Surface snow properties effect on millimeter-wave backscatter

The authors examine the relationship between 94-GHz backscatter from snow cover and the properties of the snow, using statistical analysis of observations made in West Germany in 1986. For terrain covered by dry snow, 94-GHz backscatter does not appear to depend significantly on any of the measured snow properties. Backscatter from wet snow is found to be sensitive to volumetric liquid water content, with the dependence inverse-exponential in form. Backscatter from wet snow is also found to depend on surface roughness, especially the cross-polarized return. Comparison of the 1986 data with similar data obtained in 1984 shows two major disagreements in the response of the vertical transmit vertical receive polarization backscattering coefficient to wet snow surface roughness, and the response of cross-polarized. The backscattering coefficient to snow surface wetness. The 1986 results are considered more reliable     

Dielectric properties of snow in the 3 to 37 GHz range

Microwave dielectric measurements of dry and wet snow were made at nine frequencies betweeo 3 and 18 GHz, and at 37 GHz, using two free-space transmission systems. The measurements were conducted during the winters of 1982 and 1983. The following parametric ranges were covered: 1) liquid water content, 0 to 12.3 percent by volume; 2) snow density, 0.09 to 0.42 g cm^-3; 3) temperature, 0 to-5 degC and-15degC (scattering-loss measurements); and 4) crystal size, 0.5 to 1.5 mm. The experimental data indicate that the dielectric behavior of wet snow closely follows the dispersion behavior of water. For dry snow, volume scattering is the dominant loss mechanism at 37 GHz. The applicability of several empirical and theoretical mixing models was evaluated using the experimental data. Both the Debye-like semi-empirical model and the theoretical Polder-Van Santen mixing model were found to describe adequately the dielectric behavior of wet snow. However, the Polder-Van Santen model provided a good fit to the measured values of the real and imaginary parts of wet snow only when the shapes of the water inclusions in snow were assumed to be both nonsymmetrical and dependent upon snow water content. The shape variation predicted by the model is consistent with the variation suggested by the physical mechanisms governing the distribution of liquid water in wet snow.     

Effect of wind on FM-CW radar backscatter from a wet snowcover

The most important factor affecting the microwave properties of a snowcover is the liquid water content (snow wetness). An FM-CW (26.5-40-GHz) radar has been used to investigate the influence of snow wetness on the magnitude of radar backscatter from a snowcover. The radar backscatter measurements from a wet snowcover on a windy day suggest that evaporative cooling due to the wind may reduce the amount of liquid water at the snowcover surface     

Snowcover Influence on Backscattering from Terrain

The effects of snowcover on the microwave backscattering from terrain in the 8-35 GHz region are examined through the analysis of experimental data and by application of a semiempirical model. The model accounts for surface backscattering contributions by the snow-air and snow-soil interfaces, and for volume backscattering contributions by the snow layer. Through comparisons of backscattering data for different terrain surfaces measured both with and without snowcover, the masking effects of snow are evaluated as a function of snow water equivalent and liquid water content. The results indicate that with dry snowcover it is not possible to discriminate between different types of ground surface (concrete, asphalt, grass, and bare ground) if the snow water equivalent is greater than about 20 cm (or a depth greater than 60 cm for a snow density of 0.3 g · cm-3). For the same density, however, if the snow is wet, a depth of 10 cm is sufficient to mask the underlying surface.     

Semi-empirical model for radar backscatter from snow at 35 and 95GHz

Radar backscatter experiments were conducted at 35 and 95 GHz to measure the response of snow-covered ground to snow depth, liquid water content, and ice crystal size. The measurements included observations over a wide angular range extending between normal incidence and 60° for all linear polarization combinations. A numerical radiative transfer model was developed and adapted to fit the experimental observations. Next, the radiative transfer model was exercised over a wide range of conditions and the generated data were used to develop relatively simple semi-empirical expressions that relate the backscattering coefficient (for each linear polarization) to incidence angle, snow depth, crystal size, and liquid water content     

Measurement and modeling of the millimeter-wave backscatterresponse of soil surfaces

The millimeter-wave (MMW) backscatter response of bare-soil was examined by conducting experimental measurements at 35 and 94 GHz using a truck-mounted polarimetric scatterometer and by developing appropriate models to relate the backscattering coefficient to the soil's surface and volume properties. The experimental measurements were conducted for three soil surfaces with different roughnesses under both dry and wet conditions. The experimental measurements indicate that in general the backscattering coefficient is comprised of a surface scattering component σ^s and a volume scattering component σ ^v. For wet soil conditions, the backscatter is dominated by surface scattering, while for dry conditions both surface and volume scattering are significant, particularly at 94 GHz. Because theoretical surface scattering models were found incapable of predicting the measured backscatter, a semiempirical surface scattering model was developed that relates the surface scattering component of the total backscatter to the roughness parameter ks, where k=2π/λ and s is the rms height, and the dielectric constant of the soil surface. Volume scattering was modeled using radiative transfer theory with the packed soil particles acting as the host material and the air voids as the scattering particles. The combined contribution of surface and volume scattering was found to provide good agreement between the model calculations and the experimental observations     

The Relation of Millimeter-Wavelength Backscatter to Surface Snow Properties

Although model results indicate that microwave backscatter and emission from snowcover should depend on dry-snow grain size and wet-snow surface roughness, there has been little or no experimental verification. In this paper, helicopter-mounted radar measure-ade ments of 94-GHz backscatter from snowcover, and ground-truth measurements of snow surface roughness, wetness, grain size, and porosity, are analyzed. For each of six polarization combinations, and separately for dry snow and wet snow, spatial mean values < ?° > of the backscatter coefficient ?° are fit to linear combinations of the cosine of incidence angle and the snow variables, the latter in the order in which the explained variance in < ?° > is most increased, provided the increase is significant. However, the significance of an included snow variable is considered questionable if the predicted response of < ?° > to that variable is small compared with the spatial standard deviations of or (typically 4-5 dB). This is the case for dry-snow grain size, porosity, and for some polarization combinations, wetness. Only the response to wet-snow surface roughness is consistently comparable in magnitude to the standard deviations of ?°. Dry-snow surface roughness, wet-snow grain size, and for most polarization combinations, porosity, made no significant improvement to the explained variances. An examination of residuals shows that the linear model is adequate for the < ?° > response to cosine of incidence angle and snow surface roughness.     

Polarimetric backscatter from fresh and metamorphic snowcover atmillimeter wavelengths

This paper presents 35, 95, and 225 GHz polarimetric radar backscatter data from snowcover. It compares measured backscatter data with detailed in situ measurements of the snowcover including microstructural anisotropies within the snowpack. Observations of backscatter mere made during melt-freeze cycles, and measurable differences in the normalized radar cross section between older metamorphic snow and fresh low-density snow were observed. In addition, these data show that the average phase difference between the copolarized terms of the scattering matrix, S_vvand S_hh , is nonzero for certain snow types. This phase difference was found to be related to snowpack features including anisotropy, wetness, density, and particle size. A simple backscatter model based on measured particle size and anisotropy is found to predict the Mueller matrix for dry snowcover with reasonable accuracy     

C-band backscatter signatures of old sea ice in thecentral Arctic during freeze-up

Radar backscatter signatures of old sea ice in the central Arctic have been measured and analyzed. A ship-mounted scatterometer was used to acquire backscattering coefficients at 5.4 GHz in the four linear polarization states and at incidence angles between 20° and 60°. Detailed in situ characterizations of the snow and ice were also made to enable comparison with theoretical backscatter models. Freeze-up conditions were prevalent during the experiment. The average backscattering coefficient was found to increase when the temperature of the ice surface layer decreased. The semi-empirical backscatter model is used to evaluate the measurements and shows that the backscatter increase is due to an increasing penetration depth, causing the volume scattering to increase. Model predictions also show that both surface and volume scattering contribute significantly at incidence angles of 20° to 26°. At these incidence angles, the dominating scattering mechanism changes from surface to volume scattering as the ice surface temperature decreases     

Brightness Temperatures of Snow Melting/Refreezing Cycles: Observations and Modeling Using a Multilayer Dense Medium Theory-Based Model

The ability of electromagnetic models to accurately predict microwave emission of a snowpack is complicated by the need to account for, among other things, nonindependent scattering by closely packed snow grains, stratigraphic variations, and the occurrence of wet snow. A multilayer dense medium model can account for the first two effects. While microwave remote sensing is well known to be capable of binary wet/dry discrimination, the ability to model brightness as a function of wetness opens up the possibility of ultimately retrieving a percentage wetness value during such hydrologically significant melting conditions. In this paper, the first application of a multilayer dense medium radiative transfer theory (DMRT) model is proposed to simulate emission from both wet and dry snow during melting and refreezing cycles. Wet snow is modeled as a mixture of ice particles surrounded by a thin film of water embedded in an air background. Melting/refreezing cycles are studied by means of brightness temperatures at 6.7, 19, and 37 GHz recorded by the University of Michigan Truck-Mounted Radiometer System at the Local Scale Observation Site during the Cold Land Processes Experiment-1 in March 2003. Input parameters to the DMRT model are obtained from snow pit measurements carried out in conjunction with the microwave observations. The comparisons between simulated and measured brightness temperatures show that the electromagnetic model is able to reproduce the brightness temperatures with an average percentage error of 3% (~8 K) and a maximum relative percentage error of around 8% (~20 K)     

Backscatter and attenuation by falling snow and rain at 96, 140,and 225 GHz

The results of measurements are presented for backscatter cross section per unit volume and attenuation for falling snow and rain at 96, 140, and 225 GHz. The attenuation due to rain is almost independent of the measurement frequency, but for snow the attenuation is considerably greater at 225 GHz than at 96 GHz. The rain attenuation generally varies with the rain accumulation rate in accordance with an aR^b relationship for a Laws and Parsons drop-size distribution where R is the rain rate and a and b are constants. The attenuation at all three frequencies is about 3 dB/km for a rain rate of 4 mm/h. The attenuation due to snow varies with airborne snow-mass concentration, with the average rates of increase being 0.9, 2.5, and 8.7 (dB/km)(g/m³) at 96, 140, and 225 GHz, respectively. Generally the attenuation for snow is lower than that for rain. The backscatter cross section per unit volume for rain at 96 GHz is about -35 dB m²/m³ for a rain rate of 4 mm/h. The backscatter from snow at 96 GHz is much lower than that from rain under equivalent accumulation rates or airborne mass concentrations. Snow backscatter at 140 GHz is comparable but higher than that at 96 GHz     

Laboratory backscatter measurements over urea ice with a snow coverat Ku band

Indoor laboratory facilities were used to measure radar backscatter at Ku band (13.9 GHz) over urea ice, which has been shown to be structurally similar to sea ice. Data were collected at angles of incidence from normal to 55°, over very thin (0 to 9 cm) ice, snow-covered ice, and ice with a hooded snow cover. The laboratory proved to be useful in creating and controlling specific physical properties of ice while keeping all other variables constant, a difficulty with measurements collected in the field. It was found that surface scattering and the dielectric constant are the dominant factors that cause variations (up to 15 dB) in the measured backscatter. The addition of a snow cover increased the surface roughness of the smooth ice, increasing the backscatter at 20° incidence angle by about 11 dB and decreasing the backscatter at normal incidence by about 6 dB. The subsequent flooding of this snow layer increased the backscatter at all angles of incidence due to the increased dielectric constant of the wet slush layer. These results indicate the importance of the snow layer in influencing the surface characteristics of the ice sheet, which in turn modifies the backscattered signal     

Backscatter from snow and ice surfaces at near incident angles

The radar backscatter of natural snow surfaces was measured at 10 GHz and 35 GHz and at grazing angles from1degto0.3deg. For horizontal polarized radiation the terrain clutter per unit area (m²) at 10 GHz of a flat snow terrain decreases from -50 dB at1degto -70 dB at0.4deg. The return is approximately 10 dB lower for vertical polarized radiation. The terrain clutter was found to depend on the free water content of the snow. The radar cross sections of ice blocks placed on the snow surface is roughly proportional to the square of the area of the ice block facing the radar at 10 and 35 GHz and is approximately 20 dBsm below the return expected for a perfectly reflecting plane surface. At 95 GHz the ice blocks become diffuse reflectors.     

Backscattering measurements of alpine snowcovers at 5.3 and 35 GHz

This paper describes two network-analyzer (NA)-based scatterometers at 5.3 (C-band) and 35 GHz (Ka-band) as well as snowcover measurements made in the Swiss and Austrian Alps between December 1993 and January 1996. First, the setup and the mode of operation of the scatterometers are discussed. Both instruments measure the backscattering coefficients γ at hh, νν, νh, and νh polarizations and for incidence angles ranging from 0 to 70°. The accuracy of γ is generally better than ±1.8 dB, and the scatterometers are well suited for signature studies of natural surfaces. During the two years, the authors performed many backscattering measurements of natural, strongly layered snowcovers and the authors investigated relationships between γ and physical parameters of the snowcover. All measurements were collected in a signature catalogue. They report on results at 40° incidence angle. They found that the combined use of active sensors at 5.3 and 35 GHz allows the discrimination of various snowcover situations, if multitemporal information is available. In addition, they observed a relationship of γ at 5.3 GHz with the integrated column height of liquid water and dependencies of γ at 35 GHz on the height of the dry snow, on the volumetric liquid water content at the snow surface, and on the thickness of the refrozen crust at the snow surface     

Inferring snow wetness using C-band data from SIR-C's polarimetricsynthetic aperture radar

In hydrological investigations, modeling and forecasting of snow melt runoff require timely information about spatial variability of snow properties, among them the liquid water content-snow wetness-in the top layer of a snow pack. The authors' polarimetric model shows that scattering mechanisms control the relationship between snow wetness and the copolarization signals in data from a multi-parameter synthetic aperture radar. Along with snow wetness, the surface roughness and local incidence angle also affect the copolarization signals, making them either larger or smaller depending on the snow parameters, surface roughness, and incidence angle. The authors base their algorithm for retrieving snow wetness from SIR-C/X-SAR on a first-order scattering model that includes both surface and volume scattering. It is applicable for incidence angles from 25°-70° and for surface roughness with rms height ⩽7 mm and correlation length ⩽25 cm. Comparison with ground measurements showed that the absolute error in snow wetness inferred from the imagery was within 2.5% at 95% confidence interval. Typically the free liquid water content of snow ranges from 0% to 15% by volume. The authors conclude that a C-band polarimetric SAR can provide useful estimates of the wetness of the top layers of seasonal snow packs     

Estimation of snow water equivalence using SIR-C/X-SAR. II. Inferring snow depth and particle size

For pt.I see ibid., vol.38, no.6, p.2465-74 (2000). The relationship between snow water equivalence (SWE) and SAR backscattering coefficients at C- and X-band (5.5 and 9.6 GHz) can be either positive or negative. Therefore, discovery of the relationship with an empirical approach is unrealistic. Instead, the authors estimate snow depth and particle size using SIR-C/X-SAR imagery from a physically-based first order backscattering model through analyses of the importance of each scattering term and its sensitivity to snow properties. Using numerically simulated backscattering values, the authors develop semi-empirical models for characterizing the snow-ground interaction terms, the relationships between the ground surface backscattering components, and the snowpack extinction properties at C-band and X-band. With these relationships, snow depth and optical equivalent grain size can be estimated from SIR-C/X-SAR measurements. Validation using three SIR-C/X-SAR images shows that the algorithm performs usefully for incidence angles greater than 300, with root mean square errors (RMSEs) of 34 cm and 0.27 mm for estimating snow depth and ice optical equivalent particle radius, respectively.     

Measurements of the propagation parameters of tree canopies at MMWfrequencies

The presence of trees in a given scene can hamper detection of nearby targets by millimeter-wave (MMW) radars especially at near grazing incidence. Proper characterization of scattering and attenuation in tree canopies is important for optimal detection algorithms. In this paper, a new technique for determining the extinction and volume backscattering coefficients in tree canopies using the measured radar backscatter response is proposed and verified experimentally. The technique, which can be applied to already available wideband radar backscatter data, is used to compute the extinction and volume backscattering coefficients of different tree canopies under various physical conditions. The dynamic range of these coefficients are presented and results at 35 GHz are compared with results at 95 GHz     

Extinction Behavior of Dry Snow in the 18-to 90-GHz Range

The extinction properties of several dry snow types were examined in the 18-to 90-GHz range. The snow types ranged from newly fallen snow to refrozen snow, and the density and mean grain size varied from 0.17 to 0.39 g/cm3 and from 0.2 to 1.6 mm, respectively. From measurements of the transmission loss as a function of sample thickness at a temperature of -15°C, the extinction coefficient and the surface scattering loss (due to surface roughness at the front and back surfaces of the snow slab) were determined for each snow type. The experimental values were compared against theoretical results computed according to the strong fluctuation theory. In general, good agreement with the experimental data was obtained at 18, 35, and 60 GHz when the grain size used in the theoretical calculations was chosen to be slightly smaller than the observed snow-particle size. However, the extinction coefficient of large-grained refrozen snow as predicted by the strong fluctuation theory is much larger at 90 GHz than the values determined experimentally. The attenuation in snow was observed to increase only slightly with increasing temperature in the -35 to -1°C range.     

Estimation of surface snow properties using combined millimeter-wave backscatter and near-infrared reflectance measurements

Knowledge of surficial snow properties such as grain size, surface roughness, and free-water content provides clues to the metamorphic state of snow on the ground, which in turn yields information on weathering processes and climatic activity. Remote sensing techniques using combined concurrent measurements of near-infrared passive reflectance and millimeter-wave radar backscatter show promise in estimating the above snow parameters. Near-infrared reflectance is strongly dependent on snow grain size and free-water content, while millimeter-wave backscatter is primarily dependent on free-water content and, to some extent, on the surface roughness. A neural-network based inversion algorithm has been developed that optimally combines near-infrared and millimeter-wave measurements for accurate estimation of the relevant snow properties. The algorithm uses reflectances at wavelengths of 1160 nm, 1260 nm and 1360 nm, as well as co-polarized and cross-polarized backscatter at a frequency of 95 GHz. The inversion algorithm has been tested using simulated data, and is seen to perform well under noise-free conditions. Under noise-added conditions, a signal-to-noise ratio of 32 dB or greater ensures acceptable errors in snow parameter estimation.