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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Evolution of Archival Storage (from Tape to Memory)

Over the last three decades, there has been a significant evolution in storage technologies supporting archival of remote sensing data. This section provides a brief survey of how these technologies have evolved. Three main technologies are considered - tape, hard disk and solid state disk. Their historical evolution is traced, summarizing how reductions in cost have helped being able to store larger volumes of data on faster media. The cost per GB of media is only one of the considerations in determining the best approach to archival storage. Active archives generally require faster response to user requests for data than permanent archives. The archive costs have to consider facilities and other capital costs, operations costs, software licenses, utilities costs, etc. For meeting requirements in any organization, typically a mix of technologies is needed.

Magnetic Storage; Technology Evolution↗
NASA NTRS2020

Satellite-based remote sensing data set of global surface water storage change from 1992 to 2018

The recent availability of freely and openly available satellite remote sensing products has enabled the implementation of global surface water monitoring at a level not previously possible. Here we present a global set of satellite-derived time series of surface water storage variations for lakes and reservoirs for a period that covers the satellite altimetry era. Our goals are to promote the use of satellite-derived products for the study of large inland water bodies and to set the stage for the expected availability of products from the Surface Water and Ocean Topography (SWOT) mission, which will vastly expand the spatial coverage of such products,expected from 2021 on. Our general strategy is to estimate global surface water storage changes (1V) in large lakes and reservoirs using a combination of paired water surface elevation (WSE) and water surface area (WSA)extent products. Specifically, we use data produced by multiple satellite altimetry missions (TOPEX/Poseidon,Jason-1, Jason-2, Jason-3, and Envisat) from 1992 on, with surface extent estimated from Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000 on. We leverage relationships between elevation and surface area (i.e., hypsometry) to produce estimates of 1 Veven during periods when either of the variables was not available. This approach is successful provided that there are strong relationships between the two variables during an overlapping period. Our target is to produce time series of1Vas well as of WSE and WSA fora set of 347 lakes and reservoirs globally for the 1992–2018 period. The data sets presented and their respective algorithm theoretical basis documents are publicly available and distributed via the Physical Oceanography Dis-tributed Active Archive Center (PO DAAC; https://podaac.jpl.nasa.gov/, last access: 13 May 2020) of NASA’sJet Propulsion Laboratory. Specifically, the WSE data set is available at https://doi.org/10.5067/UCLRS-GREV2(Birkett et al., 2019), the WSA data set is available at https://doi.org/10.5067/UCLRS-AREV2 (Khandelwal and Kumar, 2019), and the 1V data set is available at https://doi.org/10.5067/UCLRS-STOV2 (Tortini et al., 2019).The records we describe represent the most complete global surface water time series available from the launch of TOPEX/Poseidon in 1992 (beginning of the satellite altimetry era) to the near present. The production of long-term, consistent, and calibrated records of surface water cycle variables such as in the data set presented here is of fundamental importance to baseline future SWOT products

Riccardo Tortini↗
NASA NTRS1999

Hyperspectral Remote Sensing and Ecological Modeling Research and Education at Mid America Remote Sensing Center (MARC): Field and Laboratory Enhancement

The purpose of this project was to establish a new hyperspectral remote sensing laboratory at the Mid-America Remote sensing Center (MARC), dedicated to in situ and laboratory measurements of environmental samples and to the manipulation, analysis, and storage of remotely sensed data for environmental monitoring and research in ecological modeling using hyperspectral remote sensing at MARC, one of three research facilities of the Center of Reservoir Research at Murray State University (MSU), a Kentucky Commonwealth Center of Excellence. The equipment purchased, a FieldSpec FR portable spectroradiometer and peripherals, and ENVI hyperspectral data processing software, allowed MARC to provide hands-on experience, education, and training for the students of the Department of Geosciences in quantitative remote sensing using hyperspectral data, Geographic Information System (GIS), digital image processing (DIP), computer, geological and geophysical mapping; to provide field support to the researchers and students collecting in situ and laboratory measurements of environmental data; to create a spectral library of the cover types and to establish a World Wide Web server to provide the spectral library to other academic, state and Federal institutions. Much of the research will soon be published in scientific journals. A World Wide Web page has been created at the web site of MARC. Results of this project are grouped in two categories, education and research accomplishments. The Principal Investigator (PI) modified remote sensing and DIP courses to introduce students to ii situ field spectra and laboratory remote sensing studies for environmental monitoring in the region by using the new equipment in the courses. The PI collected in situ measurements using the spectroradiometer for the ER-2 mission to Puerto Rico project for the Moderate Resolution Imaging Spectrometer (MODIS) Airborne Simulator (MAS). Currently MARC is mapping water quality in Kentucky Lake and vegetation in the Land-Between-the Lakes (LBL) using Landsat-TM data. A Landsat-TM scene of the same day was obtained to relate ground measurements to the satellite data. A spectral library has been created for overstory species in LBL. Some of the methods, such as NPDF and IDFD techniques for spectral unmixing and reduction of effects of shadows in classifications- comparison of hyperspectral classification techniques, and spectral nonlinear and linear unmixing techniques, are being tested using the laboratory.

Cetin, Haluk↗
NASA NTRS1993

Optimization of microscopic and macroscopic second order optical nonlinearities

Nonlinear optical materials (NLO) can be used to extend the useful frequency range of lasers. Frequency generation is important for laser-based remote sensing and optical data storage. Another NLO effect, the electro-optic effect, can be used to modulate the amplitude, phase, or polarization state of an optical beam. Applications of this effect in telecommunications and in integrated optics include the impression of information on an optical carrier signal or routing of optical signals between fiber optic channels. In order to utilize these effects most effectively, it is necessary to synthesize materials which respond to applied fields very efficiently. In this talk, it will be shown how the development of a fundamental understanding of the science of nonlinear optics can lead to a rational approach to organic molecules and materials with optimized properties. In some cases, figures of merit for newly developed materials are more than an order of magnitude higher than those of currently employed materials. Some of these materials are being examined for phased-array radar and other electro-optic switching applications.

Marder, Seth R.↗
NASA NTRS1982

Yield model development project implementation plan

Tasks remaining to be completed are summarized for the following major project elements: (1) evaluation of crop yield models; (2) crop yield model research and development; (3) data acquisition processing, and storage; (4) related yield research: defining spectral and/or remote sensing data requirements; developing input for driving and testing crop growth/yield models; real time testing of wheat plant process models) and (5) project management and support.

Ambroziak, R. A.↗
NASA NTRS1977

Parametric design of ground data processing/support systems for advanced sensor systems

A parametric system design technique has been applied to ground data processing/support systems for advanced sensor applications. The system establishes a direct link between budget analysts and system planners. Three primary phases are identified: the definition of requirements, system design, and system costing. The system is evaluated for three cases: (1) a study of ground data handling systems for earth resource satellites, (2) a ground data mass storage and processing system for agricultural remote-sensing studies, and (3) a parametric study of shuttle era data processing support required for atmospheric and space physics.

Denny, C.↗
NASA NTRS2021

Tonlé Sap Food Security and Agriculture II: Evaluating Changes in Ecosystem Vitality and Freshwater Healthin the Tonlé Sap Basin using Remotely Sensed Data

The Tonlé Sap Lake and river basin in central Cambodia provide critical ecosystem services to the region, including fisheries, agricultural irrigation, hydropower, and biodiverse habitats. Deforestation, increased pumping for farming, and effects of climate change such as droughts and forest fires threaten the health of the lake and food security in the region. This project built upon the previous term through a partnership with Conservation International (CI), the Cambodian Ministry of Water Resources and Meteorology, and the Tonlé Sap Authority to assess ecosystem vitality and implement CI’s Freshwater Health Index (FHI) tool, in an effort to prioritize resource expenditure and highlight areas of concern. Due to the COVID-19 pandemic and related travel restrictions, partners had not been able to readily collect in situ data for the past year, which make up the majority of FHI inputs. To help fill this data gap, we developed a methodology for using Gravity Recovery and Climate Experiment (GRACE) satellite data to calculate groundwater storage depletion, and a Python Application Programming Interface for processing and formatting remotely-sensed data for the Soil and Water Assessment Tool (SWAT) model. We then used SWAT to model nutrient flows and of phosphorous, nitrogen and suspended sediments amounts in the basin from October 2000 to December 2020. These outputs served as inputs for the FHI and provided policy makers with robust monitoring information to aid decision-making in the area and safeguard the lake’s vital fisheries and biodiversity.

Justine Spore↗
NASA NTRS2021

NASA’s MODIS/VIIRS Global Water Reservoir Product Suite from Moderate Resolution Remote Sensing Data

Global reservoir information can not only benefit local water management but can also improve our understanding of the hydrological cycle. This information includes water area, elevation, and storage; evaporation rate and volume values; and other characteristics. However, operational wall-to-wall reservoir storage and evaporation monitoring information is lacking on a global scale. Here we introduce NASA’s new MODIS/VIIRS Global Water Reservoir product suite based on moderate resolution remote sensing data—the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Visible Infrared Imaging Radiometer Suite (VIIRS). This product consists of 8-day (MxD28C2 and VNP28C2) and monthly (MxD28C3 and VNP28C3) measurements for 164 large reservoirs (MxD stands for the product from both Terra (MOD) or Aqua (MYD) satellites). The 8-day product provides area, elevation, and storage values, which were generated by first extracting water areas from surface reflectance data and then applying the area estimations to the pre-established Area–Elevation (A–E) relationships. These values were then further aggregated to monthly, with the evaporation rate and volume information added. The evaporation rate and volume values were calculated after the Lake Temperature and Evaporation Model (LTEM) using MODIS/VIIRS land surface temperature product and meteorological data from the Global Land Data Assimilation System (GLDAS). Validation results show that the 250 m area classifications from MODIS agree well with the high-resolution classifications from Landsat (R^(2) = 0.99). Validation of elevation and storage products for twelve Indian reservoirs show good agreement in terms of R^(2) values (0.71–0.96 for elevation, and 0.79–0.96 for storage) and normalized root-mean-square error (NRMSE) values (5.08–19.34% for elevation, and 6.39–18.77% for storage). The evaporation rate results for two reservoirs (Lake Nasser and Lake Mead) agree well with in situ measurements (R^(2) values of 0.61 and 0.66, and NRMSE values of 16.25% and 21.76%). Furthermore, preliminary results from the VIIRS reservoir product have shown good consistency with the MODIS based product, confirming the continuity of this 20-year product suite. This new global water reservoir product suite can provide valuable information with regard to water-sources-related studies, applications, management, and hydrological modeling and change analysis such as drought monitoring.

Yao Li↗
DOE OSTI2020

Competing effects of soil fertility and toxicity on tropical greening

Tropical forests are expected to green up with increasing atmospheric CO 2 concentrations, but primary productivity may be limited by soil nutrient availability. However, rarely have canopy-scale measurements been assessed against soil measurements in the tropics. Here, we sought to assess remotely sensed canopy greenness against steep soil nutrient gradients across 50 1-ha mature forest plots in Panama. Contrary to expectations, increases in in situ extractable soil phosphorus (P) and base cations (K, Mg) corresponded to declines in remotely sensed mean annual canopy greenness (r 2 = 0.77– 0.85; p < 0.1), controlling for precipitation. The reason for this inverse relationship appears to be that litterfall also increased with increasing soil P and cation availability (r 2 = 0.88–0.98; p < 0.1), resulting in a decline in greenness with increasing annual litterfall (r 2 = 0.94; p < 0.1). As such, greater soil nutrient availability corresponded to greater leaf turnover, resulting in decreased greenness. However, these decreases in greenness with increasing soil P and cations were countered by increases in greenness with increasing soil nitrogen (N) (r 2 = 0.14; p < 0.1), which had no significant relationship with litterfall, likely reflecting a direct effect of soil N on leaf chlorophyll content, but not on litterfall rates. In addition, greenness increased with extractable soil aluminum (Al) (r 2 = 0.97; p < 0.1), but Al had no significant relationship with litterfall, suggesting a physiological adaptation of plants to high levels of toxic metals. Thus, spatial gradients in canopy greenness are not necessarily positive indicators of soil nutrient scarcity. Using a novel remote sensing index of canopy greenness limitation, we assessed how observed greenness compares with potential greenness. We found a strong relationship with soil N only (r 2 = 0.65; p < 0.1), suggesting that tropical canopy greenness in Panama is predominantly limited by soil N, even if plant productivity (e.g., litterfall) responds to rock-derived nutrients. Moreover, greenness limitation was also significantly correlated with fine root biomass and soil carbon stocks (r 2 = 0.62–0.71; p < 0.1), suggesting a feedback from soil N to canopy greenness to soil carbon storage. Altogether, these data point to the potential utility of a remote sensing product for assessing belowground properties in tropical ecosystems.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2009

An Intelligent Archive Testbed Incorporating Data Mining

Many significant advances have occurred during the last two decades in remote sensing instrumentation, computation, storage, and communication technology. A series of Earth observing satellites have been launched by U.S. and international agencies and have been operating and collecting global data on a regular basis. These advances have created a data rich environment for scientific research and applications. NASA s Earth Observing System (EOS) Data and Information System (EOSDIS) has been operational since August 1994 with support for pre-EOS data. Currently, EOSDIS supports all the EOS missions including Terra (1999), Aqua (2002), ICESat (2002) and Aura (2004). EOSDIS has been effectively capturing, processing and archiving several terabytes of standard data products each day. It has also been distributing these data products at a rate of several terabytes per day to a diverse and globally distributed user community (Ramapriyan et al. 2009). There are other NASA-sponsored data system activities including measurement-based systems such as the Ocean Data Processing System and the Precipitation Processing system, and several projects under the Research, Education and Applications Solutions Network (REASoN), Making Earth Science Data Records for Use in Research Environments (MEaSUREs), and the Advancing Collaborative Connections for Earth-Sun System Science (ACCESS) programs. Together, these activities provide a rich set of resources constituting a value chain for users to obtain data at various levels ranging from raw radiances to interdisciplinary model outputs. The result has been a significant leap in our understanding of the Earth systems that all humans depend on for their enjoyment, livelihood, and survival. The trend in the community today is towards many distributed sets of providers of data and services. Despite this, visions for the future include users being able to locate, fuse and utilize data with location transparency and high degree of interoperability, and being able to convert data to information and usable knowledge in an efficient, convenient manner, aided significantly by automation (Ramapriyan et al. 2004; NASA 2005). We can look upon the distributed provider environment with capabilities to convert data to information and to knowledge as an Intelligent Archive in the Context of a Knowledge Building system (IA-KBS). Some of the key capabilities of an IA-KBS are: Virtual Product Generation, Significant Event Detection, Automated Data Quality Assessment, Large-Scale Data Mining, Dynamic Feedback Loop, and Data Discovery and Efficient Requesting (Ramapriyan et al. 2004).

Ramapriyan, H.↗
DOE OSTI2026

Sedimentation and Nonlinear Trapping in Texas Reservoirs Identified Using Remote Sensing and Bathymetric Survey Records

Decreasing reservoir storage capacity due to sedimentation poses great challenges to aging U.S. reservoirs, as it reduces the efficacy and reliability of their socio‐economic services. However, systematic assessments of reservoir sedimentation rates and associated issues remain limited because of sparse and infrequent bathymetry survey data. In this study, we use remote sensing‐driven estimates of sediment concentrations to identify regions experiencing rapid reservoir capacity loss, as observed in repeated bathymetry surveys. Our analysis focuses on Texas, where one of the most reliable state‐level reservoir capacity loss data sets is available through a unique long‐term monitoring program by the Texas Water Development Board. We find that reservoirs with large storage capacities and high sedimentation rate are concentrated in Northeast Texas. We also show that reduced forest, increased barren land, and erosive soil properties are co‐varying with high reservoir sedimentation rates. Temporal changes in the longitudinal gradient of the remotely sensed sediment flux highlight the nonlinear nature of sediment trapping processes, and can be used to estimate the reservoir storage capacity loss over time. In combination with standard bathymetry surveys, our approach shows potential for more cost‐effective and frequent assessment of reservoir storage loss.

Lee, Jiyong [ORNL] (ORCID:0000000198957406)↗
NASA NTRS1975

Future remote-sensing programs

User requirements and methods developed to fulfill them are discussed. Quick-look data, data storage on computer-compatible tape, and an integrated capability for production of images from the whole class of earth-viewing satellites are among the new developments briefly described. The increased capability of LANDSAT-C and Nimbus G and the needs of specialized applications such as, urban land use planning, cartography, accurate measurement of small agricultural fields, thermal mapping and coastal zone management are examined. The affect of the space shuttle on remote sensing technology through increased capability is considered.

Schweickart, R. L.↗
DOE OSTI2024

Zero-Power Analog Optical Processing

The motivation behind this research is the growing challenge of handling the massive amounts of data generated by modern imaging systems. Conventional digital image processing techniques are struggling to keep pace with the demands of high-resolution and high-speed imaging systems for remote sensing due to their high-power consumption and data storage requirements. We present a novel approach based on analog photonics to address this challenge. The proposed system utilizes a silicon-photonics-based image encoder positioned after image formation and initial optical-to-electrical conversion. The photonic encoder compresses image data using a passive disordered photonic structure to perform kernel-type random projections of the raw data. The compressed data is then processed by a back-end neural network, which reconstructs the original image with high fidelity (structural similarity exceeding 90%). Our proposed approach has the potential to compress images with ~ 1000X lower power consumption compared to digital approaches with data rates exceeding 1 terapixel/second.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2001

Advances in Land Data Assimilation Systems

Assimilation of remotely sensed land surface observations into regional to global scale numerical models have the potential to significantly advance our ability, to assess, understand, and predict surface water, energy, and carbon cycles. This session seeks to assess the state-of-the-art in data assimilation methods for integrating land surface remote sensing and modeling, with a focus on practical applications and techniques. Assimilated land surface variables of interest include (but are not limited to, soil moisture, surface temperature, snowpack, streamflow, vegetation dynamics, and carbon storage. Contributions describing the development of practical land surface data assimilation methods, multivariate land surface data assimilation strategies, evaluation of the required accuracy and resolution of remote sensing observations, the effects of scale, process complexity, and uncertainty on data assimilation, and the optimal treatment of model and observation errors are encouraged.

Houser, Paul R.↗
NASA NTRS1999

Enhancement of Capabilities in Hyperspectral and Radar Remote Sensing for Environmental Assessment and Monitoring

The University of Utah, Department of Geography has developed a research and instructional program in satellite remote sensing and image processing. The University requested funds for the purchase of software licenses, mass storage for massive hyperspectral imager data sets, upgrades for the central data server to handle the additional storage capacity, a spectroradiometer for field data collection. These purchases have been made. This equipment will support research in one of the newest and most rapidly expanding areas of remote sensing.

Hepner, George F.↗
NASA NTRS1998

Fractal Characterization of Multitemporal Scaled Remote Sensing Data

Scale is an "innate" concept in geographic information systems. It is recognized as something that is intrinsic to the ingestion, storage, manipulation, analysis, modeling, and output of space and time data within a GIS purview, yet the relative meaning and ramifications of scaling spatial and temporal data from this perspective remain enigmatic. As GISs become more sophisticated as a product of more robust software and more powerful computer systems, there is an urgent need to examine the issue of scale, and its relationship to the whole body of spatiotemporal data, as imparted in GISS. Scale is fundamental to the characterization of geo-spatial data as represented in GISS, but we have relatively little insight on the effects of, or how to measure the effects of, scale in representing multiscaled data; i.e., data that are acquired in different formats (e.g., map, digital) and exist in varying spatial, temporal, and in the case of remote sensing data, radiometric, configurations. This is particularly true in the emerging era of Integrated GISs (IGIS), wherein spatial data in a variety of formats (e.g., raster, vector) are combined with multiscaled remote sensing data, capable of performing highly sophisticated space-time data analyses and modeling. Moreover, the complexities associated with the integration of multiscaled data sets in a multitude of formats are exacerbated by the confusion of what the term "scale" is from a multidisciplinary perspective; i.e., "scale" takes on significantly different meanings depending upon one's disciplinary background and spatial perspective which can lead to substantive confusion in the input, manipulation, analyses, and output of IGISs (Quattrochi, 1993). Hence, we must begin to look at the universality of scale and begin to develop the theory, methods, and techniques necessary to advance knowledge on the "Science of Scale" across a wide number of spatial disciplines that use GISs.

Quattrochi, Dale A.↗
NASA NTRS1977

CAMSP: Classification and Mensuration Software Package

Batch/interactive system can be used to analyze any remotely sensed Earth resources data. Configuration requirements are: 300K bytes of main storage, 200K bytes of storage for IMS, 600K bytes of storage for LCS, five 2314 disk drives, seven 9-track tape drives, and one cluster of digital television equipment.

Source record↗
NASA NTRS2015

Modelling Freshwater Resources at the Global Scale: Challenges and Prospects

Quantification of spatially and temporally resolved water flows and water storage variations for all land areas of the globe is required to assess water resources, water scarcity and flood hazards, and to understand the Earth system. This quantification is done with the help of global hydrological models (GHMs). What are the challenges and prospects in the development and application of GHMs? Seven important challenges are presented. (1) Data scarcity makes quantification of human water use difficult even though significant progress has been achieved in the last decade. (2) Uncertainty of meteorological input data strongly affects model outputs. (3) The reaction of vegetation to changing climate and CO2 concentrations is uncertain and not taken into account in most GHMs that serve to estimate climate change impacts. (4) Reasons for discrepant responses of GHMs to changing climate have yet to be identified. (5) More accurate estimates of monthly time series of water availability and use are needed to provide good indicators of water scarcity. (6) Integration of gradient-based groundwater modelling into GHMs is necessary for a better simulation of groundwater-surface water interactions and capillary rise. (7) Detection and attribution of human interference with freshwater systems by using GHMs are constrained by data of insufficient quality but also GHM uncertainty itself. Regarding prospects for progress, we propose to decrease the uncertainty of GHM output by making better use of in situ and remotely sensed observations of output variables such as river discharge or total water storage variations by multi-criteria validation, calibration or data assimilation. Finally, we present an initiative that works towards the vision of hyper resolution global hydrological modelling where GHM outputs would be provided at a 1-km resolution with reasonable accuracy.

Global hydrological model↗