High-resolution change estimation of soil moisture using L-band radiometer and Radar observations made during the SMEX02 experiments

The soil moisture experiments held during June-July 2002 (SMEX02) at Iowa demonstrated the potential of the L-band radiometer (PALS) in estimation of near surface soil moisture under dense vegetation canopy conditions. The L-band radar was also shown to be sensitive to near surface soil moisture. However, the spatial resolution of a typical satellite L-band radiometer is of the order of tens of kilometers, which is not sufficient to serve the full range of science needs for land surface hydrology and weather modeling applications. Disaggregation schemes for deriving subpixel estimates of soil moisture from radiometer data using higher resolution radar observations may provide the means for making available global soil moisture observations at a much finer scale. This paper presents a simple approach for estimation of change in soil moisture at a higher (radar) spatial resolution by combining L-band copolarized radar backscattering coefficients and L-band radiometric brightness temperatures. Sensitivity of AIRSAR L-band copolarized channels has been demonstrated by comparison with in situ soil moisture measurements as well as PALS brightness temperatures. The change estimation algorithm has been applied to coincident PALS and AIRSAR datasets acquired during the SMEX02 campaign. Using AIRSAR data aggregated to a 100-m resolution, PALS radiometer estimates of soil moisture change at a 400-m resolution have been disaggregated to 100-m resolution. The effect of surface roughness variability on the change estimation algorithm has been explained using integral equation model (IEM) simulations. A simulation experiment using synthetic data has been performed to analyze the performance of the algorithm over a region undergoing gradual wetting and dry down.     

Sensitivity, spatial heterogeneity, and scaling of C-band microwave brightness temperatures for land hydrology studies

Soil moisture is one of the most important hydrological variables that characterizes the land surface water and energy balance. Measurements from space suffer from the problem of subpixel heterogeneity, i.e., soil moisture has spatial variability at all scales; therefore, it is important to realize the exact physical implication of the single value of the satellite measurements. In this paper, we study the sensitivity of C-band passive microwave brightness temperatures to various land surface variables. The issue of heterogeneity and its role in interpretation of single spatially averaged value of satellite brightness temperature is investigated. Finally, we use the brightness temperatures from the Scanning Multichannel Microwave Radiometer to characterize spatial variability and to understand the variation of this variability with scale.     

A combined modeling and multispectral/multiresolution remote sensing approach for disaggregation of surface soil moisture: application to SMOS configuration

A new physically based disaggregation method is developed to improve the spatial resolution of the surface soil moisture extracted from the Soil Moisture and Ocean Salinity (SMOS) data. The approach combines the 40-km resolution SMOS multiangular brightness temperatures and 1-km resolution auxiliary data composed of visible, near-infrared, and thermal infrared remote sensing data and all the surface variables involved in the modeling of land surface-atmosphere interaction available at this scale (soil texture, atmospheric forcing, etc.). The method successively estimates a relative spatial distribution of soil moisture with fine-scale auxiliary data, and normalizes this distribution at SMOS resolution with SMOS data. The main assumption relies on the relationship between the radiometric soil temperature inverted from the thermal infrared and the microwave soil moisture. Based on synthetic data generated with a land surface model, it is shown that the radiometric soil temperature can be used as a tracer of the spatial variability of the 0-5 cm soil moisture. A sensitivity analysis shows that the algorithm remains stable for big uncertainties in auxiliary data and that the uncertainty in SMOS observation seems to be the limiting factor. Finally, a simple application to the SGP97/AVHRR data illustrates the usefulness of the approach.     

A Simple Method to Disaggregate Passive Microwave-Based Soil Moisture

This paper develops two alternative approaches for downscaling passive microwave-derived soil moisture. Ground and airborne data collected over the Walnut Gulch experimental watershed during the Monsoon'90 experiment were used to test these approaches. These data consisted of eight micrometeorological stations (METFLUX) and six flights of the L-band Push Broom Microwave Radiometer (PBMR). For each PBMR flight, the 180-m resolution L-band pixels covering the eight METFLUX sites were first aggregated to generate a 500-m ldquocoarse-scalerdquo passive microwave pixel. The coarse-scale-derived soil moisture was then downscaled to the 180-m resolution using two different surface soil moisture indexes (SMIs): (1) the evaporative fraction (EF), which is the ratio of the evapotranspiration to the total energy available at the surface; and (2) the actual EF (AEF), which is defined as the ratio of the actual-to-potential evapotranspiration. It is well known that both SMIs depend on the surface soil moisture. However, they are also influenced by other factors such as vegetation cover, soil type, root-zone soil moisture, and atmospheric conditions. In order to decouple the influence of soil moisture from the other factors, a land surface model was used to account for the heterogeneity of vegetation cover, soil type, and atmospheric conditions. The overall accuracy in the downscaled values was evaluated to 3% (vol.) for EF and 2% (vol.) for AEF under cloud-free conditions. These results illustrate the potential use of satellite-based estimates of instantaneous evapotranspiration on clear-sky days for downscaling the coarse-resolution passive microwave soil moisture.     

Beam-filling effect correction with subpixel cloud fraction using a neural network

In this study, the effects of cloud inhomogeneity on microwave rain rate retrievals are investigated. A single-channel (85 GHz) empirically based algorithm using a neural network approach is presented. The objective is to correct the beam-filling error (BFE), that might occur because of the inherent variability within coarse microwave pixels, with subpixel information. To this aim, we used the Tropical Rainfall Measuring Mission passive microwave, thermal infrared and radar data. The integration of spatial information into the retrieval algorithm enables us to partially overcome the BFE. We use two parameters which characterize the horizontal cloud inhomogeneity within the microwave radiometer field of view, and we add them to simulated brightness temperatures as inputs of the neural network algorithm. The first one is the cloud fraction derived from infrared measurement, and the second corresponds to the fraction of the rainy area derived from radar measurements. The output rain rates were validated using the Precipitation Radar data. It was found that adding cloud fraction of microwave pixels, can lead to more accurate retrievals. Instantaneous precipitation estimates demonstrated correlations of /spl sim/0.6-0.7 and /spl sim/0.7-0.8 with radar-derived rain rates, for ocean and land retrievals respectively. In spite of the problem inherent in deriving the cloud (or rain) fraction, the initial validation results presented in this study are reasonably encouraging and show the advantage of utilizing the information from different sensors in order to optimize the retrieval of rainfall.     

Spatial resolution and processing tradeoffs for HYDROS: application of reconstruction and resolution enhancement techniques

Recent developments in reconstruction and resolution enhancement for microwave instruments suggest a possible tradeoff between computation, resolution, and downlink data rate based on postcollection reconstruction/resolution enhancement processing. The Hydrospheric State (HYDROS) mission is designed to measure global soil moisture and freeze/thaw state in support of weather and climate prediction, water, energy, and carbon cycle studies, and natural hazards monitoring. It will use an active and passive L-band microwave system that optimizes measurement accuracy, spatial resolution, and coverage. The active channels use synthetic aperture radar-type processing to achieve fine spatial resolution, requiring a relatively high downlink data rate and ground processor complexity. To support real-time applications and processing, an optional postcollection reconstruction and resolution enhancement method is investigated. With this option, much lower rate real-aperture radar data are used along with ground-based postprocessing algorithms to enhance the resolution of the observations to achieve the desired 10-km resolution. Several approaches are investigated in this paper. It is determined that a reconstruction/resolution enhancement technique combining both forward- and aft-looking measurements enables estimation of 10-km resolution or better backscatter values at acceptable accuracy. Key tradeoffs to achieve this goal are considered.     

A method for retrieving high-resolution surface soil moisture from hydros L-band radiometer and Radar observations

NASA's Earth System Science Pathfinder Hydrospheric States (Hydros) mission will provide the first global scale space-borne observations of Earth's soil moisture using both L-band microwave radiometer and radar technologies. In preparation for the Hydros mission, an observation system simulation experiment (OSSE) has been conducted. As a part of this OSSE, the potential for retrieving useful surface soil moisture at spatial resolutions of 9 and 3 km was explored. The approach involved optimally merging relatively accurate 36-km radiometer brightness temperature and relatively noisy 3-km radar backscatter cross section observations using a Bayesian method. Based on the Hydros OSSE data sets with low and high noises added to the simulated observations or model parameters, the Bayesian method performed better than direct inversion of either the brightness temperature or radar backscatter observations alone. The root-mean-square errors of 9-km soil moisture retrievals from the Bayesian merging method were reduced by 0.5 %vol/vol and 1.4 %vol/vol from the errors of direct radar inversions for the entire OSSE domain of all 34 consecutive days for the low and high noise data sets, respectively. Improvement in soil moisture estimates using the Bayesian merging method over the direct inversions of radar or radiometer data were even more significant for soil moisture retrieval at 3-km resolution. However, to address the representativeness of these results at the global and multiyear scales, further performance comparison studies are needed, particularly with actual field data.     

The hydrosphere State (hydros) Satellite mission: an Earth system pathfinder for global mapping of soil moisture and land freeze/thaw

The Hydrosphere State Mission (Hydros) is a pathfinder mission in the National Aeronautics and Space Administration (NASA) Earth System Science Pathfinder Program (ESSP). The objective of the mission is to provide exploratory global measurements of the earth's soil moisture at 10-km resolution with two- to three-days revisit and land-surface freeze/thaw conditions at 3-km resolution with one- to two-days revisit. The mission builds on the heritage of ground-based and airborne passive and active low-frequency microwave measurements that have demonstrated and validated the effectiveness of the measurements and associated algorithms for estimating the amount and phase (frozen or thawed) of surface soil moisture. The mission data will enable advances in weather and climate prediction and in mapping processes that link the water, energy, and carbon cycles. The Hydros instrument is a combined radar and radiometer system operating at 1.26 GHz (with VV, HH, and HV polarizations) and 1.41 GHz (with H, V, and U polarizations), respectively. The radar and the radiometer share the aperture of a 6-m antenna with a look-angle of 39/spl deg/ with respect to nadir. The lightweight deployable mesh antenna is rotated at 14.6 rpm to provide a constant look-angle scan across a swath width of 1000 km. The wide swath provides global coverage that meet the revisit requirements. The radiometer measurements allow retrieval of soil moisture in diverse (nonforested) landscapes with a resolution of 40 km. The radar measurements allow the retrieval of soil moisture at relatively high resolution (3 km). The mission includes combined radar/radiometer data products that will use the synergy of the two sensors to deliver enhanced-quality 10-km resolution soil moisture estimates. In this paper, the science requirements and their traceability to the instrument design are outlined. A review of the underlying measurement physics and key instrument performance parameters are also presented.     

Statistical characteristics of simulated radar imagery from baresoil surfaces: effects of surface roughness and soil moisturevariability

The potential of high-resolution radar imagery to estimate various hydrological parameters, such as soil moisture, has long been recognized. Image simulation is one approach to study the interrelationships between the radar response and the underlying ground parameters. In order to perform realistic simulations, the authors incorporated the effects of naturally occurring spatial variability and spatial correlations of those ground parameters that affect the radar response, primarily surface roughness and soil moisture. Surface roughness and soil moisture images were generated for a hypothetical 100×100 m bare soil surface area at 1 m resolution using valid probability distributions and correlation lengths. These values were then used to obtain copolarized radar scattering coefficients at 2 GHz (L band) and 10 GHz (X band) frequencies using appropriate backscatter models, which were then converted to a digital number within 0-255 gray scale in order to generate radar images. The effect of surface roughness variability causes variability in the radar image, which is more apparent under smooth soil conditions. On the other hand, the inherent spatial pattern in soil moisture tends to cause similar patterns in the radar image under rougher soil conditions. The maximum difference between contrast-enhanced mean values of the radar image digital number due to moisture variations occurs at surface roughness values in the 1.5-2.0 cm range     

Soil moisture and rainfall estimation over a semiarid environmentwith the ESTAR microwave radiometer

The application of an airborne electronically steered thinned array L-band radiometer (ESTAR) for soil moisture mapping was investigated over the semiarid rangeland Walnut Gulch Watershed in southeastern Arizona. During the experiment, antecedent rainfall and evaporation were very different and resulted in a wide range of soil moisture conditions. The high spatial variability of rainfall events within this region resulted in moisture conditions with distinct spatial patterns. Analysis showed a correlation between the decrease in brightness temperature after a rainfall and the amount of rain. The sensor's performance was verified using two approaches. First, the microwave data were used to predict soil moisture, and the predictions were compared to ground observations of soil moisture. A second verification used an extensive data set collected the previous year at the same site with a conventional L-band push broom microwave radiometer (PBMR). Both tests showed that the ESTAR is capable of providing soil moisture with the same level of accuracy as existing systems     

Ultrawideband Radar Measurements of Thickness of Snow Over Sea Ice

An accurate knowledge of snow thickness and its variability over sea ice is crucial in determining the overall polar heat and freshwater budget, which influences the global climate. Recently, algorithms have been developed to extract snow thicknesses from satellite passive microwave data. However, validation of these data over the large footprint of the passive microwave sensor has been a challenge. The only method used thus far has been with meter sticks during ship cruises. To address this problem, we developed an ultrawideband frequency-modulated continuous-wave radar to measure the snow thickness over sea ice. We synthesized a very linear chirp signal by using a phase-locked loop with a digitally generated chirp signal as a reference to obtain a fine-range resolution. The radar operates over the frequency range from 2-8 GHz. We made snow-thickness measurements over the Antarctic sea ice by operating the radar from a sled in September and October 2003. We performed radar measurements over 11 stations with varying snow thicknesses between 4 and 85 cm. We observed an excellent agreement between radar estimates of snow thickness with physical measurements, achieving a correlation coefficient of 0.95 and a vertical resolution of about 3 cm. Comparison of simulated radar waveforms using a simple transmission line model with the measurements confirms our expectations that echoes from snow-covered sea ice are dominated by reflections from air-snow and snow-ice interfaces.     

Multiple resolution analysis of L-band brightness temperature forsoil moisture

Passive microwave Earth observing systems provide coarse resolution data. Heterogeneity in physical characteristics will typically be present within footprints, especially over land. How this affects the development and validation of methods of retrieving soil moisture has not been verified. In this study, aircraft-based 1.4 GHz microwave radiometer data were collected sit several altitudes over test sites where soil moisture was measured concurrently. The use of multiple flightlines at lower altitudes allowed the direct comparison of different spatial resolutions using independent samples over the same ground location. Results showed that the brightness temperature data from 1.4 GHz sensor in this study region provides the same mean values for an area regardless of the spatial resolution of the original data. The relationship between brightness temperature and soil moisture was similar at different resolutions. These results suggest that soil moisture retrieval methods developed using high resolution data can be extrapolated to satellite scales     

Radar measurement of soil moisture content

The effect of soil moisture on the radar backscattering coefficient was investigated by measuring the 4-8 GHz spectral response from two types of bare-soil fields: slightly rough and very rough, in terms of the wavelength. An FM-CW radar system mounted atop a 75-ft truck-mounted boom was used to measure the return at 10 frequency points across the 4-8 GHz band, at 8 different look angles (0degthrough70deg), and for all polarization combinations. A total of 17 sets of data were collected covering the range 4-36 percent soil moisture content by weight. The results indicate that the radar response to soil moisture content is highly dependent on the surface roughness, microwave frequency, and look angle. The response seems to be linear, however, over the range 15-30 percent moisture content for all angles, frequencies, polarizations, and surface conditions.     

The structure of red-infrared scattergrams of semivegetatedlandscapes

A physically based linear stochastic geometric canopy-soil reflectance model is presented for characterizing spatial variability of semivegetated landscapers at subpixel and regional scales. Landscapes are conceptualized as stochastic geometric surfaces, incorporating not only the variability in geometric elements but also the variability in vegetation and soil background reflectance, which can be important in some scenes. The model is used to investigate several possible mechanisms which contribute to the often observed characteristic triangular shape of red-infrared scattergrams of semivegetated landscapes. Scattergrams of simulated semivegetated scenes are analyzed with respect to the scales of the satellite pixel and subpixel components     

Influence of near-surface soil moisture on regional scale heatfluxes: model results using microwave remote sensing data fromSGP97

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_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 ~40×260 km) using a remote sensing model (called the two-source energy Balance-soil moisture, TSEB_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_c, correlations (R) between W and LE varied from 0.4 to 0.6 (R~0.5 on average), while correlations between W and H varied from -0.3 to -0.7 (R~-0.6 on average). The W-LE and W-H correlations were dramatically higher when variability due to f_c was considered by using NDVI as a surrogate for f_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_c. Typically, |R|≳0.9 for data sorted by NDVI having values ≲0.5 or f_c ≲0.5, while |R|≲0.5 for the data sorted under high canopy cover where NDVI≳0.6 or f_c≳0.7     

Utilization of Active Microwave Roughness Measurements to Improve Passive Microwave Soil Moisture Estimates Over Bare Soils

Investigators have researched operational microwave techniques for the remote estimation of soil moisture for sometime now. Both active and passive microwave sensors respond to variations in soil moisture, but also respond to vegetation and roughness parameters. This has led to research in multisensor techniques which account for the interference. Previously, techniques have been developed which used visible and infrared bands (similar to Landsat) to compensate for the vegetation masking on the L-band passive radiometer's response to soil moisture. In contrast, this study compensates for the surface roughness effect by using microwave scatterometer data on the same L-band radiometer. It was found that the L-band radiometer's capability to estimate soil moisture over bare fields was significantly improved when surface roughness was accounted for with scatterometers.     

Mapping the spatial distribution and time evolution of snow water equivalent with passive microwave measurements

This paper presents an algorithm that estimates the spatial distribution and temporal evolution of snow water equivalent and snow depth based on passive remote sensing measurements. It combines the inversion of passive microwave remote sensing measurements via dense media radiative transfer modeling results with snow accumulation and melt model predictions to yield improved estimates of snow depth and snow water equivalent, at a pixel resolution of 5 arc-min. In the inversion, snow grain size evolution is constrained based on pattern matching by using the local snow temperature history. This algorithm is applied to produce spatial snow maps of Upper Rio Grande River basin in Colorado. The simulation results are compared with that of the snow accumulation and melt model and a linear regression method. The quantitative comparison with the ground truth measurements from four Snowpack Telemetry (SNOTEL) sites in the basin shows that this algorithm is able to improve the estimation of snow parameters.     

ESTAR: a synthetic aperture microwave radiometer for remote sensingapplications

ESTAR represents a new technology being developed for passive microwave remote sensing of the environment from space. The instrument employs an interferometric technique called aperture synthesis in which the coherent product from pairs of antennas is measured as a function of pair spacing. Substantial reductions in the antenna aperture needed for a given spatial resolution can be achieved with this technique. As a result, aperture synthesis could lead to practical passive microwave remote sensing instruments in space to measure parameters such as soil moisture and ocean salinity which require observations at long wavelengths and, therefore, large antennas. ESTAR is an L-band, aircraft built as part of research to develop this technique ESTAR is a hybrid real-and-synthetic aperture radiometer which employs stick antennas to achieve resolution along track and uses aperture synthesis to achieve resolution across track. Experiments to validate the instrument's ability to measure soil moisture have recently been conducted at the USDA watersheds at Walnut Gulch in Arizona and the Little Washita River in Oklahoma. The results of both experiments indicate that a valid image reconstruction and calibration have been obtained for this remote sensing technique     

Improved Spatial Mapping of Rainfall Events with Spaceborne SAR Imagery

The Seasat satellite acquired the first spaceborne synthetic-aperture radar (SAR) images of the earth's surface, in 1978, at a frequency of 1.275 GHz (L-band) in a like-polarization mode at incidence angles of 23 ± 30. Although this may not be the optimum system configuration for radar remote sensing of soil moisture, interpretation of two Seasat images of Iowa demonstrates the sensitivity of microwave backscatter to soil moisture content. In both scenes, increased image brightness, which represents more radar backscatter, can be related to previous rainfall activity in the two areas. Comparison of these images with ground-based rainfall observations illustrates the increased spatial coverage of the rainfall event that can be obtained from the satellite SAR data. These data can then be color-enhanced by a digital computer to produce aesthetically pleasing output products for the user community. When the methodology for extracting accurate information about soil moisture status from radar data is developed, it will prove useful in a wide variety of agronomic and hydrological investigations.