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NASA NTRS2012

U.S. Government Open Internet Access to Sub-meter Satellite Data

The National Geospatial-Intelligence Agency (NGA) has contracted United States commercial remote sensing companies GeoEye and Digital Globe to provide very high resolution commercial quality satellite imagery to federal/state government agencies and those projects/people who support government interests. Under NextView contract terms, those engaged in official government programs/projects can gain online access to NGA's vast global archive. Additionally, data from vendor's archives of IKONOS-2 (IK-2), OrbView-3 (OB-3), GeoEye-1 (GE-1), QuickBird-1 (QB-1), WorldView-1 (WV-1), and WorldView-2 (WV-2), sensors can also be requested under these agreements. We report here the current extent of this archive, how to gain access, and the applications of these data by Earth science investigators to improve discoverability and community use of these data. Satellite commercial quality imagery (CQI) at very high resolution (< 1 m) (here after referred to as CQI) over the past decade has become an important data source to U.S. federal, state, and local governments for many different purposes. The rapid growth of free global CQI data has been slow to disseminate to NASA Earth Science community and programs such as the Land-Cover Land-Use Change (LCLUC) program which sees potential benefit from unprecedented access. This article evolved from a workshop held on February 23rd, 2012 between representatives from NGA, NASA, and NASA LCLUC Scientists discussion on how to extend this resource to a broader license approved community. Many investigators are unaware of NGA's archive availability or find it difficult to access CQI data from NGA. Results of studies, both quality and breadth, could be improved with CQI data by combining them with other moderate to coarse resolution passive optical Earth observation remote sensing satellites, or with RADAR or LiDAR instruments to better understand Earth system dynamics at the scale of human activities. We provide the evolution of this effort, a guide for qualified user access, and describe current to potential use of these data in earth science.

Neigh, Christopher S. R>↗
NASA NTRS2014

Physical Science Informatics: Providing Open Science Access to Microheater Array Boiling Experiment Data

The Physical Science Informatics (PSI) system is the next step in this an effort to make NASA sponsored flight data available to the scientific and engineering community, along with the general public. The experimental data, from six overall disciplines, Combustion Science, Fluid Physics, Complex Fluids, Fundamental Physics, and Materials Science, will present some unique challenges. Besides data in textual or numerical format, large portions of both the raw and analyzed data for many of these experiments are digital images and video, requiring large data storage requirements. In addition, the accessible data will include experiment design and engineering data (including applicable drawings), any analytical or numerical models, publications, reports, and patents, and any commercial products developed as a result of the research. This objective of paper includes the following: Present the preliminary layout (Figure 2) of MABE data within the PSI database. Obtain feedback on the layout. Present the procedure to obtain access to this database.

nucleate boiling↗
NASA NTRS2018

An Initial Validation of Landsat 5 and 7 Derived Surface Water Temperature for U.S. Lakes, Reservoirs, and Estuaries

The United States Harmful Algal Bloom and Hypoxia Research Control Act of 2014 identified the need for forecasting and monitoring harmful algal blooms (HAB) in lakes, reservoirs, and estuaries across the nation. Temperature is a driver in HAB forecasting models that affects both HAB growth rates and toxin production. Therefore, temperature data derived from the U.S. Geological Survey Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus thermal band products were validated across 35 lakes and reservoirs, and 24 estuaries. In situ data from the Water Quality Portal (WQP) were used for validation. The WQP serves data collected by state, federal, and tribal groups. Discrete in situ temperature data included measurements at 11,910 U.S. lakes and reservoirs from 1980 through 2015. Landsat temperature measurements could include 170,240 lakes and reservoirs once an operational product is achieved. The Landsat-derived temperature mean absolute error was 1.34 C in lake pixels (is) greater than180 m from land, 4.89 C at the land-water boundary, and 1.11 C in estuaries based on comparison against discrete surface in situ measurements. This is the first study to quantify Landsat resolvable U.S. lakes and reservoirs, and large-scale validation of an operational satellite provisional temperature climate data record algorithm. Due to the high performance of open water pixels, Landsat satellite data may supplement traditional in situ sampling by providing data for most U.S. lakes, reservoirs, and estuaries over consistent seasonal intervals (even with cloud cover) for an extended period of record of more than 35 years.

Schaeffer, Blake A.↗
NASA NTRS2013

Limitations of Phased Array Beamforming in Open Rotor Noise Source Imaging

Phased array beamforming results of the F31/A31 historical baseline counter-rotating open rotor blade set were investigated for measurement data taken on the NASA Counter-Rotating Open Rotor Propulsion Rig in the 9- by 15-Foot Low-Speed Wind Tunnel of NASA Glenn Research Center as well as data produced using the LINPROP open rotor tone noise code. The planar microphone array was positioned broadside and parallel to the axis of the open rotor, roughly 2.3 rotor diameters away. The results provide insight as to why the apparent noise sources of the blade passing frequency tones and interaction tones appear at their nominal Mach radii instead of at the actual noise sources, even if those locations are not on the blades. Contour maps corresponding to the sound fields produced by the radiating sound waves, taken from the simulations, are used to illustrate how the interaction patterns of circumferential spinning modes of rotating coherent noise sources interact with the phased array, often giving misleading results, as the apparent sources do not always show where the actual noise sources are located. This suggests that a more sophisticated source model would be required to accurately locate the sources of each tone. The results of this study also have implications with regard to the shielding of open rotor sources by airframe empennages.

Horvath, Csaba↗
NASA NTRS2010

Advances in Measuring the Apparent Optical Properties (AOPs) of Optically Complex Waters

This report documents new technology used to measure the apparent optical properties (AOPs) of optically complex waters. The principal objective is to be prepared for the launch of next-generation ocean color satellites with the most capable commercial off-the-shelf (COTS) instrumentation. An enhanced COTS radiometer was the starting point for designing and testing the new sensors. The follow-on steps were to apply the lessons learned towards a new in-water profiler based on a kite-shaped backplane for mounting the light sensors. The next level of sophistication involved evaluating new radiometers emerging from a development activity based on so-called microradiometers. The exploitation of microradiometers resulted in an in-water profiling system, which includes a sensor networking capability to control ancillary sensors like a shadowband or global positioning system (GPS) device. A principal advantage of microradiometers is their flexibility in producing, interconnecting, and maintaining instruments. The full problem set for collecting sea-truth data--whether in coastal waters or the open ocean-- involves other aspects of data collection that were improved for instruments measuring both AOPs and inherent optical properties (IOPs), if the uncertainty budget is to be minimized. New capabilities associated with deploying solar references were developed as well as a compact solution for recovering in-water instrument systems from small boats.

Morrow, John H.↗
NASA NTRS2013

NASA GES DISC support of CO2 Data from OCO-2, ACOS, and AIRS

NASA Goddard Earth Sciences Data and Information Services Centers (GES DISC) is the data center assigned to archive and distribute current AIRS, ACOS data and data from the upcoming OCO-2 mission. The GES DISC archives and supports data containing information on CO2 as well as other atmospheric composition, atmospheric dynamics, modeling and precipitation. Along with the data stewardship, an important mission of GES DISC is to facilitate access to and enhance the usability of data as well as to broaden the user base. GES DISC strives to promote the awareness of science content and novelty of the data by working with Science Team members and releasing news articles as appropriate. Analysis of events that are of interest to the general public, and that help in understanding the goals of NASA Earth Observing missions, have been among most popular practices.Users have unrestricted access to a user-friendly search interface, Mirador, that allows temporal, spatial, keyword and event searches, as well as an ontology-driven drill down. Variable subsetting, format conversion, quality screening, and quick browse, are among the services available in Mirador. The majority of the GES DISC data are also accessible through OPeNDAP (Open-source Project for a Network Data Access Protocol) and WMS (Web Map Service). These services add more options for specialized subsetting, format conversion, image viewing and contributing to data interoperability.

Wei, Jennifer C↗
NASA NTRS2017

GES DISC Greenhouse Gas Data Sets and Associated Services

NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) archives and distributes rich collections of data on atmospheric greenhouse gases from multiple missions. Hosted data include those from the Atmospheric Infrared Sounder (AIRS) mission (which has observed CO2, CH4, ozone, and water vapor since 2002); legacy water vapor and ozone retrievals from TIROS Operational Vertical Sounder (TOVS); and Upper Atmosphere Research Satellite (UARS) going back to the early 1980s. GES DISC also archives and supports data from seven projects of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) program that have ozone and water vapor records. Greenhouse gases data from the A-Train satellite constellation is also available: (1) Aura-Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) ozone, nitrous oxide, and water vapor since 2004; (2) Greenhouse Gases Observing Satellite (GOSAT) CO2 observations since 2009 from the Atmospheric CO2 Observations from Space (ACOS) task; and (3) Orbiting Carbon Observatory-2 (OCO-2) CO2 data since 2014. The most recent related data set that the GES DISC archives is methane flux for North America, as part of NASAs Carbon Monitoring System (CMS) project. This dataset contains estimates of methane emission in North America based on an inversion of the GEOS-Chem chemical transport model constrained by GOSAT observations (Turner et al., 2015). Along with data stewardship, an important focus area of the GES DISC is to enhance the usability of its data and broaden its user base. Users have unrestricted access to a new user-friendly search interface, which includes many services such as variable subsetting, format conversion, quality screening, and quick browse. The majority of the GES DISC data sets are also accessible through Open-source Project for a Network Data Access Protocol (OPeNDAP) and Web Coverage Service (WCS). The latter two services provide more options for specialized subsetting, format conversion, and image viewing. Additional data exploration, data preview, and preliminary analysis capabilities are available via NASA Giovanni, which obviates the need forusers to download the data (Acker and Leptoukh, 2007). Giovanni provides a bridge between the data and science and has been very successful in extending GES DISC data to educational users and to users with limited resources.

data ordering↗
NASA NTRSDate not supplied

Open Science Practices at the Community Coordinated Modeling Center

Open Science is defined as “the principle and practice of making research products and processes available to all, while respecting diverse cultures, maintaining security and privacy, and fostering collaborations, reproducibility, and equity” by Federal Agencies. The CCMC has been practicing open science based on FAIR (Findable, Accessible, Interoperable and Reusable) principle by providing access to the state-of -the art space science and space weather models to users around the world through various simulation services such as Runs-on-Request, Instant Runs, Real time runs on iSWA system. The CCMC also provides a wide range of tools and framework to help users easily utilize modeled data. One of the tools is the official NASA open-sourced software called Kamodo. Kamodo allows users to work with complex space weather models and data with little or no coding experience. Additionally, to support transparent model validation efforts, the CCMC is providing an integrated and flexible framework called CAMEL. CAMEL allows users to seamlessly compare model outputs with observational data sets. Currently, we are working on a user-friendly database of the papers and research that used CCMC services, so that the future users will have open access to previously performed research by other users and its details. In this presentation, we will show the open tools and resources provided by the CCMC. Furthermore, we will share our new efforts to support open data and open science results.

Ja Soon Shim↗
NASA NTRS2017

A New Architecture for Visualization: Open Mission Control Technologies

Open Mission Control Technologies (MCT) is a new architecture for visualisation of mission data. Driven by requirements for new mission capabilities, including distributed mission operations, access to data anywhere, customization by users, synthesis of multiple data sources, and flexibility for multi-mission adaptation, Open MCT provides users with an integrated customizable environment. Developed at NASAs Ames Research Center (ARC), in collaboration with NASAs Advanced Multimission Operations System (AMMOS) and NASAs Jet Propulsion Laboratory (JPL), Open MCT is getting its first mission use on the Jason 3 Mission, and is also available in the testbed for the Mars 2020 Rover and for development use for NASAs Resource Prospector Lunar Rover. The open source nature of the project provides for use outside of space missions, including open source contributions from a community of users. The defining features of Open MCT for mission users are data integration, end user composition and multiple views. Data integration provides access to mission data across domains in one place, making data such as activities, timelines, telemetry, imagery, event timers and procedures available in one place, without application switching. End user composition provides users with layouts, which act as a canvas to assemble visualisations. Multiple views provide the capability to view the same data in different ways, with live switching of data views in place. Open MCT is browser based, and works on the desktop as well as tablets and phones, providing access to data anywhere. An early use case for mobile data access took place on the Resource Prospector (RP) Mission Distributed Operations Test, in which rover engineers in the field were able to view telemetry on their phones. We envision this capability providing decision support to on console operators from off duty personnel. The plug-in architecture also allows for adaptation for different mission capabilities. Different data types and capabilities may be added or removed using plugins. An API provides a means to write new capabilities and to create data adaptors. Data plugins exist for mission data sources for NASA missions. Adaptors have been written by international and commercial users. Open MCT is open source. Open source enables collaborative development across organizations and also makes the product available outside of the space community, providing a potential source of usage and ideas to drive product design and development. The combination of open source with an Apache 2 license, and distribution on GitHub, has enabled an active community of users and contributors. The spectrum of users for Open MCT is, to our knowledge, unprecedented for mission software. In addition to our NASA users, we have, through open source, had users and inquires on projects ranging from Internet of Things, to radio hobbyists, to farming projects. We have an active community of contributors, enabling a flow of ideas inside and outside of the space community.

Trimble, Jay↗
NASA NTRSDate not supplied

GL4U: Bioinformatics training for students and educators using space omics data

NASA’s GeneLab project provides researchers open access to space-relevant experiment multi-omics data that can be mined to understand the effects of spaceflight on biological systems. To maximize the number of scientists who understand and utilize GeneLab data and data processing pipelines, GeneLab has created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab team plans to host two annual data processing bootcamps, one for college-level students (direct) and one for college educators (indirect – training of trainers), in which participants learn to analyze GeneLab’s space-relevant omics data. The GL4U direct training pilot program was conducted in June 2021. During the pilot, students participated in a week-long bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze RNA sequence data. This pilot demonstrated the capacity of GL4U for training young scientists and encouraging data re-use. During the educator pilot, scheduled for June 2022, educators will receive materials and training to enable them to run the bootcamp at their home institutions or alternatively to adapt the content to implement within existing courses, thereby extending the reach of this initiative.

Amanda Marie Saravia-butler↗
NASA NTRSDate not supplied

GL4U: Bioinformatics Training for Students and Educators Using Space Omics Data

NASA’s GeneLab project provides researchers open access to space-relevant experiment multi-omics data that can be mined to understand the effects of spaceflight on biological systems. To maximize the number of scientists who understand and utilize GeneLab data and data processing pipelines, GeneLab has created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab team plans to host two annual data processing bootcamps, one for college-level students (direct) and one for college educators (indirect – training of trainers), in which participants learn to analyze GeneLab’s space-relevant omics data. The GL4U direct training pilot program was conducted in June 2021. During the pilot, students participated in a week-long bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze RNA sequence data. This pilot demonstrated the capacity of GL4U for training young scientists and encouraging data re-use. During the educator pilot, scheduled for June 2022, educators will receive materials and training to enable them to run the bootcamp at their home institutions or alternatively to adapt the content to implement within existing courses, thereby extending the reach of this initiative.

Amanda Marie Saravia-butler↗
NASA NTRS2020

Connecting Users and Applications with Po.daac Hosted GHRSST Data

The 80+ GHRSST public datasets represent a rich resource for sea surface temperature research and applications given their time series length, resolution, spatial coverage, varying measurement types and processing levels, and availability in the full spectrum of PO.DAAC tools and services ecosystem. The PO.DAAC has created a publicly accessible recipe suite for the user community to perform straightforward yet powerful computations on GHRSST data using python recipes, Jupyter notebooks, R, Matlab, and the NCO programming language. These recipes include numerical computations for regional and global SST trends, anomaly derivations, EOF analysis, climate signal reproduction, and ocean phenology. For example, one recipe reproduces a famous SST based warming figure from the Fourth National Climate Assessment (USA) while another focuses on quantifying the regional changes in ocean SST phenology. Most are python-based while some contain hybrid calls and leverage the NCO programming interface too. All are available on the PO.DAAC user forum (https://podaac.jpl.nasa.gov/forum/) and/or via the open source NASA GitHub repository (https://github.com/nasa/podaac_tools_and_services). Several are available in the Jupyter notebook framework including podaacypy (https://github.com/nasa/podaacpy), a recipe for GHRSST granule metadata discovery and application, and more recently a Jupyter notebook developed to support data analysis and visualization of a cloud-based Zarr formatted Level 4 MUR dataset in the AWS Open Data Registry. Throughout the summer of 2020, the PO.DAAC intends to add and migrate more of its numerical recipes to the Jupyter notebook framework and publish them on its open source GitHub repository.

Gentemann, Chelle↗
NASA NTRS1983

Development INTERDATA 8/32 computer system

The capabilities of the Interdata 8/32 minicomputer were examined regarding data and word processing, editing, retrieval, and budgeting as well as data management demands of the user groups in the network. Based on four projected needs: (1) a hands on (open shop) computer for data analysis with large core and disc capability; (2) the expected requirements of the NASA data networks; (3) the need for intermittent large core capacity for theoretical modeling; (4) the ability to access data rapidly either directly from tape or from core onto hard copy, the system proved useful and adequate for the planned requirements.

Sonett, C. P.↗
NASA NTRS2012

Tropical Rainfall Measuring Mission (TRMM) Precipitation Data and Services for Research and Applications

Precipitation is a critical component of the Earth's hydrological cycle. Launched on 27 November 1997, TRMM is a joint U.S.-Japan satellite mission to provide the first detailed and comprehensive data set of the four-dimensional distribution of rainfall and latent heating over vastly under-sampled tropical and subtropical oceans and continents (40 S - 40 N). Over the past 14 years, TRMM has been a major data source for meteorological, hydrological and other research and application activities around the world. The purpose of this short article is to inform that the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) provides TRMM archive and near-real-time precipitation data sets and services for research and applications. TRMM data consist of orbital data from TRMM instruments at the sensor s resolution, gridded data at a range of spatial and temporal resolutions, subsets, ground-based instrument data, and ancillary data. Data analysis, display, and delivery are facilitated by the following services: (1) Mirador (data search and access); (2) TOVAS (TRMM Online Visualization and Analysis System); (3) OPeNDAP (Open-source Project for a Network Data Access Protocol); (4) GrADS Data Server (GDS); and (5) Open Geospatial Consortium (OGC) Web Map Service (WMS) for the GIS community. Precipitation data application services are available to support a wide variety of applications around the world. Future plans include enhanced and new services to address data related issues from the user community. Meanwhile, the GES DISC is preparing for the Global Precipitation Measurement (GPM) mission which is scheduled for launch in 2014.

Liu, Zhong↗
NASA NTRSDate not supplied

Biomass Harmonization and SAR Analysis with the Multi-mission Algorithm and Analysis Platform (MAAP)

The Multi‐mission Algorithm and Analysis Platform (MAAP) is a collaborative effort between NASA and the European Space Agency (ESA) to support above ground biomass (AGB) research in an open science framework. MAAP brings together relevant data, algorithms, and computing capabilities in a common cloud environment to address the challenges of sharing and processing data from field, airborne and satellite measurements. MAAP was publicly released in October 2021, providing computing capabilities co-located with the data, a collaborative coding and analysis environment, and a set of interoperable tools and algorithms developed to support the estimation and visualization of data. MAAP has allowed scientists from both North America and Europe to collaborate on the generation and analysis/visualization of data derived from multiple, discipline-adjacent missions in an open, collaborative environment that has reached beyond traditional scientific investigation. MAAP has been used to support multiple scientific activities. To date, existing LiDAR data from multiple platforms has been calibrated with field measurements and combined for more comprehensive and accurate estimates of above ground biomass AGB; these LiDAR platforms include airborne (e.g. LVIS), the International Space Station (NASA’s Global Ecosystem Dynamics Investigation (GEDI), and satellites (e.g. ICESat-2). The current challenge is to effectively and seamlessly combine the aforementioned LiDAR-based data with new data sources such as P-band RADAR from ESA’s upcoming BIOMASS mission, existing ESA Sentinel-1 C-band SAR, and the 30 PB/yr of high cadence global coverage L-band SAR data from the upcoming NASA-ISRO SAR (NISAR) mission. Recent analysis using MAAP merged ICESat-2 and optical data (Harmonized Landsat Sentinel) produced the most comprehensively precise estimate of boreal-wide AGB to date. Another effort using MAAP is the production and open distribution of global comparisons of AGB map estimates, including from ICESat-2 and GEDI, to bolster stakeholder uptake for policy applications. These map estimates will feed into the Intergovernmental Panel on Climate Change (IPCC) database, likely aiding the next Global Carbon Stocktake of the UNFCCC. Furthermore, the biomass retrieval intercomparison exercise BRIX-2 could benefit from the MAAP providing standardized test cases (based on airborne campaign and spaceborne data) allowing the community to develop and apply retrieval algorithms based on these test cases, while forthcoming SAR data training curricula could also use the MAAP as a teaching and learning platform. The MAAP is meeting the challenges inherent in international, open science collaboration and large scale computing with a platform that is entirely open source and cloud native, using open standards for data access, manipulation, protocols, and formats. The MAAP data system consists of a dedicated data store whose data is indexed in an online catalog conforming to established metadata, application programmatic interfaces (APIs), and service interface standards, using an implementation of the open sourced NASA Common Metadata Repository. Federation of user identities allows users from either NASA or ESA to access and consume services from the other using a unified metadata catalog for the data utilized across the ESA and NASA MAAP platforms. Similarly, we are exploring how to increase interoperability to achieve a common approach to packaging, orchestrating and executing algorithms, with interoperable access to data for subsetting, fast browse, and cloud-optimized access, all using interoperable standards such as those from the Open Geospatial Consortium (OGC). Designed for interoperability, ESA and NASA utilize a common architecture for the software platform. It provides a cloud-based algorithm development environment (ADE) that enables scientists to develop algorithms collaboratively with access to the MAAP data catalog as well as other data archives. MAAP provides an Eclipse Che-based ADE supporting both Python and R languages, popular in this biomass community. Algorithms developed and containerized within the ADE can be deployed to run to thousands of computational nodes in the MAAP’s data processing system (DPS), dramatically speeding up processing and giving scientists a rapid, iterative turnaround of results. NASA’s implementation of the DPS is based on the Hybrid Science Data System (HySDS) framework, used by NASA flight projects to produce Earth science standard products.

cloud computing↗
NASA NTRSDate not supplied

Open Science Approach to Analyze Climate-Crop Relationships in the US Leveraging GES DISC and Galaxy Workflows

Understanding the intricate relationship between climate variability and agricultural production is crucial for ensuring food security. This study investigates the impact of climate parameters, such as temperature, precipitation, and soil moisture, on major US crop yields. Adopting an open science approach, the study analyzes the impact of climate on agricultural production in the United States. The Galaxy workflow engine serves as the primary tool for integrating climate data from the Goddard Earth Sciences Data and Information Services Center (GES DISC), retrieved via the Giovanni system, with yield statistics from the United States Department of Agriculture’s National Agricultural Statistics Service (USDA NASS). Extensions for reading, preprocessing, and analyzing external data have been developed, enabling the creation of workflows within the Galaxy platform. The development of a reproducible workflow allows for the calculation of seasonal climate averages, which are then assessed for their correlation with crop yields. This methodology ensures the replicability of the research, promoting transparency and collaboration in the scientific community. Correlational and regression analyses have been applied to different sub-zones and crops. The findings from this research offer valuable insights into the relationship between climate parameters and crop yields. These insights contribute to a deeper understanding of climate-crop relationships, providing a solid foundation for informed decision-making in the agricultural sector. The high correlation values indicate a significant relationship between climate parameters and crop yields, underscoring the importance of considering climate factors in agricultural planning and policymaking. This research also exemplifies the power of open science in advancing our understanding of complex environmental and agricultural phenomena. By leveraging open data and services, it provides a robust and replicable framework for future studies in this critical field.

Open science↗