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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

3D Vegetation Mapping Using UAVSAR, LVIS, and LIDAR Data Acquisition Methods

The overarching objective of this ongoing project is to assess the role of vegetation within climate change. Forests capture carbon, a green house gas, from the atmosphere. Thus, any change, whether, natural (e.g. growth, fire, death) or due to anthropogenic activity (e.g. logging, burning, urbanization) may have a significant impact on the Earth's carbon cycle. Through the use of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) and NASA's Laser Vegetation Imaging Sensor (LVIS), which are airborne Light Detection and Ranging (LIDAR) remote sensing technologies, we gather data to estimate the amount of carbon contained in forests and how the content changes over time. UAVSAR and LVIS sensors were sent all over the world with the objective of mapping out terrain to gather tree canopy height and biomass data; This data is in turn used to correlate vegetation with the global carbon cycle around the world.

Uninhabited Aerial Vehicle Synthetic Aperture Rada↗
NASA NTRSDate not supplied

Radar Measurements of Orbital Debris from the Haystack Ultra-wideband Satellite Imaging Radar (HUSIR): 2020 to 2022

For over three decades, the NASA Orbital Debris Program Office (ODPO) has partnered with the U.S. Department of Defense and the Massachusetts Institute of Technology’s Lincoln Laboratory (MIT/LL) to collect radar data on the orbital debris (OD) environment. These radar measurements are used to statistically measure cm and sub-cm debris in low Earth orbit (LEO) that is used to define the current debris environment, monitor how the environment evolves, and support predictions of the future debris environment. Currently, the principal ground-based radar sensor used by the ODPO is the MIT/LL Haystack Ultrawideband Satellite Imaging Radar (HUSIR). HUSIR provides data on OD down to approximately 5 mm in size at altitudes below 1000 km. This paper will deliver an overview of recent HUSIR measurements of the LEO debris environment for calendar years 2020 to 2022. Process improvements will be highlighted along with results of interest, including the effects of the 15 November 2021 Russian direct ascent anti-satellite (ASAT) test on the defunct COSMOS 1408 spacecraft.

Jessica A. Arnold↗
NASA NTRS2023

WET Water Resources: A Google Earth Engine Python API Tool to Automate Wetland Extent Mapping Using Radar Satellite Sensors for Wetland Management and Monitoring

Wetland ecosystems are annually or seasonally wet transition zones between land and water. They provide a range of ecosystem services such as water filtration, flood mitigation, and carbon sequestration, as well as hosting biodiversity hotspots. Although they fulfill fundamental physical and natural processes, wetland extent and health are threatened by anthropogenic influences related to urbanization, population increase, pollution, and climate change. Recognizing the need to quantitatively monitor changes in these recently threatened ecosystems in a timely and cost-effective way, we developed a Google Earth Engine (GEE) Python API tool for automated wetland extent mapping using optical and radar satellite sensors that can be applied globally. The tool will significantly improve wetland change analysis and monitoring as SAR data provides high resolution (5-10 m) imagery, unaffected by cloud cover and light availability (day vs. night), common limitations for other remotely sensed sensors. The tool utilizes Copernicus Sentinel-1 C-band and NISAR L-band (once operational and available on the GEE repository) synthetic aperture radar (SAR) imagery. During image preprocessing, we applied a Terra Moderate Resolution Imaging Spectroradiometer (MODIS) snow product to determine regional snow coverage, which affects land classification sensitivity. Calibration and validation were conducted through a historical change and sensitivity analysis of the Sudd wetland located in central Sudan. The tool was the first of its kind, as it enables NISAR data processing through an open-source GEE repository, further expanding and improving the utility of NASA Earth observations and contributing to NASA Open Science initiatives. We anticipate the tool will be used by researchers and practitioners interested in wetland monitoring and management.

Inundation↗
NASA NTRSDate not supplied

Radar Measurements of Orbital Debris from the Haystack Ultra-wideband Satellite Imaging Radar (HUSIR): 2020-2021

For over three decades, the NASA Orbital Debris Program Office (ODPO) has partnered with the U.S. Department of Defense and the Massachusetts Institute of Technology’s Lincoln Laboratory (MIT/LL) to collect radar data on the orbital debris (OD) environment. These radar measurements are used to statistically measure centimeter (cm) and sub-cm debris in low Earth orbit (LEO) that is used to define the current debris environment, monitor how the environment evolves, and support predictions of the future debris environment. Currently, the principal ground-based radar sensor used by the ODPO is the MIT/LL Haystack Ultrawideband Satellite Imaging Radar (HUSIR). HUSIR provides data on OD down to approximately 5 mm in size at altitudes below 1000 km. This paper will deliver an overview of recent HUSIR measurements of the LEO debris environment for calendar years 2020 to 2022. Process improvements will be highlighted along with results of interest, including the effects of the 15 November 2021 Russian direct ascent anti-satellite (ASAT) test on the defunct Cosmos 1408 spacecraft.

Jessica A Arnold↗
NASA NTRS2010

Satellite Data Simulator Unit: A Multisensor, Multispectral Satellite Simulator Package

Several multisensor simulator packages are being developed by different research groups across the world. Such simulator packages [e.g., COSP , CRTM, ECSIM, RTTO, ISSARS (under development), and SDSU (this article), among others] share overall aims, although some are targeted more on particular satellite programs or specific applications (for research purposes or for operational use) than others. The SDSU or Satellite Data Simulator Unit is a general-purpose simulator composed of Fortran 90 codes and applicable to spaceborne microwave radiometer, radar, and visible/infrared imagers including, but not limited to, the sensors listed in a table. That shows satellite programs particularly suitable for multisensor data analysis: some are single satellite missions carrying two or more instruments, while others are constellations of satellites flying in formation. The TRMM and A-Train are ongoing satellite missions carrying diverse sensors that observe clouds and precipitation, and will be continued or augmented within the decade to come by future multisensor missions such as the GPM and Earth-CARE. The ultimate goals of these present and proposed satellite programs are not restricted to clouds and precipitation but are to better understand their interactions with atmospheric dynamics/chemistry and feedback to climate. The SDSU's applicability is not technically limited to hydrometeor measurements either, but may be extended to air temperature and humidity observations by tuning the SDSU to sounding channels. As such, the SDSU and other multisensor simulators would potentially contribute to a broad area of climate and atmospheric sciences. The SDSU is not optimized to any particular orbital geometry of satellites. The SDSU is applicable not only to low-Earth orbiting platforms as listed in Table 1, but also to geostationary meteorological satellites. Although no geosynchronous satellite carries microwave instruments at present or in the near future, the SDSU would be useful for future geostationary satellites with a microwave radiometer and/or a radar aboard, which could become more feasible as engineering challenges are met. In this short article, the SDSU algorithm architecture and potential applications are reviewed in brief.

Masunaga, Hirohiko↗
NASA NTRS2002

Convectively Induced Turbulence Encountered During NASA's Fall-2000 Flight Experiments

Aircraft encounters with atmospheric turbulence are a leading cause of in-flight injuries aboard commercial airliners and cost the airlines millions of dollars each year. Most of these injuries are due to encounters with turbulence in and around convection. In a recent study of 44 turbulence accident reports between 1990 and 1996, 82% of the cases were found to be near or within convective activity (Kaplan et al. 1999). According to NTSB accident reports, pilots' descriptions of these turbulence encounters include 'abrupt', 'in Instrument Meteorological Conditions (IMC)', 'saw nothing on the weather radar', and 'the encounter occurred while deviating around' convective activity. Though the FAA has provided guidelines for aircraft operating in convective environments, turbulence detection capability could decrease the number of injuries by alerting pilots of a potential encounter. The National Aeronautics and Space Administration, through its Aviation Safety Program, is addressing turbulence hazards through research, flight experiments, and data analysis. Primary focus of this program element is the characterization of turbulence and its environment, as well as the development and testing of hazard estimation algorithms for both radar and in situ detection. The ultimate goal is to operationally test sensors that will provide ample warning prior to hazardous turbulence encounters. In order to collect data for support of these activities, NASA-Langley's B-757 research aircraft was directed into regions favorable for convectively induced turbulence (CIT). On these flights, the airborne predictive wind shear (PWS) radar, augmented with algorithms designed for turbulence detection, was operated in real time to test this capability. In this paper, we present the results of two research flights when turbulence was encountered. Described is an overview of the flights, the general radar performance, and details of four encounters with severe turbulence.

Hamilton, David W.↗
NASA NTRS1993

MAC Europe 1991 campaign: AIRSAR/AVIRIS data integration for agricultural test site classification

During summer 1991, multi-sensor data were acquired over the Italian test site 'Otrepo Pavese', an agricultural flat area in Northern Italy. This area has been the Telespazio pilot test site for experimental activities related to agriculture applications. The aim of the investigation described in the following paper is to assess the amount of information contained in the AIRSAR (Airborne Synthetic Aperture Radar) and AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) data, and to evaluate classification results obtained from each sensor data separately and from the combined dataset. All classifications are examined by means of the resulting confusion matrices and Khat coefficients. Improvements of the classification results obtained by using the integrated dataset are finally evaluated.

Sangiovanni, S.↗
DOE OSTI2024

Statistical Relations among Solid Precipitation, Atmospheric Moisture and Cloud Parameters in the Arctic

Observations collected during cold-season precipitation periods at Utquagvik, Alaska and at the multidisciplinary drifting observatory for the study of Arctic climate (MOSAiC) are used to statistically analyze the relations among the atmospheric water cycle parameters including the columnar supercooled liquid and ice amounts (expressed as liquid-water and ice-water paths, i.e., LWP and IWP), the integrated water vapor (IWV) and the near-surface snowfall rate. Data come from radar and radiometer-based retrievals and from optical precipitation sensors. While the correlation between snowfall rate and LWP is rather weak, correlation coefficients between radar-derived snowfall rate and IWP are high (~0.8), which is explained, in part, by the generally low LWP/IWP ratios during significant precipitation. Correlation coefficients between snowfall rate and IWV are moderate (~0.45). Correlations are generally weaker if snowfall is estimated by optical sensors, which is, in part, due to blowing snow. Correlation coefficients between near-surface temperature and snowfall rates are low (r < 0.3). The results from the Alaska and MOSAiC sites are generally similar. These results are not very sensitive to the amount of time averaging (e.g., 15 min averaging versus daily averages). Observationally based relations among the water cycle parameters are informative about atmospheric moisture conversion processes and can be used for model evaluations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1983

The development of a power spectral density processor for C and L band airborne radar scatterometer sensor systems

A real-time signal processor was developed for the NASA/JSC L-and C-band airborne radar scatterometer sensor systems. The purpose of the effort was to reduce ground data processing costs. Conversion of two quadrature channels of data (like and cross polarized) was made to obtain Power Spectral Density (PSD) values. A chirp-z transform (CZT) approach was used to filter the Doppler return signal and improved high frequency and angular resolution was realized. The processors have been tested with record signals and excellent results were obtained. CZT filtering can be readily applied to scatterometers operating at other wavelengths by altering the sample frequency. The design of the hardware and software and the results of the performance tests are described in detail.

Harrison, D. A., III↗
NASA NTRS1987

The Spaceborne Imaging Radar program: SIR-C - The next step toward EOS

The NASA Shuttle Imaging Radar SIR-C experiments will investigate earth surface and environment phenomena to deepen understanding of terra firma, biosphere, hydrosphere, cryosphere, and atmosphere components of the earth system, capitalizing on the observational capabilities of orbiting multiparameter radar sensors alone or in combination with other sensors. The SIR-C sensor encompasses an antenna array, an exciter, receivers, a data-handling network, and the ground SAR processor. It will be possible to steer the antenna beam electronically, so that the radar look angle can be varied.

Evans, Diane↗
NASA NTRS1994

Characterizing the wake vortex signature for an active line of sight remote sensor

A recurring phenomenon, described as a wake vortex, develops as an aircraft approaches the runway to land. As the aircraft moves along the runway, each of the wing tips generates a spiraling and expanding cone of air. During the lifetime of this turbulent event, conditions exist over the runway which can be hazardous to following aircraft, particularly when a small aircraft is following a large aircraft. Left to themselves, these twin vortex patterns will converge toward each other near the center of the runway, harmlessly dissipating through interaction with each other or by contact with the ground. Unfortunately, the time necessary to disperse the vortex is often not predictable, and at busy airports can severely impact terminal area productivity. Rudimentary methods of avoidance are in place. Generally, time delays between landing aircraft are based on what is required to protect a small aircraft. Existing ambient wind conditions can complicate the situation. Reliable detection and tracking of a wake vortex hazard is a major technical problem which can significantly impact runway productivity. Landing minimums could be determined on the basis of the actual hazard rather than imposed on the basis of a worst case scenario. This work focuses on using a windfield description of a wake vortex to generate line-of-sight Doppler velocity truth data appropriate to an arbitrarily located active sensor such as a high resolution radar or lidar. The goal is to isolate a range Doppler signature of the vortex phenomenon that can be used to improve detection. Results are presented based on use of a simplified model of a wake vortex pattern. However, it is important to note that the method of analysis can easily be applied to any vortex model used to generate a windfield snapshot. Results involving several scan strategies are shown for a point sensor with a range resolution of 1 to 4 meters. Vortex signatures presented appear to offer potential for detection and tracking.

Heil, Robert Milton↗
NASA NTRS2010

The Geostationary Lightning Mapper (GLM) for the GOES-R Series Next Generation Operational Environmental Satellite Constellation

The next generation Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2015 is a follow on to the existing GOES system currently operating over the Western Hemisphere. The system will aid in forecasting severe storms and tornado activity, and convective weather impacts on aviation safety and efficiency. The system provides products including lightning, cloud properties, rainfall rate, volcanic ash, air quality, hurricane intensity, and fire/hot spot characterization. Advancements over current GOES include a new capability for total lightning detection (cloud and cloud-to-ground flashes) from the Geostationary Lightning Mapper (GLM), and improved spectral, spatial, and temporal resolution for the 16-channel Advanced Baseline Imager (ABI). The Geostationary Lightning Mapper (GLM), an optical transient detector will map total (in-cloud and cloud-to-ground) lightning flashes continuously day and night with near-uniform spatial resolution of 8 km with a product refresh rate of less than 20 sec over the Americas and adjacent oceanic regions, from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the higher level algorithms and applications using the GLM alone and decision aids incorporating information from the ABI, ground-based weather radar, and numerical models. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional lightning networks are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time total lightning mapping data are also being provided in an experimental mode to selected National Weather Service (NWS) national centers and forecast offices via the GOES-R Proving Ground to help improve our understanding of the application of these data in operational settings and facilitate early on-orbit user readiness for this new capability.

Goodman, Steven J.↗
NASA NTRS2022

Neurosymbolic Hybrid Approach to Driver Collision Warning

There are two main algorithmic approaches to autonomous driving systems: (1) An end-to-end system in which a single deep neural network learns to map sensory input directly into appropriate warning and driving responses. (2) A mediated hybrid recognition system in which a system is created by combining independent modules that detect each semantic feature. While some researchers believe that deep learning can solve any problem, others believe that a more engineered and symbolic approach is needed to cope with complex environments with less data. Deep learning alone has achieved state-of-the-art results in many areas, from complex gameplay to predicting protein structures. In particular, in image classification and recognition, deep learning models have achieved accuracies as high as humans. But sometimes it can be very difficult to debug if the deep learning model doesn't work. Deep learning models can be vulnerable and are very sensitive to changes in data distribution. Generalization can be problematic. It's usually hard to prove why it works or doesn't. Deep learning models can also be vulnerable to adversarial attacks. Here, we combine deep learning-based object recognition and tracking with an adaptive neurosymbolic network agent, called the Non-Axiomatic Reasoning System (NARS), that can adapt to its environment by building concepts based on perceptual sequences. We achieved an improved intersection-over-union (IOU) object recognition performance of 0.65 in the adaptive retraining model compared to IOU 0.31 in the COCO data pre-trained model. We improved the object detection limits using RADAR sensors in a simulated environment, and demonstrated the weaving car detection capability by combining deep learning-based object detection and tracking with a neurosymbolic model.

Wang, Pei↗
NASA NTRS1990

The Magellan Venus explorer's guide

The Magellan radar-mapping mission to the planet Venus is described. Scientific highlights include the history of U.S. and Soviet missions, as well as ground-based radar observations, that have provided the current knowledge about the surface of Venus. Descriptions of the major Venusian surface features include controversial theories about the origin of some of the features. The organization of the Magellan science investigators into discipline-related task groups for data-analysis purposes is presented. The design of the Magellan spacecraft and the ability of its radar sensor to conduct radar imaging, altimetry, and radiometry measurements are discussed. Other topics report on the May 1989 launch, the interplanetary cruise, the Venus orbit-insertion maneuver, and the in-orbit mapping strategy. The objectives of a possible extended mission emphasize the gravity experiment and explain why high-resolution gravity data cannot be acquired during the primary mission. A focus on the people of Magellan reveals how they fly the spacecraft and prepare for major mission events. Special items of interest associated with the Magellan mission are contained in windows interspersed throughout the text. Finally, short summaries describe the major objectives and schedules for several exciting space missions planned to take us into the 21st century.

Young, Carolynn↗
NASA NTRS1972

Meteorological rocket data processor and results from the solar eclipse of 7 March 1970.

A comprehensive digital computer program is described which automatically processes precision digitized data from conventional rocketsondes and radars to produce accurately corrected temperatures and winds together with estimated uncertainties at each point. Underlying models for the systematic errors in the Arcasonde 1A temperature sensor and the radar-tracked parachute wind sensor, and for the uncertainty in corrected results are presented. Techniques used to automatically edit, synchronize and filter data are also given. Performance is illustrated by graphical output from the eight meteorological soundings in the rocket series conducted at Wallops Island, Va., during the Mar. 7, 1970 solar eclipse. An operational version (METROK) of the program is in use at NASA Wallops Station.

Staffanson, F. L.↗
NASA NTRS2018

Lake Chad Total Surface Water Area as Derived from Land Surface Temperature and Radar Remote Sensing Data

Lake Chad, located in the middle of the African Sahel belt, underwent dramatic decreases in the 1970s and 1980s leaving less than ten percent of its 1960s surface water extent as open water. In this paper, we present an extended record (dry seasons 1988-2016) of the total surface water area of the lake (including both open water and flooded vegetation) derived using Land Surface Temperature (LST) data (dry seasons 2000-2016) from the NASA Terra MODIS sensor and EUMETSAT Meteosat-based LST measurements (dry seasons 1988-2001) from an earlier study. We also examine the total surface water area for Lake Chad using radar data (dry seasons 2015-2016) from the ESA Sentinel-1a mission. For the limited number of radar data sets available to us (18 data sets), we find on average a close match between the estimates from these data and the corresponding estimates from LST, though we find spatial differences in the estimates using the two types of data. We use these spatial differences to adjust the record (dry seasons 2000-2016) from MODIS LST. Then we use the adjusted record to remove the bias of the existing LST record (dry seasons 1988-2001) derived from Meteosat measurements and combine the two records. From this composite, extended record, we plot the total surface water area of the lake for the dry seasons of 1988-1989 through 2016-2017. We find for the dry seasons of 1988-1989 to 2016-2017 that the maximum total surface water area of the lake was approximately 16,800 sq. km (February and May, 2000), the minimum total surface water area of the lake was approximately 6400 sq. km (November, 1990), and the average was approximately 12,700 sq. km. Further, we find the total surface water area of the lake to be highly variable during this period, with an average rate of increase of approximately 143 sq. km per year.

Policelli, Frederick↗
NASA NTRS1986

Overview of the Shuttle Imaging Radar-B preliminary scientific results

Data collected with the Shuttle Imaging Radar-B (SIR-B) on the October 5, 1985 Shuttle mission are discussed. The design and capabilities of the sensor which operates in a fixed illumination geometry and has incidence angles between 15 and 60 deg with 1 deg increments are described. Problems encountered with the SIR-B during the mission are examined. the The radar stereo imaging capability of the sensor was verified and three-dimensional images of the earth surface were obtained. The oceanography experiments provided significant data on ocean wave and internal wave patterns, oil spills, and ice zones. The geological images revealed that the sensor can evaluate penetration effect in dry soil from buried receivers and the existence of subsurface dry channels in the Egyptian desert was validated. The use of multiincidence angle imaging to classify terrain units and derive vegetation maps and the development of terrain maps are confirmed.

Elachi, C.↗
NASA NTRS1999

TRMM and its Connection to the Global Water Cycle

The importance of quantitative knowledge of tropical rainfall, its associated latent heating and variability is summarized in the context of the global hydrologic cycle. Much of the tropics is covered by oceans. What land exists, is covered largely by rainforests that are only thinly populated. The only way to adequately measure the global tropical rainfall for climate and general circulation models is from space. The Tropical Rainfall Measuring Mission (TRMM) orbit is inclined 35 degrees leading to good sampling in the tropics and a rapid precession to study the diurnal cycle of precipitation. The precipitation instrument complement consists of the first rain radar to be flown in space (PR), a multi-channel passive microwave sensor (TMI) and a five-channel VIS/IR (VIRS) sensor. The precipitation radar operates at a frequency of 13.6 GHz. The swath width is 220 km, with a horizontal resolution of 4 km and the vertical resolution of 250 m. The minimum detectable signal from the precipitation radar has been measured at - 17 dBZ. The TMI instrument is designed similar to the SSM/I with two important changes. The 22.235 GHz water vapor absorption channel of the SSM/I was moved to 21.3 GHz in order to avoid saturation in the tropics and 10.7 GHz V&H polarized channels were added to expand the dynamic range of rainfall estimates. The resolution of the TMI varies from 4.6 km at 85 GHz to 36 km at 10.7 GHz. The visible and infrared sensor (VIRS) measures radiation at 0.63, 1.6, 3.75, 10.8 and 12.0 microns. The spatial resolution of all five VIRS channels is 2 km at nadir. In addition to the three primary rainfall instruments, TRMM will also carry a Lightning Imaging Sensor (LIS) and a Clouds and the Earth's Radiant Energy System (CERES) instrument. This presentation will focus primarily on the advances in our understanding of tropical rain systems needed to interpret the TRMM data. Global averages, as well as case studies from TRMM radar (PR), the TRMM Microwave Imager (TMI) and Visible and Infrared Sensor (VIRS) will be presented. Comparisons and contrasts among the different sensors will be drawn. Results will also be compared to previous rainfall climatologies generated from the SSM/I instrument. In particular this paper will focus on the synergy between the TRMM radar and passive microwave radiometer and what we have learned from its synergy.

Kummerow, Christian↗