parameters describing a vegetated land surface. These data were collected over ponderosa pine in central Oregon near longitude and latitude -121 37 25 and 44 29 56. The JPL interferometric TOPSAR system was flown at the standard 8-km altitude, and also at 4-km and 2-km altitudes, in a race track. A reference line including the above coordinates was maintained at 35 deg for both the north-east heading and the return southwest heading, at all altitudes. In addition to the three altitudes for interferometry, one line was flown with full zero-baseline polarimetry at the 8-km altitude. A preliminary analysis of part of the data collected suggests that they are consistent with one of two physical models describing the vegetation: 1) a single-layer, randomly oriented forest volume with a very strong ground return or 2) a multilayered randomly oriented volume; a homogeneous, single-layer model with no ground return cannot account for the multialtitude correlation amplitudes. Below the inconsistency of the data with a single-layer model is followed by analysis scenarios which include either the ground or a layered structure. The ground returns suggested by this preliminary analysis seem too strong to be plausible, but parameters describing a two-layer compare reasonably well to a field-measured probability distribution of tree heights in the area.

Treuhaft, Robert N.↗

NASA NTRS1995

Analysis of Gaspra lightcurves using Galileo shape and photometric models

Galileo-based models for the shape of 951 Gaspra and the global-average photometric behavior of its surface have been used to model a representative subset of the asteroid's telescopic lightcurves. Fitting the synthetic lightcurves to the observed timing of lightcurve extrema, and knowing the orientation of Gaspra's axes at the time of the Galileo flyby, leads to a sidereal rotation period for the asteroid of 7.042024 +/- 0.000020 hr, a slight change from the period reported by Magnusson et al. (1992). Initially, the shapes, amplitudes, and absolute photometry of the synthetic and observed lightcurves agree with each other to within 0.05-0.1 mag. Small modifications to the Gaspra shape model on sides of the asteroid poorly imaged by Galileo (changes of 700 m or less in the southern hemisphere at longitudes 90 deg-270 deg W) reduce the typical discrepancies to approximately 0.05 mag in lightcurve shape and less than 0.03 mag in absolute photometry. The result demonstrates that Earth-based lightcurves can be used to refine the shape of a spacecraft-imaged irregular object in areas that are poorly constrained by the spacecraft observations. The consistency and phase-angle dependence of the Galileo-based model for Gaspra photometry, supports the accuracy of the absolute calibration of the Galileo SSI camera, and confirms the Earth-based determination of the V-filter geometric albedo of the asteroid (0.22 +/- 0.03; Tholen et al., submitted for publication). Remaining discrepancies between the synthetic and observed lightcurves show no indication of systematic latitudinal variations in albedo and also cannot be explained entirely by isolated albedo spots. These discrepancies are most likely caused by (1) small, remaining, hard-to-constrain errors in the Gaspra shape model and/or (2) moderate variations in macroscopic roughness across the asteroid's surface, in particular making longitudes 130 deg to 300 deg W moderately rougher than the opposite hemisphere.

Simonelli, Damon P.↗

NASA NTRSDate not supplied

Pushing the Limits of Aquatic Remote Sensing: Synthetic Data and Deep Learning for Fast Inverse Emulation of A Coupled Ocean-Atmosphere Radiative Transfer Model

The inversion of electromagnetic information to physical and biological properties of the water column is a notoriously difficult problem, yet fundamental to our ability of understanding aquatic processes on large time and space scales. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will discuss research integrating advanced biological and radiative modeling, high-end computation, and machine learning to develop a portable global processor for simultaneous retrieval of atmosphere and water optics for diverse aquatic systems from the open and coastal ocean to optically extreme inland waters and harmful algal blooms. We will discuss some of the basic concepts behind the forward modeling approach including DEAP, the novel Distributed Equivalent Algal Populations model, for developing large spectral libraries of aquatic particle optics to aid in our ability to distinguish phytoplankton functional types (PFTs) and inorganic material, as well as other factors which enable comprehensive modeling from the benthos to top-of-atmosphere (TOA). This information is being used to understand how we can leverage next-generation deep learning methods for maximum information retrieval and rapid image processing, while also providing capabilities to identify minimum sensor spectral requirements necessary for certain aquatic applications. Further, I will touch on how we envision this research to enable the aquatic community for science discovery and how we are moving closer towards the capability for high-fidelity global analysis of aquatic ecosystems.

Jeremy Alan Kravitz↗

NASA NTRS2013

3D Radiative Transfer Effects in Multi-Angle/Multi-Spectral Radio-Polarimetric Signals from a Mixture of Clouds and Aerosols Viewed by a Non-Imaging Sensor

When observing a spatially complex mix of aerosols and clouds in a single relatively large field-of-view, nature entangles their signals non-linearly through polarized radiation transport processes that unfold in the 3D position and direction spaces. In contrast, any practical forward model in a retrieval algorithm will use only 1D vector radiative transfer (vRT) in a linear mixing technique. We assess the difference between the observed and predicted signals using synthetic data from a high-fidelity 3D vRT model with clouds generated using a Large Eddy Simulation model and an aerosol climatology. We find that this difference is signal--not noise--for the Aerosol Polarimetry Sensor (APS), an instrument developed by NASA. Moreover, the worst case scenario is also the most interesting case, namely, when the aerosol burden is large, hence hase the most impact on the cloud microphysics and dynamics. Based on our findings, we formulate a mitigation strategy for these unresolved cloud adjacency effects assuming that some spatial information is available about the structure of the clouds at higher resolution from "context" cameras, as was planned for NASA's ill-fated Glory mission that was to carry the APS but failed to reach orbit. Application to POLDER (POLarization and Directionality of Earth Reflectances) data from the period when PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) was in the A-train is briefly discussed.

APS↗

NASA NTRS2024

Extending the Utility of Space-Borne Snow Water Equivalent Observations Over Vegetated Areas With Data Assimilation

Snow is a vital component of the earth system, yet no snow-focused satellite remote sensing platform currently exists. In this study, we investigate how synthetic observations of snow water equivalent (SWE) representative of a synthetic aperture radar remote sensing platform could improve spatiotemporal estimates of snowpack. We use a fraternal twin observing system simulation experiment, specifically investigating how much snow simulated using widely used models and forcing data could be improved by assimilating synthetic observations of SWE. We focus this study across a 24° x 37° domain in the western USA and Canada, simulating snow at 250 m resolution and hourly time steps in water year 2019. We perform two data assimilation experiments, including (1) a simulation excluding synthetic observations in forests where canopies obstruct remote sensing retrievals and (2) a simulation inferring snow distribution in forested grid cells using synthetic observations from nearby canopy-free grid cells. Results found that, relative to a nature run, or assumed true simulation of snow evolution, assimilating synthetic SWE observations improved average SWE biases at maximum snowpack timing in shrub, grass, crop, bare-ground, and wetland land cover types from 14 %, to within 1 %. However, forested grid cells contained a disproportionate amount of SWE volume. In forests, SWE mean absolute errors at the time of maximum snow volume were 111 mm and average SWE biases were on the order of 150 %. Here the data assimilation approach that estimated forest SWE using observations from the nearest canopy-free grid cells substantially improved these SWE biases (18 %) and the SWE mean absolute error (27 mm). Simulations employing data assimilation also improved estimates of the temporal evolution of both SWE and runoff, even in spring snowmelt periods when melting snow and high snow liquid water content prevented synthetic SWE retrievals. In fact, in the Upper Colorado River region, melt-season SWE biases were improved from 63 % to within 1 %, and the Nash–Sutcliffe efficiency of runoff improved from −2.59 to 0.22. These results demonstrate the value of data assimilation and a snow-focused globally relevant remote sensing platform for improving the characterization of SWE and associated water availability.

Justin Pflug↗

NASA NTRS1995

Model atmospheres for M (sub)dwarf stars. 1: The base model grid

We have calculated a grid of more than 700 model atmospheres valid for a wide range of parameters encompassing the coolest known M dwarfs, M subdwarfs, and brown dwarf candidates: 1500 less than or equal to T(sub eff) less than or equal to 4000 K, 3.5 less than or equal to log g less than or equal to 5.5, and -4.0 less than or equal to (M/H) less than or equal to +0.5. Our equation of state includes 105 molecules and up to 27 ionization stages of 39 elements. In the calculations of the base grid of model atmospheres presented here, we include over 300 molecular bands of four molecules (TiO, VO, CaH, FeH) in the JOLA approximation, the water opacity of Ludwig (1971), collision-induced opacities, b-f and f-f atomic processes, as well as about 2 million spectral lines selected from a list with more than 42 million atomic and 24 million molecular (H2, CH, NH, OH, MgH, SiH, C2, CN, CO, SiO) lines. High-resolution synthetic spectra are obtained using an opacity sampling method. The model atmospheres and spectra are calculated with the generalized stellar atmosphere code PHOENIX, assuming LTE, plane-parallel geometry, energy (radiative plus convective) conservation, and hydrostatic equilibrium. The model spectra give close agreement with observations of M dwarfs across a wide spectral range from the blue to the near-IR, with one notable exception: the fit to the water bands. We discuss several practical applications of our model grid, e.g., broadband colors derived from the synthetic spectra. In light of current efforts to identify genuine brown dwarfs, we also show how low-resolution spectra of cool dwarfs vary with surface gravity, and how the high-regulation line profile of the Li I resonance doublet depends on the Li abundance.

Allard, France↗

NASA NTRS1996

Spectrum Synthesis of Hot Water in Sunspots and Selected Cool Stars

Very recently, Partridge and Schwenke completed an elaborate theoretical computation of the potential energy surface and dipole moment function for H2O. They have used their results to predict the positions and strengths of nearly 308 million lines. This line tabulation is the most complete now available. It extends to sufficiently high excitations that the spectra of M-stars may be modelled with greater accuracy than ever before provided the predicted line parameters of Partridge and Schwenke are themselves accurate. We have computed synthetic sunspot spectra using the Partridge and Schwenke line list and the sunspot umbral models of Maltby et al. In this display, we compare these synthetic spectra with published high resolution sunspot atlases. We demonstrate the extent to which the new line list successfully predicts the sunspot spectrum and suggest where improvements are necessary. Using the new tabulation, we also illustrate the extent to which hot stellar blankets the H, K and L passbands for select K and M star model atmospheres.

Carbon, D. F.↗

NASA NTRS1978

Mathematical modeling and simulation of the space shuttle imaging radar antennas

Simulations of space shuttle synthetic aperture radar antennas under the influence of space environmental conditions were carried out at L, C, and X-band. Mathematical difficulties in modeling large, non-planar array antennas are discussed, and an approximate modeling technique is presented. Results for several antenna error conditions are illustrated in far-field profile patterns, earth surface footprint contours, and summary graphs.

Campbell, R. W.↗