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All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Arctic Tundra Vegetation Functional Types Based on Photosynthetic Physiology and Optical Properties

Non-vascular plants (lichens and mosses) are significant components of tundra landscapes and may respond to climate change differently from vascular plants affecting ecosystem carbon balance. Remote sensing provides critical tools for monitoring plant cover types, as optical signals provide a way to scale from plot measurements to regional estimates of biophysical properties, for which spatial-temporal patterns may be analyzed. Gas exchange measurements were collected for pure patches of key vegetation functional types (lichens, mosses, and vascular plants) in sedge tundra at Barrow, AK. These functional types were found to have three significantly different values of light use efficiency (LUE) with values of 0.013 plus or minus 0.0002, 0.0018 plus or minus 0.0002, and 0.0012 plus or minus 0.0001 mol C mol (exp -1) absorbed quanta for vascular plants, mosses and lichens, respectively. Discriminant analysis of the spectra reflectance of these patches identified five spectral bands that separated each of these vegetation functional types as well as nongreen material (bare soil, standing water, and dead leaves). These results were tested along a 100 m transect where midsummer spectral reflectance and vegetation coverage were measured at one meter intervals. Along the transect, area-averaged canopy LUE estimated from coverage fractions of the three functional types varied widely, even over short distances. The patch-level statistical discriminant functions applied to in situ hyperspectral reflectance data collected along the transect successfully unmixed cover fractions of the vegetation functional types. The unmixing functions, developed from the transect data, were applied to 30 m spatial resolution Earth Observing-1 Hyperion imaging spectrometer data to examine variability in distribution of the vegetation functional types for an area near Barrow, AK. Spatial variability of LUE was derived from the observed functional type distributions. Across this landscape, a fivefold variation in tundra LUE was observed. LUE calculated from the functional type cover fractions was also correlated to a spectral vegetation index developed to detect vegetation chlorophyll content. The concurrence of these alternate methods suggest that hyperspectral remote sensing can distinguish functionally distinct vegetation types and can be used to develop regional estimates of photosynthetic LUE in tundra landscapes.

VEGETATION↗
NASA NTRS2014

Abiotic Ozone and Oxygen in Atmospheres Similar to Prebiotic Earth

The search for life on planets outside our solar system will use spectroscopic identification of atmospheric biosignatures. The most robust remotely detectable potential biosignature is considered to be the detection of oxygen (O 2 ) or ozone (O 3 ) simultaneous to methane (CH 4 ) at levels indicating fluxes from the planetary surface in excess of those that could be produced abiotically. Here we use an altitude-dependent photochemical model with the enhanced lower boundary conditions necessary to carefully explore abiotic O 2 and O 3 production on lifeless planets with a wide variety of volcanic gas fluxes and stellar energy distributions. On some of these worlds, we predict limited O 2 and O 3 buildup, caused by fast chemical production of these gases. This results in detectable abiotic O 3 and CH 4 features in the UV-visible, but no detectable abiotic O 2 features. Thus, simultaneous detection of O 3 and CH 4 by a UV-visible mission is not a strong biosignature without proper contextual information. Discrimination between biological and abiotic sources of O 2 and O 3 is possible through analysis of the stellar and atmospheric context-particularly redox state and O atom inventory-of the planet in question. Specifically, understanding the spectral characteristics of the star and obtaining a broad wavelength range for planetary spectra should allow more robust identification of false positives for life. This highlights the importance of wide spectral coverage for future exoplanet characterization missions. Specifically, discrimination between true and false positives may require spectral observations that extend into infrared wavelengths and provide contextual information on the planet's atmospheric chemistry.

planets and satellites↗
NASA NTRS2016

Identifying Planetary Biosignature Impostors: Spectral Features of CO and O4 Resulting from Abiotic O2/O3 Production

O2 and O3 have been long considered the most robust individual biosignature gases in a planetary atmosphere, yet multiple mechanisms that may produce them in the absence of life have been described. However, these abiotic planetary mechanisms modify the environment in potentially identifiable ways. Here we briefly discuss two of the most detectable spectral discriminants for abiotic O2/O3: CO and O4. We produce the first explicit self-consistent simulations of these spectral discriminants as they may be seen by James Webb Space Telescope (JWST). If JWST-NIRISS (Near InfraRed Imager and Slitless Spectrograph) and/or NIRSpec (Near InfraRed Spectograph) observe CO (2.35, 4.6 micrometers) in conjunction with CO2 (1.6, 2.0, 4.3 micrometers) in the transmission spectrum of a terrestrial planet it could indicate robust CO2 photolysis and suggest that a future detection of O2 or O3 might not be biogenic. Strong O4 bands seen in transmission at 1.06 and 1.27 micrometers could be diagnostic of a post-runaway O2-dominated atmosphere from massive H-escape. We find that for these false positive scenarios, CO at 2.35 micrometers, CO2 at 2.0 and 4.3 micrometers, and O4 at 1.27 micrometers are all stronger features in transmission than O2/O3 and could be detected with sigal to noise ratios greater than or approximately 3 for an Earth-size planet orbiting a nearby M dwarf star with as few as 10 transits, assuming photon-limited noise. O4 bands could also be sought in UV/VIS/NIR reflected light (at 0.345, 0.36, 0.38, 0.445, 0.475, 0.53, 0.57, 0.63, 1.06, and 1.27 micrometers) by a next generation direct imaging telescope such as LUVOIR (Large Ultraviolet Visible Infrared)/HDST (High-Definition Space Telescope) or HabEx (Habitable-Exoplanet Imaging Mission) and would indicate an oxygen atmosphere too massive to be biologically produced.

planetary atmosphere↗
NASA NTRS2017

3-D-Observation of Matrix of MIL 090657 Meteorite by Absorption-Phase Tomography

MIL 090657 meteorite (CR2.7) is one of the least altered primitive carbonaceous c hondrites [1]. This meteorite has amorphous silicates like GEMS (glass with embedded metal and sulfide), which are characteristically contained in cometary dust, in matrix [2,3] as with the Paris meteorite [4]. Three lithologies have been recognized; lithology-1 (L 1) dominated by submicron anhydrous silicates, lithology-2 (L2) by GEMS-like amorphous silicates and lithology-3 (L3) by phyllosilicates [2]. Organic materials are abundant in L 1 and L2 [2,3]. L 1 and L2 were further divided into sub-lithology respectively based on their textures and compositions [5]. These studies were performed by 2D SEM and TEM observations of sample surfaces and thin sections that are unable to reveal what constitute each lithology and how these lithologies are distributed and related to each other. This information will provide important insights into alteration and aggregation processes on asteroids and in the early solar nebula. In this study, MIL 090657 matrix was examined in 3D using two types of X-ray tomography; DET (dual-energy tomography) [6] and SIXM (scanning-imaging X-ray microscopy) [7]. Mineral phases can be discriminated based on absorption contrasts at two different X-ray energies in DET. In SIXM, materials composed of light elements such as water or organic materials can be identified based on phase and absorption contrasts. By combining these methods, we can discriminate not only organic materials from voids but also hydrous alteration products, such as hydrated silicates and carbonates, from anhydrous minerals [8]. In this study, we first observed cross sections of MIL 090657 matrix fragments C1 00 mm) in detail using FE-SEM/ EDS. Based on the results, three house-shaped samples (3 0 -50 mm) were extracted from L 1, L2 and their boundary (H1, H3 and H5, respectively) using FIB. 3D imaging of these samples were conducted at BL47XU of SPring-8, a synchrotron radiation facility, with -30-40 nm/ voxel and - 70-80 nm/ voxel at 7keV and 8keV in DET and -100 nm/ voxel at 8keV in SIXM. We found new lithologies that we named L4, L5 and L6 in H1 and H3 in addition to L 1 and L2. L4. L5 and L6 are mainly composed of probably phyllosilicates with different Fe contents. Sulfide and framboidal magnetite were recognized in L4. L5 includes magnetite and carbonate and L6 includes anhydrous silicates having cracks inside. L 1, L2, L4 and L5 are porous while few voids were observed in L6. L4 adjoins to L 1 with boundary, which is not very distinct. L2, L5 and L6 adjoin to each other, and the boundaries of L6 with L2 and L5 are clear. In H5, coarse mineral grains C5-1 0 mm) such as Fe-metal and enstatite are present in L 1 and L2. L 1-L2 boundary is not sharp in 3 D. In conclusion, we found a variety of lithologies by 3D observation for the first time, suggesting that t he MIL 0 90657 meteorite experienced complex alteration and aggregation histories. As L2 is dominated by amorphous silicates, which are extremely susceptible to aqueous alteration, t his is p resumed to be t he most primitive lithology. The contact between L2 and phyllosilicate-bearing lithologies (L5 and L6) with clear boundaries indicates that they were aggregated after aqueous alteration of L5 and L6. The indistinct boundary between L 1 and L2 is suggesting that these two lithologies might originally be the same aggregate composed of amorphous silicates and coarse mineral grains. L 1 might have experienced weak aqueous alteration followed by mild thermal alteration [2], while L2 did not undergo aqueous alteration.

Miyama, Sugimoto↗
NASA NTRS2019

Development of Large-Scale Magnetic Calorimeter Arrays

We describe performance of large-scale arrays of metallic magnetic calorimeters (MMCs) we are developing to meet requirements of the Lynx X-ray Microcalorimeter (LXM) instrument in the astrophysics mission concept Lynx. We have fabricated prototypes with 55,800 x-ray pixels thermally connected to 5,688 MMC sensors. Subarrays demonstrate three types of pixels, which have different energy and spatial resolution goals in LXM. Pixel pitch is 50 or 25 µm. For two subarrays, use of position sensitive detectors, in a "hydra" configuration with 25 x-ray absorbers per sensor, helps achieve a large focal plane by increasing pixel count relative to readout channels. Since each absorber has a different thermal link to its sensor, it generates a different pulse shape and enables discrimination of pixel position. Superconducting wiring from all sensors was brought out to the perimeter of the overall array using multiple buried metal layers planarized by Chemical Mechanical Planarization to achieve high critical current, low inductance, and high fabrication yield. An automated, algorithmic approach was used to layout the complex wiring pattern. For readout with existing small arrays of Superconducting Quantum Interference (SQUID) amplifiers, 112 selected pixels were connected to wire bond pads. In hydras, we successfully identified 25 pulse shapes using rise-time and pulse height. Pulse shapes were similar to simulations. We measured noise and responsivity to substrate temperature and applied x-ray pulses. While we designed sensors to have high inductance to relative to stray inductance of wiring, we had only mismatched (lower input inductance) SQUIDs available in our current apparatus, giving non-optimized resolution. We compared measured and calculated energy resolutions. We have the required sensitivity to reach < 3 eV FWHM resolution for 6 keV x-rays, required for the LXM Main Array, and the ability to discriminate the 25 different absorber pixels down to energies < 300 eV.

microcalorimeters↗
NASA NTRS2019

The Cumulus and Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD)

Low clouds continue to contribute greatly to the uncertainty in cloud feedback estimates. Depending on whether a region is dominated by cumulus (Cu) or stratocumulus (Sc) clouds, the interannual low-cloud feedback is somewhat different in both spaceborne and large-eddy simulation studies. Therefore, simulating the correct amount and variation of the Cu and Sc cloud distributions could be crucial to predict future cloud feedbacks. Here we document spatial distributions and profiles of Sc and Cu clouds derived from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat measurements. For this purpose, we create a new dataset called the Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD), which identifies Sc, broken Sc, Cu under Sc, Cu with stratiform outflow and Cu. To separate the Cu from Sc, we design an original method based on the cloud height, horizontal extent, vertical variability and horizontal continuity, which is separately applied to both CALIPSO and combined CloudSat–CALIPSO observations. First, the choice of parameters used in the discrimination algorithm is investigated and validated in selected Cu, Sc and Sc–Cu transition case studies. Then, the global statistics are compared against those from existing passive- and active-sensor satellite observations. Our results indicate that the cloud optical thickness – as used in passive-sensor observations – is not a sufficient parameter to discriminate Cu from Sc clouds, in agreement with previous literature. Using clustering-derived datasets shows better results although one cannot completely separate cloud types with such an approach. On the contrary, classifying Cu and Sc clouds and the transition between them based on their geometrical shape and spatial heterogeneity leads to spatial distributions consistent with prior knowledge of these clouds, from ground-based, ship-based and field campaigns. Furthermore, we show that our method improves existing Sc–Cu classifications by using additional information on cloud height and vertical cloud fraction variation. Finally, the CASCCAD datasets provide a basis to evaluate shallow convection and stratocumulus clouds on a global scale in climate models and potentially improve our understanding of low-level cloud feedbacks. The CASCCAD dataset (Cesana, 2019, https://doi.org/10.5281/zenodo.2667637) is available on the Goddard Institute for Space Studies (GISS) website at https://data.giss.nasa.gov/clouds/casccad/ (last access: 5 November 2019) and on the zenodo website at https://zenodo.org/record/2667637 (last access: 5 November 2019).

Cesana, Gregory V.↗
NASA NTRSDate not supplied

Examining Soil Freeze/Thaw Remote Sensing Using NASA’s SLAPex Freeze/Thaw Airborne Campaign

Around half the exposed land of the Northern Hemisphere experiences seasonal freezing and thawing. The changes to the soil thermal, hydraulic, and mechanical properties are large and these, in turn, exert significant controls on the water, energy, and carbon cycles of the affected regions. Wide-area freeze/thaw discrimination via microwave remote sensing is possible using both passive and active techniques. We explore freeze/thaw remote sensing using observations from NASA’s Scanning L-band Active Passive (SLAP) airborne sensor during the SLAPex Freeze/Thaw campaign near Winnipeg, Canada in November, 2015. SLAP is an airborne simulator of the Soil Moisture Active Passive (SMAP) satellite mission. The SLAPex Freeze/Thaw dataset is a unique dataset that allows comparison of airborne-derived freeze/thaw state (plus soil moisture for thawed areas) against in-situ ground truth, satellite-derived freeze/thaw from SMAP, as well as tower-based passive microwave freeze/thaw observations. The sensitivity of passive and active microwave sensing to different depths of freezing and different dielectric models (when soil moisture is retrieved) is examined, and the spatial scaling of heterogeneous frozen/thawed pixels is also explored. Implications for the accuracy of satellite-based freeze/thaw discrimination are discussed.

Edward Kim↗
NASA NTRS2020

Position-Sensitive Magnetic Calorimeters for Lynx

We describe the performance of position-sensitive metallic magnetic calorimeter X-ray detectors for potential future astrophysics missions, such as Lynx. The motivation behind this is to achieve a large focal plane area by increasing the number of pixels compared to the number of readout channels (called hydra). The detector consists of a 5×5 array of gold absorbers thermally coupled to a paramagnetic sensor. Since each absorber has a different thermal link to the sensor, it generates a different pulse shape and enables discrimination of pixel position. Recently, we have been successfully able to discriminate 25 averaged pulse shapes by means of rise-time and pulse height. The measured pulse shapes and distribution agree well with the modeling results. The estimated energy resolutions based on the pulse height and noise data we measured are 2.8–3.7 eV for MnKα X-ray photons at 50 mK.

Magnetic calorimeters↗
NASA NTRSDate not supplied

Remote Sensing of Lineage Functional Types for Modeling and Monitoring Biodiversity

Hyperspectral remote sensing has the potential to continuously scale plant function and plant diversity information from landscape to global extents. Numerous studies have indicated that VSWIR (400-2500 nm) reflectance properties of vegetation capture evolutionarily conserved biochemical, structural, and other functional attributes of plant species. Spectral properties conserved in plants provide the opportunity to both 1) aggregate species into lineages with improved classification accuracy and 2) link those lineages directly to plant traits. Full realization of this goal will enable parameterization of Land Surface Models (LSMs) with remotely sensed information, e.g., canopy nitrogen, and better representations of biodiversity and functional diversity in biogeographic studies. In this study, we use hyperspectral AVIRIS data from the 2013 HyspIRI campaign over the Southern Sierra Nevada, California flight box to investigate the potential for incorporating evolutionary thinking into landcover classification. We link the airborne hyperspectral data with vegetation plot data from roughly 1372 surveys and a phylogeny representing 1361 species. We aggregate species into lineages ranging from species level groups down to similar number of Plant Functional Types as often used in LSMs. We assessed the ability of Random Forest and Partial Least Squares Discriminant Analysis to discriminate across these different phylogenetic scales and determine the optimal number of lineages to classify. Although there are some temporal and spatial differences in our training data, our best approaches achieved moderate classification accuracy (Kappa > 0.65). Given an optimal number of lineages, we explored approaches to improve classifications including machine learning and unmixing approaches. This work suggests that lineage-based methods may be a promising way to leverage the huge amounts of data that will come from high resolution and high return interval hyperspectral data planned for the Surface Biology and Geology mission with sparsely sampled existing ground-based ecological data.

Hyperspectral↗
NASA NTRSDate not supplied

Artificial Neural Networks to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗
NASA NTRSDate not supplied

Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗
NASA NTRS2020

Microscopic Object Classification through Passive Motion Observations with Holographic Microscopy

Digital holographic microscopy provides the ability to observe throughout a volume that is large compared to its resolution element without the need to refocus through the volume. This capability enables simultaneous observations of large numbers of small objects within such a volume. We have constructed a microscope that can observe a volume 0.4 x 0.4 x 1.0 µm with submicrometer resolution for observation of microorganisms and minerals in liquid environments in earth and on potential planetary missions. Because environmental samples are likely to contain mixtures of inorganics and microorganisms that are of comparable sizes near the resolution limit of the instrument, discrimination of living and non-living objects may be difficult. The motion of motile organisms can be used to readily distinguish them from non-motile objects (live or inorganic), but additional methods are required to distinguish non-motile organisms and inorganic objects that are of comparable size, but different composition and structure. In this paper we evaluate the use of passive motion to make this discrimination by evaluating diffusion and buoyancy characteristics of objects in the field of view.

Lindensmith, Christian↗
NASA NTRS2021

Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

This research uses machine-learned computational analyses to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is from a rodent model exposed to ≤ 15 cGy of individual Galactic Cosmic Radiation (GCR) ions: 4He, 16O, 28Si, 48Ti, or 56Fe, expected for a Lunar or Mars mission. This work investigates rats at a subject-based level and uses performance scores taken before irradiation to predict impairment in Attentional Set-shifting (ATSET) data post-irradiation. Here, the worst performing rats of the control group define the impairment thresholds based on population analyses via cumulative distribution functions, leading to the labeling of impairment for each subject. A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the Simple Discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the Compound Discrimination (CD) stage. On a subject-based level, implementing Machine Learning (ML) classifiers such as the Gaussian Naïve Bayes, Support Vector Machine, and Artificial Neural Networks identifies rats that have a higher tendency for impairment after GCR exposure. The algorithms employ the experimental prescreenperformance scores as multidimensional input features to predict each rodent’s susceptibility to cognitive impairment due to space radiation exposure. The receiver operating characteristic and the precision-recall curves of the ML models show a better prediction of impairment when 56Feis the ion in question in both SD and CD stages. They, however, do not depict impairment due to 4Hein SD and 28Siin CD, suggesting no dose-dependent impairment response in these cases. One key finding of our study is that prescreen performance scores can be used to predict the ATSET performance impairments. This result is significant to crewed space missions as it supports the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. Future research can focus on constructing ML ensemble methods to integrate the findings from the methodologies implemented in this study for morerobust predictionsof cognitive decrements due to space radiation exposure.

space radiation↗
NASA NTRS2021

Mapping Boreal Forest Spruce Beetle Health Status at the Individual Crown Scale Using Fused Spectral and Structural Data

The frequency and severity of spruce bark beetle outbreaks are increasing in boreal forests leading to widespread tree mortality and fuel conditions promoting extreme wildfire. Detection of beetle infestation is a forest health monitoring (FHM) priority but is hampered by the challenges of detecting early stage (“green”) attack from the air. There is indication that green stage might be detected from vertical gradients of spectral data or from shortwave infrared information distributed within a single crown. To evaluate the efficacy of discriminating “non-infested”, “green”, and “dead” health statuses at the landscape scale in Alaska, USA, this study conducted spectral and structural fusion of data from: (1) Unoccupied aerial vehicle (UAV) multispectral (6 cm) + structure from motion point clouds (~700 pts per sq. m); and (2) Goddard Lidar Hyperspectral Thermal (G-LiHT) hyperspectral (400 to 1000 nm, 0.5 m) + SWIR-band lidar (~32 pts per sq.m). We achieved 78% accuracy for all three health statuses using spectral + structural fusion from either UAV or G-LiHT and 97% accuracy for non-infested/dead using G-LiHT. We confirm that UAV 3D spectral (e.g., greenness above versus below median height in crown) and lidar apparent reflectance metrics (e.g., mean reflectance at 99th percentile height in crown), are of high value, perhaps capturing the vertical gradient of needle degradation. In most classification exercises, UAV accuracy was lower than G-LiHT indicating that collecting ultra-high spatial resolution data might be less important than high spectral resolution information. While the value of passive optical spectral information was largely confined to the discrimination of non-infested versus dead crowns, G-LiHT hyperspectral band selection (~400, 675, 755, and 940 nm) could inform future FHM mission planning regarding optimal wavelengths for this task. Interestingly, the selected regions mostly did not align with the band designations for our UAV multispectral data but do correspond to, e.g., Sentinel-2 red edge bands, suggesting a path forward for moderate scale bark beetle detection when paired with suitable structural data.

Janice Cessna↗
NASA NTRS2022

Evaluation of the Tau-Omega Model Over Bare and Wheat-Covered Flat and Periodic Soil Surfaces at P- and L-band

It has been over ten years since the successful launch of the first-ever dedicated satellite for global soil moisture monitoring; Soil Moisture and Ocean Salinity (SMOS). Looking towards the future, P-band (0.3-1 GHz) is a promising technique to replace or enhance the L-band (1.4 GHz) SMOS and SMAP (Soil Moisture Active Passive) missions because of an expected reduction in roughness and vegetation impact, leading to an improved soil moisture accuracy over rougher soil surfaces and more densely vegetated areas. Accordingly, this investigation evaluated the tau-omega model at P-band (0.75 GHz) using a tower-based experiment in Victoria, Australia, where brightness temperature observations were collected concurrently at P- and L-band over bare and wheat-covered flat and periodic soil surfaces. The potential to retrieve soil moisture without discriminating periodic and flat surfaces was investigated by applying the roughness and vegetation parameters calibrated for flat soil to retrieve the moisture of periodic soil. Results showed that P-band had a comparable RMSE across different roughness configurations (variations less than 0.016 m3/m3) for both bare and wheat-covered soil, while the L-band RMSE was only comparable for wheat-covered soil, indicating that periodic surfaces did not need to be discriminated in such scenarios. Conversely, a difference of 0.022 m3/m3 was observed for L-band with bare soil. A reduced vegetation impact was also demonstrated at P-band, with an RMSE of 0.029 m3/m3 achieved when completely ignoring the wheat existence with under 4-kg/m2 vegetation water content, whereas at L-band the RMSE increased to 0.063 m3/m3. This study therefore paves the way for a successful P-band radiometer mission for obtaining more accurate global soil moisture information.

P-band↗
NASA NTRSDate not supplied

Development of an Inertial Sensor-based Methodology for Spacesuited Geology Task Assessments during Simulated Lunar Extravehicular Activities

Lunar surface exploration during Artemis missions will require the specific skill set of geology sampling. Apollo astronauts had extensive training and used specialized tools to collect lunar rocks, core samples, pebbles, sand, and dust. The inflexibility of the pressurized Apollo spacesuits forced sampling to be taken at a standstill posture. However, new exploration spacesuits are expected to incorporate advanced materials and joint bearings, allowing for greater mobility and a wider range of functional postures. Thus, science and exploration during Artemis missions will likely involve a variety of standing, squatting, and kneeling postures. In preparation for future lunar exploration missions, NASA provides geologic training to astronauts and other mission personnel. This professional training with a spacesuit in simulated lunar environments will enhance performance and reduce risk of injury to astronauts on the lunar surface. However, anecdotally, untrained or newly trained people wearing prototype planetary spacesuits have been observed to performing motions differently than a trained geologist would when conducting the same geology sampling tasks. Therefore, a tool for evaluating geology postures at extravehicular activity (EVA) training facilities becomes required. In this paper, we introduce a novel inertial measurement unit (IMU)-based method of geology task assessments in spacesuited conditions during simulated lunar EVAs. As a case study, two subjects (one geologist and one non-geologist) participated and donned the Mark III prototype planetary spacesuit during offloading with the spreader bar gimbal in NASA’s Active Response Gravity Offload System (ARGOS). For automated geology task assessments, the spacesuit was instrumented with three wireless IMUs (APDM Opal, OR, USA): one on the chest and one each on the left and right ankle bearings. Then subjects performed geology tasks using various tools (rake, trench, hammer chisel, scoop, and drive tube) for 45 minutes each. The chest IMU measured the torso tilt angle in the sagittal plane. We used an ensemble learning method with the ankle IMUs to discriminate between standing and kneeling activities. IMU data were processed using custom MATLAB (Mathworks, MA, USA) software. In our case study, the developed method was able to discriminate differences in standing and kneeling activity levels between subjects who were all highly experienced with spacesuited testing. Our preliminary data showed one subject maintained the constant and lower range of the upper body tilt angle while both standing and kneeling, while the other subject showed more variation of the upper body tilt angle and preferred bending the upper body rather than changing from standing to kneeling posture and vice versa. While geology experience may be a factor, these results need further investigation as suit sizing and ARGOS offloading configurations have been proven to have a significant influence on suited ARGOS tasks. Also, more subjects will be needed to complete these tasks for validation. IMU-based geology task assessments can provide useful information for geology training programs. Additionally, our IMU-based posture analysis can provide new insights into how to evaluate spacesuited geology task characteristics of astronauts during simulated lunar EVAs.

Kyoung Jae Kim↗
NASA NTRS2022

Precipitation Phase Determination by Brightness Temperatures From ATMS

Previous studies used the temperature-related variables from model outputs (e.g., 2-m temperature) for precipitation phase determination (i.e., rain–snow separation). This study presents a new idea for precipitation phase determination using brightness temperatures (TBs) from Advanced Technology Microwave Sounder (ATMS). It is found that TB-based phase discrimination shows comparable determination performance to that from model outputs over land. In contrast, TB-based phase discrimination over the ocean performs noticeably worse than that from model outputs. Further analyses reveal that the phase determination performance over land from TBs slightly depends on the satellite’s local zenith angle. However, the determination performance over the ocean strongly depends on the satellite’s local zenith angle, with the skill score decreasing sharply from 0.74 near the nadir to 0.55 near the edge. These results imply that over land, TBs may be used directly for phase determination, which can be extended to other operational and future microwave sounders with similar channels available.

Yalei You↗