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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Analyzing Emerging Low-Carbon Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 degrees C or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Under baseline projections (i.e., no decarbonization goals), neither process reaches parity with the incumbent technology across several environmental metrics. Under the decarbonization scenarios, the underlying sectoral shifts result in declining impacts over time, compared to 2020 levels, except for metal depletion levels, which increase. The background shifts postulate a heavily decarbonized economy and energy system, which help technologies reach parity with SMR between 2040-2050 (RCP2.6) and 2030-2040 (RCP1.9) for global warming. Despite declines across several other metrics over time, neither PtH2 technology break even with SMR by 2100 besides for global warming.

decarbonizing↗
DOE OSTI2024

Towards Prospective LCA Using Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) Framework for Analyzing Emerging Low-Carbon Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 Degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Additionally we compare our results by linking two other prospective models with LiAISON - GCAM(Global Change Assessment Model) and ReEDS (Regional Energy Deployment System) to analyze the effect of changing background scenarios using varying predictions in life cycle analysis.

emissions↗
NASA NTRS2003

Work measurement for estimating food preparation time of a bioregenerative diet

During space missions, such as the prospective Mars mission, crew labor time is a strictly limited resource. The diet for such a mission (based on crops grown in a bioregenerative life support system) will require astronauts to prepare their meals essentially from raw ingredients. Time spent on food processing and preparation is time lost for other purposes. Recipe design and diet planning for a space mission should therefore incorporate the time required to prepare the recipes as a critical factor. In this study, videotape analysis of an experienced chef was used to develop a database of recipe preparation time. The measurements were highly consistent among different measurement teams. Data analysis revealed a wide variation between the active times of different recipes, underscoring the need for optimization of diet planning. Potential uses of the database developed in this study are discussed and illustrated in this work.

NASA Discipline Life Support Systems↗
NASA NTRS2015

Aram Chaos: a Long Lived Subsurface Aqueous Environment with Strong Water Resources Potential for Human Missions on Mars

Aram Chaos, Mars is a crater 280 kilometers in diameter with elevations circa. minus 2 to minus 3 kilometers below datum that provides a compelling landing site for future human explorers as it features multiple scientific regions of interest (ROI) paired with a rich extensible Resource ROI that features poly-hydrated sulfates [1]. The geologic history of Aram Chaos suggests several past episodes of groundwater recharge and infilling by liquid water, ice, and other materials [1-3]. The creation of the fractured region with no known terrestrial equivalent may have been caused by melting of deep ice reservoirs that triggered the collapse of terrain followed by catastrophic water outflows over the region. Aram Chaos is of particular scientific interest because it is hypothesized that the chaotic terrain may be the source of water that contributed to the creation of nearby valleys such as Ares Vallis flowing toward Chryse Planitia. The liquid water was likely sourced as groundwater and therefore represents water derived from a protected subsurface environment making it a compelling astrobiological site [2]. The past history of water is also represented by high concentrations of hematite, Fe-oxyhydroxides, mono-hydrated and poly-hydrated sulfates [1, 2]. Poly-hydrated sulfates are likely to contain abundant water that evolves at temperatures below 500 degrees Centigrade thus conferring Aram Chaos a potentially high value for early in-situ resource utilization (ISRU) [4]. The geologic history also calls for future prospecting of deep ice deposits and possibly liquid water via deep drilling. The most recent stratigraphic units in the central part of Aram Chaos are not fractured, and are part of a dome-shaped formation that features bright, poorly-consolidated material that contains both hydrated sulfates and ferric oxides according to OMEGA (Observatoire pour la Minéralogie, l'Eau, les Glaces et l'Activité) data [5]. These surface material characteristics are preliminary indications of their potential use in civil engineering activities that involve regolith moving and hauling, while further study is needed to assess traverse-ability challenges. The widespread distribution of sulfates is also of interest as a resource for the use of sulfur as a binding compound in regolith-based concrete for constructions. The terrain depressions caused by the rock fracturing events may challenge surface mobility but also suggest the possibility of using such natural features for additional shielding from space radiation and as emplacement of nuclear surface power reactors for the same reason. The high concentration of hematite (up to 16 percent) in some of the smoother recent terrains of the central part of Aram Chaos [2] is a favorable attribute for metal extraction ISRU to create iron-based feedstock for in-situ fabrication of replacement parts or their repairs. Preliminary data on Aram Chaos indicate that it offers a combination of many critical criteria for human missions to the surface of Mars: equatorial region at low Mars Orbiter Laser Altimeter (MOLA), evidence of hydrated minerals over large areas and at high concentrations tied to historic evidence of liquid water over long periods.

Sibille, L.↗
NASA NTRS2007

Hypovolemia Induced Orthostatic Hypotension in Presyncopal Astronauts and Normal Subjects Relates to Hypo-Sympathetic Responsiveness

Circulating blood volume is reduced during spaceflight, leaving astronauts hemodynamically compromised after landing. Because of this hypovolemia, crew members are able to withstand a postflight 10 minute upright tilt test only if they are able to mount a hyper-sympathetic response. Previous work from this laboratory has shown that about 30% of astronauts, primarily female, have postflight sympathetic responses to tilt that are equal to or less than their preflight responses and thus, they become presyncopal. Part of the mission of the cardiovascular lab at the Johnson Space Center is to identify susceptible crewmembers before flight so that individualized countermeasures can be prescribed. The goal of this study was to develop a ground based model of hypovolemia that could be used for this purpose We tested the hypothesis that hypovolemia alone, in the absence of spaceflight, would reproduce the landing day rate of presyncope during upright tilt in normal volunteers. Further, we hypothesized that, during hypovolemia, subjects who had sympathetic responses that were equal to or less than their normovolemic responses would become presyncopal during upright tilt tests. We studied 20 subjects, 13 male and 7 female, on two separate occasions: during normovolemia and hypovolemia. We induced hypovolemia with intravenous furosemide 40 hours prior to the experiment day, followed by a 10MEq Na diet. On the normovolemia and hypovolemia test days, plasma volume, tilt tolerance and supine and standing arterial pressure, heart rate and plasma norepinephrine levels were measured. A two factor, repeated measures analysis of variance was performed to examine the differences between group (presyncopal vs. non-presyncopal) and day (normovolemia vs. hypovolemia) effects. There were no differences in baseline arterial pressure between normovolemia and hypovolemia or between presyncopal and non-presyncopal groups, but heart rates were higher with hypovolemia in both groups (presyncopal: 70 5 bpm vs. 63 3 bpm, P = 0.003, non-presyncopal: 59 2 bpm vs. 52 2 bpm, P = 0.003). Similar to patterns reported after flight, non-presyncopal subjects had greater norepinephrine responses to tilt during hypovolemia compared to normovolemia (580 79 vs. 298 37 pg/ml, P < 0.05), but presyncopal subjects did not (180 44 vs. 145 32 pg/ml, P = NS). This new model has the potential to accelerate the development of countermeasures and save flight resources. It can be used to identify astronauts who will become presyncopal on landing day, so that prospective, individualized countermeasures can be developed. In addition, it can also be used to screen candidate countermeasures prior to requests for bed rest or inflight resources.

Meck, Janice V.↗
DOE OSTI2024

Prospective Seal Unit Spatial Extent Database for U.S. Sedimentary Basins

The Prospective Seal Unit Spatial Extent Database for U.S. Sedimentary Basins contains a series of spatial datasets representing spatial extents of publicly available data for caprock and seal rock units within the Appalachian Basin, Denver-Julesburg Basin, Great Valley Basin (Sacramento and San Joaquin Basins), Illinois Basin, Michigan Basin, San Juan Basin, U.S. Gulf Coast Basin, and Williston Basin. The database is designed to support carbon storage feasibility and resources assessment for carbon transport and storage (CTS) projects while displaying the spatial extent of prospective seal units and provide a guide to the original data source. This database leverages publicly available data resources from authoritative sources (e.g. U.S. Geological Survey, State Geologic Surveys, and published reports), and aims to help guide users to understand the seal unit's spatial coverage and data gaps from the regional to sub-basin/field scale. The database is organized by seal unit/formation, including the spatial extent for data found to be available for the seal unit. The various datasets represented include spatial extents of the lithologic formation, depth to top structural contour maps, and thickness/isopach maps. Included in this submission are the following resources: 1. Geodatabase/Dataset: “prospective-seal-unit-extents-2025.gdb” 2. ReadMe: “readme-prospective-seal-unit-spatial-extent-dataset-2025.pdf” 3. Data Catalog: “prospective-seal-unit-spatial-extents-data-catalog-2025.xlsx” 4. Data Sources Key: “data-source.csv” Please see NETL disclaimers here: https://netl.doe.gov/home/disclaimer

Basin↗
DOE OSTI2024

Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are identifying hidden geothermal resources in the USA and designing profitable enhanced geothermal systems (EGS). Many non-obvious processes and parameters could characterize geothermal resources and could control the ultimate energy potential of geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize geothermal resources, but this data is sparse and multi-scale. This has hindered attempts to leverage the datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) give promise to overcome these issues. Modern ML methods and tools can (1) analyze large datasets, (2) assimilate model ensembles that include a multitude of inputs and outputs, (3) process sparse datasets, (4) perform transfer learning between sites with different data quality, (5) extract hidden geothermal signatures from field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. In this work, we implement ML-based geothermal exploration and an enhanced geothermal systems (EGS) design tool to achieve the above goals. Our exploration tool is GeoThermalCloud (GTC) EGS design tool is GeoDT-ML. GTC (github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. It enables the identification of critical measurements needed to identify geothermal resource signatures. GeoDT-ML (github.com/SmartTensors/GeoThermalCloud.jl/tree/master/) adds coupling to GeoDT (https://github.com/GeoDesignTool/GeoDT.git) for stochastic EGS design optimization and performance prediction. GeoDT-ML leverages recent advances in deep learning and high-performance computing. Contributors to this effort include LANL, PNNL, Google, Stanford, and Julia Computing.

15 GEOTHERMAL ENERGY↗
NASA NTRS1981

National Aeronautics and Space Administration fundamental research program. Information utilization and evaluation

In the second half of the 1980's NASA can expect to face difficult choices among alternative fundamental and applied research, and development projects that could potentially lead to improvements in the information systems used to manage renewable resources. The working group on information utilization and evaluation believes that effective choices cannot be made without a better understanding of the current and prospective problems and opportunities involved in the application of remote sensing to improve renewable research information systems. A renewable resources information system is defined in a broad context to include a flow of data/information from: acquisition through processing, storage, integration with other data, analysis, graphic presentation, decision making, and assessment of the affects of those decisions.

Estes, J. E.↗
DOE OSTI2024

Modular Subsurface Sensors and Integrated Software for Advanced Subsurface Characterization and Monitoring using Unoccupied Vehicles

The advent and subsequent proliferation of autonomous airborne, waterborne, and groundbased vehicles (i.e., “drones”) promises to broadly transform the geosciences and associated industries, including fossil energy exploration and development, mineral resource exploration and development, water-resource management, and environmental remediation. For geophysical characterization and monitoring, the prospect of programming highly repeatable and low-cost drone missions for subsurface imaging will allow for deployments in hazardous and previously inaccessible areas. Coupled with autonomous workflows for data processing, management, and visualization, drone-based geophysical characterization and monitoring will enable unprecedented, real-time insight into diverse subsurface properties and processes of scientific and engineering importance. Toward this end, the objectives of this Lab Directed Research and Development (LDRD) project were to develop new (1) instrumentation for dronebased electromagnetic induction (EMI) geophysical imaging, including separated transmitter and receivers and associated electronics, (2) software for real-time data telemetry, processing, management, and visualization. Although EMI has been previously deployed using unoccupied aerial systems (UASs), these applications failed to capitalize on the game-changing capabilities of drone platforms. Whereas drone-based data acquisition allows for collection of rich, three-dimensional (3D) multi-offset/multi-angle configurations between transmitters and receivers, past efforts have relied on conventional instrumentation that was designed for ground-based data collection with the transmitter and a single receiver housed in the same unit; nor did these previous applications demonstrate real-time delivery of results to support rapid management decisions in the field. In this 1-year project, we (1) designed and constructed new lightweight independent transmitter and receiver antenna platforms that communicate with a laptop computer; (2) developed software to control data acquisition, manage/transfer data, and visualize data as its collected; and (3) demonstrated the operation of the new hardware and software systems in a ground-based field test. Our work entails major technological advances for EMI and established a foundation on which to build a new drone-based, real-time geophysical EMI imaging capability to support diverse challenges facing the nation.

47 OTHER INSTRUMENTATION↗
NASA NTRS1976

Inputs requested from earth resources remote sensing data users regarding LANDSAT-C mission requirements and data needs

Inputs from prospective LANDSAT-C data users are requested to aid NASA in defining LANDSAT-C mission and data requirements and in making decisions regarding the scheduling of satellite operations and ground data processing operations. Design specifications, multispectral band scanner performance characteristics, satellite schedule operations, and types of available data products are briefly described.

Source record↗
NASA NTRS2022

The VIPER Mission, a Resource-Mapping Mission on Another Celestial Body

The Volatiles Investigation Polar Exploration Rover (VIPER) mission is a lunar polar volatiles prospecting mission developed through NASA’s Science Mission Directorate (SMD) Planetary Science Division with launch in late 2023. VIPER’s mission goals are to (1) characterize the distribution and physical state of lunar polar water and other volatiles in lunar cold traps and regolith and (2)provide the data necessary for NASA to evaluate potential return of In-Situ Resource Utilization (ISRU) from lunar polar regions. VIPER will be optimized for lunar regions that receive prolonged periods of sunlight (short lunar nights). The mission duration will be more than 90 Earth days and result in a traverse distance of up to 20 km. The mission includes a rover-borne payload that can locate surface and near-subsurface volatiles, excavate, and analyze samples of the volatile-bearing regolith, and demonstrate the form, extractability, and usefulness of the materials.

The Volatiles Investigation Polar Exploration Rove↗
NASA NTRS1980

Pattern recognition for remote sensing - Progress and prospects

An overview is given of the current state of automatic image pattern recognition as applied to remote sensing of the earth's resources. The framework for the discussion is provided by four important aspects of the remote sensing problem: scene information content, characterization of scene information, information extraction methods, and the net value of extractable information. Outstanding problems are surveyed, as are the prospects for future developments. The effect of increasingly complex data bases and the rapidly evolving digital computer technology are highlighted.

Swain, P. H.↗
DOE OSTI2024

Building a Prospective LCA Framework to Analyze Emerging Technologies in a Dynamic System Context

NREL's Lifecycle Analysis Integration into Opensource Numerical models (LiAISON) framework computes temporally explicit life cycle impacts and resource uses for specific technologies (foreground) in a dynamic system context (background). LiAISON computes results for a series of environmental mid-points enabling an analysis of prospective tradeoffs of emerging technologies toward 2100. This prospective feature is of critical importance when analyzing present-day emerging technologies whose large-scale impacts during deployment phases will occur in different, future system contexts. LiAISON systematically accounts for dynamic system changes by applying an integrated background of the future energy-economy-land-climate system, generated by exogenous integrated assessment models (IAMs). Using IAM scenarios, LiAISON generates a time-series of life cycle inventory (LCI) databases, which are then used to calculate the impacts per functional unit per time step. This expands current practice of using static, future system assumptions, e.g., a specific grid-mix each year. Further, IAM scenarios are provided in a standardized format of shared-socioeconomic pathways (SSP) and representative concentration pathways (RCP) combinations. These are coherent, regularly, published, and peer-reviewed scenario combinations that establish a reproducible and standardized societal and climate mitigation futures context. They are comparable across IAMs and expand the system boundary of the traditional LCA by including dimensions such as societal and behavioral changes. We apply the framework to assess two emerging Power-to-Hydrogen processes, high temperature electrolysis using solid oxide fuel cell (HT-SOE) and polymer electrolyte membrane electrolysis (PEME). We compare the technologies to a baseline Hydrogen production process via steam methane reforming. Despite the decarbonized electricity systems' beneficial effects on the PtH2 processes' carbon intensities, we find environmental tradeoffs, which require technology improvements via learning-by-doing to be alleviated. Future work via ongoing collaborations will focus on linking the framework to other energy-economy-land-climate models and open-source life cycle inventory databases.

ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2016

Launch Lock Mechanism for Resource Prospector Rover

The Resource Prospector Rover is being designed to carry the RESOLVE (Regolith Environment Science, and Oxygen Lunar Volatile Extraction) payload on a mission to the Moon to prospect for water ice. This is a joint project between KSC Swamp Works UB-R1 and JSC. JSC is building the Resource Prospector 2015 (RP15) rover and KSC designed and fabricated a Launch-Lock (LL) hold down mechanism for the rover. The LL mechanism will attach and support the rover on a Lunar Lander during launch and transit to the moon, then release the RP15 rover after touchdown on the lunar surface. This report presents the design and development of the LL mechanism and its unique features which make it suitable for this lunar exploration mission. An EDU (engineering development unit) prototype of the LL has been built and tested at KSC which is the subject of this paper.

Lunar Lander↗
DOE OSTI2020

Effect of Fluorination on Lithium Transport and Short-Range Order in Disordered-Rocksalt-Type Lithium-Ion Battery Cathodes

Fluorine substitution is a critical enabler for improving the cycle life and energy density of disordered rocksalt (DRX) Li-ion battery cathode materials which offer prospects for high energy density cathodes, without the reliance on limited mineral resources. Due to the strong Li–F interaction, fluorine also is expected to modify the short-range cation order in these materials which is critical for Li-ion transport. In this work, density functional theory and Monte Carlo simulations are combined to investigate the impact of Li–F short-range ordering on the formation of Li percolation and diffusion in DRX materials. Additionally, the modeling reveals that F substitution is always beneficial at sufficiently high concentrations and can, surprisingly, even facilitate percolation in compounds without Li excess, giving them the ability to incorporate more transition metal redox capacity and thereby higher energy density. It is found that for F levels below 15%, its effect can be beneficial or disadvantageous depending on the intrinsic short-range order in the unfluorinated oxide, while for high fluorination levels the effects are always beneficial. Using extensive simulations, a map is also presented showing the trade-off between transition-metal capacity, Li-transport, and synthetic accessibility, and two of the more extreme predictions are experimentally confirmed.

25 ENERGY STORAGE↗
NASA NTRS1974

Feasibility study of a swept frequency electromagnetic probe (SWEEP) using inductive coupling for the determination of subsurface conductivity of the earth and water prospecting in arid regions

Techniques developed for electromagnetic probing of the lunar interior, and techniques developed for the generation of high power audio frequencies were combined to make practical a magnetic inductive coupling system for the rapid measurement of ground conductivity profiles which are helpful when prospecting for the presence and quality of subsurface water. A system which involves the measurement of the direction, intensity, and time phase of the magnetic field observed near the surface of the earth at a distance from a horizontal coil energized so as to create a field that penetrates the earth was designed and studied to deduce the conductivity and stratification of the subsurface. Theoretical studies and a rudimentary experiment in an arid region showed that the approach is conceptually valid and that this geophysical prospecting technique can be developed for the economical exploration of subterranean water resources.

Latorraca, G. A.↗
NASA NTRS1973

Remote sensors - Prospects and limitations

Review of the history, present status, and future prospects and limitations of remote sensing systems for satellite-based earth resources surveys. The objectives and special areas of interest of ongoing sensor development experiments are summarized, and the measurement and performance goals of current potential sensor research is discussed.

Lehmann, J.↗