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At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input

Passive microwave observations from various spaceborne sensors have been linked to the soil moisture of the Earth's surface layer. A new generation of passive microwave sensors are dedicated to retrieving this variable and make observations in the single theoretically optimal L-band frequency (1-2 GHz). Previous generations of passive microwave sensors made observations in a range of higher frequencies, allowing for simultaneous estimation of additional variables required for solving the radiative transfer equation. One of these additional variables is land surface temperature, which plays a unique role in the radiative transfer equation and has an influence on the final quality of retrieved soil moisture anomalies. This study presents an optimization procedure for soil moisture retrievals through a quasi-global precipitation-based verification technique, the so-called Rvalue metric. Various land surface temperature scenarios were evaluated in which biases were added to an existing linear regression, specifically focusing on improving the skills to capture the temporal variability of soil moisture. We focus on the relative quality of the day-time (01:30 pm) observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), as these are theoretically most challenging due to the thermal equilibrium theory, and existing studies indicate that larger improvements are possible for these observations compared to their night-time (01:30 am) equivalent. Soil moisture data used in this study were retrieved through the Land Parameter Retrieval Model (LPRM), and in line with theory, both satellite paths show a unique and distinct degradation as a function of vegetation density. Both the ascending (01:30 pm) and descending (01:30 am) paths of the publicly available and widely used AMSR-E LPRM soil moisture products were used for benchmarking purposes. Several scenarios were employed in which the land surface temperature input for the radiative transfer was varied by imposing a bias on an existing regression. These scenarios were evaluated through the Rvalue technique, resulting in optimal bias values on top of this regression. In a next step, these optimal bias values were incorporated in order to re-calibrate the existing linear regression, resulting in a quasi-global uniform LST relation for day-time observations. In a final step, day-time soil moisture retrievals using the re-calibrated land surface temperature relation were again validated through the Rvalue technique. Results indicate an average increasing Rvalue of 16.5%, which indicates a better performance obtained through the re-calibration. This number was confirmed through an independent Triple Collocation verification over the same domain, demonstrating an average root mean square error reduction of 15.3%. Furthermore, a comparison against an extensive in situ database (679 stations) also indicates a generally higher quality for the re-calibrated dataset. Besides the improved day-time dataset, this study furthermore provides insights on the relative quality of soil moisture retrieved from AMSR-E's day- and night-time observations.

soil moisture↗
NASA NTRSDate not supplied

Communicating Metrics of Land Surface Temperature Variability Using Multi-sensor Machine Learning

Land surface temperature (LST) is a key climate observable used to detect changes in the Earth’s surface energy budget that influence carbon and water cycles. Land surface temperature exhibits strong diurnal variability, which geostationary satellites can observe at scale thanks to their temporal resolution. Due to anthropogenic climate and land use changes, the surface energy balance has been considerably modified and may be described by changes in diurnal temperature range and extremes. Using high performance computing and datasets from the NASA Earth Exchange, we exploit co-located, co-temporal observations from low-earth orbit (LEO) and geostationary (GEO) sensors to develop a deep learning-based method for LEO-to-GEO algorithm emulation. Our model is trained to predict MODIS Terra LST from GOES-16 thermal bands and achieves validation error <2K. Application of the model to unseen times of day (observed by MODIS Aqua) and a new GEO sensor (Himawari-8) observing an unseen spatial domain, demonstrate the generalization of the deep learning model across space, time and spectra. Further, time series clustering approaches are examined with the objective of identifying key indicators of change in diurnal cycling and extremes on a continental scale. Communicating LST variability observed by geostationary satellites can have impacts in multiple disciplines, from understanding of snow, vegetation and soil dynamics, to recognizing trends in heat events relevant to human health.

Kate Duffy↗
NASA NTRSDate not supplied

LEO Sensor to GEO Sensor Algorithm Transfer Models for Land Surface Temperature

Land surface temperature (LST) is a key climate observable used to detect changes in the Earth’s surface energy budget that influence carbon and water cycles. Land surface temperature exhibits strong diurnal variability, which geostationary satellites can observe at scale thanks to their temporal resolution. Due to anthropogenic climate and land use changes, the surface energy balance has been considerably modified and may be described by changes in diurnal temperature range and extremes. Using high performance computing and datasets from the NASA Earth Exchange, we exploit co-located, co-temporal observations from low-earth orbit (LEO) and geostationary (GEO) sensors to develop a deep learning-based method for LEO-to-GEO algorithm emulation. Our model is trained to predict MODIS Terra LST from GOES-16 thermal bands and achieves validation error <2K. Application of the model to unseen times of day (observed by MODIS Aqua) and a new GEO sensor (Himawari-8) observing an unseen spatial domain, demonstrate the generalization of the deep learning model across space, time and spectra. Communicating diurnal LST variability observed by geostationary satellites can have impacts in multiple disciplines, from understanding of snow, vegetation and soil dynamics, to recognizing trends in heat events relevant to human health.

Kate Marie Duffy↗
NASA NTRS2004

Exposure and Terrestrial Histories of New Lunar and Martian Meteorites

Cosmogenic nuclide studies of lunar and Martian meteorites have contributed significantly to our understanding of these objects. By measuring a combination of cosmogenic stable- and radionuclides, we can determine a number of important properties of those meteorites. Most lunar meteorites have complex cosmic ray exposure histories, having been exposed both at some depth on the lunar surface (2 irradiation) before their ejection and as small bodies in space (4 irradiation) during transport from the Moon to the Earth. On the other hand, we have not observed evidence of complex exposure history for any Martian meteorites, so far. These exposures were then followed by residence on Earth s surface, a time commonly referred to as the terrestrial age. In addition to their complement of galactic cosmic ray (GCR) produced nuclides some lunar and Martian meteorites contain nuclides produced by solar cosmic rays (SCR). Unraveling the complex history of these objects requires the measurement of at least four cosmogenic nuclides. The specific goals of these measurements are to constrain or set limits on the following shielding or exposure parameters: (1) the depth of the sample at the time of ejection from the Moon or Mars; (2) the transit time (4 exposure age) from ejection off the lunar or Martian surface to the time of capture by the Earth and (3) the terrestrial residence time. The sum of the transit time and residence time yield an ejection age. The ejection age, in conjunction with the sample depth on the Moon or Mars, can then be used to model impact and ejection mechanisms.

Nishiizumi, K.↗
NASA NTRS1988

Earth Observing System (EOS) advanced altimetry

In the post-TOPEX era, satellite radar altimeters will be developed with the capability of measuring the earth's surface topography over a wide swath of coverage, rather than just at the satellite's nadir. The identification of potential spacecraft flight missions in the future was studied. The best opportunity was found to be the Earth Observing System (EOS). It is felt that an instrument system that has a broad appeal to the earth sciences community stands a much better chance of being selected as an EOS instrument. Consequently, the Topography and Rain Radar Imager (TARRI) will be proposed as a system that has the capability to profile the Earth's topography regardless of the surface type. The horizontal and height resolutions of interest are obviously significantly different over land, ice, and water; but, the use of radar to provide an all-weather observation capability is applicable to the whole earth. The scientific guidance for the design and development of this instrument and the eventual scientific utilization of the data produced by the TARRI will be provided by seven science teams. The teams are formed around scientific disciplines and are titled: Geology/Geophysics, Hydrology/Rain, Oceanography, Ice/Snow, Geodesy/Orbit/Attitude, Cartography, and Surface Properties/Techniques.

Parsons, C. L.↗
NASA NTRSDate not supplied

Clouds and the Earth's Radiant Energy System

The Clouds and the Earth's Radiant Energy System (CERES) is a key component of the Earth Observing System (EOS) program. The CERES instrument provides radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions are a follow-on to the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument (PFM) was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the EOS flagship Terra on December 18, 1999, and two additional CERES instruments (FM3 and FM4) were launched on board EOS Aqua on May 4,2002. [Mission Objectives] The scientific justification for the CERES measurements can be summarized by three assertions: (1) changes in the radiative energy balance of the Earth-atmosphere system can cause long-term climate changes (e.g., carbon dioxide inducing global warming); (2) besides the systematic diurnal and seasonal cycles of incoming solar energy, changes in cloud properties (amount, height, optical thickness) cause the largest changes of the Earth's radiative energy balance; and (3) cloud physics is one of the weakest components of current climate models used to predict potential global climate change. CERES has four main objectives: 1) For climate change analysis, provide a continuation of the ERBE record of radiative fluxes at the top of the atmosphere (TOA), analyzed using the same algorithms that produced the ERBE data. 2) Double the accuracy of estimates of radiative fluxes at TOA and the Earth's surface. 3) Provide the first long-term global estimates of the radiative fluxes within the Earth's atmosphere. 4) Provide cloud property estimates that are consistent with the radiative fluxes from surface to TOA. [Temporal_Coverage: Start_Date=1997-12-27; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180].

Wielicki, Bruce A.↗
NASA NTRS1988

Astronaut's guide to terrestrial impact craters

Impact cratering of the earth's surface is discussed and compared with lunar craters. The basic types found on earth are either simple craters or complex impact structures and basins. Meteorite fragments and shock metamorphism provide evidence of a crater's formation by meteorite impact. Known craters on earth are ordered by location and a few principal facts are given for each crater and the general terrain in which it is located. A satellite picture of each crater and maps identifying crater locations are provided.

Grieve, R. A. F.↗
NASA NTRS1992

Approach to derivation of SIR-C science requirements for calibration

Many of the experiments proposed for the forthcoming SIR-C mission require calibrated data, for example those which emphasize (1) deriving quantitative geophysical information (e.g., surface roughness and dielectric constant), (2) monitoring daily and seasonal changes in the Earth's surface (e.g., soil moisture), (3) extending local case studies to regional and worldwide scales, and (4) using SIR-C data with other spaceborne sensors (e.g., ERS-1, JERS-1, and Radarsat). There are three different aspects to the SIR-C calibration problem: radiometric and geometric calibration, which have been previously reported, and polarimetric calibration. The study described in this paper is an attempt at determining the science requirements for polarimetric calibration for SIR-C. A model describing the effect of miscalibration is presented first, followed by an example describing how to assess the calibration requirements specific to an experiment. The effects of miscalibration on some commonly used polarimetric parameters are also discussed. It is shown that polarimetric calibration requirements are strongly application dependent. In consequence, the SIR-C investigators are advised to assess the calibration requirements of their own experiment. A set of numbers summarizing SIR-C polarimetric calibration goals concludes this paper.

Dubois, Pascale C.↗
NASA NTRS1993

Application of the target decomposition theorem to a polarimetric random media model

With advances in polarimetric radar measurements of land surfaces, the need for understanding the underlying scattering mechanisms and dominant target features has become the focus of many studies. In particular, the maximum use of the polarimetric information to identify and/or separate parameters related to the surface features such as vegetation thickness, structure, water content, and soil surface characteristics will enhance the possibility of using polarimetric radars for monitoring the earth's surface from space. In this paper, Cloude's decomposition theorem is applied to a polarimetric random media model to simulate the radar measurements of vegetated canopies. The vegetated canopies are modeled as a three layer discrete random medium with leaves and branches in the first layer, tree trunks in the second layer, and a half space of homogeneous ground with rough interface as the bottom layer. The distorted born approximation (DBA) has been used to compute full Mueller matrix of the canopy, using canonical dielectric objects such as thin discs and cylinders as leaves, branches, and trunks, respectively. The Mueller matrix and the derived covariance matrix contain information on the second order statistics of radar signals at various polarizations from the canopy. To decompose the covariance matrix to its constituent targets, the eigenvalues and eigenvectors of the covariance matrix are computed in terms of the physical parameters of the canopy. In addition, each eigenvector explicitly shows the scattering mechanisms such as odd and even reflections in the canopy. Cloude's decomposition theorem is applied using the Pauli spin matrices as a basis and an expression for the degree of disorder or the entropy for the vegetated surface is found. Then, the physical parameters estimated from in situ measurements are used in the random media to obtain realistic covariance matrices. As a result, the sensitivity of the eigenvalue spectrums and the coefficients resulting from the target decomposition theorem to the physical parameters of the canopy are examined and the possible use of Cloude's theorem to estimate vegetation and soil parameters is discussed.

Saatchi, Sasan S.↗
NASA NTRS2017

Recent Global Warming as Observed by AIRS and Depicted in GISSTEMP and MERRA-2

AIRS Version-6 monthly mean level-3 surface temperature products confirm the result, depicted in the GISSTEMP dataset, that the earth's surface temperature has been warming since early 2015, though not before that. AIRS is at a higher spatial resolution than GISSTEMP, and produces sharper spatial features which are otherwise in excellent agreement with those of GISSTEMP. Version-6 AO Ts anomalies are consistent with those of Version-6 AIRS/AMSU. Version-7 AO anomalies should be even more accurate, especially at high latitudes. ARCs of MERRA-2 Ts anomalies are spurious as a result of a discontinuity which occurred somewhere between 2007 and 2008. This decreases global mean trends.

surface temperatur↗
NASA NTRSDate not supplied

Integrated Design Results for the MSR SRC Mars Ascent Vehicle

The primary mission of the NASA Mars Sample Return (MSR) Campaign is to return samples of the Martian surface to Earth for scientific study. As part of this campaign, NASA is developing a Mars Ascent Vehicle (MAV). This vehicle must survive an approximate two-year journey to the Martian surface as a payload aboard a separate lander spacecraft. After residing on the surface for another year, the MAV will carry a payload of samples into orbit. From there, following ejection from the MAV, the samples will rendezvous with an Earth return spacecraft for capture, and ultimately, return to Earth. The design of the MAV represents a number of unique challenges, as no launch vehicle has ever left the surface of a planet other than Earth. Although conceptual designs for a MAV have been in various levels of development since the 1970s, none have achieved the level of fidelity and support that exists in the current MSR-MAV design. Early MSR-MAV concept studies examined multiple methods of propulsion, ultimately deciding that a Two-Stage-to-Orbit (TSTO) solid propulsion vehicle would provide the most capable performance in a Martian environment. Following this key architecture decision, the vehicle design was further matured to a Solid-Solid Guided-Guided (SSGG) architecture for NASA Key Decision Point A (KDP-A). Although the SSGG design was able to meet all mission constraints, concerns were raised regarding limited mass margin on other elements of the MSR campaign at such an early phase. A design challenge was issued to reduce MAV total mass by as much as possible. It was ultimately determined that by moving a number of components of the vehicle second stage to the first stage, the overall vehicle mass could be reduced significantly. The new design features a much smaller and completely unguided second stage. This paper describes the resultant Solid-Solid Guided-Unguided (SSGU) MAV design concept developed as part of the Systems Requirement Cycle (SRC). This design was developed primarily by NASA Marshall Space Flight Center (MSFC), in association with NASA Jet Propulsion Laboratory (JPL) and NASA Langley Research Center (LaRC). The TSTO vehicle includes one solid rocket motor per stage. As the vehicle second stage is unguided, it features spin-stabilization to maintain vehicle stability during flight. An electromechanically actuated Thrust Vector Control (TVC) system and a monopropellant Reaction Control System (RCS) are employed for active guidance on the first stage. The vehicle is designed to deliver up to 0.47kg of Martian samples to a Mars circular orbit of 380km at 27° inclination. Due to the extremely unique design constraints of this mission, and a recent transition to a Risk Class A posture, the MAV team was compelled to devise unconventional solutions to the vehicle design. The detailed design and analysis of these subsystems and the vehicle as a whole are discussed in this paper relative to all of the engineering disciplines involved.

MAV↗
NASA NTRS2020

Cloud Detection over Snow and Ice with Oxygen A- and B-band Observations from the Earth Polychromatic Imaging Camera (EPIC)

Satellite cloud detection over snow and ice has been difficult for passive remote sensing instruments due to the lack of contrast between clouds and cold/bright surfaces; cloud mask algorithms often heavily rely on shortwave infrared (IR) channels over such surfaces. The Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) does not have infrared channels, which makes cloud detection over snow and ice surfaces even more challenging. This study investigates the methodology of applying EPIC's two oxygen absorption band pair ratios in the A band (764, 780 nm) and B band (688, 680 nm) for cloud detection over the snow and ice surfaces. We develop a novel elevation and zenith-angle-dependent threshold scheme based on radiative transfer model simulations that achieves significant improvements over the existing algorithm. When compared against a composite cloud mask based on geosynchronous Earth orbit (GEO) and low Earth orbit (LEO) sensors, the positive detection rate over snow and ice surfaces increased from around 36 % to 65 % while the false detection rate dropped from 50 % to 10 % for observations of January 2016 and 2017. The improvement in July is less substantial due to relatively better performance in the current algorithm. The new algorithm is applicable for all snow and ice surfaces including Antarctic, sea ice, high-latitude snow, and high-altitude glacier regions. This method is less reliable when clouds are optically thin or below 3 km because the sensitivity is low in oxygen band ratios for these cases.

EPIC↗
NASA NTRS1978

The tektite problem

Small glassy pebbles, called tektites, are found in widely scattered locations around the world. These tektites appear much like volcanic glass obsidian, but their chemical composition is different from that of any terrestrial lava and they contain far less water and none of obsidian's characteristic microcrystals. No one has ever found the mother lode of a field of tektites. They cannot, therefore, be the product of terrestrial volcanism. Recently acquired knowledge about the moon's surface confirms earlier indications that tektites cannot be bits of lunar soil propelled to the earth by the impact of meteorites on the moon. According to one of two remaining possibilities tektites are bits of terrestrial sedimentary rock excavated by meteorites striking the earth's surface, melted by the heat of impact, and congealed into glass as they travel above the atmosphere to the scattered sites where they are found. The other possibility is that tektites are the remains of gobs of lava fired at the earth by volcanic activity on the moon.

Okeefe, J. A.↗
NASA NTRS1997

Internal Physical Features of a Land Surface Model Employing a Tangent Linear Model

The Earth's land surface, including its biomass, is an integral part of the Earth's weather and climate system. Land surface heterogeneity, such as the type and amount of vegetative covering., has a profound effect on local weather variability and therefore on regional variations of the global climate. Surface conditions affect local weather and climate through a number of mechanisms. First, they determine the re-distribution of the net radiative energy received at the surface, through the atmosphere, from the sun. A certain fraction of this energy increases the surface ground temperature, another warms the near-surface atmosphere, and the rest evaporates surface water, which in turn creates clouds and causes precipitation. Second, they determine how much rainfall and snowmelt can be stored in the soil and how much instead runs off into waterways. Finally, surface conditions influence the near-surface concentration and distribution of greenhouse gases such as carbon dioxide. The processes through which these mechanisms interact with the atmosphere can be modeled mathematically, to within some degree of uncertainty, on the basis of underlying physical principles. Such a land surface model provides predictive capability for surface variables including ground temperature, surface humidity, and soil moisture and temperature. This information is important for agriculture and industry, as well as for addressing fundamental scientific questions concerning global and local climate change. In this study we apply a methodology known as tangent linear modeling to help us understand more deeply, the behavior of the Mosaic land surface model, a model that has been developed over the past several years at NASA/GSFC. This methodology allows us to examine, directly and quantitatively, the dependence of prediction errors in land surface variables upon different vegetation conditions. The work also highlights the importance of accurate soil moisture information. Although surface variables are predicted imperfectly due to inherent uncertainties in the modeling process, our study suggests how satellite observations can be combined with the model, through land surface data assimilation, to improve their prediction.

Yang, Runhua↗
NASA NTRS1979

A technique for remotely measuring surface pressure from a satellite using a multicolor laser ranging system

The optical path length from a satellite to the earth's surface is strongly dependent on the atmospheric pressure along the propagation path. Surface pressure can be measured by using a multicolor laser ranging system to observe the change with wavelength in the optical path length from the satellite to a ground target. The equations which relate surface pressure to the differential path lengths are derived and the accuracy of the pressure measurement is evaluated in terms of the ranging system parameters. The results indicate that pressure accuracies of a few millibars appear feasible.

Gardner, C. S.↗
NASA NTRS1979

Technique for remotely measuring surface pressure from a satellite using a multicolor laser ranging system

The optical path length from a satellite to the earth's surface is strongly dependent on the atmospheric pressure along the propagation path. It is shown that surface pressures can be measured by the use of a multicolor laser ranging system to observe the change with wavelength in the optical path length from the satellite to a ground target. Equations are derived which relate surface pressure to the differential path lengths. In addition, the accuracy of the pressure measurement is evaluated in terms of the ranging system parameters. It is concluded that the results indicate that pressure accuracies of a few millibars appear feasible.

Gardner, C. S.↗
NASA NTRS1985

Rail accelerator technology and applications

Rail accelerators offer a viable means of launching ton-size payloads from the Earth's surface to space. The results of two mission studies which indicate that an Earth-to-Space Rail Launcher (ESRL) system is not only technically feasible but also economically beneficial, particularly when large amounts of bulk cago are to be delivered to space are given. An in-house experimental program at the Lewis Research Center (LeRC) was conducted in parallel with the mission studies with the objective of examining technical feasibility issues. A 1 m long - 12.5 by 12.5 mm bore rail accelerator as designed with clear polycarbonate sidewalls to visually observe the plasma armature acceleration. The general character of plasma/projectile dynamics is described for a typical test firing.

Zana, L. M.↗
NASA NTRS1988

A new proton fluence model for E greater than 10 MeV

Researchers describe a new engineering model for the fluence of protons with energies greater than 10 MeV. The data set used is a combination of observations made primarily from the Earth's surface between 1956 and 1963 and observations made from spacecraft in the vicinity of Earth between 1963 and 1985. With this data set we find that the distinction between ordinary proton events and anomalously large proton events made in earlier work disappears. The greater than 10 MeV fluences at 1 AU calculated with the new model are about twice those expected on the basis of models now in use. In contrast to earlier models, results do not depend critically on the fluence from any one event.

Feynman, Joan↗