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NASA NTRSDate not supplied

Quantum Leap: Evaluating the Feasibility of Quantum Machine Learning Using NASA Earth Observational Data

This study explores the feasibility of leveraging quantum machine learning (QML) to analyze NASA Earth Observational (EO) data for climate change research, with a particular focus on the phenomenon of ”crop frosting” which has become more prevalent due to climate change. We implemented and evaluated two QML models, the Variational Quantum Classifier (VQC) and Quantum Support Vector Classifier (QSVC), in both simulated and real quantum computing environments using a 127 qubit IBM quantum processor. Our study emphasizes the scientific rigor in comparing these quantum models with a classical Support Vector Machine (SVM) classifier, highlighting their performance in processing climate data. The results offer valuable insights into the potential scientific advantages, limitations, and scalability of QML for analyzing EO datasets, thus paving the way for more advanced climate modeling and predictive analytics using quantum computing. We showcased how Environmental Interaction Knowledge Graphs (EIKGs) and Digital Twins (DTs) can be integrated into this study. This research underscores the transformative potential of Classical and QML leveraging KGs and DT to address the multifaceted challenges posed by climate change.

Quantum Computing↗
NASA NTRS1973

Operational earth resources data handling system for the 1980's

Results of a recent study of data handling requirements for future operational earth observation systems are reported. Such systems in the 1980's may have 10-20 meter resolution and generate .2 tecabits of data per day, with peak rates of 0.8 gigabits per second based on the dominant requirements of agriculture. System alternatives are considered that will handle such data. Data relayed to the facility are recorded at high rates and then processed at lower speed by computer. Hardwired special digital logic computers may be used with an appropriate classification algorithm for crop recognition. Optical mass memories now in the prototype stage will handle the 10 tecabits and 800 megabit per second read-in rate required.

Van Vleck, E. M.↗
NASA NTRS2018

Developing a Standard for Earth Observation Data Preservation Content - A Path to Future Usability

For datasets to be usable, many pieces of information in addition to the data themselves are essential. During the active parts of the lifecycle of dataset generating projects, the needed information is usually accessible through individuals familiar with the various aspects of the projects. However, the utility of datasets tends to outlive the lives of projects, by several decades in many cases. Thus it is essential to capture all the relevant information about the datasets, data, metadata and associate knowledge that is sufficient to read, understand, interpret and reuse the datasets, while the projects are still active. The capture and preservation should be such that the data are usable when no consultation is available from the original project participants. Identification of specific categories of content through an international standard is beneficial to the user communities of the future, so that projects involving Earth observations and generating data products can consistently plan for preservation and future usability of the project outcomes. While there are existing standards that address archival and preservation in general, there are no existing international standards or specifications today to address what content should be preserved. The standard, ISO 19165-1, titled "Geographic Information - Preservation of digital data and metadata Part 1: Fundamentals" considers geographic information preservation in general. It acknowledges that "specific content items needed to preserve the full provenance and context of the data and associated metadata depend on the needs of the designated community and types of datasets (e.g., maps, remotely sensed data from satellites and airborne instruments, physical samples). Follow-up parts to this standard may be developed detailing content items appropriate to individual disciplines." NASA proposed an extension to this standard, titled "Geographic information -- Preservation of digital data and metadata -- Part 2: Content specifications for Earth observation data and derived digital products." The development of this extension is in progress with participation by an international team representing nine countries. The purpose of this paper is to introduce this standard and report on its status.

Remote Sensing; Data Systems; Open Data;↗
NASA NTRS1983

A comparison of Frequency Domain Multiple Access (FDMA) and Time Domain Multiple Access (TDMA) approaches to satellite service for low data rate Earth stations

A technological and economic assessment is made of providing low data rate service to small earth stations by satellite at Ka-band. Various Frequency Domain Multiple Access (FDMA) and Time Domain Multiple Access (TDMA) scenarios are examined and compared on the basis of cost to the end user. Very small stations (1 to 2 meters in diameter) are found not to be viable alternatives to available terrestrial services. However, medium size (3 to 5 meters) earth stations appear to be very competitive if a minimum throughput of about 1.5 Mbs is maintained. This constrains the use of such terminals to large users and shared use by smaller users. No advantage was found to the use of FDMA. TDMA had a slight advantage from a total system viewpoint and a very significant advantage in the space segment (about 1/3 the required payload weight for an equivalent capacity).

Stevens, G.↗
NASA NTRS1993

Applications of wavelet-based compression to multidimensional Earth science data

A data compression algorithm involving vector quantization (VQ) and the discrete wavelet transform (DWT) is applied to two different types of multidimensional digital earth-science data. The algorithms (WVQ) is optimized for each particular application through an optimization procedure that assigns VQ parameters to the wavelet transform subbands subject to constraints on compression ratio and encoding complexity. Preliminary results of compressing global ocean model data generated on a Thinking Machines CM-200 supercomputer are presented. The WVQ scheme is used in both a predictive and nonpredictive mode. Parameters generated by the optimization algorithm are reported, as are signal-to-noise (SNR) measurements of actual quantized data. The problem of extrapolating hydrodynamic variables across the continental landmasses in order to compute the DWT on a rectangular grid is discussed. Results are also presented for compressing Landsat TM 7-band data using the WVQ scheme. The formulation of the optimization problem is presented along with SNR measurements of actual quantized data. Postprocessing applications are considered in which the seven spectral bands are clustered into 256 clusters using a k-means algorithm and analyzed using the Los Alamos multispectral data analysis program, SPECTRUM, both before and after being compressed using the WVQ program.

Bradley, Jonathan N.↗
NASA NTRS2015

Earth Science Data Fusion with Event Building Approach

Objectives of the NASA Information And Data System (NAIADS) project are to develop a prototype of a conceptually new middleware framework to modernize and significantly improve efficiency of the Earth Science data fusion, big data processing and analytics. The key components of the NAIADS include: Service Oriented Architecture (SOA) multi-lingual framework, multi-sensor coincident data Predictor, fast into-memory data Staging, multi-sensor data-Event Builder, complete data-Event streaming (a work flow with minimized IO), on-line data processing control and analytics services. The NAIADS project is leveraging CLARA framework, developed in Jefferson Lab, and integrated with the ZeroMQ messaging library. The science services are prototyped and incorporated into the system. Merging the SCIAMACHY Level-1 observations and MODIS/Terra Level-2 (Clouds and Aerosols) data products, and ECMWF re- analysis will be used for NAIADS demonstration and performance tests in compute Cloud and Cluster environments.

Lukashin, C.↗
NASA NTRS2010

Terra, Aqua, and Aura Direct Broadcast - Providing Earth Science Data for Realtime Applications

The need for realtime data to aid in disaster management and monitoring has been clearly demonstrated for the past several years, e.g., during the tsunami in Indonesia in 2004, the hurricane Katrina in 2005, fires, etc. Users want (and often require) the means to get earth observation data for operational regional use as soon as they are generated by satellites. This is especially true for events that can cause loss of human life and/or property. To meet this need, NASA's Earth Observing System (EOS) satellites, Terra and Aqua, provide realtime data useful to disaster management teams. This paper describes the satellites, their Direct Broadcast (DB) capabilities, the data uses, what it takes to deploy a DB ground station, and the future of the DB.

Kelly, Angelita C.↗
NASA NTRS2018

NASA GIBS and Worldview: Visualizing NASA's Earth Science Data for All to Explore

For more than 20 years, the NASA Earth Observing System (EOS) has operated dozens of remote sensing satellites collecting nearly 15 Petabytes of data that span thousands of science parameters. Within these observations are keys the Earth Scientists have used to unlock many discoveries that we now understand about our planet. Also contained within these observations are a myriad of opportunities for learning and education. The challenge is making them accessible to educators and students in intuitive and simple ways so that effort can be spent on lesson enrichment and not overcoming technical hurdles. The NASA Global Imagery Browse Services (GIBS) system and NASA Worldview website provide a unique view into EOS data through daily full resolution visualizations of hundreds of earth science parameters. For many of these parameters, visualizations are available within hours of acquisition from the satellite. For others, visualizations are available for the entire mission of the satellite. Accompanying the visualizations are visual aids such as color legends, place names, and orbit tracks. By using these visualizations, educators and students can observe natural phenomena that enrich a scientific education.

Cechini, Matthew↗
NASA NTRS2019

NASA GIBS and Worldview: Visualizing NASA's Earth Science Data for All to Explore

For more than 20 years, the NASA Earth Observing System (EOS) has operated dozens of remote sensing satellites collecting nearly 15 Petabytes of data that span thousands of science parameters. Within these observations are keys the Earth Scientists have used to unlock many discoveries that we now understand about our planet. Also contained within these observations are a myriad of opportunities for learning and education. The challenge is making them accessible to educators and students in intuitive and simple ways so that effort can be spent on lesson enrichment and not overcoming technical hurdles.The NASA Global Imagery Browse Services (GIBS) system and NASA Worldview interactive mapping site provide a unique view into EOS data through daily full resolution visualizations of hundreds of Earth science parameters. For many of these parameters, visualizations are available within hours of acquisition from the satellite. For others, visualizations are available for the entire mission of the satellite. Accompanying the visualizations are visual aids such as color legends, place names, and orbit tracks. By using these visualizations, educators and students can observe natural phenomena that enrich a scientific education.This presentation will provide an overview of the visualizations available in NASA GIBS and Worldview and how they are accessed. Specific attention will be given to the newer capabilities and accomplishments, including: Support for geostationary sub-daily visualizations, Enhanced support for vector-based visualizations, Improved Worldview tour and snapshot capabilities, New imagery products across a growing set of scientific areas.

Cechini, Matthew↗
NASA NTRS1990

Study and prototype of data system interactions for the Earth Observing System Data and Information System

A crucial part of the Earth Observing System (EOS) is its Data and Information System (EOSDIS). The success of EOS depends not only on its instruments and science studies, but also on its ability to help scientists integrate data sets of geophysical and biological measurements taken by various instruments and investigators. NASA contractors have completed Phase B studies of EOSDIS, in particular its architecture, functionality, and user interfacing. At this point in time, it may seem impossible to exercise the EOSDIS or any of its components since they do not exist; i.e., if the EOSDIS is accepted as a totally new system, distinct from any existing DIS. However, if EOSDIS is seen as evolving from existing data systems, then some limited prototyping studies can be conducted by using currently functioning systems. In support of both the EOSDIS Science Advisory Panel and the EOSDIS Project, a prototyping activity was carried out by a cross section of interdisciplinary scientists. That prototyping activity is summarized and some conclusions are drawn that can be used by NASA-Goddard to evaluate and modify the specifications soon to be released in an RFP to build EOSDIS.

Emmitt, G. D.↗
NASA NTRS2019

NASA GIBS and Worldview: Leveraging FOSS for NASA Earth Science Data Visualizations

The NASA Global Imagery Browse Services (GIBS) and Worldview interactive mapping site leverage scientific and community best practices, open source software, and public standards to provide a scalable, compliant, and authoritative source for NASA Earth Observing System (EOS) Earth science data visualizations. GIBS and Worldview allow end users to easily and quickly interact with more than 800 full resolution pre-generated raster- and vector-based visualizations. This interactive discovery approach relies on visual observation and identification of phenomena that are not as simply identified otherwise. This eLightning presentation will exhibit the broad set of capabilities and visualization layers made possible through the GIBS and Worldview open source software. Specific dependencies on, and contributions to, open source software will be highlighted. Additionally, opportunities for future improvements for better interoperability and reuse through open source software will be discussed.

NASA Global Imagery Browse Services (GIBS)↗
NASA NTRS1994

Automatic cataloguing and characterization of Earth science data using SE-trees

In the future, NASA's Earth Observing System (EOS) platforms will produce enormous amounts of remote sensing image data that will be stored in the EOS Data Information System. For the past several years, the Intelligent Data Management group at Goddard's Information Science and Technology Office has been researching techniques for automatically cataloguing and characterizing image data (ADCC) from EOS into a distributed database. At the core of the approach, scientists will be able to retrieve data based upon the contents of the imagery. The ability to automatically classify imagery is key to the success of contents-based search. We report results from experiments applying a novel machine learning framework, based on Set-Enumeration (SE) trees, to the ADCC domain. We experiment with two images: one taken from the Blackhills region in South Dakota; and the other from the Washington DC area. In a classical machine learning experimentation approach, an image's pixels are randomly partitioned into training (i.e. including ground truth or survey data) and testing sets. The prediction model is built using the pixels in the training set, and its performance is estimated using the testing set. With the first Blackhills image, we perform various experiments achieving an accuracy level of 83.2 percent, compared to 72.7 percent using a Back Propagation Neural Network (BPNN) and 65.3 percent using a Gaussain Maximum Likelihood Classifier (GMLC). However, with the Washington DC image, we were only able to achieve 71.4 percent, compared with 67.7 percent reported for the BPNN model and 62.3 percent for the GMLC.

Rymon, Ron↗
NASA NTRS2010

Tracking Provenance of Earth Science Data

Tremendous volumes of data have been captured, archived and analyzed. Sensors, algorithms and processing systems for transforming and analyzing the data are evolving over time. Web Portals and Services can create transient data sets on-demand. Data are transferred from organization to organization with additional transformations at every stage. Provenance in this context refers to the source of data and a record of the process that led to its current state. It encompasses the documentation of a variety of artifacts related to particular data. Provenance is important for understanding and using scientific datasets, and critical for independent confirmation of scientific results. Managing provenance throughout scientific data processing has gained interest lately and there are a variety of approaches. Large scale scientific datasets consisting of thousands to millions of individual data files and processes offer particular challenges. This paper uses the analogy of art history provenance to explore some of the concerns of applying provenance tracking to earth science data. It also illustrates some of the provenance issues with examples drawn from the Ozone Monitoring Instrument (OMI) Data Processing System (OMIDAPS) run at NASA's Goddard Space Flight Center by the first author.

Tilmes, Curt↗
NASA NTRSDate not supplied

Earth Observation Data Provenance for Future Climate Research - Requirements and Challenges

Observations and measurements of the Earth’s environment have been collected from space since the 1960’s. Flight Projects, airborne and field campaigns have developed and operated multi-year missions with global observing instruments, and Principle Investigator Science Teams have developed algorithms, calibrated, and derived a wide variety of Earth system environmental parameters. These data are archived and distributed for research purposes by NASA’s ESDIS Project and Distributed Active Archive Centers (DAACs). They are expected to be an important basis for Earth science and climate change research extending well beyond their observation times, the Principle Investigators/Science Teams research projects and life of the Flight Projects.

John Moses↗
NASA NTRS1976

Program on Earth Observation Data Management Systems (EODMS), appendixes

The needs of state, regional, and local agencies involved in natural resources management in Illinois, Iowa, Minnesota, Missouri, and Wisconsin are investigated to determine the design of satellite remotely sensed derivable information products. It is concluded that an operational Earth Observation Data Management System (EODMS) will be most beneficial if it provides a full range of services - from raw data acquisition to interpretation and dissemination of final information products. Included is a cost and performance analysis of alternative processing centers, and an assessment of the impacts of policy, regulation, and government structure on implementing large scale use of remote sensing technology in this community of users.

Eastwood, L. F., Jr.↗
NASA NTRS2019

A Unified Level of Service Model for NASA Earth Science Data Stewardship

During the past year, the Interagency Implementation and Concepts Team (IMPACT) reviewed existing service models in use at various NASA data centers in an effort to produce a unified, cohesive, and comprehensive Level of Service Model for all of NASA Distributed Active Archive Centers (DAACs). NASA DAACs are responsible for ensuring NASA Earth Science data are accurately and securely ingested, distributed, supported, and preserved. The term “Service" as used here refers to the spectrum of data management activities and outputs provided by DAACs in support of the data cared fo by each data cente. The unified Level-of- Service (LoS) model described in this presentation utilizes both the NASA-defined data product category and the data processing level to easily identify an appropriate level-of-service to be applied to a data product throughout the full data life cycle. This LoS model is to be used by DAACs when appraising incoming data in order to determine the appropriate and required services to provide. The LoS model utilizes a 3-level system in which services build upon previous levels and thereby require greater commitment and effort both on the part of the DAAC personnel and the data producer at the highest level. The LoS model description also contains examples of ways to communicate with data producers and data users what services can be expected, thereby bringing more consistent user experiences across the enterprise. In this presentation, we will outline the features of the LoS model and describe how it relates to the FAIR data practices and the NOAA Maturity Matrix model.

Smith, Deborah↗
NASA NTRS2003

Goddard Earth Observing System - Data Assimilation System (GEOS-4 DAS) Data Products at the NASA Goddard Earth Science DISC DAAC

This list contains the names of data products from the Goddard Earth Observing System - Data Assimilation System (GEOS-4 DAS) at the Goddard Earth Science (GES) Data and Information Services Center (DISC) Distributed Active Archive Center (DAAC). The list divides the data products into Assimilated Synoptic Files and Assimilated Time Averaged Files.

Source record↗