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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

CERES Single Scanner Satellite Footprint, TOA, Surface Fluxes and Clouds (SSF)- Test data in HDF (CER_SSF_TRMM-PFM-VIRS_Subset-Edition1)

The Single Scanner Footprint TOA/Surface Fluxes and Clouds (SSF) product contains one hour of instantaneous Clouds and the Earth's Radiant Energy System (CERES) data for a single scanner instrument. The SSF combines instantaneous CERES data with scene information from a higher-resolution imager such as Visible/Infrared Scanner (VIRS) on TRMM or Moderate-Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua. Scene identification and cloud properties are defined at the higher imager resolution and these data are averaged over the larger CERES footprint. For each CERES footprint, the SSF contains the number of cloud layers and for each layer the cloud amount, height, temperature, pressure, optical depth, emissivity, ice and liquid water path, and water particle size. The SSF also contains the CERES filtered radiances for the total, shortwave (SW), and window (WN) channels and the unfiltered SW, longwave (LW), and WN radiances. The SW, LW, and WN radiances at spacecraft altitude are converted to Top-of-the-Atmosphere (TOA) fluxes based on the imager defined scene. These TOA fluxes are used to estimate surface fluxes. Only footprints with adequate imager coverage are included on CER_SSF_TRMM-PFM-VIRS_Subset_Edition1the SSF which is much less than the full set of footprints on the CERES ES-8 product. The following CERES SSF data sets are currently available: CER_SSF_TRMM-PFM-VIRS_Edition1 CER_SSF_TRMM-PFM-VIRS_Subset_Edition1 CER_SSF_TRMM-PFM-VIRS_Edition2A CER_SSF_TRMM-SIM-VIRS_Edition2_VIRSonly CER_SSF_TRMM-PFM-VIRS_Edition2A-TransOps CER_SSF_TRMM-PFM-VIRS_Edition2B-TransOps CER_SSF_TRMM-PFM-VIRS_Edition2B CER_SSF_Terra-FM1-MODIS_Edition1A CER_SSF_Terra-FM1-MODIS_Edition1A CER_SSF_Terra-FM1-MODIS_Edition2A CER_SSF_Terra-FM2-MODIS_Edition2A CER_SSF_Terra-FM1-MODIS_Edition2B CER_SSF_Terra-FM2-MODIS_Edition2B CER_SSF_Aqua-FM4-MODIS_Beta1 CER_SSF_Aqua-FM3-MODIS_Beta2 CER_SSF_Aqua-FM4-MODIS_Beta2. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1998-01-01; Stop_Date=1998-08-31] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Temporal_Resolution=1 hour; Temporal_Resolution_Range=Hourly - < Daily].

CLOUD LAYER AREA↗
NASA NTRSDate not supplied

CERES ERBE-like Instantaneous TOA Estimates (ES-8) in HDF (CER_ES8_Aqua-FM4_Edition1-CV)

The ES-8 archival data product contains a 24-hour, single-satellite, instantaneous view of scanner fluxes at the top-of-atmosphere (TOA) reduced from spacecraft altitude unfiltered radiances using Earth Radiation Budget Experiment (ERBE) scanner Inversion algorithms and the ERBE shortwave (SW) and longwave (LW) Angular Distribution Models (ADMs). The ES-8 also includes the total (TOT), SW, LW, and window (WN) channel radiometric data; SW, LW, and WN unfiltered radiance values; and the ERBE scene identification for each measurement. These data are organized according to the CERES 3.3-second scan into 6.6-second records. As long as there is one valid scanner measurement within a record, the ES-8 record will be generated. The following CERES ES8 data sets are currently available: CER_ES8_TRMM-PFM_Edition1 CER_ES8_TRMM-PFM_Edition2 CER_ES8_TRMM-PFM_Transient-Ops2 CER_ES8_Terra-FM1_Edition1 CER_ES8_Terra-FM2_Edition1 CER_ES8_Terra-FM1_Edition2 CER_ES8_Terra-FM2_Edition2 CER_ES8_Aqua-FM3_Edition1 CER_ES8_Aqua-FM4_Edition1 CER_ES8_Aqua-FM3_Edition2 CER_ES8_Aqua-FM4_Edition2 CER_ES8_Aqua-FM3_Edition1-CV CER_ES8_Aqua-FM4_Edition1-CV CER_ES8_Terra-FM1_Edition1-CV CER_ES8_Terra-FM1_Edition1-CV. [Location=GLOBAL] [Temporal_Coverage: Start_Date=1997-12-27; Stop_Date=2005-03-29] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Temporal_Resolution=1 day; Temporal_Resolution_Range=Daily - < Weekly].

TOA FLUX↗
NASA NTRSDate not supplied

MISR Level 1B1 Local Mode Radiance Data (MIB1LM_V1)

The results of two types of processing are included in this product. First, the Radiance Scaling operation converts the camera's digital number output to a measure of energy incident on the front optical surface. The measurement is expressed in units called radiance (energy per unit area, wavelength, and solid angle) as defined by the International Standard (SI). Second, Radiance Conditioning modifies the radiances to remove instrument-dependent effects. Specifically, image sharpening is applied, and focal-plane scattering is removed. Additionally, all radiances are adjusted to remove slight spectral sensitivity differences among the 1504 detector elements of each spectral band and each camera. In addition to the Level 1B1 radiometric product for MISR's Global Mode imagery, there is a separate Level 1B1 product for each high-resolution Local Mode scene. The Radiometric Product contains spectral radiances for all MISR channels (four spectral bands and nine cameras). Each radiance value represents the incident radiance averaged over the sensor's total band response. Processing includes both radiance scaling and conditioning steps. Radiance scaling converts the Level 1A data from digital counts to radiances using coefficients derived in combination with the On-Board Calibrator (OBC) and vicarious calibrations. The OBC contains Spectralon calibration panels which are deployed monthly and reflect sunlight into the cameras. The OBC detector standards then measure this reflected light to provide the calibration. Vicarious field campaigns are conducted less frequently but provide an independent methodology useful for reducing systematic errors. Radiance conditioning removes undesirable instrument effects. Image enhancement is provided by deconvolving the scene with the sensor's point-spread-function. Additionally, in-band scaling adjusts the reported radiances to correspond to a nominal band response profile. This frees the Level 2 software from the need to correct for detector element non-uniformities. No out-of-band correction is done for this product, nor are the data geometrically corrected or resampled at this point. In summary, the Level 1B1 Product contains the Data Numbers (DNs) radiometrically-scaled to radiances with no geometric resampling. [Location=GLOBAL] [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=275 m for red band only; Longitude_Resolution=275 m for red band only; Horizontal_Resolution_Range=250 km - < 500 km or approximately 2.5 degrees - < 5.0 degrees; Temporal_Resolution=about 15 orbits/day].

NEAR IR IMAGERY↗
NASA NTRSDate not supplied

MISR Level 1B1 Radiance Data (MI1B1_V1)

The results of two types of processing are included in this product. First, the Radiance Scaling operation converts the camera's digital number output to a measure of energy incident on the front optical surface. The measurement is expressed in units called radiance (energy per unit area, wavelength, and solid angle) as defined by the International Standard (SI). Second, Radiance Conditioning modifies the radiances to remove instrument-dependent effects. Specifically, image sharpening is applied, and focal-plane scattering is removed. Additionally, all radiances are adjusted to remove slight spectral sensitivity differences among the 1504 detector elements of each spectral band and each camera. The Radiometric Product contains spectral radiances for all MISR channels (four spectral bands and nine cameras). Each radiance value represents the incident radiance averaged over the sensor's total band response. Processing includes both radiance scaling and conditioning steps. Radiance scaling converts the Level 1A data from digital counts to radiances using coefficients derived in combination with the On-Board Calibrator (OBC) and vicarious calibrations. The OBC contains Spectralon calibration panels which are deployed monthly and reflect sunlight into the cameras. The OBC detector standards then measure this reflected light to provide the calibration. Vicarious field campaigns are conducted less frequently but provide an independent methodology useful for reducing systematic errors. Radiance conditioning removes undesirable instrument effects. Image enhancement is provided by deconvolving the scene with the sensor's point-spread-function. Additionally, in-band scaling adjusts the reported radiances to correspond to a nominal band response profile. This frees the Level 2 software from the need to correct for detector element non-uniformities. No out-of-band correction is done for this product, nor are the data geometrically corrected or resampled at this point. In summary, the Level 1B1 Product contains the Data Numbers (DNs) radiometrically-scaled to radiances with no geometric resampling. [Location=GLOBAL] [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=275 m for red band only; Longitude_Resolution=275 m for red band only; Horizontal_Resolution_Range=250 meters - < 500 meters; Temporal_Resolution=about 15 orbits/day; Temporal_Resolution_Range=Daily - < Weekly, about 15 orbits/day, Daily - < Weekly].

EOSDIS↗
NASA NTRSDate not supplied

MISR Level 2 TOA/Cloud Albedo parameters (MIL2TCAL_V1)

The TOA/Cloud Albedo data contain albedo values, including finely-sampled or local (2.2 km) TOA albedos registered to the RLRA, and two coarsely-sampled (35.2 km resolution) TOA albedos projected to 30-km altitude. The local (2.2 km) albedos do not take the obscuration of cloud features into account, so they should only be treated as traditional albedos when the number of obscured pixels is low. The restrictive and expansive albedos are both available at 35.2 km resolution: the restrictive albedos are only calculated using the radiation upwelling from the pixel under consideration, whereas the expansive albedos use all the radiation emanating from the surrounding area. Therefore, the expansive albedo is closer to the traditional definition of top-of-atmosphere albedos. [Location=GLOBAL] [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180] [Data_Resolution: Latitude_Resolution=35.2 km; Longitude_Resolution=35.2 km; Horizontal_Resolution_Range=10 km - < 50 km or approximately .09 degree - < .5 degree; Temporal_Resolution=about 15 orbits/day].

CLOUD TEXTURE↗
NASA NTRSDate not supplied

MISR radiometric camera-by-camera Cloud Mask (MIRCCM_V3)

The MISR instrument consists of nine pushbroom cameras which measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four cameras pointing forward and four cameras pointing aftward. It takes 7 minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid, are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally gaussian, centered at 443, 555, 670, and 865 nm. The RCCM is derived from the radiance values, and is calculated independently for each camera. Therefore, the amount of apparent cloudiness will vary with view angle, with the oblique view angles generally being more cloudy than the near-nadir ones. Since the RCCM is calculated primarily from the radiance values, it does not work well over snow and ice and will usually confuse clear snow/ice with cloud. It works the best over clear-sky ocean, but other surface types are also of quite good quality. The RCCM product also contains a glint mask for each camera, and this mask is set to true whenever the scattering angles indicate that glint could be possible. This glint mask is not masked out over land; users must do this step themselves. [Location=GLOBAL] [Temporal_Coverage: Start_Date=2000-02-24; Stop_Date=] [Spatial_Coverage: Southernmost_Latitude=-90; Northernmost_Latitude=90; Westernmost_Longitude=-180; Easternmost_Longitude=180].

CLOUD MASK↗
NASA NTRS2018

NASA's EOSDIS Near Term Challenges

Given the long-term requirements, and the rapid pace of information technology and changing expectations of the user community, the ESDIS Project has had to evolve EOSDIS continually over the past three decades. However, many challenges remain. One near-term challenge is the enormous quantity of new data that will need to be managed by the EOSDIS. With the upcoming launch of the latest NASA missions coupled with existing operational missions and field campaigns, EOSDIS can expect to handle as much as 50 petabytes of data per year. In perspective, this is twice the size of the current existing archive, which took over 21 years to collect. Another continuing challenge is the disparate requirements of a diverse science community. Maintaining rigorous long-term data preservation, supporting ease of discovery and access, incorporating user feedback, enabling reanalysis/ reprocessing, and agile integration of new data sources, continue to be the Project's objectives.

Remote Sensing↗
NASA NTRSDate not supplied

"Giovanni at 20: Consistency and Persistency in Making Earth Remote Sensing Data Available (and Useful) to the Earth Science Community"

Since its creation in the year 2000, the NASA Geospatial Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni) has been an exemplar of how to provide remotely-sensed satellite data and related Earth science datasets to a broad and globally diverse researcher community. One of the hallmarks of Giovanni system usage is that it is common to see practitioners of many branches of science – particularly those in fields not traditionally associated with remote-sensing data, such as animal behavior and paleooceanography – both accessing and employing datasets which the system provides. This usage pattern is attested to by the diversity of published science citing Giovanni. Giovanni has evolved through several versions, each of which increased analytical and visualization options while also enhancing ease-of-use. Information resources supporting Giovanni from the Goddard Earth Sciences Data and Information Services Center (GES DISC), which hosts the system, start with basic mapping and plotting functions and extend to “How-to” recipes demonstrating interusability with other data analysis systems and software. Giovanni is now being developed for use in a cloud environment, potentially expanding datasets the system can be applied to, and also improving performance for data having high spatial and temporal resolution. This presentation covers the system’s historical success with interesting examples of Giovanni-citing research, and will apply its current capabilities to a multi-dataset examination of derecho events associated with mesoscale convective phenomena in the summer of 2020, indicating how Giovanni is prepared for ongoing support of Earth science in a changing world.

Giovanni Derecho visualization↗
NASA NTRS2000

Earth Remote Sensing Center of Excellence at Scripps Institution of Oceanography

We developed advanced communications and networking capability and satellite reception systems for Earth science to improve the ability of scientists at Scripps Institution of Oceanography (SIO) to conduct interdisciplinary research. With matching funds from the SIO Director's office we implemented a "virtual center" utilizing modern networking hardware and software to enhance access for researchers and students to unique satellite and in situ data sets. The center provides facilities and data access to graduate students as well as research scientists at SIO, and outside SIO. Our private sector partners installed and testes and advanced X-band data acquisition system for satellite data capture relevant for Earth science research and applications. Some of the commercial applications of these partners have been developed (or are under development) with NASA SBIR resources. The X-band system collected RADARSAT, ERS-2 and MODIS imagery. Perhaps most importantly, this COE brought together - for the first time - an interdisciplinary team of SIO scientists with interests in Earth remote sensing. The collaboration extended beyond our infrastructure and research accomplishments leading to a dialog that resulted in a report with strong recommendations to the SIO community for enhancing satellite remote sensing at SIO.

Mitchell, B. Greg↗
NASA NTRS1994

An adaptive technique to maximize lossless image data compression of satellite images

Data compression will pay an increasingly important role in the storage and transmission of image data within NASA science programs as the Earth Observing System comes into operation. It is important that the science data be preserved at the fidelity the instrument and the satellite communication systems were designed to produce. Lossless compression must therefore be applied, at least, to archive the processed instrument data. In this paper, we present an analysis of the performance of lossless compression techniques and develop an adaptive approach which applied image remapping, feature-based image segmentation to determine regions of similar entropy and high-order arithmetic coding to obtain significant improvements over the use of conventional compression techniques alone. Image remapping is used to transform the original image into a lower entropy state. Several techniques were tested on satellite images including differential pulse code modulation, bi-linear interpolation, and block-based linear predictive coding. The results of these experiments are discussed and trade-offs between computation requirements and entropy reductions are used to identify the optimum approach for a variety of satellite images. Further entropy reduction can be achieved by segmenting the image based on local entropy properties then applying a coding technique which maximizes compression for the region. Experimental results are presented showing the effect of different coding techniques for regions of different entropy. A rule-base is developed through which the technique giving the best compression is selected. The paper concludes that maximum compression can be achieved cost effectively and at acceptable performance rates with a combination of techniques which are selected based on image contextual information.

Stewart, Robert J.↗
NASA NTRS1994

CESDIS

CESDIS, the Center of Excellence in Space Data and Information Sciences was developed jointly by NASA, Universities Space Research Association (USRA), and the University of Maryland in 1988 to focus on the design of advanced computing techniques and data systems to support NASA Earth and space science research programs. CESDIS is operated by USRA under contract to NASA. The Director, Associate Director, Staff Scientists, and administrative staff are located on-site at NASA's Goddard Space Flight Center in Greenbelt, Maryland. The primary CESDIS mission is to increase the connection between computer science and engineering research programs at colleges and universities and NASA groups working with computer applications in Earth and space science. Research areas of primary interest at CESDIS include: 1) High performance computing, especially software design and performance evaluation for massively parallel machines; 2) Parallel input/output and data storage systems for high performance parallel computers; 3) Data base and intelligent data management systems for parallel computers; 4) Image processing; 5) Digital libraries; and 6) Data compression. CESDIS funds multiyear projects at U. S. universities and colleges. Proposals are accepted in response to calls for proposals and are selected on the basis of peer reviews. Funds are provided to support faculty and graduate students working at their home institutions. Project personnel visit Goddard during academic recess periods to attend workshops, present seminars, and collaborate with NASA scientists on research projects. Additionally, CESDIS takes on specific research tasks of shorter duration for computer science research requested by NASA Goddard scientists.

Source record↗
NASA NTRS2001

ECHO Responds to NASA's Earth Science User Community

Over the past decade NASA has designed, built, evolved, and operated the Earth Observing System Data and Information System (EOSDIS) Information Management System (IMS) in order to provide user access to NASA's Earth Science data holdings. During this time revolutionary advances in technology have driven changes in NASA's approach to providing an IMS service. This paper will describe NASA's strategic planning and approach to build and evolve the EOSDIS IMS and to serve the evolving needs of NASA's Earth Science community. It discusses the original strategic plan and how lessons learned help to form a new plan, a new approach and a new system. It discusses the original technologies and how they have evolved to today.

Pfister, Robin↗
DOE OSTI2023

A Geo-Data Science Method for Assessing Unconventional Rare-Earth Element Resources in Sedimentary Systems

Abstract Rare-earth elements (REEs) supply raw materials that constitute many of our modern critical infrastructure, defense, technology, and electrification needs. Despite REE accumulations occurring in conventional bedrock and ion-adsorption deposits sourced from weathering of igneous rocks, unconventional host materials such as coal and related sedimentary strata have been identified as promising sources of REEs to meet growing demand. To maximize the potential of unconventional resources such as REE-coal systems, new approaches are needed overcome challenges from mineral systems with no known deposits and areas with sparse geochemical data. This article presents a systematic knowledge-data resource assessment method for predicting and identifying REE resource potential and occurrence in these unconventional systems. The method utilizes a geologic and geospatial knowledge-data approach informed and guided by REE accumulation mechanisms to systematically assess and identify areas of higher enrichment. An assessment of the Powder River Basin is presented as a test case to demonstrate the method workflow and results. The key output is a potential enrichment score map reported with varying confidence levels based on the amount of supporting evidence. Results from the test case indicate several locations with promising potential for different types of coal-REE deposits, demonstrating the viability of the method for exploration and assessment of unconventional REE resources. The method is flexible by design and, with sufficient applicable knowledge and data, can be adapted for assessing critical mineral systems in other sedimentary systems as well.

58 GEOSCIENCES↗
NASA NTRSDate not supplied

Maximizing Earth Science Observations with Data Harmonization: Harmonized Landsat/Sentinel-2

In August 2021, NASA released science quality harmonized Landsat/Sentinel-2 products for both cloud-based access and direct download from the Land Processes Distributed Active Archive Center (LP DAAC). These 30-meter products, HLSS30 (Sentinel-2 component) and HLSL30 (Landsat component) are placed on the same grid and are generated and distributed fully in the cloud. Similarly, ESA is prototyping harmonized Landsat Sentinel-2 products at 10-meter resolution. The data production and science teams from NASA and ESA will present technical details on the data production system, data product status and availability, and the benefit of harmonizing instruments with similar sensing characteristics between the agencies.

Earth Science↗
NASA NTRS2009

Utilizing Remote Sensing Data to Ascertain Soil Moisture Applications and Air Quality Conditions

Recognizing the significance of NASA remote sensing Earth science data in monitoring and better understanding our planet's natural environment, NASA Earth Applied Sciences has implemented the 'Decision Support Through Earth Science Research Results' program. Several applications support systems through collaborations with benefiting organizations have been implemented. The Goddard Earth Sciences Data and Information Services Center (GES DISC) has participated in this program on two projects (one complete, one ongoing), and has had opportune ad hoc collaborations utilizing NASA Earth science data. GES DISC's understanding of Earth science missions and resulting data and information enables the GES DISC to identify challenges that come with bringing science data to research applications. In this presentation we describe applications research projects utilizing NASA Earth science data and a variety of resulting GES DISC applications support system project experiences. In addition, defining metrics that really evaluate success will be exemplified.

Leptoukh, Gregory↗
NASA NTRS2001

Simple, Scalable, Script-Based Science Processor (S4P)

The development and deployment of data processing systems to process Earth Observing System (EOS) data has proven to be costly and prone to technical and schedule risk. Integration of science algorithms into a robust operational system has been difficult. The core processing system, based on commercial tools, has demonstrated limitations at the rates needed to produce the several terabytes per day for EOS, primarily due to job management overhead. This has motivated an evolution in the EOS Data Information System toward a more distributed one incorporating Science Investigator-led Processing Systems (SIPS). As part of this evolution, the Goddard Earth Sciences Distributed Active Archive Center (GES DAAC) has developed a simplified processing system to accommodate the increased load expected with the advent of reprocessing and launch of a second satellite. This system, the Simple, Scalable, Script-based Science Processor (S42) may also serve as a resource for future SIPS. The current EOSDIS Core System was designed to be general, resulting in a large, complex mix of commercial and custom software. In contrast, many simpler systems, such as the EROS Data Center AVHRR IKM system, rely on a simple directory structure to drive processing, with directories representing different stages of production. The system passes input data to a directory, and the output data is placed in a "downstream" directory. The GES DAAC's Simple Scalable Script-based Science Processing System is based on the latter concept, but with modifications to allow varied science algorithms and improve portability. It uses a factory assembly-line paradigm: when work orders arrive at a station, an executable is run, and output work orders are sent to downstream stations. The stations are implemented as UNIX directories, while work orders are simple ASCII files. The core S4P infrastructure consists of a Perl program called stationmaster, which detects newly arrived work orders and forks a job to run the appropriate executable (registered in a configuration file for that station). Although S4P is written in Perl, the executables associated with a station can be any program that can be run from the command line, i.e., non-interactively. An S4P instance is typically monitored using a simple Graphical User Interface. However, the reliance of S4P on UNIX files and directories also allows visibility into the state of stations and jobs using standard operating system commands, permitting remote monitor/control over low-bandwidth connections. S4P is being used as the foundation for several small- to medium-size systems for data mining, on-demand subsetting, processing of direct broadcast Moderate Resolution Imaging Spectroradiometer (MODIS) data, and Quick-Response MODIS processing. It has also been used to implement a large-scale system to process MODIS Level 1 and Level 2 Standard Products, which will ultimately process close to 2 TB/day.

Lynnes, Christopher↗
NASA NTRS2015

Architectures Toward Reusable Science Data Systems

Science Data Systems (SDS) comprise an important class of data processing systems that support product generation from remote sensors and in-situ observations. These systems enable research into new science data products, replication of experiments and verification of results. NASA has been building systems for satellite data processing since the first Earth observing satellites launched and is continuing development of systems to support NASA science research and NOAAs Earth observing satellite operations. The basic data processing workflows and scenarios continue to be valid for remote sensor observations research as well as for the complex multi-instrument operational satellite data systems being built today. System functions such as ingest, product generation and distribution need to be configured and performed in a consistent and repeatable way with an emphasis on scalability. This paper will examine the key architectural elements of several NASA satellite data processing systems currently in operation and under development that make them suitable for scaling and reuse. Examples of architectural elements that have become attractive include virtual machine environments, standard data product formats, metadata content and file naming, workflow and job management frameworks, data acquisition, search, and distribution protocols. By highlighting key elements and implementation experience we expect to find architectures that will outlast their original application and be readily adaptable for new applications. Concepts and principles are explored that lead to sound guidance for SDS developers and strategists.

Data Processing↗
NASA NTRS1991

High-speed, cross-continental networks - A necessity

The networking requirements that the Earth Observing System (EOS) program has levied on NASA and the national and international operational networking communities is addressed. The objectives of the EOS program are listed and include the development and operation of an observing system to acquire essential, global earth science data on a long-term, sustained basis and in a manner which maximizes the scientific utility of the data and simplifies data analysis. Network design issues are also discussed, including protocol selection, the Transport Control Protocol/Internet Protocol suite, the Open Systems Interconnect network suite, and the internet evolution. Open and closed network issues are also discussed. A number of networks now in place are assessed including NASNet, and ESNet. These offer possible options and/or solutions to be considered by the NASA Headquarters EOS Data and Information Systems Program.

Wong, C. M.↗