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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Weighted slit extraction of low-dispersion IUE spectral data

The weighted slit extraction has been chosen by the IUE project as the standard extraction for low-dispersion spectra in the final IUE archives. In order to properly weight the data within the extraction slit and obtain the optimal evaluation of the true spectrum, a noise model is derived for IUE data. The exclusion of cosmic rays is demonstrated and the improvement in signal-to-noise ratio is quantified for spectra taken with a wide range of flux level. Problematic spectra, such as trailed spectra, multiple spectra, discontinuous spectra, and emission-line spectra, are extracted to demonstrate the robust nature of the method. Comparisons are made between the standard IUE Standard Image Processing System (IUESIPS) extraction, nonstandard narrow slit extraction, trailed IUESIPS extraction, and weighted extraction for these data. Other weighted slit extractions are briefly discussed. The advantages of the weighted slit extraction are: (1) the extraction improves the signal-to-noise ratio of the spectrum while conserving total flux; (2) most of the cosmic rays are automatically removed; (3) the output includes an error estimate for each point in the flux spectrum; and (4) the algorithm can be run in a batch mode that requires no special input parameters.

Kinney, A. L.↗
NASA NTRS1993

ROWS wave spectral data collected in SAXON-FPN, November 1990

High-resolution directional wave spectra obtained with the NASA Ku-band radar ocean wave spectrometer (ROWS) on the Naval Research Laboratory P-3 aircraft during SAXON-FPN (SAR and X-Band Ocean Nonlinearities Experiment-Forschungsplattform Nordsee) experiments in the North Sea in November 1990 are presented. This experiment was the first in which the ROWS was operated with its new pc-based high-speed digital data acquisition system.

Jackson, F.↗
NASA NTRS2000

BOREAS TE-23 Canopy Architecture and Spectral Data from Hemispherical Photographs

The Boreal Ecosystem-Atmospheric Study (BOREAS) TE-23 (Terrestrial Ecology) team collected hemispherical photographs in support of its efforts to characterize and interpret information on estimates of canopy architecture and radiative transfer properties for most BOREAS study sites. Various Old Aspen (OA), Old Black Spruce (OBS), Old Jack Pine (OJP), Young Jack Pine (YJP), and Young Aspen (YA) sites in the boreal forest were measured from May to August 1994. The hemispherical photographs were used to derive values of leaf area index (LAI), leaf angle, gap fraction, and clumping index. This documentation describes these derived values. The derived data are stored in tabular ASCII files. The hemispherical photographs are stored in the original set of 42 CD-ROMs that were supplied by TE-23. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Rich, Paul M.↗
NASA NTRS2001

Spectral Data Assimilation for Merging Satellite Ocean Color Imagery

The Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) project was initiated 'to develop a methodology and operational capability to combine data products from various ocean color missions in a manner that ensures the best possible global coverage and best exploits the complementary missions of the sensors'. While the merging of data from multiple sources has already been implemented for sea surface temperature (SST), altimetry or clouds, this is a new topic in ocean color science. Merging ocean color data has obvious advantage in terms of spatial and temporal coverage of the global ocean but data merging should also result in new, more diverse and improved data products with lower uncertainties. The merging of ocean color satellite data is generally considered at the level of the global gridded products, i.e., NASA Level-3 and, more specifically, for the merging of chlorophyll a concentration data, [Ch1]. This approach limits computational issues and deals with a unique, simple quantity, [Ch1]. However, ocean color data merging can also be conducted at the level of water-leaving radiances, LwN(lamda), using either empirical or semi-analytical algorithms. Although more challenging, this latter approach is potentially extremely powerful as it can achieve good consistency in the final data products and can take advantage of both the specificities and differences of each source of data. The demonstration of the feasibility of such approach is the main objective of our SIMBIOS work. Here, we use a semi-analytical algorithm to merge Rrs (or, equivalently LwN) data from different sources to estimate [Ch1]. The model also allows for the retrieval of other quantities such as the combined absorption of colored detrital particulate and dissolved organic matter at 443 nm, a(sub cdm)(443) and the particulate backscattering, b(sub bp)(443).

Siegel, David A.↗
NASA NTRS2005

Thermodynamic and cloud parameter retrieval using infrared spectral data

High-resolution infrared radiance spectra obtained from near nadir observations provide atmospheric, surface, and cloud property information. A fast radiative transfer model, including cloud effects, is used for atmospheric profile and cloud parameter retrieval. The retrieval algorithm is presented along with its application to recent field experiment data from the NPOESS Airborne Sounding Testbed - Interferometer (NAST-I). The retrieval accuracy dependence on cloud properties is discussed. It is shown that relatively accurate temperature and moisture retrievals can be achieved below optically thin clouds. For optically thick clouds, accurate temperature and moisture profiles down to cloud top level are obtained. For both optically thin and thick cloud situations, the cloud top height can be retrieved with an accuracy of approximately 1.0 km. Preliminary NAST-I retrieval results from the recent Atlantic-THORPEX Regional Campaign (ATReC) are presented and compared with coincident observations obtained from dropsondes and the nadir-pointing Cloud Physics Lidar (CPL).

Zhou, Daniel K.↗
NASA NTRS2000

On-chip learning of hyper-spectral data for real time target recognition

As the focus of our present paper, we have used the cascade error projection (CEP) learning algorithm (shown to be hardware-implementable) with on-chip learning (OCL) scheme to obtain three orders of magnitude speed-up in target recognition compared to software-based learning schemes. Thus, it is shown, real time learning as well as data processing for target recognition can be achieved.

on-chip learning Cascade Error Projection real tim↗
NASA NTRS2004

Initial Processing of Infrared Spectral Data

The Atmospheric Infrared Spectrometer (AIRS) Science Processing System is a collection of computer programs, denoted product generation executives (PGEs), for processing the readings of the AIRS suite of infrared and microwave instruments orbiting the Earth aboard NASA's Aqua spacecraft. Following from level 0 (representing raw AIRS data), the PGEs and their data products are denoted by alphanumeric labels (1A, 1B, and 2) that signify the successive stages of processing. Once level-0 data have been received, the level-1A PGEs begin processing, performing such basic housekeeping tasks as ensuring that all the Level-0 data are present and ordering the data according to observation times. The level-1A PGEs then perform geolocation-refinement calculations and conversions of raw data numbers to engineering units. Finally, the level-1A data are grouped into packages, denoted granules, each of which contain the data from a six-minute observation period. The granules are forwarded, along with calibration data, to the Level-1B PGEs for processing into calibrated, geolocated radiance products. The Level-2 PGEs, which are not yet operational, are intended to process the level-1B data into temperature and humidity profiles, and other geophysical properties.

De Picciotto, Solomon↗
NASA NTRS1989

Estimating forest productivity with Thematic Mapper and biogeographical data

Spectral data from the Landsat Thematic Mapper (TM) on three forest exosystems (the southern Illinois, the Great Smoky Mountains regions in Tennessee and North Carolina, and the central Adirondack Mountains in New York) were used in conjunction with ground-collected measures of forest productivity and such information as the area's slope, aspect, elevation, and soil and vegetation types, to develop models of regional forest productivity. It is shown that the models developed may be used to estimate the productivity of a region with a high degree of confidence, but that the reliability of single-pixel estimates is poor. The characteristics of a given ecosystem determine which spectral values are most closely related to forest productivity. Thus, mid-IR, NIR, and visible bands are most significant in Illinois and New York, while the thermal band is relatively more important in the Smokies.

Cook, Elizabeth A.↗
NASA NTRS1993

Objective determination of image end-members in spectral mixture analysis of AVIRIS data

Spectral mixture analysis has been shown to be a powerful, multifaceted tool for analysis of multi- and hyper-spectral data. Applications of AVIRIS data have ranged from mapping soils and bedrock to ecosystem studies. During the first phase of the approach, a set of end-members are selected from an image cube (image end-members) that best account for its spectral variance within a constrained, linear least squares mixing model. These image end-members are usually selected using a priori knowledge and successive trial and error solutions to refine the total number and physical location of the end-members. However, in many situations a more objective method of determining these essential components is desired. We approach the problem of image end-member determination objectively by using the inherent variance of the data. Unlike purely statistical methods such as factor analysis, this approach derives solutions that conform to a physically realistic model.

Tompkins, Stefanie↗