Electronically Scanned Thinned Array Radiometer Research Articles

Development of a deterministic downscaling algorithm for remote sensing soil moisture footprint using soil and vegetation classifications

[1] Soil moisture (SM) at the local scale is required to account for small-scale spatial heterogeneity of land surface because many hydrological processes manifest at scales ranging from cm to km. Although remote sensing (RS) platforms provide large-scale soil moisture dynamics, scale discrepancy between observation scale (e.g., approximately several kilometers) and modeling scale (e.g., few hundred meters) leads to uncertainties in the performance of land surface hydrologic models. To overcome this drawback, we developed a new deterministic downscaling algorithm (DDA) for estimating fine-scale soil moisture with pixel-based RS soil moisture and evapotranspiration (ET) products using a genetic algorithm. This approach was evaluated under various synthetic and field experiments (Little Washita-LW 13 and 21, Oklahoma) conditions including homogeneous and heterogeneous land surface conditions composed of different soil textures and vegetations. Our algorithm is based on determining effective soil hydraulic properties for different subpixels within a RS pixel and estimating the long-term soil moisture dynamics of individual subpixels using the hydrological model with the extracted soil hydraulic parameters. The soil moisture dynamics of subpixels from synthetic experiments matched well with the observations under heterogeneous land surface condition, although uncertainties (Mean Bias Error, MBE: −0.073 to −0.049) exist. Field experiments have typically more variations due to weather conditions, measurement errors, unknown bottom boundary conditions, and scale discrepancy between remote sensing pixel and model grid resolution. However, the soil moisture estimates of individual subpixels (from the airborne Electronically Scanned Thinned Array Radiometer (ESTAR) footprints of 800 m × 800 m) downscaled by this approach matched well (R: 0.724 to −0.914, MBE: −0.203 to −0.169 for the LW 13; R: 0.343–0.865, MBE: −0.165 to −0.122 for the LW 21) with the in situ local scale soil moisture measurements during Southern Great Plains Experiment 1997 (SGP97). The good correspondence of observed soil water characteristics θ(h) functions (from the soil core samples) and genetic algorithm (GA) searched soil parameters at the LW 13 and 21 sites demonstrated the robustness of the algorithm. Although the algorithm is tested under limited conditions at field scale, this approach improves the availability of remotely sensed soil moisture product at finer resolution for various land surface and hydrological model applications.

Comparison of Statistical and Multifractal Properties of Soil Moisture and Brightness Temperature From ESTAR and PSR During SGP99

Global soil moisture products are based on passive microwave sensors of brightness temperature at different frequencies, including Land C-bands. Two airborne sensors used to develop and test retrieval algorithms for soil moisture estimation are the L-band Electronically Scanned Thinned Array Radiometer (ESTAR) and the C-band Polarimetric Scanning Radiometer (PSR). In this letter, we compare the statistical and multifractal properties of soil moisture and brightness temperature from ESTAR and PSR during the Southern Great Plains Experiment in 1999. We show that differences between products are minimized at a support scale close to the satellite resolution. Then, we compare the subfootprint variabilities of each product in eight coarse domains of 25.6 × 25.6 km2. Scale-invariance and multifractal properties were found in all the three available soil moisture products, but multifractality was negligible in brightness temperature fields, implying that the nonlinear transformations in retrieval algorithms introduce the multifractal behavior in soil moisture. Subfootprint variability and the multifractal behavior for diverse wetness conditions are also different for the three products. Since discrepancies between the subfootprint variabilities of PSR and ESTAR brightness temperatures are small, the factors leading to the differences among the soil moisture products are mainly due to the level of detail of the retrieval algorithm and to the spatial variability of forcing data and parameters related to surface roughness and vegetation.

Near‐surface soil moisture assimilation for quantifying effective soil hydraulic properties using genetic algorithms: 2. Using airborne remote sensing during SGP97 and SMEX02

Pixel‐based effective soil hydraulic parameters are crucial inputs for large‐scale hydroclimatic modeling. In this paper, we extend/apply a genetic algorithm (GA) approach for estimating these parameters at the scale of an airborne remote sensing (RS) footprint. To estimate these parameters, we used a time series of near‐surface RS soil moisture data to invert a physically based soil‐water‐atmosphere‐plant (SWAP) model with a (multipopulated) modified‐microGA. Uncertainties in the solutions were examined in two ways: (1) by solving the inverse problem under various combinations of modeling conditions in a respective way; and (2) the same as the first method but the inverse solutions were determined in a collective way aimed at finding the robust solutions for all the modeling conditions (ensembles). A cross validation of the derived soil hydraulic parameters was done to check their effectiveness for all the modeling conditions used. For our case studies, we considered three electronically scanned thinned array radiometer (ESTAR) footprints in Oklahoma and four polarimetric scanning radiometer (PSR) footprints in Iowa during the Southern Great Plains 1997 (SGP97) Hydrology Experiment and Soil Moisture Experiment 2002 (SMEX02) campaigns, respectively. The results clearly showed the promising potentials of near‐surface RS soil moisture data combined with inverse modeling for determining average soil hydrologic properties at the footprint scale. Our cross validation showed that parameters derived by method 1 under water table (bottom boundary) conditions are applicable also for free‐draining conditions. However, parameters derived under free‐draining conditions generally produced too wet near‐surface soil moisture when applied under water table conditions. Method 2, on the other hand, produced robust parameter sets applicable for all modeling conditions used. These results were validated using distributed in situ soil moisture and soil hydraulic properties measurements, and texture‐based data from the UNSODA database. In this study, we conclude that inverse modeling of RS soil moisture data is a promising approach for parameter estimation at large measurement support scale. Nevertheless, the derived effective soil hydraulic parameters are subject to the uncertainties of remotely sensed soil moisture data and from the assumptions used in the soil‐water‐atmosphere‐plant modeling. Method 2 provides a flexible framework for accounting these sources of uncertainties in the inverse estimation of large‐scale soil hydraulic properties. We have illustrated this flexibility by combining multiple data sources and various modeling conditions in our large‐scale inverse modeling.

Root Zone Soil Moisture Assessment Using Remote Sensing and Vadose Zone Modeling

Soil moisture is an important hydrologic state variable critical to successful hydroclimatic and environmental predictions. Soil moisture varies both in space and time because of spatio‐temporal variations in precipitation, soil properties, topographic features, and vegetation characteristics. In recent years, air‐ and space‐borne remote sensing campaigns have successfully demonstrated the use of passive microwave remote sensing to map soil moisture status near the soil surface (≈0–0.05 m below the ground) at various spatial scales. In this study root zone (e.g., ≈0–0.6 m below the ground) soil moisture distributions were estimated across the Little Washita watershed (Oklahoma) by assimilating near‐surface soil moisture data from remote sensing measurements using the Electronically Scanned Thinned Array Radiometer (ESTAR) with an ensemble Kalman filter (EnKF) technique coupled with a numerical one‐dimensional vadose zone flow model (HYDRUS‐ET). The resulting distributed root zone soil moisture assessment tool (SMAT) is based on the concept of having parallel noninteracting streamtubes (hydrologic units) within a geographic information system (GIS) platform. The simulated soil moisture distribution at various depths and locations within the watershed were compared with measured profile soil moisture data using time domain reflectometry (TDR). A reasonable agreement was found under favorable conditions between footprint‐scale model estimations and point‐scale field soil moisture measurements in the root zone. However, uncertainties introduced by precipitation and soil hydraulic properties caused suboptimal performance of the integrated model. The SMAT holds great promise and offers flexibility to incorporate various data assimilation techniques, scaling, and other hydrological complexities across large landscapes. The integrated model can be useful for simulating profile soil moisture estimation and for predicting transient soil moisture behavior for a range of hydrological and environmental applications.

Optimal multiscale Kalman filter for assimilation of near‐surface soil moisture into land surface models

We undertake an alternative and novel approach to assimilation of near‐surface soil moisture into land surface models by means of an extension of multiscale Kalman filtering (MKF). While most data assimilation studies rely on the assumption of spatially independent near‐surface soil moisture observations to attain computational tractability in large‐scale problems, MKF allows us to explicitly and very efficiently model the spatial dependence and scaling properties of near‐surface soil moisture fields. Furthermore, MKF has the appealing ability to cope with model predictions and observations made at different spatial scales. Yet another essential feature of our approach is that we resort to the use of the expectation maximization (EM) algorithm in conjunction with MKF so that the statistical parameters inherent to MKF may be optimally determined directly from the data at hand and allowed to vary over time. This constitutes a significant advantage since these parameters (e.g., observation and model error noise variances) essentially determine the performance of the assimilation approach and have so far been most commonly prescribed heuristically and not allowed to evolve in time. We test our approach by assimilating the near‐surface soil moisture fields derived from electronically scanned thinned array radiometer (ESTAR) during the Southern Great Plains Hydrology experiment of 1997 (SGP97) into the three‐layer variable infiltration capacity (VIC‐3L) land surface model. The results show that assimilation significantly improves the short‐term predictions of soil moisture and energy fluxes from VIC‐3L, especially with regard to capturing the spatial structure of these state variables. Additionally, we find that allowing the statistical parameters of the assimilation algorithm to evolve in time allows for an adequate representation of the time‐varying uncertainties in land surface model predictions.

Soil moisture retrieval during the Southern Great Plains Hydrology Experiment 1999: A comparison between experimental remote sensing data and operational products

Soil moisture images for the Southern Great Plains Hydrology Experiment (SGP99) were derived from airborne passive microwave measurements obtained from the electronically scanned thinned array radiometer (ESTAR) and the polarimetric scanning radiometer (PSR) at L‐band and C‐band, respectively. In order to mimic operational products, a robust retrieval method is chosen and the corrections for vegetation and surface roughness are based on average values from the literature. Validation against field measurements results in root‐mean‐square errors and biases of 2.9% and 4.2% in volumetric soil moisture for the ESTAR data set and 4.6% and 6.0% for the PSR product. For this sparsely vegetated experiment region, the quality of the C‐band derived soil moisture is therefore comparable to the corresponding L‐band product. The accuracies of the operational data sets, namely, the European Centre for Medium‐Range Weather Forecasts (ECMWF) 40 year reanalysis product (ERA40) and the European Remote Sensing Satellite (ERS) scatterometer derived soil moisture, are quantified based on the high‐resolution PSR soil moisture images for the SGP99 region. The ERA40 reanalysis comprises soil moisture data for four soil layers at the T159 spectral resolution. The top 7 cm layer soil moisture product is in reasonable agreement with the C‐band derived soil moisture data sets. Temporal and spatial patterns are well represented, but a significant wet bias of up to 14.4% (ERA40) is present in dry conditions. In order to evaluate the quality of the operational data sets on a longer temporal timescale, in situ soil moisture measurements at the Little Washita National Oceanic and Atmospheric Administration/Atmospheric Turbulence and Diffusion Division (NOAA/ATDD) site are used to analyze time series for June 1997 to December 1998. The temporal evolution of soil moisture is captured reasonably well by the ERA40 product and the ERS scatterometer derived surface soil moisture data set. RMS errors were found to be 5.6% and 5.7%, respectively. The study shows that passive microwave remote sensing has the potential to improve operational products.

Using a Microwave Emission Model to Estimate Soil Moisture from ESTAR Observations during SGP99

Abstract The 1999 Southern Great Plains Hydrology Experiment (SGP99) provides comprehensive datasets for evaluating microwave remote sensing of soil moisture algorithms that involve complex physical properties of soils and vegetation. The Land Surface Microwave Emission Model (LSMEM) is presented and used to retrieve soil moisture from brightness temperatures collected by the airborne Electronically Scanned Thinned Array Radiometer (ESTAR) L-band radiometer. Soil moisture maps for the SGP99 domain are retrieved using LSMEM, surface temperatures computed using the Variable Infiltration Capacity (VIC) land surface model, standard soil datasets, and vegetation parameters estimated through remote sensing. The retrieved soil moisture is validated using field-scale and area-averaged soil moisture collected as part of the SGP99 experiment, and had a rms range for the area-averaged soil moisture of 1.8%–2.8% volumetric soil moisture.

A downscaling framework for L band radiobrightness temperature imagery

In this paper we introduce a general downscaling framework and apply it to L band microwave radiobrightness temperature fields retrieved from electronically scanned thinned array radiometer (ESTAR). The gist of the downscaling scheme presented in this paper is the statistical characterization of scale‐invariant properties of the wavelet coefficients or fluctuations from long memory 1/f processes. We test the proposed downscaling framework with the radiobrightness temperature images collected during the Southern Great Plains hydrology experiment of 1997. We produce realizations of radiobrightness temperature at 800‐m resolution given a mean‐area value at approximately 30‐km resolution (the near‐future expected operational scale). The results obtained evince that the proposed downscaling methodology is capable of accurately preserving the variability and overall structure of spatial dependence of the observed radiobrightness temperature fields.

Estimating surface soil moisture with the scanning low frequency microwave radiometer (SLFMR) during the Southern Great Plains 1997 (SGP97) hydrology experiment

The scanning low frequency microwave radiometer (SLFMR) was used to map surface soil moisture (0–5 cm depth) during the Southern Great Plains 1997 (SGP97) hydrology experiment. On June 29, July 2, and July 3, surface soil moisture maps with a pixel resolution of 200 m were obtained using a soil moisture retrieval algorithm, developed for L-band (1.4 GHz frequency, 21 cm wavelength) passive microwave data. In comparison with the 800 m resolution data from the electronically scanned thinned array radiometer (ESTAR), the higher resolution SLFMR data required a more site specific calibration. After calibration root mean square difference (RMSD) between model and observed surface soil moisture observations were on the order of 5%. Although the higher pixel resolution generally provided brightness temperatures of individual fields, it is also meant the greater spatial variability in land cover properties (primarily vegetation cover) were affecting the microwave observations and had to be accounted for in the soil moisture algorithm. Parameters in the soil moisture algorithm required local recalibration, particularly for the heavily vegetated fields, in order to account for vegetation effects on the microwave brightness temperatures. Thus having microwave data at resolutions that differentiate field boundaries with sharp contrasts in vegetation cover amounts will likely require greater variation in parameter values (and more uncertainty) be assigned to the soil moisture algorithm than at coarser resolutions. This result indicates that parameter values in the soil moisture algorithm may be resolution dependent under certain land cover conditions, particularly at resolutions that discriminate field boundaries.

Using GIS in passive microwave soil moisture mapping and geostatistical analysis

Soil moisture is an important hydrologic variable in both natural and agricultural ecosystems. Recent experiments, based on remote sensing data, provided observations of soil moisture distributions at a regional scale. One of the instruments is the Electronically Scanned Thinned Array Radiometer (ESTAR). Deployed on the aircraft, it measures brightness temperature (with the spatial resolution about 400 m) that can be converted into estimates of volumetric soil moisture. The soil moisture retrieval algorithm requires additional data layers (like soil physical temperature, land cover, and soil texture) that come from different sources and have to be combined together in a uniform GIS. The objective of this paper is to report results of the GIS use to map soil surface moisture from passive microwave measurements and to quantify spatial structure of soil moisture distributions. The data were collected across a 10 000 km 2 area in Oklahoma during June and July 1997. The precipitation data and ground measurements from the test sites were also incorporated into GIS. The GIS data layers enable soil moisture distribution analysis with geostatistical tools. Semi-variograms of soil moisture were nested and showed the presence of two scales. Rainfall was the dominant factor influencing soil moisture distribution at the regional scale, whereas soil texture was important at the local scale. Information about soil moisture, obtained with remote sensing techniques over space and time, and organized in a GIS with complementary environmental data layers, can be used in landscape pattern analysis, landscape models, land use assessment and management.

Spatio-temporal evolution and time-stable characteristics of soil moisture within remote sensing footprints with varying soil, slope, and vegetation

Air-borne passive microwave remote sensors measure soil moisture at the footprint scale, a scale of several hundred square meters or kilometers that encompasses different characteristic combinations of soil, topography, vegetation, and climate. Studies of within-footprint variability of soil moisture are needed to determine the factors governing hydrologic processes and their relative importance, as well as to test the efficacy of remote sensors. Gridded ground-based impedance probe water content data and aircraft-mounted Electronically Scanned Thinned Array Radiometer (ESTAR) pixel-average soil moisture data were used to investigate the spatio-temporal evolution and time-stable characteristics of soil moisture in three selected (LW03, LW13, LW21) footprints from the Southern Great Plains 1997 (SGP97) Hydrology Experiment. Better time-stable features were observed within a footprint containing sandy loam soil than within two pixels containing silty loam soil. Additionally, flat topography with split wheat/grass land cover produced the largest spatio-temporal variability and the least time stability in soil moisture patterns. A comparison of ground-based and remote sensing data showed that ESTAR footprint-average soil moisture was well calibrated for the LW03 pixel with sandy loam soil, rolling topography, and pasture land cover, but improved calibration is warranted for the LW13 (silty loam soil, rolling topography, pasture land) and LW21 (silty loam soil, flat topography, split vegetation of wheat and grass land with tillage practice) pixels. Footprint-scale variability and associated nonlinear soil moisture dynamics may prove to be critical in the regional-scale hydroclimatic models.

Verification of Patch- and Regional-Scale Energy Balance Estimates Derived from Microwave and Optical Remote Sensing during SGP97

Abstract The 1997 Southern Great Plains Hydrology Experiment (SGP97) was designed and conducted to extend surface soil moisture retrieval algorithms based on passive microwave observations to coarser resolutions, larger regions with more diverse conditions, and longer time periods. The L-band Electronically Scanned Thinned Array Radiometer (ESTAR) on an airborne platform was used for daily mapping of surface soil moisture over an area of approximately 40 km × 260 km for a 1-month period. Results showed that the soil moisture retrieval algorithm performed the same as in previous investigations, demonstrating consistency of both the retrieval and the instrument. This soil moisture product at 800-m pixel resolution together with land use and fractional vegetation cover information is used in a remote sensing model for computing spatially distributed fluxes over the SGP97 domain. Validation of the model output is performed at the patch scale using tower-based measurements and at regional scale using aircraft ...

ESTAR measurements during the Southern Great Plains experiment (SGP99)

During the Southern Great Plains experiment (SGP99), the electronically scanned thinned array radiometer (ESTAR) mapped L-band brightness temperature over a swath about 50-km wide and 300 km long, extending west from Oklahoma City, OK, to El Reno, OK, and north from the Little Washita River watershed to the Kansas border. ESTAR flew on the NASA P-3B Orion aircraft at an altitude of 7.6 km, and maps were made on seven days between July 8-20, 1999. The brightness temperature maps reflect the patterns of soil moisture expected from rainfall and are consistent with values of soil moisture observed at the research sites within the SGP99 study area and with previous measurements in this area. The data add to the resources for hydrologic modeling in this area and are further validation of the technology represented by ESTAR as a potential path to a future mission to map soil moisture globally from space.

Influence of near-surface soil moisture on regional scale heat fluxes: model results using microwave remote sensing data from SGP97

During the 1997 Southern Great Plains Hydrology Experiment (SGP97), passive microwave observations using the L-band electronically scanned thinned array radiometer (ESTAR) were used to extend surface soil moisture retrieval algorithms to coarser resolutions and larger regions with more diverse conditions. This near-surface soil moisture product (W) at 800 m pixel resolution together with land use and fractional vegetation cover (f/sub c/) estimated from normalized difference vegetation index (NDVI) was used for computing spatially distributed sensible (H) and latent (LE) heat fluxes over the SGP97 domain (an area /spl sim/40/spl times/260 km) using a remote sensing model (called the two-source energy Balance-soil moisture, TSEB/sub SM/, model). With regional maps of W and the heat fluxes, spatial correlations were computed to evaluate the influence of W on H and LE. For the whole SGP97 domain and full range in f/sub c/, correlations (R) between W and LE varied from 0.4 to 0.6 (R/spl sim/0.5 on average), while correlations between W and H varied from -0.3 to -0.7 (R/spl sim/-0.6 on average). The W-LE and W-H correlations were dramatically higher when variability due to f/sub c/ was considered by using NDVI as a surrogate for f/sub c/ and computing R between heat fluxes and corresponding W values under similar fractional vegetation cover conditions. The results showed a steady decline in correlation with increasing NDVI or f/sub c/. Typically, |R|/spl gsim/0.9 for data sorted by NDVI having values /spl lsim/0.5 or f/sub c//spl lsim/0.5, while |R|/spl lsim/0.5 for the data sorted under high canopy cover where NDVI/spl gsim/0.6 or f/sub c//spl gsim/0.7.

The impact on area‐averaged heat fluxes from using remotely sensed data at different resolutions: A case study with Washita '92 data

Landscape‐scale fluxes are derived using an energy balance model with remotely sensed input data at different pixel resolutions. The model explicitly evaluates energy flux contributions from the soil and vegetation using remotely sensed near‐surface soil moisture and normalized difference vegetation index (NDVI) to define key model variables. The remotely sensed data used in the model were collected as part of the Washita '92 Experiment conducted in the Little Washita watershed, a subhumid catchment in southwest Oklahoma. Near‐surface soil moisture maps covering approximately an 800 km2 area at 0.2 km resolution were generated using aircraft‐based passive microwave observations from the L band electronically scanned thinned array radiometer (ESTAR). A spatially distributed NDVI map was derived from a SPOT satellite image. Daily values of the midday Bowen ratio (BO, ratio of sensible to latent heat flux) computed by the model indicated that areas with low vegetation cover or bare soil and senescent vegetation were drying out significantly (i.e., dramatic increases in BO) while other areas with higher vegetation cover showed smaller increases in BO in response to a drying soil surface. After a few days of drying, BO values computed by the model ranged from ∼0.1 to values ≳2 over the image. The resulting spatially distributed maps of BO suggested a heterogeneous surface at the field or patch scale. A satellite‐based L band ESTAR would have a pixel resolution on the order of 101 km or larger (i.e., landscape scale). Since fluxes are nonlinearly related to model input variables/parameters, defining model inputs at resolutions significantly larger than the patch scale may cause significant errors in flux calculations. Potential errors in flux calculations were investigated by evaluating differences in area‐averaged flux estimates for the ∼18 × 45 km study area using the model with the remotely sensed near‐surface soil moisture and NDVI data at the following pixel resolutions: (1) 0.2 km (original pixel resolution), (2) 9 km, and (3) the whole image, which has an effective pixel resolution on the order of 28 km. Differences in the area‐ averaged sensible and latent fluxes computed by the model at the three resolutions were within 10%. Such minor discrepancies in the area‐average fluxes suggests that the heterogeneity in soil moisture and vegetation type and cover was not of the type to affect regional flux predictions using significantly coarser resolution information. There was a consistent decrease in area‐averaged latent heat flux estimates at the coarser resolution caused by biases in area‐averaged values of the other three energy flux components. The consistent decrease in area‐average latent heat flux caused the area‐averaged midday BO to increase with decreasing pixel resolution resulting in ≈15% increase from 200 m to using the full image. These results, however, depended on the method used in aggregating leaf area index to the coarser resolutions, indicating that techniques for scaling up key model parameters have a significant effect on area‐average flux predictions.

Soil moisture mapping at regional scales using microwave radiometry: the Southern Great Plains Hydrology Experiment

Surface soil moisture retrieval algorithms based on passive microwave observations, developed and verified at high spatial resolution, were evaluated in a regional scale experiment. Using previous investigations as a base, the Southern Great Plains Hydrology Experiment (SGP97) was designed and conducted to extend the algorithm to coarser resolutions, larger regions with more diverse conditions, and longer time periods. The L-band electronically scanned thinned array radiometer (ESTAR) was used for daily mapping of surface soil moisture over an area greater than 10000 km/sup 2/ for a one month period. Results show that the soil moisture retrieval algorithm performed the same as in previous investigations, demonstrating consistency of both the retrieval and the instrument. Error levels were on the order of 3% for area Integrated averages of sites used for validation. This result showed that for the coarser resolution used that the theory and techniques employed in the algorithm apply at this scale. Spatial patterns observed in the Little Washita Watershed in previous investigations were also observed. These results showed that soil texture dominated the spatial pattern at this scale. However, the regional soil moisture patterns were a reflection of the spatially variable rainfall and soil texture patterns were not as obvious.

The role of mutual coupling in the performance of synthetic aperture radiometers

This paper describes research to evaluate the role of mutual coupling between antennas in the performance of synthetic aperture radiometers. Measurements, taken from the aircraft prototype instrument ESTAR (electronically scanned thinned array radiometer), are presented to demonstrate the importance of mutual coupling. Theory for the ESTAR configuration (an array of dipoles) is presented and checked against the measurements. This theory is then used to assess the effects of small displacements in the elementary antennas in the synthesis array of a hypothetical sensor in space. The measurement errors that result from small displacements (as small as a few hundredths of a wavelength) can be important for applications such as the measurement of soil moisture. Therefore the design approach for future systems should include the control of these and should also include the capability to perform onboard calibration to monitor and remedy detrimental effects. A synthetic aperture radiometer is particularly suited to the measurement of mutual coupling because the basic hardware (a correlation radiometer) measures the product for each antenna pair in the array. Hence, in conclusion, this paper proposes a concept for monitoring changes in mutual coupling to correct for such effects in formation of images.

Statistical characterization of remotely sensed soil moisture images

Using ESTAR (Electronically Scanned Thinned Array Radiometer) passive microwave remotely sensed soil moisture data from the Washita 92 experiment, we examine the appropriateness of several probability density functions as a candidate for soil moisture distribution. Although the normal distribution is found to approximate the data well, it is shown that such a distribution cannot capture the inherent spatial structure of soil moisture. We confirm the recent finding that the variance of soil moisture images follows a power law decay and show that such a scaling is valid over a wider range of scales than previously reported. We also relate the scaling exponent of the variance to highly correlated cluster structure: in soil moisture images. Our analysis of scaling for higher-order moments suggests that soil moisture images may not be simple scaling.

Mapping surface soil moisture using an aircraft-based passive microwave instrument: algorithm and example

Microwave remote sensing at L-band (21 cm wavelength) can provide a direct measurement of the surface soil moisture for a range of cover conditions and within reasonable error bounds. Surface soil moisture observations are rare and, therefore, the use of these data in hydrology and other disciplines has not been fully explored or developed. Without satellite-based observing systems, the only way to collect these data in large-scale studies is with an aircraft platform. Recently, aircraft systems such as the push broom microwave radiometer (PBMR) and the electronically scanned thinned array radiometer (ESTAR) have been developed to facilitate such investigations. In addition, field experiments have attempted to collect the passive microwave data as part of an integrated set of hydrologic data. One of the most ambitious of these investigations was the Washita'92 experiment. Preliminary analysis of these data has shown that the microwave observations are indicative of deterministic spatial and temporal variations in the surface soil moisture. Users of these data should be aware of a number of issues related to using aircraft-based systems and practical approaches to applying soil moisture estimation algorithms to large data sets. This paper outlines the process of mapping surface soil moisture from an aircraft-based passive microwave radiometer system for the Washita'92 experiment.

Large area mapping of soil moisture using the ESTAR passive microwave radiometer in Washita'92

Washita'92 was a large-scale study of remote sensing and hydrology conducted on the Little Washita watershed in southwest Oklahoma. Data collection during the experiment included passive microwave observations using an L-band electronically scanned thinned array radiometer ( ESTAR) and surface soil moisture observations at sites distributed over the area. Data were collected on 8 days over a 9-day period in June 1992. The watershed was saturated with a great deal of standing water at the outset of the study. During the experiment there was no rainfall and surface soil moisture observations exhibited a drydown pattern over the period. Significant variations in the level and rate of change in surface soil moisture were noted over areas dominated by different soil textures. ESTAR data were processed to produce brightness temperature maps of a 740 sq. km. area on each of the 8 days. These data exhibited significant spatial and temporal patterns. Spatial patterns were clearly associated with soil textures and temporal patterns with drainage and evaporative processes. Relationships between the groundsampled soil moisture and the brightness temperatures were consistent with previous results. Spatial averaging of both variables was analyzed to study scaling of soil moisture over a mixed landscape. Results of these studies showed that a strong correlation is retained at these scales, suggesting that mapping surface moisture for large footprints may provide important information for regional studies.