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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

A multi-frequency radiometric measurement of soil moisture content over bare and vegetated fields

An experiment on soil moisture remote sensing was conducted during July to September 1981 on bare, grass, and alfalfa fields at frequencies of 0.6, 1.4, 5.0, and 10.6 GHz with radiometers mounted on mobile towers. The results confirm the frequency dependence of sensitivity reduction due to the presence of vegetation cover. For the type of vegetated fields reported here, the vegetation effect is appreciable even at 0.6 GHz. Measurements over bare soil show that when the soil is wet, the measured brightness temperature is lowest at 5.0 GHz and highest at 0.6 GHz, a result contrary to the expectation based on the estimated dielectric permittivity of soil-water mixtures and the current radiative transfer model in that frequency range.

Wang, J. R.↗
NASA NTRS1987

Vegetation spatial variability and its effect on vegetation indices

Landsat MSS data were used to simulate low resolution satellite data, such as NOAA AVHRR, to quantify the fractional vegetation cover within a pixel and relate the fractional cover to the normalized difference vegetation index (NDVI) and the simple ratio (SR). The MSS data were converted to radiances from which the NDVI and SR values for the simulated pixels were determined. Each simulated pixel was divided into clusters using an unsupervised classification program. Spatial and spectral analysis provided a means of combining clusters representing similar surface characteristics into vegetated and non-vegetated areas. Analysis showed an average error of 12.7 per cent in determining these areas. NDVI values less than 0.3 represented fractional vegetated areas of 5 per cent or less, while a value of 0.7 or higher represented fractional vegetated areas greater than 80 per cent. Regression analysis showed a strong linear relation between fractional vegetation area and the NDVI and SR values; correlation values were 0.89 and 0.95 respectively. The range of NDVI values calculated from the MSS data agrees well with field studies.

Ormsby, J. P.↗
NASA NTRS1991

Correspondence of surface temperatures and terrain variables over a tallgrass prairie

The time-dependent correspondence between maps of surface brightness temperature derived from Landsat Thematic Mapper data and mapped terrain variables over a tallgrass prairie in northeastern Kansas is examined. Individual terrain variables including burning treatment, vegetation cover type (agriculture, prairie, woody vegetation), hillslope position, and greenness exhibit varying degrees of association with surface temperature. Burning treatment is most strongly associated with mid-morning surface temperature. Examination of terrain strata based on combinations of terrain variables, notably burning treatment and hillslope position, suggest that terrain variables interact in affecting surface temperature. Interaction between hillslope position, burning treatment, and surface temperature is more important in August than in May.

Friedl, Mark A.↗
NASA NTRS1991

Separation of vegetation and rock signatures in Thematic Mapper and polarimetric SAR images

A technique for separating classes of surface roughness and vegetation in semiarid regions using polarimetric synthetic aperture radar (SAR) and Thematic Mapper data is developed. Polarimetric SAR data may be used to separate areas with less than 10 percent vegetation cover where one suite of model inversions may be applicable for derivation of surface roughness. In more vegetated regions, visible/near-infrared data provide a mechanism for determining the relative contribution of vegetation to multiple scattering in the SAR data. Thus, the synergistic use of the two data sets provides a more complete characterization of surface units and cover. This result is critical when trying to monitor dielectric constant variations related to changes in moisture content of soil and vegetation cover in arid to semiarid regions. A strategy for automating a procedure for measurement of surface roughness is proposed.

Evans, Diane L.↗
NASA NTRS2014

Ten Years of Forest Cover Change in the Sierra Nevada Detected Using Landsat Satellite Image Analysis

The Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) methodology was applied to detected changes in forest vegetation cover for areas burned by wildfires in the Sierra Nevada Mountains of California between the periods of 1975- 79 and 1995-1999. Results for areas burned by wildfire between 1995 and 1999 confirmed the importance of regrowing forest vegetation over 17% of the combined burned areas. A notable fraction (12%) of the entire 5-km (unburned) buffer area outside the 1995-199 fires perimeters showed decline in forest cover, and not nearly as many regrowing forest areas, covering only 3% of all the 1995-1999 buffer areas combined. Areas burned by wildfire between 1975 and 1979 confirmed the importance of disturbed (or declining evergreen) vegetation covering 13% of the combined 1975- 1979 burned areas. Based on comparison of these results to ground-based survey data, the LEDAPS methodology should be capable of fulfilling much of the need for consistent, low-cost monitoring of changes due to climate and biological factors in western forest regrowth following stand-replacing disturbances.

Vegetation↗
NASA NTRS1989

On estimating total daily evapotranspiration from remote surface temperature measurements

A method for calculating daily evapotranspiration from the daily surface energy budget using remotely sensed surface temperature and several meteorological variables is presented. Vaules of the coefficients are determined from simulations with a one-dimensional boundary layer model with vegetation cover. Model constants are obtained for vegetation and bare soil at two air temperature and wind speed levels over a range of surface roughness and wind speeds. A different means of estimating the daily evapotranspiration based on the time rate of increase of surface temperature during the morning is also considered. Both the equations using our model-derived constants and field measurements are evaluated, and a discussion of sources of error in the use of the formulation is given.

Carlson, Toby N.↗
NASA NTRS2001

Spectral Unmixing of Vegetation, Soil and Dry Carbon in Arid Regions: Comparing Multispectral and Hyperspectral Observations

Remote sensing of vegetation cover and condition is critically needed to understand the impacts of land use and climate variability in and and semi-arid regions. However, remote sensing of vegetation change in these environments is difficult for several reasons. First, individual plant canopies are typically small and do not reach the spatial scale of typical Landsat-like satellite image pixels. Second, the phenological status and subsequent dry carbon (or non-photosynthetic) fraction of plant canopies varies dramatically in both space and time throughout and and semi-arid regions. Detection of only the 'green' part of the vegetation using a metric such as the normalized difference vegetation index (NDVI) thus yields limited information on the presence and condition of plants in these ecosystems. Monitoring of both photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) is needed to understand a range of ecosystem characteristics including vegetation presence, cover and abundance, physiological and biogeochemical functioning, drought severity, fire fuel load, disturbance events and recovery from disturbance.

Asner, Gregory P.↗
NASA NTRS2010

Spectral Indices to Monitor Nitrogen-Driven Carbon Uptake in Field Corn

Climate change is heavily impacted by changing vegetation cover and productivity with large scale monitoring of vegetation only possible with remote sensing techniques. The goal of this effort was to evaluate existing reflectance (R) spectroscopic methods for determining vegetation parameters related to photosynthetic function and carbon (C) dynamics in plants. Since nitrogen (N) is a key constituent of photosynthetic pigments and C fixing enzymes, biological C sequestration is regulated in part by N availability. Spectral R information was obtained from field corn grown at four N application rates (0, 70, 140, 280 kg N/ha). A hierarchy of spectral observations were obtained: leaf and canopy with a spectral radiometer; aircraft with the AISA sensor; and satellite with EO-1 Hyperion. A number of spectral R indices were calculated from these hyperspectral observations and compared to geo-located biophysical measures of plant growth and physiological condition. Top performing indices included the R derivative index D730/D705 and the normalized difference of R750 vs. R705 (ND705), both of which differentiated three of the four N fertilization rates at multiple observation levels and yielded high correlations to these carbon parameters: light use efficiency (LUE); C:N ratio; and crop grain yield. These results advocate the use of hyperspectral sensors for remotely monitoring carbon cycle dynamics in managed terrestrial ecosystems.

Corp, Lawrence A.↗
NASA NTRS1979

Detection and mapping of hydrothermally altered rocks in the vicinity of the comstock lode, Virginia Range, Nevada, using enhanced LANDSAT images

The author has identified the following significant results. LANDSAT images enhanced by the band-ratioing method can be used for reconnaissance alteration mapping in moderately heavily vegetated semiarid terrain as well as in sparsely vegetated to semiarid terrain where the technique was originally developed. Significant vegetation cover in a scene, however, requires the use of MSS ratios 4/5, 4/6, and 6/7 rather than 4/5, 5/6, and 6/7, and requires careful interpretation of the results. Supplemental information suitable to vegetation identification and cover estimates, such as standard LANDSAT false-color composites and low altitude aerial photographs of selected areas is desirable.

Ashley, R. P.↗
NASA NTRS1981

Satellite estimation of the surface energy balance, moisture availability and thermal inertia

A method for inferring the distribution of surface heat and evaporative fluxes and the ground moisture availability and thermal inertia (ground conductive capacity) is used to analyze two urbanized areas, Los Angeles and St. Louis. The technique employs infrared satellite temperature measurements in conjunction with a one-dimensional boundary-layer model. Results show that there is a marked reduction of evaporation and moisture availability and a corresponding elevation of sensible heat flux over urbanized areas and over cropped areas with low vegetative cover. Conversely, low heat flux and high evaporation characterize vegetated and, especially, forested areas. Warm urban centers appear directly related to a reduction in vegetation, which normally allows for a greater fraction of available radiant energy to be converted into latent heat flux. The distribution of thermal inertia was surprisingly ill-defined and its variation between urban and rural areas was quite small. Thus, the increased heat storage within the urban fabric, which has been proposed as the underlying cause of the nocturnal heat island, may be caused mainly by enhanced daytime surface heating which occurs because of surface dryness, rather than by large spatial variations in the conductivity of the surface.

Carlson, T. N.↗
NASA NTRS2021

Augmenting Landsat time series with Harmonized Landsat Sentinel-2 data products: Assessment of spectral correspondence

An increase in the temporal revisit of satellite data is often sought to increase the likelihood of obtaining cloud- and shadow-free observations as well as to improve mapping of rapidly- or seasonally-changing features. Currently, as a tandem, Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and −8 Operational Land Imager (OLI) provide an acquisition opportunity on an 8-day revisit interval. Sentinel-2A and -2B MultiSpectral Instrument (MSI), with a wider swath, have a 5-day revisit interval at the equator. Due to robust pre- and post-launch cross-calibration, it has been possible for NASA to produce the Harmonized Landsat Sentinel-2 (HLS) data product from Landsat-8 OLI and Sentinel-2 MSI: L30 and S30, respectively. Knowledge of the agreement of HLS outputs (especially S30) with historic Landsat surface reflectance products will inform the ability to integrate historic time-series information with new and more frequent measures as delivered by HLS. In this research, we control for acquisition date and data source to cross-compare the HLS data (L30, S30) with established Landsat-8 OLI surface-reflectance measures as delivered by the USGS (hereafter BAP, Best Available Pixel). S30 and L30 were found to have high agreement (R = 0.87–0.96) for spectral channels and an r = 0.99 for Normalized Burn Ratio (NBR) with low relative root-mean-square difference values (1.7%–3.3%). Agreement between L30 and BAP was lower, with R values ranging from 0.85 to 0.92 for spectral channels and R = 0.94 for NBR. S30 and BAP had the lowest agreement, with R values ranging from 0.71 to 0.85 for spectral channels and r = 0.90 for NBR. Comparisons indicated a stronger agreement at latitudes above 55° N. Some dependency between spectral agreement and land cover was found, with stronger correspondence for non-vegetated cover types. The level of agreement between S30 and BAP reported herein would enable integration of HLS outputs with historic Landsat data. The resulting increased temporal frequency of data allows for improvements to current cloud screening practices and increases data density and the likelihood of temporal proximity to target date for pixel compositing approaches. Furthermore, additional within-year observations will enable change products with a higher temporal fidelity and allow for the incorporation of phenological trends into land cover classification algorithms.

Michael A. Wulder↗
NASA NTRS2010

Satellite Gravimetry Applied to Drought Monitoring

Near-surface wetness conditions change rapidly with the weather, which limits their usefulness as drought indicators. Deeper stores of water, including root-zone soil wetness and groundwater, portend longer-term weather trends and climate variations, thus they are well suited for quantifying droughts. However, the existing in situ networks for monitoring these variables suffer from significant discontinuities (short records and spatial undersampling), as well as the inherent human and mechanical errors associated with the soil moisture and groundwater observation. Remote sensing is a promising alternative, but standard remote sensors, which measure various wavelengths of light emitted or reflected from Earth's surface and atmosphere, can only directly detect wetness conditions within the first few centimeters of the land s surface. Such sensors include the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) C-band passive microwave measurement system on the National Aeronautic and Space Administration's (NASA) Aqua satellite, and the combined active and passive L-band microwave system currently under development for NASA's planned Soil Moisture Active Passive (SMAP) satellite mission. These instruments are sensitive to water as deep as the top 2 cm and 5 cm of the soil column, respectively, with the specific depth depending on vegetation cover. Thermal infrared (TIR) imaging has been used to infer water stored in the full root zone, with limitations: auxiliary information including soil grain size is required, the TIR temperature versus soil water content curve becomes flat as wetness increases, and dense vegetation and cloud cover impede measurement. Numerical models of land surface hydrology are another potential solution, but the quality of output from such models is limited by errors in the input data and tradeoffs between model realism and computational efficiency. This chapter is divided into eight sections, the next of which describes the theory behind satellite gravimetry. Following that is a summary of the GRACE mission and how hydrological information is gleaned from its gravity products. The fourth section provides examples of hydrological science enabled by GRACE. The fifth and sixth sections list the challenging aspects of GRACE derived hydrology data and how they are being overcome, including the use of data assimilation. The seventh section describes recent progress in applying GRACE for drought monitoring, including the development of new soil moisture and drought indicator products, and that is followed by a discussion of future prospects in satellite gravimetry based drought monitoring.

Rodell, Matthew↗
NASA NTRS1993

Synergism of active and passive microwave data for estimating bare surface soil moisture

Active and passive microwave sensors were applied effectively to the problem of estimating the surface soil moisture in a variety of environmental conditions. Research to date has shown that both types of sensors are also sensitive to the surface roughness and the vegetation cover. In estimating the soil moisture, the effect of the vegetation and roughness are often corrected either by acquiring multi-configuration (frequency and polarization) data or by adjusting the surface parameters in order to match the model predictions to the measured data. Due to the limitations on multi-configuration spaceborne data and the lack of a priori knowledge of the surface characteristics for parameter adjustments, it was suggested that the synergistic use of the sensors may improve the estimation of the soil moisture over the extreme range of naturally occurring soil and vegetation conditions. To investigate this problem, the backscattering and emission from a bare soil surface using the classical rough surface scattering theory were modeled. The model combines the small perturbation and the Kirchhoff approximations in conjunction with the Peak formulation to cover a wide range of surface roughness parameters with respect to frequency for both active and passive measurements. In this approach, the same analytical method was used to calculate the backscattering and emissivity. Therefore, the active and passive simulations can be combined at various polarizations and frequencies in order to estimate the soil moisture more actively. As a result, it is shown that (1) the emissivity is less dependent on the surface correlation length, (2) the ratio of the backscattering coefficient (HH) over the surface reflectivity (H) is almost independent of the soil moisture for a wide range of surface roughness, and (3) this ratio can be approximated as a linear function of the surface rms height. The results were compared with the data obtained by a multi-frequency radiometer-scatterometer system working at frequencies between 3.0 GHz to 11.0 GHz. The data were acquired over bare soil surfaces with moisture variations due to freezing and thawing and roughness changes due to rain and erosion. The model predictions are shown to be in reasonable agreement with the data. In addition, it was shown that the same ratio when calculated from the data shows almost no dependence on the soil moisture. Finally, a simple technique which combines the backscattering coefficient at HH polarization (active sensing) with the emissivity at H polarization (passive sensing) is suggested for retrieving the soil moisture from bare soil surfaces.

Saatchi, Sasan S.↗
NASA NTRS1974

User oriented ERTS-1 images

Photographic reproduction of ERTS-1 images are capable of displaying only a portion of the total information available from the multispectral scanner. Methods are being developed to generate ERTS-1 images oriented towards special users such as agriculturists, foresters, and hydrologists by applying image enhancement techniques and interactive statistical classification schemes. Spatial boundaries and linear features can be emphasized and delineated using simple filters. Linear and nonlinear transformations can be applied to the spectral data to emphasize certain ground information. An automatic classification scheme was developed to identify particular ground cover classes such as fallow, grain, rape seed or various vegetation covers. The scheme applies the maximum likelihood decision rule to the spectral information and classifies the ERTS-1 image on a pixel by pixel basis. Preliminary results indicate that the classifier has limited success in distinguishing crops, but is well adapted for identifying different types of vegetation.

Shlien, S.↗
NASA NTRS1991

Remote sensing of earth terrain

Abstracts from 46 refereed journal and conference papers are presented for research on remote sensing of earth terrain. The topics covered related to remote sensing include the following: mathematical models, vegetation cover, sea ice, finite difference theory, electromagnetic waves, polarimetry, neural networks, random media, synthetic aperture radar, electromagnetic bias, and others.

Kong, Jin AU↗
NASA NTRS1991

Utilization of surface cover composition to improve the microwave determination of snow water equivalent in a mountain basin

Satellite microwave data have been used to derive areal snow water equivalent in flat homogeneous areas. Over heterogeneous mountainous areas different algorithms are needed to retrieve the water equivalent of the snow cover. A mixed pixel model based on the percentage of vegetation cover within a pixel has been developed to simulate the microwave brightness temperatures for the Rio Grande basin in southwestern Colorado. A relationship between the difference in microwave-brightness temperature at two different frequencies (37- and 18-GHz horizontal polarization), and the basin-wide average snow water equivalent was obtained. The areal snow-water equivalent values derived from the model were consistent with values generated by a reliable snowmelt run-off model using snow-cover extent data.

Chang, A. T. C.↗
NASA NTRS2013

Estimating Root Mean Square Errors in Remotely Sensed Soil Moisture over Continental Scale Domains

Root Mean Square Errors (RMSE) in the soil moisture anomaly time series obtained from the Advanced Scatterometer (ASCAT) and the Advanced Microwave Scanning Radiometer (AMSR-E; using the Land Parameter Retrieval Model) are estimated over a continental scale domain centered on North America, using two methods: triple colocation (RMSETC ) and error propagation through the soil moisture retrieval models (RMSEEP ). In the absence of an established consensus for the climatology of soil moisture over large domains, presenting a RMSE in soil moisture units requires that it be specified relative to a selected reference data set. To avoid the complications that arise from the use of a reference, the RMSE is presented as a fraction of the time series standard deviation (fRMSE). For both sensors, the fRMSETC and fRMSEEP show similar spatial patterns of relatively highlow errors, and the mean fRMSE for each land cover class is consistent with expectations. Triple colocation is also shown to be surprisingly robust to representativity differences between the soil moisture data sets used, and it is believed to accurately estimate the fRMSE in the remotely sensed soil moisture anomaly time series. Comparing the ASCAT and AMSR-E fRMSETC shows that both data sets have very similar accuracy across a range of land cover classes, although the AMSR-E accuracy is more directly related to vegetation cover. In general, both data sets have good skill up to moderate vegetation conditions.

Remotely sensed soil moisture validation↗
NASA NTRS2016

Linking Land Use Changes to Surface Water Quality Variability in Lake Victoria: Some Insights From Remote Sensing (GC41B-1101)

Various land use changes driven by urbanization, conversion of grasslands and woodlands into farmlands, intensification of agricultural practices, deforestation, land fragmentation and degradation are taking place in Africa. In Kenya, agriculture is the main driver of land use conversions. The impacts of these land use changes are observable in land cover maps, and eventually in the hydrological systems. Reduction or change of natural vegetation cover types increases the speed of surface runoff and reduces water and nutrient retention capacities. This can lead to high nutrient inputs into lakes, resulting in eutrophication, siltation and infestation of floating aquatic vegetation. To assess if changes in land use could be contributing to increased phytoplankton blooms and sediment loads into Lake Victoria, we analyzed land use land cover data from Landsat, as well as surface chlorophyll-a and total suspended matter from MODIS-Aqua sensor.

water quality↗