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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Comparison of sea ice parameters retrieved from passive microwave (SSM/I), Landsat MSS and AVHRR imagery

Ice concentrations derived from passive microwave data from the Special Sensing Microwave Imager (SSM/I), the Landsat MSS, and the AVHRR are compared for the validation of SSM/I-derived sea-ice products. Case studies with data from the Beaufort, Bering, East Greenland, and Weddell Seas are conducted to evaluate the performance of the NASA Team SSM/I algorithm under various sea-ice conditions. The results show that SSM/I ice concentrations are accurate with a mean error of 15 percent using globally derived tie points.

Schweiger, Axel J.↗
NASA NTRS1990

Progress in microwave remote sensing of soil moisture

Significant progress has been made in the application of microwave remote sensing for measuring soil moisture. Both passive and active systems have demonstrated the capability for measuring soil moisture. However, several questions are still unresolved regarding the optimal instrument configuration and other target characteristics, such as roughness and vegetation. In addition, the most likely disciplines for using these data, agriculture and hydrology, do not currently possess adequate models or procedures for using these new data.

Engman, Edwin T.↗
NASA NTRS1997

Analysis of Smart Composite Structures Including Debonding

Smart composite structures with distributed sensors and actuators have the capability to actively respond to a changing environment while offering significant weight savings and additional passive controllability through ply tailoring. Piezoelectric sensing and actuation of composite laminates is the most promising concept due to the static and dynamic control capabilities. Essential to the implementation of these smart composites are the development of accurate and efficient modeling techniques and experimental validation. This research addresses each of these important topics. A refined higher order theory is developed to model composite structures with surface bonded or embedded piezoelectric transducers. These transducers are used as both sensors and actuators for closed loop control. The theory accurately captures the transverse shear deformation through the thickness of the smart composite laminate while satisfying stress free boundary conditions on the free surfaces. The theory is extended to include the effect of debonding at the actuator-laminate interface. The developed analytical model is implemented using the finite element method utilizing an induced strain approach for computational efficiency. This allows general laminate geometries and boundary conditions to be analyzed. The state space control equations are developed to allow flexibility in the design of the control system. Circuit concepts are also discussed. Static and dynamic results of smart composite structures, obtained using the higher order theory, are correlated with available analytical data. Comparisons, including debonded laminates, are also made with a general purpose finite element code and available experimental data. Overall, very good agreement is observed. Convergence of the finite element implementation of the higher order theory is shown with exact solutions. Additional results demonstrate the utility of the developed theory to study piezoelectric actuation of composite laminates with pre-existing debonding. Significant changes in the modes shapes and reductions in the control authority result due to partially debonded actuators. An experimental investigation addresses practical issues, such as circuit design and implementation, associated with piezoelectric sensing and actuation of composite laminates. Composite specimens with piezoelectric transducers were designed, constructed and tested to validate the higher order theory. These specimens were tested with various stacking sequences, debonding lengths and gains for both open and closed loop cases. Frequency changes of 15% and damping on the order of more than 20% of critical damping, via closed loop control, was achieved. Correlation with the higher order theory is very good. Debonding is shown to adversely affect the open and closed loop frequencies, damping ratios, settling time and control authority.

Chattopadhyay, Aditi↗
NASA NTRS1997

Combined High-Resolution Active and Passive Imaging of Ocean Surface Winds from Aircraft

A unique complement of passive and active microwave imaging and sensing instruments for observing ocean surface emission and scattering signatures were integrated onto the NASA Wallops Flight Facility's Orion P-3B aircraft (N426NA) for the purpose of studying the signature of ocean surface winds. The complement included: (1) a, four-band (X, K, Ka, and W) tri-polarimetric scanning radiometer (PSR), (2) a C-band ocean surface scatterometer (CSCAT), (3) a Ka-band conical-scanning polarimetric radiometer (KASPR), (4) a nadir-viewing Ka-band polarimetric radiometer, (KAPOL), (5) a 21- and 31-GHz zenith-viewing cloud and water vapor radiometer (CWVR), and (6) a radar ocean wave spectrometer (ROWS). The above Ocean Winds Imaging (OWI) complement was flown during January-March, 1997 over the Labrador Sea. Conically-scanned brightness temperature and backscatter imagery were observed over open ocean for a variety of wind speeds and cloud conditions. Presented herein are the results of a preliminary intercomparison of data from several of the OWI instruments.

Gasiewski, A. J.↗
NASA NTRS2003

Development of Structural Health Management Technology for Aerospace Vehicles

As part of the overall goal of developing Integrated Vehicle Health Management (IVHM) systems for aerospace vehicles, NASA has focused considerable resources on the development of technologies for Structural Health Management (SHM). The motivations for these efforts are to increase the safety and reliability of aerospace structural systems, while at the same time decreasing operating and maintenance costs. Research and development of SHM technologies has been supported under a variety of programs for both aircraft and spacecraft including the Space Launch Initiative, X-33, Next Generation Launch Technology, and Aviation Safety Program. The major focus of much of the research to date has been on the development and testing of sensor technologies. A wide range of sensor technologies are under consideration including fiber-optic sensors, active and passive acoustic sensors, electromagnetic sensors, wireless sensing systems, MEMS, and nanosensors. Because of their numerous advantages for aerospace applications, most notably being extremely light weight, fiber-optic sensors are one of the leading candidates and have received considerable attention.

Prosser, W. H.↗
NASA NTRS2008

Modeling the Impact of Drizzle and 3D Cloud Structure on Remote Sensing of Effective Radius

Remote sensing of cloud particle size with passive sensors like MODIS is an important tool for cloud microphysical studies. As a measure of the radiatively relevant droplet size, effective radius can be retrieved with different combinations of visible through shortwave infrared channels. MODIS observations sometimes show significantly larger effective radii in marine boundary layer cloud fields derived from the 1.6 and 2.1 pm channel observations than for 3.7 pm retrievals. Possible explanations range from 3D radiative transport effects and sub-pixel cloud inhomogeneity to the impact of drizzle formation on the droplet distribution. To investigate the potential influence of these factors, we use LES boundary layer cloud simulations in combination with 3D Monte Carlo simulations of MODIS observations. LES simulations of warm cloud spectral microphysics for cases of marine stratus and broken stratocumulus, each for two different values of cloud condensation nuclei density, produce cloud structures comprising droplet size distributions with and without drizzle size drops. In this study, synthetic MODIS observations generated from 3D radiative transport simulations that consider the full droplet size distribution will be generated for each scene. The operational MODIS effective radius retrievals will then be applied to the simulated reflectances and the results compared with the LES microphysics.

Platnick, Steven↗
NASA NTRS2017

Operational Implementation of Sea Ice Concentration Estimates from the AMSR2 Sensor

An operation implementation of a passive microwave sea ice concentration algorithm to support NOAA's operational mission is presented. The NASA team 2 algorithm, previously developed for the NASA advanced microwave scanning radiometer for the Earth observing system (AMSR-E) product suite, is adapted for operational use with the JAXA AMSR2 sensor through several enhancements. First, the algorithm is modified to process individual swaths and provide concentration from the most recent swaths instead of a 24-hour average. A latency (time since observation) field and a 24-hour concentration range (maximum-minimum) are included to provide indications of data timeliness and variability. Concentration from the Bootstrap algorithm is a secondary field to provide complementary sea ice information. A quality flag is implemented to provide information on interpolation, filtering, and other quality control steps. The AMSR2 concentration fields are compared with a different AMSR2 passive microwave product, and then validated via comparison with sea ice concentration from the Suomi visible and infrared imaging radiometer suite. This validation indicates the AMSR2 concentrations have a bias of 3.9% and an RMSE of 11.0% in the Arctic, and a bias of 4.45% and RMSE of 8.8% in the Antarctic. In most cases, the NOAA operational requirements for accuracy are met. However, in low-concentration regimes, such as during melt and near the ice edge, errors are higher because of the limitations of passive microwave sensors and the algorithm retrieval.

Arctic↗
NASA NTRS2017

RFI Statistical Distribution and Missed Detection in Aquarius Radiometer Measurements

Aquarius is an microwave active/passive sensor whose main goal is to globally estimate sea surface salinity from space. Two instruments, a radar scatterometer and a radiometer, operate at L-band observing the same surface footprint almost simultaneously. The sensitivity to sea surface salinity (SSS) is given by the radiometer, while the scatterometer measurements provide a correction for sea surface roughness. Although the primary objective is the measurement of SSS, the instrument combination operates continuously, acquiring data over land and sea ice as well. Radio Frequency Interference (RFI) can occur in both the radiometer and the scatterometer bands of operation, and for this reason detection and mitigation of RFI was included in the data processing of both active and passive instruments. This paper will focus on the RFI processing for the Aquarius radiometer only and provide an update on the efforts to reduce the amount of missed RFI detection.

RFI↗
NASA NTRS2020

Spurious Signal in SMAP Fourth Stokes Parameter

The radiometer on the NASA Soil Moisture Active/Passive (SMAP) mission is a fully polarimetric instrument that operates at L-band in the spectrum window at 1400-1427 MHz protected for passive use only. A unique feature of the radiometer is the fully digital back-end which permits direct computation of the third and fourth Stokes parameters: the real and imaginary part of the correlation of signal at horizontal and vertical polarizations, respectively. In particular, the SMAP conical scanning geometry provides the opportunity to look at the global distribution of the fourth Stokes parameter, TA4, at constant incidence angle (40°). A striking feature of TA4 is the existence of a strong (±10 K) spurious signal at coastlines. This article provides examples of the spikes and an explanation of the cause. Simulations have shown that the spurious signal is associated with antenna imperfections, such as cross polarization coupling and phase mismatch between polarizations.

radiometry↗
NASA NTRSDate not supplied

FORWARD AND INVERSE L-BAND RADIATIVE TRANSFER MODELING OVER THE DRY CHACO, USING SMOS OBSERVATIONS, LAND SURFACE MODELING AND IN SITU DATA

Passive microwave L-band remote sensing is well known for its sensitivity to surface soil moisture over land.The signal is also affected by other dynamic variables such as vegetation and soil salinity. In this research, L-band microwave observations of the Soil Moisture Ocean Salinity (SMOS) mission are used to explore soil surface salinity, moisture and vegetation, in the Argentinean Dry Chaco, an area with possible emerging dryland salinity. A Radiative Transfer Model (RTM) with inclusion of a correction to the soil’s dielectric constant for salinity was used in forward and inverse mode, using either in situ data or Catchment Land Surface Model (CLSM) simulations as RTM input. The forward analysis pointed out shortcomings in the modeled soil moisture and soil surface temperature estimates. The impact of salinity on forward simulations over the Dry Chaco was limited.The RTM inversion using 10 years of SMOS brightness temperature observations resulted in realistic estimates of vegetation and roughness, both with and withouto ptimizing a correction term for the dielectric constant in terms of salinity equivalents. However,the retrieval of the correction term to the dielectric constant was very uncertain and not representative of soil surface salinity, but rather of open water, texture uncertainty or soil moisture bias.

L-band↗
NASA NTRS2021

Informing Improvements in Freeze/Thaw State Classification Using Subpixel Temperature

Freeze/thaw (FT) processes at the earth’s surface can have a considerable effect on global carbon, energy, and hydrologic cycles. Therefore, an accurate representation of FT is valuable to adequately monitor and model these processes. In this study, we assess the relationship between satellite-based FT products and modeled surface and soil temperatures over North America. In addition, hourly land surface temperature (LST) from the Geostationary Operational Environmental Satellite (GOES) system is also compared to FT classifications. Utilizing the higher spatial resolution temperatures (5 km), we assess subgrid-scale variability and its relationship to coarser microwave FT classifications (>25 km). We also examine product agreement and subpixel characteristics across the land cover, climate, and topography. FT classifications are shown to vary widely depending on these variables, leading to an ambiguous definition of frozen and thawed states. Our results suggest that current products can characterize FT transitions with consistent subfreezing surface characteristics in far northern regions (>50 °N). However, uncertainty associated with FT classifications is shown to increase considerably as latitude decreases. Our results also suggest that fractional FT products, utilizing data inputs, such as LST, would provide a considerable improvement in mountainous regions with high intergrid cell heterogeneity, in regions characterized by ephemeral FT events (i.e., regions <40 °N), as well as during freeze and thaw onset periods. This study also provides insight to improving the representation of surface FT state by providing a clearer definition of the subpixel scale temperature characteristics that govern existing frozen classifications.

Earth observing systems↗
NASA NTRS2022

Development of SMAP Retrievals for Forested Regions: SMAPVEX19-22 and SMAPVEX22-Boreal

The retrieval of soil moisture (SM) under forest canopy has long been an important goal for low frequency remote sensing. The NASA Soil Moisture Active Passive (SMAP) mission is engaged at three separate experiment sites to improve its SM retrieval algorithm in forested areas. Two of the sites are located in the deciduous forest region in Massachusetts and New York, US and one is located in southern boreal forest zone in Saskatchewan, Canada. Each site has a SM measurement network of about 20 stations spread out over an area of about 30 km, which covers the SMAP radiometer footprint. In 2022, intensive observations will be carried out at each site which involve deployments of an airborne instrument, which is similar to the SMAP instrument, and intensive manual measurements of SM, surface and vegetation. The measurements also include tower-based radiometer observations with ground truth measurements within the instrument footprint. Here we show some early results using the networks and SMAP measurements to analyze the sensitivity of the SMAP L-band measurements to SM changes in forested area and the impact of the vegetation to the signal. The results suggest an upper limit for vegetation attenuation accounting for surface roughness effect and relate that to the values used in the current SMAP SM products.

Yueh, Simon H.↗
NASA NTRS1999

Remote Sensing of Rain

The first problem addressed concerns passive-microwave rain retrievals. Most current approaches start by building off-line a cloud-model-derived database. Given data, the retrieval algorithms search the database for the microwave temperatures "closest" to the observed data, then after some fine-tuning (performed in different ways by different implementations) the rain is estimated to be that which corresponds to the selected (and fine-tuned) set of database temperatures. These approaches have three drawbacks: they cannot properly take into account the ambiguities which arise from the fact that several rain scenarios can produce the same observed temperatures; they are quite inefficient since they require manipulating a large database along with often complex "fine-tuning" procedures; and they cannot refine their estimates if additional data is available. This past year we have derived closed formulae relating observed microwave brightness temperatures, T(sub b), and the underlying rain rates, R: average T(sub b) =f (rain) and average rain = g (T(sub b)), along with the corresponding covariance matrices. These results are sufficient to describe the conditional probabilities p(R/T(sub b)) and p(T(sub b)/R) to second order. Progress has also been made towards deriving a robust description of the rain drop size distribution (DSD). The widespread approach consisting in parameterizing the DSD as a gamma-distribution in terms of the drop diameter D suffers from the facts that, in reality, the DSD is not a smooth function of D and that the largely arbitrary Gamma model imposes unintended behavior, which has implications on any quantities derived from the DSD model. We have therefore developed a non-parametric yet practical description of the DSD, which is particularly well-suited for use in remote-sensing applications. The diagram on the left shows a comparison between an actual DSD sample and the truncated non-parametric representation. One figure shows the relation between radar reflectivity and rain rate derived using this representation. Validation of the Tropical Rainfall Measuring Mission (TRMM) radar-radiometer combined R and DSD algorithm is underway. This algorithm was designed to make optimal use of the instantaneous reflectivity profiles measured by the TRMM radar and the microwave brightness temperatures measured by the TRMM passive radiometer. So far, it appears to be the most reliable TRMM rain algorithm.

Haddad, Ziad S.↗
NASA NTRS2018

Assimilation of Satellite Derived Soil Moisture Profiles into a Crop Modeling System for Robust Yield Estimates

Soil Moisture Measurement - Remote Sensing. - Microwave (MW) Remote Sensing: Physically based and quantitative in nature; Based on difference in dielectric constant; Coarse spatial resolution 25-40 km; Shallow SM estimation depth 0-5 cm (approx.); All weather capabilities (e.g. Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E), Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP) etc.) - Thermal Infrared (TIR) Remote Sensing: Indirect SM retrieval through energy flux estimations; Relatively better spatial resolution 1-10 km; Root-zone moisture retrieval capability; Can not penetrate through clouds, hence data gaps (e.g. Surface Energy Balance Algorithm for Land (SEBAL), Atmospheric Land Exchange Inverse (ALEXI) etc.)

SERVIR↗
DOE OSTI2021

All-Digital Plug and Play Passive RFID Sensors for Energy Efficient Building Control

This is the final report for the project “All-Digital Plug and Play Passive RFID Sensors for Energy Efficient Building Control”, funded by DOE, and performed by Clemson University, Phase IV Engineering and Harvard University from October 1, 2016 to December 31, 2020. The main objective of this project is to develop, demonstrate and pre-commercialize a novel, plug & play, battery-free, wireless sensor technology to enable low-cost (<$10 per node) indoor and outdoor temperature and humidity measurement for energy efficient building controls and operations. The proposed technology is based on the novel concept of all-digital sensing and its seamless integration with the passive RFID technology. This project focuses on the design, fabrication, material optimization, interrogation electronics, validation, and demonstration of the novel sensor nodes for building applications. The specific objectives of this research program include: (1) Design, fabricate and optimize a compact, robust, and high-resolution digitizer, which could convert rotation angle into digital numbers. (2) Develop the multi-physics-based modeling and simulation of the temperature/humidity transducer to derive a rational design of the architecture, dimension, structure, materials (i.e., mechanical, electrical, thermal and hygroscopic) properties and functions of the sensor node. (3) Design, fabricate and optimize the bi-material based humidity sensitive coil which could linearly transduce the environmental relative humidity variations to rotation angles. (4) Design, fabricate and optimize an UHF RFID platform which could support long-range passive wireless communications of 8-bit digital numbers. (5) Design, fabricate and optimize the miniaturized all-digital sensor using MEMS technology. (6) Validate the integrated all-digital sensing system in a real building environment to test the system’s performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRSDate not supplied

Challenges in Remote-Sensing of Hail: Examining the Performance and Biases of Satellite Hail Retrievals Using Aqua MODIS Visible/IR and AMSR-E Passive-Microwave Observations

Hail poses threats to myriad aspects of human life and society, infrastructure, and agriculture. Scientifically, hail can often cause large errors in precipitation retrieval and estimation, posing challenges to establishing the current climatology of severe storms and their future trend in a changing Earth system. Fortunately, hailstorms exhibit distinct signatures in spaceborne remote-sensing datasets (e.g. overshooting cloud tops in visible/IR, or brightness temperature depressions in passive-microwave imagery). Approaches that leverage these signatures, however, are not without their pitfalls,: passive-microwave channels have large footprints and exhibit non-uniform beam filling. Visible/IR instruments have fine horizontal resolution but are limited by their insensitivity to processes occurring below cloud top. Large horizontal areas of smaller scatterers may also meaningfully lower the brightness temperatures, especially if they are able to occupy large portions of the footprint. Radiative transfer simulations show that low frequencies such as 19- and 37-GHz can be scattered to extremely low brightness temperatures by high concentrations of smaller (graupel-sized) ice scatterers, especially in larger features that are more likely to occupy the footprint, which may cause climatologies to overestimate the frequency severe hail. To address this, we investigate the nearly simultaneous and colocated MODIS (visible/IR) and AMSR-E (passive-microwave) onboard the Aqua satellite to leverage both datasets together, pairing AMSR-E and MODIS signatures of severe convection with ground-based weather radar, severe weather reports, and environmental parameters defined by the MERRA-2 reanalysis over CONUS, and then explore the performance and challenges of the algorithm when we expand outside the United States into six different geographical regimes throughout the Aqua domain.

Sarah D Bang↗
NASA NTRS2012

Assimilation of Passive and Active Microwave Soil Moisture Retrievals

Root-zone soil moisture is an important control over the partition of land surface energy and moisture, and the assimilation of remotely sensed near-surface soil moisture has been shown to improve model profile soil moisture [1]. To date, efforts to assimilate remotely sensed near-surface soil moisture at large scales have focused on soil moisture derived from the passive microwave Advanced Microwave Scanning Radiometer (AMSR-E) and the active Advanced Scatterometer (ASCAT; together with its predecessor on the European Remote Sensing satellites (ERS. The assimilation of passive and active microwave soil moisture observations has not yet been directly compared, and so this study compares the impact of assimilating ASCAT and AMSR-E soil moisture data, both separately and together. Since the soil moisture retrieval skill from active and passive microwave data is thought to differ according to surface characteristics [2], the impact of each assimilation on the model soil moisture skill is assessed according to land cover type, by comparison to in situ soil moisture observations.

Draper, C. S.↗