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NASA NTRS2023

What’s That Supposed to Mean? Capturing Micro-Behaviors in Teams

Future long-duration space exploration (LDSE) crews will require extensive coordination, cooperation, and team functioning as they face a myriad of challenges rooted in both taskwork and teamwork (Bell et al., 2015; Landon et al., 2018). While exposed to extreme conditions, crew members must navigate living and working together in prolonged confinement. Moreover, astronaut teams are becoming increasingly diverse, introducing significant variability in team composition. This increasing diversity, alongside traditional constraints of LDSE, introduces additional challenges into effective team functioning. To date, most methods for capturing team functioning rely on self-report measures. Such measures are prone to several limitations, including but not limited to social desirability bias, halo effect, and leniency effects (Trull & Ebner-Priemer, 2013), which skew data and limit nuanced understandings of phenomena at play. Self-report measures broadly capture team functioning, lending the nature of such methods to identifying underlying “macro”-behaviors (i.e., behaviors that are long-standing and last over time). However, team functioning is far more complex than a series of macro-behaviors, rendering reliance on self-report data deficient for accurate measurement. Recent research demonstrates the potential of alternative methods for capturing team functioning, such as speech and physiological data (Chaffin et al., 2017; Murray & Oertel, 2018). Consequently, these methods are more suitable for capturing micro-behaviors: brief, often unconscious expressions that affect the extent to which an individual feels included by others around them (Paletz et al., 2013). Micro-behaviors can be further classified into microaggressions (i.e., subtle, negative exchanges; Keller & Galgay, 2010) or micro-affirmations (i.e., subtle, positive exchanges; Kyte et al. 2020), both of which influence team functioning. Due to the subtle nature of micro-behaviors, contextual factors have a significant impact when determining if it is aggressive or affirmative. Additionally, several iterations of microbehaviors can have lingering effects on team interactions. For example, the use of “mm-hmm” by a crew member can function as both a micro-affirmation and micro-aggression. Specifically, it can be indication of active listening (i.e., micro-affirmation) or as an expression of annoyance (i.e., aggression) depending on the context in which it occurs. Auditory features (e.g., tone, frequency) can help delineate between the two forms; however, the contextual factors (e.g., previous interactions between team members, crew demographics) add a layer of complexity that render auditory features alone as insufficient to capture micro-behaviors. Consequently, this paper seeks to provide a novel approach in which multi-modal data (i.e., auditory features and contextual features) are used in a random-forest model to better identify distinguishing characteristics between micro-affirmations and micro-aggressions. In turn, detected micro-behaviors are used to predict team performance, thereby demonstrating the value of capturing micro-behaviors as supplemental data to macro-behaviors.

Sydney R. Begerowski↗

NASA NTRS2023

What’s That Supposed to Mean? Capturing Micro-Behaviors in Teams

Future long-duration space exploration (LDSE) crews will require extensive coordination, cooperation, and team functioning as they face a myriad of challenges rooted in both taskwork and teamwork (Bell et al., 2015; Landon et al., 2018). While exposed to extreme conditions, crew members must navigate living and working together in prolonged confinement. Moreover, astronaut teams are becoming increasingly diverse, introducing significant variability in team composition. This increasing diversity, alongside traditional constraints of LDSE, introduces additional challenges into effective team functioning. To date, most methods for capturing team functioning rely on self-report measures. Such measures are prone to several limitations, including but not limited to social desirability bias, halo effect, and leniency effects (Trull & Ebner-Priemer, 2013), which skew data and limit nuanced understandings of phenomena at play. Self-report measures broadly capture team functioning, lending the nature of such methods to identifying underlying “macro”-behaviors (i.e., behaviors that are long-standing and last over time). However, team functioning is far more complex than a series of macro-behaviors, rendering reliance on self-report data deficient for accurate measurement. Recent research demonstrates the potential of alternative methods for capturing team functioning, such as speech and physiological data (Chaffin et al., 2017; Murray & Oertel, 2018). Consequently, these methods are more suitable for capturing micro-behaviors: brief, often unconscious expressions that affect the extent to which an individual feels included by others around them (Paletz et al., 2013). Micro-behaviors can be further classified into micro-aggressions (i.e., subtle, negative exchanges; Keller & Galgay, 2010) or micro-affirmations (i.e., subtle, positive exchanges; Kyte et al. 2020), both of which influence team functioning. Due to the subtle nature of micro-behaviors, contextual factors have a significant impact when determining if it is aggressive or affirmative. Additionally, several iterations of micro-behaviors can have lingering effects on team interactions. For example, the use of “mm-hmm” by a crew member can function as both a micro-affirmation and micro-aggression. Specifically, it can be indication of active listening (i.e., micro-affirmation) or as an expression of annoyance (i.e., aggression) depending on the context in which it occurs. Auditory features (e.g., tone, frequency) can help delineate between the two forms; however, the contextual factors (e.g., previous interactions between team members, crew demographics) add a layer of complexity that render auditory features alone as insufficient to capture micro-behaviors. Consequently, this paper seeks to provide a novel approach in which multi-modal data (i.e., auditory features and contextual features) are used in a random-forest model to better identify distinguishing characteristics between micro-affirmations and micro-aggressions. In turn, detected micro-behaviors are used to predict team performance, thereby demonstrating the value of capturing micro-behaviors as supplemental data to macro-behaviors.

Sydney Begerowski↗

NASA NTRS2017

GEONEX: Land Monitoring From a New Generation of Geostationary Satellite Sensors

The latest generation of geostationary satellites carry sensors such as ABI (Advanced Baseline Imager on GOES-16) and the AHI (Advanced Himawari Imager on Himawari) that closely mimic the spatial and spectral characteristics of Earth Observing System flagship MODIS for monitoring land surface conditions. More importantly they provide observations at 5-15 minute intervals. Such high frequency data offer exciting possibilities for producing robust estimates of land surface conditions by overcoming cloud cover, enabling studies of diurnally varying local-to-regional biosphere-atmosphere interactions, and operational decision-making in agriculture, forestry and disaster management. But the data come with challenges that need special attention. For instance, geostationary data feature changing sun angle at constant view for each pixel, which is reciprocal to sun-synchronous observations, and thus require careful adaptation of EOS algorithms. Our goal is to produce a set of land surface products from geostationary sensors by leveraging NASA's investments in EOS algorithms and in the data/compute facility NEX. The land surface variables of interest include atmospherically corrected surface reflectances, snow cover, vegetation indices and leaf area index (LAI)/fraction of photosynthetically absorbed radiation (FPAR), as well as land surface temperature and fires. In order to get ready to produce operational products over the US from GOES-16 starting 2018, we have utilized 18 months of data from Himawari AHI over Australia to test the production pipeline and the performance of various algorithms for our initial tests. The end-to-end processing pipeline consists of a suite of modules to (a) perform calibration and automatic georeference correction of the AHI L1b data, (b) adopt the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to produce surface spectral reflectances along with compositing schemes and QA, and (c) modify relevant EOS retrieval algorithms (e.g., LAI and FPAR, GPP, etc.) for subsequent science product generation. Initial evaluation of Himawari AHI products against standard MODIS products indicate general agreement, suggesting that data from geostationary sensors can augment low earth orbit (LEO) satellite observations.

geostationary↗

NASA NTRS2021

Investigation of several proxies to estimate sulfuric acid concentration in volcanic plume conditions

Sulfuric acid (H2SO4) is commonly accepted as a key precursor for atmospheric new particle formation (NPF). However, direct measurements of [H2SO4] remain challenging, thus preventing the determination of this important quantity, and, consequently, a complete understanding of its contribution to the NPF process. Several proxies have been developed to bridge the gaps, but their ability to predict [H2SO4] in very specific conditions such as those encountered in volcanic plumes (including in particular high sulphur dioxide mixing ratios) has not been evaluated so far. In this context, the main objective of the present study was to develop new proxies for daytime [H2SO4] in volcanic plume conditions and compare their performance to that of the proxies available in the literature. In specific, the data collected at Maïdo during the OCTAVE 2018 campaign, in the volcanic eruption plume of the Piton de la Fournaise, were first used to derive seven proxies based on the knowledge of sulphur dioxide (SO2) mixing ratio, global radiation, condensation sink (CS) and relative humidity (RH). In three of the seven proxies (F1–F3), all variables were given equal weight in the prediction of [H2SO4], while adjusted powers were allowed for the different variables in the other four proxies (A1–A4). Proxies A1–A4 were overall found to perform better compared to F1–F3, with, in specific, improved predictive ability for [H2SO4] > 2 × 108 per cu.cm. The CS was observed to play an important role in regulating [H2SO4], while, in contrast, the inclusion of RH did not improve the predictions. A last expression accounting for an additional sink term related to cluster formation, S1, was also tested and showed a very good predictive ability over the whole range of measured [H2SO4]. The newly developed proxies were in a second step further evaluated using airborne measurements performed in the passive degassing plume of Etna during the STRAP 2016 campaign. Increased correlations between observed and predicted [H2SO4] were obtained when the dependence of predicted [H2SO4] over CS was the lowest, and when the dependence over [SO2] was concurrently the highest. The best predictions were finally retrieved by the simple formulation of F2 (in which [SO2] and radiation alone were assumed to explain the variations of [H2SO4] with equal contributions), with a pre factor adapted to the STRAP data. All in all, our results illustrate the fairly good capacity of the proxy available in the literature to describe [H2SO4] in volcanic plume conditions, but highlight at the same time the benefit of the newly developed proxies for the prediction of the highest concentrations ([H2SO4] > 2–3 × 108 per cu.cm). Also, the contrasting behaviours of the new proxies in the two investigated datasets indicate that in volcanic plumes like in other environments, the relevance of a proxy can be affected by changes in environmental conditions, and that location specific coefficients do logically improve the predictions.

new particle formation↗

NASA NTRS2018

An Integrated Platform for Mission Performance: Goddard Space Flight Center's Meta Information System

Successful management of NASA Goddard Space Flight Centers portfolio of mission projects (i.e. spacecraft, spacecraft instruments / subsystems, and ground systems) depends upon numerous processes, data and information resources coming together throughout the life-cycle of the projects to enable sound decision-making, risk reduction, conformance with requirements and continual improvement. In the summer of 2012, GSFC SMA management recognized that the set of legacy data systems used to support the SMA mission and responsibilities at GSFC were not effective, integrated nor on par with best practices. Accordingly, GSFC SMA management launched a project to transform the GSFC electronic management system to create and deploy an integrated, state-of-the-market information system for process performance, data management and analytics that is currently operating in support of GSFC overall mission performance. The information system, known as Meta, provides controlled, process-based applications, integrated data management, and analytics for a robust set of integrated applications. Since the inception of the project, a lean, award-winning SMA project team has thus far designed, configured and managed the system to serve as an integrated platform for 18 applications that support key processes for GSFC and the Science Mission Directorate at NASA headquarters, such as internal Management System assessments, Supplier Assessments, Supplier Research & Analysis, Problem Reporting for in-house mission projects, On-orbit Anomaly Reporting for GSFC-operated spacecraft, GSFC Project Life-Cycle Reviews, Risk Management and Reporting, and SMA Project Team Reporting. The system has experienced steady growth in utilization with 1,917 users (NASA employees and support contractor personnel) and 44 gigabytes of data and related files managed by the system as of the end of April 2018. Overall, Meta has achieved increasing levels of process management and real-time data / information integration to enable improved process performance, richer analytics and informed decision-making. This paper will present and examine the need, goals, approach and design driving the management and operation of the Meta information system and its ongoing growth as an integrated platform for advancing NASA mission performance.

Integration↗

NASA NTRS2019

TCL3 UTM (UAS Traffic Management) Flight Tests, Airspace Operations Laboratory (AOL) Report

The Technology Capability Level-3 (TCL3) flight tests were conducted at six different test sites located across the USA from March to May of 2018. The campaign resulted in over 830 data collection flights using 28 different aircraft and involving 20 flight crews. Flights not only varied in duration, but also in the environments and terrains over which they flew. The TCL3 tests highlighted four different types of tests: three tests focused on Communication, Navigation and Surveillance (CNS); six tests focused on Sense and Avoid (SAA) technologies; six tests focused on USS Data and Information Exchange (DAT); and five tests focused on exploring fundamental Concepts of the project (CON). This document presents data collected during the TCL3 tests that informed the operator’s experiences—the quality of the unmanned aerial system (UAS) Service Supplier (USS) information that the operator was provided with, the usefulness of this information, and the usability of the automation, both while airborne and on the ground. It is intended to complement the reports written by the test sites and the quantitative reports and presentations of the UAS Traffic Management (UTM) project. With the goal of instructing what the minimum information requirements and/or best practices might be in TCL3 operations, the driving enquiry was: How do you get the information you need, when you need it, to successfully fly a UAS in UTM airspace? This enquiry touches on two requirements for displays, which are to provide adequate situation awareness (SA) and to share information through a USS. The six test sites participating in the TCL3 tests flew a subset of the 20 tests (outlined above), with most sites working on a subset of each of the four types: Communications, Navigation and Surveillance (CNS); DAT; CON; and Sense and Avoid (SAA). The, mainly qualitative, data addressed in this report was collected by the AOL (Airspace Operations Laboratory) both on-site and remotely for each test. The data consists of the contents of end-of-day debriefs, end-of-day surveys, observer notes, and flight test information, all submitted as part of the Data Management Plan (DMP).

Martin, Lynne↗

NASA NTRS2019

Reconstruction of the Adaptable Deployable Entry and Placement Technology Sounding Rocket One Flight Test

The Adaptable Deployable Entry and Placement Technology Sounding Rocket One flight test is a demonstration experiment for deployable atmospheric decelerator technologies. The suborbital flight test occurred on 12 September 2018, at the White Sands Missile Range. Data from on-board and ground-based sensors were collected, from which the as-flown trajectory was reconstructed using an iterative extended Kalman filter-smoother. This paper describes the methodology, test vehicle instrumentation, and data analysis results from the flight test trajectory reconstruction.

Tynis, Jake A.↗

NASA NTRS2020

Cambridge Urban Development - Quantifying Changes in Urban Albedo with NASA Earth Observations to Reduce Urban Heat Island Effect in Cambridge, Massachusetts

The urban heat island (UHI) effect occurs when urban areas have temperatures that are warmer on average than the surrounding suburban and rural regions. Low albedo surfaces traditionally found in urban landscapes, such as dark asphalt and rooftops, absorb solar irradiance and reemit heat, which contributes to the UHI effect. Cambridge,Massachusetts,part of the Boston metropolitan area,expects severe impacts on human health,infrastructure, and local environmental features due to increasing urban heat. The Massachusetts – Boston NASA DEVELOP team partnered with the City of Cambridge Community Development Department and the American Geophysical Union’s Thriving Earth Exchange to inform ongoing planning and heat mitigation efforts. High Resolution Orthoimagery (HRO) and National Agriculture Imagery Program (NAIP) scenes were analyzed in conjunction with ancillary datasets to assess changes in rooftop albedo between 2008 and 2018. Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to determine nighttime land surface temperature (LST) between 2003 and 2019. Lastly, temperature anomalies were calculated using seasonally averaged nighttime LST values obtained from Aqua MODIS to display ‘hot spots’ for summers between 2004 and 2019. On average, albedo increased by 0.12 across the city, with the majority of positive change in commercial areas. Analysis of mean summer nighttime LST between 2003 and 2019 revealed a non-significant increase in temperature, although temperature anomaly maps demonstrated that Cambridge was warmer on average than rural areas. These results were incorporated in the interactive Cambridge Urban Heat Dashboard, which allows users to explore temporal trends in albedo.

Nicole Ramberg-Pihl↗

NASA NTRS2020

Cambridge Urban Development: Quantifying Changes in Urban Albedo with NASA Earth Observations to Reduce Urban Heat Island Effect in Cambridge, Massachusetts

The urban heat island (UHI) effect occurs when urban areas have temperatures that are warmer on average than the surrounding suburban and rural regions. Low albedo surfaces traditionally found in urban landscapes, such as dark asphalt and rooftops, absorb solar irradiance and reemit heat, which contributes to the UHI effect. Cambridge, Massachusetts, part of the Boston metropolitan area, expects severe impacts on human health, infrastructure, and local environmental features due to increasing urban heat. The Massachusetts – Boston NASA DEVELOP team partnered with the City of Cambridge Community Development Department and the American Geophysical Union’s Thriving Earth Exchange to inform ongoing planning and heat mitigation efforts. High Resolution Orthoimagery (HRO) and National Agriculture Imagery Program (NAIP) scenes were analyzed in conjunction with ancillary datasets to assess changes in rooftop albedo between 2008 and 2018. Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to determine nighttime land surface temperature (LST) between 2003 and 2019. Lastly, temperature anomalies were calculated using seasonally averaged nighttime LST values obtained from Aqua MODIS to display ‘hot spots’ for summers between 2004 and 2019. On average, albedo increased by 0.12 across the city, with the majority of positive change in commercial areas. Analysis of mean summer nighttime LST between 2003 and 2019 revealed a non-significant increase in temperature, although temperature anomaly maps demonstrated that Cambridge was warmer on average than rural areas. These results were incorporated in the interactive Cambridge Urban Heat Dashboard, which allows users to explore temporal trends in albedo.

Nicole Ramberg-Pihl↗

NASA NTRSDate not supplied

The Response of the Ozone Layer Under Abrupt 4xCO2 in CMIP6

Previous studies indicate a possible role of stratospheric ozone chemistry feedbacks in the climate response to 4xCO2, either via a reduction in equilibrium climate sensitivity (ECS) (Nowack et al., 2015), or via changes in the tropospheric circulation (Chiodo and Polvani, 2017). However, these effects are subject to uncertainty. Part of the uncertainty may stem from the dependency of the feedback on the pattern of the ozone response, as the radiative efficiency of ozone largely depends on its vertical distribution (Lacis et al., 1990). Here, an analysis is presented of the ozone layer response to 4xCO2 in chemistry–climate models (CCMs) which participated to CMIP intercomparisons. In a previous study using CMIP5 models, it has been shown that under 4xCO2, ozone decreases in the tropical lower stratosphere, and increases over the high latitudes and throughout the upper stratosphere. It was also found that a substantial portion of the spread in the tropical column ozone is tied to inter-model spread in tropical upwelling, which is in turn partly tied to ECS (Chiodo et al., 2018). Here, we revisit this connection using4xCO2 data from CMIP6, thereby exploiting the larger number of CCMs than in CMIP5. In addition, we explore the linearity of the ozone response, by complementing the analysis with simulations using transient CO2 forcing (1pc/year). We show that the pattern of the ozone response in is similar to CMIP5. In some models (e.g. WACCM), we find larger ozone responses in CMIP6 than in CMIP5, partly because of the larger ECS and thus larger upwelling response in the tropical pipe. In this presentation, we will discuss the relationship between radiative forcing, transport and ozone, as well as further implications for CMIP6 models.

Ozone layer↗

NASA NTRS2021

Tracking the Rates and Mechanisms of Canopy Damage and Recovery Following Hurricane Maria Using Multitemporal Lidar Data

Hurricane Maria, a Category 4 storm, snapped and uprooted canopy trees, removed large branches, and defoliated vegetation across Puerto Rico. The magnitude of forest damages and the rates and mechanisms of forest recovery following Maria provide important benchmarks for understanding the ecology of extreme events. We used airborne Lidar data acquired before (2017) and after Maria (2018, 2020) to quantify landscape-scale changes in forest structure along a 439-ha elevational gradient (100–800 m) in the Luquillo Experimental Forest. Damages from Maria were widespread, with 73% of the study area losing ≥ 1 m in canopy height (mean = −7.1 m). Taller forests at lower elevations suffered more damage than shorter forests above 600 m. Yet only 13.5% of the study area had canopy heights ≤ 2 m in 2018, a typical threshold for forest gaps, highlighting the importance of damaged trees and advanced regeneration on post-storm forest structure. Heterogeneous patterns of regrowth and recruitment yielded shorter and more open forests by 2020. Nearly 45% of forests experienced initial height loss > 1 m (2017–2018) followed by rapid height gain > 1 m (2018–2020), whereas 21.6% of forests with initial height losses showed little or no height gain, and 17.8% of forests exhibited no height changes larger than ± 1 m in either period. Canopy layers < 10 m tall accounted for most increases in canopy height and fractional cover between 2018 and 2020, with gains split evenly between height growth and lateral crown expansion by surviving individuals. These findings benchmark rates of gap formation, crown expansion, and canopy closure following hurricane damage and highlight the diversity of ecosystem impacts from heterogeneous spatial patterns and vertical stratification of forest regrowth following a major disturbance event.

forest structure↗

NASA NTRS2021

Tracking the Rates and Mechanisms of Canopy Damage and Recovery Following Hurricane Maria Using Multitemporal Lidar Data

forest structure↗

NASA NTRS2019

Micro-meteoroid and orbital debris radar from Goldstone Radar observations

Micro-meteoroid and orbital debris (MMOD) refers to millions of micrometeoroids and orbital debris orbiting the Earth, which can come from remaining parts of spacecraft from previous missions. These debris objects keep colliding and creating new pieces of debris, with very different sizes ranging from millimeters to meters. About 99.3% of the debris objects have a size < 1 cm. These debris objects, traveling at speeds of 10 km/s, can cause significant damage to spacecraft, satellites, and astronauts. Despite the historic use of shielding to protect vital spacecraft components, MMOD still present a potential hazard for future missions and operations involving humans in space. Goldstone’s orbital debris radar (ODR) enhances the NASA orbital debris model with radar observation data, providing vital information on orbital debris detections: size, Doppler, range, and orbit inclination angle. These observations currently provide the only access to information about this size class (2 mm to 10 mm) of orbital debris. Goldstone’s ODR experiments operate as a bistatic radar, with overlapping beams providing a wide distribution of ranges. From 2004 to early 2018, Goldstone’s ODR has been collecting orbital debris data from altitudes covering 280 km to 3000 km. Since the decommissioning of the receive antenna in early 2018, Goldstone’s range coverage is reduced to a 300 km window within a 600 km to 1000 km potential range coverage, depending on the pointing geometry and antenna station used. We are investigating alternative transmit receive configurations at Goldstone to recover our previous wide range coverage.

Rodriguez-Alvarez, Nereida↗

NASA NTRSDate not supplied

ICESat-2 Mission: Status, Outlook, and Contributions to Polar Science

The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) mission carries the Advanced Topographic Laser Altimeter System (ATLAS) lidar to measure the changing height of Earth surface. After more than 3 years of science data collection, ATLAS has emitted well over a trillion laser pulses and continues to operate nominally. The ICESat-2 data products were initially released in May 2019, and have been used in over 150 peer-reviewed publications to date. Recent community white papers have noted the importance of ice elevation measurements for the coming decades, and as CryoSat-2 has entered it’s second decade and future missions are uncertain, ICESat-2 will be a critical part of the Earth System Observatory for the 2020s. In the polar regions, ICESat-2 has enabled year-round sea ice freeboard measurements as well as ice sheet elevation changes on seasonal, annual, and decadal time scales when combined with other missions. These data enable measurement of change at fine spatial and temporal scales to help understand or characterize the processes driving these observations. As of this presentation, Release 005 is the current version of the along-track data products, including surface-specific products for sea ice, land ice, ground and canopy height, ocean, and inland water heights. In addition, first versions of gridded data products for land ice, sea ice, and ocean are all available. Data spans the start of the mission (14 October 2018) through early 2022. The ICESat-2 project has also begun to produce and distribute QuickLook products with a nominal 3-day latency (as opposed to the ~45-day latency of the final data products). Initial versions of these QuickLook products have elevation accuracy of ~3 meters, and geolocation accuracy of ~50 meters. All products are available through the National Snow and Ice Data Center (nsidc.org). This presentation will summarize the current state and health of the observatory, current state of the ICESat-2 data products, data product quality assessment, and outlook for the future.

ICESat-2↗

NASA NTRS2020

Evaluation and Validation of a High Spatial Resolution Satellite Soil Moisture Product over the Continental United States

The soil moisture (SM) data retrieved from the Soil Moisture Active and Passive (SMAP) satellite are available at a 9 km grid spacing since April 2015. This product can provide valuable information for research and applications in hydrology and other related fields. However, the resolution may be too coarse for applications at catchment or field scale. In this study, an established downscaling methodology, which had a major modification regarding its application on the SMAP 33 km domain, was implemented to develop a 1 km soil moisture product based on the SMAP 9 km data. The algorithm proposed here is based on the thermal inertia principle and developed by modeling the relationship between surface temperature difference and SM for different Normalized Difference Vegetation Index (NDVI) classes. The model functions were established and tuned using data from the NASA’s Land Information System (LIS) North America Land Data Assimilation System (NLDAS) and remotely sensed VISible/InfRared (VIS/IR) reflectance data from Long Term Data Record (LTDR) AVHRR (Advanced Very High Resolution Radiometer) for the growing season months of April-September 1981–2018. These were then implemented using the MODIS (Moderate Resolution Imaging Spectroradiometer) data over the Continental United States (CONUS) domain. Validation activities were carried out using in situ measurements distributed through the International Soil Moisture Network (ISMN). The validation results computed using the 1 km SM data showed that the R2, unbiased RMSE (root mean square error) and bias were improved relative to the 9 km SMAP product by 0.045, 0.018m3/m3 and 0.001m3/m3, respectively. The 1 km SM also exhibited a strong time-series autocorrelation. Further accuracy assessment analyses indicated that precipitation might contribute to the uncertainties in both the 9 km SMAP and 1 km downscaled SMAP SM products.

SMAP↗

NASA NTRSDate not supplied

Comparisons of CFD Simulations of Icing Wind Tunnel Clouds with Experiments Conducted at the NASA Propulsions Systems Laboratory

This paper evaluates simulation predictions against experimental test data of icing clouds that were produced during 2018 ice crystal icing physics tests conducted at the NASA Propulsion Systems Laboratory icing wind tunnel. Aero-thermal and cloud parameters are set and known upstream at the tunnel inlet and spray system, but change as the cloud and air thermodynamically interact as the flowing masses reach the tunnel test section. Utilizing the ANSYS Fluent Discrete Phase Model function, 3D computational fluid dynamics (CFD) simulations were performed, capturing the thermodynamic interactions between the test parameters, and providing predictions of the aero thermal and cloud conditions at the tunnel test section. Simulation predictions were compared with test data that were measured at the tunnel exit plane. Evaluations focused on the cloud concentration (total water content), humidity content, and air temperature. CFD simulation predictions showed areas of agreement and disagreement. Simulations showed that cloud concentration profiles at the test section are strongly related to the initial spray nozzle pattern used at the tunnel inlet. Experimental data suggest that greater dispersion of the cloud occurred as the simulated cloud predicted areas of high and low cloud concentration compared to test data profiles. Simulations, however, captured the magnitude and location of the change in humidity content and the change in air temperature due to the presence of the cloud reasonably well, when compared to test data. This result would suggest that while the ANSYS simulation did not fully predict the spreading of the cloud as measured during experiment, it did capture evaporation and the molecular movements of air and vapor relatively well.

CFD↗

NASA NTRSDate not supplied

Comparisons of CFD Simulations of Icing Wind Tunnel Clouds with Experiments Conducted at the NASA Propulsions Systems Laboratory

CFD↗

NASA NTRS2021

Automated Uas Measurements of Reflectance and Solar Induced Florescence (SIF) for Assessment Of the Dinamics in Photosynthetic Function, Application for Maze (Zea Mays L.) in Greenbelt, Maryland, Us

For accurate assessment of vegetation function there is a strong need for advancement in the remote sensing methods and instruments, to enable monitoring of the parameters governing photosynthesis at the temporal frequency relevant to their dynamics and at a spatial scale that allows practical assessment and management. Currently, no single sensor can provide data at the desired high temporal, spectral and spatial resolutions. Our field measurements obtained using the integrated UAS Piccolo system during the summers of2017 and 2018 demonstrate that science quality reflectance and solar induced fluorescence (SIF) data can be retrieved with high temporal frequency using small Unmanned Aerial Systems (UAS). The implemented approach facilitates data comparisons through space and time, and the integration with other spectral satellite and airborne data. This investigation contributes for bridging the gap in Earth observation between field and airborne measurements by implementing autonomous methods for obtaining high temporal frequency spectroscopy measurements from an Unmanned Aerial Systems (UAS) at various illumination conditions. An advancement in the Intelligent Payload Module (IPM) facilitated the implementation of an optimization workflow to collect spectral data for characterizing vegetation reflectance and solar induced fluorescence (SIF).

Petya Campbell↗