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

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

A Preview of AMSR: Airborne C-band Microwave Radiometer (ACMR) Observations from SGP99

Although L-band is generally considered ideal for passive microwave sensing of soil moisture, near-future satellite observing systems such as Advanced Mechanically Scanned Radiometer (AMSR) will provide C-band data for several years before any L-band data might become available. The Southern Great Plains'99 (SGP99) Experiment was designed to generate C-band observations suitable for testing and refinement of AMSR-era soil moisture retrieval algorithms. C-band data collected using the Airborne C-band Microwave Radiometer (ACMR), a new high-accuracy NASA/GSFC instrument, clearly demonstrated a strong response to a 9-day drydown event as well as to differences between the northern (cooler & wetter) and southern (warmer & dryer) areas covered by the P-3 flights. For example, the H-polarized brightness temperatures observed during the first three days of the drydown increased up to 50 K in the northern areas. These observations represent a preview of what we can expect from AMSR, albeit at 3-km spatial resolution vs. approximately 60 km for AMSR. Initial results of soil-vegetation microwave modeling will also be presented to estimate the relative contributions of soil physical temperature, canopy physical temperature, soil moisture, and canopy moisture. Significant radio-frequency interference (RFI) was evident during the experiment, and amelioration strategies will be discussed. The net effect of RFI (an upward bias in brightnesses) when averaged over an AMSR footprint is expected to be more subtle.

Kim, Edward↗
NASA NTRS1985

Passive microwave soil moisture research

The AgRISTARS Soil Moisture Project has made significant progress in the quantification of microwave sensor capabilities for soil moisture remote sensing. The 21-cm wavelength has been verified to be the best single channel for radiometric observations of soil moisture. It has also been found that other remote sensing approaches used in conjunction with L-band passive data are more successful than multiple wavelength microwave radiometry in this application. AgRISTARS studies have also improved current understanding of noise factors affecting the interpretability of microwave emission data. The absorption of soil emission by vegetation has been quantified, although this effect is less important than absorption effects for microwave radiometry.

Schmugge, T. J.↗
NASA NTRS2004

Landcover Based Optimal Deconvolution of PALS L-band Microwave Brightness Temperature

An optimal de-convolution (ODC) technique has been developed to estimate microwave brightness temperatures of agricultural fields using microwave radiometer observations. The technique is applied to airborne measurements taken by the Passive and Active L and S band (PALS) sensor in Iowa during Soil Moisture Experiments in 2002 (SMEX02). Agricultural fields in the study area were predominantly soybeans and corn. The brightness temperatures of corn and soybeans were observed to be significantly different because of large differences in vegetation biomass. PALS observations have significant over-sampling; observations were made about 100 m apart and the sensor footprint extends to about 400 m. Conventionally, observations of this type are averaged to produce smooth spatial data fields of brightness temperatures. However, the conventional approach is in contrast to reality in which the brightness temperatures are in fact strongly dependent on landcover, which is characterized by sharp boundaries. In this study, we mathematically de-convolve the observations into brightness temperature at the field scale (500-800m) using the sensor antenna response function. The result is more accurate spatial representation of field-scale brightness temperatures, which may in turn lead to more accurate soil moisture retrieval.

Limaye, Ashutosh S.↗
NASA NTRS2006

The Australian National Airborne Field Experiment 2005: Soil Moisture Remote Sensing at 60 Meter Resolution and Up

Spatially-distributed soil moisture observations have applications spanning a wide range of spatial resolutions from the very local needs of individual farmers to the progressively larger areas of interest to weather forecasters, water resource managers, and global climate modelers. To date, the most promising approach for space-based remote sensing of soil moisture makes use of passive microwave emission radiometers at L-band frequencies (1-2 GHz). Several soil moisture-sensing satellites have been proposed in recent years, with the European Space Agency's Soil Moisture Ocean Salinity (SMOS) mission scheduled to be launched first in a couple years. While such a microwave-based approach has the advantage of essentially allweather operation, satellite size limits spatial resolution to 10's of km. Whether used at this native resolution or in conjunction with some type of downscaling technique to generate soil moisture estimates on a finer-scale grid, the effects of subpixel spatial variability play a critical role. The soil moisture variability is typically affected by factors such as vegetation, topography, surface roughness, and soil texture. Understanding and these factors is the key to achieving accurate soil moisture retrievals at any scale. Indeed, the ability to compensate for these factors ultimately limits the achievable spatial resolution and/or accuracy of the retrieval. Over the last 20 years, a series of airborne campaigns in the USA have supported the development of algorithms for spaceborne soil moisture retrieval. The most important observations involved imagery from passive microwave radiometers. The early campaigns proved that the retrieval worked for larger and larger footprints, up to satellite-scale footprints. These provided the solid basis for proposing the satellite missions. More recent campaigns have explored other aspects such as retrieval performance through greater amounts of vegetation. All of these campaigns featured extensive ground truth collection over a range of grid spacings, to provide a basis for examining the effects of subpixel variability. However, the native footprint size of the airborne L-band radiometers was always a few hundred meters. During the recently completed (November, 2005) National Airborne Field Experiment (NAFE) campaign in Australia, a compact L-band radiometer was deployed on a small aircraft. This new combination permitted routine observations at native resolutions as high as 60 meters, substantially finer than in previous airborne soil moisture campaigns, as well as satellite footprint areal coverage. The radiometer, the Polarimetric L-band Microwave Radiometer (PLMR) performed extremely well and operations included extensive calibration-related observations. Thus, along with the extensive fine-scale ground truth, the NAFE dataset includes all the ingredients for the first scaling studies involving very-high-native resolution soil moisture observations and the effects of vegetation, roughness, etc. A brief overview of the NAFE will be presented, then examples of the airborne observations with resolutions from 60 m to 1 km will be shown, and early results from scaling studies will be discussed.

Kim, E. J.↗
NASA NTRS1980

Passive microwave remote sensing of soil moisture - The effect of tilled row structure

The tilled row structure in agricultural fields is one of the important factors affecting observations of microwave emission from such fields. Measurements of this effect were performed with L-band and X-band radiometers mounted on a mobile truck on a bare 40 m x 45 m row tilled field; the soil moisture content during measurements ranged from 10 to 30% by dry weight. Results showed that the variations of the antenna temperatures with incident angle changed with the azimuth angle measured from the row direction. It is found that the observed difference between horizontally and vertically polarized antenna temperatures is due to the change in the local angle of field emission within the antenna field of view caused by the large-scale row structure.

Wang, J. R.↗
NASA NTRS2006

Observed Scaling Behavior of Snow and Soil Moisture Signatures from the CLPX and NAFE Campaigns

Seasonal snow and soil moisture (SM) are important elements of the Earth's water cycle, acting as storage elements and by modulating water and energy exchanges between the land surface and the atmosphere and space. Accurate estimates of snow water equivalent (SWE) and SM also play an important role in weather and climate models over a range of scales. There are many advantages for the retrieval of snow parameters using passive microwave sensors. However, the current scale mismatch between many high-resolution applications and available satellite snow sensor footprints (approx.25km) presents challenges. There is also a scale mismatch for planned L-band soil moisture satellite measurements with their large (50-100 km) passive microwave footprints. Combined radiometer-radar schemes are being explored as possible aids, but this is still an area of ongoing research. Retrieval accuracies are limited in part by complex, and potentially nonlinear relationships between SWE or SM and microwave emission signatures. We will use airborne and satellite observations from two field campaigns to examine the scaling behavior of brightness temperatures and SWE and SM across scales from 100 meters to 10's of kilometers. The results will provide examples of the appropriate spatial sampling scales for new sensors, and guidance for downscaling schemes. The two campaigns are NASA's Cold Land Processes Experiment- 1 (CLPX- 1 : Colorado, 2002 & 2003), and the Australian National Airborne Field Experiment (NAFE, Nov, 2005). The analyses will also illustrate the roles and spatial scales of the underlying phenomena (e.g., vegetation, topography) that control subpixel heterogeneity.

Kim, Edward J.↗
NASA NTRS2006

Mitigation of Terrestrial Radar Interference in L-Band Spaceborne Microwave Radiometers

Terrestrial radars operating in the 1215-1400 MHz radio-location and navigation spectrum allocation are important for air traffic safety, homeland security, and national defense. For low-frequency observations of soil moisture and ocean salinity, Earth-observing microwave radiometers are allocated Earth- Exploration Satellite Service (EESS) spectrum for operating at 1400-1427 MHz. The proximity of powerful long-range radars to the passive allocation makes observing a challenge. Three aspects of mitigation to RFI are discussed in this paper: survivability, operability, and excisability (SOE). Modeling and simulations of NASA's Hydros and Aquarius radiometers were performed to examine the impacts of radar interference. The results are applied to the three aspects of mitigation SOE and the affects on the radiometer requirements are discussed.

Piepmeier, Jeffrey R.↗
NASA NTRS1978

Microwave remote sensing of sea ice in the AIDJEX Main Experiment

A microwave remote sensing program of sea ice in the Beaufort Sea was conducted during the Arctic Ice Dynamics Joint Experiment (AIDJEX). Several types of both passive and active sensors were used to perform surface and aircraft measurements during all seasons of the year. In situ observations were made of physical properties (salinity, temperature, density, surface roughness), dielectric properties, and passive microwave measurements were made of first-year, multiyear, and first-year/multiyear mixtures. Airborne passive microwave measurements were performed with the electronically scanning microwave radiometer while airborne active microwave measurements were performed by synthetic aperture radar, X- and L-band radar, and a scatterometer.

Campbell, W. J.↗
NASA NTRS2007

Using Remotely-Sensed Estimates of Soil Moisture to Infer Soil Texture and Hydraulic Properties across a Semi-arid Watershed

Near-surface soil moisture is a critical component of land surface energy and water balance studies encompassing a wide range of disciplines. However, the processes of infiltration, runoff, and evapotranspiration in the vadose zone of the soil are not easy to quantify or predict because of the difficulty in accurately representing soil texture and hydraulic properties in land surface models. This study approaches the problem of parameterizing soils from a unique perspective based on components originally developed for operational estimation of soil moisture for mobility assessments. Estimates of near-surface soil moisture derived from passive (L-band) microwave remote sensing were acquired on six dates during the Monsoon '90 experiment in southeastern Arizona, and used to calibrate hydraulic properties in an offline land surface model and infer information on the soil conditions of the region. Specifically, a robust parameter estimation tool (PEST) was used to calibrate the Noah land surface model and run at very high spatial resolution across the Walnut Gulch Experimental Watershed. Errors in simulated versus observed soil moisture were minimized by adjusting the soil texture, which in turn controls the hydraulic properties through the use of pedotransfer functions. By estimating a continuous range of widely applicable soil properties such as sand, silt, and clay percentages rather than applying rigid soil texture classes, lookup tables, or large parameter sets as in previous studies, the physical accuracy and consistency of the resulting soils could then be assessed. In addition, the sensitivity of this calibration method to the number and timing of microwave retrievals is determined in relation to the temporal patterns in precipitation and soil drying. The resultant soil properties were applied to an extended time period demonstrating the improvement in simulated soil moisture over that using default or county-level soil parameters. The methodology is also applied to an independent case at Walnut Gulch using a new soil moisture product from active (C-band) radar imagery with much lower spatial and temporal resolution. Overall, results demonstrate the potential to gain physically meaningful soils information using simple parameter estimation with few but appropriately timed remote sensing retrievals.

Santanello, Joseph A.↗
NASA NTRS1992

On the application of multifrequency polarimetric radar observations for sea-ice classification

The use of multifrequency polarimetric radar imagery to enhance the ability to separate different sea-ice types using single-frequency, single-polarization synthetic aperture radar (SAR) data is investigated. Backscatter characteristics of six radiometrically and polarimetrically distinct sea-ice types are selected in an unsupervised range-dependent analysis of multifrequency polarimetric SAR data using the maximum a posteriori (MAP) polarimetric classifier. Maximum ice discrimination is achieved with combined C- and L-band full polarimetry, and collocated passive microwave imagery suggests greater than 90 percent classification accuracy. C-band VV-pol alone achieves only 68 percent relative accuracy because it confuses multiyear and rough compressed first year ice. L-band, relative classification accuracy is 75 percent, 83 percent, and 85 percent, using HH-pol, HH- and VV-combined, or the full polarimetry, respectively. P-band is less accurate. Combinations of two frequencies at a single polarization show the greatest improvement over a single channel.

Rignot, Eric↗
NASA NTRS1998

A Conceptual Approach to Assimilating Remote Sensing Data to Improve Soil Moisture Profile Estimates in a Surface Flux/Hydrology Model: Overview - Part 1

Knowledge of the amount of water in the soil is of great importance to many earth science disciplines. Soil moisture is a key variable in controlling the exchange of water and energy between the land surface and the atmosphere. Thus, soil moisture information is valuable in a wide range of applications including weather and climate, runoff potential and flood control, early warning of droughts, irrigation, crop yield forecasting, soil erosion, reservoir management, geotechnical engineering, and water quality. Despite the importance of soil moisture information, widespread and continuous measurements of soil moisture are not possible today. Although many earth surface conditions can be measured from satellites, we still cannot adequately measure soil moisture from space. Research in soil moisture remote sensing began in the mid 1970s shortly after the surge in satellite development. Recent advances in remote sensing have shown that soil moisture can be measured, at least qualitatively, by several methods. Quantitative measurements of moisture in the soil surface layer have been most successful using both passive and active microwave remote sensing, although complications arise from surface roughness and vegetation type and density. Early attempts to measure soil moisture from space-borne microwave instruments were hindered by what is now considered sub-optimal wavelengths (shorter than 5 cm) and the coarse spatial resolution of the measurements. L-band frequencies between 1 and 3 GHz (10-30 cm) have been deemed optimal for detection of soil moisture in the upper few centimeters of soil. The Electronically Steered Thinned Array Radiometer (ESTAR), an aircraft-based instrument operating a 1,4 GHz, has shown great promise for soil moisture determination. Initiatives are underway to develop a similar instrument for space. Existing space-borne synthetic aperture radars (SARS) operating at C- and L-band have also shown some potential to detect surface wetness. The advantage of radar is its much higher resolution than passive microwave systems, but it is currently hampered by surface roughness effects and the lack of a good algorithm based on a single frequency and single polarization. In addition, its repeat frequency is generally low (about 40 days). In the meantime, two new radiometers offer some hope for remote sensing of soil moisture from space. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), launched in November 1997, possesses a 10.65 GHz channel and the Advanced Microwave Scanning Radiometer (AMSR) on both the ADEOS-11 and Earth Observing System AM-1 platforms to be launched in 1999 possesses a 6.9 GHz channel. Aside from issues about interference from vegetation, the coarse resolution of these data will provide considerable challenges pertaining to their application. The resolution of TMI is about 45 km and that of AMSR is about 70 km. These resolutions are grossly inconsistent with the scale of soil moisture processes and the spatial variability of factors that control soil moisture. Scale disparities such as these are forcing us to rethink how we assimilate data of various scales in hydrologic models. Of particular interest is how to assimilate soil moisture data by reconciling the scale disparity between what we can expect from present and future remote sensing measurements of soil moisture and modeling soil moisture processes. It is because of this disparity between the resolution of space-based sensors and the scale of data needed for capturing the spatial variability of soil moisture and related properties that remote sensing of soil moisture has not met with more widespread success. Within a single footprint of current sensors at the wavelengths optimal for this application, in most cases there is enormous heterogeneity in soil moisture created by differences in landcover, soils and topography, as well as variability in antecedent precipitation. It is difficult to interpret the meaning of 'mean' soil moisture under such conditions and even more difficult to apply such a value. Because of the non-linear relationships between near-surface soil moisture and other variables of interest, such as surface energy fluxes and runoff, mean soil moisture has little applicability at such large scales. It is for these reasons that the use of remote sensing in conjunction with a hydrologic model appears to be of benefit in capturing the complete spatial and temporal structure of soil moisture. This paper is Part I of a four-part series describing a method for intermittently assimilating remotely-sensed soil moisture information to improve performance of a distributed land surface hydrology model. The method, summarized in section II, involves the following components, each of which is detailed in the indicated section of the paper or subsequent papers in this series: Forward radiative transfer model methods (section II and Part IV); Use of a Kalman filter to assimilate remotely-sensed soil moisture estimates with the model profile (section II and Part IV); Application of a soil hydrology model to capture the continuous evolution of the soil moisture profile within and below the root zone (section III); Statistical aggregation techniques (section IV and Part II); Disaggregation techniques using a neural network approach (section IV and Part III); and Maximum likelihood and Bayesian algorithms for inversely solving for the soil moisture profile in the upper few cm (Part IV).

Crosson, William L.↗
NASA NTRS1991

Microwave backscattering and emission model for grass canopies

A two-layer model is developed that treats the grass canopy as a collection of randomly oriented elliptical dielectric discs over a layer of thatch with underlying soil surface. The distorted Born approximation in conjunction with the Peake formulation is used to calculate the backscattering coefficient and the emissivity from the canopy. Two particular features of this model which are unique for grass canopies are the variation of the canopy structure and the presence of the thatch layer. The basic parameters in the model such as the size and orientation of grass blades, dielectric constant of soil and vegetation, and thickness and water content of the thatch layer have been obtained from ground truth data. To interpret the available experimental observations of grasslands, numerical results from both passive and active models at L-band (1.4 GHz) are generated and various scattering and emission properties of the grass canopies are discussed.

Saatchi, Sasan S.↗
NASA NTRS2009

The Soil Moisture Active and Passive Mission (SMAP): Science and Applications

The Soil Moisture Active and Passive mission (SMAP) will provide global maps of soil moisture content and surface freeze/thaw state. Global measurements of these variables are critical for terrestrial water and carbon cycle applications. The SMAP observatory consists of two multipolarization L-band sensors, a radar and radiometer, that share a deployable-mesh reflector antenna. The combined observations from the two sensors will allow accurate estimation of soil moisture at hydrometeorological (10 km) and hydroclimatological (40 km) spatial scales. The rotating antenna configuration provides conical scans of the Earth surface at a constant look angle. The wide-swath (1000 km) measurements will allow global mapping of soil moisture and its freeze/thaw state with 2-3 days revisit. Freeze/thaw in boreal latitudes will be mapped using the radar at 3 km resolution with 1-2 days revisit. The synergy of active and passive observations enables measurements of soil moisture and freeze/thaw state with unprecedented resolution, sensitivity, area coverage and revisit.

radiometry↗
NASA NTRS1994

Microwave backscattering and emission model for grass canopies

Microwave radar and radiometer measurements of grasslands indicate a substantial reduction in sensor sensitivity to soil moisture in the presence of a thatch layer. When this layer is wet it masks changes in the underlying soil, making the canopy appear warm in the case of passive sensors (radiometer) and decreasing backscatter in the active case (scatterometer). A model for a grass canopy with thatch will be presented in this paper to explain this behavior and to compare with observations. The canopy model consists of three layers: grass, thatch, and the underlying soil. The grass blades are modeled by elongated elliptical discs and the thatch is modeled as a collection of disk shaped water droplets (i.e., the dry matter is neglected). The ground is homogeneous and flat. The distorted Born approximation is used to compute the radar cross section of this three layer canopy and the emissivity is computed from the radar cross section using the Peake formulation for the passive problem. Results are computed at L-band (1.4 GHz) and C-band (4.75 GHz) using canopy parameters (i.e., plant geometry, soil moisture, plant moisture, etc.) representative of Konza Prairie grasslands. The results are compared to C-band scatterometer measurements and L-band radiometer measurements at these grasslands.

TRT-THEORETICAL↗
NASA NTRS2003

RFI at L-band in Synthetic Aperture Radiometers

The spectral window at 1.413 GHz (L-band), set aside for passive use only, is critical for passive remote sensing of the earth from space. It is the largest spectral window available in the long wavelength end of the microwave spectrum where measurements are needed to monitor parameters of the surface such as soil moisture and sea surface salinity. The sensitivity to these parameters is rapidly lost at higher frequencies and is compromised by the ionosphere and antenna size at lower frequencies. Instruments for remote sensing from space in this spectral window are being developed by NASA (Aquarius) and ESA (SMOS) and are expected to be in orbit in a few years (2006). Although the band at 1.413 GHz is protected for passive use, RFI is a common problem. For example, the synthetic aperture radiometer, ESTAR (L-band, Horizontal polarization), has frequently experienced problems with RFI. During the Southern Great Plains Experiments (1997 and 1999), ESTAR experienced RFI significant enough to warrant changes in flight lines. The largest sources of RFI were identified as originating in airports and a likely source is air traffic control radar. In experiments in the vicinity of Richmond, VA, RFI in the form of periodic spikes was recorded, again suggestive of radar. However, in most cases the sources of the RFI are unknown. RFI is a sufficiently common problem that the first step in processing ESTAR data is a screening for RFI (a filter is used to detect large, rapid changes in brightness). Recently, measurements have been made with a new synthetic aperture radiometer, 2D-STAR. Examples of RFI observed simultaneously with ESTAR and the new synthetic aperture radiometer will be presented. 2D-STAR is an airborne instrument designed to develop the technology of aperture synthesis in two dimensions. It employs dual polarized patch antennas arranged in a cross configuration (+). Synthesis in two dimensions offers the potential for optimal thinning, but because of the wide field of view of the individual antennas it is potentially more susceptible to RFI. The 2D-STAR instrument was flown together with ESTAR in a series of test flights on the NASA P-3 aircraft near the Wallops Flight Facility during the summer of 2002. Both instruments experienced RFI over common locations. Examples will be presented and a comparison between instruments presented.

LeVine, David M.↗
NASA NTRS2010

L Band Brightness Temperature from Forest: Comparison of Approximate Techniques

In this paper, three approximate physical microwave radiometry models have been used to calculate brightness temperatures from a forest canopy at L-band. These models are (1) tau-omega model (zero order scattering approximation to radiative transfer equations), (2) successive order of scattering model up to first order (first order scattering approximation to the radiative transfer equations), and (3) Peak technique utilizing the active solution obtained from the Distorted Born Approximation (DBA). These models are physically-based and treat vegetation as a layer of discrete scatterers over a rough surface. Vegetation components within the canopy are represented by canonical shapes such as dielectric discs and cylinders. The tau-omega model is based on a zero-order solution to the radiative transfer (RT) equations. The model ignores scattering except for the effect of the scatterers in the attenuation of the emission through the vegetation. Application of the tau-omega model to data acquired during airborne and ground-based campaigns over the years has solidified scientific understanding of microwave interactions with different landscapes. In particular, shrubland, grasslands, agricultural crops, and light to moderate vegetation have been investigated. Its applicability to areas with a significant tree fraction is unknown. The first order scattering model is based on an iterative solution of the RT equation up to the first order. The first order solution is obtained by substituting the zeroth-order solution into the scattering source term and then solving the resulting radiative transfer equations. This formulation adds a new scattering term to the tau-omega model. It represents emission by particles in the layer and emission by the ground that is scattered once by particles in the layer. The resulting model represents an improvement over the standard zero-order solution (the tau-omega model) since it accounts for the scattered vegetation and ground radiation that can have a pronounced effect on the observed brightness temperature. The third model is based on the Peake formulation in conjunction with the DBA. The procedure for calculation of forest emission is accomplished by first calculating the bistatic scattering cross section for each type of scatterer, then by using the DBA to calculate specular albedo of the ground and the diffused albedo of the layer. Once the albedos are determined, Peake s principle relating active and passive problems can be used to determine the effective emissivity of the forest layer.

Kurum, Mehmet↗
NASA NTRS1986

Observations of the seasonal variability of soil moisture and vegetation cover over Africa using satellite microwave radiometry

Multispectral passive microwave data from the scanning multichannel microwave radiometer (SMMR) on the Nimbus-7 satellite were processed selectively for a 1 yr period over Africa. The data show a wide dynamic range of brightness temperature (180 to 290 K), corresponding to variations in surface features such as moisture, temperature, vegetation, roughness, and large-scale topography. It appears that soil moisture variability is detectable with the SMMR over large regions of Africa. To what extent roughness and vegetation affect this capability is not clear. The lowest SMMR frequency is C-band (6.6 GHz), thus any soil moisture sensitivity at this frequency would be much improved by a sensor at L-band (1 to 2 GHz) less affected by roughness and vegetation.

Njoku, Eni G.↗
NASA NTRS2000

ESTAR Measurements During SGP-99

The synthetic aperture radiometer, ESTAR, provided L-band brightness temperature maps of the experiment site during the Southern Great Plains Experiment in 1999. ESTAR flew on the NASA P-3 aircraft at an altitude of 7.6 km and mapped a swath about 50 km wide and about 300 km long. The area mapped extended west from Oklahoma City to El Reno and north from the Little Washita River watershed to the Kansas border. The flight lines and mapping were done in a manner similar to that used in 1997 during SGP-97. ESTAR is a prototype designed to develop the technology of aperture synthesis for passive microwave remote sensing. It is actually a hybrid that uses real aperture to obtain resolution along track and synthetic aperture to obtain resolution across track. ESTAR operates in the band at 1.413 GHz set aside for passive use and images at horizontal polarization in the equivalent of a cross-track scan. The scan is done in software as part of image reconstruction. Calibration consists of blackbody observed before and after each flight and a local body of water (Lake Kaw). ESTAR arrived in Oklahoma on July 7, 1999 and flew mapping missions on July 8,9, 11, 14, 15, 19 and 20. The aircraft was down on July 10 by design and was down again on July 12-13 and 16-18 due to mechanical problems. ESTAR data for July I I is questionable because of a hardware failure. The brightness temperature maps reflect the patterns of soil moisture observed in the SGP99 study area and compare well with measurements made by ESTAR in other experiments in this region (e.g. SGP -97 and Washita-92).

LeVine, D. M.↗