The potential of times series of C-Band SAR data to monitor dry andshallow snow cover

A study was conducted to assess the potential of C-band synthetic aperture radar (SAR) data to determine the snow water equivalent (SWE). A multitemporal (three winters) SAR data set was obtained using the Convair-580 from the Canada Centre for Remote Sensing (CCRS) over a watershed in the Appalachian Mountains in Southern Quebec, Canada. The SAR data were relatively calibrated using extended targets (coniferous stands). Extensive ground measurements were done simultaneously to each of the seven flights, in order to measure the snow cover characteristics (depth, density, SWE, liquid water content, temperature, and dielectric profiles) as well as the soil characteristics (moisture, temperature). To estimate the SWE of a given snowpack, a model which links the scattering coefficient to the physical parameters of the snow cover and the underlying soil has been developed. The model is based on the ratio of the scattering coefficient of a field covered by snow to the scattering coefficient of a field without snow. The analysis has revealed that volume scattering from a shallow dry snow cover (SWE<20 cm) is undetectable. The backscattering power is dominated by soil surface scattering, the latter varying with the decrease of liquid water content in the surface layer with decreasing soil temperature below 0°C. Then, the scattering ratio decreases proportionally to the dielectric constant of the soil in winter. Furthermore, a unique relationship for three acquisition dates has been found between the thermal resistance, R, of the snow pack and the backscattering power ratio. Then, the spatial distribution of the power ratio should depict the spatial distribution of R, given spatially uniform climatological conditions over the study area. Since linear relationships between SWE and R have been observed, it should be possible to estimate the SWE of shallow dry snow cover with C-band SAR data using few ground truthing data in an open area when the soil is frozen     

On the estimation of snow depth from microwave radiometricmeasurements

Multiple-channel microwave radiometric measurements made over Alaska at aircraft (near 90 and 183 GHz) and satellite (at 37 and 85 GHz) altitudes are used to study the effect of atmospheric absorption on the estimation of snow depth. The estimation is based on the radiative transfer calculations using an early theoretical model of Mie scattering of single-size particles. It is shown that the radiometric correction for the effect of atmospheric absorption is important even at 37 GHz for a reliable estimation of snow depth. Under a dry atmosphere and based on single-frequency radiometric measurements, the underestimation of snow depth could amount to 50% at 85 GHz and 20-30% at 37 GHz if the effect of atmospheric absorption is not taken into account. The snow depths estimated from the 90-GHz aircraft and 85-GHz satellite measurements are found to be in reasonable agreement. However, there is a discrepancy in the snow depth estimated from the 37-GHz (at both vertical and horizontal polarizations) and 85-GHz satellite measurements     

A prototype AMSR-E global snow area and snow depth algorithm

A methodologically simple approach to estimate snow depth from spaceborne microwave instruments is described. The scattering signal observed in multifrequency passive microwave data is used to detect snow cover. Wet snow, frozen ground, precipitation, and other anomalous scattering signals are screened using established methods. The results from two different approaches (a simple time and continentwide static approach and a space and time dynamic approach) to estimating snow depth were compared. The static approach, based on radiative transfer calculations, assumes a temporally constant grain size and density. The dynamic approach assumes that snowpack properties are spatially and temporally dynamic and requires two simple empirical models of density and snowpack grain radius evolution, plus a dense media radiative transfer model based on the quasicrystalline approximation and sticky particle theory. To test the approaches, a four-year record of daily snow depth measurements at 71 meteorological stations plus passive microwave data from the Special Sensor Microwave Imager, land cover data and a digital elevation model were used. In addition, testing was performed for a global dataset of over 1000 World Meteorological Organization meteorological stations recording snow depth during the 2000-2001 winter season. When compared with the snow depth data, the new algorithm had an average error of 23 cm for the one-year dataset and 21 cm for the four-year dataset (131% and 94% relative error, respectively). More importantly, the dynamic algorithm tended to underestimate the snow depth less than the static algorithm. This approach will be developed further and implemented for use with the Advanced Microwave Scanning Radiometer-Earth Observing System aboard Aqua.     

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.     

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.     

Retrieval of snow parameters by iterative inversion of a neuralnetwork

The inversion of snow parameters from passive microwave remote sensing measurements is performed, using an iterative inversion of a neural network (NN) trained with a dense-media multiple-scattering model. Inversion of four parameters is performed based on five brightness temperatures. The four parameters are mean grain size of ice particles in snow, snow density, snow temperature, and snow depth. Iterative inversion of a data-driven forward NN model is justified on a theoretical and methodological basis. An error analysis is performed, comparing iterative inversion of a forward model with the use of an explicit inverse for the retrieval of independent snow parameters from their corresponding measurements. The NN iterative inversion algorithm is further illustrated by reconstructing a synthetic terrain of snow parameters from their corresponding measurements, inverting all four parameters simultaneously. The reconstructed parameter contours are in good agreement with the original synthetic parameter contours     

Monitoring of melting refreezing cycles of snow with microwave radiometers: the Microwave Alpine Snow Melting Experiment (MASMEx 2002-2003)

A study of the melting cycle of snow was carried out by using ground-based microwave radiometers, which operated continuously 24 h/day from late March to mid-May in 2002 and from mid-February to early May in 2003. The experiment took place on the eastern Italian Alps and included micrometeorological and conventional snow measurements as well. The measurements confirmed the high sensitivity of microwave emission at 19 and 37 GHz to the melting-refreezing cycles of snow. Moreover, micrometeorological data made it possible to simulate snow density, temperature, and liquid water content through a hydrological snowpack model and provided additional insight into these processes. Simulations obtained with a two-layer electromagnetic model based on the strong fluctuation theory and driven by the output of the hydrological snowpack model were consistent with experimental data and allowed interpretation of both variation in microwave emission during the melting and refreezing phases and in discerning the contributions of the upper and lower layers of snow as well as of the underlying ground surface.     

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.     

New methods to infer snow albedo from the MISR instrument with applications to the Greenland ice sheet

Snow-covered surfaces have a very high surface albedo, thereby allowing little energy to be absorbed by the snowpack. As the snowpack ages and/or begins to melt, the snow albedo decreases and more solar energy is absorbed by the snowpack. Therefore, accurate estimation of snow albedo is essential for monitoring the state of the cryosphere. This paper examines the retrieval of snow albedo using data from the Multi-angle Imaging SpectroRadiometer (MISR) instrument over the Greenland ice sheet. Two different methods are developed and examined to derive the snow albedo: one based on the spectral information from MISR and one utilizing the angular information from the MISR instrument. The latter method is based on a statistical relationship between in situ albedo measurements and the MISR red channel reflectance at all MISR viewing angles and is found to give good agreement with the ground-based measurements. Good agreement is also found using the spectral information, although the method is more sensitive to instrument calibration, snow bidirectional reflectance distribution function models, and narrowband-to-broadband relationships. In general, using either method retrieves snow surface albedo values that are within about 6% of that measured at the stations in Greenland.     

Inversion of snow parameters from passive microwave remote sensingmeasurements by a neural network trained with a multiple scatteringmodel

The inversion of snow parameters from passive microwave remote sensing measurements is performed with a neural network trained with a dense-media multiple-scattering model. The input-output pairs generated by the scattering model are used to train the neural network. Simultaneous inversion of three parameters, mean-grain size of ice particles in snow, snow density, and snow temperature from five brightness temperatures, is reported. It is shown that the neural network gives good results for simulated data. The absolute percentage errors for mean-grain size of ice particles and snow density are less than 10%, and the absolute error for snow temperature is less than 3 K. The neural network with the trained weighting coefficients of the three-parameter model is also used to invert SSMI data taken over the Antarctic region     

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.     

The use of ERS-1 SAR data in snow melt monitoring

ERS-1 SAR data, airborne data and in situ snow data were acquired for the Sodankyla test site in northern Finland for the winters of 1991-1992 and 1992-1993. The test area consists of sparsely forested areas (pine, mixed forests, and mires) and open areas (bogs, lakes, clear-cut areas, and urban area). A set of multitemporal ERS-1 SAR images covering the two winters have been analyzed and the results have been compared with in situ surveys and a digital land-use map. The results indicate that even in the presence of forest canopies (1) wet snow can be distinguished from other soil/snow conditions (dry snow and bare ground), and (2) snow melt maps can be derived from SAR images. Snow-melt maps indicate areas fully covered with wet snow, partly melted areas and snow-free areas     

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     

Microwave emission from dry snow: a comparison of experimental andmodel results

Field measurements of microwave emission from snow-covered soil were carried out in 1996, 1997, and 1999 on the Italian Alps using a three-frequency dual polarized microwave system. At the same time, nivological time measurements were carried out using standard methods and an electromagnetic contact probe. Collected data confirmed the possibility of separating wet from dry snow and of estimating the water equivalent of dry snow. Simulations performed by means of a model based on the dense medium radiative theory (DMRT) were able to reproduce experimental data very well     

Measurement of directional and spectral signatures of light reflectance by snow

The bidirectional reflection distribution functions (BRDF) of snow have been measured at high spectral resolution at various locations in Finland (Vuotso, Hyytia/spl uml/la/spl uml/, Sodankyla/spl uml/, Kilpisja/spl uml/rvi, Rovaniemi, Sodankyla/spl uml/ again). The measured snow types include fresh, new snow, both needle-like and hexagonal flakes, old, loose snow, and melting and refrozen snow. All snow types show strong forward scattering as previously reported, but there also appeared to be some enhancement in the backward directions that has not been reported in much detail. The grain size gives a clear signal at near-infrared, which was observed previously. A nontrivial dependence on grain shape was also observed, which has been ignored previously. Melting snow has a distinct forward feature not observable in dry snow: first a maximum in specular direction, a minimum after that, and then again brightening forward. There is a spectral signal at 1250/1350 nm that could be useful for wetness recovery in particular, even when the topography or BRDF model is not known. Density dependence was observed, partially contradicting earlier measurements. Microtopographic roughness slightly increases backscattering as expected. Much more detailed information about snow could be observed using hyperspectral, multidirectional remote sensing techniques than with current instruments. Measurements of more snow types need to be taken, especially dirty snow, snow/vegetation composites, and rough snow surfaces.     

Reflective properties of natural snow: approximate asymptotic theory versus in situ measurements

Results of measurements of the bidirectional reflection function of snow for the solar zenith angle close to 54/spl deg/ are compared with a recently developed snow optical model based on the representation of snow grains as fractal particles. The model has a high accuracy out of the principal plane for the observation zenith angles smaller than 60/spl deg/. However, the accuracy is reduced in the principal plane. Specular light reflection by partially oriented snow plates on the snow surface not accounted for by the model can play a role for measurements in the principal plane. The model discussed can be used for the grain size retrieval using both ground and spaceborne measurements of the snow reflectance. This is supported by a high accuracy of the model in a broad spectral range 545-2120 nm as demonstrated in this work.     

Metamorphic signature of snow revealed in SSM/I measurements

Brightness temperatures (19, 22, 37, 85 GHz) measured by the special sensor microwave/imager (SSM/I) are analyzed using data from the snow monitoring network within the former Soviet Union during the 1987-1988 winter period. It is shown that in the beginning of winter, the SSM/I measurements display the classical snow scattering signature, i.e., the brightness temperatures decrease with increasing depth, and the largest decrease occurs at the highest frequency. Dramatic deviations from this pattern are observed in the middle of winter, where the brightness temperature approaches a minimum and then begins to increase despite the fact that the snow depth remains constant or continues to grow. The two-stream radiative transfer model is combined with results from dense media theory to help explain the phenomenon. Model results suggest that the increase in brightness temperature is due to a decrease of the single scattering albedo as the snowpack ages. This decrease of the albedo is related to changes in the snow crystalline structure due to metamorphism. Consequences for the interpretation of satellite measurements and development of algorithms for deriving snow water equivalent are discussed     

Influence of land-cover category on brightness temperature of snow

A helicopter-borne multifrequency radiometer (24, 34, 48 and 94 GHz vertical polarization) was used to investigate the behavior of the brightness temperature of snow in Sodankyla (latitude: 67.41 N, longitude: 26.58 E), Northern Finland. The measurements were carried out during dry snow, wet snow, and snow-free conditions. The angle of incidence was 45° in all measurements. The measurements and the main results are presented. The analysis is focused on the effect of vegetation and land type on the brightness temperature of snow. The main topics of this paper are: (a) the general behavior of the brightness temperature of snow for different land types, (b) the effect of forest vegetation on the brightness temperature of snow, and (c) the capability of the radiometer system to monitor snow extent in forests during the melting period     

Passive microwave remote sensing of snow constrained byhydrological simulations

This paper describes a snow parameter retrieval algorithm from passive microwave remote sensing measurements. The three components of the retrieval algorithm include a dense media radiative transfer (DMRT) model, which is based on the quasicrystalline approximation (QCA) with the sticky particle assumption, a physically-based snow hydrology model (SHM) that incorporates meteorological and topographical data, and a neural network (NN) for computational efficient inversions. The DMRT model relates physical snow parameters to brightness temperatures. The SHM simulates the mass and heat balance and provides initial guesses for the neural network. The NN is used to speed up the inversion of parameters. The retrieval algorithm can provide speedy parameter retrievals for desired temporal and spatial resolutions, Four channels of brightness temperature measurements: 19V, 19H, 37V, and 37H are used. The algorithm was applied to stations in the northern hemisphere. Two sets of results are shown. For these cases, the authors use ground-truth precipitation data, and estimates of snow water equivalent (SWE) from SHM give good results. For the second set, a weather forecast model is used to provide precipitation inputs for SHM. Additional constraints in grain size and density are used. They show that inversion results compare favorably with ground truth observations     

Integrating in situ and multiscale passive microwave data for estimation of subgrid scale snow water equivalent distribution and variability

New multiscale research datasets were acquired in central Saskatchewan, Canada during February 2003 to quantify the effect of spatially heterogeneous land cover and snowpack properties on passive microwave snow water equivalent (SWE) retrievals. Microwave brightness temperature data at various spatial resolutions were acquired from tower and airborne microwave radiometers, complemented by spaceborne Special Sensor Microwave/Imager (SSM/I) data for a 25/spl times/25 km study area centered on the Old Jack Pine tower in the Boreal Ecosystem Research and Monitoring Sites (BERMS). To best address scaling issues, the airborne data were acquired over an intensively spaced grid of north-south and east-west oriented flight lines. A coincident ground sampling program characterized in situ snow cover for all representative land cover types found in the study area. A suite of micrometeorological data from seven sites within the study area was acquired to aid interpretation of the passive microwave brightness temperatures. The in situ data were used to determine variability in SWE, snow depth, and density within and between forest stands and land cover types within the 25/spl times/25 km SSM/I grid cell. Statistically significant subgrid scale SWE variability in this mixed forest environment was controlled by variations in snow depth, not density. Spaceborne passive microwave SWE retrievals derived using the Meteorological Service of Canada land cover sensitive algorithm suite were near the center of the normally distributed in situ measurements, providing a reasonable estimate of the mean grid cell SWE. A realistic level of SWE variability was captured by the high-resolution airborne data, showing that passive microwave retrievals are capable of capturing stand-to-stand SWE variability if the imaging footprint is sufficiently small.