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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

NASA Tech Briefs, January 2012

Contents of this issue are: (1) Energy-Based Tetrahedron Sensor for High-Temperature, High-Pressure Environments (2) Handheld Universal Diagnostic Sensor (3) Large-Area Vacuum Ultraviolet Sensors (4) Fiber Bragg Grating Sensor System for Monitoring Smart Composite Aerospace Structures (5) Health-Enabled Smart Sensor Fusion Technology (6) Extended-Range Passive RFID and Sensor Tags (7) Hybrid Collaborative Learning for Classification and Clustering in Sensor Networks (8) Self-Healing, Inflatable, Rigidizable Shelter (9) Improvements in Cold-Plate Fabrication (10) Technique for Radiometer and Antenna Array Calibration - TRAAC (11) Real-Time Cognitive Computing Architecture for Data Fusion in a Dynamic Environment (12) Programmable Digital Controller (13) Use of CCSDS Packets Over SpaceWire to Control Hardware (14) Key Decision Record Creation and Approval Module (15) Enhanced Graphics for Extended Scale Range (16) Debris Examination Using Ballistic and Radar Integrated Software (17) Data Distribution System (DDS) and Solar Dynamic Observatory Ground Station (SDOGS) (18) Integration Manager (19) Eclipse-Free-Time Assessment Tool for IRIS (20) Automated and Manual Rocket Crater Measurement Software (21) MATLAB Stability and Control Toolbox Trim and Static Stability Module (22) Patched Conic Trajectory Code (23) Ring Image Analyzer (24) SureTrak Probability of Impact Display (25) Implementation of a Non-Metallic Barrier in an Electric Motor (26) Multi-Mission Radioisotope Thermoelectric Generator Heat Exchangers for the Mars Science Laboratory Rover (27) Uniform Dust Distributor for Testing Radiative Emittance of Dust-Coated Surfaces (28) MicroProbe Small Unmanned Aerial System (29) Highly Stable and Active Catalyst for Sabatier Reactions (30) Better Proton-Conducting Polymers for Fuel-Cell Membranes (31) CCD Camera Lens Interface for Real-Time Theodolite Alignment (32) Peregrine 100-km Sounding Rocket Project (33) SOFIA Closed- and Open-Door Aerodynamic Analyses (34) Sonic Thermometer for High-Altitude Balloons (35) Near-Infrared Photon-Counting Camera for High-Sensitivity Observations (36) Integrated Optics Achromatic Nuller for Stellar Interferometry (37) High-Speed Digital Interferometry (38) Ultra-Miniature Lidar Scanner for Launch Range Data Collection (39) Shape and Color Features for Object Recognition Search (40) Explanation Capabilities for Behavior-Based Robot Control (41) A DNA-Inspired Encryption Methodology for Secure, Mobile Ad Hoc Networks (42) Quality Control Method for a Micro-Nano-Channel Microfabricated Device (43) Corner-Cube Retroreflector Instrument for Advanced Lunar Laser Ranging (44) Electrospray Collection of Lunar Dust (45) Fabrication of a Kilopixel Array of Superconducting Microcalorimeters with Microstripline Wiring Spacecraft Attitude Tracking and Maneuver Using Combined Magnetic Actuators (46) Coherent Detector for Near-Angle Scattering and Polarization Characterization of Telescope Mirror Coatings

Source record↗
NASA NTRSDate not supplied

FloodPlanet: High-Resolution Commercial Imagery for Training and Validation of Deep Learning-Based Models of Inundation Extent

Flooding events are becoming increasingly frequent worldwide and are known to cause extensive damage. Public optical and radar satellite imagery can be used to detect large areas of inundation in rural areas, however, long revisit times and coarse spatial resolution limit applications for short-lived events and urban areas. Commercial constellations such as those operated by Planet offer increased spatial and temporal resolution and can supplement mapping efforts to provide more information to disaster response, relief, and mitigation efforts. Deep learning requires high quality labeled data for training across coincident sensors. The FloodPlanet dataset presented here contains labeled surface water for 18 events across the world based on Planetscope imagery with coincident Harmonized Landsat Sentinel-2 ( HLS) or Sentinel-1 and builds upon the previously existing Sen1Floods11, xBD, and NASA Sentinel-1 datasets. Sen1Floods11 includes 4,831 512x512 pixel overlapping tiles of coincident Sentinel-1 and Sentinel-2 data observing 11 flood events across the world from 2017-2019. The dataset contains a combination of automated and hand-labeled surface water for use in training and validation of inundation modeling efforts. The xBD dataset identifies flood-damaged buildings and indicates the scale of damage to each (none, minor, moderate, and major) from four flood events which occurred in the United States, India, Nepal, and Bangladesh from the same time period. The NASA dataset contains hand-labeled water bodies observed in Sentinel-1 imagery during five flood events within the 2017-2019 period. The effort presented here utilizes observations from these previously investigated flood events to generate labels of surface water at the 3-5m spatial resolution provided by Planetscope and facilitate the comparison between public and commercial data. A data pipeline was built which uses clustering algorithms to pick the most suitable overlapping chips between the public data and PlanetScope data for manual labeling. Labels were created manually using NASA’s ImageLabeler tool and include areas of high- and low-confidence water. The high confidence designation is reserved for areas of open, unobstructed water while low confidence is used for areas of suspected water beneath vegetation, clouds, or cloud shadows. Expected to be released in late 2022, the FloodPlanet dataset will include tiled imagery with a unique ID for each 1024x1024 pixel tile, 7 bands of HLS data, and high- and low-confidence flood labels in both shapefile and tiff formats. The authors will follow Spatial Temporal Access Catalog (STAC) guidelines to release FloodPlanet on the Radiant Earth ML hub, which hosts public datasets for machine learning.

Alexander Melancon↗
NASA NTRS1983

Satellite microwave radiances correlated with radar rain rates over land

The characteristics of upwelling microwave radiation from raindrops as measured by satellite sensors are examined. The scanning multichannel microwave radiometers on board the Nimbus 7 and Seasat satellites have the capability of quantifying the perpendicularly polarized antenna temperatures at 37, 21, 18, 10.7, and 6.6 GHz. The instruments scan the earth at a constant 50 deg angle to the surface with a footprint that varies from 20-70 km. Radar rainfall measurements have an accuracy of within 60 percent, whereas a series of test measurements using SMMR data in comparison with radar data for rainfall in the same areas showed that the microwave data depicted rainfall rates with less than 1.55 mm/h error. Details of the rainfall rate algorithms used to treat the satellite microwave data are provided, noting that the identification of rainfall rates is dependent on quantifying the amount the upwelling radiance is reduced due to rainfall.

Spencer, R. W.↗
NASA NTRS1983

Spaceborne SAR sensor architecture

Spaceborne imaging radars for the decade of the '90s will be called upon to provide increased illumination parameter flexibility, polar orbital coverage, and to operate from the same platform with other advanced sensors such as multilinear arrays. The potential information content in accurately merged microwave infrared, or microwave visible images is enormous by comparison to either radar images alone or visible/infrared images alone, since microwave images are principally responsive to surface geometry whereas visible/infrared images are highly sensitive to surface chemistry. A strawman system concept in which a SAR and multilinear array (MLA) are flown on a polar orbiting free flyer at 800 km altitude is discussed. Data would be relayed to ground via the TDRSS (or equivalent).

Carver, K. R.↗
NASA NTRS2026

PERSIANN-Unet: A Global Deep Learning Framework for Near-Real-Time Precipitation Estimation Using Infrared Data

Access to high-quality, high-resolution, near-real-time precipitation data is essential for hydrological and meteorological research and disaster mitigation. Traditional tools such as rain gauges and radar networks, though effective, have limitations, including sparse coverage in remote areas and high operational costs. Satellite data, with its global coverage and high spatial and temporal resolutions, mitigates limitations in coverage. Satellite precipitation products like Hydro Estimator (HE), Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) utilize both geosynchronous thermal infrared (IR) and passive microwave (PMW) data in their operation. PMW sensors offer detailed atmospheric profiles but suffer from higher latency, whereas IR sensors provide lower latency but only capture cloud-top information. Despite this constraint, IR data remains attractive for low-latency precipitation estimation. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have further improved satellite precipitation retrievals. This study introduces PERSIANN-Unet (PUnet or PERSIANN V3), a quasi-global algorithm covering 60°N–60°S that combines IR data, monthly climatology, and the UNet architecture to produce half-hourly precipitation estimates at 0.04° resolution. The product is evaluated against HE, IMERG, and PDIR-Now for 2022–2023. Results show that PUnet closely matches its training target, IMERG V07 Final, at the global scale, and performance is further evaluated against Stage IV as a reference over CONUS. Training PUnet on IMERG (2016–2021) leverages a high-quality, integrated PMW IR-gauge precipitation product while developing an IR-based framework not reliant on PMW availability. By operating on a single global image, PUnet avoids tile partitioning and blending steps, reducing edge discontinuities, and produces more spatially consistent precipitation fields across hemispheres.

Phu Nguyen↗
DOE OSTI2021

Thresholding Analysis and Feature Extraction from 3D Ground Penetrating Radar Data for Noninvasive Assessment of Peanut Yield

This study explores the efficacy of utilizing a novel ground penetrating radar (GPR) acquisition platform and data analysis methods to quantify peanut yield for breeding selection, agronomic research, and producer management and harvest applications. Sixty plots comprising different peanut market types were scanned with a multichannel, air-launched GPR antenna. Image thresholding analysis was performed on 3D GPR data from four of the channels to extract features that were correlated to peanut yield with the objective of developing a noninvasive high-throughput peanut phenotyping and yield-monitoring methodology. Plot-level GPR data were summarized using mean, standard deviation, sum, and the number of nonzero values (counts) below or above different percentile threshold values. Best results were obtained for data below the percentile threshold for mean, standard deviation and sum. Data both below and above the percentile threshold generated good correlations for count. Correlating individual GPR features to yield generated correlations of up to 39% explained variability, while combining GPR features in multiple linear regression models generated up to 51% explained variability. The correlations increased when regression models were developed separately for each peanut type. This research demonstrates that a systematic search of thresholding range, analysis window size, and data summary statistics is necessary for successful application of this type of analysis. The results also establish that thresholding analysis of GPR data is an appropriate methodology for noninvasive assessment of peanut yield, which could be further developed for high-throughput phenotyping and yield-monitoring, adding a new sensor and new capabilities to the growing set of digital agriculture technologies.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Analysis of RFI Statistics for Aquarius RFI Detection and Mitigation Improvements

Aquarius is an L-band active/passive sensor designed to globally map sea surface salinity from space. Two instruments, a radar scatterometer and a radiometer, observe the same surface footprint almost simultaneously. The radiometer is the primary instrument for sensing sea surface salinity (SSS), while the scatterometer is included to provide a correction for sea surface roughness, which is a primary source of error in the salinity retrieval. Although the primary objective is the measurement of SSS, the instrument combination operates continuously, acquiring data over land and sea ice as well. An important feature of the data processing includes detection and mitigation of Radio Frequency Interference (RFI) which is done separately for both active and passive instruments. Correcting for RFI is particularly critical over ocean because of the high accuracy required in the brightness temperature measurements for SSS retrieval. It is also necessary for applications of the Aquarius data over land, where man-made interference is widespread, even though less accuracy is required in this case. This paper will provide an overview of the current status of the Aquarius RFI processing and an update on the ongoing work on the improvement of the RFI detection and mitigation performance.

Microwave remote sensing↗
NASA NTRS1987

SPACE/COHMEX data inventory document

During the period June to July 1986, NASA conducted the Satellite Precipitation and Cloud Experiment (SPACE) in the central Tennessee, northern Alabama, and northeastern Mississippi area. In addition to SPACE, the Microburst and Severe Thunderstorm (MIST) Program, sponsored by the National Science Foundation, and the FAA-Lincoln Laboratory Operational Weather Study (FLOWS) sponsored by the Federal Aviation Administration, operated concurrently under the acronym of COHMEX (Cooperative Huntsville Meteorological Experiment). The COHMEX field program incorporated measurements from remote sensors flown on high altitude aircraft (ER-2 and U-2), Doppler and conventional radars, rawinsondes, satellites, cloud physics research aircraft, and various surface observational systems.

Williams, S. F.↗
NASA NTRS1975

Space Tug Avionics Definition Study. Volume 5: Cost and Programmatics

The baseline avionics system features a central digital computer that integrates the functions of all the space tug subsystems by means of a redundant digital data bus. The central computer consists of dual central processor units, dual input/output processors, and a fault tolerant memory, utilizing internal redundancy and error checking. Three electronically steerable phased arrays provide downlink transmission from any tug attitude directly to ground or via TDRS. Six laser gyros and six accelerometers in a dodecahedron configuration make up the inertial measurement unit. Both a scanning laser radar and a TV system, employing strobe lamps, are required as acquisition and docking sensors. Primary dc power at a nominal 28 volts is supplied from dual lightweight, thermally integrated fuel cells which operate from propellant grade reactants out of the main tanks.

Source record↗
NASA NTRS1977

Application of remote sensing for planning purposes

Types of remotely sensed data are many and varied but, all are primarily dependent on the sensor platform and the kind of sensing system used. A sensor platform is the type of aircraft or satellite to which a sensing system is attached; each platform has its own inherent advantages and disadvantages. Selected attributes of several current or recently used platforms are outlined. Though sensing systems are highly varied, they may be divided into various operational categories such as cameras, electromechanical scanners, and radars.

Hughes, T. H.↗
NASA NTRS1971

Results of scatterometer systems analysis for NASA/MSC Earth Observation Sensor Evaluation Program.

Radar scatterometers have applications in the NASA/MSC Earth Observation Aircraft Program. Over a period of several years, several missions have been flown over both land and ocean. In this paper a system evaluation of the NASA/MSC 13.3-GHz Scatterometer System is presented. The effects of phase error between the Scatterometer channels, antenna pattern deviations, aircraft attitude deviations, environmental changes, and other related factors such as processing errors, system repeatability, and propeller modulation, were established. Furthermore, the reduction in system errors and calibration improvement was investigated by taking into account these parameter deviations. Typical scatterometer data samples are presented.

Krishen, K.↗
NASA NTRS1990

A sensor fusion field experiment in forest ecosystem dynamics

The background of the Forest Ecosystem Dynamics field campaign is presented, a progress report on the analysis of the collected data and related modeling activities is provided, and plans for future experiments at different points in the phenological cycle are outlined. The ecological overview of the study site is presented, and attention is focused on forest stands, needles, and atmospheric measurements. Sensor deployment and thermal and microwave observations are discussed, along with two examples of the optical radiation measurements obtained during the experiment in support of radiative transfer modeling. Future activities pertaining to an archival system, synthetic aperture radar, carbon acquisition modeling, and upcoming field experiments are considered.

Smith, James A.↗
NASA NTRS2008

The Global Precipitation Measurement (GPM) Project

The GIobd Precipitation hleasurement (GPM) mission is an international cooperati~ee ffort to advance weather, climate, and hydrological predictions through space-based precipitation measurements. The Core Observatory will be a reference standard to uniform11 calibrate data from a constellatism of spacecraft with passive microuave sensors. GP3l mission data will be used for scientific research as well as societal applications. GPM is being developed under a partnership between the United States (US) National .Aeronautics and Space Administration (XASA) and the Japanese Aerospace and Exploration Agency (JAYA). NASA is developing the Core Observatory, a Low-Inclination Constellation Observatory, two GPM Rlicrowave Imager (GXII) instruments. Ground Validation System and Precipitation Processing System for the GPRl mission. JAXA will provide a Dual-frequency Precipitation Radar (DPR) for installation on the Core satellite and launch services for the Core Observatory. Other US agencies and international partners contribute to the GPkf mission by providing precipitation measurements obtained from their own spacecraft and,'or providing ground-based precipitation measurements to support ground validation activities. The GPM Core Observatory will be placed in a low earth orbit (-400 krn) with 65-degree inclination, in order to calibrate partner instruments in a variety of orbits. The Core Observatory accommodates 3 instruments. The GkfI instrument provides measurements of precipitation intensity and distribution. The DPR consists of Ka and Ku band instruments, and provides threedimensional measurements of cloud structure, precipitation particle size distribution and precipitation intensitj and distribution. The instruments are key drivers for GPM Core Observatory overall size (1 1.6m x 6.5m x 5.0m) and mass (3500kg), as well as the significant (-1 950U.3 power requirement. The Core Spacecraft is being built in-house at Goddard Space Flight Center. The spacecraft structure consists of an aluminum lower bus structure. composite upper bus structure, '-axis steerable High Gain Antenna System on a dual-hinged boom, and two deploy able solar arraq s. The propulsion system features twelve thrusters and a single Composite Overlv~apP ressure Vessel tank. The GPhl Core spacecraft is one of the first large spacecraft developed to be demiseable (i.e. burn up upon atmospheric reentry j. The spacecraft dernissable components-- structure. propulsion tank, lithium-ion battery, sotar array md reaction wheels. are a unique fcature.

Azarbarazin, Ardeshir Art↗
NASA NTRS2011

Spaceborne Sensors Track Marine Debris Circulation in the Gulf of Mexico

Marine debris is a problem for coastal areas throughout the world, including the Gulf of Mexico. To aid the NOAA Marine Debris Program in monitoring marine debris dispersal and regulating marine debris practices, sea surface height and height anomaly data provided by the Colorado Center for Astrodynamics Research at the University of Colorado, Boulder, were utilized to help assess trash and other discarded items that routinely wash ashore in southeastern Texas, at Padre Island National Seashore. These data were generated from the NASA radar altimeter satellites TOPEX/Poseidon, Jason 1, and Jason 2, as well as the European altimeter satellites ERS-1, ERS-2 (European Remote Sensing Satellite), and ENVISAT (Environmental Satellite). Sea surface temperature data from MODIS were used to study of the dynamics of the Loop Current. Sea surface height and MODIS data analysis were used to show that warm water in the core of eddies, which periodically separate from the Loop Current, can be as high as 30 cm above the surrounding water. These eddies are known to directly transfer marine debris to the western continental shelf and the elevated area of water can be tracked using satellite radar altimeter data. Additionally, using sea surface height, geostrophic velocity, and particle path data, foretracking and backtracking simulations were created. These simulation runs demonstrated that marine debris on Padre Island National Seashore may arise from a variety of sources, such as commercial fishing/shrimping, the oil and gas industry, recreational boaters, and from rivers that empty into the Gulf of Mexico.

Reahard, Ross↗
DOE OSTI2020

Thunderstorm Cloud-Type Classification from Space-Based Lightning Imagers

The organization and structure of thunderstorms determines the extent and severity of their hazards to the general public and their consequences for the Earth system. Distinguishing vigorous convective regions that produce heavy rain and hail from adjacent regions of stratiform clouds or overhanging anvil clouds that produce light to no rainfall is valuable in operations and physical research. Cloud-type algorithms that partition convection from stratiform regions have been developed for space-based radar, passive microwave, and now Geostationary Operational Environmental Satellites (GOES) Advanced Baseline Imager (ABI) multispectral products. However, there are limitations for each of these products including temporal availability, spatial coverage, and the degree to which they based on cloud microphysics. We report we have developed a cloud-type algorithm for GOES Geostationary Lightning Mapper (GLM) observations that identifies convective/nonconvective regions in thunderstorms based on signatures of interactions with nonconvective charge structures in the lightning flash data. The GLM sensor permits a rapid (20 s) update cycle over the combined GOES-16–GOES-17 domain across all hours of the day. Storm regions that do not produce lightning will not be classified by our algorithm, however. The GLM cloud-type product is intended to provide situational awareness of electrified nonconvective clouds and to complement other cloud-type retrievals by providing a contemporary assessment tied to lightning physics. We propose that a future combined ABI–GLM cloud-type algorithm would be a valuable product that could draw from the strengths of each instrument and approach.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1977

Technology advances in active and passive microwave sensing through 1985

The capabilities of passive and active microwave sensors are discussed. The Nimbus-G and Seasat-A scanning multichannel microwave spectrometer, the Seasat-A radar altimeter, scatterometer and synthetic aperture radar represent the first systematic attempt at exploring a wide variety of applications utilizing microwave sensing techniques and are indicators of the directions in which the pertinent technology is likely to evolve. The trend is toward high resolution multi-frequency imagers spanning wide frequency ranges and wide swaths requiring sophisticated receivers, real-time data processors and most importantly, complex antennas.

Barath, F. T.↗
NASA NTRS1987

Autonomous control procedures for shuttle rendezvous proximity operations

The results are presented of a study which uses fuzzy sets to model a Space Shuttle pilot's reasoning and actions while performing rendezvous proximity operation maneuvers. In this model fuzzy sets are used to simulate smooth and continuous actions as would be expected from an experienced pilot and to simulate common sense reasoning in the decision process. The present model assumes visual information available to the Shuttle pilot from the Shuttle Crew Optical Alignment Sighting (COAS) device and the overhead window and rendezvous radar sensor information available to him from an onboard display. This model will be used in a flight analysis simulator to perform studies requiring a large number of runs, each of which currently needs an engineer in the loop to supply the piloting decisions. This work has much broader implications in control of robots such as the Flight Telerobotic Servicer, in automated pilot control and attitude control, and in advisory and evaluation functions that could be used for flight data monitoring or for testing of various rule sets in flight preparation.

Lea, Robert N.↗
NASA NTRS1993

Estimation of cirrus cloud particle fallspeeds from vertically pointing Doppler radar

The First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment 2 (FIRE 2) was conducted in Coffeyville, Kansas in late 1991 to study the microphysical and radiative properties of cirrus clouds. A variety of active and passive remote sensors were employed, including an 8-mm-wavelength cloud-sensing Doppler radar developed at the Wave Propagation Laboratory (WPL). The radar, having excellent sensitivity to cloud particles (-30 dBZ at 10 km), good spatial resolution (37 m), and velocity precision (.05 ms -1), is an excellent tool for observing cirrus clouds. Having this radar directed toward the zenith for long periods of time during FIRE 2 permitted the reflectivity-weighted particle fallspeed to be related to reflectivity which allowed a separation of ice particle fallspeeds from vertical air motions. Additionally, such relationships proved useful in other multi-sensor techniques for determining vertical profiles of ice particle characteristic size and ice water content in cirrus clouds. The analysis method and the results of applying it to cirrus cloud reflectivity and velocity data collected during FIRE 2 are discussed.

Orr, Brad W.↗