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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

Microwave remote sensing laboratory design

Application of active and passive microwave remote sensing to the study of ocean pollution is discussed. Previous research efforts, both in the field and in the laboratory were surveyed to derive guidance for the design of a laboratory program of research. The essential issues include: choice of radar or radiometry as the observational technique; choice of laboratory or field as the research site; choice of operating frequency; tank sizes and material; techniques for wave generation and appropriate wavelength spectrum; methods for controlling and disposing of pollutants used in the research; and pollutants other than oil which could or should be studied.

Friedman, E.↗
NASA NTRS2016

Spaceborne Remote Sensing of Aerosol Type: Global Distribution, Model Evaluation and Translation into Chemical Speciation

It is essential to evaluate and refine aerosol classification methods applied to passive satellite remote sensing. We have developed an aerosol classification algorithm (called Specified Clustering and Mahalanobis Classification, SCMC) that assigns an aerosol type to multi-parameter retrievals by spaceborne, airborne or ground-based passive remote sensing instruments [1]. The aerosol types identified by our scheme are pure dust, polluted dust, urban-industrialdeveloped economy, urban-industrialdeveloping economy, dark biomass smoke, light biomass smoke and pure marine. We apply the SCMC method to inversions from the ground-based AErosol RObotic NETwork (AERONET [2]) and retrievals from the space-borne Polarization and Directionality of Earths Reflectances instrument (POLDER, [3]). The POLDER retrievals that we use differ from the standard POLDER retrievals [4] as they make full use of multi-angle, multispectral polarimetric data [5]. We analyze agreement in the aerosol types inferred from both AERONET and POLDER and evaluate GEOS-Chem [6] simulations over the globe. Finally, we use in-situ observations from the SEAC4RS airborne field experiment to bridge the gap between remote sensing-inferred qualitative SCMC aerosol types and their corresponding quantitative chemical speciation. We apply the SCMC method to airborne in-situ observations from the NASA Langley Aerosol Research Group Experiment (LARGE, [7]) and the Differential Aerosol Sizing and Hygroscopicity Spectrometer Probe (DASH-SP, [8]) instruments; we then relate each coarsely defined SCMC type to a sum of percentage of individual aerosol species, using in-situ observations from the Particle Analysis by Laser Mass Spectrometry (PALMS, [9]), the Soluble Acidic Gases and Aerosol (SAGA, [10]), and the High - Resolution Time - of - Flight Aerosol Mass Spectrometer (HR ToF AMS, [11]).

airborne↗
NASA NTRS2018

On the Ability of Space- Based Passive and Active Remote Sensing Observations of CO2 to Detect Flux Perturbations to the Carbon Cycle

Space-borne observations of CO2 are vital to gaining understanding of the carbon cycle in regions of the world that are difficult to measure directly, such as the tropical terrestrial biosphere, the high northern and southern latitudes, and in developing nations such as China. Measurements from passive instruments such as GOSAT (Greenhouse Gases Observing Satellite) and OCO-2 (Orbiting Carbon Observatory 2), however, are constrained by solar zenith angle limitations as well as sensitivity to the presence of clouds and aerosols. Active measurements such as those in development for the Active Sensing of CO2 Emissions over Nights, Days and Seasons (ASCENDS) mission show strong potential for making measurements in the high-latitude winter and in cloudy regions. In this work we examine the enhanced flux constraint provided by the improved coverage from an active measurement such as ASCENDS. The simulation studies presented here show that with sufficient precision, ASCENDS will detect permafrost thaw and fossil fuel emissions shifts at annual and seasonal time scales, even in the presence of transport errors, representativeness errors, and biogenic flux errors. While OCO-2 can detect some of these perturbations at the annual scale, the seasonal sampling provided by ASCENDS provides the stronger constraint. Plain Language Summary: Active and passive remote sensors show the potential to provide unprecedented information on the carbon cycle. With the all-season sampling, active remote sensors are more capable of constraining high-latitude emissions. The reduced sensitivity to cloud and aerosol also makes active sensors more capable of providing information in cloudy and polluted scenes with sufficient accuracy. These experiments account for errors that are fundamental to the top-down approach for constraining emissions, and even including these sources of error, we show that satellite remote sensors are critical for understanding the carbon cycle.

tropical terrestrial biosphere↗
NASA NTRS2003

Optical Reflectance and Fluorescence for Detecting Nitrogen Needs in Zea mays L.

Nitrogen (N) status in field grown corn (Zea mays L.) was assessed using spectral techniques. Passive reflectance remote sensing and, both passive and active fluorescence sensing methods were investigated. Reflectance and fluorescence methods are reported to detect changes in the primary plant pigments (chlorophylls a and b; carotenoids) in higher plant species. As a general rule, foliar chlorophyll a (Chl a) and chlorophyll b (Chl b) usually exist in approx.3:l ratio. In plants under stress, Chl b content is affected before Chl a reductions occur. For reflectance, a version of the chlorophyll absorption in reflectance index (CARI) method was tested with narrow bands from the Airborne Imaging Spectroradiometer for Applications (ASIA). CARI minimizes the effects of soil background on the signal from green canopies. A modified CARI (MCARI) was used to track total Chl a levels in the red dip of the spectrum from the corn canopy. A second MCARI was used to track the auxiliary plant pigments (Chl b and the carotenoids) in the yellow/orange/red edge part of the reflectance spectrum. The difference between these two MCARI indices detected variations in N levels across the field plot canopies using ASIA data. At the leaf level, ratios of fluorescence emissions in the blue, green, red and far-red wavelengths sensed responses that were associated with the plant pigments, and were indicative of energy transfer in the photosynthetic process. N stressed corn stands could be distinguish from those with optimally applied N with fluorescence emission spectra obtained from individual corn leaves. Both reflectance and fluorescence methods are sensitive in detecting corn N needs and may be especially powerful in monitoring crop conditions if both types of information can be combined.

McMurtrey, J. E.↗
NASA NTRS2026

Satellite Optical Remote Sensing of Clouds and Aerosols: From Particle Single-Scattering and Gaseous Absorption Through Radiative Transfer to Retrieval Products

Clouds and aerosols are fundamental regulators of Earth’s radiation budget and climate system, influencing both solar and terrestrial radiation through scattering, absorption, and emission processes. Accurate characterization of their physical and radiative properties from space requires a rigorous understanding of particle single-scattering, gaseous absorption, and radiative transfer in the atmosphere, as well as reliable inversion methods. This review synthesizes the physical foundations and algorithmic implementations of satellite-based passive optical remote sensing of clouds and aerosols, spanning the ultraviolet to thermal infrared spectral range. Beginning with electromagnetic scattering theory and state-of-the-art methods for computing single-scattering by nonspherical particles and computationally efficient methods for accounting for atmospheric absorption, we discuss the radiative transfer framework underpinning cloud and aerosol retrievals. The connection between single-scattering and multiple-scattering is rigorously formulated. We then summarize operational and research-grade retrieval techniques, including cloud masking and thermodynamic phase determination, CO₂ slicing for cloud-top pressure, the Nakajima-King shortwave bi-spectral, and infrared split-window approaches for cloud optical thickness and effective particle size, inversion algorithms for determining aerosol properties from multi-spectral and/or multi-angle radiometric and polarimetric measurements, and active-passive sensing synergy. Examples of the global cloud and aerosol climatologies are illustrated using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multi-angle Imaging SpectroRadiometer (MISR). Furthermore, the unique strengths of active remote sensing techniques based on spaceborne lidar observations are briefly elaborated in the context of studying ice clouds composed of randomly and horizontally oriented ice crystals, which is a significant challenge for conventional passive remote sensing techniques. By connecting physical theory to practical retrievals, this review highlights both the maturity of current methodologies and the remaining challenges in reducing uncertainties in particle morphology, vertical structure, absorption, and aerosol-cloud interactions. Furthermore, the impact of artificial intelligence (AI) on atmospheric remote sensing is briefly addressed.

Aerosols↗
NASA NTRSDate not supplied

Single-Scattering Properties of Melting Precipitation for GPM Passive Microwave and Radar Remote Sensing Applications

Over the past two decades, detailed computational simulations of the intricate three dimensional structures of ice-phase crystals and aggregates of those crystals have been developed. The microwave single-scattering properties of these simulated ice particles have been computed and used to improve quantitative estimates of snow rates and to better deRne the vertical structure of snow water contents in deep convective systems, as derived from satellite-borne passive microwave and/or radar remote sensing measurements. The same icephase particles have more recently been used as the starting point for simulations of melting precipitation using computational melting methods. In the current study, a heuristic melting method, as well as a physically-based melting procedure based on smoothed-particle hydrodynamics, are applied to ice particle models to describe the full evolution of the particles from dry snow to liquid drops. The discrete dipole approximation is utilized to calculate the single-scattering properties of the mixed-phase particles throughout the melting process. Then, the properties of the particles are “mapped” into simpliRed microphysical simulations of particle size spectra in the melting layers of stratiform, precipitating clouds. The bulk single-scattering properties of the melting layers and the sensitivity of their properties to modeling assumptions are explored, and the implications for combined radar-radiometer precipitation remote sensing from GPM are discussed.

William S Olson↗
NASA NTRS1986

Radar scene generation for tactical decision aids

The Mueller matrix and polarization covariance matrix for polarimetric radar systems was studied. The clutter was modeled by a layer of random permittivity, described by a three-dimensional correlation function, with variance, and horizontal and vertical correlation lengths. This model was applied, using the wave theory with Born approximations carried to the second order, to find the backscattering elements of the polarimetric matrices. Theoretical predictions are matched with experimental data for vegetation fields. The strong fluctuation theory was used to derive the backscattering cross sections. A two-layer anisotropic random medium model was developed for the active and passive microwave remote sensing of ice fields. A three-layer random medium model was adopted to study the volume scattering effects for the active and passive microwave remote sensing of snow-covered ice fields.

Kong, J. A.↗
NASA NTRS2018

An Assessment of the Differences Between Spatial Resolution and Grid Size for the SMAP Enhanced Soil Moisture Product over Homogeneous Sites

Satellite-based passive microwave remote sensing typically involves a scanning antenna that makes measurements at irregularly spaced locations. These locations can change on a day to day basis. Soil moisture products derived from satellite-based passive microwave remote sensing are usually resampled to a fixed Earth grid that facilitates their use in applications. In many cases the grid size is finer than the actual spatial resolution of the observation, and often this difference is not well understood by the user. Here, this issue was examined for the Soil Moisture Active Passive (SMAP) enhanced version of the passive-based soil moisture product, which has a grid size of 9-km and a nominal spatial resolution of 33-km. In situ observations from core validation sites were used to compute comparison metrics. For sites that satisfied the established reliability and scaling criteria, the impact of validating the 9-km grid product with in situ data collected over a 9-km versus a 33-km domain was very small for the sites studied (0.039 cu. m/cu. m unbiased root mean square difference for the 9-km case versus 0.037 cu. m/cu. m for the 33-km case). This result does not mean that the resolution of the product is 9-km but that for the conditions studied here the soil moisture estimated from in situ observations over 9-km is a close approximation of the soil moisture estimated from in situ observations over the 33-km resolution. The implication is that using the enhanced SMAP product at its grid resolution of 9-km should not introduce large errors in most applications.

Soil moisture↗
NASA NTRS1984

Remote sensing of soil moisture

Four major objectives are proposed: (1) to study the sensitivity of active and passive microwave remote sensing approaches to soil moisture variations; (2) to investigate the effect of vegetation cover on microwave backscatter and emission; (3) to test theoretical models of microwave backscatter and emission from a natural terrain against the observations obtained from SIR-B and aircraft radiometer flights; and (4) to estimate vegetation biomass with airborne visible and infrared sensors.

Wang, J. R.↗
NASA NTRS1974

Estimation of sunlight penetration in the sea for remote sensing

There is a need for a simple theoretical approach to the calculation of sunlight penetration depths suitable for passive multispectral remote sensing of water resources. An earlier paper presented an approach which is readily adapted to this calculation and which provides reasonably good agreement with more accurate but time-consuming radiative transfer models. The needed modifications are described and the model is used to calculate the penetration of sunlight into clear ocean water at several wavelengths throughout the visible portion of the spectrum. Calculations for both clear and turbid water are carried out for the two visible channels of the multispectral scanner on NASA's ERTS-1 satellite. The effect of a reflective bottom on the upwelling light field is discussed. Measurement parameters needed for the passive remote determination of water depths are identified and the use of submerged reflective panels for surface truth measurements is discussed.

Mccluney, W. R.↗
NASA NTRS1981

A survey of passive microwave and hybrid remote sensing of precipitation

It is noted that for practical realization of new information on precipitation during this decade, satellite observing systems must be coupled into surface-based observations and computer models of weather systems as they develop. Methods to combine the satellite/surface-based/model capabilities are discussed and several precipitation estimation pilot studies are proposed and outlined.

Weinman, J. A.↗
NASA NTRS1993

Intercomparisons between passive and active microwave remote sensing, and hydrological modeling for soil moisture

Soil moisture estimations from a distributed hydrological model and two microwave sensors were compared with ground measurements collected during the MAC-HYDRO'90 experiment. The comparison was done with the purpose of evaluating the performance of the hydrological model and examining the limitations of remote sensing techniques used in soil moisture estimation. An image integration technique was used to integrate and analyze rainfall, soil properties, land cover, topography, and remote sensing imagery. Results indicate that the hydrological model and microwave sensors successfully picked up temporal variations of soil moisture and that the spatial soil moisture pattern may be remotely sensed with reasonable accuracy using existing algorithms.

Wood, E. F.↗