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No current satellite measurement system can provide soil moisture data from space, and previous process studies and algorithm development activities for microwave remote sensing of soil moisture have been limited in their spatial extent. To remedy this situation and build a strong foundation for a soil moisture space mission, an EOS interdisciplinary science hydrology experiment called Southern Great Plains '97 (SGP97) was conducted by NASA and USDA in Oklahoma during June-July, 1997 in order to acquire data at spatial and temporal scales more typical of satellite systems. A core activity of SGP97 involved mapping surface soil moisture with the ESTAR (Electronically Scanned Thinned Array Radiometer) airborne L band microwave radiometer on a daily basis for a month over an 11,000 km/sup 2/ area at 800 m resolution. To provide continuous 24-hour microwave brightness temperature measurements over two representative surface covers (winter wheat and grazed pasture) to complement the once-a-day ESTAR data, the two-frequency truck-mounted passive microwave SLMR (S and L Microwave Radiometer) system was also deployed to the Department of Energy's ARM/CART Central Facility site in the northern part of the SGP97 test region for the month-long experiment. The availability of both truck-based and aircraft L band brightness temperature measurements over the same site in SGP97 offers a unique opportunity to investigate the effects of pixel heterogeneity and scaling on soil moisture retrieval. The detailed diurnal time series data available from the SLMR system also permits validation and refinement of coupled energy/water balance models at the point scale before these models are extrapolated to the entire SGP97 test area.

During 1992 and 1994 NASA/GSFC, USDA, and Princeton University conducted hydrology field experiments in the Little Washita River watershed near Chickasha, Oklahoma, with a goal of characterizing the spatial and temporal variability of soil moisture using microwave sensors from ground, aircraft, and space platforms. A major objective of these activities included the subsequent incorporation of the microwave-derived soil moisture patterns in models of larger scale water balance and partial area hydrology. While work is continuing to improve the accuracy of microwave soil moisture inversion algorithms for both bare and vegetated soils, the impact of errors in estimated soil moisture on hydrological modeling of the watershed has yet to be addressed. In this study a coupled water and energy balance model operating within a topographic framework was used to predict surface soil moisture fields for the Little Washita watershed for an eight-day period in June, 1992 which covered a wide range of soil moisture conditions. The model was first driven by meteorological forcing data, and the model-generated soil moisture fields are compared in space and time to those produced for the watershed by the airborne passive microwave ESTAR sensor for the same time period. In a second analysis, the model was initialized by the remote sensing data, and subsequent model predictions of soil moisture are compared to measured values.

The spatial and temporal distribution of soil moisture can be viewed as a key descriptor of hydrologic processes in a watershed. If SAR is to be a useful tool for studying and identifying these hydrologic processes, then the effects of topography must be separable from the response due to soil moisture within any microwave data set. This is especially true in mountainous terrain where much stronger reflections are observed from radar-facing slopes than from slopes facing away from the radar. To investigate the impact of topography on the accuracy of SAR-derived soil moisture estimates, AIRSAR data were analyzed from a field experiment conducted in the mountainous semiarid Reynolds Creek watershed near Boise, Idaho during 1991. Reynolds Creek watershed is almost entirely covered with sparse sagebrush vegetation which is essentially transparent at L-band wavelengths. In an attempt to correct for topography, a USGS 30 m DEM of Reynolds Creek was registered to the AIRSAR data, and the changes in radar cross-section area and antenna pattern for each pixel due to slopes in the range and azimuth directions were computed using JPL's POLCAL 4.0 software. Initial results indicate that the 30 m resolution of the USGS DEM may be too coarse for terrain correction of the AIRSAR data for this area. Use of higher resolution DEM for sub-watersheds within Reynolds Creek produced improved calibration of the terrain effect, and increased the correlation between radar backscatter and soil moisture. >

Passive microwave radiometry experiments have typically employed a single daily observation of the land surface, usually at high spatial resolution and often at a single wavelength. There exists the potential of new information about land surface parameters by increasing the temporal resolution, decreasing the spatial resolution and by using multifrequency sensor packages. To facilitate some of these goals, a dual frequency (L and S band) single beam radiometer system (SLMR) that is capable of both truck and aircraft operation has been developed. During the summer of 1993, truck based field experiments designed to complete the calibration/verification of the instrument as well as to collect high temporal resolution data over well controlled plots were conducted. Results show diurnal multiday observations may provide a great deal of information on the state (profile soil moisture) and flux (evaporation) variables influencing the surface condition being observed. >

During June, 1992 NASA and USDA conducted a hydrology field experiment in the Little Washita River Watershed near Chickasha, Oklahoma, with a goal of characterizing the spatial and temporal variability of soil moisture using microwave sensors. As part of this experiment, truck radar data at 1.6 GHz acquired over a large corn field were used to validate the performance of a vegetation model in which discrete scatter random media techniques are employed to calculate vegetation transmissivity and scattering. These parameters were then used in a soil moisture prediction algorithm utilizing aircraft microwave data from the 1.4 GHz ESTAR radiometer. Over the 8 flight days of the experiment (during which soil moisture decreased over 20% from the saturated conditions at the start), the match of predicted to measured soil moisture was excellent, with an average absolute error of less than 1.5%. For the corn, canopy encountered in Washita '92 (2.1 m tall with canopy water content of 4.05 kg/m/sup 2/), the inclusion of the small single scattering albedo in the algorithm slightly improved the prediction of soil moisture. >

A major objective of soil moisture-related hydrological research during NASA's SIR-C/X-SAR mission was to determine and compare soil moisture patterns within humid watersheds using SAR data, ground-based measurements, and hydrologic modeling. Currently available soil moisture inversion methods using active microwave data are only accurate when applied to bare and slightly vegetated surfaces. Moreover, as the surface dries down, the number of pixels that can provide estimated soil moisture by these radar inversion methods decreases, leading to less accuracy and confidence in the retrieved soil moisture fields at the watershed scale. The impact of these errors in microwave-derived soil moisture on hydrological modeling of vegetated watersheds has yet to be addressed. In this study a coupled water and energy balance model operating within a topographic framework is used to predict surface soil moisture for both bare and vegetated areas. In the first model run, the hydrological model is initialized using a standard baseflow approach, while in the second model run, soil moisture values derived from SIR-C radar data are used for initialization. The results, which compare favorably with ground measurements, demonstrate the utility of combining radar-derived surface soil moisture information with basin-scale hydrological modeling.

This paper demonstrates the technique to estimate ground surface and vegetation scattering components, based on the backscattering model and the radar decomposition theory, under configuration of multi-temporal L-band polarimetric SAR measurement. This technique can be used to estimate soil moisture of vegetated surfaces.

During the first SIR-C mission in April, 1994, a three-frequency truck-mounted radar system was used to obtain radar backscatter data coincident with the AIRSAR and SIR-C radars for representative test fields in the Little Washita River watershed near Chickasha, Oklahoma, one of the designated hydrology supersites for the SIR-C experiment. Analysis of the resulting radar data sets indicates that (1) despite windy ground conditions, the truck radar operated in a very stable manner, producing accurate and repeatable measurements at both like and cross polarizations; and (2) both the Shuttle and truck radars were able to follow changing soil moisture conditions in most of the test fields quite well.

During June, 1992 NASA, USDA, and Princeton University conducted a hydrology field experiment in the Little Washita River watershed near Chickasha, Oklahoma with a goal of characterizing the spatial and temporal variability of soil moisture using microwave sensors. As part of this experiment, several time series of radar images obtained by NASA/JPL's airborne AIRSAR system over agricultural and pasture test sites were analyzed for their utility to soil moisture estimation. Two existing microwave backscatter models using copolarization ratios which were originally developed for base soils were examined in terms of their performance in the Washita '92 bare fields and their potential role in a radar-based approach to determining soil moisture through vegetation canopies.

For more than two decades radar backscattering signals at a frequency range of 1-10 GHz have been demonstrated by a number of investigators to depend on surface soil moisture. Most of these past studies were limited to the forward problem in that the measured backscatter was directly correlated to the ground-measured soil moisture. Attempts to invert the experimentally measured radar backscattering signals to soil moisture values occurred only recently. The authors examined three out of these four attempts with respect to the SIR-C observations over a test site some 60 km southwest of Oklahoma City, Oklahoma. The results showed some promise, but also suggested the need of a close scrutiny of these inversion algorithms.

This study shows 1) a semi-empirical surface emission model and 2) the properties that can be used to improve soil moisture estimation for vegetated surfaces under the change detection concept, based on numerically simulated data using the IEM model.

Soil moisture is a critical state variable in land surface hydrology, and has a significant influence on interactions between the land surface and the atmosphere through the exchange of water vapor and sensible heat. Assessment of the ability of passive microwave remote sensing to measure accurately the spatial and temporal variations of soil moisture is an essential first step for the successful incorporation of soil moisture data in many important applications such as mesoscale weather forecasting, climate modeling, agricultural productivity, watershed hydrology, ecological processes, and natural hazards research. An important part of this assessment is to understand the. relationship between soil moisture derived from microwave remote sensing and soil moisture as used and predicted by various models of the land surface water and energy balance, including soil-vegetation atmosphere transfer schemes (SVATS). Characterization of the relationship between microwave-derived and SVAT-modeled soil moisture is important at both point and distributed spatial scales, and this issue forms the basis of our current research. Two models used in this study include (1) the new modular vectorized version of the University of Michigan Land Surface Process/Radiobrightness (LSP/R) model. and (2) the NOAH Land Surface Model by NOAA NCEP, Oregon State University, the Army, and the Office of Hydrology. Both models are initialized with appropriate soil and vegetation parameters and are driven by local meteorological data.

An EOS interdisciplinary science hydrology experiment called Southern Great Plains '97 (SGP97) was conducted by NASA and USDA in Oklahoma during June-July, 1997 in order to acquire data at spatial and temporal scales more typical of possible future satellite systems. A core activity of SGP97 involved mapping surface soil moisture with the ESTAR (Electronically Scanned Thinned Array Radiometer) airborne L-band microwave radiometer on a daily basis for a month over an 11,000 km/sup 2/ area at 800 m resolution. To provide continuous 24-hour microwave brightness temperature measurements over two representative surface covers (winter wheat and grazed pasture) to complement the once-a-day ESTAR data, the two-frequency truck-mounted passive microwave SLMR (S and L Microwave Radiometer) system was deployed to the Department of Energy's ARM/CART Central Facility site near Lament, Oklahoma in the northern part of the SGP97 test region for the month-long experiment. The detailed diurnal time series data available from the SLMR system permits validation and refinement of coupled energy/water balance models at the point scale before these models are extrapolated to the entire SGP97 test area.

The objectives of this study were: to examine the sensitivity of radar backscatter, to estimate soil moisture under a corn plot and to evaluate the effectiveness and sensitivity of a Radiative Transfer Model (RTM), adapted from the earlier work of Njoku and Kong, (1977) in predicting brightness temperature from a grass plot. Microwave radar measurements were collected from plots of different vegetation cover types, vegetation density, and moisture conditions during the Huntsville 1998 field experiment. A large amount of ground data on brightness temperatures, soil moisture, and vegetation characteristics (e.g., biomass, and water content) were collected. The experiments were conducted at Alabama A&M University's, Winfred Thomas Agricultural Research Station, located near Hazel Green, Alabama. Six plots, one 50 X 60 m smooth bare plot, one 50 X 60 m grass plot, and four 30 X 50 m corn plots at full, 2/3, 1/2, and 1/3 densities were used. Radar backscatter data were obtained from a ground based truck mounted radar operating at L, C, and X bands (1.6, 4.75, and 10 GHz) with four linear polarization HH, HV, VV, and VH and two incidence angles (15 and 45 degrees). Soil moisture values were determined using Water Content Reflectometry (WCR). Three types of soil temperature sensors (Infrared Thermometer, Thermistor, and a 4-sensor averaging thermocouple probes) were used. A discrete backscatter approach model and RTM were evaluated. Comparisons between model prediction and experimental observation for HH polarization indicated good agreement for a corn full plot. The direct-reflected scattering coefficient is found to be the most dominant term for both polarization and the backscatter is also highly sensitive to soil moisture. The trends in time variation of brightness temperature are in agreement with the experimental results and the numerical results are within a few percent of the experimental results. The vegetation corrections as estimated by the Jackson and Schmugge method are very small. Detailed examination of the vegetation canopy contribution including the geometry of the canopy, the various absorption and scattering mechanisms are necessary.

Theory and experiments have shown that passive microwave radiometers can be used to measure soil moisture. However, the presence of a vegetative cover alters the measurement that might be obtained under bare conditions. Two significant obstacles to the practical use of this approach are deterministically accounting for the effect of vegetation; and developing algorithms for extracting soil moisture from observations of a vegetation-soil complex. The presence of a vegetation canopy reduces the sensitivity of passive microwave instruments to soil moisture variations. Data collected using truck-mounted microwave radiometers were used to examine the specific effects of corn and soybean canopies. >

Experiments were conducted using truck-based microwave radiometers operating at 1.41- (L-band) and 2.65-GHz (S-band) horizontal polarization to observe small plots during and following sprinkler irrigation. These experiments were conducted on a sandy loam soil in 1993 and a silt loam in 1995. Sandy loam soils typically have higher infiltration capabilities than clays, and in the authors' studies, they were not able to exceed this with the irrigation system. The observed brightness temperature (T/sub B/) quickly reached a nominally constant value during irrigation. When the irrigation was stopped, the T/sub B/ began to increase as drainage took place. Contributing depth-related differences were observed for L- and S-band as expected. The irrigation rates in 1995 with the silt loam soil exceeded the saturated conductivity of the soil. During irrigation, the T/sub B/ values exhibited a phenomenon that had not been previously observed and identified and is associated with coherent interference. The Land S-band exhibited similar patterns but were not identical due to contributing depth. These results suggested the existence of a sharp dielectric boundary (wet over dry soil) that was increasing in depth with time. The temporal description of the wetting front boundary was used with a coherent radiative transfer model to predict T/sub B/ for L- and S-band.

An EOS interdisciplinary science hydrology experiment conducted by NASA and USDA called Southern Great Plains '97 (SGP97) took place from June 18 to July 16, 1997 in Oklahoma, with a goal of mapping surface soil moisture with an airborne L-band microwave radiometer on a daily basis for a month over an 11000 km2 area at 800 m resolution. Operated at a scale equivalent to a GCIP ISA, this experiment offers a unique data set for examining the applicability of microwave soil moisture retrieval algorithms at spatial and temporal scales more typical of satellite systems, as well as the value of soil moisture information to regional scale hydrology, weather, and land-atmosphere interactions. The spatial area covered ranged from the Little Washita River watershed in the south to the Department of Energy's ARM/CART Central Facility near the Kansas border in the north. Extensive ground measurements of soil moisture were collected at the Little Washita watershed and the Central Facility area as well as USDA's El Reno watershed west of Oklahoma City in conjunction with the aircraft mapping. A total of 18 complete missions and 3 partial missions were successfully flown with the ESTAR airborne L-band 1.4 GHz microwave radiometer during the experimental period. Besides the core mapping by ESTAR, the experiment included a comprehensive flux measurement component, enhanced ground measurement of soil and vegetation properties, extensive soil moisture sampling through both gravimetric and TDR techniques, other aircraft remote sensing instruments and access to automated micrometeorological and soil profile measurements from the three research instrument networks in the SGP97 area.

In a previous study, the usefulness of incorporating microwave-derived soil moisture information in a statistical version of a hydrological model was presented. In the present study the TOPLATS hydrological model was initialized with surface soil moisture fields derived from the ESTAR airborne L-band microwave radiometer for the Washita '92 experiment in the Little Washita River watershed in Oklahoma. After only a single initialization with ESTAR data on the wet day at the start of the study period, the hydrological model was able to produce more accurate predictions of soil moisture than a standard hydrological initialization with streamflow data over an eight-day soil moisture drydown. However, when the model soil moisture was compared to ESTAR soil moisture on each of the eight days, the spatial patterns of the model errors were not random-soil moisture was overpredicted in the sandy soils in the center of the watershed while underpredicted in the loamier soils to the east and west. Since the statistical version of the hydrological model is driven by the watershed average of the hydrological parameters, it is difficult to conclude whether these modeling results are due to errors in the input hydrological parameters obtained from a soils data base or due to errors resulting from the aggregation processes in the model. Thus, the objective of this paper is to use the fully distributed version of the hydrological model to explore the detailed representation of the dynamics of hydrological processes in the watershed, so that the effect of aggregation at the macroscale can be determined.

One of the present efforts in the field of microwave remote sensing is to estimate soil water status throughout the soil profile using radiometers. This study describes a simple, ideal algorithm relating microwave brightness temperature (TB) and soil water status in the upper (10 cm) and lower (greater than 10 cm) soil depths. The algorithm description considers a simple, homogeneous, and sandy soil system with negligible amounts of capillary-stored water, organic matter content, and surface roughness. The microwave TB was estimated from the dielectric constant, k, as a function of the amount of water remaining in the soil during drainage. The effect of downward fluxing, free water on the value of TB was greater and occurred over a shorter period than that of the slower draining water near field capacity. The predictability of the algorithm decreased as the moisture content decreased to that of approximately field capacity.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Requiring continuing education (CE) for RN relicensure began in the 1970s and has remained a controversial issue over the past 25 years. This article is a description of the process used by nurses in one state to investigate the complex issues surrounding mandatory continuing education for relicensure and the factors that led state nursing leaders to support the continuation of a voluntary CE system. Included are the findings of a survey of all states currently requiring continuing education for relicensure. The primary reason given for mandating CE was to protect the public, however, no data are available to support this premise.

This paper reports preliminary results of a study using L-band SAR imagery to estimate soil moisture and surface roughness for bare fields in a mountain watershed. The small perturbation model requires that the surface roughness profile deviates only slightly from that of a smooth surface. This condition is seldom met by most natural surfaces. Thus, the classic treatment of the scattering model has only limited usage. In this study, we show a simplified model derived from the integral equation model for estimation of soil moisture and surface roughness. We then test this model using JPL L-band AIRSAR data.

Mapping land cover and vegetation characteristics on a regional scale is critical to soil moisture retrieval using microwave remote sensing. In aircraft-based experiments such as the National Airborne Field Experiment 2006 (NAFE 06), it is challenging to provide accurate high resolution vegetation information, especially on a daily basis. A technique proposed in previous studies was adapted here to the heterogenous conditions encountered in NAFE 06, which included a hydrologically complex landscape consisting of both irrigated and dryland agriculture. Using field vegetation sampling and ground-based reflectance measurements, the knowledge base for relating the Normalized Difference Water Index (NDWI) and the vegetation water content was extended to a greater diversity of agricultural crops, which included dryland and irrigated wheat, alfalfa, and canola. Critical to the generation of vegetation water content maps, the land cover for this region was determined from satellite visible/infrared imagery and ground surveys with an accuracy of 95.5% and a kappa coefficient of 0.95. The vegetation water content was estimated with a root mean square error of 0.33 kg/sq m. The results of this investigation contribute to a more robust database of global vegetation water content observations and demonstrate that the approach can be applied with high accuracy. Keywords: Vegetation, field experimentation, thematic mapper, NDWI, agriculture.

While our field's response practices have changed dramatically over the past two decades to involve more student comments on their own texts, empirical studies have lagged far behind classroom practices, focusing almost exclusively on teachers' written comments as texts. By broadening our notion of response-and acknowledging the many and varied ways that teachers respond to student writing as well as the many and varied ways that students influence and interpret those responses-we will be able to narrow the gap between our teaching practices and our research questions.

A series of field experiments was conducted to extend the database available for verifying agricultural-row-effects models of emissivity. Observations were made using a truck-mounted, 21-cm, 1.4-GHz passive microwave radiometer for a range of soil moisture conditions on three bare soil plots: a flat control field, a furrowed field with row height 15 cm and row spacing 75 cm, and a furrowed field with row height 30 cm and row spacing 90 cm. An examination of model predictions for the test conditions indicated that although the composite roughness model tended to overestimate the effect of row structure on emissivity, it reproduced the observations fairly well. Both simulation results and the field measurements indicated that if an error of +or-0.03 in estimating emissivity, could be tolerated, then a reliable prediction of equivalent smooth field emissivity can be made for furrowed fields with row-height/row-spacing ratio less than 0.2, which encompasses most dry-land agricultural planting practices. >

In order to understand the interactions of soil properties and microwave emission better, a series of field experiments were conducted in 1984. Small plots were measured with a truck-mounted passive microwave radiometer operating at 1.4 GHz. The microwave data were collected concurrently with ground observations of soil moisture and bulk density. Treatment effects included different soil moisture contents and bulk densities. Evaluations of the data showed that commonly used models of the dielectric properties of wet soils could not explain the observations. This conclusion was based on the fact that the roughness parameters determined through optimization were significantly larger than those observed in similar other investigations. These discrepancies are most likely due to inadequate characterization of the effects of soil structure. Commonly used models assume a homogeneous three phase mixture of soil solids, air, and water. Under tilled conditions the soil is actually a two phase mixture of aggregates and voids. Appropriate dielectric models for this tilled condition were evaluated and found to match the observed data. These results indicate that previous conclusions concerning the effects of surface roughness in tilled fields may be incorrect, and the improved model may explain some of the inconsistencies encountered in roughness modeling.

Ground-based microwave radiometer experiments were performed to investigate the effects of moisture, temperature, and roughness on microwave emission from bare soils. Measurements were made at frequencies of 0.6-0.9, 1.4, and 10.7 GHz using van-mounted radiometers to observe prepared soil sites in Kern County, CA. The sites were instrumented for monitoring soil characteristics and surface meteorological conditions. Brightness temperature variations of approximately 15 K at 1.4 GHz and 25 K at 10.7 GHz were observed as a result of diurnal changes in the soil temperature. Increasing the soil moisture content from 2 to 15 percent by volume resulted in brightness temperature decreases of approximately 70 K at 0.775 and 1.4 GHz, and 40 K at 10.7 GHz, depending, to a lesser extent, on polarization and viewing angle. The results show the significance of soil temperature in deriving soil moisture from microwave radiometer measurements. Comparisons of the microwave measurements with theoretical predictions using a smooth surface model show reasonable agreement and support previous results of this nature obtained with other soil types. Approximately equal sensitivity to soil moisture was observed at 0.775 and 1.4 GHz, although the sampling depth is greater at the lower frequency.

A comparison of the soil moisture response of three 1–4 GHz radiometers from truck and aircraft platforms at a variety of lest sites indicates that microwave remote sensing of bare soil produces repeatable and quantifiable results regardless of geographic location and sensor system used. The combined microwave sensitivity of these data sets appears to be of the order of − 3–4deg K per 1 per cent change in volumetric soil moisture for L-band wavelengths ( − 2–7 deg K per 1 per cent soil moisture if watersheds characterized by vegetation and surface roughness are included). With the addition of appropriate algorithms to handle the effect of roughness and vegetation, these results demonstrate the potential of microwave remote sensing for estimating soil moisture over large areas.

A field experiment was conducted utilizing artificial arrangements of plant components during the summer of 1982 to examine the effects of corn canopy structure and plant water content on microwave emission. Truck-mounted microwave radiometers at C (5 GHz) and L (1.4 GHz) band sensed vertically and horizontally polarized radiation concurrent with ground observations of soil moisture and vegetation parameters. Results indicate that the orientation of cut stalks and the distribution of their dielectric properties through the canopy layer can influence the microwave emission measured from a vegetation/soil scene. The magnitude of this effect varies with polarization and frequency and with the amount of water in the plant, disappearing at low levels of vegetation water content. Although many of the canopy structures and orientations studied in this experiment are somewhat artificial, they serve to improve understanding of microwave energy interactions within a vegetation canopy and to aid in the development of appropriate physically based vegetation models.

Extensive studies conducted by several researchers using truck-mounted active microwave sensors have shown the sensitivity of these sensors to soil moisture variations. The logical extension of these results is the evaluation of similar systems at lower resolutions typical of operational systems. Data collected during a series of aircraft flights in 1978 and 1980 over four rangeland watersheds located near Chickasha, Oklahoma, were analyzed in this study. These data included scatterometer measurements made at 1.6 and 4.75 GHz using a NASA aircraft and ground observations of soil moisture for a wide range of moisture conditions. Data were analyzed for consistency and compared to previous truck and aircraft results. Results indicate that the sensor system is capable of providing consistent estimates of soil moisture under the conditions tested.

An experiment on remote sensing of soil moisture content was conducted over bare fields with microwave radiometers at the frequencies of 1.4, 5, and 10.7 GHz, during July-September of 1981. Three bare fields with different surface roughnesses and soil textures were prepared for the experiment. Ground-truth acquisition of soil temperatures and moisture contents for 5 layers down to the depths of 15 cm was made concurrently with radiometric measurements. The experimental results show that the effect of surface roughness is to increase the soil's brightness temperature and to reduce the slope of regression between brightness temperature and moisture content. The slopes of regression for soils with different textures are found to be comparable and the effect of soil texture is reflected in the difference of regression line intercepts at brightness-temperature axis. The result is consistent with laboratory measurement of soil's dielectric permittivity. Measurements on wet smooth bare fields give lower brightness temperatures at 5 than at 1.4 GHz. This phenomenon is not expected from current radiative transfer theory, using laboratory measurements of the relationship between dielectric permittivity and moisture content for different soil-water mixtures at frequencies of

Useful quantitative information about soil properties may be obtained by calibrating energy and moisture balance models with remotely sensed data. In this study, a soil physics model solves heat and moisture flux equations in the soil profile and is driven by the surface energy balance. Model-generated surface temperature and soil moisture and temperature profiles are then used in a microwave emission model to predict the soil brightness temperature. The model hydraulic parameters are varied until the predicted temperatures agree with the remotely sensed measurements. Using this method, values were estimated for hydraulic conductivity, matric potential and soil moisture at saturation, and a soil texture parameter. The conductivity agreed with values measured with an infiltration ring and the other parameters agreed with values in the literature.

A series of 10 aircraft flights was conducted over agricultural fields to evaluate relationships between observed surface soil moisture and soil moisture predicted using passive microwave sensor observations. An a priori approach was used to predict values of surface soil moisture for three types of fields: tilled corn, no-till corn with soybean stubble, and idle fields with corn stubble. Acceptable predictions were obtained for the tilled corn fields, while poor results were obtained for the others. The source of error is suspected to be the density and orientation of the surface stubble layer; however, further research is needed to verify this explanation. Temporal comparisons between observed, microwave predicted, and soil water-simulated moisture values showed similar patterns for tilled well-drained fields. Divergences between the observed and simulated measurements were apparent on poorly drained fields. This result may be of value in locating and mapping hydrologic contributing areas.

During the four years of the AgRISTARS Program, significant progress was made in quantifying the capabilities of microwave sensors for the remote sensing of soil moisture. In this paper we discuss the results of numerous field and aircraft experiments, analysis of spacecraft data, and modeling activities which examined the various noise factors such as roughness and vegetation that affect the interpretability of microwave emission measurements. While determining that a 21-cm wavelength radiometer was the best single sensor for soil moisture research, these studies demonstrated that a multisensor approach will provide more accurate soil moisture information for a wider range of naturally occurrring conditions.

Accurate assessment of preplanting soil moisture conditions is necessary for good agricultural management, and can have a significant influence on crop yield in the Texas Panhandle region. The Texas High Plains Underground Water Conservation District invests considerable time and money in developing a soil moisture deficit map each year in the hopes of achieving optimal use of irrigation water. Microwave sensors are responsive to surface soil moisture and, if used in this application, can provide timely and detailed information on root zone soil moisture. For this reason, an experiment was conducted in 1984 to evaluate the potential of aircraft-mounted passive microwave sensors. Microwave radiometer data were collected over a 2700 km2 area near Lubbock, Texas, with a processed resolution of 0.32 km2. These data were ground registered and converted to estimates of soil moisture using an appropriate model and land cover and soil texture information. Analyses indicate that the system provides an efficient means for mapping variations in soil moisture over large areas.