Search NASA⌕ Search

SEARCH · Search NASA

Results for “Knowledge Acquisition”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The False Dilemma: Rethinking AF Science and Technology Officer Talent Management

In 2019, Secretary of the Air Force (SECAF) Heather Wilson launched the 2030 Science and Technology (S&T) Strategy by stating, “The advantage will go to those who create the best technologies and who integrate and field them in creative operational ways that provide military advantages.” In 2021, 15% of Air Force general officers responsible for creating and integrating this technological edge had a science, technology, engineering, and math (STEM) graduate degree, and less than 1% of generals had a STEM doctorate. As one point of comparison, at least 32% and 13% of founding CEOs of Fortune 500 technology companies had a master’s and doctoral STEM degree, respectively. The gulf is larger when you consider that these CEOs used their technical degrees throughout their careers whereas most general officers do not. In large organizations, if the leader does not possess technical knowledge, it is difficult to drive innovation systems and connect ideas to reality. The evidence for this disconnect is abundant in acquisition challenges for high-tech systems, significant pushback to the “revolution in military affairs”, and failure of the “Third Offset” to take hold and deliver capabilities to offset Chinese and Russian capabilities. Indeed, when the former Air Force and Space Force Chief Software Officer, Nicolas M. Chaillan, offered his resignation, one of the main reasons for leaving was “the failure of OSD and the Joint Staff to deliver on their own alleged top ‘priority’, JADC2 – they couldn’t ‘walk the walk.’” Senior leaders have recognized this disconnect, and several inquiries and studies regarding STEM competency have been conducted. Many have posed a false dilemma: a large, STEM-cognizant or small, STEM-expert force. However, current and future STEM human capital needs are managed at the unit and functional area level. This leads to “silos” and the tactical “needs of the present” dominating the strategic “needs of the future”, highlighted by only two functional areas systematically tracking future STEM needs. Therefore, the decision on force structure appears to have been historically made by default through these and other perceived structural realities, and a vast majority of recommendations have focused on the small STEM community – an exercise in diminishing returns. In essence, the solution often boils down to finding unicorns – officers that have chartered the unforgiving pathway through the traditional “gates” to general officer while obtaining sufficient STEM proficiency along the way. Fortunately for the Air Force, these officers exist, albeit at a rate below what the evidence would suggest is necessary. However, this approach fundamentally limits the ability to develop a deep pool of officers with the ability to create and integrate technologies and ensures the Dunning-Kruger effect is prevalent. The Dunning-Kruger effect is a cognitive bias where those with limited knowledge in an area lack the expertise necessary to recognize their lack of expertise and consequently are prone to overestimate their knowledge and performance. In short, we need less leaders overestimating their STEM knowledge and more with hard-earned STEM competency required to “walk the walk” to create and integrate technologies for military advantage. This will not be accomplished by restricting the emphasis to the traditional scientists and engineering (S&E) career fields – the pool, only 10% of line officers, and general officer progression is just too small. Fortunately, the recent SECAF’s Management Initiatives and Chief of Staff’s (CSAF) Action Orders, both containing emphasis on organic expertise to “accrue advantage in military-technological competition,” present an opportunity to truly develop a framework for the force of the future.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Williston Basin Carbon Ore, Rare Earth, and Critical Minerals (CORE-CM) Program

The U.S. Department of Energy’s Office of Fossil Energy awarded 13 CORE-CM programs as part of the CORE-CM Initiative, designed to develop the technology and upstream and midstream supply chains to extract rare-earth elements (REEs) and critical minerals (CMs) from the nation’s coal supplies. The intent is to catalyze regional economic growth and job creation while strengthening the use of domestic resources. The Williston Basin CORE-CM Program aims to drive the expansion and transformation of coal and coal-based resource usage within the Williston Basin to produce REEs, CMs, and nonfuel carbon-based products (CBPs). The work constitutes Phase 1 of a long-term program. The objectives of Phase 1 are to identify the existing knowledge base and gaps and to develop a series of assessments/plans, including an initial basinal assessment; a characterization and data acquisition plan; a waste stream reuse plan; a basinal strategies assessment for infrastructure, industries, and business; a technology assessment, development, and field testing plan; a technology innovation center plan(s); and a stakeholder outreach and education plan. The goal of this project is to initiate the development of a new industry in the resource-rich Williston Basin that will catalyze economic growth and job creation through a coalition team of private industry, university, and state, local, and federal government entities. A coalition team of nearly 30 private industry, university, and state, local, and federal government partners was formed for this program. Partners include the University of North Dakota Energy & Environmental Research Center, Institute for Energy Studies, and Nistler College; North Dakota State University; Montana Tech University; Pacific Northwest National Laboratory; Critical Materials Institute; North Dakota Geological Survey; South Dakota Geological Survey; U.S. Geological Survey; North American Coal Corporation; BNI Energy; Basin Electric Power Cooperative; Minnkota Power Cooperative; and many more.

Kay, John P.↗
DOE OSTI2023

Williston Basin Carbon Ore, Rare Earth, and Critical Minerals (CORE-CM) Program

The U.S. Department of Energy’s (DOE’s) Office of Fossil Energy awarded 13 Carbon Ore, Rare-Earth and Critical Minerals (CORE-CM) programs as part of the CORE-CM Initiative, designed to develop the technology and upstream and midstream supply chains to extract rare-earth elements (REEs) and CMs from the nation’s coal supplies. The intent is to catalyze regional economic growth and job creation while strengthening the use of domestic resources. The Williston Basin CORE-CM Program aims to drive the expansion and transformation of coal and coal-based resource usage within the Williston Basin to produce REEs, CMs, and nonfuel carbon-based products (CBPs). The work constitutes Phase 1 of a long-term program. The objectives of Phase 1 are to identify the existing knowledge base and gaps and to develop a series of assessments/plans, including an initial basinal assessment; a characterization and data acquisition plan; a waste stream reuse plan; a basinal strategies assessment for infrastructure, industries, and business; a technology assessment, development, and field-testing plan; a technology innovation center plan(s); and a stakeholder outreach and education plan. Originally, the CORE-CM programs were to do little if any sample collection as part of the Phase 1 program. As part of the ongoing interactions with DOE regarding the progress of the 13 CORE-CM programs, DOE has decided to extend Phase 1 with additional funding to allow all of the Phase 1 CORE-CM programs to collect and characterize more samples that have potential to be sources of REEs and CMs. The goal of this project is to initiate the development of a new industry in the resource-rich Williston Basin that will generate economic growth and job creation through a coalition team of private industry; university; and state, local, and federal government entities. A coalition team of nearly 30 private industry; university; and state, local, and federal government partners was formed for this program. Partners include the University of North Dakota Energy & Environmental Research Center, Institute for Energy Studies, and Nistler College; North Dakota State University; Montana Tech University; Pacific Northwest National Laboratory; Critical Materials Institute; North Dakota Geological Survey; South Dakota Geological Survey; U.S. Geological Survey; North American Coal Corporation; BNI Energy; Basin Electric Power Cooperative; Minnkota Power Cooperative; and many more.

Kay, John P.↗
NASA NTRS2004

Simulation Based Acquisition for NASA's Office of Exploration Systems

In January 2004, President George W. Bush unveiled his vision for NASA to advance U.S. scientific, security, and economic interests through a robust space exploration program. This vision includes the goal to extend human presence across the solar system, starting with a human return to the Moon no later than 2020, in preparation for human exploration of Mars and other destinations. In response to this vision, NASA has created the Office of Exploration Systems (OExS) to develop the innovative technologies, knowledge, and infrastructures to explore and support decisions about human exploration destinations, including the development of a new Crew Exploration Vehicle (CEV). Within the OExS organization, NASA is implementing Simulation Based Acquisition (SBA), a robust Modeling & Simulation (M&S) environment integrated across all acquisition phases and programs/teams, to make the realization of the President s vision more certain. Executed properly, SBA will foster better informed, timelier, and more defensible decisions throughout the acquisition life cycle. By doing so, SBA will improve the quality of NASA systems and speed their development, at less cost and risk than would otherwise be the case. SBA is a comprehensive, Enterprise-wide endeavor that necessitates an evolved culture, a revised spiral acquisition process, and an infrastructure of advanced Information Technology (IT) capabilities. SBA encompasses all project phases (from requirements analysis and concept formulation through design, manufacture, training, and operations), professional disciplines, and activities that can benefit from employing SBA capabilities. SBA capabilities include: developing and assessing system concepts and designs; planning manufacturing, assembly, transport, and launch; training crews, maintainers, launch personnel, and controllers; planning and monitoring missions; responding to emergencies by evaluating effects and exploring solutions; and communicating across the OExS enterprise, within the Government, and with the general public. The SBA process features empowered collaborative teams (including industry partners) to integrate requirements, acquisition, training, operations, and sustainment. The SBA process also utilizes an increased reliance on and investment in M&S to reduce design risk. SBA originated as a joint Industry and Department of Defense (DoD) initiative to define and integrate an acquisition process that employs robust, collaborative use of M&S technology across acquisition phases and programs. The SBA process was successfully implemented in the Air Force s Joint Strike Fighter (JSF) Program.

Hale, Joe↗
DOE OSTI2020

A Hybrid-Learning Algorithm for Online Dynamic State Estimation in Multimachine Power Systems

With the increasing penetration of distributed generators in the smart grids, having knowledge of rapid real-time electromechanical dynamic states has become crucial to system stability control. Conventional Supervisory Control and Data Acquisition (SCADA)-based dynamic state estimation (DSE) techniques are limited by the slow sampling rates, while the emerging phasor measurement units (PMUs) technology enables rapid real-time measurements at network nodes. Using generator bus terminal voltages, we propose a hybrid-learning DSE (HL-DSE) algorithm to estimate the synchronous machine rotor angle and speed in real time. The HL-DSE takes the power system model into account and trains neuroestimators with real-time data in an online manner. Compared with traditional DSE methods, the HL-DSE overcomes limitations by using a data-driven approach in conjunction with the physical power system model. The time efficiency, accuracy, convergence, and robustness of the proposed algorithm are tested under noises and fault conditions in both small- and large-scale test systems. Simulation results show that the proposed HL-DSE is much more computationally efficient than widely used Kalman filter (KF)-based methods while maintaining comparable accuracy and robustness. In particular, HL-DSE is over 100 times faster than square-root unscented KF (SR-UKF) and 80 times faster than extended KF (EKF). The advantages and challenges of the HL-DSE are also discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Seedling nitrogen uptake and rhizodeposition between mycorrhizal types

Tree mycorrhizal associations are associated with patterns in N cycling and soil organic matter (SOM) storage, however, we still lack a mechanistic understanding of what tree and fungal traits drive these patterns and how they will respond to global changes in soil N availability. To address this knowledge gap, we investigated how arbuscular mycorrhizal (AM)- and ectomycorrhizal (EcM)-associated seedlings alter rhizodeposition in response to increased inorganic N acquisition. Specifically, we conducted this greenhouse experiment in a sealed labeling chamber with an enriched 13Carbon atmosphere and 15Nitrogen enriched fertilizer over the course of five months from April 2021 - August 2021. To include the variability across tree species, we grew eight species of seedlings belonging to eight families that were either arbuscular (Acer rubrum, Nyssa sylvatica, Thuja occidentalis, and Prunus seritina) or ectomycorrhizal-associated (Quercus rubra, Tilia americana, Pinus strobus, and Betula lenta). We measured rhizodeposition (mg 13C), plant N uptake from fertilizer (mg N), net soil carbon, and the abundance of mycorrhizal fungi (ITS sequencing and qPCR). We also characterized fungal (ITS2) and bacterial (16S) soil communities.The data from this project are ".csv" files that can up downloaded into a folder, and then run in the associated R markdown scripts after changing the source folder location at the top of the script. These data include raw outputs and processed files (using the R markdown files) for seedling growth and biomass, 15N content, soil 13C content, raw reads and processed file versions for fungal and bacterial ASVS, and a final summary file used for modeling. R software is needed to run these data, and the packages needed are listed at the top of the R markdown file.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Decision Support System to Compile Environmental Mitigations from Hydropower Licensing Documents

The process of deciphering, extracting, and compiling information from texts dense with domain-specific terminology and technical jargon is a challenging endeavor. It demands considerable expertise and deep knowledge in the respective field, resulting in a labor-intensive process when executed by humans. Furthermore, the task of identifying multiple class labels in extensive texts presents a challenge due to intra- and inter-reader variability, making the process time-consuming and costly.We’re introducing a user-friendly graphical interface, fortified with a BERT model-powered decision support system. This advanced system aims to augment efficiency, curtail data collection time, and sustain high precision in data acquisition. It is instrumental in deciphering and synthesizing intricate texts teeming with a spectrum of expressions, even within similar mitigation categories. Such tasks traditionally demand substantial human effort and specialized knowledge in the domain.Our system is specifically engineered for the task of extracting environmental mitigation information to promote sustainable hydropower development from licenses issued by the Federal Energy Regulatory Commission (FERC). These license documents are comprehensive, each containing over 15,000 words and requiring the identification of 135 different class labels. We anticipate that our system will boost reading speed, improve the consistency of classification outputs among readers, and contribute to the development of a robust scientific database of environmental mitigations associated with the 2,000+ non-federal hydropower facilities licensed by FERC in the United States.

Yoon, Hong-Jun [ORNL] (ORCID:0000000254505878)↗
NASA NTRS1990

Real time data acquisition for expert systems in Unix workstations at Space Shuttle Mission Control

A distributed system of proprietary engineering-class workstations is incorporated into NASA's Space Shuttle Mission-Control Center to increase the automation of mission control. The Real-Time Data System (RTDS) allows the operator to utilize expert knowledge in the display program for system modeling and evaluation. RTDS applications are reviewed including: (1) telemetry-animated communications schematics; (2) workstation displays of systems such as the Space Shuttle remote manipulator; and (3) a workstation emulation of shuttle flight instrumentation. The hard and soft real-time constraints are described including computer data acquisition, and the support techniques for the real-time expert systems include major frame buffers for logging and distribution as well as noise filtering. The incorporation of the workstations allows smaller programming teams to implement real-time telemetry systems that can improve operations and flight testing.

Muratore, John F.↗
DOE OSTI2022

Deep transfer operator learning for partial differential equations under conditional shift

Transfer learning enables the transfer of knowledge gained while learning to perform one task (source) to a related but different task (target), hence addressing the expense of data acquisition and labelling, potential computational power limitations and dataset distribution mismatches. Here, we propose a new transfer learning framework for task-specific learning (functional regression in partial differential equations) under conditional shift based on the deep operator network (DeepONet). Task-specific operator learning is accomplished by fine-tuning task-specific layers of the target DeepONet using a hybrid loss function that allows for the matching of individual target samples while also preserving the global properties of the conditional distribution of the target data. Inspired by conditional embedding operator theory, we minimize the statistical distance between labelled target data and the surrogate prediction on unlabelled target data by embedding conditional distributions onto a reproducing kernel Hilbert space. We demonstrate the advantages of our approach for various transfer learning scenarios involving nonlinear partial differential equations under diverse conditions due to shifts in the geometric domain and model dynamics. Our transfer learning framework enables fast and efficient learning of heterogeneous tasks despite considerable differences between the source and target domains.

42 ENGINEERING↗
NASA NTRS1992

Domain-specific functional software testing: A progress report

Software Engineering is a knowledge intensive activity that involves defining, designing, developing, and maintaining software systems. In order to build effective systems to support Software Engineering activities, Artificial Intelligence techniques are needed. The application of Artificial Intelligence technology to Software Engineering is called Knowledge-based Software Engineering (KBSE). The goal of KBSE is to change the software life cycle such that software maintenance and evolution occur by modifying the specifications and then rederiving the implementation rather than by directly modifying the implementation. The use of domain knowledge in developing KBSE systems is crucial. Our work is mainly related to one area of KBSE that is called automatic specification acquisition. One example is the WATSON prototype on which our current work is based. WATSON is an automatic programming system for formalizing specifications for telephone switching software mainly restricted to POTS, i.e., plain old telephone service. Our current approach differentiates itself from other approaches in two antagonistic ways. On the one hand, we address a large and complex real-world problem instead of a 'toy domain' as in many research prototypes. On the other hand, to allow such scaling, we had to relax the ambitious goal of complete automatic programming, to the easier task of automatic testing.

Nonnenmann, Uwe↗
DOE OSTI2021

Burst-mode dual-comb spectroscopy

We introduce a new modality of dual-comb spectroscopy that enables a simplified and powerful new approach for time-resolved measurements with increased acquisition rates. This "burst mode" form of dual-comb spectroscopy (BM-DCS) relies on the multiplexing of each probe pulse into a short train of pulses. With this approach we demonstrate a time-resolved series of absorption-based spectroscopic measurements of a laser-induced plasma using only a single laser ablation shot and identify 22 Nd lines not previously reported in the literature. The transmission spectra spanned 3.1 THz and were acquired at an effective acquisition rate of 25 kHz with 40 ms time-resolution. This simple modification to 100 MHz level dual-comb systems provides a flexible approach for studying transient and low duty-cycle events such as laser-induced plasmas, combustion, and explosive reactions.

(280.5395) Plasma Diagnostics, broadband laser spe↗
NASA NTRS1972

Multiple asteroid flyby opportunities in the 1970's and 1980's.

Trajectory characteristics are described for several representative multiple asteroid flyby (MAF) missions possible in the 1970's and early 1980's, many of which are constrained to include a flyby of Ceres, the largest asteroid. It is shown that flybys of three or four asteroids are possible with spacecraft having a delta-V capability of less than 1 km per sec. The major technical problems associated with MAF missions are due to the small size and poorly known orbits of aster oids. These factors make acquisition, guidance, and science measurements difficult, especially for the very small asteroids. Yet, MAF missions are expected to provide a significant increase in knowledge of basic properties of asteroids and to lay the groundwork for more sophisticated rendezvous and sample return missions.

Brooks, D. R.↗
NASA NTRSDate not supplied

Medical System Requirements Development for Lunar Operations

The major health hazards of spaceflight include higher levels of damaging radiation, altered gravity, extended periods of isolation and confinement, a closed and potentially hostile living environment, and the stress associated with being a long distance from Earth. As we increase the duration of lunar stays with foreseeable communication latencies and disruptions, there will be a progressive need for crew to maintain their own health and independently respond to critical medical events. The Exploration Medical Capability element of the NASA Human Research Program is developing a set of Medical System requirements for lunar transit and surface operations. These requirements specify the capabilities, processes and procedures of a habitat Medical System needed for a range of conditions known to occur during spaceflight. Requirement text is written so as not to constrain innovative design solutions necessary for a resilient system. The requirement set includes attributes and functions the Medical System imposes on eight additional habitat systems. A key property of the Medical System is the provision of medical knowledge that will be stored, updated, analyzed, and secured within a Habitat Data System. A Task Performance Support System will aid in medical data acquisition and interpretation, crew training, medical condition prevention, diagnosis and treatment, provide interactive procedures, and track medical inventory. A Wellness System will focus on the provision of countermeasures to prevent, mitigate or treat adverse physical and behavioral health effects while the Medical System recommends adjustments to these countermeasures to maintain crew health. An Environmental Monitoring System will share out-of-bounds readings of air and water quality, acoustics, and radiation exposure levels with the Medical System to help identify issues before they affect crew health and performance. A Communications System will provide secured and private consultations between crew and the ground medical team and their loved ones on Earth. The Medical System also imposes requirements on a Research & Testbed System, fostering advanced medical science such as human research. A Waste Management System provides biohazard waste containment and waste disposal options (recycle and reuse). An Extravehicular Activity System supports crew health during lunar surface activities. And finally, a Maintenance Support System ensures that medical equipment is performing as expected. These requirements are being specifically developed for lunar surface operations but could help to identify Medical System requirements for any space habitat (e.g., I-Hab, commercial endeavors, etc.).

technology↗
NASA NTRS1988

Goldstone solar system radar

Planning, direction, experimental design, and coordination of data-acquisition and engineering activities in support of all Goldstone planetary radar astronomy were performed. This work demands familiarity with the various components of a planetary radar telescope (transmitter, receiver, antenna, computer hardware and software) as well as knowledge of how the entire system must function as a cohesive unit to meet the particular scientific objectives at hand in a given observation. Support radar data-processing facilities, currently being used for virtually all Goldstone data reduction includes: a VAX 11/780 computer system, an FPS 5210 array processor, terminals, tape drives, and image-display devices, as well as a large body of data-reduction software to accommodate the variety of data-acquisition formats and strategems. Successful 113-cm radar observation of Callisto and the near-Earth asteroid 1981 Midas and Goldstone/VLA radar observations of Saturn's rings were obtained. Quick-look verification programs from data taken with phase-coded cw (i.e., ranging) waveforms, applicable to Venus, the Moon, and small bodies were completed. Definition of scientific and engineering requirements on instrument performance, radar system configuration, and personnel, for all 1988 Goldstone radar investigations was accomplished.

Jurgens, Raymond F.↗
DOE OSTI2021

Connect the Dots: In Situ 4-D Seismic Monitoring of CO 2 Storage With Spatio-Temporal CNNs

4-D seismic imaging has been widely used in CO 2 sequestration projects to monitor the fluid flow in the volumetric subsurface region that is not sampled by wells. Ideally, real-time monitoring and near-future forecasting would provide site operators with great insights to understand the dynamics of the subsurface reservoir and assess any potential risks. However, due to obstacles such as high deployment cost, availability of acquisition equipment, exclusion zones around surface structures, only very sparse seismic imaging data can be obtained during monitoring. That leads to an unavoidable and growing knowledge gap over time. The operator needs to understand the fluid flow throughout the project lifetime and the seismic data are only available at a limited number of times. This is insufficient for understanding reservoir behavior. To overcome those challenges, we have developed spatio-temporal neural-network-based models that can produce high-fidelity interpolated or extrapolated images effectively and efficiently. Specifically, our models are built on an autoencoder, and incorporate the long short-term memory (LSTM) structure with a new loss function regularized by optical flow. We validate the performance of our models using real 4-D post-stack seismic imaging data acquired at the Sleipner CO 2 sequestration field. We employ two different strategies in evaluating our models. Numerically, we compare our models with different baseline approaches using classic pixel-based metrics. We also conduct a blind survey and collect a total of 20 responses from domain experts to evaluate the quality of data generated by our models. Finally, via both numerical and expert evaluation, we conclude that our models can produce high-quality 2-D/3-D seismic imaging data at a reasonable cost, offering the possibility of real-time monitoring or even near-future forecasting of the CO 2 storage reservoir.

4-D seismic imaging↗
NASA NTRS1991

Investigation and modification of a commercially available tactile sensor suited for robotic applications

Active (dynamic) tactile sensing was explored using a commercially available tactile array sensor. This task requires the redesign of the sensor interface and a full understanding of the old sensor hardware implementation. There were different stages to this research; the first stage involved the reverse engineering of the old tactile sensor. The second stage had to do with the exploration of the characteristics and behavior of the tactile sensor pad. The next stage dealt with the redesign of the sensor interface using the knowledge gained from the previous two stages. Finally, in the last stage, software to control the tactile sensor was developed to aid in the data acquisition process.

Creus, Carolina↗
NASA NTRS2014

Software Suite to Support In-Flight Characterization of Remote Sensing Systems

A characterization software suite was developed to facilitate NASA's in-flight characterization of commercial remote sensing systems. Characterization of aerial and satellite systems requires knowledge of ground characteristics, or ground truth. This information is typically obtained with instruments taking measurements prior to or during a remote sensing system overpass. Acquired ground-truth data, which can consist of hundreds of measurements with different data formats, must be processed before it can be used in the characterization. Accurate in-flight characterization of remote sensing systems relies on multiple field data acquisitions that are efficiently processed, with minimal error. To address the need for timely, reproducible ground-truth data, a characterization software suite was developed to automate the data processing methods. The characterization software suite is engineering code, requiring some prior knowledge and expertise to run. The suite consists of component scripts for each of the three main in-flight characterization types: radiometric, geometric, and spatial. The component scripts for the radiometric characterization operate primarily by reading the raw data acquired by the field instruments, combining it with other applicable information, and then reducing it to a format that is appropriate for input into MODTRAN (MODerate resolution atmospheric TRANsmission), an Air Force Research Laboratory-developed radiative transport code used to predict at-sensor measurements. The geometric scripts operate by comparing identified target locations from the remote sensing image to known target locations, producing circular error statistics defined by the Federal Geographic Data Committee Standards. The spatial scripts analyze a target edge within the image, and produce estimates of Relative Edge Response and the value of the Modulation Transfer Function at the Nyquist frequency. The software suite enables rapid, efficient, automated processing of ground truth data, which has been used to provide reproducible characterizations on a number of commercial remote sensing systems. Overall, this characterization software suite improves the reliability of ground-truth data processing techniques that are required for remote sensing system in-flight characterizations.

Stanley, Thomas↗
NASA NTRS2021

Augmenting Landsat time series with Harmonized Landsat Sentinel-2 data products: Assessment of spectral correspondence

An increase in the temporal revisit of satellite data is often sought to increase the likelihood of obtaining cloud- and shadow-free observations as well as to improve mapping of rapidly- or seasonally-changing features. Currently, as a tandem, Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and −8 Operational Land Imager (OLI) provide an acquisition opportunity on an 8-day revisit interval. Sentinel-2A and -2B MultiSpectral Instrument (MSI), with a wider swath, have a 5-day revisit interval at the equator. Due to robust pre- and post-launch cross-calibration, it has been possible for NASA to produce the Harmonized Landsat Sentinel-2 (HLS) data product from Landsat-8 OLI and Sentinel-2 MSI: L30 and S30, respectively. Knowledge of the agreement of HLS outputs (especially S30) with historic Landsat surface reflectance products will inform the ability to integrate historic time-series information with new and more frequent measures as delivered by HLS. In this research, we control for acquisition date and data source to cross-compare the HLS data (L30, S30) with established Landsat-8 OLI surface-reflectance measures as delivered by the USGS (hereafter BAP, Best Available Pixel). S30 and L30 were found to have high agreement (R = 0.87–0.96) for spectral channels and an r = 0.99 for Normalized Burn Ratio (NBR) with low relative root-mean-square difference values (1.7%–3.3%). Agreement between L30 and BAP was lower, with R values ranging from 0.85 to 0.92 for spectral channels and R = 0.94 for NBR. S30 and BAP had the lowest agreement, with R values ranging from 0.71 to 0.85 for spectral channels and r = 0.90 for NBR. Comparisons indicated a stronger agreement at latitudes above 55° N. Some dependency between spectral agreement and land cover was found, with stronger correspondence for non-vegetated cover types. The level of agreement between S30 and BAP reported herein would enable integration of HLS outputs with historic Landsat data. The resulting increased temporal frequency of data allows for improvements to current cloud screening practices and increases data density and the likelihood of temporal proximity to target date for pixel compositing approaches. Furthermore, additional within-year observations will enable change products with a higher temporal fidelity and allow for the incorporation of phenological trends into land cover classification algorithms.

Michael A. Wulder↗