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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Modeling the Effect of Vegetation on Passive Microwave Remote Sensing of Soil Moisture

The effect of vegetation on passive microwave remote sensing of soil moisture is studied. The radiative transfer modeling work of Njoku and Kong is applied to a stratified medium of which the upper layer is treated as a layer of vegetation. An effective dielectric constant for this vegetation layer is computed using estimates of the dielectric constant of individual components of the vegetation layer. The horizontally-polarized brightness temperature is then computed as a function of the incidence angle. Model predictions are used to compare with the data obtained in the Huntsville '96, remote sensing of soil moisture experiment, and with predictions obtained using a correction procedure of Jackson and Schmugge.

Liu, Y. P.↗
NASA NTRS2007

NASA's Potential Contributions to Avalanche Forecasting Using Active and Passive Microwave Measurements

This Candidate Solution is based on using active and passive microwave measurements acquired from NASA satellites to improve USDA (U.S. Department of Agriculture) Forest Service forecasting of avalanche danger. Regional Avalanche Centers prepare avalanche forecasts using ground measurements of snowpack and mountain weather conditions. In this Solution, range of the in situ observations is extended by adding remote sensing measurements of snow depth, snow water equivalent, and snowfall rate acquired by satellite missions that include Aqua, CloudSat, future GPM (Global Precipitation Measurement), and the proposed SCLP (Snow and Cold Land Processes). Measurements of snowpack conditions and time evolution are improved by combining the in situ and satellite observations with a snow model. Recurring snow observations from NASA satellites increase accuracy of avalanche forecasting, which helps the public and the managers of public facilities make better avalanche safety decisions.

Blonski, Slawomir↗
NASA NTRS2009

A Blended Global Snow Product using Visible, Passive Microwave and Scatterometer Satellite Data

A joint U.S. Air Force/NASA blended, global snow product that utilizes Earth Observation System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and QuikSCAT (Quick Scatterometer) (QSCAT) data has been developed. Existing snow products derived from these sensors have been blended into a single, global, daily, user-friendly product by employing a newly-developed Air Force Weather Agency (AFWA)/National Aeronautics and Space Administration (NASA) Snow Algorithm (ANSA). This initial blended-snow product uses minimal modeling to expeditiously yield improved snow products, which include snow cover extent, fractional snow cover, snow water equivalent (SWE), onset of snowmelt, and identification of actively melting snow cover. The blended snow products are currently 25-km resolution. These products are validated with data from the lower Great Lakes region of the U.S., from Colorado during the Cold Lands Processes Experiment (CLPX), and from Finland. The AMSR-E product is especially useful in detecting snow through clouds; however, passive microwave data miss snow in those regions where the snow cover is thin, along the margins of the continental snowline, and on the lee side of the Rocky Mountains, for instance. In these regions, the MODIS product can map shallow snow cover under cloud-free conditions. The confidence for mapping snow cover extent is greater with the MODIS product than with the microwave product when cloud-free MODIS observations are available. Therefore, the MODIS product is used as the default for detecting snow cover. The passive microwave product is used as the default only in those areas where MODIS data are not applicable due to the presence of clouds and darkness. The AMSR-E snow product is used in association with the difference between ascending and descending satellite passes or Diurnal Amplitude Variations (DAV) to detect the onset of melt, and a QSCAT product will be used to map areas of snow that are actively melting.

Foster, James L.↗
NASA NTRS2007

Thin Sea-Ice Thickness as Inferred from Passive Microwave and In Situ Observations

Since microwave radiometric signals from sea-ice strongly reflect physical conditions of a layer near the ice surface, a relationship of brightness temperature with thickness is possible especially during the early stages of ice growth. Sea ice is most saline during formation stage and as the salinity decreases with time while at the same time the thickness of the sea ice increases, a corresponding change in the dielectric properties and hence the brightness temperature may occur. This study examines the extent to which the relationships of thickness with brightness temperature (and with emissivity) hold for thin sea-ice, approximately less than 0.2 -0.3 m, using near concurrent measurements of sea-ice thickness in the Sea of Okhotsk from a ship and passive microwave brightness temperature data from an over-flying aircraft. The results show that the brightness temperature and emissivity increase with ice thickness for the frequency range of 10-37 GHz. The relationship is more pronounced at lower frequencies and at the horizontal polarization. We also established an empirical relationship between ice thickness and salinity in the layer near the ice surface from a field experiment, which qualitatively support the idea that changes in the near-surface brine characteristics contribute to the observed thickness-brightness temperature/emissivity relationship. Our results suggest that for thin ice, passive microwave radiometric signals contain, ice thickness information which can be utilized in polar process studies.

Naoki, Kazuhiro↗
NASA NTRS1991

Synergic inversion technique for active and passive microwave remote sensing of the ocean

A unified approach for combining active and passive microwave measurements for remote sensing applications is described. A synergic inversion technique has been developed and applied to the retrieval of geophysical parameters of the ocean surface and of the atmosphere. It is based on the combination of radiometric and radar measurements at the electromagnetic and cell level and not only on the correction of radar measurements by radiometric measurements, or conversely. Such a combination is performed through a common quantity: the bistatic scattering coefficient of the observed surface. This is used in a direct model to simulate combined measurements from active and passive sensors. It requires a rather complete and accurate calculation of the scattering of microwaves by the rough sea surface. Comparisons with experimental models derived for scatterometers and radar altimeters or with data derived from radiometric measurements show that the results of this unified approach are consistent with those from separate microwave sensors, at least for a foam-free sea surface.

Sobieski, Piotr↗
NASA NTRS2000

Implications of RFI at L-Band on Passive Microwave Remote Sensing of Soil Moisture from Space: Experience with ESTAR During SGP97

Passive microwave remote sensing in the protected band at 1.413 GHz (L-band) is important for monitoring parameters of the ocean and land surface such as soil moisture and sea surface salinity. These parameters are needed for understanding energy exchange between the surface and atmosphere and therefore are important for improving our understanding of weather and climate change. Although the band at 1.413 GHz is protected for passive use, airborne radiometers experience problems with RFI. For example, during the Southern Great Plains Experiment (1997) the ESTAR radiometer experienced significant RFI associated with airports, presumably air traffic control radar. This is a potential problem for future sensors in space planned for remote sensing of the earth surface in this frequency band.

LeVine, David M.↗
NASA NTRS2019

Exploring the Utility of Machine Learning-Based Passive Microwave Brightness Temperature Data Assimilation over Terrestrial Snow in High Mountain Asia

This study explores the use of a support vector machine (SVM) as the observation operator within a passive microwave brightness temperature data assimilation framework (herein SVM-DA) to enhance the characterization of snow water equivalent (SWE) over High Mountain Asia (HMA). A series of synthetic twin experiments were conducted with the NASA Land Information System (LIS) at a number of locations across HMA. Overall, the SVM-DA framework is effective at improving SWE estimates (~70% reduction in RMSE relative to the Open Loop) for SWE depths less than 200 mm during dry snowpack conditions. The SVM-DA framework also improves SWE estimates in deep, wet snow (~45% reduction in RMSE) when snow liquid water is well estimated by the land surface model, but can lead to model degradation when snow liquid water estimates diverge from values used during SVM training. In particular, two key challenges of using the SVM-DA framework were observed over deep, wet snowpacks. First, variations in snow liquid water content dominate the brightness temperature spectral difference (TB) signal associated with emission from a wet snowpack, which can lead to abrupt changes in SWE during the analysis update. Second, the ensemble of SVM-based predictions can collapse (i.e., yield a near-zero standard deviation across the ensemble) when prior estimates of snow are outside the range of snow inputs used during the SVM training procedure. Such a scenario can lead to the presence of spurious error correlations between SWE and TB, and as a consequence, can result in degraded SWE estimates from the analysis update. These degraded analysis updates can be largely mitigated by applying rule-based approaches. For example, restricting the SWE update when the standard deviation of the predicted TB is greater than 0.05 K helps prevent the occurrence of filter divergence. Similarly, adding a thin layer (i.e., 5 mm) of SWE when the synthetic TB is larger than 5 K can improve SVM-DA performance in the presence of a precipitation dry bias. The study demonstrates that a carefully constructed SVM-DA framework cognizant of the inherent limitations of passive microwave-based SWE estimation holds promise for snow mass data assimilation.

Kwon, Yonghwan↗
NASA NTRS1992

Surface-based passive microwave studies of multiyear sea ice

Results are presented on surface-based multifrequency passive microwave observations of multiyear (MY) sea ice in the eastern Arctic Basin, the Beaufort Sea, the Canadian archipelago, and the northern Greenland Sea. The analyses of these data show that the magnitude of the spectral gradient of emissivity is directly related to the existence and the thickness of a decomposed surface ice layer with very high porosity. Spectra for melt ponds with a frozen surface layer closely resembled those of lake ice and showed a positive spectral gradient. The variance among emissivity spectra for MY ice was caused primarily by the distributions of melt ponds and by the presence of significant amounts of scattering inhomogeneities in the snow and the upper 20-30-cm layer of the ice.

Grenfell, T. C.↗
NASA NTRS2001

Seasonal Snow Extent and Snow Volume in South America Using SSM/I Passive Microwave Data

Seasonal snow cover in South America was examined in this study using passive microwave satellite data from the Special Sensor Microwave Imagers (SSM/I) on board Defense Meteorological Satellite Program (DMSP) satellites. For the period from 1992-1998, both snow cover extent and snow depth (snow mass) were investigated during the winter months (May-August) in the Patagonia region of Argentina. Since above normal temperatures in this region are typically above freezing, the coldest winter month was found to be not only the month having the most extensive snow cover but also the month having the deepest snows. For the seven-year period of this study, the average snow cover extent (May-August) was about 0.46 million sq km and the average monthly snow mass was about 1.18 x 10(exp 13) kg. July 1992 was the month having the greatest snow extent (nearly 0.8 million sq km) and snow mass (approximately 2.6 x 10(exp 13) kg).

Foster, James L.↗
NASA NTRS1984

Passive microwave characteristics of the Bering Sea ice cover during Marginal Ice Zone Experiment (MIZEX) West

Passive microwave measurements of the Bering Sea were made with the NASA CV-990 airborne laboratory during February. Microwave data were obtained with imaging and dual-polarized, fixed-beam radiometers in a range of frequencies from 10 to 183 GHz. The high resolution imagery at 92 GHz provides a particularly good description of the marginal ice zone delineating regions of open water, ice compactness, and ice-edge structure. Analysis of the fixed-beam data shows that spectral differences increase with a decrease in ice thickness. Polarization at 18 and 37 GHz distinguishes among new, young, and first-year sea ice types.

Cavalieri, D. J.↗
NASA NTRS1992

Passive microwave precipitation mapping and retrieval simulation

A numerical simulation of the satellite-based multispectral passive microwave mapping and precipitation retrieval process is described. The simulation contains three major components: (1) the forward radiative transfer calculation; (2) the sensor observation simulation; and (3) the precipitation retrieval algorithm. Secondary components include a Karhunen-Loeve transformation module and antenna pattern deconvolution module. The simulation facilitates the investigation of candidate precipitation retrieval algorithms using sub-Nyquist brightness temperature imagery, and the comparison of the relative merits of proposed radiometric systems such as the EOS multispectral imaging microwave radiometer (MIMR), the tropical rainfall measurement mission (TRMM) microwave imager (TRMM TMI), and the geosynchronous satellite sensors. A procedure for retrieving rain rates using a statistical iterative approach is outlined.

Skofronick, G. M.↗
NASA NTRS2002

Passive Microwave Studies of Atmospheric Precipitation and State

The principal contributions of this research on novel passive microwave spectral techniques are in the areas of: (1) global precipitation mapping using the opaque spectral bands on research and operational weather satellites, (2) development and analysis of extensive aircraft observational imaging data sets obtained using the MIT instrument NAST-M near 54 and 118 GHz over hurricanes and weather ranging from tropical to polar; simultaneous data from the 8500-channel infrared spectrometer NAST-I was obtained and analyzed separately, (3) estimation of hydrometeor diameters in cell tops using data from aircraft and spacecraft, (4) continued improvement of expressions for atmospheric transmittance at millimeter and sub-millimeter wavelengths, (5) development and airborne use of spectrometers operating near 183- and 425-GHz bands, appropriate to practical systems in geosynchronous orbit, and (6) preliminary studies of the design and performance of future geosynchronous microwave sounders for temperature and humidity profiles and for continuous monitoring of regional precipitation through most clouds. This work was a natural extension of work under NASA Grant NAG5-2545 and its predecessors. This earlier work had developed improved airborne imaging microwave spectrometers and had shown their sensitivity to precipitation altitude and character. They also had prepared the foundations for precipitation estimation using the opaque microwave bands. The field demonstration and improvement of these capabilities was then a central part of the present research reported here, during which period the first AMSU data became available and several hurricanes were overflown by NAST-M, yielding unique data about their microwave signatures. This present work has in turn helped lay the foundation for future progress in incorporating the opaque microwave channels in systems for climatologically precise global precipitation mapping from current and future operational satellites. Extension of these techniques to global snowfall mapping, even over ice and snow, is one such opportunity signaled by this research.

Staelin, David H.↗
NASA NTRS1996

The Perennial Ice Cover of the Beaufort Sea from Active and Passive Microwave Observations

We examine the perennial ice concentration in the Beaufort Sea using active and passive microwave observations. The definition of multiyear ice provides a condition which states that areas of multiyear ice during the winter should be nearly equivalent to the ice area during the previous summer's minima. We compare the ice type and concentration estimates from SSM/I and ERS-1 SAR data over a seasonal cycle from January 1992 to January 1993.

Sea↗
NASA NTRS2021

Surface Melting Over the Greenland Ice Sheet Derived From Enhanced Resolution Passive Microwave Brightness Temperatures (1979–2019)

Surface melting is a major component of the Greenland ice sheet surface mass balance, and it affects sea level rise through direct runoff and the modulation of ice dynamics and hydrological processes, supraglacially, englacially and subglacially. Passive microwave (PMW) brightness temperature observations are of paramount importance in studying the spatial and temporal evolution of surface melting due to their long temporal coverage (1979–present) and high temporal resolution (daily). However, a major limitation of PMW datasets has been the relatively coarse spatial resolution, which has historically been of the order of tens of kilometers. Here, we use a newly released PMW dataset (37 GHz, horizontal polarization) made available through a NASA “Making Earth System Data Records for Use in Research Environments” (MeASUREs) program to study the spatiotemporal evolution of surface melting over the Greenland ice sheet at an enhanced spatial resolution of 3.125 km. We assess the outputs of different detection algorithms using data collected by automatic weather stations (AWSs) and the outputs of the Modèle Atmosphérique Régional (MAR) regional climate model. We found that sporadic melting is well captured using a dynamic algorithm based on the outputs of the Microwave Emission Model of Layered Snowpack (MEMLS), whereas a fixed threshold of 245 K is capable of detecting persistent melt. Our results indicate that, during the reference period from 1979 to 2019 (from 1988 to 2019), surface melting over the ice sheet increased in terms of both duration, up to 4.5 (2.9) d per decade, and extension, up to 6.9 % (3.6 %) of the entire ice sheet surface extent per decade, according to the MEMLS algorithm. Furthermore, the melting season started up to 4.0 (2.5) d earlier and ended 7.0 (3.9) d later per decade. We also explored the information content of the enhanced-resolution dataset with respect to the one at 25 km and MAR outputs using a semi-variogram approach. We found that the enhanced product is more sensitive to local-scale processes, thereby confirming the potential of this new enhanced product for monitoring surface melting over Greenland at a higher spatial resolution than the historical products and for monitoring its impact on sea level rise. This offers the opportunity to improve our understanding of the processes driving melting, to validate modeled melt extent at high resolution and, potentially, to assimilate these data in climate models.

Surface melting↗
NASA NTRS1977

A multichannel passive microwave atmospheric temperature sounding system

The development of a small, lightweight, low-power, seven channel passive microwave radiometer system for use on the Defense Meteorological Satellite Program (DMSP) was described. The 50-60 GHz sensor system operates in the region of an intense atmospheric oxygen absorption band to provide atmospheric temperature profiles to 30 kilometer altitudes on a global basis.

Louappe, M. E.↗