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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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At least 145 records · Page 8

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NASA NTRS2006

Aviation Applications for Satellite-Based Observations of Cloud Properties, Convection Initiation, In-flight Icing, Turbulence and Volcanic Ash

Advanced Satellite Aviation Weather Products (ASAP) was jointly initiated by the NASA Applied Sciences Program and the NASA Aviation Safety and Security Program in 2002. The initiative provides a valuable bridge for transitioning new and existing satellite information and products into Federal Aviation Administration (FAA) Aviation Weather Research Program (AWRP) efforts to increase the safety and efficiency of the airspace system. The ASAP project addresses hazards such as convective weather, turbulence (clear-air and cloud-induced), icing and volcanic ash and is particularly applicable in extending the monitoring of weather over data-sparse areas such as the oceans and other observationally remote locations. ASAP research is conducted by scientists from NASA, the FAA AWRP's Product Development Teams (PDT), NOAA and the academic research community. In this paper we provide a summary of activities since the inception of ASAP that emphasize the use of current-generation satellite technologies toward observing and mitigating specified aviation hazards. A brief overview of future ASAP goals is also provided in light of the next generation of satellite sensors (e.g., hyperspectral; high spatial resolution) to become operational in the 2006-2013 timeframe.

Mecikalski, John R.↗

NASA NTRS2007

An Overview of the CBERS-2 Satellite and Comparison of the CBERS-2 CCD Data with the L5 TM Data

CBERS satellite carries on-board a multi sensor payload with different spatial resolutions and collection frequencies. HRCCD (High Resolution CCD Camera), IRMSS (Infrared Multispectral Scanner), and WFI (Wide-Field Imager). The CCD and the WFI camera operate in the VNIR regions, while the IRMSS operates in SWIR and thermal region. In addition to the imaging payload, the satellite carries a Data Collection System (DCS) and Space Environment Monitor (SEM).

Chandler, Gyanesh↗

NASA NTRS2001

Using Multiple Space Assests with In-Situ Measurements to Track Flooding in Thailand

Increasing numbers of space assets can enable coordinated measurements of flooding phenomena to enhance tracking of extreme events. We describe the use of space and ground measurements to target further measurements as part of a flood monitoring system in Thailand. We utilize rapidly delivered MODIS data to detect major areas of flooding and the target the Earth Observing One Advanced Land Imager sensor to acquire higher spatial resolution data. Automatic surface water extent mapping products delivered to interested parties. We are also working to extend our network to include in-situ sensing networks and additional space assets.

flooding↗

NASA NTRSDate not supplied

Assessing the Feasibility of Using Various Earth Observations to Monitor Environmental Trends Associated with River Flow Impediments Near Energy Intake Structures

Grassing events, characterized by the release of river vegetation in large quantities, are often responsible for major flow impediments surrounding industrial water intake structures. These sudden flow impediments halt critical municipal functions, such as cooling at energy generating facilities, leading to widespread energy disruptions for surrounding communities. Because the origin and cause of grassing events in rivers are largely unknown, employees at these facilities can only reactively respond after they occur by pausing energy generation to manually remove the accumulation of aquatic vegetation. In 2023, the Dresden Generating Station, in collaboration with the United States Geological Survey, began looking into using remote sensing methodologies to locate floating aquatic vegetation surrounding their intake structure located along the Kankakee River in northern Illinois. This project contributed to this effort by conducting a case study that compared the performance, practicality, and feasibility of using various Earth observations (Landsat 9 OLI-2, Landsat 8 OLI, Sentinel-2 MSI, and DOVE PlanetScope) and vegetation indices (NDVI, EVI, SAVI, and GCI) to monitor aquatic vegetation in the Kankakee River. Additionally, this study incorporated several environmental metrics to identify potential triggers for grassing events. Results from this project found that while Landsat sensors provide a lower spatial resolution than commercial satellite Earth observations, areas of aquatic vegetation were similarly identified; therefore, it may not be necessary to acquire expensive, high-resolution datasets for continued monitoring. Finally, preliminary correlation analyses showed a potential negative relationship between the river discharge and the presence of aquatic vegetation (-0.875 correlation coefficient), suggesting that periods of low flow could lead to large releases in aquatic vegetation.

Marisa Smedsrud↗

NASA NTRS1989

MAMS - A high spatial resolution multispectral scanner

The Multispectral Atmospheric Mapping Sensor (MAMS) is an airborne scanner which collects imagery in eight visible and three IR bands, with ground resolution of 100 m at a nominal aircraft altitude of 20 km. The visible channels provide continuous spectral coverage from 0.42 to 1.05 microns, and the IR channels measure upwelling and reflected radiation at combinations of 3.7, 6.5, 11.1, and 12.5 microns. These include channels nearly identical to the Landsat TM channels 1-4, and similar to those on the VISSR Atmospheric Sounder (VAS) and AVHRR instruments. The high spatial resolution permits investigation of surface thermal and atmospheric water vapor structure at scales not available from current stabilities. Significant modifications have been made since engineering flights in 1985 to improve the radiometric performance. The increased data quality and recent multidisciplinary applications of this data are presented.

Atkinson, Robert J.↗

NASA NTRS2018

Improving Imaging Instrument Spatial Resolution Using Software

In order to overcome spatial resolution limitations associated with physical sensor limitations when using smallsats and cubesats, we utilize an image processing technology referred to as Super-Resolution (SR). In general, software approaches are increasingly considered in connection with smaller satellites for which size, mass and power constraints limit the sensor capabilities. Being able to perform hardware vs. software trades might enable more capabilities for a lower cost. This paper describes recent experiments conducted to optimize the spatial enhancement of acquired observations using multiple sub-pixel shifted low resolution image.

Image Processing↗

NASA NTRS2018

Improving the Spatial Resolution of Imaging Instruments Using Software

Image Processing↗

NASA NTRSDate not supplied

Intercomparing Open Source Surface Water Extent Mapping Products & Software Packages

The open source revolution of Earth Observation (EO) science has resulted in increased openness of EO data, workflows to transform that data into end products (e.g. surface water extent maps), and the products themselves. However, this revolution has oversaturated decision-makers with products that can give conflicting results. Thus, it is increasingly crucial for scientists to communicate their methodologies and assumptions so scientific products can be used accurately. Recognizing this challenge, SERVIR – a joint initiative between NASA, USAID, and geospatial organizations in Asia, Africa, and Latin America – is conducting a regional intercomparison of open source surface water extent products and packages. SERVIR’s Hindu Kush Himalaya and Southeast Asia “hubs” have developed satellite-based surface water mapping services involving customizable code packages that are operationally run at each hub. These services are regionally and locally tailored to inform specific decisions and early actions. Conversely, the scientific community has released surface water products that are global or near-global, but are not customizable. These packages and products employ different methodologies and sensors, causing decision-makers to evaluate trade-offs related to physical sensor characteristics (e.g. spectral, temporal, and spatial resolution, and latency). We will discuss the tradeoffs, strengths, and weaknesses of the sensor characteristics and methodologies associated with each product/package, and provide preliminary results of a validation effort intercomparing products/packages for case studies in South and Southeast Asia. Understanding the strengths and weaknesses of these products is crucial in both the aftermath of a flood event and in preparing for future floods.

Micky Maganini↗

NASA NTRS1985

Impact of Thematic Mapper Sensor Characteristics on Classification Accuracy

A fixed effect, three factor (two levels per factor) analysis of variance was used to quantitatively assess the significance of the improved spectral, spatial and radiometric resolution capabilities of the LANDSAT-4 thematic mapper sensor relative to the familiar MSS sensor. TM data acquired over the Washington, D.C. area were progressively degraded in spectral, spatial and radiometric characteristics to simulate the MSS, and classification accuracies were derived in a consistent manner for all eight treatments in the ANOVA design. Statistical testing of the significance of differences in classification accuracies between treatments indicated that the increased number of spectral bands and the improved quantization capabilities afforded by the TM sensor design would lead to significant improvements in classification accuracies attainable relative to MSS. In contrast, however, the improved spatial resolution provided by the TM sensor did not enhance classification accuracy. This latter result was felt to be more a function of the type of classification algorithms available.

Williams, D. L.↗

DOE OSTI2022

Distributed Brillouin fiber laser sensor

Brillouin fiber sensors can provide distributed strain and temperature measurements over long distances in standard off-the-shelf fiber by measuring the Brillouin frequency shift as a function of position along a fiber. The primary drawback of these systems is their limited sensitivity, which results from the challenge in identifying the Brillouin frequency shift to within a small fraction of the Brillouin linewidth. In this work, we introduce a technique that overcomes this fundamental limitation by establishing a series of lasing modes that experience Brillouin amplification at discrete spatial locations in the test fiber. The linewidth narrowing and high intensity associated with the lasing transition enable precise measurements of this lasing frequency. As an initial demonstration, we present a sensor that simultaneously excites 40 lasing modes in a 400 m fiber, providing a measurement of the strain at 40 discrete locations with a spatial resolution of 4 m. Each sensor exhibits a minimum detectable strain as low as 4 nε/Hz 1/2 with a dynamic range of >5 mε and a bandwidth of ~10 kHz. As the first demonstration that Brillouin lasing can be used for distributed fiber sensing, this work establishes an approach that could enable ultra-high strain sensitivity using off-the-shelf fiber.

47 OTHER INSTRUMENTATION↗

NASA NTRS2017

Evaluation of the VIIRS BRDF, Albedo and NBAR Products Suite and an Assessment of Continuity with the Long Term MODIS Record

Bidirectional Reflectance Distribution Function (BRDF) model parameters, Albedo quantities, and Nadir BRDF Adjusted Reflectance (NBAR) products derived from the Visible Infrared Imaging Radiometer Suite (VIIRS), on the Suomi-NPP (National Polar-orbiting Partnership) satellite are evaluated with spatially representative in situ tower albedometer measurements and through comparison with the MODerate Resolution Imaging Spectroradiometer (MODIS) long term record. White Sky Albedo (WSA), Black Sky Albedo (BSA), NBAR, and Quality Assurance (QA) results show that the VIIRS algorithm is performing well at the global scale and is able to provide global data products comparable with the heritage MODIS (with an absolute bias of 0.0069 for shortwave broadband WSA) despite the spectral, angular, and effective spatial resolution differences between the two sensors. Both VIIRS and MODIS albedo are shown to agree well with in situ albedo measurements at a number of spatially representative sites. This evaluation provides confidence that the high quality, daily VIIRS BRDF model parameters, Albedo, and NBAR products will be able to provide the long term continuity required by the modeling and monitoring communities.

VIIRS↗

DOE OSTI2020

Machine-Learning Enabled Evaluation of Probability of Piping Degradation In Secondary Systems of Nuclear Power Plants

The transition to condition-based, risk-informed automated maintenance will contribute to a significant reduction of operations and maintenance costs that account for the majority of nuclear power generation costs. Furthermore, of the operations and maintenance costs in U.S. plants, approximately 80% are labor costs. To address the issue of rising operating costs and economic viability, technologies used to perform online monitoring of piping and other secondary system structural components in commercial nuclear power plants (NPPs) are under evaluation. These online monitoring systems have the potential to identify when a more detailed inspection is needed using real time measurements, rather than at a pre-determined inspection interval thus reducing the maintenance cost. This paper describes distributed high-temperature stable fiber sensors fabricated in optical fibers through a roll-to-roll laser direct writing process using femtosecond lasers. Using phase-sensitive optical time domain reflectometry, distributed acoustic and vibration sensors can be developed and deployed to critical components and systems in NPPs to perform active measurements with spatial resolution down to 0.5-meter throughout the piping systems. Complex acoustic and vibration signatures harnessed by distributed sensors are registered and analyzed by artificial intelligence algorithms for degradation detection and flaw identification. Piping elbows with machined-in flaws were instrumented with fiber sensors. High-spatial-resolution data were used to develop and validate machine learning algorithms, including both linear and nonlinear regression, and classification. Additionally, classification and sensor analysis were also performed for data analysis. The paper concludes with recommendations and future work on applications of machine learning enabled high-resolution fiber sensors for piping degradation monitoring in current or future NPPs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRSDate not supplied

Towards a program of record of inland water quality: Exploiting present and heritage multispectral sensors for maximum information extraction

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This research exploits recent advancements in bio-optical modeling, cloud computing, and machine learning to enhance our capacity to leverage present and heritage satellite data. Recent research suggests that sensors with low spectral resolution, such as Sentinel 2 and Landsat missions, contain enough hidden spectral variation which can be exploited using data-driven approaches. The availability of three decades of archival imagery will open doors to discover global trends of eutrophication and increased cyanobacteria dominance and provide valuable insight to the development of predictive methodologies. Preliminary efforts in synthetic emulation of global natural inland waters will be discussed and contextualized against satellite radiometric measurement uncertainty, satellite data product uncertainties and causal signal ambiguity over the visible wavelength range, supported by high quality field and image data for selected inland aquatic sites. Insights on water quality estimation via data-driven machine learning models versus matrix inversions will be discussed, and how we can exploit spectral-spatial relationships in high spatial resolution data. A cross-sensor synergistic approach with detailed uncertainty analysis based on optical water types, will allow for unprecedented global snapshots of fine scale ecological dynamics of inland waters.

Inland↗

NASA NTRS1983

The influence of sensor and flight parameters on texture in radar images

Texture is known to be important in the analysis of radar images for geologic applications. It has previously been shown that texture features derived from the grey level co-occurrence matrix (GLCM) can be used to separate large scale texture in radar images. Here the influence of sensor parameters, specifically the spatial and radiometric resolution and flight parameters, i.e., the orientation of the surface structure relative to the sensor, on the ability to classify texture based on the GLCM features is investigated. It was found that changing these sensor and flight parameters greatly affects the usefulness of the GLCM for classifying texture on radar images.

Frost, V. S.↗

NASA NTRS1983

The Influence of Sensor and Flight Parameters on Texture in Radar Images

Texture is known to be important in the analysis of radar images for geologic applications. It was previously shown that texture features derived from the grey-level co-occurrence matrix (GLCM) can be used to separate large scale texture in radar images. The influence of sensor parameters, specifically the spatial and radiometric resolution and flight parameters, i.e., the orientation of the surface structure relative to the sensor, on the ability to classify texture based on the GLCM features is investigated. It was found that changing these sensor and flight parameters greatly affects the usefulness of the GLCM for classifying texture on radar images.

Frost, V. S.↗

NASA NTRS1984

The influence of sensor and flight parameters on texture in radar images

Frost, V. S.↗

NASA NTRS2024

Investigating the Spatial and Temporal Limitations for Remote Sensing of Wildfire Smoke Using Satellite and Airborne Imagers During FIREX_AQ

Starting from point sources, wildfire smoke is important in the global aerosol system. The ability to characterize smoke near-source is key to modeling smoke dispersion and predicting air quality. With hemispheric views and 10-min refresh, imagers in Geostationary (GEO) orbit have advantages monitoring smoke over once-per-day sensors in low-earth orbit (LEO). However, both can be inadequate in capturing the characteristics of smoke plumes close to their sources due to too-coarse spatial resolution (both detector and product resolution), too-sparse temporal resolution (from LEO sensors), and too-conservative masking. In addition to satellite observations, the Fire Influence on Regional to Global Environments and Air Quality experiment offered sub-orbital enhanced-MODIS Airborne Simulator (eMAS) imagery at 50 m pixel resolution—including multiple eMAS flight tracks over individual fires in short time periods. It provided opportunity to explore smoke plume characterization at various spatial and temporal scales and quantify the limitations of space sensors for describing smoke magnitude near source as well as its temporal evolution. Here we applied modified aerosol algorithm to different imagers, relaxing its masking to estimate smoke's aerosol optical depth (AOD) as close as possible to its source. We found that GEO sensors with nominal 1 km spatial resolution can match the much finer resolution eMAS retrieved mean plume AOD, as long as the retrieval spatial resolution is finer than the width of the plumes. However, the plume's maximum AOD may be drastically underestimated by satellite products.

remote sensing↗

NASA NTRS1996

Passive Microwave Remote Sensing of Soil Moisture

Microwave remote sensing provides a unique capability for direct observation of soil moisture. Remote measurements from space afford the possibility of obtaining frequent, global sampling of soil moisture over a large fraction of the Earth's land surface. Microwave measurements have the benefit of being largely unaffected by cloud cover and variable surface solar illumination, but accurate soil moisture estimates are limited to regions that have either bare soil or low to moderate amounts of vegetation cover. A particular advantage of passive microwave sensors is that in the absence of significant vegetation cover soil moisture is the dominant effect on the received signal. The spatial resolutions of passive Microwave soil moisture sensors currently considered for space operation are in the range 10-20 km. The most useful frequency range for soil moisture sensing is 1-5 GHz. System design considerations include optimum choice of frequencies, polarizations, and scanning configurations, based on trade-offs between requirements for high vegetation penetration capability, freedom from electromagnetic interference, manageable antenna size and complexity, and the requirement that a sufficient number of information channels be available to correct for perturbing geophysical effects. This paper outlines the basic principles of the passive microwave technique for soil moisture sensing, and reviews briefly the status of current retrieval methods. Particularly promising are methods for optimally assimilating passive microwave data into hydrologic models. Further studies are needed to investigate the effects on microwave observations of within-footprint spatial heterogeneity of vegetation cover and subsurface soil characteristics, and to assess the limitations imposed by heterogeneity on the retrievability of large-scale soil moisture information from remote observations.

Njoku, Eni G.↗