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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Global Assimilation of L-Band Brightness Temperature Observations from SMAP and SMOS into the Catchment Land Surface Model and Contribution to the Skill of Soil Moisture Estimates

The Soil Moisture Active Passive (SMAP) and Soil Moisture Ocean Salinity (SMOS) missions provide global observations of L-band (1.4 GHz) passive microwave brightness temperature (Tb) observations at a resolution of ~40 km every 2-3 days. These observations have been available since 2015 from SMAP and since 2010 from SMOS.Soil moisture estimates from the separate assimilation of SMAP and SMOS Tb observations into land surface models were previously shown to improve over model-only estimates, thereby demonstrating the value of assimilating L-band Tb observations for soil moisture estimation. The assimilation experiments documented in the literature do not, however, establish whether the joint assimilation of SMAP and SMOS Tbs improves the skill of the resulting soil moisture estimates beyond what can be achieved with either set of observations alone. Moreover, because the published SMAP and SMOS results used different assimilation systems and configurations and applied different evaluation data and procedures, it is unclear whether the assimilation of SMAP-only or SMOS-only Tbs results in better soil moisture skill. In this presentation, we compare the results of three separate assimilation experiments for the period from April 2015 to present. The experiments utilize the SMAP Level-4 Soil Moisture (L4_SM) algorithm, which assimilates L-band Tb observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter. Specifically, the three experiments presented here assimilate (i) SMAP Tbs only, as in the L4_SM product, (ii) SMOS Tbs only, after interpolation of 40° incidence angle, and (iii) both SMAP and SMOS Tbs. In all other respects, the configuration of three experiments is identical, as is the validation of the output. Preliminary results suggest that the joint assimilation of SMAP and SMOS Tbs yields the most skillful soil moisture estimates when compared to independent in situ measurements. Moreover, the skill of the SMAP-only assimilation exceeds that of the SMOS-only assimilation. The presentation provides an in-depth evaluation of the skill of the assimilation estimates vs. independent in situ and satellite measurements and in terms of statistics of the observation-minus-forecast residuals and analysis increments.

Reichle, Rolf↗
DOE OSTI2023

Integration of a physics-based direct normal irradiance ($\mathrm{DNI}$) model to enhance the National Solar Radiation Database ($\mathrm{NSRDB}$)

The National Solar Radiation Database (NSRDB) is an extensively used dataset that furnishes satellite-retrieved solar resource data across the United States and an expanding list of other countries. Although the NSRDB uses a physical model to compute global horizontal irradiance (GHI), it currently employs an empirical approach based on surface observations to estimate cloudy-sky direct normal irradiance (DNI). Recently, a new physics-based approach, known as the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to improve DNI forecasting. FARMS-DNI integrates direct and scattered solar radiances within the circumsolar region, resulting in improved day-ahead forecasting of DNI by incorporating it into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study incorporates FARMS-DNI into the NSRDB algorithm to produce high-spatiotemporal-resolution DNI data from satellite data. The accuracy of the NSRDB based on FARMS-DNI is analyzed using surface observations from 19 sites situated within the National Oceanic and Atmospheric Administration (NOAA) Surface Radiation Budget (SURFRAD) and Solar Radiation (SOLRAD) networks, the University of Oregon (UO) network, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) network, and at the National Renewable Energy Laboratory (NREL). The results demonstrate that FARMS-DNI reduces the significant overestimation of DNI in the conventional NSRDB at all surface sites, particularly in cloud overcast conditions classified using both satellite retrievals and surface observations. Consequently, this new model can effectively improve the overall accuracy of the NSRDB. The results also suggest that further improvement of DNI estimates at individual time steps, however, requires advanced satellite techniques and precise identification of clouds and retrieval of cloud properties.

14 SOLAR ENERGY↗
NASA NTRS1996

The Information Content of Interferometric Synthetic Aperture Radar: Vegetation and Underlying Surface Topography

This paper first gives a heuristic description of the sensitivity of Interferometric Synthetic Aperture Radar to vertical vegetation distributions and underlying surface topography. A parameter estimation scenario is then described in which the Interferometric Synthetic Aperture Radar cross-correlation amplitude and phase are the observations from which vegetation and surface topographic parameters are estimated. It is shown that, even in the homogeneous-layer model of the vegetation, the number of parameters needed to describe the vegetation and underlying topography exceeds the number of Interferometric Synthetic Aperture Radar observations for single-baseline, single-frequency, single-incidence-angle, single-polarization Interferometric Synthetic Aperture Radar. Using ancillary ground-truth data to compensate for the underdetermination of the parameters, forest depths are estimated from the INSAR data. A recently-analyzed multibaseline data set is also discussed and the potential for stand-alone Interferometric Synthetic Aperture Radar parameter estimation is assessed. The potential of combining the information content of Interferometric Synthetic Aperture Radar with that of infrared/optical remote sensing data is briefly discussed.

Treuhaft, Robert N.↗
NASA NTRS1990

Ground and aircraft infrared observations over a partially-vegetated area

Consideration is given to the feasibility of estimated a reliable composite surface temperature over a partially-vegetated and furrowed cotton field with an intensive ground sampling routing using hand-held radiometers. The ground-based data are compared with surface temperatures taken from an aircraft at an altitude of about 150 m. A simple model is presented for computing the composite surface temperature using component temperatures of the soil and vegetation and row crop dimensions and orientations. The results of the modeling are compared with the ground-based and airborne radiometric measurements. It is found that estimates of the composite surface temperature comparable to low-flying aircraft observations over row crops with sparse cover may be obtained using ground-based estimates of average canopy temperature.

Kustas, W. P.↗
DOE OSTI2022

Quantum states of cylindrical surface charge density for modeling plasmonic circuit elements: Nanowires, nanorods, cavities, and waveguides

Nanostructures in the form of ellipsoids, prolate spheroids, rings, and cylinders are known to exhibit resonant surface and cavity modes with applications in nanophotonics and plasmonics and, more recently, in novel quantum experiments, in which control of plasmons and their interactions with plasmons, photons, phonons, excitons, and quantum emitters are desired. Nanorods and nanowires are examples of plasmonic structures with spectral properties of potential use as interconnects and circuit components. Additionally, estimates of the surface properties of these components are needed in circuit design and integrated systems. Here, we present a quantum Hamiltonian for the cylindrical surface charge density. We then study the photon excitation of plasmons on the cylindrical surface and calculate their scattering and radiative decay rate. Nonradiative decay of plasmons induces an efficient heating of the nanoparticle and can photoacoustically excite mechanical oscillations. Computational calculations are also presented for the plasmonic modes and the ensuing excitations of nanomechanical eigenmodes of nanoparticles with near-cylindrical symmetries.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Inverse Modeling of Hydrologic Parameters in CLM4 via Generalized Polynomial Chaos in the Bayesian Framework

In this work, generalized polynomial chaos (gPC) expansion for land surface model parameter estimation is evaluated. We perform inverse modeling and compute the posterior distribution of the critical hydrological parameters that are subject to great uncertainty in the Community Land Model (CLM) for a given value of the output LH. The unknown parameters include those that have been identified as the most influential factors on the simulations of surface and subsurface runoff, latent and sensible heat fluxes, and soil moisture in CLM4.0. We set up the inversion problem in the Bayesian framework in two steps: (i) building a surrogate model expressing the input–output mapping, and (ii) performing inverse modeling and computing the posterior distributions of the input parameters using observation data for a given value of the output LH. The development of the surrogate model is carried out with a Bayesian procedure based on the variable selection methods that use gPC expansions. Our approach accounts for bases selection uncertainty and quantifies the importance of the gPC terms, and, hence, all of the input parameters, via the associated posterior probabilities.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2012

Real-Time Frequency Response Estimation Using Joined-Wing SensorCraft Aeroelastic Wind-Tunnel Data

A new method is presented for estimating frequency responses and their uncertainties from wind-tunnel data in real time. The method uses orthogonal phase-optimized multi- sine excitation inputs and a recursive Fourier transform with a least-squares estimator. The method was first demonstrated with an F-16 nonlinear flight simulation and results showed that accurate short period frequency responses were obtained within 10 seconds. The method was then applied to wind-tunnel data from a previous aeroelastic test of the Joined- Wing SensorCraft. Frequency responses describing bending strains from simultaneous control surface excitations were estimated in a time-efficient manner.

Grauer, Jared A↗
NASA NTRS1986

Regional dust deposits on Mars - Physical properties, age, and history

This paper presents a description of the use of Viking infrared thermal mapper (IRTM), earth-based radar, and visual observations for the study of the existence of regional dust deposits. It is pointed out that these observations provide estimates of particle size, rock abundance, surface texture, thermal emissivity, and albedo. These estimates can be used to characterize surface deposits and to determine the degree of surface mantling. Attention is given to the regolith properties, atmospheric dust properties, and a model for formation of low-inertia regions. It is found that global dust storms deposit currently approximately 25 microns of material per year in the equatorial region. Over geologic time this value may vary from 0 to 250 microns due to variations in atmospheric conditions produced by orbital variations.

Christensen, P. R.↗
NASA NTRS1986

Downward longwave surface radiation from sun-synchronous satellite data - Validation of methodology

An extensive study has been carried out to validate a satellite technique for estimating downward longwave radiation at the surface. The technique, mostly developed earlier, uses operational sun-synchronous satellite data and a radiative transfer model to provide the surface flux estimates. The satellite-derived fluxes were compared directly with corresponding ground-measured fluxes at four different sites in the United States for a common one-year period. This provided a study of seasonal variations as well as a diversity of meteorological conditions. Dome heating errors in the ground-measured fluxes were also investigated and were corrected prior to the comparisons. Comparison of the monthly averaged fluxes from the satellite and ground sources for all four sites for the entire year showed a correlation coefficient of 0.98 and a standard error of estimate of 10 W/sq m. A brief description of the technique is provided, and the results validating the technique are presented.

Darnell, W. L.↗
NASA NTRS1987

Martian terrains

Terrain studies of candidate landing sites for a future rover/sample-return mission to Mars are being conducted to evaluate the geologic and trafficability aspects of each site. An optimum site should have geologic units of widely diverse ages and chemical compositions occurring in close enough proximity and in smooth enough terrain so that a roving vehicle of limited traverse ability (+ or - 100 km) could collect representative samples. In FY 1986, geologic maps were compiled at 1:500,000 and 1:2 million scales of the Mangala Valles, Kasei Valles, Chasma Boreale (north polar), and Planum Australe (south polar) areas, and a study was begun of the topography and surface roughness characteristics of the Mangala Valles site. Geologic mapping has been greatly facilitated by specially enhanced, high-resolution Viking photographs, which clarify stratigraphic relations of units unrecognized earlier. Photoclinometric profiles of topographic features provide width and depth measurements of four classes of channels, the thickness of some volcanic units, and the throw on some faults. Estimates of the surface roughness of units are calculated using a newly developed USGS computer program and using measurements derived from Earth-based radar.

Masursky, Harold↗
NASA NTRS2011

Combining Observations in the Reflective Solar and Thermal Domains for Improved Mapping of Carbon, Water and Energy FLuxes

This study investigates the utility of integrating remotely sensed estimates of leaf chlorophyll (C(sub ab)) into a thermal-based Two-Source Energy Balance (TSEB) model that estimates land-surface CO2 and energy fluxes using an analytical, light-use-efficiency (LUE) based model of canopy resistance. Day to day variations in nominal LUE (LUE(sub n)) were assessed for a corn crop field in Maryland U.S.A. through model calibration with CO2 flux tower observations. The optimized daily LUE(sub n) values were then compared to estimates of C(sub ab) integrated from gridded maps of chlorophyll content weighted over the tower flux source area. Changes in Cab exhibited a curvilinear relationship with corresponding changes in daily calibrated LUE(sub n) values derived from the tower flux data, and hourly water, energy and carbon flux estimation accuracies from TSEB were significantly improved when using C(sub ab) for delineating spatio-temporal variations in LUE(sub n). The results demonstrate the synergy between thermal infrared and shortwave reflective wavebands in producing valuable remote sensing data for monitoring of carbon and water fluxes.

Houborg, Rasmus↗
NASA NTRS1977

An Evaluation of Total Solar Reflectance and Spectral Band Ratioing Techniques for Estimating Soil Water Content

For several days in March of 1975, reflected solar radiation measurements were obtained from smooth and rough surfaces of wet, drying, and continually dry Avondale loam at Phoenix, Arizona, with pyranometers located 50 cm above the ground surface and a multispectral scanner flown at a 300-m height. The simple summation of the different band radiances measured by the multispectral scanner proved equally as good as the pyranometer data for estimating surface soil water content if the multispectral scanner data were standardized with respect to the intensity of incoming solar radiation or the reflected radiance from a reference surface, such as the continually dry soil. Without this means of standardization, multispectral scanner data are most useful in a spectral band ratioing context. Our results indicated that, for the bands used, no significant information on soil water content could be obtained by band ratioing. Thus the variability in soil water content should insignificantly affect soil-type discrimination based on identification of type-specific spectral signatures. Therefore remote sensing, conducted in the 0.4- to 1.0-micron wavelength region of the solar spectrum, would seem to be much More suited to identifying crop and soil types than to estimating of soil water content.

Reginato, R. J.↗
NASA NTRS2001

Soot Optical Property Study

Recent past studies of soot reaction processes in laminar premixed and nonpremixed flames generally have used the intrusive technique of thermophoretic sampling and analysis by transmission electron microscopy (TEM) to observe soot structure and obtain important fundamental information about soot particle properties, such as soot primary particle diameters, the rate of change of soot primary particle diameter as a function of time (or rate of soot surface growth or oxidation), the amount of soot particle reactive surface area per unit volume, the number of primary soot particles per unit volume, and the rate of formation of primary soot particles (or the rate of soot primary particle nucleation). Given the soot volume per unit volume of the flame (or the soot volume fraction), all these properties are readily found from a measurement of the soot primary particle diameter (which usually is nearly a constant for each location within a laminar flame). This approach is not possible within freely propagating flames, however, because soot properties at given positions in such flames vary relatively rapidly as a function of time in the soot formation and oxidation regions compared to the relatively lengthy sampling times needed to accumulate adequate soot samples and to minimize effects of soot collected on the sampling grid as it moves to and from the sampling position through other portions of the flame. Thus, nonintrusive optical methods must be used to find the soot primary particle diameters needed to define the soot surface reaction properties mentioned earlier. Unfortunately, approximate nonintrusive methods used during early studies of soot reaction properties in flames, found from laser scattering and absorption measurements analyzed assuming either Rayleigh scattering or Mie scattering from polydisperse effective soot particles having the same mass of soot as individual soot aggregates, have not been found to be an effective way to estimate the soot surface reaction area per unit volume. Thus, alternative nonintrusive optical methods of finding these properties must be sought, which was the objective of this phase of the investigation. The alternative method used here involves use of the Rayleigh-Debye-Gans-Polydisperse-Fractal-Aggregate (RDG-PFA) scattering approximation for soot aggregates in flames. Thus, the development of this method will be discussed next before describing its evaluation as a means of nonintrusively measuring soot primary particle diameters in soot-containing flames.

Aung, K. T.↗
NASA NTRS2014

Improvement of Aerosol Optical Depth Retrieval over Hong Kong from a Geostationary Meteorological Satellite Using Critical Reflectance with Background Optical Depth Correction

Despite continuous efforts to retrieve aerosol optical depth (AOD) using a conventional 5-channelmeteorological imager in geostationary orbit, the accuracy in urban areas has been poorer than other areas primarily due to complex urban surface properties and mixed aerosol types from different emission sources. The two largest error sources in aerosol retrieval have been aerosol type selection and surface reflectance. In selecting the aerosol type from a single visible channel, the season-dependent aerosol optical properties were adopted from longterm measurements of Aerosol Robotic Network (AERONET) sun-photometers. With the aerosol optical properties obtained fromthe AERONET inversion data, look-up tableswere calculated by using a radiative transfer code: the Second Simulation of the Satellite Signal in the Solar Spectrum (6S). Surface reflectance was estimated using the clear sky composite method, awidely used technique for geostationary retrievals. Over East Asia, the AOD retrieved from the Meteorological Imager showed good agreement, although the values were affected by cloud contamination errors. However, the conventional retrieval of the AOD over Hong Kong was largely underestimated due to the lack of information on the aerosol type and surface properties. To detect spatial and temporal variation of aerosol type over the area, the critical reflectance method, a technique to retrieve single scattering albedo (SSA), was applied. Additionally, the background aerosol effect was corrected to improve the accuracy of the surface reflectance over Hong Kong. The AOD retrieved froma modified algorithmwas compared to the collocated data measured by AERONET in Hong Kong. The comparison showed that the new aerosol type selection using the critical reflectance and the corrected surface reflectance significantly improved the accuracy of AODs in Hong Kong areas,with a correlation coefficient increase from0.65 to 0.76 and a regression line change from τMI [basic algorithm] = 0.41τAERONET + 0.16 to τMI [new algorithm] = 0.70τAERONET + 0.01.

aerosols↗
NASA NTRS2023

Fine particulate concentrations over East Asia derived from aerosols measured by the Advanced Himawari Imager using machine learning

Fine particulate matter with a diameter below 2.5 μm (PM 2.5 ) is deleterious to the cardiovascular and respiratory systems. It is often difficult to assess the effects of PM 2.5 on human health over regions with limited ground monitoring sites, especially in East Asia. As an alternative, we estimated near-surface PM 2.5 concentrations by analyzing Advanced Himawari Imager (AHI) Yonsei Aerosol Retrieval (YAER) products. This study incorporates daytime data for East Asia covering the Korean Peninsula, China, Japan, Southeast Asia, and southern Mongolia. We collocated AHI YAER product pixels with meteorological, land-cover, and other ancillary data for the period from March 2018 to February 2019. To estimate PM 2.5 concentrations over wide areas spanning many countries displaying various relationships between aerosol optical depth and PM 2.5 , monthly models were developed by considering both the spatial and temporal characteristics of ground-based PM 2.5 measurements. Random forest machine learning model estimated ground-level mass concentrations of PM 2.5 ; subsequent 10-fold cross validation (CV) yielded a CV R 2 value of 0.81 and a CV root mean squared error (RMSE) of 12.3 μg m -3 . We investigated the spatial pattern of PM 2.5 concentrations over multiple countries and seasonal variation in PM 2.5 concentrations. Diurnal variation of a severe PM 2.5 event in the Korean Peninsula was investigated as a case study. The model captured the extremely heterogeneous spatial distribution of PM 2.5 concentrations peaked around local noon. To measure the capability of the developed model to estimate PM 2.5 concentrations in areas with few in-situ data, its predictive performance was evaluated using a dataset independent of the training process with an R 2 of 0.60 and RMSE of 8.18 μg m −3 . This study demonstrates the potential for satellite-based PM 2.5 estimation for areas with insufficient measuring stations.

Pm2.5↗
DOE OSTI2022

Using soil library hyperspectral reflectance and machine learning to predict soil organic carbon: Assessing potential of airborne and spaceborne optical soil sensing

Soil organic carbon (SOC) is a key variable to determine soil functioning, ecosystem services, and global carbon cycles. Spectroscopy, particularly optical hyperspectral reflectance coupled with machine learning, can provide rapid, efficient, and cost-effective quantification of SOC. However, how to exploit soil hyperspectral reflectance to predict SOC concentration, and the potential performance of airborne and satellite data for predicting surface SOC at large scales remain relatively underknown. Here, this study utilized a continental-scale soil laboratory spectral library (37,540 full-pedon 350–2500 nm reflectance spectra with SOC concentration of 0–780 g·kg –1 across the US) to thoroughly evaluate seven machine learning algorithms including Partial-Least Squares Regression (PLSR), Random Forest (RF), K-Nearest Neighbors (KNN), Ridge, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) along with four preprocessed spectra, i.e. original, vector normalization, continuum removal, and first-order derivative, to quantify SOC concentration. Furthermore, by using the coupled soil-vegetation-atmosphere radiative transfer model, we simulated twelve airborne and spaceborne hyper/multi-spectral remote sensing data from surface bare soil laboratory spectra to evaluate their potential for estimating SOC concentration of surface bare soils. Results show that LSTM achieved best predictive performance of quantifying SOC concentration for the whole data sets (R 2 = 0.96, RMSE = 30.81 g·kg –1 ), mineral soils (SOC ≤ 120 g·kg –1 , R 2 = 0.71, RMSE = 10.60 g·kg –1 ), and organic soils (SOC > 120 g·kg –1 , R 2 = 0.78, RMSE = 62.31 g·kg –1 ). Spectral data preprocessing, particularly the first-order derivative, improved the performance of PLSR, RF, Ridge, KNN, and ANN, but not LSTM or CNN. We found that the SOC models of mineral and organic soils should be distinguished given their distinct spectral signatures. Finally, we identified that the shortwave infrared is vital for airborne and spaceborne hyperspectral sensors to monitor surface SOC. This study highlights the high accuracy of LSTM with hyperspectral/multispectral data to mitigate a certain level of noise (soil moisture <0.4 m 3 ·m –3 , green leaf area < 0.3 m 2 ·m –2 , plant residue <0.4 m 2 ·m –2 ) for quantifying surface SOC concentration. Forthcoming satellite hyperspectral missions like Surface Biology and Geology (SBG) have a high potential for future global soil carbon monitoring, while high-resolution satellite multispectral fusion data can be an alternative.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1982

Proceedings of the NASA Workshop on Surface Fitting

Surface fitting techniques and their utilization are addressed. Surface representation, approximation, and interpolation are discussed. Along with statistical estimation problems associated with surface fitting.

Guseman, L. F., Jr.↗
NASA NTRS2014

Surface Albedo Darkening from wildfires in Northern Sub-Saharan Africa

Wildfires are recognized as a key physical disturbance of terrestrial ecosystems and a major source of atmospheric trace gases and aerosols. They are known to produce changes in landscape patterns and lead to changes in surface albedo that can persist for long periods. Here, we estimate the darkening of surface albedo due to wildfires in different land cover ecosystems in the Northern Sub-Saharan Africa using data from the Moderate Resolution Imaging Spectroradiometer (MODIS). We determined a decrease in albedo after fires over most land cover types (e.g. woody savannas: (-0.00352 0.00003) and savannas: (- 0.003910.00003), which together accounted for >86% of the total MODIS fire count between 2003 and 2011). Grasslands had a higher value (-0.00454 0.00003) than the savannas, but accounted for only about 5% of the total fire count. A few other land cover types (e.g. Deciduous broad leaf: (0.00062 0.00015), and barren: 0.00027 0.00019), showed an increase in albedo after fires, but accounted for less than 1% of the total fires. Albedo change due to wildfires is more important during the fire season (October-February). The albedo recovery progresses rapidly during the first year after fires, where savannas show the greatest recovery (>77%) within one year, while deciduous broadleaf, permanent wetlands and barren lands show the least one-year recovery (56%). The persistence of surface albedo darkening in most land cover types is limited to about six to seven years, after which at least 98% of the burnt pixels recover to their pre-fire albedo.

Wildfires↗