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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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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 NTRS1972

Functional design for operational earth resources ground data processing

The author has identified the following significant results. Study emphasis was on developing a unified concept for the required ground system, capable of handling data from all viable acquisition platforms and sensor groupings envisaged as supporting operational earth survey programs. The platforms considered include both manned and unmanned spacecraft in near earth orbit, and continued use of low and high altitude aircraft. The sensor systems include both imaging and nonimaging devices, operated both passively and actively, from the ultraviolet to the microwave regions of the electromagnetic spectrum.

Baldwin, C. J.↗

NASA NTRS1987

Investigation of multi-dimensional algorithms using active and passive microwave data for ice concentration determination

A discussion is presented concerning the effect of variations in ice surface characteristics on the concentration calculations, in order to ascertain how active and passive microwave data can be optimally combined. Attention is given to dual-polarized, multifrequency passive microwave sensors and to single-channel SAR, in virtue of their availability in the near future. An examination of SAR intensity data at both 1 and 10 GHz indicates that the latter are much less sensitive to ice type and surface condition variations such as wetness and ridging. The Advanced Multichannel Microwave Radiometer is discussed.

Cavalieri, Donald J.↗

NASA NTRS1974

A remote sensing study of Pacific hurricane Ava

Aircraft, Skylab, NOAA-2, ATS-3, and NIMBUS-5 recently obtained a variety of measurements of Pacific hurricane Ava. These measurements are unusually broad in scope and include satellite observed passive microwave emissivities at 13.9 and 19.5 GHz, active microwave scattering cross-sections at 13.9 GHz, and near infrared and visible images. Essentially simultaneous aircraft measurements of wind speed, waves, whitecaps, 1.4 and 13-15 GHz passive microwave emissivities, 1.4 GHz active microwave images, sea surface temperatures, pressure fields, and aerosol size distributions were also obtained. A brief description of sensors and platforms is presented along with some in-depth details of results obtained.

Ross, D.↗

NASA NTRS2022

A Comprehensive Machine Learning Study to Classify Precipitation Type over Land from Global Precipitation Measurement Microwave Imager (GPM-GMI) Measurements

Precipitation type is a key parameter used for better retrieval of precipitation characteristics as well as to understand the cloud–convection–precipitation coupling processes. Ice crystals and water droplets inherently exhibit different characteristics in different precipitation regimes (e.g., convection, stratiform), which reflect on satellite remote sensing measurements that help us distinguish them. The Global Precipitation Measurement (GPM) Core Observatory’s microwave imager (GMI) and dual-frequency precipitation radar (DPR) together provide ample information on global precipitation characteristics. As an active sensor, the DPR provides an accurate precipitation type assignment, while passive sensors such as the GMI are traditionally only used for empirical understanding of precipitation regimes. Using collocated precipitation type flags from the DPR as the “truth”, this paper employs machine learning (ML) models to train and test the predictability and accuracy of using passive GMI-only observations together with ancillary information from a reanalysis and GMI surface emissivity retrieval products. Out of six ML models, four simple ones (support vector machine, neural network, random forest, and gradient boosting) and the 1-D convolutional neural network (CNN) model are identified to produce 90–94% prediction accuracy globally for five types of precipitation (convective, stratiform, mixture, no precipitation, and other precipitation), which is much more robust than previous similar effort. One novelty of this work is to introduce data augmentation (subsampling and bootstrapping) to handle extremely unbalanced samples in each category. A careful evaluation of the impact matrices demonstrates that the polarization difference (PD), brightness temperature (Tc) and surface emissivity at high-frequency channels dominate the decision process, which is consistent with the physical understanding of polarized microwave radiative transfer over different surface types, as well as in snow and liquid clouds with different microphysical properties. Furthermore, the view-angle dependency artifact that the DPR’s precipitation flag bears with does not propagate into the conical-viewing GMI retrievals. This work provides a new and promising way for future physics-based ML retrieval algorithm development.

machine learning/artificial intelligence↗

NASA NTRSDate not supplied

Space‐Based Observations for Understanding Changes in the Arctic‐Boreal Zone

A recent, interdisciplinary survey of more than 40 international remote sensing experts reviewed the strengths and limitations of current space-based observational capabilities of the Arctic Boreal Zone (ABZ) and made recommendations for improving upon these current capabilities (Duncan et al., 2019). General recommendations focused on the complementarity of sub-orbital and satellite observations, the need for measurement continuity, and the importance of stakeholder engagement. The development of a comprehensive and robust sub-orbital portion of an ABZ observing network can act to fill some temporal gaps in satellite coverage, provide detail unobtainable from space, and is necessary for validation and interpretation of satellite data. A priority across all types of observations is the continuation, enhancement, and/or creation of long-term, multi-satellite, climate-quality, and self-consistent data records of ABZ components, such as surface temperature, energy fluxes, or sea ice extent and volume, for improved determination of trends. Long-term passive satellite observations currently represent the only feasible option for monitoring change of the ABZ at broad spatial scales required to address pressing science challenges. Active sensors are particularly attractive for the ABZ as they do not depend on reflected sunlight and so join passive microwave sensors in having a significant advantage over passive visible and infrared sensors in the low-light conditions that are typical of the ABZ for several months of the year. Application of consistent retrieval algorithms to multiple data sets, as well as careful characterization of satellite instruments and their temporal evolution, helps to ensure data quality and consistency. Among the needs ranked as “Most Important” are those associated with gaining a process-based understanding of the ABZ carbon cycle and hydrologic cycle (which includes sea level rise) as they have the potential to affect a large portion of Earth’s population.

Remote sensing↗

NASA NTRSDate not supplied

Examining Soil Freeze/Thaw Remote Sensing Using NASA’s SLAPex Freeze/Thaw Airborne Campaign

Around half the exposed land of the Northern Hemisphere experiences seasonal freezing and thawing. The changes to the soil thermal, hydraulic, and mechanical properties are large and these, in turn, exert significant controls on the water, energy, and carbon cycles of the affected regions. Wide-area freeze/thaw discrimination via microwave remote sensing is possible using both passive and active techniques. We explore freeze/thaw remote sensing using observations from NASA’s Scanning L-band Active Passive (SLAP) airborne sensor during the SLAPex Freeze/Thaw campaign near Winnipeg, Canada in November, 2015. SLAP is an airborne simulator of the Soil Moisture Active Passive (SMAP) satellite mission. The SLAPex Freeze/Thaw dataset is a unique dataset that allows comparison of airborne-derived freeze/thaw state (plus soil moisture for thawed areas) against in-situ ground truth, satellite-derived freeze/thaw from SMAP, as well as tower-based passive microwave freeze/thaw observations. The sensitivity of passive and active microwave sensing to different depths of freezing and different dielectric models (when soil moisture is retrieved) is examined, and the spatial scaling of heterogeneous frozen/thawed pixels is also explored. Implications for the accuracy of satellite-based freeze/thaw discrimination are discussed.

Edward Kim↗

NASA NTRS2004

Landcover Based Optimal Deconvolution of PALS L-band Microwave Brightness Temperature

An optimal de-convolution (ODC) technique has been developed to estimate microwave brightness temperatures of agricultural fields using microwave radiometer observations. The technique is applied to airborne measurements taken by the Passive and Active L and S band (PALS) sensor in Iowa during Soil Moisture Experiments in 2002 (SMEX02). Agricultural fields in the study area were predominantly soybeans and corn. The brightness temperatures of corn and soybeans were observed to be significantly different because of large differences in vegetation biomass. PALS observations have significant over-sampling; observations were made about 100 m apart and the sensor footprint extends to about 400 m. Conventionally, observations of this type are averaged to produce smooth spatial data fields of brightness temperatures. However, the conventional approach is in contrast to reality in which the brightness temperatures are in fact strongly dependent on landcover, which is characterized by sharp boundaries. In this study, we mathematically de-convolve the observations into brightness temperature at the field scale (500-800m) using the sensor antenna response function. The result is more accurate spatial representation of field-scale brightness temperatures, which may in turn lead to more accurate soil moisture retrieval.

Limaye, Ashutosh S.↗

NASA NTRS2004

Application of SeaWinds Scatterometer and TMI-SSM/I Rain Rates to Hurricane Analysis and Forecasting

Results provided by two different assimilation methodologies involving data from passive and active space-borne microwave instruments are presented. The impact of the precipitation estimates produced by the TRMM Microwave Imager (TMI) and Special Sensor Microwave/Imager (SSM/I) in a previously developed 1D variational continuous assimilation algorithm for assimilating tropical rainfall is shown on two hurricane cases. Results on the impact of the SeaWinds scatterometer on the intensity and track forecast of a mid-Atlantic hurricane are also presented. This work is the outcome of a collaborative effort between NASA and NOAA and indicates the substantial improvement in tropical cyclone forecasting that can result from the assimilation of space-based data in global atmospheric models.

Atlas, Robert↗

NASA NTRS1993

Topographic slope from the SAR interferometric phase gradient

A new algorithm for the direct calculation of topographic slope maps from synthetic aperture radar (SAR) interferograms is presented. The algorithm derives slope maps without first requiring the creation of a digital elevational model (DEM) from the interferogram, thus obviating the need for high SNR in the interferogram and altitude calibration points for the scene. SAR data useful for interferometry has been collected by the Active Microwave Imager on board the ERS-1 satellite, when it was in a short period repeat orbit. Two passes of the radar sensor form a cross-track interferometric baseline. For a point target at some position (x,y,h), the interferometric phase difference phi is proportional to the difference in path lengths for the two sensor positions to the scatterer. Given the phase difference as measured in the complex interferogram and an accurate baseline geometry, the position of the scatterer, most significantly the height h, can be determined through triangulation. The interferometric phase measurement however is known only modulo 2-pi, and hence it is necessary to determine the correct multiple of 2-pi to add to the phase at each point to obtain an estimate of the actual phase with respect to an absolute datum. This phase unwrapping process is required for creating DEM's, and is difficult or impossible for regions of low SNR or SAR image layover. The new algorithm described here derives slope maps without requiring phase unwrapping.

Werner, Charles L.↗

NASA NTRS2019

Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters

Since 2009, three low frequency microwave sensors have been launched into space with the capability of global monitoring of sea surface salinity (SSS). The European Space Agency's (ESA's) Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), onboard the Soil Moisture and Ocean Salinity mission (SMOS), and National Aeronautics and Space Administration's (NASA's) Aquarius and Soil Moisture Active Passive mission (SMAP) use L-band radiometry to measure SSS. There are notable differences in the instrumental approaches, as well as in the retrieval algorithms. We compare the salinity retrieved from these three spaceborne sensors to in situ observations from theArgo network of drifting floats, and we analyze some possible causes for the differences. We present comparisons of the long-term global spatial distribution, the temporal variability for a set of regions of interest and statistical distributions. We analyze some of the possible causes for the differences between the various satellite SSS products by reprocessing the retrievals from Aquarius brightness temperatures changing the model for the sea water dielectric constant and the ancillary product for the sea surface temperature. We quantify the impact of these changes on the differences in SSS between Aquarius and SMOS.We also identify the impact of the corrections for atmospheric effectsrecently modified in the Aquarius SSS retrievals. All three satellites exhibit SSS errors with a strong dependence on sea surface temperature, but this dependence varies significantly with the sensor. We show that these differences are first and foremost due to the dielectric constant model, then to atmospheric corrections and to a lesser extent to the ancillary product of the sea surface temperature.

SSS↗

NASA NTRS1985

Microwave remote sensing from space

Spaceborne microwave remote sensors provide perspectives of the earth surface and atmosphere which are of unique value in scientific studies of geomorphology, oceanic waves and topography, atmospheric water vapor and temperatures, vegetation classification and stress, ice types and dynamics, and hydrological characteristics. Microwave radars and radiometers offer enhanced sensitivities to the geometrical characteristics of the earth's surface and its cover, to water in all its forms - soil and vegetation moisture, ice, wetlands, oceans, and atmospheric water vapor, and can provide high-resolution imagery of the earth's surface independent of cloud cover or sun angle. A brief review of the historical development and principles of active and passive microwave remote sensing is presented, with emphasis on the unique characteristics of the information obtainable in the microwave spectrum and the value of this information to global geoscientific studies. Various spaceborne microwave remote sensors are described, with applications to geology, planetology, oceanography, glaciology, land biology, meteorology, and hydrology. A discussion of future microwave remote sensor technological developments and challenges is presented, along with a summary of future missions being planned by several countries.

Carver, K. R.↗

NASA NTRS1975

Earth observations

The development of the Earth Resources Technology Satellites for making earth observations from space is discussed. The programmatic elements of the ERTS program are defined. The specific results of the program which are considered are as follows: (1) analysis of snowmelt runoff, (2) analysis of Mississippi River floods of 1973, (3) global ice surveys with an electrically scanning microwave radiometer, (4) the microwave signature of snow fields, (5) analysis of strip mining activities, (6) sea surface studies using microwave techniques during the Bering Sea experiment, (7) cloud types and measurement of rainfall, and (8) performance of various remote sensors.

Source record↗

NASA NTRS1984

Satellite observations of sea ice

Attention is given to techniques developed by NASA for the satellite acquisition of polar ice cover data, as well as for the spotlighting of selected ice cover areas in order to reveal detailed ice pack features. Of the visible, IR and microwave sensors available aboard satellites, only microwave units operate day and night and in all weather conditions to provide global synoptic coverage. Microwave may be either passive or active; the former include the Electrically Scanning Microwave Radiometer aboard Nimbus-5 and the Scanning Multichannel Microwave Radiometer of Seasat and Nimbus-7, while the latter are represented by synthetic aperture radars and radar altimeters aboard Seasat.

Thomas, R. H.↗

NASA NTRS1981

Availability of Seasat synthetic aperture radar imagery

One of the sensors of the Seasat satellite was a synthetic aperture radar (SAR), which was operated over both oceanic and land areas. The Seasat SAR was an active imaging microwave system which transmitted and received horizontally polarized radiation at L-band frequency (1.275 GHz, 23-cm wavelength). Extensive coverage was obtained over North America, Europe, the Eastern Pacific Ocean, the Western and Northern Atlantic Ocean, and the Beaufort Sea. Surface coverage was also acquired over limited portions of Central America, the Gulf of Mexico, and the Caribbean Sea. The majority of the data has been optically processed in a so-called survey mode. Each optically processed Seasat pass has a swath width of 100 km at a scale of 1:500,000. A subset of the SAR data has been digitally processed in a so-called high-resolution mode. Digital processing has been performed upon selected scenes with dimensions of 100 x 100 km. Digitally processed imagery has a nominal ground resolution of 25 m.

Holt, B.↗

NASA NTRS2017

Active and Passive 3D Vector Radiative Transfer with Preferentially-Aligned Ice Particles

For the purposes of interpreting active (radar) and passive (radiometer) microwave and millimeter wave remote sensing data, we have constructed a consistent radiative transfer modeling framework to simulate the responses for arbitrary sensors with differing sensing geometries and hardware configurations. As part of this work, we have implemented a recent method for calculating the electromagnetic properties of individual ice crystals and snow flakes. These calculations will allow us to exploit polarized remote sensing observations to discriminate different particles types and elucidate dynamics of cloud and precipitating systems.

Adams, Ian S.↗

DOE OSTI2021

Facile microwave synthesis of zirconium metal-organic framework thin films on gold and silicon and application to sensor functionalization

Zirconium-based metal-organic frameworks, including UiO-66 and related frameworks, have become the focus of considerable research in the area of chemical warfare agent (CWA) decontamination. However, little work has been reported exploring these metal-organic frameworks (MOFs) for CWA sensing applications. For many sensing approaches, the growth of high-quality thin films of the active material is required, and thin film growth methods must be compatible with complex device architectures. Several approaches to synthesize thin films of UiO-66 have been described but many of these existing methods are complex or time consuming. In this work, we describe the development of a simple and rapid microwave assisted synthesis of oriented UiO-66 thin films on unmodified silicon (Si) and gold (Au) substrates. Thin films of UiO-66 and UiO-66-NH 2 can be grown in as little as 2 min on gold substrates and 30 min on Si substrates. The film morphology and orientation are characterized and the effects of reaction time and temperature on thin film growth on Au are investigated. Both reaction time and temperature impact the overgrowth of protruding discrete crystallites in the thin film layer but, surprisingly, no strong correlation is observed between film thickness and reaction time or temperature. We also briefly describe the synthesis of Zr/Ce solid solution thin films of UiO-66 on Au and report the first synthesis of a solid solution thin film MOF. Finally, we demonstrate the utility of the microwave method for the facile functionalization of two sensor architectures, plasmonic nanohole arrays and microresonators, with UiO-66 thin films.

36 MATERIALS SCIENCE↗

NASA NTRS1977

Microwave remote sensing from space for earth resource surveys

The concepts of radar remote sensing and microwave radiometry are discussed and their utility in earth resource sensing is examined. The direct relationship between the character of the remotely sensed data and the level of decision making for which the data are appropriate is considered. Applications of active and a passive microwave sensing covered include hydrology, land use, mapping, vegetation classification, environmental monitoring, coastal features and processes, geology, and ice and snow. Approved and proposed microwave sensors are described and the use of space shuttle as a development platform is evaluated.

Source record↗