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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Searching for Water Ice at the Lunar North Pole Using High-Resolution Images and Radar

Permanently shadowed regions (PSRs) at the lunar poles are potential reservoirs of frozen volatiles, and are therefore high-priority exploration targets. PSRs trap water and other volatiles because their annual maximum temperatures (40-100K) are lower than the sublimation temperatures of these species (i.e. H2O approx.104K). Previous studies using various remote sensing techniques have not been able to definitively characterize the distribution or abundance of ice in lunar PSRs. The purpose of this study is to search for signs of ice in PSRs using two complimentary remote sensing techniques: radar and visible images.

Mitchell, J. L.↗
NASA NTRS2011

DISCOVER-AQ: An Overview and Initial Comparisons of NO2 with OMI Observations

The first deployment of the Earth Venture -1 DISCOVER-AQ (Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality) project was conducted during July 2011 in the Baltimore-Washington region. Two aircraft (a P-3B for in-situ sampling and a King Air for remote sensing) were used along with an extensive array of surface-based in-situ and remote sensing instrumentation. Fourteen flight days were accomplished by both aircraft and over 250 profiles of trace gases and aerosols were performed by the P-3B over surface air quality monitoring stations, which were specially outfitted with sunphotometers and Pandora UV/Vis spectrometers. The King Air flew with the High Spectral Resolution Lidar for aerosols and the ACAM UV/Vis spectrometer for trace gases. This suite of observations allows linkage of surface air quality with the vertical distributions of gases and aerosols, with remotely-sensed column amounts observed from the surface and from the King Air, and with satellite observations from Aura (OMI and TES), GOME-2, MODIS and GOES. The DISCOVER-AQ data will allow determination of under what conditions satellite retrievals are indicative of surface air quality, and they will be useful in planning new satellites. In addition to an overview of the project, a preliminary comparison of tropospheric column NO2 densities from the integration of in-situ P-3B observations, from the Pandoras and ACAM, and from the new Goddard OMI NO2 algorithm will be presented.

Pickering, Kenneth↗
NASA NTRS1981

Synoptic thermal and oceanographic parameter distributions in the New York Bight Apex

Concurrent surface water measurements made from a moving oceanographic research vessel were used to calibrate and interpret remotely sensed data collected over a plume in the New York Bight Apex on 23 June 1977. Multiple regression techniques were used to develop equations to map synoptic distributions of chlorophyll a and total suspended matter in the remotely sensed scene. Thermal (which did not have surface calibration values) and water quality parameter distributions indicated a cold mass of water in the Bight Apex with an overflowing nutrient-rich warm water plume that originated in the Sandy Hook Bay and flowed south near the New Jersey shoreline. Data analysis indicates that remotely sensed data may be particularly useful for studying physical and biological processes in the top several metres of surface water at plume boundaries.

Johnson, R. W.↗
NASA NTRS2002

Remote Sensing of Aerosol and their Effect on Climate

Smoke aerosol, pollution or dust affects the distribution of sunlight reaching the Earth. It enhances reflection of sunlight back to space and absorption in the atmosphere. Aerosol particles also affect cloud formation, thickness and formation of precipitation. The concentration of aerosol and their properties vary rapidly, due to the heterogeneity of sources and a short lifetime of about a week. Satellite and ground based network of remote sensing instruments were developed for continues measurement of the global distribution of aerosol and variation of their concentration and properties. I shall present examples of such measurements from the Terra spacecraft and the Aerosol Robotic Network, and explore the pathways to integrate these measurements into models that calculate the aerosol radiative forcing of climate.

Kaufman, Yoram↗
NASA NTRS2000

BOREAS HYD-3 Snow Measurements

The Boreal Ecosystem-Atmosphere Study (BOREAS) Hydrology (HYD)-3 team collected several data sets related to the hydrology of forested areas. This data set contains measurements of snow depth, snow density in three cm intervals, an integrated snow pack density and snow water equivalent (SWE), and snow pack physical properties from snow pit evaluation taken in 1994 and 1996. The data were collected from several sites in both the southern study area (SSA) and the northern study area (NSA). A variety of standard tools were used to measure the snow pack properties, including a meter stick (snow depth), a 100 cc snow density cutter, a dial stem thermometer, and the Canadian snow sampler as used by HYD-4 to obtain a snow pack-integrated measure of SWE. This study was undertaken to predict spatial distributions of snow properties important to the hydrology, remote sensing signatures, and the transmissivity of gases through the snow. The data are available in tabular ASCII files. The snow measurement data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

Hardy, Janet P.↗
NASA NTRS1991

Dust in the Mars atmosphere

The amount of dust suspended in the Martian atmosphere is highly variable with location and with time. The opacity of the sky is best known at the two Viking Lander sites, where visual, vertical-column optical depth never fell below a value of a few tenths during the 1.25 Mars years of observations and yet exceeded 2 to 3 during two great dust storms in 1977. Elsewhere on the planet, optical depths have been estimated from orbiter visible imaging of surface contrasts and from mapping of infrared emission from the surface and the overlying (dusty) atmosphere. In many cases these opacities (and thus dust amounts) may be uncertain by as much as a factor of two. Viking Lander observations of twilight indicate that the background dust haze is more or less uniformly mixed with altitude in the lower atmosphere. Observations from spacecraft indicate that there may be come seasonal variations to the height of these dust hazes, which sometimes extend above 30 km. (Ice haze layers may occur as high as 80 km.) The existing observations do not constrain the composition or the size distribution of the suspended dust particles very well. Remote sensing observations depend principally upon the product of the number of particles, the geometric cross-sections (and so particle size and shape), and the extinction efficiency of the particles (and so the particle composition), as integrated over the particle size distribution and along the line of sight. While the observed variation of dust opacity with wavelength constrains these quantities, it does not often permit the unique determination of the individual properties of the suspended dust. A size distribution having a cross-section weighted mean particle radius of 2.5 microns was deduced from a synthesis of the IR thermal emission spectra observed in the Southern Hemisphere by Mariner 9 during the 1971 global dust storm. Although the IR thermal emission is relatively insensitive to the sub-micron sized particles which tend to dominate visible opacity, this same size distribution was consistent with modeling of the sky brightness variation near the sun, as seen through the background haze above the Viking lander sites in the Northern Hemisphere.

Zurek, R. W.↗
NASA NTRS1973

Mississippi Sound remote sensing study

A study of the Mississippi Sound was initiated in early 1971 by personnel of NASA Earth Resources Laboratory. Four separate seasonal experiments consisting of quasi-synoptic remote and surface measurements over the entire area were planned. Approximately 80 stations distributed throughout Mississippi Sound were occupied. Surface water temperature and secchi extinction depth were measured at each station and water samples were collected for water quality analyses. The surface distribution of three water parameters of interest from a remote sensing standpoint - temperature, salinity and chlorophyll content - are displayed in map form. Areal variations in these parameters are related to tides and winds. A brief discussion of the general problem of radiative measurements of water temperature is followed by a comparison of remotely measured temperatures (PRT-5) to surface vessel measurements.

Atwell, B. H.↗
NASA NTRS2023

Sources of L-Band RFI Determined From Kurtosis Using the SMAP Radiometer

Among the parameters of the signal measured by the SMAP radiometer to identify RFI is kurtosis which indicates the departure of the received signal from a gaussian distribution and therefor helpful in identifying RFI. But the kurtosis has the potential to provide additional information. This manuscript reports results of an investigation examining the connection between the level of kurtosis (K) and the characteristics of the source of the RFI. It is shown, for example, that the level of can be used to distinguish the short pulse, relatively wideband RFI associated with radar from the narrow bandwidth signals associated with faulty consumer electronics.

Radio Frequency Interference↗
NASA NTRS1975

Solutions to Kuessner's integral equation in unsteady flow using local basis functions

The computational procedure and numerical results are presented for a new method to solve Kuessner's integral equation in the case of subsonic compressible flow about harmonically oscillating planar surfaces with controls. Kuessner's equation is a linear transformation from pressure to normalwash. The unknown pressure is expanded in terms of prescribed basis functions and the unknown basis function coefficients are determined in the usual manner by satisfying the given normalwash distribution either collocationally or in the complex least squares sense. The present method of solution differs from previous ones in that the basis functions are defined in a continuous fashion over a relatively small portion of the aerodynamic surface and are zero elsewhere. This method, termed the local basis function method, combines the smoothness and accuracy of distribution methods with the simplicity and versatility of panel methods. Predictions by the local basis function method for unsteady flow are shown to be in excellent agreement with other methods. Also, potential improvements to the present method and extensions to more general classes of solutions are discussed.

Fromme, J. A.↗
NASA NTRS1975

Particulate size distribution cascade analyzer for spacecraft contamination monitoring

A cascade particulate analyzer was developed for nearly real time measurement of the contaminating particulate size distribution in the spacecraft interior ambient environment and as a real time total impacting particulate mass monitor under vacuum conditions. The analyzer has four stages, the first stage is a basic 10 MHz quartz crystal microbalance used widely on spacecraft (such as Skylab) for contamination monitoring purposes. In this application the front sensing crystal is coated with a low vapor pressure adhesive grease which captures impacting particles. This first stage has a wide viewing angle and measures total particulate mass impacting the crystal while the unit is exposed to the vacuum environment. The remaining three states form an aerodynamic impaction cascade with individual quartz crystal microbalances at each stage acting as accumulated mass sensing elements. These three stages thus give relative mass distribution of particulates in three ranges, particles having effective diameter greater than 5 micron, particles between 1 and 5 micron diameter and particles 0.3 to 1 micron diameter.

Wallace, D. A.↗
NASA NTRS1998

A Conceptual Approach to Assimilating Remote Sensing Data to Improve Soil Moisture Profile Estimates in a Surface Flux/Hydrology Model: Overview - Part 1

Knowledge of the amount of water in the soil is of great importance to many earth science disciplines. Soil moisture is a key variable in controlling the exchange of water and energy between the land surface and the atmosphere. Thus, soil moisture information is valuable in a wide range of applications including weather and climate, runoff potential and flood control, early warning of droughts, irrigation, crop yield forecasting, soil erosion, reservoir management, geotechnical engineering, and water quality. Despite the importance of soil moisture information, widespread and continuous measurements of soil moisture are not possible today. Although many earth surface conditions can be measured from satellites, we still cannot adequately measure soil moisture from space. Research in soil moisture remote sensing began in the mid 1970s shortly after the surge in satellite development. Recent advances in remote sensing have shown that soil moisture can be measured, at least qualitatively, by several methods. Quantitative measurements of moisture in the soil surface layer have been most successful using both passive and active microwave remote sensing, although complications arise from surface roughness and vegetation type and density. Early attempts to measure soil moisture from space-borne microwave instruments were hindered by what is now considered sub-optimal wavelengths (shorter than 5 cm) and the coarse spatial resolution of the measurements. L-band frequencies between 1 and 3 GHz (10-30 cm) have been deemed optimal for detection of soil moisture in the upper few centimeters of soil. The Electronically Steered Thinned Array Radiometer (ESTAR), an aircraft-based instrument operating a 1,4 GHz, has shown great promise for soil moisture determination. Initiatives are underway to develop a similar instrument for space. Existing space-borne synthetic aperture radars (SARS) operating at C- and L-band have also shown some potential to detect surface wetness. The advantage of radar is its much higher resolution than passive microwave systems, but it is currently hampered by surface roughness effects and the lack of a good algorithm based on a single frequency and single polarization. In addition, its repeat frequency is generally low (about 40 days). In the meantime, two new radiometers offer some hope for remote sensing of soil moisture from space. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), launched in November 1997, possesses a 10.65 GHz channel and the Advanced Microwave Scanning Radiometer (AMSR) on both the ADEOS-11 and Earth Observing System AM-1 platforms to be launched in 1999 possesses a 6.9 GHz channel. Aside from issues about interference from vegetation, the coarse resolution of these data will provide considerable challenges pertaining to their application. The resolution of TMI is about 45 km and that of AMSR is about 70 km. These resolutions are grossly inconsistent with the scale of soil moisture processes and the spatial variability of factors that control soil moisture. Scale disparities such as these are forcing us to rethink how we assimilate data of various scales in hydrologic models. Of particular interest is how to assimilate soil moisture data by reconciling the scale disparity between what we can expect from present and future remote sensing measurements of soil moisture and modeling soil moisture processes. It is because of this disparity between the resolution of space-based sensors and the scale of data needed for capturing the spatial variability of soil moisture and related properties that remote sensing of soil moisture has not met with more widespread success. Within a single footprint of current sensors at the wavelengths optimal for this application, in most cases there is enormous heterogeneity in soil moisture created by differences in landcover, soils and topography, as well as variability in antecedent precipitation. It is difficult to interpret the meaning of 'mean' soil moisture under such conditions and even more difficult to apply such a value. Because of the non-linear relationships between near-surface soil moisture and other variables of interest, such as surface energy fluxes and runoff, mean soil moisture has little applicability at such large scales. It is for these reasons that the use of remote sensing in conjunction with a hydrologic model appears to be of benefit in capturing the complete spatial and temporal structure of soil moisture. This paper is Part I of a four-part series describing a method for intermittently assimilating remotely-sensed soil moisture information to improve performance of a distributed land surface hydrology model. The method, summarized in section II, involves the following components, each of which is detailed in the indicated section of the paper or subsequent papers in this series: Forward radiative transfer model methods (section II and Part IV); Use of a Kalman filter to assimilate remotely-sensed soil moisture estimates with the model profile (section II and Part IV); Application of a soil hydrology model to capture the continuous evolution of the soil moisture profile within and below the root zone (section III); Statistical aggregation techniques (section IV and Part II); Disaggregation techniques using a neural network approach (section IV and Part III); and Maximum likelihood and Bayesian algorithms for inversely solving for the soil moisture profile in the upper few cm (Part IV).

Crosson, William L.↗
NASA NTRS1979

Transducer with a sense of touch

Matrix of pressure sensors determines shape and pressure distribution of object in contact with its surface. Output can be used to develop pressure map of objects' surface and displayed as array of alphanumeric symbols on video monitor.

Bejczy, A. K.↗
NASA NTRS1982

Remote sensing of cloud parameters

Day and night mapping of the global distributions of the horizontal cloud covers and the corresponding cloud-top pressure levels are derived from the same set of infrared radiance data used to retrieve clear-column temperature profiles. General formulation of the problem is presented with illustrations for the simple case of a single layer of non-reflecting clouds. Experimental verification is obtained using 15 micron data measured by the NOAA-VTPR infrared sounder. After correcting for water vapor emission, the results show that the effective cloud cover derived from 15 micron data is less than that obtained from visible data.

Chahine, M. T.↗
NASA NTRS2002

NASA planetary data: applying planetary satellite remote sensing data in the classroom

NASA supports several data archiving and distribution mechanisms that provide a means whereby scientists can participate in education and outreach through the use of technology for data and information dissemination. The Planetary Data System (PDS) is sponsored by NASA's Office of Space Science. Its purpose is to ensure the long-term usability of NASA data and to stimulate advanced research. In addition, the NASA Regional Planetary Image Facility (RPIF), an international system of planetary image libraries, maintains photographic and digital data as well as mission documentation and cartographic data.

Regional Plaentary Image Facility RPIF Planetary D↗
NASA NTRS2012

Spatial Distribution of Accuracy of Aerosol Retrievals from Multiple Satellite Sensors

Remote sensing of aerosols from space has been a subject of extensive research, with multiple sensors retrieving aerosol properties globally on a daily or weekly basis. The diverse algorithms used for these retrievals operate on different types of reflected signals based on different assumptions about the underlying physical phenomena. Depending on the actual retrieval conditions and especially on the geographical location of the sensed aerosol parcels, the combination of these factors might be advantageous for one or more of the sensors and unfavorable for others, resulting in disagreements between similar aerosol parameters retrieved from different sensors. In this presentation, we will demonstrate the use of the Multi-sensor Aerosol Products Sampling System (MAPSS) to analyze and intercompare aerosol retrievals from multiple spaceborne sensors, including MODIS (on Terra and Aqua), MISR, OMI, POLDER, CALIOP, and SeaWiFS. Based on this intercomparison, we are determining geographical locations where these products provide the greatest accuracy of the retrievals and identifying the products that are the most suitable for retrieval at these locations. The analyses are performed by comparing quality-screened satellite aerosol products to available collocated ground-based aerosol observations from the Aerosol Robotic Network (AERONET) stations, during the period of 2006-2010 when all the satellite sensors were operating concurrently. Furthermore, we will discuss results of a statistical approach that is applied to the collocated data to detect and remove potential data outliers that can bias the results of the analysis.

Petrenko, Maksym↗
NASA NTRS2024

Extending the Utility of Space-Borne Snow Water Equivalent Observations Over Vegetated Areas With Data Assimilation

Snow is a vital component of the earth system, yet no snow-focused satellite remote sensing platform currently exists. In this study, we investigate how synthetic observations of snow water equivalent (SWE) representative of a synthetic aperture radar remote sensing platform could improve spatiotemporal estimates of snowpack. We use a fraternal twin observing system simulation experiment, specifically investigating how much snow simulated using widely used models and forcing data could be improved by assimilating synthetic observations of SWE. We focus this study across a 24° x 37° domain in the western USA and Canada, simulating snow at 250 m resolution and hourly time steps in water year 2019. We perform two data assimilation experiments, including (1) a simulation excluding synthetic observations in forests where canopies obstruct remote sensing retrievals and (2) a simulation inferring snow distribution in forested grid cells using synthetic observations from nearby canopy-free grid cells. Results found that, relative to a nature run, or assumed true simulation of snow evolution, assimilating synthetic SWE observations improved average SWE biases at maximum snowpack timing in shrub, grass, crop, bare-ground, and wetland land cover types from 14 %, to within 1 %. However, forested grid cells contained a disproportionate amount of SWE volume. In forests, SWE mean absolute errors at the time of maximum snow volume were 111 mm and average SWE biases were on the order of 150 %. Here the data assimilation approach that estimated forest SWE using observations from the nearest canopy-free grid cells substantially improved these SWE biases (18 %) and the SWE mean absolute error (27 mm). Simulations employing data assimilation also improved estimates of the temporal evolution of both SWE and runoff, even in spring snowmelt periods when melting snow and high snow liquid water content prevented synthetic SWE retrievals. In fact, in the Upper Colorado River region, melt-season SWE biases were improved from 63 % to within 1 %, and the Nash–Sutcliffe efficiency of runoff improved from −2.59 to 0.22. These results demonstrate the value of data assimilation and a snow-focused globally relevant remote sensing platform for improving the characterization of SWE and associated water availability.

Justin Pflug↗
NASA NTRS2015

Remote Sensing of Aerosol in the Terrestrial Atmosphere from Space: New Missions

The distribution and properties of atmospheric aerosols on a global scale are not well known in terms of determination of their effects on climate. This mostly is due to extreme variability of aerosol concentrations, properties, sources, and types. Aerosol climate impact is comparable to the effect of greenhouse gases, but its influence is more difficult to measure, especially with respect to aerosol microphysical properties and the evaluation of anthropogenic aerosol effect. There are many satellite missions studying aerosol distribution in the terrestrial atmosphere, such as MISR/Terra, OMI/Aura, AVHHR, MODIS/Terra and Aqua, CALIOP/CALIPSO. To improve the quality of data and climate models, and to reduce aerosol climate forcing uncertainties, several new missions are planned. The gap in orbital instruments for studying aerosol microphysics has arisen after the Glory mission failed during launch in 2011. In this review paper, we describe several planned aerosol space missions, including the Ukrainian project Aerosol-UA that obtains data using a multi-channel scanning polarimeter and wide-angle polarimetric camera. The project is designed for remote sensing of the aerosol microphysics and cloud properties on a global scale.

Aerosol↗
NASA NTRS2003

Accessing and Understanding MODIS Data

The National Aeronautics and Space Administration (NASA) launched the Terra satellite in December 1999, as part of the Earth Science Enterprise promotion of interdisciplinary studies of the integrated Earth system. Aqua, the second satellite from the series of EOS constellation, was launched in May 2002. Both satellites carry the MODerate resolution Imaging Spectroradiometer (MODIS) instrument. MODIS data are processed at the Goddard Space Flight Center, Greenbelt, MD, and then archived and distributed by the Distributed Active Archive Centers (DAACs). Data products from the MODIS sensors present new challenges to remote sensing scientists due to specialized production level, data format, and map projection. MODIS data are distributed as calibrated radiances and as higher level products such as: surface reflectance, water-leaving radiances, ocean color and sea surface temperature, land surface kinetic temperature, vegetation indices, leaf area index, land cover, snow cover, sea ice extent, cloud mask, atmospheric profiles, aerosol properties, and many other geophysical parameters. MODIS data are stored in HDF- EOS format in both swath format and in several different map projections. This tutorial guides users through data set characteristics as well as search and order interfaces, data unpacking, data subsetting, and potential applications of the data. A CD-ROM with sample data sets, and software tools for working with the data will be provided to the course participants.

Leptoukh, Gregory↗