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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Managing Global Satellite Data: The GHRSST-PP

This presentation examines the management of satellite data, specifically the GODAE (Global Ocean Data Assimilation Experiment) High Resolution Sea Surface Temperature Pilot Project (GHRSST-PP). The objective of the GHRSST-PP is to produce high quality, enhanced Level 2 SST products (known as L2P) from a number of satellite infrared and microwave sources. Topics covered include data organization, access and data discovery, as well as historical continuity.

Global High Resolution Sea Surface Pilot Project (↗
NASA NTRS1985

Multispectral, thermal infrared satellite data for geologic applications

The value of multispectral thermal infrared satellite data for geologic mapping was assessed, applying the principal component and canonical analysis techniques to the images of the central part of the Arabian Peninsula (a 200 x 300 km area). Low resolution thermal infrared (TIR) data from the Nimbus 5 Surface Composition Mapping Radiometer (SCMR) and the NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) were used. Color images included an 8.8 micrometer (SCMR) and 3.7 and 10.8 micrometer (AVHRR-night) data, ratioed AVHRR day/night TIR data, ratioed AVHRR reflected radiation data, and transformed 8- and 10-band TIR plus reflected radiation data. The results clearly demonstrated the potential geologic value of multispectral TIR data. Igneous and metamorphic units could be separated as a class (although not from each other except for young calc-alkaline granites). Some previously unmapped extensions of mapped faults below thick sedimentary units could be delineated. No single enhancement technique displayed all the potential information, implying that they should be used together.

Blodget, H. W.↗
NASA NTRS1987

Deriving surface albedo measurements from narrow band satellite data

A target calibration procedure for obtaining surface albedo from satellite data is presented. The methodology addresses two key issues, the calibration of remotely-sensed, discrete wavelength, digital data and the derivation of an albedo measurement (defined over the solar short wave spectrum) from spectrally limited observations. Twenty-seven Landsat observations, calibrated with urban targets (building roof-tops and parking lots), are used to derive spatial and seasonal patterns of surface reflectance and albedo for four land cover types: city, suburb, farm and forest.

Brest, Christopher L.↗
NASA NTRS2012

A Project to Map and Monitor Baldcypress Forests in Coastal Louisiana, Using Landsat, MODIS, and ASTER Satellite Data

Cypress swamp forests of Louisiana offer many important ecological and economic benefits: wildlife habitat, forest products, storm buffers, water quality, and recreation. Such forests are also threatened by multiple factors: subsidence, salt water intrusion, sea level rise, persistent flooding, hydrologic modification, hurricanes, insect and nutria damage, timber harvesting, and land use conversion. Unfortunately, there are many information gaps regarding the type, location, extent, and condition of these forests. Better more up to date swamp forest mapping products are needed to aid coastal forest conservation and restoration work (e.g., through the Coastal Forest Conservation Initiative or CFCI). In response, a collaborative project was initiated to develop, test and demonstrate cypress swamp forest mapping products, using NASA supported Landsat, ASTER, and MODIS satellite data. Research Objectives are: Develop, test, and demonstrate use of Landsat and ASTER data for computing new cypress forest classification products and Landsat, ASTER, and MODIS satellite data for detecting and monitoring swamp forest change

Spruce, Joseph↗
NASA NTRSDate not supplied

GeoNEX-SUBSETS: Cutouts of geostationary satellite data over long-term monitoring sites

Satellite remote sensing data are important tool for extrapolating the knowledge obtained at the Fluxnet towers. We introduce our GEO-NEX geostationary satellite subset products, which make it easy to compare between ground observation and Geo-NEX products. The MODIS subset has been frequently used for validating ecosystem models developed at flux towers and upscaling the observed flux data to regional scales. However, MODIS on the polar orbiting satellites can observe target regions only once a day, while the Fluxnet eddy-covariance data are compiled as sub-hourly datasets. As a results, summarizing the sub-hourly flux data into daily statistics is necessary for the comparison between MODIS and Flux data. Here, the new generation geostationary satellite sensors (GOES-16/17 ABI and Himawari-8/9 AHI) has the high-frequent observation feature (10 minutes) in addition to similar spectral band and spatial resolution with MODIS. The high frequent observation allows us to compare the flux diurnal cycle with geostationary satellite sensor data. Some studies have already shown the effective utilization of time series of geostationary satellite data for ecosystem modeling. We are producing NEX Level-1G products, which is the gridded Top-of-Atmosphere reflectance and brightness temperature data of geostationary satellite sensors. We cut out the NEX Level-1G data using the same file format with the MODIS subset except for the projection. The other data products (e.g., surface reflectance, land surface temperature, vegetation indices and climate data) will be also added upon their availability. We selected the ground observation sites from Fluxnet, Phenocam, and AERONET networks. The NEX subset data will be provided through NASA NEX data portal.

GeoNEX↗
DOE OSTI2024

Statistical Validation of Multiple Related Data Sets—Case Study Using Interstellar Boundary Explorer Satellite Data

Abstract Space scientists often face the question of whether data collected by different instruments are measurements of the same source population. This paper proposes a statistical validation method for evaluating the agreement between such related data sets. It offers a detailed case study focused on validating a new data set from the Interstellar Boundary Explorer (IBEX) mission, which serves as a practical how-to guide for similar analyses. Since 2008, the IBEX satellite has been gathering data on heliospheric energetic neutral atoms (ENAs) while being exposed to various sources of background noise, such as cosmic rays and solar energetic particles. The IBEX mission initially released only a qualified triple-coincidence (qABC) data product, which was designed to provide observations of ENAs free of background contamination. Further measurements revealed that the qABC data were in fact susceptible to contamination, having relatively low ENA counts and high background rates. To mitigate this issue, the mission team recently considered releasing a certain qualified double-coincidence (qBC) data product, which has roughly twice the detection rate of the qABC data product. This paper presents a simulation-based validation of the new qBC data product against the already-released qABC data product. The results show that the qBCs can plausibly be said to be measuring the same source population as the qABCs up to an average absolute deviation of 3.6%. Visual diagnostics provide additional confirmation of source rate coherence across data products. The framework introduced here is general and can be applied to other validation problems both within and outside the field of space physics.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1980

GOES satellite data maps areas of extreme cold in Colorado

Geostationary Operational Environmental Satellite (GOES) enhanced infrared (IR) imagery depicted very cold temperatures over Colorado on the morning of 8 December 1978. The situation was unusual because skies were clear and the cold temperatures were not associated with high cloud tops. Instead, satellite data mapped large areas that were experiencing extremely cold surface temperatures. The GOES data were also examined using the Colorado State University interactive data processing system and it was found that the cold IR readings corresponded well with early morning low temperatures over the state. GOES data can be of use in monitoring surface temperatures and can, in certain situations, provide detailed spatial and temporal information over regions experiencing extreme temperatures.

Maddox, R. A.↗
NASA NTRS1983

Vegetation monitoring and classification using NOAA/AVHRR satellite data

A vegetation gradient model, based on a new surface hydrologic index and NOAA/AVHRR meteorological satellite data, has been analyzed along a 1300 km east-west transect across the state of Texas. The model was developed to test the potential usefulness of such low-resolution data for vegetation stratification and monitoring. Normalized Difference values (ratio of AVHRR bands 1 and 2, considered to be an index of greenness) were determined and evaluated against climatological and vegetation characteristics at 50 sample locations (regular intervals of 0.25 deg longitude) along the transect on five days in 1980. Statistical treatment of the data indicate that a multivariate model incorporating satellite-measured spectral greenness values and a surface hydrologic factor offer promise as a new technique for regional-scale vegetation stratification and monitoring.

Greegor, D. H., Jr.↗
NASA NTRSDate not supplied

Tropospheric Ozone in the NASA GEOS Model: Effects of Satellite Data Assimilation and Improvements in Background NO x Chemistry

Tropospheric ozone is a major atmospheric oxidant, the primary source of the hydroxyl radical, and a greenhouse gas. In the troposphere, it is produced from the oxidation of carbon monoxide and volatile organic compounds in the presence of nitrogen oxides (NO x =NO+NO 2 ) or has its origins in the stratosphere. The tropospheric ozone burdens in global atmospheric chemistry models disagree by a factor of 1.5, indicating an incomplete understanding of ozone sources and sinks. Here we examine the tropospheric ozone simulation in NASA Global Modeling and Assimilation Office’s (GMAO’s) GEOS model with the GEOS-Chem chemistry mechanism (version 14.2), using observations from satellites, aircraft, ozonesondes, and surface sites. This modeling framework is used to produce GMAO’s GEOS Composition Forecasts (GEOS-CF), and it has been updated extensively with new emission inventories, improved model physics, satellite data assimilation capability, and an up-to-date chemical mechanism that includes tropospheric halogen chemistry and NOx recycling from particulate nitrate. Earlier versions of the model using GEOS-Chem version 12.0 showed an underestimate in tropospheric ozone in the northern hemisphere. We evaluate the ozone simulation in the updated model, focusing on the effects of assimilating satellite NO 2 data from the Ozone Monitoring Instrument (OMI), constraining stratospheric ozone to satellite data, and updates to the NOx chemistry. We will examine changes in the vertical profiles of tropospheric ozone over the US in support of the Tropospheric Emissions: Monitoring of Pollution (TEMPO), and discuss implications for the tropospheric ozone budget.

Viral Shah↗
DOE OSTI2022

Cross-scale sensing of field-level crop residue cover: Integrating field photos, airborne hyperspectral imaging, and satellite data

Conservation tillage practices can bring benefits to agricultural sustainability. Accurate spatial and temporal resolved information of field-scale crop residue cover, which reflects tillage intensity, is highly valuable for evaluating the outcomes of government conservation programs and voluntary ecosystem service markets, as well as facilitating agroecosystem modeling to quantify cropland biogeochemical processes. Remote sensing has the potential to cost-effectively detect crop residue cover, however, existing regional-scale studies were limited by insufficient ground truth data, scale mismatch between coarse satellite pixels and ground data, and the lack of key spectral data for detecting crop residues. Therefore, this study developed an innovative cross-sensing framework to integrate proximal sensing, airborne hyperspectral imaging, and satellite Earth Observation through deep learning to quantify field-level crop residue cover fractions at the regional scale. Specifically, we have collected intensive ground orthographic photos and conducted airborne hyperspectral surveys at corn and soybean fields of Champaign and nearby counties in Illinois, the heartland of the U.S. Corn Belt. Through semi-automatic labeling aided by ResNet-50 and superpixel image segmentation, we obtained 6719 records of ground residue fractions. With these ground data, we developed the 1-dimensional convolution neural network (CNN) model using airborne hyperspectral reflectance, which has 0.5m spatial resolution and 3–5 nm spectral resolution from 400 to 2400 nm, to predict residue fractions. By applying the CNN model to airborne pixels, we augmented “ground truth” data of crop residues and further combined them with Harmonized Landsat and Sentinel-2 (HLS) satellite data to quantify regional residue fractions at 30 m resolution. Results show that airborne hyperspectral imagery with CNN can accurately detect residue fractions (R 2 = 0.82, relative RMSE = 11.73%) to effectively generate quasi “ground truth” data to support satellite upscaling to all fields. With independent ground data for testing, we found that the ground-airborne-satellite integrative framework achieved better predictions in estimating crop residue cover (R 2 = 0.67, relative RMSE = 17.53%) than the conventional ground-satellite upscaling (R 2 = 0.22, relative RMSE = 32.09%). Here we also found that the shortwave infrared wavelengths, particularly 2100–2300 nm, are vital for predicting crop residue cover. Sentinel-2 and Landsat-8 data have a comparable capability to track residue fractions due to similar shortwave infrared wavelengths. This study highlights the high accuracy of hyperspectral imaging to detect agroecosystem tillage management practices and the advantages of cross-scale sensing to cost-effectively integrate multi-source data to quantify field-level agroecosystem variables across scales.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1980

A study of universal modulation techniques applied to satellite data collection

A universal modulation and frequency control system for use with data collection platform (DCP) transmitters is examined. The final design discussed can, under software/firmwave control, generate all of the specific digital data modulation formats currently used in the NASA satellite data collection service and can simultaneously synthesize the proper RF carrier frequencies employed. A novel technique for DCP time and frequency control is presented. The emissions of NBS radio station WWV/WWVH are received, detected, and finally decoded in microcomputer software to generate a highly accurate time base for the platform; with the assistance of external hardware, the microcomputer also directs the recalibration of all DCP oscillators to achieve very high frequency accuracies and low drift rates versus temperature, supply voltage, and time. The final programmable DCP design also employs direct microcomputer control of data reduction, formatting, transmitter switching, and system power management.

Source record↗
NASA NTRS1985

Determination of volcanic dust deposition from El Chichon using ground and satellite data

Major volcanic eruptions of El Chichon on 28 March 1982, 3 April 1982 and 4 April 1982 produced heavy tephra falls in surrounding areas. Data from the NOAA-6 and NOAA-7 satellites were used to determine the extent of the tephra falls. The satellite data showed a larger area of tephra deposition than was reported from conventional ground measurements.

Tucker, C. J.↗
NASA NTRS1999

Global Seasonal Climatologies of Ocean Chlorophyll: Blending In situ and Satellite Data for the CZCS Era

The historical archives of in situ (National Oceanographic Data Center) and satellite (Coastal Zone Color Scanner) chlorophyll data were combined using the blended analysis method of Reynolds [1988] in an attempt to construct an improved climatological seasonal representation of global chlorophyll distributions. The results of the blended analysis differed dramatically from the CZCS representation: global chlorophyll estimates increased 8-35% in the blended analysis depending upon season. Regional differences were even larger, up to 140% in the equatorial Indian Ocean in summer (during the southwest monsoon). Tropical Pacific chlorophyll values increased 25-41%. The results suggested that the CZCS generally underestimates chlorophyll. Regional and seasonal differences in the blended analysis were sufficiently large as to produce a different representation of global chlorophyll distributions than otherwise inferred from CZCS data alone. Analyses of primary production and biogeochemical cycles may be substantially impacted by these results.

Gregg, Watson W.↗
NASA NTRS1993

Further studies to extend and test the area-time-integral technique applied to satellite data

The principal goal of this project is to establish relationships that would allow application of area-time integral (ATI) calculations based upon satellite data to estimate rainfall volumes. The research has been pursued using two different approaches, which for convenience can be designated as the 'fixed-threshold approach' and the 'variable-threshold approach'. In the former approach, an attempt is made to determine a single temperature threshold in the satellite infrared data that would yield ATI values for identifiable cloud clusters which are most closely related to the corresponding rainfall amounts. Results thus far have indicated that a strong correlation exists between the rain volumes and the satellite ATI values, but the optimum threshold for this relationship seems to differ from one geographic location to another. The difference is probably related to differences in the basic precipitation mechanisms that dominate in the different regions. The average rainfall rate associated with each cloudy pixel is also found to vary across the spectrum of ATI values. Work on the second, or 'variable-threshold', approach for determining the satellite ATI values was essentially suspended during this period due to exhaustion of project funds. Most of the ATI work thus far has dealt with cloud clusters from the Lagrangian or 'floating-target' point of view. For many purposes, however, the Eulerian or 'fixed-target' perspective is more appropriate. For a very large target area encompassing entire cluster life histories, the rain volume-ATI relationship would obviously be the same in either case. The important question for the Eulerian perspective is how small the fixed area can be made while maintaining consistency in that relationship.

Smith, Paul L.↗
NASA NTRS1986

Downward longwave surface radiation from sun-synchronous satellite data - Validation of methodology

An extensive study has been carried out to validate a satellite technique for estimating downward longwave radiation at the surface. The technique, mostly developed earlier, uses operational sun-synchronous satellite data and a radiative transfer model to provide the surface flux estimates. The satellite-derived fluxes were compared directly with corresponding ground-measured fluxes at four different sites in the United States for a common one-year period. This provided a study of seasonal variations as well as a diversity of meteorological conditions. Dome heating errors in the ground-measured fluxes were also investigated and were corrected prior to the comparisons. Comparison of the monthly averaged fluxes from the satellite and ground sources for all four sites for the entire year showed a correlation coefficient of 0.98 and a standard error of estimate of 10 W/sq m. A brief description of the technique is provided, and the results validating the technique are presented.

Darnell, W. L.↗
NASA NTRS1989

Analysis and use of VAS satellite data

Four interrelated investigations have examined the analysis and use of VAS satellite data. A case study of VAS-derived mesoscale stability parameters suggested that they would have been a useful supplement to conventional data in the forecasting of thunderstorms on the day of interest. A second investigation examined the roles of first guess and VAS radiometric data in producing sounding retrievals. Broad-scale patterns of the first guess, radiances, and retrievals frequently were similar, whereas small-scale retrieval features, especially in the dew points, were often of uncertain origin. Two research tasks considered 6.7 micron middle tropospheric water vapor imagery. The first utilized radiosonde data to examine causes for two areas of warm brightness temperature. Subsidence associated with a translating jet streak was important. The second task involving water vapor imagery investigated simulated imagery created from LAMPS output and a radiative transfer algorithm. Simulated image patterns were found to compare favorably with those actually observed by VAS. Furthermore, the mass/momentum fields from LAMPS were powerful tools for understanding causes for the image configurations.

Fuelberg, Henry E.↗