Ice sheet internal temperature profile plays a key role in the study of ice sheet thermodynamics and ice sheet mass balance. Wideband radiometric remote sensing measurement has been demonstrated to be an effective approach for estimating the ice sheet internal temperature profile. In this study, we make model analysis of the sensitivity of emission observation on different frequency channel to ice sheet internal temperature at different depth, aiming at assessing the value of each channel in deriving ice sheet internal temperature profile. As the emission contribution depth profile can give insight into the emission compositions and emission sensitivity to the ice sheet internal properties at different depth, this study analyses the impact on ice sheet emission contribution depth profile due to the uncertainties of ice sheet temperature profile based on Comprehensive Layer Emission Model (CLEM). The correlation of different frequency channel are further discussed. The analysis results give guidance to construct retrieval approach for better extraction and utilization of the information on ice sheet internal temperature profile providing by the wideband emission observation.

Reported radiometric measurements indicate that incoherent and coherent radiative transfer processes characterize the microwave emission features of layered irregular and inhomogeneous medium. In order to develop a forward emission model to be capable of considering the medium and boundary scattering and coherent boundary interaction that may exist in general layered medium, in this article, we present a comprehensive layer emission model (CLEM) based on the scattering operator formulation. We introduce wave-based coherent multiple reflection operators to account for coherent boundary interaction and first integrate them with the intensity-based multiple scattering processes, allowing the comprehensive description of rough boundary scattering, volume scattering, medium/boundary interaction, and coherent boundary interaction in the framework of CLEM. Simulations and analyses for ice- and snow-covered ground cases are conducted based on CLEM to evaluate the coherent boundary interaction and different impacting factors. Validation on CLEM is conducted with emission observations of snow-covered terrain in campaign Nordic Snow Radar Experiment (NoSREx) 2010–2013 during dry snow period and time-series emission observations of frozen soil during freezing and thawing processes. CLEM simulation results show a good agreement with measurements. For NoSREx, root-mean-square errors (RMSEs) at L- to Ka-band are below 5.5 K for both polarizations, which are of different levels of promotion compared with the incoherent model simulations, especially for horizontal polarization at L- and X-band. Application to frozen soil case illustrates the capability of CLEM to explain coherent oscillation feature with impact from incoherent scattering effect.

Combined use of active and passive remote sensing can provide complementary information to simultaneously characterize the internal structure and internal physical properties of ice sheet. Well constraints of layering structure and density variation are the precondition to retrieve ice sheet internal temperature profile using radiometric measurements. This paper makes further improvement to the combined active and passive remote sensing approach presented in our previous preliminary study, mainly in density variation profile characterization, emission modeling, and density variation estimation method. In the combined approach, ice-penetrating radar-echo profile is used to estimate the ice sheet density variation, correcting the input density parameter for ice sheet emission model when interpretating the ice sheet emission observations. Validation of the improved combined approach is conducted with SMOS data and radar profiles collected by CReSIS around several ice core sites in Greenland. Evaluation of availability and advantage of the improved combined approach is performed based on the results.

Microwave emission from layered inhomogeneous medium is affected by both incoherent scattering and coherent interaction within the layer. Reported emission observations of snow-covered surface and ice sheet indicate that effects of layer boundary interference are more prominent at low frequencies. This paper presents a comprehensive layer emission model (CLEM) for layered inhomogeneous medium that fully accounts for incoherent scattering and coherent boundary interactions within the layer. The CLEM model is based on scattering operator (matrix doubling) formulation, which provides the basic framework for integrating rough boundary scattering, volume scattering, and coherent multiple reflections at layer boundaries. Coherent boundary interactions are accounted for through novel integrated wave- and intensity-based layer scattering operators. Initial model validation using published Elbara-II data and SodRad data shows good agreement between CLEM predictions and measured results.

Wideband radiometric measurements can be used to retrieve the internal temperature of ice sheet [1]. However, emission from ice sheet is affected by many factors including layering structure, density profile and grain size distribution. These factors are not well constrained due to the lack of ice sheet characterization data, and thus severely limit the ability to retrieve ice internal temperature profile using radiometric measurements. This paper presents a combined active and passive remote sensing approach, in which ice-penetrating radar provides information that constraint the ice sheet parameters, to improve estimation of ice internal temperature profile using radiometric measurements. In this study, radar reflectivity measurements are used for estimating ice density variations, which are input parameters for the ice sheet emission model. Model analysis of emission from ice layer with internal temperature and structural profiles, and comparison with SMOS data are conducted to assess the viability of the combined approach.

Wind is a major factor in the formation of sea surface foam. Foam layer parameters, such as thickness and coverage are functions of wind speed. Microwave emissivity of sea surface is sensitive to thickness of its foam cover. Foam thickness plays a vital role in emissivity from sea surface with foam cover. The average thickness of foam layer is can be estimated from sea surface wind speed. In this study, the foam layer emissivity is simulated using the incoherent multiple scattering model at different wind speeds and observation frequencies. Model predictions show that emissivities at different frequencies are sensitive to average foam thickness (and wind speed) at different ranges. Model Comparisons with AMSR2 measurements show good agreement on how foam emissivity varies with wind speed; they also show better correlation for V-polarized emissivity at 36.5GHz and 6.9GHz at wind speed of 5 m/s to 30 m/s.

Clutter is present in most applications of radar imaging and can negatively impact target location and identification by adding unwanted image artifacts. Time-varying clutter, such as vegetation in the presence of wind, can be difficult to model and remove. A second-order stochastic model for time-varying clutter is proposed and validated. This model captures second-order statistics and estimates directional multiscale correlation between target space image pixels. Directional multiscale correlation is estimated using a linear system of wavelet decomposition coefficients at the desired scale, resulting in a flexible multiscale model that can be used with clutter removal techniques. This model is evaluated using simulated data and physical ISAR measurements of time-varying vegetation.

Scatterometers provide consistent observations of ocean surface wind with reliable quality. In tropical regions, Quality Control (QC) rejects observations effected by rain clouds to guarantee the quality of wind products obtained with references to Geophysical Model Functions (GMFs). GMFs map scatterometer observed normalized radar cross-sections (NRCS) to wind fields without considering rain effects. The rejected groups have information of both rains and winds, and can be utilized for quantitative modelling of rain effects. Wind scatterometers usually operate at C-or Ku-band, they are affected differently by rain clouds due to differences in sensing wavelengths. Collocated observations with both frequencies would enable quantitative evaluation of rain effects. This study exploits these collocated C- and Ku-band measurements and seeks to ultimately develop a method for improving surface wind retrieval using a rain-cloud correction factor, with the preliminary model outline proposed. A vector radiative transfer based layer scattering model for rain clouds above ocean surface that accounts for scattering and attenuation effects of rain column and sea surface roughness will be used in quantitative analysis of rain cloud effects on surface wind retrieval at C- and Ku-band. Rain drop size distribution (DSD) and surface modifications from rains under different wind speed are considered in the model. For rain-free condition, a modified IEM surface scattering model is utilized in the quantitative analysis. Model validation and evaluation of the proposed correction factor are conducted using collocated data obtained from existing C- and Ku-band scatterometer observations, with time lag less than 5 minutes and spatial difference less than 25 km, and references to rain rates from the Meteosat Second Generation (MSG) Satellite.

Inverse Synthetic Aperture Radar (ISAR) imaging methods are well established. These methods utilize a variety of tools to estimate the spatial distribution of target energy from measurements in the k-space domain. These methods include inverse Fourier techniques, subspace methods such as MUSIC, and sparse optimization such as Compressive Sensing (CS). All these methods assume a linear signal model with a tolerable amount of additive Gaussian noise. However, in many real-world ISAR measurement scenarios, a significant amount of multiplicative noise or clutter may be present. Current linear imaging methods are not generally well suited for multiplicative noise, as they rely significantly on phase information that can be heavily distorted or randomized under random multiplicative processes. This paper will present a method for imaging a target in the presence of a specific type of time-varying multiplicative noise using a convolutional classification neural network [1] - [4] .

Imaging a weak target in the presence of strong clutter is a common problem associated with many radar imaging applications. Current ISAR imaging techniques assume an ideal target scene with Gaussian additive noise and coherent clutter backscatter will manifest as image artifacts. This paper will propose a method for separating target and clutter using compressive sensing techniques combined with a basis matrix formed with measured clutter data. The resulting images will be compared to traditional imaging methods.

In this letter, we investigate volume-scattering effects on microwave emission from a foam-covered ocean surface by a comparative study of emissivities and polarization indexes of a numerical radiative transfer model and an incoherent emission model. The matrix doubling method is applied in the numerical model, which fully accounts for the multiple scattering in the foam layer and at the interfaces. The incoherent emission model considers incoherent interactions between air-foam and foam-seawater interfaces but ignores the volume scattering in the foam layer. Model analyses show that foam volume scattering affects both the magnitude and polarization index of emission from the foam-covered ocean surface. Foam volume scattering reduces the rate of increase in emissivity with increasing foam water fraction. Results also show that polarization index is more sensitive to volume scattering at a large observation angle and at a low foam water fraction. Comparison between model predictions and experimental measurements accentuate the need to account for foam-scattering effects in interpreting emission measurements from the foam-covered ocean surface.

In this work we derive a novel clustering scheme for hyperspectral pixels according to the material they sense. We utilize statistical correlations that pixels sensing the same material exhibit. Specifically, kernel learning is combined with a norm-one regularized canonical correlations framework that can perform data clustering on nonlinearly dependent data. To tackle the derived minimization formulation we employ gradient descent iterations that enable a computationally efficient determination of proper sparse clustering matrices. Extensive numerical tests on real hyperspectral images reveal that the proposed approach, in spite of being unsupervised, can outperform existing supervised and unsupervised techniques especially in the presence of missing pixels that may be caused by malfunctioning in the data acquisition system.

Many methods have been developed over the last several decades to provide spatial imaging from backscattered ISAR data, including range-Doppler processing, subspace techniques, and more recently, compressive sensing. Range-Doppler processing generally has a lower resolution than subspace and compressive sensing techniques. Subspace techniques provide a high resolution image and perform well in the presence of noise, but require a sufficiently high measurement bandwidth. In addition, in the presence of significant noise, subspace dimensionality can be difficult to determine. Compressive sensing can provide a very high resolution image under ideal circumstances but generally performs poorly in the presence of noise. This paper proposes a filtered compressive sensing method that improves compressive sensing ISAR imaging over direct methods.

A polarimetric scattering model for inhomogenenous layer with vertical profile is applied to analyze backscattering from soil layer at L-band. The soil layer is modeled as discrete random medium with water content that varies with depth and bounded by randomly rough surfaces. The soil is modeled as air voids embedded in host medium consisting of a mixture of solid minerals and water. The air-soil and soil-lower half-space interfaces are modeled using the Integral-Equation-Model (IEM) for surface scattering. Soil layer scattering model predictions, computed using measured parameters, agreed well with the published backscattering measurements. Soil layer Model analyses showed that at L-band the backscattering is dominated by the top surface when the soil is optically thick. At a smaller optical layer thickness, both the air-soil interface and the interactions between the two boundaries contribute to the total soil layer backscattering. Due to the small volume scattering coefficient, the effect of soil moisture profile is negligible on the total soil backscattering at L-band.

This paper presents a numerical study of microwave brightness temperature of residual foam cover on ocean surface. The foam layer is modeled using Kelvin's Tetrakaidecahedron structure. Absorption and scattering of the foam layer, i.e. the layer phase matrix, are calculated using a multilevel UV method to accelerate method of moment (MoM) solution of Maxwell's equations. Matrix doubling method, which accounts for interactions between the foam volume and interfaces, is then utilized to determine the total emission from the foam layer. The calculated foam layer absorption rates at a frequency range of 1.5GHz to 36.5GHz are presented and analyzed for both H and V polarizations. Brightness temperatures at 1.5GHz for both planar and rough interface with different water fractions in the foam layer are simulated and analyzed. Effects of foam-boundary interactions on foam layer emission are discussed.

SAR is a complex system that integrates two major parts: data collector and image formatter [1-2]. In the phase of data collection, radar transmits electromagnetic waves toward the target and receives the scattered waves. The transmitted signal can be modulated into certain types, commonly linearly frequency modulated with pulse or continuous waveform. The process involves signal transmission from generator, through various types of guided device, to antenna, by which the signal is radiated into free space, and then undergoes propagation. The measured scattered signal been made in bistatic or monostatic configurations is essentially in time-frequency (delay time - Doppler frequency) domain. The role of image formatter is then to map the time-frequency data into spatial domain where the targets are located. The mapping from the data domain to image domain, and eventually, into target or object domain must minimize both geometric and radiometric distortions. Essentially, two models that define the SAR operational process: physical model and system model. This paper concentrates on the physical process of a SAR system from wave scattering to imaging. System simulation based on the stationary (frequency modulation continuous wave) FMCW is developed and implemented for both point target and extended target. To further validate the simulation and thus our physical understanding of the imaging chain, measurements at aniconic chamber with two mental spheres and two dielectric spheres displaced with varying spacing were conducted. Good agreement between the simulated by extend target model and real measured SAR images is obtained.

Traditional subspace-based image processing treats the target as a collection of scattering points whose phase component is dependent only on phase change due to target motion. Physical scattering mechanisms can result in magnitude and phase components outside this model and defocusing of scatterers in the resulting image. Two methods are presented which can compensate for the defocusing of targets in subspace-based images due to physical scattering phenomena. The focusing effectiveness of these two methods are demonstrated and compared.

A microwave backscattering model for ground-based remote sensing of precipitation is developed and used to analyze back scattering measurements from snowfall and rain type precipitation. Backscattering from entire precipitation region is calculated by solving vector radiative transfer (VRT) equations. Geophysical parameters for rain or snow are determined by considering altitude and latent range. VRT equations are solved numerically by using matrix doubling method to take into account multiple-scattering effects. Results from model analyses agree well with measured radar data.

In ISAR Multiple Signal Classification (MUSIC) imaging, the target is generally modeled as a collection of point scatterers. This simplistic model is robust but does not accurately image other scattering phenomena such as physical optics scattering from targets that are large with respect to wavelength. Utilization of the point scattering model for MUSIC imaging of physical optics scattering results in the distribution of target energy across several image pixels. This abstract introduces a method for separating scattering returns from objects with distinct size and shape or with different scattering mechanisms. A new scattering model is proposed to estimate physical optics scattering from spherical scatterers. Using constrained MUSIC techniques, returns from scatterers with a specific size and shape can be eliminated from the super-resolution ISAR image.

The amount of the grain in bulk silos is the most important issue in commercial care. Therefore many level measurement methods have been used to measure the level of solids in silos. Existing methods, however, are generally based on one-point measurement which makes the three dimensional (3D) level measurement impractical. Microwave radar based systems can be used to 3D perception but the multiple scatterings occurred from metallic walls of the silo, makes it impossible. In this study we present the preliminary results of our compressive sensing based reconstruction algorithm to enhance backscattering signals inside a grain silo. The method proposed here eliminates the effect of multiple scattering form silo wall and gives the accurate reading of the grain level. The effectiveness of the recommend CS-based reconstruction method, which will be able to extend to 3D level perception, was verified through a real data of bulk silo.

A rain column model is used analyze back scattering from precipitation in the presence of hail. Backscattering from the entire precipitation column comprising hail and rain that accounts for multiple-scattering and boundary interactions are determined by solving the vector radiative transfer (VRT) equations. Precipitation column is partitioned into sublayers and Mie scattering calculation is used to determine phase matrix for differential layers consist of hail stones and rain drops. The raindrops and hail stones are represented by using spherical shaped scatterers. Microphysical properties of rain and hail such as shape, composition and size distribution are considered in the model. Results from model analyses agree well with published measurement results.

Radar scattering center separation by means of the statistical properties of the data allows an algorithm to adapt to the scattering environment. Through the assumption of non-Gaussianity for scattering centers of interest it is feasible to employ a source separation technique base on finding the most non-Gaussian features within the data. A well known technique to accomplish extraction of components with this statistical property is Independent Component Analysis (ICA). This work explores the use of ICA in extracting scattering center components from complex frequency domain Radar data.

A combined T-matrix algorithm and vector radiative transfer layered precipitation model has been used to analyze scattering from rain column. The raindrop is modeled as spheroid with complex permittivity. The phase matrices for differential layers rain-drop scattering within the rain column are computed using the T-matrix approach, and backscattering from the entire rain column that accounts for multiple-scattering and boundary interactions are determined by solving the vector radiative transfer equations. Model analyses and some data comparisons were conducted at Ku-Band using published measured/observed parameters. Results show that model predictions agree well with published TRMM measurements. More extensive model validation and analyses will be provided in the full paper.

Fourier techniques are often used in radar imaging and feature extraction of ISAR data. One drawback is the appearance of artifacts due to scatterer interactions within the target. This paper defines a modified bicoherence, based on a specific target scattering model, to distinguish target scattering centers from target interactions and develop target-specific features for identification. Third-order statistics such as the bicoherence measure the asymmetric properties of distributions and are therefore zero for Gaussian processes such as additive white Gaussian noise. In addition to scatterer range, the model-based bicoherence described in this paper can also estimate the scatterer separation distance. This target information can be used for defining a feature set for target identification that is independent of target aspect angle, assuming the target is modeled as a collection of point scatterers. This information can also be used to determine subspace separation in eigenspace techniques such as the MUSIC algorithm, thereby increasing ISAR image accuracy.

A numerical approach to model the microwave emission from soil with vegetation cover is developed. The vegetation cover is represented as an irregular layer on top of a homogeneous half space representing the soil. The phase matrix of the layer components as well as the emission characteristics have been estimated using the Finite-Difference Time-Domain (FDTD) Method. The phase matrix was then integrated into a layer model that accounts for the scattering between the layer and soil, using the radiative transfer theory. The effects of interface roughness on emission are incorporated into the model through the surface phase matrices, which are computed using the Integral Equation Model (IEM). The validity of the phase matrices computation is tested against theoretical models. The microwave emission model predictions are compared to field measurements, showing a good agreement.

This paper provides an overview of the results from application of the combined MUSIC-ICA algorithm to a fully polarimetric radar dataset. The results show that the application of the algorithm over two of the three dimensions of the data cube formed from the polarization, range and angle data spaces produces unique results which can yield information on target signatures, which would otherwise be indiscernible. The practical application of this combined algorithm has the potential to yield added robustness to target identification and characterization.

The scatterometer can be significantly affected by rain, especially those operated on Ku-band. The effects are related to many parameters, such as rain rate and frequency. In this paper, a model which takes all the parameters into accounted is presented. The effects of all the parameters played on sea surface back scattering with precipitation are analyzed using this model. The analyzed results indicate that rain volume fraction and frequency are key factors, while influence of raindrop size polarization is less obvious.

This paper demonstrates a technique for super-resolution ISAR imaging using polarimetry to determine the signal subspace dimensionality for the MUSIC algorithm. By creating a feature vector, polarimetric information from the target is used to accurately determine the number of primary scatterers in a simple target geometry with added noise. This information is used to determine the signal subspace dimension, which is used in the MUSIC super-resolution imaging algorithm, resulting in high resolution target images in the presence of additive noise.

This paper presents an overview of the potential for the use of the combined MUSIC and ICA algorithms for scattering center separation in radar imaging. This work uses the combination of these algorithms applied to the specific problem of scattering center extraction from a complex radar target. The radar data is first processed using ICA to extract the independent scattering components. These components are then processed using the MUSIC algorithm to isolate the signal subspace and form an image. The target data used in this paper was generated using the turntable ISAR system at the University of Texas at Arlington. The results of this paper show the viability of the combination of these signal processing algorithms to enhance the users ability to extract scattering centers within a complex target.

This paper presents a method for subspace dimension estimation which is critical for accurate ISAR image construction using the MUSIC method. The proposed method examines the distribution of correlation matrix eigenvalues after various amount of spatial smoothing to derive an ideal amount of spatial smoothing and the corresponding eigenvalue threshold. When used to separate the signal and noise subspaces, this eigenvalue threshold provides an accurate estimate of the number of scattering centers on the target. The proposed method is tested with simulated ISAR data and compared with a fixed-threshold method. Accuracy over SNR and number of scatterers is presented as well as example images.

In this letter, we investigated an inversion technique to estimate snow water equivalence (SWE) under Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) sensor configurations. Through our numerical simulations by the advanced integral equation model (AIEM), we found that the ground surface emission signals at 18.7 and 36.5 GHz were highly correlated regardless of the ground surface properties (dielectric and roughness properties) and can be well described by a linear function. It leads to a new development for describing the relationship between snow emission signals observed at 18.7 and 36.5 GHz as a linear function. The intercept (A) and slope (B) of this linear equation depend only on snow properties and can be estimated from the observations directly. This development provides a new technique that separates the snowpack and ground surface emission signals. With the parameterized snow emission model from a simulated database that was derived using a multiscattering microwave emission model (dense medium radiative transfer model-AIEM-matrix doubling) over dry snow covers, we developed an algorithm to estimate the SWE using the microwave radiometer measurements. Evaluations on this technique using both the model simulated data and the field experimental data with the airborne Polarimetric Scanning Radiometer data from National Aeronautics and Space Administration Cold Land Processes Experiment 2003 showed promising results, with root-mean-square errors of 32.8 and 31.85 mm, respectively. This newly developed inversion method has the advantages over the AMSR-E SWE baseline algorithm when applied to high-resolution airborne observations.

A subspace extraction approach for detection of targets embedded in the clutter is presented in this work. Subspace extraction approach that makes use of both Compressive Sampling and Principal Component Analysis (PCA) is presented in this paper. Inverse Synthetic Aperture Radar (ISAR) Imaging measurement data is used to validate the proposed approach. Experimental results of targets above rough surface with intermediate roughness are presented. Results showed the dimensionality of an intermediate scale rough surface is generally larger than the dimensionality of the finite size targets. Results showed that by compressing the dimensionality through compressive sampling and extracting the principal components, significant improvement in target subspace extraction can be achieved.

This paper investigated the emission behavior at 18.7 GHz, 36.5 GHz and 89 GHz over the snow-cover surface and after snow completely removed surface at the Local Scale Observation Site (LSOS) in Fraser, Colorado, USA with 55° incidence angle) using one-layer and two-layer emission model, which is based on the radiative transfer by Matrix Doubling approach with the dense media theory and the surface scattering Model. From the comparisons with the GBMR-7 observation on Feb. 21, both the two-layer emission model and one-layer emission model could predict the observed brightness temperature over snow-covered surface well, but the polarization difference predicted by two-layer emission model was relatively smaller than one-layer model did. In addition, we attempted to interpret the emission magnitude and polarization separation of snow-removed surface by incorporating a transition layer below the soil medium. We also demonstrated the effect of snow fraction on the brightness temperature difference at 18.7 GHz and 36.5 GHz over snow-cover surface with the field observation and model simulation.

A 3D-FDTD algorithm is developed and used to compute the emissivity of finite-size and arbitrary-shape objects. Under thermal equilibrium, the emissivity of an object is the same as its absorptivity. The absorptivity is a function of both the scattering cross section and the absorption cross section of the object; in this study, these cross sections were computed using the FDTD approach. Emissivity values for spherical, cylindrical and landmine-like objects as function of observation angle, polarization and permittivity are generated and presented in this paper.

This paper presents a novel cavity backed slot (CBS) antenna for subsurface sensing applications. The CBS antenna is designed to be "matched" in the two-half-space configuration (one half space air; and the other ground) over a relatively wide frequency band. As a result, when attached onto ground surface, it is able to efficiently couple microwave power into and out of the ground. In this study, CBS antennas with operating frequency range [8.5 GHz, 13.6 GHz] are designed to detect objects buried in sand. Data acquisition is carried out using a sandpit with size (125 cm times 100 cm times 80 cm) as the test bed. One transmitting CBS antenna is fixed at the center of the sandpit and one receiving antenna is physically moved along a rectangular grid (i.e., multi-static measurement). An inverse synthetic aperture radar (ISAR) algorithm is adopted for inverse processing. A 4-inch-diameter metallic sphere is used as calibration target; and three targets are tested, including a 3-inch-diameter metallic sphere, a T-shaped copper target, and a landmine simulant. Imaging results are presented and compared with those obtained using horn antennas (which are not "matched" to the air-ground interface). Better signal-to-clutter ratios are demonstrated by the proposed CBS antennas.

Microwave scattering from snow is difficult to model due to the complexity and heterogeneity of natural snow. In this paper, we developed a multi-layer, multi-scattering model based on recent theoretical advances in snow and surface modeling. In the proposed multi-layer model, Matrix Doubling method is used to account for scattering from each snow layer; and Advanced Integral Equation Model (AIEM) is incorporated into the model to describe surface scattering. Comparisons were made between the model predictions and field observations from truck-mounted L- and Ku-band scatterometers (frequencies are 1.25 GHz and 15.5 GHz) at Local-Scale Observation Site (LSOS) of NASA Cold- land Processes Field Experiment (CLPX) during Third Intensive Observation Period (IOP3). It was found that model predictions were in good agreement with field observations with proper particle size selected. Analysis on scatterer shape, multiple scattering and snow stratification effects were also made based on model simulations.

A novel method, which employs both a matrix doubling algorithm and the first-order solution of a radiative transfer (RT) equation for modeling microwave backscattering from forest, is presented in the paper. The method is based on the assumption that a forest canopy can be divided into a number of distinct horizontal vegetation layers over a dielectric half-space rough surface. The scattering phase matrix of each layer is calculated by either matrix doubling to account for the multiple-scattering effect or first-order solution of an RT equation, depending on the scattering characteristics of the layer. The first-order solution of the RT equation is used for the trunk layer while the matrix doubling technique is applied to both the crown layer and understory. The advanced integral equation model and reflectivity matrix are used to calculate the noncoherent and coherent surface boundary conditions. Comparisons between model predictions and field measurements on radar backscattering coefficients for a walnut orchard showed a good agreement at both L-band and X-band and for all three polarizations. Comparative analyses of model predictions for backscattering from a forest medium calculated using the combined model, first-order RT model, and the standard matrix doubling model were also presented. Understory effects, that can significantly change the weight of each scattering mechanism, were also evaluated by using the combined method.

We evaluate the capability of the microwave emission model that including the Dense Media Radiative Transfer Model (DMRT) and AIEM for simulation of dry snow emissivity. We compared the model predictions with the ground experimental measurements. The comparison shows our snow microwave emission model agrees quite well with the measurements.

A dense medium phase matrix developed based on the concept of random lattice perturbation is employed in the radiative transfer theory to calculate the coand cross-polarized backscatter from a layer of randomly distributed spherical scatterers. The position randomness properties are characterized by the variance and correlation function of scatterer positions within the medium. The dense medium phase matrix differs from the conventional one in two major aspects, i.e., there is an amplitude and a phase correction. These corrections account for the effects of close spacing and position correlation between scatterers in a dense discrete random medium. This study shows that phase coherency and close-spacing amplitude modifications are two separate corrections necessary for an electrically dense medium. Results indicate that there is a need to distinguish between spatially and electrically dense medium. The phase correction is found to have a greater impact on cross-polarized than like-polarized backscatter coefficients; the converse is true of the amplitude correction. Backscattering calculations from the theory are compared with measurements from controlled microwave experiments on random media consisting of closely packed spheres, and from field measurements of dry snowpack. Predictions from such a theory agree well with the measured data.

In the derivation of the conventional scattering phase matrix of a discrete random medium, the far-field approximation is usually assumed. In this paper, the phase matrix of a dense discrete random medium is developed by relaxing the far-field approximation and accounting for the effect of volume fraction and randomness properties characterized by the variance and correlation function of scatterer positions within the medium. The final expression for the phase matrix differs from the conventional one in two major aspects: there is an amplitude and a phase correction. The concept used in the derivation is analogous to the antenna array theory. The phase matrix for a collection of scatterers is found to be the Stokes matrix of the single scatterer multiplied by a dense medium phase correction factor. The close spacing amplitude correction appears inside the Stokes matrix. When the scatterers are uncorrelated, the phase correction factor approaches unity. The phase matrix is used to calculate the volume scattering coefficients for a unit volume of spherical scatterers, and the results are compared with calculations from other theories, numerical simulations, and laboratory measurements. Results indicate that there should be a distinction between physically dense medium and electrically dense medium.

An examination of soil particles from very fine to medium sand surfaces has indicated that they are generally on the order of 50 to 500 /spl mu/m. Thus, at an incident wavelength around 0.6 /spl mu/m, the incident light should "see" microscopic roughness features on the particle rather than its microscopic features. It is anticipated that the macroscopic features of a soil particle are responsible for the shadowing and tilting. Note that these smaller scales of roughness may-still be larger than the incident wavelength. In view of this physical structure, a soil particle is modeled as a layer with two arbitrarily oriented surface boundaries to simulate the overall roughness effect. A scattering phase function is then developed for this layer by considering wave scattering from and propagating through it. A probability distribution function for the orientation of the layer boundaries is assumed for the calculation of this phase function. After the phase function is developed, it is incorporated into a matrix doubling algorithm calculate the backscattering coefficients for a half space of soil particles. Preliminary results indicate that backscattering is dominated by the small scales of roughness riding on the particle, and those large scales of roughness are responsible for tilting and shadowing.

Experiments were performed at the U.S. Army Cold Regions Research and Engineering Laboratory (CRREL) in Hanover, NH, to precisely determine the relative contributions of surface and volume scattering from saline ice that has well-known surface roughness characteristics. The ice growth phase of the experiment made use of two 6-ft diameter tanks and a 6-ft diameter mold with known roughness statistical parameters of rms height=0.25 cm and Gaussian correlation (correlation length=2.0 cm). One tank was used for growing a moderately thick saline ice sheet with very smooth surface, and the other was used for growing a thin layer of freshwater ice over the surface mold. The latter resulted in a layer with one statistically known rough boundary and one smooth boundary. Wide-bandwidth, multiple incidence angle backscattering measurements were performed, first on the bare saline ice sheet and then on the same sheet after the thin freshwater ice sheet was placed on top of it. Results indicate that the surface scattering dominates over saline ice volume scattering at all frequencies for low incidence angles for both the very smooth and Gaussian rough surfaces. The significance of volume scattering depends strongly on angle of incidence, frequency, volume scattering albedo, surface roughness, and surface correlation function.>

Randomly rough surface patches in three dimensions are generated on the computer. The FD-TD method is used to compute scattering from surface patches by converting the Maxwell's equations into difference equations using a central difference approximation for the space and time derivatives. The volume of grids above the rough surface is divided into the total field and the scattered field regions. In between these two regions, obliquely incident waves are generated. To reduce computation, the volume of grids is chosen to be small, and a transformation is used to convert the scattered field into far zone fields for bistatic scattering coefficient calculations. Possible errors near the edge of the surface due to the use of a relatively small volume are suppressed by introducing a windowing function. Very good agreements are obtained between the results obtained by this method and those calculated by an integral equation method (IEM) for scattering from randomly rough perfectly conducting and dielectric surfaces.>

The special properties of a robust radiative transfer model for scattering from layers of inhomogeneous rough-boundary slabs are presented. The model is applied to backscattering from saline and desalinated ice. Comparisons are made at single and multiple frequencies with some of the most complete sets of measurement data available, using measured physical and electrical characteristics of the ice as inputs to the model where possible. The results show close agreement. For example, for the saline ice backscatter data set, which consisted of measurements at two like and two cross polarizations at 5 and 13.9 GHz, the agreement with model predictions is within 2 dB except at 13.9-GHz cross polarization. Backscattering from >15-cm-thick saline ice is generally dominated by scattering from the top surface while backscattering from>