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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Intercomparisons of Total Precipitable Water from Satellite and Other Long Term Data Sets

Global water vapor data sets from satellite (NVAP) and reanalysis (NCEP and DAO) are intercompared for a 5 year period (1988-1992). Global average indicates that the NCEP and DAO reanalyses are dryer than NVAP over much of the period. Spatial patterns of the NCEP/NVAP and DAO/NVAP differences show regional variations in the 60 month climatogical fields. For example, in the Eastern Pacific just south of the equator NVAP is much dryer than the NCEP and more moist than DAO. Rather large discrepancies exist in other regions as well. North Africa, Saudi Arabia, India, and Australia show that NVAP TPW values are more moist than the NCEP and DAO model analyses by 4-8 mm. In general, the NCEP and DAO exhibits a tendency to be dryer than NVAP over the tropical ocean region. Over the Americas little differences exist except over the west coast. The shapes of the differences fields over the Eastern Pacific region are significantly different between NCEP/NVAP and DAO/NVAP differences. Over South America and Central Africa, DAO TPW values are significantly higher than those of NVAP. These difference fields show monthly and seasonal variability as well. These results will be highlighted in the paper and on the poster.

Chang, Fong-Chiau↗
NASA NTRS1991

An evaluation of water vapor radiometer data for calibration of the wet path delay in very long baseline interferometry experiments

The internal consistency of the baseline-length measurements derived from analysis of several independent VLBI experiments is an estimate of the measurement precision. The paper investigates whether the inclusion of water vapor radiometer (WVR) data as an absolute calibration of the propagation delay due to water vapor improves the precision of VLBI baseline-length measurements. The paper analyzes 28 International Radio Interferometric Surveying runs between June 1988 and January 1989; WVR measurements were made during each session. The addition of WVR data decreased the scatter of the length measurements of the baselines by 5-10 percent. The observed reduction in the scatter of the baseline lengths is less than what is expected from the behavior of the formal errors, which suggest that the baseline-length measurement precision should improve 10-20 percent if WVR data are included in the analysis. The discrepancy between the formal errors and the baseline-length results can be explained as the consequence of systematic errors in the dry-mapping function parameters, instrumental biases in the WVR and the barometer, or both.

Kuehn, C. E.↗
NASA NTRS1973

Development of a two channel linear discriminant function for detecting and identifying surface water using ERTS-1 data

A method for detecting surface water using ERTS-1 multispectral scanner digital data is described. The several computer programs for processing multispectral data are described, along with the manner in which these programs were used. The use of the LARSAA program to test and demonstrate the discriminant function is explained. The discriminant function itself was later implemented in an operational program which performs only those specific operations necessary to test each data point for an indication of surface water in the scene. Several possible sources of difficulty in the use of the function are identified, and suggestions are made for avoiding or correcting those difficulties.

Source record↗
NASA NTRS1998

Assessing the Geomorphic Evolution and Hydrographic Changes Induced by Winter Storms along the Louisiana Coast

The influence that cold front passages have on Louisiana coastal environments, including land loss and land building processes, has been the primary topic of this multidisciplinary research. This research has combined meteorological, remote sensing, and coastal expertise from the University of Wisconsin (UW) and Louisiana State University (LSU). Analyzed data sets include remotely sensed radiometric data (AVHRR on NOAA-12,13,14, Multispectral Atmospheric Mapping Sensor (MAMS) and MODIS Airborne Simulator (MAS) on NASA ER-2), U.S. Army Corps of Engineers (USACE) water level data, water quality data from the Coastal Studies Institute (CSI) at LSU, USACE river discharge data, National Weather Service (NWS) and CSI wind in sitzi measurements, geomorphic measurements from aerial photography (NASA ER-2 and Learjet), and CSI ground based sediment burial pipes (for monitoring topographic change along the Louisiana coast) and sediment cores. The work reported here-in is a continuation of an initial investigation into coastal Louisiana landform modification by cold front systems. That initial effort demonstrated the importance of cold front winds in the Atchafalaya Bay sediment plume distribution (Moeller et al.), documented the sediment transport and deposition process of the western Louisiana coast (Huh et al.) and developed tools (e.g. water types identification, suspended solids estimation) from multispectral radiometric data for application to the current study. This study has extended that work, developing a Geomorphic Impact Index (GI(sup 2)) for relating atmospheric forcing to coastal response and new tools to measure water motion and sediment transport.

Menzel, W. Paul↗
NASA NTRS1981

Evaluation of HCMM data for assessing soil moisture and water table depth

Data were analyzed for variations in eastern South Dakota. Soil moisture in the 0-4 cm layer could be estimated with 1-mm soil temperatures throughout the growing season of a rainfed barley crop (% cover ranging from 30% to 90%) with an r squared = 0.81. Empirical equations were developed to reduce the effect of canopy cover when radiometrically estimating the 1-mm soil temperature, r squared = 0.88. The corrective equations were applied to an aircraft simulation of HCMM data for a diversity of crop types and land cover conditions to estimate the 0-4 cm soil moisture. The average difference between observed and measured soil moisture was 1.6% of field capacity. HCMM data were used to estimate the soil moisture for four dates with an r squared = 0.55 after correction for crop conditions. Location of shallow alluvial aquifers could be accomplished with HCMM predawn data. After correction of HCMM day data for vegetation differences, equations were developed for predicting water table depths within the aquifer (r=0.8).

Moore, D. G.↗
NASA NTRS1977

Land use change detection with LANDSAT-2 data for monitoring and predicting regional water quality degradation

The author has identified the following significant results. Comparison between LANDSAT 1 and 2 imagery of Arkansas provided evidence of significant land use changes during the 1972-75 time period. Analysis of Arkansas historical water quality information has shown conclusively that whereas point source pollution generally can be detected by use of water quality data collected by state and federal agencies, sampling methodologies for nonpoint source contamination attributable to surface runoff are totally inadequate. The expensive undertaking of monitoring all nonpoint sources for numerous watersheds can be lessened by implementing LANDSAT change detection analyses.

Macdonald, H.↗
NASA NTRS1995

Impact of geostationary satellite water vapor channel data on weather analysis and forecasting

Preliminary results from NWP impact studies are indicating that upper-tropospheric wind information provided by tracking motions in sequences of geostationary satellite water vapor imagery can positively influence forecasts on regional scales, and possibly on global scales as well. The data are complimentary to cloud-tracked winds by providing data in cloud-free regions, as well as comparable in quality. First results from GOES-8 winds are encouraging, and further efforts and model impacts will be directed towards optimizing these data in numerical weather prediction (NWP). Assuming successful launches of GOES-J and GMS-5 satellites in 1995, high quality and resolution water vapor imagers will be available to provide nearly complete global upper-tropospheric wind coverage.

Velden, Christopher S.↗
NASA NTRSDate not supplied

Leveraging High Resolution Remotely Sensed Evapotranspiration Data to Assess Water Availability and Vulnerability in the Memphis Aquifer Area in West Tennessee

The Memphis aquifer is an indispensable source of water for municipal, agricultural, and industrial use across west Tennessee. The aquifer's water is thousands of years old and in pristine condition due to the natural geological filtering by the aquifer’s confining clay layers. To sustain the aquifer’s water supply, recharge must happen at a rate efficient enough to keep up with pumping from the aquifer. In partnership with Protect Our Aquifer and the University of Memphis Center for Applied Earth Science and Engineering Research, this project examined multiple factors to discover thriving areas of recharge in west Tennessee. The team used Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation data, ECOsystem Spaceborne Thermal Radiometer on Space Station (ECOSTRESS) evapotranspiration data, Landsat 8 and National Land Cover Database (NLCD) to track landcover change, and the North American Land Data Assimilation System (NLDAS) Noah land surface model to estimate monthly runoff. These factors were averaged seasonally from 2019 to 2022 to understand seasonal relationships within the study area. Precipitation and evapotranspiration data were used to create water balance maps and with the evaporative stress index (ESI), landcover, and runoff maps, the team pinpointed locations that are most conducive to recharge. The team determined that areas where the water balance is low, ESI is high, and where there is sufficient pervious landcover are locations where recharge happens most effectively. Identification of effective recharge locations may be used by the partners to prioritize areas in need of protection before they become impacted by industrialization and urbanization.

Lauren Webster↗
NASA NTRSDate not supplied

Synthetic Hyperspectral Data for Global Water Quality Algorithm Development

Eutrophication and increasing prevalence of potentially toxic algal blooms (cyanoHABs) among global inland water bodies have become a major ecological concern and require direct attention. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local aquatic processes, to spatially resolved global products. Planned aquatic biogeochemistry remote sensing data products from hyperspectral imagers such as NASA’s Surface Biology and Geology (SBG) mission and relevant aquatic sensor sensitivity precursor airborne imaging spectrometer data provide unprecedented radiometric resolution and sensor sensitivity for characterizing complex aquatic ecosystems. However, scarcity of high-quality freshwater in-situ optical data hinders our capability to develop and validate robust retrieval algorithms. A state-of-the-art synthetic dataset of paired top-of-atmosphere, bottom-of-atmosphere, and optical and biogeophysical data was developed through radiative transfer modeling to simulate natural freshwater ecosystems. A synthetic or precursor dataset for SBG is being used to train robust machine learning models to derive water quality products pertinent to SBG mission objectives. The dataset is also used to show the potential of performing vigorous aquatic sensitivity studies and explored pathways for how best to optimize hyperspectral data for machine learning development. A processing pipeline and resultant global synthetic/precursor dataset for inland waters is presented to establish the innovation for water quality studies of inland waters globally. Optical Society of America Imaging and Applied Optics Congress, Hyperspectral Imaging and Sounding of the Environment (OSA HISE) Meeting, 19-23 July 2021, Virtual Meeting, https://www.osa.org/enus/meetings/osa_meetings/optical_sensors_and_sensing_congress/program/hyperspectral_imaging_and_sounding_of_the_environm/

Synthetic↗
NASA NTRSDate not supplied

SAGE III/ISS Validation of Ozone and Water Vapor V5.2 Data Using Collocated Balloon-Borne Measurements

The Stratospheric Aerosol and Gas Experiment III (SAGE III) instrument installed on the International Space Station (ISS) has completed nearly four years of data collection and production of science data products. The SAGE III/ISS is a solar and lunar occultation instrument that scans the light from the Sun and Moon through the limb of the Earth’s atmosphere to produce vertical profiles of aerosol, ozone, water vapor, and other trace gases. It continues the legacy of previous SAGE instruments dating back to the 1970s to provide data continuity of stratospheric constituents critical for assessing trends in the ozone layer. This presentation shows the validation results of comparing SAGE III/ISS ozone and water vapor vertical profiles from the newly released v5.2 science product with those of balloon launched ozonesonde and frost point hygrometer measurements and satellite data from the Microwave Limb Sounder.

S Kizer↗
NASA NTRS2012

Moisture Fluxes Derived from EOS Aqua Satellite Data for the North Water Polynya Over 2003-2009

Satellite data were applied to calculate the moisture flux from the North Water polynya during a series of events spanning 2003-2009. The fluxes were calculated using bulk aerodynamic formulas with the stability effects according to the Monin-Obukhov similarity theory. Input parameters were taken from three sources: air relative humidity, air temperature, and surface temperature from the Atmospheric Infrared Sounder (AIRS) onboard NASA's Earth Observing System (EOS) Aqua satellite, sea ice concentration from the Advanced Microwave Scanning Radiometer (AMSR-E, also onboard Aqua), and wind speed from the ECMWF ERA-Interim reanalysis. Our results show the progression of the moisture fluxes from the polynya during each event, as well as their atmospheric effects after the polynya has closed up. These results were compared to results from studies on other polynyas, and fall within one standard deviation of the moisture flux estimates from these studies. Although the estimated moisture fluxes over the entire study region from AIRS are smaller in magnitude than ERA-Interim, they are more accurate due to improved temperature and relative humidity profiles and ice concentration estimates over the polynya. Error estimates were calculated to be 5.56 x10(exp -3) g/sq. m/ s, only 25% of the total moisture flux, thus suggesting that AIRS and AMSR-E can be used with confidence to study smaller scale features in the Arctic sea ice pack and can capture their atmospheric effects. These findings bode well for larger-scale studies of moisture fluxes over the entire Arctic Ocean and the thinning ice pack.

Boisvert, Linette N.↗
NASA NTRS2003

Using MGS TES Data to Understand Water Cycling in Mars' North Polar Region

The Martian water cycle is one of the three annual cycles on Mars, dust and CO2 being the other two. Despite the fact that detailed spacecraft data, including global and annual coverage in a variety of wavelengths, have been taken of Mars spanning more than 25 years, there are many outstanding questions regarding the water cycle. There is very little exposed water on Mars today, in either the atmosphere or on the surface although there is geological evidence of catastrophic flooding and continuously running water in past epochs in Mars' history as well as recent (within about 10,000 years ago) evidence for running water in the form of gullies. While there is little water in the atmosphere, water- ice clouds do form and produce seasonal clouds caused by general circulation and by storms. These clouds may in turn be controlling the cycling of the water within the general circulation.

Tamppari, L. K.↗
NASA NTRS2003

Adaptation of a Hyperspectral Atmospheric Correction Algorithm for Multi-spectral Ocean Color Data in Coastal Waters

This SIMBIOS contract supports several activities over its three-year time-span. These include certain computational aspects of atmospheric correction, including the modification of our hyperspectral atmospheric correction algorithm Tafkaa for various multi-spectral instruments, such as SeaWiFS, MODIS, and GLI. Additionally, since absorbing aerosols are becoming common in many coastal areas, we are making the model calculations to incorporate various absorbing aerosol models into tables used by our Tafkaa atmospheric correction algorithm. Finally, we have developed the algorithms to use MODIS data to characterize thin cirrus effects on aerosol retrieval.

Gao, Bo-Cai↗
NASA NTRS2019

An L-Band Brightness Temperature Disaggregation Method Using S-Band Radiometer Data for the Water Cycle Observation Mission (WCOM)

The Water Cycle Observation Mission (WCOM) will build upon previous L and C band passive microwave soil moisture satellite missions. WCOM will consist of a passive microwave synthetic aperture radiometer operating at L, S, and C bands. The WCOM requirements for passive soil moisture are to estimate soil moisture in the top 5 cm of soil layer with an error less than 0.04 cu m/cu m, at 15 km resolution and with a 3-day revisit. A new set of algorithms for these multi-frequency platforms will need to be developed for estimating the data products at the desired resolution. To accomplish this, a brightness temperature (TB) downscaling methodology is developed that uses passive S-band TB (30 km) to downscale L-band TB (50 km) and to estimate soil moisture at a 30 km resolution, based on the linear relationships between the passive signals of L-band and S-band. To test this downscaling method, analysis was performed using PALS data from the Soil Moisture Experiments in 2002 (SMEX02). For this study, 4 km L-band observations were downscaled to 800 m. The root mean square errors (RMSE) between the downscaled TBL at 800m with the observed TBL at 800m are 2.63 K and 1.60 K for H and V polarizations respectively. The results also showed that it was possible to use these disaggregated TB to estimate soil moisture to meet the mission requirement of 0.04 cu m/cu m. These results showed that we can obtain higher resolution soil moisture from L band passive TB with a high accuracy (<0.04 cu m/cu m) by using S-band information.

Disaggregation↗
NASA NTRS2020

Monitoring Water-Related Ecosystems with Earth Observation Data in Support of Sustainable Development Goal (SDG) 6 Reporting

Lack of national data on water-related ecosystems is a major challenge to achieving the Sustainable Development Goal (SDG) 6 targets by 2030. Monitoring surface water extent, wetlands, and water quality from space can be an important asset for many countries in support of SDG 6reporting. We demonstrate the potential for Earth observation (EO) data to support country reporting for SDG Indicator 6.6.1, ‘Change in the extent of water-related ecosystems over time’ and identify important considerations for countries using these data for SDG reporting. The spatial extent of water-related ecosystems, and the partial quality of water within these ecosystems is investigated for seven countries. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 5, 7, and 8 with Shuttle Radar Topography Mission (SRTM) are used to measure surface water extent at 250 m and 30 m spatial resolution, respectively, in Cambodia, Jamaica, Peru, the Philippines, Senegal, Uganda, and Zambia. The extent of mangroves is mapped at 30 m spatial resolution using Landsat 8 Operational Land Imager (OLI), Sentinel-1, and SRTM data for Jamaica, Peru, and Senegal. Using Landsat 8 and Sentinel 2A imagery, total suspended solids and chlorophyll-a are mapped overtime for a select number of large surface water bodies in Peru, Senegal, and Zambia. All of the EO datasets used are of global coverage and publicly available at no cost. The temporal consistency and long time-series of many of the datasets enable replicability over time, making reporting of change from baseline values consistent and systematic. We find that statistical comparisons between different surface water data products can help provide some degree of confidence for countries during their validation process and highlight the need for accuracy assessments when using EO-based land change data for SDG reporting. We also raise concern that EO data in the context of SDG Indicator 6.6.1reporting may be more challenging for some countries, such as small island nations, than others to use in assessing the extent of water-related ecosystems due to scale limitations and climate variability. Country-driven validation of the EO data products remains a priority to ensure successful data integration in support of SDG Indicator 6.6.1 reporting. Multi-country studies such as this one can be valuable tools for helping to guide the evolution of SDG monitoring methodologies and provide a useful resource for countries reporting on water-related ecosystems. The EO data analyses and statistical methods used in this study can be easily replicated for country-driven validation of EO data products in the future.

Raha Hakimdavar↗
NASA NTRS2020

Monitoring Water-Related Ecosystems with Earth Observation Data in Support of Sustainable Development Goal (SDG) 6 Reporting

Lack of national data on water-related ecosystems is a major challenge to achieving the Sustainable Development Goal (SDG) 6 targets by 2030. Monitoring surface water extent, wetlands, and water quality from space can be an important asset for many countries in support of SDG 6 reporting. We demonstrate the potential for Earth observation (EO) data to support country reporting for SDG Indicator 6.6.1, ‘Change in the extent of water-related ecosystems over time’ and identify important considerations for countries using these data for SDG reporting. The spatial extent of water-related ecosystems, and the partial quality of water within these ecosystems is investigated for seven countries. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 5, 7, and 8 with Shuttle Radar Topography Mission (SRTM) are used to measure surface water extent at 250 m and 30 m spatial resolution, respectively, in Cambodia, Jamaica, Peru, the Philippines, Senegal, Uganda, and Zambia. The extent of mangroves is mapped at 30 m spatial resolution using Landsat 8 Operational Land Imager (OLI), Sentinel-1, and SRTM data for Jamaica, Peru, and Senegal. Using Landsat 8 and Sentinel 2A imagery, total suspended solids and chlorophyll-a are mapped over time for a select number of large surface water bodies in Peru, Senegal, and Zambia. All of the EO datasets used are of global coverage and publicly available at no cost. The temporal consistency and long time-series of many of the datasets enable replicability over time, making reporting of change from baseline values consistent and systematic. We find that statistical comparisons between different surface water data products can help provide some degree of confidence for countries during their validation process and highlight the need for accuracy assessments when using EO-based land change data for SDG reporting. We also raise concern that EO data in the context of SDG Indicator 6.6.1 reporting may be more challenging for some countries, such as small island nations, than others to use in assessing the extent of water-related ecosystems due to scale limitations and climate variability. Country-driven validation of the EO data products remains a priority to ensure successful data integration in support of SDG Indicator 6.6.1 reporting. Multi-country studies such as this one can be valuable tools for helping to guide the evolution of SDG monitoring methodologies and provide a useful resource for countries reporting on water-related ecosystems. The EO data analyses and statistical methods used in this study can be easily replicated for country-driven validation of EO data products in the future.

water-related ecosystems; surface water extent; ma↗