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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Calculations of radar backscattering coefficient of vegetation-covered soils

The present investigation has the objective to develop a simple 'user's' model for simulating the measured radar backscattering coefficients from vegetation-covered fields in conjunction with the data obtained by Jackson et al. (1980, 1982). The theoretical work reported by Fung and Eom (1981) provides the basis for the model. Certain modifications are related to a consideration of the effect of a vegetation canopy. The first part of the model is concerned with a description of scatter from rough bare soil, while the second part takes into account the effect of a vegetation cover. It is shown that the measured angular distribution of the backscattering coefficient of vegetation-covered fields can be satisfactory reproduced by using the developed model.

Mo, T.↗
NASA NTRS2012

A Parameterized Inversion Model for Soil Moisture and Biomass from Polarimetric Backscattering Coefficients

A semi-empirical algorithm for the retrieval of soil moisture, root mean square (RMS) height and biomass from polarimetric SAR data is explained and analyzed in this paper. The algorithm is a simplification of the distorted Born model. It takes into account the physical scattering phenomenon and has three major components: volume, double-bounce and surface. This simplified model uses the three backscattering coefficients ( sigma HH, sigma HV and sigma vv) at low-frequency (P-band). The inversion process uses the Levenberg-Marquardt non-linear least-squares method to estimate the structural parameters. The estimation process is entirely explained in this paper, from initialization of the unknowns to retrievals. A sensitivity analysis is also done where the initial values in the inversion process are varying randomly. The results show that the inversion process is not really sensitive to initial values and a major part of the retrievals has a root-mean-square error lower than 5% for soil moisture, 24 Mg/ha for biomass and 0.49 cm for roughness, considering a soil moisture of 40%, roughness equal to 3cm and biomass varying from 0 to 500 Mg/ha with a mean of 161 Mg/ha

soil moisture↗
NASA NTRS1972

Microwave properties of geological materials: Studies of penetration depth and moisture effects

Summarized are the results of two of a series of controlled experiments performed during the summer of 1970. The first series of experiments to determine penetration depth showed the value of having a modeled response and data reduced in real time for examination in the field. The results suggest a radiometric method for measuring the microwave properties of materials in bulk and are applicable to studies of sea ice and other naturally layered media. The sensitivity of the microwave emission to changes in moisture content has inspired a number of airborne and ground based investigations. Although the effect is dominent under certain conditions, the complicating factors of soil type, roughness, vegetation, etc. seem to govern the conclusions regarding its application at this time.

Blinn, J. C., III↗
NASA NTRS1980

Thermal microwave emissions from vegetated fields: A comparison between theory and experiment

The radiometric measurements over bare field and fields covered with grass, soybean, corn, and alfalfa were made with 1.4 GHz and 5 GHz microwave radiometers during August - October 1978. The measured results are compared with radiative transfer theory treating the vegetated fields as a two layer random medium. It is found that the presence of a vegetation cover generally gives a higher brightness temperature T(B) than that expected from a bare soil. The amount of this T(B) excess increases in the vegetation biomass and in the frequency of the observed radiation. The results of radiative transfer calculations generally match well with the experimental data, however, a detailed analysis also strongly suggests the need of incorporating soil surface roughness effect into the radiative transfer theory in order to better interpret the experimental data.

Wang, J. R.↗
NASA NTRS2010

Sub-10-Micron and Respirable Particles in Lunar Soils

Based on published lunar soil grain size distribution data, we estimate that 1-3% of the mass of typical mature lunar soils is comprised of grains less than 2.5 micrometers in diameter. These particles are in the respirable range (small enough to be inhaled). Estimates are used because the early methods of obtaining grain size distributions did not give reliable results below about 10 micrometers. Grain size analyses of Apollo 11 soil 10084 by a laser diffraction technique shows that this soil contains roughly 2% by volume in the respirable grain size, in agreement with our prior estimate.

Cooper, Bonnie L.↗
NASA NTRS1975

Effects of roughness on the radar response to soil moisture of bare ground

The radar response to soil moisture content was experimentally determined for three different bare fields with considerably different surface roughnesses at eight frequencies in the 2 to 8 GHz band and for Horizontal transmit-Horizontal receive (HH) and Vertical transmit-Vertical receive (VV) polarizations. Analysis of the data indicated that the effect of roughness on the radar backscattering coefficient can be minimized by proper choice of the radar parameters. If, in addition, sensitivity to soil moisture variations and system design constraints are considered, the following radar parameters for an operational soil moisture mapper are recommended: frequency= 4 GHz, angle of incidence range= 7 deg to 15 deg and either HH or VV polarization. The corresponding sensitivity is about 0.25 db/ 0.01 gram/cubic cm.

Batlivala, P. P.↗
NASA NTRS1992

Modeling and measurement of microwave emission and backscattering from bare soil surfaces

A multifrequency ground-based radiometer-scatterometer system working at frequencies between 3.0 GHz and 11.0 GHz has been used to study the effect of soil moisture and roughness on microwave emission and backscattering. The freezing and thawing effect of the soil surface and the changes of the surface roughness due to rain and erosion are reported. To analyze the combined active and passive data, a scattering model based on physical optics approximation for the low frequency and geometrical optics approximation for high frequency has been developed. The model is used to calculate the bistatic scattering coefficients from the surface. By considering the conservation of energy, the result has been integrated over a hemisphere above the surface to calculate the emissivity. The backscattering and emission model has been coupled with the observed data in order to extract soil moisture and surface roughness.

Saatchi, S.↗
NASA NTRS1990

Bidirectional measurements of surface reflectance for view angle corrections of oblique imagery

An apparatus for acquiring bidirectional reflectance-factor data was constructed and used over four surface types. Data sets were obtained over a headed wheat canopy, bare soil having several different roughness conditions, playa (dry lake bed), and gypsum sand. Results are presented in terms of relative bidirectional reflectance factors (BRFs) as a function of view angle at a number of solar zenith angles, nadir BRFs as a function of solar zenith angles, and, for wheat, vegetation indices as related to view and solar zenith angles. The wheat canopy exhibited the largest BRF changes with view angle. BRFs for the red and the NIR bands measured over wheat did not have the same relationship with view angle. NIR/Red ratios calculated from nadir BRFs changed by nearly a factor of 2 when the solar zenith angle changed from 20 to 50 degs. BRF versus view angle relationships were similar for soils having smooth and intermediate rough surfaces but were considerably different for the roughest surface. Nadir BRF versus solar-zenith angle relationships were distinctly different for the three soil roughness levels. Of the various surfaces, BRFs for gypsum sand changed the least with view angle (10 percent at 30 degs).

Jackson, R. D.↗
NASA NTRS2014

Assimilation of SMOS Retrieved Soil Moisture into the Land Information System

Soil moisture is a crucial variable for weather prediction because of its influence on evaporation and surface heat fluxes. It is also of critical importance for drought and flood monitoring and prediction and for public health applications such as monitoring vector-borne diseases. Land surface modeling benefits greatly from regular updates with soil moisture observations via data assimilation. Satellite remote sensing is the only practical observation type for this purpose in most areas due to its worldwide coverage. The newest operational satellite sensor for soil moisture is the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument aboard the Soil Moisture and Ocean Salinity (SMOS) satellite. The NASA Short-term Prediction Research and Transition Center (SPoRT) has implemented the assimilation of SMOS soil moisture observations into the NASA Land Information System (LIS), an integrated modeling and data assimilation software platform. We present results from assimilating SMOS observations into the Noah 3.2 land surface model within LIS. The SMOS MIRAS is an L-band radiometer launched by the European Space Agency in 2009, from which we assimilate Level 2 retrievals [1] into LIS-Noah. The measurements are sensitive to soil moisture concentration in roughly the top 2.5 cm of soil. The retrievals have a target volumetric accuracy of 4% at a resolution of 35-50 km. Sensitivity is reduced where precipitation, snowcover, frozen soil, or dense vegetation is present. Due to the satellite's polar orbit, the instrument achieves global coverage twice daily at most mid- and low-latitude locations, with only small gaps between swaths.

Blankenship, Clay B.↗
NASA NTRS1980

Soil moisture sensing with microwave techniques

Microwave approaches for the remote sensing of soil moisture are discussed, with the advantages described as follows: (1) the all-weather capability, (2) the greater penetration depth into the soil and through vegetation than with optical or infrared sensors, and (3) the large changes in the dielectric properties of soil produced by changes in water content. Both active and passive microwave approaches are discussed. The dependence of the relationship between microwave response and soil moisture on such things as soil texture, surface roughness, vegetative cover and nonuniform moisture and temperature profiles is analyzed from both the experimental and theoretical viewpoints. The dielectric properties of the soil are analyzed quantitatively, as these control the reflective and emissive properties of the soil surface, and a model for estimating a soil's dielectric properties from its texture and moisture content is also presented. Emissivity is calculated using the Fresnel equation of electromagnetic theory, and reflectivity is shown to be decreased by surface roughness, while the backscatter coefficient increases. It is demonstrated, that microwave radiometers are sensitive to soil moisture for a wide range of surface conditions, and that the longer wavelengths are best for soil moisture sensing.

Schmugge, T.↗
NASA NTRS1973

Radar measurement of soil moisture content

The effect of soil moisture on the radar backscattering coefficient was investigated by measuring the 4-8 GHz spectral response from two types of bare-soil fields: slightly rough and very rough, in terms of the wavelength. An FM-CW radar system was used to measure the return at 10 frequency points across the 4-8 GHz band, at different look angles, and for all polarization combinations. The results indicate that the radar response to soil moisture content is highly dependent on the surface roughness, microwave frequency, and look angle. The response seems to be linear over the range 15%-30% moisture content for all angles, frequencies, polarizations and surface conditions.

Ulaby, F. T.↗
NASA NTRS1974

Radar measurement of soil moisture content

The effect of soil moisture on the radar backscattering coefficient was investigated by measuring the 4- to 8-GHz spectral response from two types of bare-soil fields: slightly rough and very rough, in terms of the wavelength. An FM-CW radar system mounted atop a 75-ft truck-mounted boom was used to measure the return at ten frequency points across the 4- to 8-GHz band, at eight different look angles (0 through 70 deg), and for all polarization combinations. A total of 17 sets of data were collected covering the range from 4 to 36% soil moisture content by weight. The results indicate that the radar response to soil moisture content is highly dependent on the surface roughness, microwave frequency, and look angle. The response seems to be linear, however, over the range from 15 to 30% moisture content for all angles, frequencies, polarizations and surface conditions.

Ulaby, F. T.↗
NASA NTRS1986

Relation between L-band soil emittance and soil water content

An experimental relation between soil emittance (E) at L-band and soil surface moisture content (M) is compared with a theoretical one. The latter depends on the soil dielectric constant, which is a function of both soil moisture content and of soil texture. It appears that a difference of 10 percent in the surface clay content causes a change in the estimate of M on the order of 0.02 cu m/cu m. This is based on calculations with a model that simulates the flow of water and energy, in combination with a radiative transfer model. It is concluded that an experimental determination of the E-M relation for each soil type is not required, and that a rough estimate of the soil texture will lead to a sufficiently accurate estimate of soil moisture from a general, theoretical relationship obtained by numerical simulation.

Stroosnijder, L.↗
NASA NTRS1990

Ground truth measurement for the analysis of airborne SAR data recorded over Oberpfaffenhofen, FRG, 1989

As a preliminary investigation to the joint multiparameter SIR-C/X-SAR shuttle experiment of NASA/JPL (USA), DLR (FRG), and PSN (Italy) which is scheduled for the year 1992 an airborne SAR campaign was conducted over Oberpfaffenhofen, FRG, in August 1989. Primarily this campaign was planned to test and verify equipment and algorithms developed at the DLR to calibrate multifrequency polarimetric SAR data. Oberpfaffenhofen is designated as one of the super test sites for the SIR-C/X-SAR experiment which will be imaged under all circumstances except severe mission errors. A super test site drives radar parameters and look directions and the recorded SAR data will be calibrated. In addition ancillary data will be available for the site. During the airborne STAR campaign conducted in the week of August 14th 1989 various sensor types were used to record remote sensing data over the calibration test site and its vicinity: the polarimetric DC-8 JPL-SAR (P-, L-, C-band), the DLR airborne SAR (C-, X-band), color infrared aerial photography (DLR), and the truck-mounted scatterometer (C- and X-band) of the Institute for Navigation, University of Stuttgart (INS). Because of this variety of different sensor types used and out of the fact that sufficiently large forested and agriculturally used areas were planned to be covered by these sensors, the interest of several German research groups involved in investigations concerning SAR land applications arose. The following groups carried out different ground-truth measurements: University of Bonn, Institute for plant cultivation (plant morphology and moisture content); University of Braunschweig, Institute for Geography (soil moisture and surface roughness); University of Freiburg, Institute for Geography (dielectric soil properties, landuse); and University of Munich, Institute for Geography (landuse inventory, plant, surface, and soil parameters). This paper presents the joint ground truth activities of the Institute for Geography, University of Munich, and the German Remote Sensing Data Center of the DLR.

Bayer, T.↗
NASA NTRS2011

Canadian Experiment for Soil Moisture in 2010 (CanEX-SM10): Overview and Preliminary Results

The Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) was carried out in Saskatchewan, Canada from 31 May to 16 June, 2010. Its main objective was to contribute to Soil Moisture and Ocean salinity (SMOS) mission validation and the pre-launch assessment of Soil Moisture and Active and Passive (SMAP) mission. During CanEx-SM10, SMOS data as well as other passive and active microwave measurements were collected by both airborne and satellite platforms. Ground-based measurements of soil (moisture, temperature, roughness, bulk density) and vegetation characteristics (Leaf Area Index, biomass, vegetation height) were conducted close in time to the airborne and satellite acquisitions. Besides, two ground-based in situ networks provided continuous measurements of meteorological conditions and soil moisture and soil temperature profiles. Two sites, each covering 33 km x 71 km (about two SMOS pixels) were selected in agricultural and boreal forested areas in order to provide contrasting soil and vegetation conditions. This paper describes the measurement strategy, provides an overview of the data sets and presents preliminary results. Over the agricultural area, the airborne L-band brightness temperatures matched up well with the SMOS data. The Radio frequency interference (RFI) observed in both SMOS and the airborne L-band radiometer data exhibited spatial and temporal variability and polarization dependency. The temporal evolution of SMOS soil moisture product matched that observed with the ground data, but the absolute soil moisture estimates did not meet the accuracy requirements (0.04 m3/m3) of the SMOS mission. AMSR-E soil moisture estimates are more closely correlated with measured soil moisture.

Magagi, Ramata↗
NASA NTRS1996

A Numerical Simulation of Scattering from One-Dimensional Inhomogeneous Dielectric Random Surfaces

In this paper, an efficient numerical solution for the scattering problem of inhomogeneous dielectric rough surfaces is presented. The inhomogeneous dielectric random surface represents a bare soil surface and is considered to be comprised of a large number of randomly positioned dielectric humps of different sizes, shapes, and dielectric constants above an impedance surface. Clods with nonuniform moisture content and rocks are modeled by inhomogeneous dielectric humps and the underlying smooth wet soil surface is modeled by an impedance surface. In this technique, an efficient numerical solution for the constituent dielectric humps over an impedance surface is obtained using Green's function derived by the exact image theory in conjunction with the method of moments. The scattered field from a sample of the rough surface is obtained by summing the scattered fields from all the individual humps of the surface coherently ignoring the effect of multiple scattering between the humps. The statistical behavior of the scattering coefficient sigma(sup 0) is obtained from the calculation of scattered fields of many different realizations of the surface. Numerical results are presented for several different roughnesses and dielectric constants of the random surfaces. The numerical technique is verified by comparing the numerical solution with the solution based on the small perturbation method and the physical optics model for homogeneous rough surfaces. This technique can be used to study the behavior of scattering coefficient and phase difference statistics of rough soil surfaces for which no analytical solution exists.

Sarabandi, Kamal↗
NASA NTRS1993

Classification of simple vegetation types using POLSAR image data

Mapping basic vegetation or land cover types is a fairly common problem in remote sensing. Knowledge of the land cover type is a key input to algorithms which estimate geophysical parameters, such as soil moisture, surface roughness, leaf area index or biomass from remotely sensed data. In an earlier paper, an algorithm for fitting a simple three-component scattering model to POLSAR data was presented. The algorithm yielded estimates for surface scatter, double-bounce scatter and volume scatter for each pixel in a POLSAR image data set. In this paper, we show how the relative levels of each of the three components can be used as inputs to simple classifier for vegetation type. Vegetation classes include no vegetation cover (e.g. bare soil or desert), low vegetation cover (e.g. grassland), moderate vegetation cover (e.g. fully developed crops), forest and urban areas. Implementation of the approach requires estimates for the three components from all three frequencies available using the NASA/JPL AIRSAR, i.e. C-, L- and P-bands. The research described in this paper was carried out by the Jet Propulsion Laboratory, California Institute of Technology under a contract with the National Aeronautics and Space Administration.

Freeman, A.↗