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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

NASA's New Laser Risk Reduction Program For Future Space Lidar Missions

NASA has been performing ground, airborne, and space-based scientific measurements since it was formed in 1958. Initial ground and airborne measurements were made with in situ instruments. By necessity, initial earth observation space-based missions were accomplished with passive remote sensing. Active microwave radar was added to the sensor repertoire in the late 1970s. A few key measurements important to NASA remain unaccomplished, however, despite the passive and radar successes. These critical measurements include space-based altimetry; and high spatial resolution profiling of aerosol properties, wind velocity, clouds, and molecular concentrations. Fortunately, a new technology, active optical radar or laser radar or lidar, has matured to the point that the last decade has seen a growing consideration of lidar for space missions. Part of the surge in consideration of lidar has been the tremendous progress in solid-state lasers fueled by advances in crystal growth quality and pump laser diode technology.

Michael J. Kavaya↗
NASA NTRS1980

Remote sensing of snow properties by passive microwave radiometry: GSFC truck experiment

Recent results indicate that microwave radiometry has the potential for inferring the snow depth and water equivalent information from snowpacks. In order to assess this potential for determining the water equivalent of a snowpack, it is necessary to understand the microwave emission and scattering behavior of the snow at various wavelengths under carefully controlled conditions. Truck-mounted microwave instrumentation was used to study the microwave characteristics of the snowpack in the Colorado Rocky Mountain region during the winters of 1977 to 78 and 7978 to 79. The spectral signatures of C, X, K sub u, and K sub a band radiometers with dual polarization were used, together with measurements of snowpack density, temperature an ram profiles, liquid water content, and rough characterization of the crystal sizes. These data compared favorably with calculated results based on recent microscopic scattering models.

Chang, A. T. C.↗
NASA NTRS1987

Snow load effect on earth's rotation and gravitational field, 1979-1985

A global, monthly snow depth data set has been generated from the Nimbus 7 satellite observations using passive microwave remote-sensing techniques. Seven years of data, 1979-1985, are analyzed to compute the snow load effects on the earth's rotation and low-degree zonal gravitational field. The resultant time series show dominant seasonal cycles. The annual peak-to-peak variation in J2 is found to be 2.3 x 10 to the -10th, that in J3 to be 1.1 x 10 to the -10th, and believed to decrease rapidly for higher degrees. The corresponding change in the length of day is 41 micro-s. The annual wobble excitation is (4.9 marc sec, -109 deg) for the prograde motion component and (4.8 marc sec, -28 deg) for the retrograde motion component. The excitation power of the Chandler wobble due to the snow load is estimated to be about 25 dB less than the power needed to maintain the observed Chandler wobble.

Chao, B. Fong↗
NASA NTRS1993

On the use of passive microwaves at 37 GHz in remote sensing of vegetation

Recently, a number of studies have investigated the use of the 37 GHz channels of the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) for vegetation monitoring and for studying synergisms between the SMMR and the NOAA Advanced Very High Resolution Radiometer (AVHRR). The approaches are promising but raise a number of issues concerning interpretation of the results, specifically on the relative effects of vegetation and other surface and atmospheric characteristics on the observed signal. This article analyzes the 37 GHz Microwave Polarization Difference Temperature (MPDT) in terms of its sensitivity to surface and atmospheric parameters. For this, a radiative transfer model is used which indicates some limitations of the MPDT index and suggests the importance of accounting for atmospheric effects in the data analysis. An alternative approach to the MPDT, including lower SMMR frequencies than 37 GHz, is discussed.

Kerr, Y. H.↗
NASA NTRS2004

A Polarimetric Extension of the van Cittert-Zernike Theorem for Use with Microwave Interferometers

The van Cittert-Zernike theorem describes the Fourier-transform relationship between an extended source and its visibility function. Developments in classical optics texts use scalar field formulations for the theorem. Here, we develop a polarimetric extension to the van Cittert-Zernike theorem with applications to passive microwave Earth remote sensing. The development provides insight into the mechanics of two-dimensional interferometric imaging, particularly the effects of polarization basis differences between the scene and the observer.

Piepmeier, J. R.↗
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 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 NTRS2005

Microwave Remote Sensing of Falling Snow

This study analyzes passive and active microwave measurements during the 2003 Wakasa Bay field experiment for understanding of the electromagnetic characteristics of frozen hydrometeors at millimeter-wave frequencies. Based on these understandings, parameterizations of the electromagnetic scattering properties of snow at millimeter-wave frequencies are developed and applied to the hydrometeor profiles obtained by airborne radar measurements. Calculated brightness temperatures and radar reflectivity are compared with the millimeter-wave measurements.

Kim, Min-Jeong↗
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 NTRS2004

Evaluation and Application of Satellite-Based Latent Heating Profile Estimation Methods

In recent years, methods for estimating atmospheric latent heating vertical structure from both passive and active microwave remote sensing have matured to the point where quantitative evaluation of these methods is the next logical step. Two approaches for heating algorithm evaluation are proposed: First, application of heating algorithms to synthetic data, based upon cloud-resolving model simulations, can be used to test the internal consistency of heating estimates in the absence of systematic errors in physical assumptions. Second, comparisons of satellite-retrieved vertical heating structures to independent ground-based estimates, such as rawinsonde-derived analyses of heating, provide an additional test. The two approaches are complementary, since systematic errors in heating indicated by the second approach may be confirmed by the first. A passive microwave and combined passive/active microwave heating retrieval algorithm are evaluated using the described approaches. In general, the passive microwave algorithm heating profile estimates are subject to biases due to the limited vertical heating structure information contained in the passive microwave observations. These biases may be partly overcome by including more environment-specific a priori information into the algorithm s database of candidate solution profiles. The combined passive/active microwave algorithm utilizes the much higher-resolution vertical structure information provided by spaceborne radar data to produce less biased estimates; however, the global spatio-temporal sampling by spaceborne radar is limited. In the present study, the passive/active microwave algorithm is used to construct a more physically-consistent and environment-specific set of candidate solution profiles for the passive microwave algorithm and to help evaluate errors in the passive algorithm s heating estimates. Although satellite estimates of latent heating are based upon instantaneous, footprint- scale data, suppression of random errors requires averaging to at least half-degree resolution. Analysis of mesoscale and larger space-time scale phenomena based upon passive and passive/active microwave heating estimates from TRMM, SSMI, and AMSR data will be presented at the conference.

Olson, William S.↗
NASA NTRS1977

Technology advances in active and passive microwave sensing through 1985

As a result of a growing awareness by the remote sensing community of the unique capabilities of passive and active microwave sensors, these instruments are expected to grow in the next decade in numbers, versatility and complexity. The Nimbus-G and Seasat-A Scanning Multichannel Microwave Spectrometer (SMMR), the Seasat-A radar altimeter, scatterometer and synthetic aperture radar represent the first systematic attempt at exploring a wide variety of applications utilizing microwave sensing techniques and are indicators of the directions in which the pertinent technology is likely to evolve. The trend is toward high resolution multi-frequency imagers spanning wide frequency ranges and wide swaths requiring sophisticated receivers, real-time data processors and most importantly, complex antennas.

Barath, F. T.↗
NASA NTRS1975

Ground truth procedures, Phoenix soil moisture

Procedures used for collection of ground truth, in connection with passive gamma and microwave remote sensing are summarized. Soil moisture and vegetation sampling procedures are described.

Jones, E. B.↗
NASA NTRS2018

Satellite Observations to Monitor Subarctic Rain-On-Snow Events

Rain-on-snow (ROS) events have been the focus of numerous studies in the past five years. Their characteristics(frequency, extent, and duration) represent a new and relevant climate indicator. However, monitoring ROS occurrences remotely using satellite observations is deemed challenging. The ROS events can be sporadic, of very different intensities, and the outcome of the rain water uncertain (either it freezes in the snow cover or runs off). Using passive and active microwave remote sensing observations, our study proposes new approaches to monitor the occurrence of ROS events.Specifically, we utilize observations from Advanced Microwave Scanning Radiometer 2 (AMSR2), and Global Precipitation Measurements (GPM) Microwave Imager (GMI), and GPM Dual-frequency Precipitation Radar (DPR). We compare our ROS detection against weather stations and recently published algorithms using a different set of microwave frequencies.

Brucker, Ludovic↗
NASA NTRS2017

Radio Frequency Interference Detection for Passive Remote Sensing Using Eigenvalue Analysis

Radio frequency interference (RFI) can corrupt passive remote sensing measurements taken with microwave radiometers. With the increasingly utilized spectrum and the push for larger bandwidth radiometers, the likelihood of RFI contamination has grown significantly. In this work, an eigenvalue-based algorithm is developed to detect the presence of RFI and provide estimates of RFI-free radiation levels. Simulated tests show that the proposed detector outperforms conventional kurtosis-based RFI detectors in the low-to-medium interference-to-noise-power-ratio (INR) regime under continuous wave (CW) and quadrature phase shift keying (QPSK) RFIs.

kurtosis↗