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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Evaluation of the Tau-Omega Model over a Dense Corn Canopy at P- and L-band

As an emerging technique, P-band (0.3-1GHz) may improve soil moisture remote sensing compared to L-band (1.4 GHz) SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) missions, because of its greater moisture retrieval depth resulting from its longer wavelength. Consequently, a number of tower-based experiments were undertaken in Victoria, Australia, to understand and quantify potential improvements. The study reported here has extended the evaluation of the tau-omega model to a scenario with a dense corn canopy whose vegetation water content reached ~20 kg/m 2 , and compared the soil moisture retrieval performance at P- and L-band. Based on the locally calibrated parameters, the results from both the SCA (Single Channel Algorithm) and DCA (Dual Channel Algorithm) approaches presented a clear reduction in vegetation impact at P-band compared to L-band. While the root-mean-square error (RMSE) for P-band did not achieve the 0.04-m 3 /m 3 target accuracy of SMOS and SMAP, i.e., 0.054 m 3 /m 3 for the SCA and 0.074 m 3 /m 3 for the DCA, this performance can be regarded as acceptable considering the extremely high vegetation water content. In comparison, the RMSEs at L-band were larger than 0.1 m 3 /m 3 for both the SCA and the DCA approaches. Additionally, DCA performed better in correlation coefficient and unbiased RMSE, while SCA performed better in RMSE at P-band due to the larger bias when using DCA. Moreover, the calibrated vegetation parameters at P-band were found to apply to broader conditions than those at L-band, likely due to the reduced vegetation impact.

P-band↗
NASA NTRS2014

L-Band Brightness Temperature Variations at Dome C and Snow Metamorphism at the Surface

The Antarctic Plateau is a promising site to monitor microwave radiometers' drift, and to inter-calibrate microwave radiometers, especially 1.4 GigaHertz (L-band) radiometers on board the Soil Moisture and Ocean Salinity (SMOS), and AquariusSAC-D missions. The Plateau is a thick ice cover, thermally stable in depth, with large dimensions, and relatively low heterogeneities. In addition, its high latitude location in the Southern Hemisphere enables frequent observations by polar-orbiting satellites, and no contaminations by radio frequency interference. At Dome C (75S, 123E), on the Antarctic Plateau, the substantial amount of in-situ snow measurements available allows us to interpret variations in space-borne microwave brightness temperature (TB) (e.g. Macelloni et al., 2007, 2013, Brucker et al., 2011, Champollion et al., 2013). However, to analyze the observations from the Aquarius radiometers, whose sensitivity is 0.15 K, the stability of the snow layers near the surface that are most susceptible to rapidly change needs to be precisely assessed. This study focuses on the spatial and temporal variations of the Aquarius TB over the Antarctic Plateau, and at Dome C in particular, to highlight the impact of snow surface metamorphism on the TB observations at L-band.

l-band↗
NASA NTRS2014

Aquarius Brightness Temperature Variations at Dome C and Snow Metamorphism at the Surface

The Antarctic Plateau is a promising site to monitor microwave radiometers' drift, and to inter-calibrate microwave radiometers, especially 1.4 GHz (L-band) radiometers on board the Soil Moisture and Ocean Salinity (SMOS), and AquariusSAC-D missions. The Plateau is a thick ice cover, thermally stable in depth, with large dimensions, and relatively low heterogeneities. In addition, its high latitude location in the Southern Hemisphere enables frequent observations by polar-orbiting satellites, and no contaminations by radio frequency interference. At Dome C (75S, 123E), on the Antarctic Plateau, the substantial amount of in-situ snow measurements available allows us to interpret variations in space-borne microwave brightness temperature (TB) (e.g. Macelloni et al., 2007, 2013, Brucker et al., 2011, Champollion et al., 2013). However, to analyze the observations from the Aquarius radiometers, whose sensitivity is 0.15 K, the stability of the snow layers near the surface that are most susceptible to rapidly change needs to be precisely assessed. This study focuses on the spatial and temporal variations of the Aquarius TB over the Antarctic Plateau, and at Dome C in particular, to highlight the impact of snow surface metamorphism on the TB observations at L-band.

l-band↗
NASA NTRS2017

Assimilation of Global Radar Backscatter and Radiometer Brightness Temperature Observations to Improve Soil Moisture and Land Evaporation Estimates

Active radar backscatter (s◦) observations from the Advanced Scatterometer (ASCAT) and passive radiometer brightness temperature (TB) observations from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated either individually or jointly into the Global Land Evaporation Amsterdam Model (GLEAM) to improve its simulations of soil moisture and land evaporation. To enable s◦ and TB assimilation, GLEAM is coupled to the Water Cloud Model and the L-band Microwave Emission from the Biosphere (L-MEB) model. The innovations, i.e. differences between observations and simulations, are mapped onto the model soil moisture states through an Ensemble Kalman Filter. The validation of surface (0-10 cm) soil moisture simulations over the period 2010-2014 against in situ measurements from the International Soil Moisture Network (ISMN) shows that assimilating s◦ or TB alone improves the average correlation of seasonal anomalies (Ran) from 0.514 to 0.547 and 0.548, respectively. The joint assimilation further improves Ran to 0.559. Associated enhancements in daily evaporative flux simulations by GLEAM are validated based on measurements from 22 FLUXNET stations. Again, the singular assimilation improves Ran from 0.502 to 0.536 and 0.533, respectively for s◦ and TB, whereas the best performance is observed for the joint assimilation (Ran = 0.546). These results demonstrate the complementary value of assimilating radar backscatter observations together with brightness temperatures for improving estimates of hydrological variables, as their joint assimilation outperforms the assimilation of each observation type separately.

GLEAM↗
NASA NTRS2016

L-Band RFI in Japan

In recent years, three instruments have been launched into orbit with the aim of producing global maps of sea surface salinity and soil moisture using the 1400-1427 MHz band: SMOS, Aquarius and SMAP. Although this frequency band is allocated to passive measurements only, RFI (Radio-Frequency Interference) is present in the data of all three missions. On a global scale, the three sensors have observed approximately the same distribution of RFI. Japan is an important exception that has implications for the design of RFI detection algorithms. RFI in Japan is caused by a large number of emitters belonging to the same system (TV receivers) and for this reason some traditional RFI detection strategies detect little to no RFI over Japan. The study of this case has led to an improvement of the approach to detect RFI in Aquarius data.

Aquarius↗
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↗
DOE OSTI2021

CO2 partial pressure and fluxes in the Amazon River Plume using in situ and remote sensing data

Estimations of the global carbon budget include a quantitative understanding of the evolving processes that occur along river-to-ocean gradients. However, high spatiotemporal resolution observations of these processes are limited. Here we present in situ measurements of the partial pressure of CO2 (pCO2) made through the Amazon River plume (ARP) during different discharge seasons, from 2010-2012. We evaluated the spatiotemporal distribution of pCO2 using Soil Moisture and Ocean Salinity (SMOS) satellite observations for each hydrologic period in the ARP. Regression models were used to estimate pCO2 at the ARP for the period of 2010-2014. From these distributions we calculated sea-air gas exchange of CO2 between the plume waters and the atmosphere (F_co2^sea). Intra-annual variability of F_co2^sea was related to discharge at the river mouth and ocean currents as well as trade winds in the plume. Climatic events during the study period had a significant impact on the F_co2^sea. Including the plume area closer to the river mouth makes the ARP a net source of CO2 with an annual net sea-air flux of 8.6 ±7.1 Tg C y-1 from 2011-2014.

Amazon↗
DOE OSTI2026

Machine Learning Eliminates Reanalysis Warm Bias and Reveals Weaker Winter Surface Cooling Over Arctic Sea Ice

The surface energy budget governs Arctic sea-ice growth/melt, yet observations are sparse, and reanalysis data sets suffer from systematic biases. Here, we train a neural network with observational data to bias-correct hourly ERA5 fluxes over Arctic ice-covered regions (≥70°N; sea-ice concentration >80%) for 1994–2024. Training data cover two full seasonal cycles and different sea-ice regimes. The neural network reduces RMSE for net shortwave radiation by ∼40%, downward longwave radiation by ∼16% and the total surface energy budget by ∼55%, eliminating the wintertime warm bias of ∼4 K in ERA5. Wintertime surface cooling is reduced by ∼50%, yielding thermodynamic ice-growth estimates of ∼80–120 cm, consistent with SMOS–CryoSat satellite thickness increases and in contrast to the 150–200 cm growth implied by ERA5. Our bias-corrected data capture the observed clear/cloudy states of the winter boundary layer and can be used to study Arctic climatology, evaluate climate models and drive sea-ice-ocean models.

Hossain, Akil [Alfred Wegener Institute for Polar ↗
DOE OSTI2021

Tower Water-Vapor Mixing Ratio Value-Added Product Report

The purpose of the Tower Water-Vapor Mixing Ratio (TWRMR) value-added product (VAP) is to calculate water-vapor mixing ratios at the 25-meter and 60-meter levels of the meteorological tower and also report best-estimate temperature, relative humidity, and pressure measurements at the 2-meter, 25-meter, and 60-meter levels at the Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) Central Facility. Because there are no barometric pressure sensors at the 25-meter and 60-meter levels on the tower, the hypsometric equation is used along with surface pressure values from the surface meteorological instrumentation (MET), the surface meteorological observation system (SMOS), or the temperature, humidity, wind, and pressure system (THWAPS) to derive barometric pressures at those altitudes (Ritsche 2008, 2011a, 2011b). After this is done, water-vapor mixing ratio can be calculated directly.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1986

The ABC of Population III

The author discusses the circumstances in which Population III stars are expected to form and examines their cosmological consequences. Consideration of their production of light, metals, and remnants places a strong constraint on their formation epoch and mass spectrum. However, these constraints would still allow Population III stars to provide the dark matter in galactic halos, a helium abundance of around 25 percent, a detectable background of gravitational waves, a possible infrared background, and the generation of large-scale cosmological structure through explosions. All of these features could be achieved by VMOs in the range 100 to 100,000 solar masses. On the other hand, the Population III scenario would be most compatible with the standard Big Bang picture if the stars were SMOs with mass around a million solar masses. Such stars could still provide the dark matter and detectable gravitational waves.

Carr, B. J.↗
NASA NTRS2003

RFI at L-band in Synthetic Aperture Radiometers

The spectral window at 1.413 GHz (L-band), set aside for passive use only, is critical for passive remote sensing of the earth from space. It is the largest spectral window available in the long wavelength end of the microwave spectrum where measurements are needed to monitor parameters of the surface such as soil moisture and sea surface salinity. The sensitivity to these parameters is rapidly lost at higher frequencies and is compromised by the ionosphere and antenna size at lower frequencies. Instruments for remote sensing from space in this spectral window are being developed by NASA (Aquarius) and ESA (SMOS) and are expected to be in orbit in a few years (2006). Although the band at 1.413 GHz is protected for passive use, RFI is a common problem. For example, the synthetic aperture radiometer, ESTAR (L-band, Horizontal polarization), has frequently experienced problems with RFI. During the Southern Great Plains Experiments (1997 and 1999), ESTAR experienced RFI significant enough to warrant changes in flight lines. The largest sources of RFI were identified as originating in airports and a likely source is air traffic control radar. In experiments in the vicinity of Richmond, VA, RFI in the form of periodic spikes was recorded, again suggestive of radar. However, in most cases the sources of the RFI are unknown. RFI is a sufficiently common problem that the first step in processing ESTAR data is a screening for RFI (a filter is used to detect large, rapid changes in brightness). Recently, measurements have been made with a new synthetic aperture radiometer, 2D-STAR. Examples of RFI observed simultaneously with ESTAR and the new synthetic aperture radiometer will be presented. 2D-STAR is an airborne instrument designed to develop the technology of aperture synthesis in two dimensions. It employs dual polarized patch antennas arranged in a cross configuration (+). Synthesis in two dimensions offers the potential for optimal thinning, but because of the wide field of view of the individual antennas it is potentially more susceptible to RFI. The 2D-STAR instrument was flown together with ESTAR in a series of test flights on the NASA P-3 aircraft near the Wallops Flight Facility during the summer of 2002. Both instruments experienced RFI over common locations. Examples will be presented and a comparison between instruments presented.

LeVine, David M.↗
NASA NTRS2004

Use of IRI to Model the Effect of Ionosphere Emission on Earth Remote Sensing at L-Band

Microwave remote sensing in the window at 1.413 GHz (L-band) set aside for passive use only is important for monitoring sea surface salinity and soil moisture. These parameters are important for understanding ocean dynamics and energy exchange between the surface and atmosphere, and both NASA and ESA plan to launch satellite sensors to monitor these parameters at L-band (Aquarius, Hydros and SMOS). The ionosphere is an important source of error for passive remote sensing at this frequency. In addition to Faraday rotation, emission from the ionosphere is also a potential source of error at L-band. As an aid for correcting for emission, a regression model is presented that relates ionosphere emission to the integrated electron density (TEC). The goal is to use TEC from sources such as TOPEX, JASON or GPS to obtain estimates of emission over the oceans where the electron density profiles needed to compute emission are not available. In addition, data will also be presented to evaluate the use of the IRI for computing emission over the ocean.

Abraham, Saji↗
NASA NTRS2005

The Impact of the Sun on Passive Remote Sensing at L-band

The sun is a strong source of radiation at L-band behaving roughly like a thermal source with a temperature ranging between 10(exp 5) - 10(exp 7) K, depending on solar activity. This is an important consideration at 1.4 GHz (the window set aside for passive use only) where future satellite sensors will operate to monitor soil moisture and sea surface salinity (e.g. SMOS, Aquarius, and Hydros). Straight forward calculations show that such a source of radiation can be a significant issue for these remote sensing applications, especially in the case of remote sensing of sea surface salinity. Radiation from the sun impacts passive remote sensing systems in several ways. First, is the solar radiation that comes directly from the sun (line-of-sight from sun to spacecraft that enters the radiometer through antenna side lobes). This is a particular problem for sensors in sunsynchronous orbits near the terminator (e.g. orbits with equatorial crossing times near 6am/6pm) because the spacecraft is in the sun most of the time. Second, is solar radiation that is reflected (specularly) from the mean surface to the radiometer. This contribution can be nearly as large as the direct ray, especially when the reflection is from the ocean surface which has a high reflection coefficient. Finally, there is "incoherent" signal reflected from the surface structure (roughness) to the radiometer antenna. Examples illustrating the significance of these terms is presented for the case of a pushbroom radiometer such as Aquarius and a conically scanning radiometer such as proposed for Hydros. Calculations are made using a set of theoretical patterns for these beams together with data on solar radiation obtained from by a worldwide network of observing stations known as Radio Solar Telescope Network (RSTN). Near solar minimum, solar contamination is not a problem unless the sun enters near the main beam. But near solar maximum, account must be made for radiation from the sun even when the signal enters in sidelobes far from the main beam.

LeVine, David M.↗
NASA NTRS2005

Impact of the Sun on Remote Sensing of Sea Surface Salinity from Space

The sun is a sufficiently strong source of radiation at L-band to be an important source of interference for radiometers on future satellite missions such as SMOS, Aquarius, and Hydros designed to monitor soil moisture and sea surface salinity. Radiation from the sun can impact passive remote sensing systems in several ways, including line-of-sight radiation that comes directly from the sun and enters through antenna side lobes and radiation that is reflected from the surface to the radiometer. Examples are presented in the case of Aquarius, a pushbroom radiometer with three beams designed to monitor sea surface salinity. Near solar minimum, solar contamination is not a problem unless the sun enters near the main beam. But near solar maximum, contamination from the sun equivalent to a change of salinity on the order of 0.1 psu can occur even when the signal enters in sidelobes far from the main beam.

COMPONENT REPORTS↗
NASA NTRS2006

The Australian National Airborne Field Experiment 2005: Soil Moisture Remote Sensing at 60 Meter Resolution and Up

Spatially-distributed soil moisture observations have applications spanning a wide range of spatial resolutions from the very local needs of individual farmers to the progressively larger areas of interest to weather forecasters, water resource managers, and global climate modelers. To date, the most promising approach for space-based remote sensing of soil moisture makes use of passive microwave emission radiometers at L-band frequencies (1-2 GHz). Several soil moisture-sensing satellites have been proposed in recent years, with the European Space Agency's Soil Moisture Ocean Salinity (SMOS) mission scheduled to be launched first in a couple years. While such a microwave-based approach has the advantage of essentially allweather operation, satellite size limits spatial resolution to 10's of km. Whether used at this native resolution or in conjunction with some type of downscaling technique to generate soil moisture estimates on a finer-scale grid, the effects of subpixel spatial variability play a critical role. The soil moisture variability is typically affected by factors such as vegetation, topography, surface roughness, and soil texture. Understanding and these factors is the key to achieving accurate soil moisture retrievals at any scale. Indeed, the ability to compensate for these factors ultimately limits the achievable spatial resolution and/or accuracy of the retrieval. Over the last 20 years, a series of airborne campaigns in the USA have supported the development of algorithms for spaceborne soil moisture retrieval. The most important observations involved imagery from passive microwave radiometers. The early campaigns proved that the retrieval worked for larger and larger footprints, up to satellite-scale footprints. These provided the solid basis for proposing the satellite missions. More recent campaigns have explored other aspects such as retrieval performance through greater amounts of vegetation. All of these campaigns featured extensive ground truth collection over a range of grid spacings, to provide a basis for examining the effects of subpixel variability. However, the native footprint size of the airborne L-band radiometers was always a few hundred meters. During the recently completed (November, 2005) National Airborne Field Experiment (NAFE) campaign in Australia, a compact L-band radiometer was deployed on a small aircraft. This new combination permitted routine observations at native resolutions as high as 60 meters, substantially finer than in previous airborne soil moisture campaigns, as well as satellite footprint areal coverage. The radiometer, the Polarimetric L-band Microwave Radiometer (PLMR) performed extremely well and operations included extensive calibration-related observations. Thus, along with the extensive fine-scale ground truth, the NAFE dataset includes all the ingredients for the first scaling studies involving very-high-native resolution soil moisture observations and the effects of vegetation, roughness, etc. A brief overview of the NAFE will be presented, then examples of the airborne observations with resolutions from 60 m to 1 km will be shown, and early results from scaling studies will be discussed.

Kim, E. J.↗
NASA NTRS2007

The Influence of Antenna Pattern on Faraday Rotation in Remote Sensing at L-band

Faraday rotation is a change in the polarization vector of electromagnetic radiation that occurs as the waves propagate from the Earth surface through the ionosphere to a spaceborne sensor. This change can cause errors in monitoring parameters at the surface such as soil moisture and sea surface salinity and it is an important consideration for radiometers on future missions in space such as NASA's Aquarius mission and ESA's SMOS mission. Two prominent strategies for compensating for Faraday rotation are using a sum of the signal at two polarizations and using the correlation between the signals at the two polarizations. These strategies work for an idealized antenna. This paper evaluates the strategies in the context of realistic antennas such as will be built for the Aquarius radiometer. Realistic antennas will make small differences that need to be included in planning for retrieval algorithms in future missions.

LeVine, David M.↗
NASA NTRS2007

Soil Moisture Retrieval During a Corn Growth Cycle using L-band (1.6 GHz) Radar Observations

New opportunities for large-scale soil moisture monitoring will emerge with the launch of two low frequency (L-band 1.4 GHz) radiometers: the Aquarius mission in 2009 and the Soil Moisture and Ocean Salinity (SMOS) mission in 2008. Soil moisture is an important land surface variable affecting water and heat exchanges between atmosphere, land surface and deeper ground water reservoirs. The data products from these sensors provide valuable information in a range of climate and hydrologic applications (e.g., nume~cal weather prediction, drought monitoring, flood forecasting, water resources management, etc.). This paper describes a unique data set that was collected during a field campaign at OPE^ (Optimizing Production Inputs for Economic and Environmental Enhancements) site in Beltsville, Maryland throughout the eompj2ete corn growing in 2002. This investigation describes a simple methodology to correct active microwave observations for vegetation effects, which could potentially be implemented in a global soil moisture monitoring algorithm. The methodology has been applied to radar observation collected during the entire corn growth season and validation against ground measurements showed that the top 5-cm soil moisture can be retrieved with an accuracy up to 0.033 [cu cm/cu cm] depending on the sensing configuration.

Joseph, Alicia T.↗
NASA NTRS2007

Salinity Remote Sensing and the Study of the Global Water Cycle

The SMOS and AquariusISAC-D satellite missions will begin a new era to map the global sea surface salinity (SSS) field and its variability from space within the next twothree years. They will provide critical data needed to study the interactions between the ocean circulation, global water cycle and climate. Key scientific issues to address are (1) mapping large expanses of the ocean where conventional SSS data do not yet exist, (2) understanding the seasonal and interannual SSS variations and the link to precipitation, evaporation and sea-ice patterns, (3) links between SSS and variations in the oceanic overturning circulation, (4) air-sea coupling processes in the tropics that influence El Nino, and (4) closing the marine freshwater budget. There is a growing body of oceanographic evidence in the form of salinity trends that portend significant changes in the hydrologic cycle. Over the past several decades, highlatitude oceans have become fresher while the subtropical oceans have become saltier. This change is slowly spreading into the subsurface ocean layers and may be affecting the strength of the ocean's therrnohaline overturning circulation. Salinity is directly linked to the ocean dynamics through the density distribution, and provides an important signature of the global water cycle. The distribution and variation of oceanic salinity is therefore attracting increasing scientific attention due to the relationship to the global water cycle and its influence on circulation, mixing, and climate processes. The oceans dominate the water cycle by providing 86% of global surface evaporation (E) and receiving 78% of global precipitation (P). Regional differences in E-P, land runoff, and the melting or freezing of ice affect the salinity of surface water. Direct observations of E-P over the ocean have large uncertainty, with discrepancies between the various state-of-the-art precipitation analyses of a factor of two or more in many regions. Quantifying the climatic influence of the oceanic water cycle requires more accurately resolving the net air-sea water flux. Measuring global SSS trends on seasonal to interannual timescales by satellite is fundamental to this problem because the SSS trends represent detectable time-integrated signals of the variable marine hydrological cycle. Satellite measurements, coupled with an array of in situ observations, will provide global synoptic SSS fields for the first time history. These data will provide a strong constraint on climate models and data assimilation efforts, which must properly represent the freshwater budget in terms of E-P, ocean advection and surface layer mixing in order to accurately simulate the true ocean state. The SSS fields will allow us to quantify the covariability between the SSS and the strong seasonal E-P cycle in the tropics and high latitudes. Field measurement campaigns to exploit satellite and in situ measurements to close the seasonal E-P cycle over an ocean region are being considered. Lastly the satellite systems will monitor and trace the large long-lived SSS anomalies from year to year that have the potential to influence El Nino and the large scale ocean circulation.

Lagerloef, G. S. E.↗