SEARCH · Search NASA

Results for “Sparse sensing”

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.

SourceResearch connectionFrom yearThrough year

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

At least 37 records · Page 2

All sources NASA NTRS DOE OSTI

NASA NTRS2023

Auroral, Ionospheric and Ground Magnetic Signatures of Magnetopause Surface Modes

Surface waves on Earth's magnetopause have a controlling effect upon global magnetospheric dynamics. Since spacecraft provide sparse in situ observation points, remote sensing these modes using ground-based instruments in the polar regions is desirable. However, many open conceptual questions on the expected signatures remain. Therefore, we provide predictions of key qualitative features expected in auroral, ionospheric, and ground magnetic observations through both magnetohydrodynamic theory and a global coupled magnetosphere-ionosphere simulation of a magnetopause surface eigenmode. These show monochromatic oscillatory field-aligned currents (FACs), due to both the surface mode and its non-resonant Alfvén coupling, are present throughout the magnetosphere. The currents peak in amplitude at the equatorward edge of the magnetopause boundary layer, not the open-closed boundary as previously thought. They also exhibit slow poleward phase motion rather than being purely evanescent. We suggest the upward FAC perturbations may result in periodic auroral brightenings. In the ionosphere, convection vortices circulate the poleward moving FAC structures. Finally, surface mode signals are predicted in the ground magnetic field, with ionospheric Hall currents rotating perturbations by approximately (but not exactly) 90° compared to the magnetosphere. Thus typical dayside magnetopause surface modes should be strongest in the East-West ground magnetic field component. Overall, all ground-based signatures of the magnetopause surface mode are predicted to have the same frequency across L-shells, amplitudes that maximize near the magnetopause's equatorward edge, and larger latitudinal scales than for field line resonance. Implications in terms of ionospheric Joule heating and geomagnetically induced currents are discussed.

M. O. Archer↗

NASA NTRS1973

Objective sea-level pressure analysis for data-sparse areas.

A computer procedure, described in an earlier study, uses the wind speed field near the ocean surface in combination with a small number of observations of pressure and wind velocity to specify the maritime sea-level pressure field. An improved version was used to analyze the pressure distribution over the North Pacific Ocean for eleven synoptic times in February 1967. Independent knowledge of the central pressures of lows is shown to reduce the analysis errors for very sparse data coverage. The application of planned remote sensing of sea-level wind speeds is shown to make a significant contribution to the quality of the analysis especially in the high gradient midlatitudes and for sparse coverage of conventional observations (such as over Southern Hemisphere oceans). Uniform distribution of the available observations of sea-level pressure and wind velocity yields results far superior to those derived from a random distribution.

Druyan, L. M.↗

NASA NTRS1972

Objective sea level pressure analysis for sparse data areas

A computer procedure was used to analyze the pressure distribution over the North Pacific Ocean for eleven synoptic times in February, 1967. Independent knowledge of the central pressures of lows is shown to reduce the analysis errors for very sparse data coverage. The application of planned remote sensing of sea-level wind speeds is shown to make a significant contribution to the quality of the analysis especially in the high gradient mid-latitudes and for sparse coverage of conventional observations (such as over Southern Hemisphere oceans). Uniform distribution of the available observations of sea-level pressure and wind velocity yields results far superior to those derived from a random distribution. A generalization of the results indicates that the average lower limit for analysis errors is between 2 and 2.5 mb based on the perfect specification of the magnitude of the sea-level pressure gradient from a known verification analysis. A less than perfect specification will derive from wind-pressure relationships applied to satellite observed wind speeds.

Druyan, L. M.↗

NASA NTRS2007

Timber Volume and Biomass Estimates in Central Siberia from Satellite Data

Mapping of boreal forest's type, structure parameters and biomass are critical for understanding the boreal forest's significance in the carbon cycle, its response to and impact on global climate change. The biggest deficiency of the existing ground based forest inventories is the uncertainty in the inventory data, particularly in remote areas of Siberia where sampling is sparse, lacking, and often decades old. Remote sensing methods can help overcome these problems. In this joint US and Russian study, we used the moderate resolution imaging spectroradiometer (MODIS) and unique waveform data of the geoscience laser altimeter system (GLAS) and produced a map of timber volume for a 10degx12deg area in Central Siberia. Using these methods, the mean timber volume for the forested area in the total study area was 203 m3/ ha. The new remote sensing methods used in this study provide a truly independent estimate of forest structure, which is not dependent on traditional ground forest inventory methods.

Ranson, K. Jon↗

NASA NTRS2022

Assimilation of SMAP Products for Improving Streamflow Simulations over Tropical Climate Region—Is Spatial Information More Important Than Temporal Information?

Streamflow is one of the key variables in the hydrological cycle. Simulation and forecasting of streamflow are challenging tasks for hydrologists, especially in sparsely gauged areas. Coarse spatial resolution remote sensing soil moisture products (equal to or larger than 9 km) are often assimilated into hydrological models to improve streamflow simulation in large catchments. This study uses the Ensemble Kalman Filter (EnKF) technique to assimilate SMAP soil moisture products at the coarse spatial resolution of 9 km (SMAP 9 km), and downscaled SMAP soil moisture product at the higher spatial resolution of 1 km (SMAP 1 km), into the Soil and Water Assessment Tool (SWAT) to investigate the usefulness of different spatial and temporal resolutions of remotely sensed soil moisture products in streamflow simulation and forecasting. The experiment was set up for eight catchments across the tropical climate of Vietnam, with varying catchment areas from 267 to 6430 km^2 during the period 2017–2019. We comprehensively evaluated the EnKF-based SWAT model in simulating streamflow at low, average, and high flow. Our results indicated that high-spatial resolution of downscaled SMAP 1 km is more beneficial in the data assimilation framework in aiding the accuracy of streamflow simulation, as compared to that of SMAP 9 km, especially for the small catchments. Our analysis on the impact of observation resolution also indicates that the improvement in the streamflow simulation with data assimilation is more significant at catchments where downscaled SMAP 1 km has fewer missing observations. This study is helpful for adding more understanding of performances of soil moisture data assimilation based hydrological modelling over the tropical climate region, and exhibits the potential use of remote sensing data assimilation in hydrology.

soil moisture↗

NASA NTRS2008

The NASA NEESPI Data Portal: Products, Information, and Services

Studies have indicated that land cover and use changes in Northern Eurasia influence global climate system. However, the procedures are not fully understood and it is challenging to understand the interactions between the land changes in this region and the global climate. Having integrated data collections form multiple disciplines are important for studies of climate and environmental changes. Remote sensed and model data are particularly important die to sparse in situ measurements in many Eurasia regions especially in Siberia. The NASA GES DISC (Goddard Earth Sciences Data and Information Services Center) NEESPI data portal has generated infrastructure to provide satellite remote sensing and numerical model data for atmospheric, land surface, and cryosphere. Data searching, subsetting, and downloading functions are available. ONe useful tool is the Web-based online data analysis and visualization system, Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure), which allows scientists to assess easily the state and dynamics of terrestrial ecosystems in Northern Eurasia and their interactions with global climate system. Recently, we have created a metadata database prototype to expand the NASA NEESPI data portal for providing a venue for NEESPI scientists fo find the desired data easily and leveraging data sharing within NEESPI projects. The database provides product level information. The desired data can be found through navigation and free text search and narrowed down by filtering with a number of constraints. In addition, we have developed a Web Map Service (WMS) prototype to allow access data and images from difference data resources.

Shen, Suhung↗

NASA NTRS1993

The red edge in arid region vegetation: 340-1060 nm spectra

The remote sensing study of vegetated regions of the world has typically been focused on the use of broad-band vegetation indices such as NDVI. Various modifications of these indices have been developed in attempts to minimize the effect of soil background, e.g., SAVI, or to reduce the effect of the atmosphere, e.g., ARVI. Most of these indices depend on the so-called 'red edge,' the sharp transition between the strong absorption of chlorophyll pigment in visible wavelengths and the strong scattering in the near-infrared from the cellular structure of leaves. These broadband indices tend to become highly inaccurate as the green canopy cover becomes sparse. The advent of high spectral resolution remote sensing instrument such as the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) has allowed the detection of narrow spectral features in vegetation and there are reports of detection of the red edge even for pixels with very low levels of green vegetation cover by Vane et al. and Elvidge et al., and to characterize algal biomass in coastal areas. Spectral mixing approaches similar to those of Smith et al. can be extended into the high spectral resolution domain allowing for the analysis of more endmembers, and potentially, discrimination between material with narrow spectral differences. Vegetation in arid regions tends to be sparse, often with small leaves such as the creosote bush. Many types of arid region vegetation spend much of the year with their leaves in a senescent state, i.e., yellow, with lowered chlorophyll pigmentation. The sparseness of the leaves of many arid region plants has the dual effect of lowering the green leaf area which can be observed and of allowing more of the sub-shrub soil to be visible which further complicates the spectrum of a region covered with arid region vegetation. Elvidge examined the spectral characteristics of dry plant materials showing significant differences in the region of the red edge and the diagnostic ligno-cellulose absorptions at 2090 nm and 2300 nm. Ray et al. detected absorption at 2100 nm in AVIRIS spectra of an abandoned field known to be covered by a great deal of dead plant litter. In order to better study arid region vegetation remote sensing data, it is necessary to better characterize the reflectance spectra of in situ, living, arid region plants.

Ray, Terrill W.↗

NASA NTRS2012

Multi-Scale Three-Dimensional Variational Data Assimilation System for Coastal Ocean Prediction

A multi-scale three-dimensional variational data assimilation system (MS-3DVAR) has been formulated and the associated software system has been developed for improving high-resolution coastal ocean prediction. This system helps improve coastal ocean prediction skill, and has been used in support of operational coastal ocean forecasting systems and field experiments. The system has been developed to improve the capability of data assimilation for assimilating, simultaneously and effectively, sparse vertical profiles and high-resolution remote sensing surface measurements into coastal ocean models, as well as constraining model biases. In this system, the cost function is decomposed into two separate units for the large- and small-scale components, respectively. As such, data assimilation is implemented sequentially from large to small scales, the background error covariance is constructed to be scale-dependent, and a scale-dependent dynamic balance is incorporated. This scheme then allows effective constraining large scales and model bias through assimilating sparse vertical profiles, and small scales through assimilating high-resolution surface measurements. This MS-3DVAR enhances the capability of the traditional 3DVAR for assimilating highly heterogeneously distributed observations, such as along-track satellite altimetry data, and particularly maximizing the extraction of information from limited numbers of vertical profile observations.

Li, Zhijin↗

NASA NTRS1982

Advanced multispectral remote sensing technology and applications; Proceedings of the Conference, Arlington, VA, May 6, 7, 1982

Sensor system concepts are discussed, taking into account a multispectral linear array instrument design, an improved earth resources sensing instrument, design tradeoffs for a multispectral linear array instrument, imaging spectrometer technologies for advanced earth remote sensing, and the design concept for an optimized earth radiation budget sensor. Topics related to supporting optics and signal processing methodologies are explored, giving attention to two mirror objective design for multispectral remote sensing, the measurement of optical modulation functions in sparsely sampled mosaic focal plane arrays, multispectral linear array focal plane signal processing, signal processing for the NASA multispectral linear array, the concept for a multiple resolution pushbroom sensor, and the evolution of the octal radiometer. Techniques and applications for multispectral data are considered along with aspects of focal plane technology.

Ando, K. J.↗

NASA NTRS2010

NASA Tech Briefs, May 2010

Topics covered include: Instrument for Analysis of Greenland's Glacier Mills Cryogenic Moisture Apparatus; A Transportable Gravity Gradiometer Based on Atom Interferometry; Three Methods of Detection of Hydrazines; Crossed, Small-Deflection Energy Analyzer for Wind/Temperature Spectrometer; Wavefront Correction for Large, Flexible Antenna Reflector; Novel Micro Strip-to-Waveguide Feed Employing a Double-Y Junction; Thin-Film Ferro Electric-Coupled Microstripline Phase Shifters With Reduced Device Hysteresis; Two-Stage, 90-GHz, Low-Noise Amplifier; A 311-GHz Fundamental Oscillator Using InP HBT Technology; FPGA Coprocessor Design for an Onboard Multi-Angle Spectro-Polarimetric Imager; Serrating Nozzle Surfaces for Complete Transfer of Droplets; Turbomolecular Pumps for Holding Gases in Open Containers; Triaxial Swirl Injector Element for Liquid-Fueled Engines; Integrated Budget Office Toolbox; PLOT3D Export Tool for Tecplot; Math Description Engine Software Development Kit; Astronaut Office Scheduling System Software; ISS Solar Array Management; Probabilistic Structural Analysis Program; SPOT Program; Integrated Hybrid System Architecture for Risk Analysis; System for Packaging Planetary Samples for Return to Earth; Offset Compound Gear Drive; Low-Dead-Volume Inlet for Vacuum Chamber; Simple Check Valves for Microfluidic Devices; A Capillary-Based Static Phase Separator for Highly Variable Wetting Conditions; Gimballing Spacecraft Thruster; Finned Carbon-Carbon Heat Pipe with Potassium Working Fluid; Lightweight Heat Pipes Made from Magnesium; Ceramic Rail-Race Ball Bearings; Improved OTEC System for a Submarine Robot; Reflector Surface Error Compensation in Dual-Reflector Antennas; Enriched Storable Oxidizers for Rocket Engines; Planar Submillimeter-Wave Mixer Technology with Integrated Antenna; Widely Tunable Mode-Hop-Free External-Cavity Quantum Cascade Laser; Non-Geiger-Mode Single-Photon Avalanche Detector with Low Excess Noise; Using Whispering-Gallery-Mode Resonators for Refractometry; RF Device for Acquiring Images of the Human Body; Reactive Collision Avoidance Algorithm; Fast Solution in Sparse LDA for Binary Classification; Modeling Common-Sense Decisions in Artificial Intelligence; Graph-Based Path-Planning for Titan Balloons; Nanolaminate Membranes as Cylindrical Telescope Reflectors; Air-Sea Spray Airborne Radar Profiler Characterizes Energy Fluxes in Hurricanes; Large Telescope Segmented Primary Mirror Alignment; and Simplified Night Sky Display System.

Source record↗

NASA NTRS1995

Quantitative remote sensing of ammonium minerals, Cedar Mountains, Esmeralda County, Nevada

Mineral-bound ammonium (NH4+) was discovered by the U.S. Geological Survey in the southern Cedar Mountains of Esmeralda County, Nevada in 1989. At 10 km in length, this site is 100 times larger than any previously known occurrence in volcanic rocks. The ammonium occurs in two hydrothermally altered, crystal-rich rhyolitic tuff units of Oligocene age, and is both structurally and stratigraphically controlled. This research uses Advanced Visible/Infrared Imaging Spectrometer (AVIRIS) data to quantitatively map the mineral-bound ammonium (buddingtonite) concentration in the altered volcanic rocks. Naturally occurring mineral-bound ammonium is fairly rare; however, it has been found to occur in gold-bearing hydrothermal deposits. Because of this association, it is thought that ammonium may be a useful too in exploration for gold and other metal deposits. Mineral-bound ammonium is produced when an ammonium ion (NH4+) replaces the alkali cation site (usually K+) in the crystal structure of silicate minerals such as feldspars, micas and clays. Buddingtonite is an ammonium feldspar. The ammonium originates in buried organic plant matter and is transported to the host rock by hydrothermal fluids. Ammonium alteration does not produce visible changes in the rock, and it is barely detectable with standard x-ray diffraction methods. It is clearly identified, however, by absorption features in short wave-infrared (SWIR) wavelengths (2.0 - 2.5 micrometers). The ammonium absorption features are believed to be caused by N-H vibrational modes and are analogous to hydroxyl (O-H) vibrational modes, only shifted slightly in wavelength. Buddingtonite absorption features in the near- and SWIR lie at 1.56, 2.02 and 2.12 micrometers. The feature at 2.12 micrometer is the strongest of the three and is the only one used in this study. The southern Cedar Mountains are sparsely vegetated and are an ideal site for a remote sensing study.

Baugh, William M.↗

NASA NTRS2019

Evaluation of Satellite-Based Rainfall Estimates in the Lower Mekong River Basin (Southeast Asia)

Satellite-based precipitation is an essential tool for regional water resource applications that requires frequent observations of meteorological forcing, particularly in areas that have sparse rain gauge networks. To fully realize the utility of remotely sensed precipitation products in watershed modeling and decision-making, a thorough evaluation of the accuracy of satellite-based rainfall and regional gauge network estimates is needed. In this study, Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42 v.7 and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) daily rainfall estimates were compared with daily rain gauge observations from 2000 to 2014 in the Lower Mekong River Basin (LMRB) in Southeast Asia. Monthly, seasonal, and annual comparisons were performed, which included the calculations of correlation coefficient, coefficient of determination, bias, root mean square error (RMSE), and mean absolute error (MAE). Our validation test showed TMPA to correctly detect precipitation or no-precipitation 64.9% of all days and CHIRPS 66.8% of all days, compared to daily in-situ rainfall measurements. The accuracy of the satellite-based products varied greatly between the wet and dry seasons. Both TMPA and CHIRPS showed higher correlation with in-situ data during the wet season (June–September) as compared to the dry season (November–January). Additionally, both performed better on a monthly than an annual time-scale when compared to in-situ data. The satellite-based products showed wet biases during months that received higher cumulative precipitation. Based on a spatial correlation analysis, the average r-value of CHIRPS was much higher than TMPA across the basin. CHIRPS correlated better than TMPA at lower elevations and for monthly rainfall accumulation less than 500 mm. While both satellite-based products performed well, as compared to rain gauge measurements, the present research shows that CHIRPS might be better at representing precipitation over the LMRB than TMPA.

Dandridge, Chelsea↗

NASA NTRS2012

Microwave Satellite Data for Hydrologic Modeling in Ungauged Basins

An innovative flood-prediction framework is developed using Tropical Rainfall Measuring Mission precipitation forcing and a proxy for river discharge from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) onboard the National Aeronautics and Space Administration's Aqua satellite. The AMSR-E-detected water surface signal was correlated with in situ measurements of streamflow in the Okavango Basin in Southern Africa as indicated by a Pearson correlation coefficient of 0.90. A distributed hydrologic model, with structural data sets derived from remote-sensing data, was calibrated to yield simulations matching the flood frequencies from the AMSR-E-detected water surface signal. Model performance during a validation period yielded a Nash-Sutcliffe efficiency of 0.84. We concluded that remote-sensing data from microwave sensors could be used to supplement stream gauges in large sparsely gauged or ungauged basins to calibrate hydrologic models. Given the global availability of all required data sets, this approach can be potentially expanded to improve flood monitoring and prediction in sparsely gauged basins throughout the world

Distributed hydrologic modeling↗

NASA NTRSDate not supplied

Compressive Sensing Application for Transient Photometric Measurements

Compressive Sensing (CS) is a mathematical theory for simultaneous data acquisition and compression. Natural phenomena may be sparse in some physical or temporal domain. If we exploit this sparsity by applying the technique of CS to obtain information, how do our measurements change as a function of domain and measurement systematics? What are the specific implications for the science and for the sensing infrastructure? In this talk, we will discuss the generalized systematic effects consequent to the application of CS to time-series photometric measurements. We assess implications for observability, sparsification, and information loss in the detection, retrieval and reconstruction process. To study time-series photometry, we explore the field of gravitational microlensing. A source star, typically in the galactic bulge, gets microlensed when there is a precise alignment of a lensing star and its planetary system, with the source star. The microlensed source star changes in flux magnification as the lensing system crosses the precise path of alignment, resulting in a microlensing curve in time domain. A high-cadence, high-resolution system, which uses low power and bandwidth is essential to obtain valuable science measurements. Hence, we discuss application of CS to gravitational microlensing data sets, which in turn can be generalized to any time-ordered photometric measurements.

Asmita Abhay Korde↗

NASA NTRS2016

Application of Compressive Sensing to Gravitational Microlensing Data and Implications for Miniaturized Space Observatories

Compressive Sensing is a technique for simultaneous acquisition and compression of data that is sparse or can be made sparse in some domain. It is currently under intense development and has been profitably employed for industrial and medical applications. We here describe the use of this technique for the processing of astronomical data. We outline the procedure as applied to exoplanet gravitational microlensing and analyze measurement results and uncertainty values. We describe implications for on-spacecraft data processing for space observatories. Our findings suggest that application of these techniques may yield significant, enabling benefits especially for power and volume-limited space applications such as miniaturized or micro-constellation satellites.

Compressive Sensing↗

NASA NTRS1972

Geologic remote sensing study of the Hayden pass-Orient Mine Area, Northern Sangre de Cristo Mountains, Colorado

Remote sensor data from a NASA Convair 990 radar flight and Mission 101 and 105 have been interpreted and evaluated. Based on interpretation of the remote sensor data, a geologic map has been prepared and compared with a second geologic map, prepared from interpretation of both remote sensor data and field data. Comparison of the two maps gives one indication of the usefulness and reliability of the remote sensor data. Color and color infrared photography provided the largest amount of valuable information. Multiband photography was of lesser value and side-looking radar imagery provided no new information that was not available on small scale photography. Thermal scanner imagery proved to be a very specialized remote sensing tool that should be applied to areas of low relief and sparse vegetation where geologic features produce known or suspected thermal contrast. Low sun angle photography may be a good alternative to side-looking radar imagery but must be flown with critical timing.

Wychgram, D. C.↗

NASA NTRS2016

Application of Compressive Sensing to Gravitational Microlensing Data and Implications for Miniaturized Space Observatories

Gravitational Microlensing↗

NASA NTRS2001

Determining Greenland Ice Sheet Accumulation Rates from Radar Remote Sensing

An important component of NASA's Program for Arctic Regional Climate Assessment (PARCA) is a mass balance investigation of the Greenland Ice Sheet. The mass balance is calculated by taking the difference between the snow accumulation and the ice discharge of the ice sheet. Uncertainties in this calculation include the snow accumulation rate, which has traditionally been determined by interpolating data from ice core samples taken throughout the ice sheet. The sparse data associated with ice cores, coupled with the high spatial and temporal resolution provided by remote sensing, have motivated scientists to investigate relationships between accumulation rate and microwave observations.

Jezek, Kenneth C.↗