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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

The Ocean Colour Climate Change Initiative: II. Spatial and Temporal Homogeneity of Satellite Data Retrieval Due to Systematic Effects in Atmospheric Correction Processors

The established procedure to access the quality of atmospheric correction processors and their underlying algorithms is the comparison of satellite data products with related in-situ measurements. Although this approach addresses the accuracy of derived geophysical properties in a straight forward fashion, it is also limited in its ability to catch systematic sensor and processor dependent behaviour of satellite products along the scan-line, which might impair the usefulness of the data in spatial analyses. The Ocean Colour Climate Change Initiative (OC-CCI) aims to create an ocean colour dataset on a global scale to meet the demands of the ecosystem modelling community. The need for products with increasing spatial and temporal resolution that also show as little systematic and random errors as possible, increases. Due to cloud cover, even temporal means can be influenced by along-scanline artefacts if the observations are not balanced and effects cannot be cancelled out mutually. These effects can arise from a multitude of results which are not easily separated, if at all. Among the sources of artefacts, there are some sensor-specific calibration issues which should lead to similar responses in all processors, as well as processor-specific features which correspond with the individual choices in the algorithms. A set of methods is proposed and applied to MERIS data over two regions of interest in the North Atlantic and the South Pacific Gyre. The normalised water leaving reflectance products of four atmospheric correction processors, which have also been evaluated in match-up analysis, is analysed in order to find and interpret systematic effects across track. These results are summed up with a semi-objective ranking and are used as a complement to the match-up analysis in the decision for the best Atmospheric Correction (AC) processor. Although the need for discussion remains concerning the absolutes by which to judge an AC processor, this example demonstrates clearly, that relying on the match-up analysis alone can lead to misjudgement.

Ocean Colour↗
NASA NTRS1980

Unsupervised classification of MSS Landsat data for mapping spatially complex vegetation

The various stages in carrying out a monocluster block unsupervised classification using Landsat MSS data are described. Procedures for carrying out these various stages were found to be far from well-established for the type of terrain being investigated, which is rugged and contains many small land cover units. Two particular difficulties were encountered: first, that of precise ground location of pixels; and, secondly, that of objectively evaluating the results. Ways in which these can be surmounted are suggested.

Townshend, J. R. G.↗
NASA NTRS1993

Neural network for processing both spatial and temporal data with time based back-propagation

Neural networks are computing systems modeled after the paradigm of the biological brain. For years, researchers using various forms of neural networks have attempted to model the brain's information processing and decision-making capabilities. Neural network algorithms have impressively demonstrated the capability of modeling spatial information. On the other hand, the application of parallel distributed models to the processing of temporal data has been severely restricted. The invention introduces a novel technique which adds the dimension of time to the well known back-propagation neural network algorithm. In the space-time neural network disclosed herein, the synaptic weights between two artificial neurons (processing elements) are replaced with an adaptable-adjustable filter. Instead of a single synaptic weight, the invention provides a plurality of weights representing not only association, but also temporal dependencies. In this case, the synaptic weights are the coefficients to the adaptable digital filters. Novelty is believed to lie in the disclosure of a processing element and a network of the processing elements which are capable of processing temporal as well as spacial data.

Villarreal, James A.↗
NASA NTRS1993

Arctic sea ice albedo - A comparison of two satellite-derived data sets

Spatial patterns of mean monthly surface albedo for May, June, and July, derived from DMSP Operational Line Scan (OLS) satellite imagery are compared with surface albedos derived from the International Satellite Cloud Climatology Program (ISCCP) monthly data set. Spatial patterns obtained by the two techniques are in general agreement, especially for June and July. Nevertheless, systematic differences in albedo of 0.05 - 0.10 are noted which are most likely related to uncertainties in the simple parameterizations used in the DMSP analyses, problems in the ISCCP cloud-clearing algorithm and other modeling simplifications. However, with respect to the eventual goal of developing a reliable automated retrieval algorithm for compiling a long-term albedo data base, these initial comparisons are very encouraging.

Schweiger, Axel J.↗
NASA NTRS2023

Spatially-Coordinated Airborne Data and Complementary Products for Aerosol, Gas, Cloud, and Meteorological Studies: the Nasa Activate Dataset

The NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) produced a unique dataset for research into aerosol–cloud–meteorology interactions, with applications extending from process-based studies to multi-scale model intercomparison and improvement as well as to remote-sensing algorithm assessments and advancements. ACTIVATE used two NASA Langley Research Center aircraft, a HU-25 Falcon and King Air, to conduct systematic and spatially coordinated flights over the northwest Atlantic Ocean, resulting in 162 joint flights and 17 other single-aircraft flights between 2020 and 2022 across all seasons. Data cover 574 and 592 cumulative flights hours for the HU-25 Falcon and King Air, respectively. The HU-25 Falcon conducted profiling at different level legs below, in, and just above boundary layer clouds (< 3 km) and obtained in situ measurements of trace gases, aerosol particles, clouds, and atmospheric state parameters. Under cloud-free conditions, the HU-25 Falcon similarly conducted profiling at different level legs within and immediately above the boundary layer. The King Air (the high-flying aircraft) flew at approximately ∼ 9 km and conducted remote sensing with a lidar and polarimeter while also launching dropsondes (785 in total). Collectively, simultaneous data from both aircraft help to characterize the same vertical column of the atmosphere. In addition to individual instrument files, data from the HU-25 Falcon aircraft are combined into “merge files” on the publicly available data archive that are created at different time resolutions of interest (e.g., 1, 5, 10, 15, 30, 60 s, or matching an individual data product's start and stop times). This paper describes the ACTIVATE flight strategy, instrument and complementary dataset products, data access and usage details, and data application notes. The data are publicly accessible through https://doi.org/10.5067/SUBORBITAL/ACTIVATE/DATA001 (ACTIVATE Science Team, 2020).

Aerosol Cloud meTeorology Interactions oVer the we↗
NASA NTRS1998

A Spatial Classifier for Multispectral Data Using Contextual Information

Connectivity describes the spatial relationship among pixels. A spatial classifier which employs the sigma probability concept of the Gaussian distribution and a type of contextual information connectivity of the pixels, is studied in this paper. This spatial classifier attempts to replicate the kind of spatial synthesis done by the human analyst during visual interpretation or to capture the spatial relationships inherent in an aerial photograph. Several classification results of the Landsat TM data using this classifier with different window sizes for capturing the contextual information are illustrated and compared.

Hung, Chih-Cheng↗
NASA NTRS2019

Parameterization-Induced Uncertainties and Impacts of Crop Management Harmonization in a Global Gridded Crop Model Ensemble

Global gridded crop models (GGCMs) combine agronomic or plant growth models with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes.

Folberth, Christian↗
NASA NTRS1993

An astrophysics data program investigation of spatial structure of supernova remnants

The final report on astrophysics data program investigation of spatial structure of supernova remnants for the period 1 Aug. 1989 to 31 Jul. 1991 is presented. The goal of the project was the study of the spatial structure of supernova remnants (SNR's) as observed in the x-ray band. A number of software tools were developed for the analysis: (1) a program to fit various geometric models to high resolution x-ray data, and (2) programs for Fourier Transform analysis of clumping in SNR's. These programs were applied to high resolution imager (HRI) data on the young galactic SNR's Tycho and Kepler with some success.

Hughes, John P.↗
NASA NTRS2012

Method and system for near-field spectroscopy using targeted deposition of nanoparticles

There is provided in one embodiment of the invention a method for analyzing a sample material using surface enhanced spectroscopy. The method comprises the steps of imaging the sample material with an atomic force microscope (AFM) to select an area of interest for analysis, depositing nanoparticles onto the area of interest with an AFM tip, illuminating the deposited nanoparticles with a spectrometer excitation beam, and disengaging the AFM tip and acquiring a localized surface enhanced spectrum. The method may further comprise the step of using the AFM tip to modulate the spectrometer excitation beam above the deposited nanoparticles to obtain improved sensitivity data and higher spatial resolution data from the sample material. The invention further comprises in one embodiment a system for analyzing a sample material using surface enhanced spectroscopy.

Anderson, Mark S.↗
NASA NTRS1983

Relating Spatial Patterns in Image Data to Scene Characteristics

In remote sensing, the primary goal is accurate scene inference, in which characteristics of the scene are inferred from the image data. More effective inference of scene characteristics can be accomplished through the use of techniques that use explicit models of spatial pattern. Spatial patterns in image data are functionally related to the size and spacing of elements in the scene and to the spatial resolution of the image data. At resolutions where variance is high, scene inference techniques should rely heavily on data from the spatial domain. As variance decreases, effective scene inference will increasingly rely on spectral data.

Strahler, A. H.↗
NASA NTRS1981

Incorporating spatial context into statistical classification of multidimensional image data

Compound decision theory is employed to develop a general statistical model for classifying image data using spatial context. The classification algorithm developed from this model exploits the tendency of certain ground-cover classes to occur more frequently in some spatial contexts than in others. A key input to this contextural classifier is a quantitative characterization of this tendency: the context function. Several methods for estimating the context function are explored, and two complementary methods are recommended. The contextural classifier is shown to produce substantial improvements in classification accuracy compared to the accuracy produced by a non-contextural uniform-priors maximum likelihood classifier when these methods of estimating the context function are used. An approximate algorithm, which cuts computational requirements by over one-half, is presented. The search for an optimal implementation is furthered by an exploration of the relative merits of using spectral classes or information classes for classification and/or context function estimation.

Bauer, M. E.↗
NASA NTRS1983

The time-space relationships among data points from multispectral spatial scanners

Multispectral scanner and thematic mapper (TM) data from Landsat satellites are discussed in terms of the perceived 'simultaineity' of the images, which are obtained by scanning techniques. Scanning the scenes ensures that the data points of the images are actually sequential, even if the scan is performed at rates that are fast relative to the motion of the spacecraft. The last datum gathered by the MSS is, in fact, taken 29 sec after the first, witih a 56 m distance being present between pixels. The spacing in uneven from band to band on the TM and analyses of the data to produce an image requires consideration of nonfixed time relationships for different locations on the scan within and among the bands. Additionally, corrective measures must be taken to compensate for instrument jitter and attitude changes.

Gordon, F.↗
NASA NTRS2016

Comparison of Spatial Correlation Parameters between Full and Model Scale Launch Vehicles

The current vibro-acoustic analysis tools require specific spatial correlation parameters as input to define the liftoff acoustic environment experienced by the launch vehicle. Until recently these parameters have not been very well defined. A comprehensive set of spatial correlation data were obtained during a scale model acoustic test conducted in 2014. From these spatial correlation data, several parameters were calculated: the decay coefficient, the diffuse to propagating ratio, and the angle of incidence. Spatial correlation data were also collected on the EFT-1 flight of the Delta IV vehicle which launched on December 5th, 2014. A comparison of the spatial correlation parameters from full scale and model scale data will be presented.

Kenny, Jeremy↗
NASA NTRS1994

Representing spatial information in a computational model for network management

While currently available relational database management systems (RDBMS) allow inclusion of spatial information in a data model, they lack tools for presenting this information in an easily comprehensible form. Computer-aided design (CAD) software packages provide adequate functions to produce drawings, but still require manual placement of symbols and features. This project has demonstrated a bridge between the data model of an RDBMS and the graphic display of a CAD system. It is shown that the CAD system can be used to control the selection of data with spatial components from the database and then quickly plot that data on a map display. It is shown that the CAD system can be used to extract data from a drawing and then control the insertion of that data into the database. These demonstrations were successful in a test environment that incorporated many features of known working environments, suggesting that the techniques developed could be adapted for practical use.

Blaisdell, James H.↗