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.     

Sensitivity of passive microwave snow depth retrievals to weather effects and snow evolution

Snow fall and snow accumulation are key climate parameters due to the snow's high albedo, its thermal insulation, and its importance to the global water cycle. Satellite passive microwave radiometers currently provide the only means for the retrieval of snow depth and/or snow water equivalent (SWE) over land as well as over sea ice from space. All algorithms make use of the frequency-dependent amount of scattering of snow over a high-emissivity surface. Specifically, the difference between 37- and 19-GHz brightness temperatures is used to determine the depth of the snow or the SWE. With the availability of the Advanced Microwave Scanning Radiometer (AMSR-E) on the National Aeronautics and Space Administration's Earth Observing System Aqua satellite (launched in May 2002), a wider range of frequencies can be utilized. In this study we investigate, using model simulations, how snow depth retrievals are affected by the evolution of the physical properties of the snow (mainly grain size growth and densification), how they are affected by variations in atmospheric conditions and, finally, how the additional channels may help to reduce errors in passive microwave snow retrievals. The sensitivity of snow depth retrievals to atmospheric water vapor is confirmed through the comparison with precipitable water retrievals from the National Oceanic and Atmospheric Administration's Advanced Microwave Sounding Unit (AMSU-B). The results suggest that a combination of the 10-, 19-, 37-, and 89-GHz channels may significantly improve retrieval accuracy. Additionally, the development of a multisensor algorithm utilizing AMSR-E and AMSU-B data may help to obtain weather-corrected snow retrievals.     

Estimating terrestrial snow depth with the TOPEX-Poseidon altimeter and radiometer

Active and passive microwave measurements obtained by the dual-frequency TOPEX-Poseidon radar altimeter from the Northern Great Plains of the United States are used to develop a snow pack radar backscatter model. The model results are compared with daily time series of surface snow observations made by the U.S. National Weather Service. The model results show that Ku-band provides more accurate snow depth determinations than does C-band. Comparing the snow depth determinations derived from the TOPEX-Poseidon nadir-looking passive microwave radiometers with the oblique-looking Satellite Sensor Microwave Imager (SSM/I) passive microwave observations and surface observations shows that both instruments accurately portray the temporal characteristics of the snow depth time series. While both retrievals consistently underestimate the actual snow depths, the TOPEX-Poseidon results are more accurate.     

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     

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.     

Automated delineation of dry and melt snow zones in Antarctica using active and passive microwave observations from space

This paper presents the algorithms and analysis results for delineating snow zones using active and passive microwave satellite remote sensing data. With a high-resolution Radarsat synthetic aperture radar (SAR) image mosaic, dry snow zones, percolation zones, wet snow zones, and blue ice patches for the Antarctic continent have been successfully delineated. A competing region growing and merging algorithm is used to initially segment the SAR images into a series of homogeneous regions. Based on the backscatter characteristics and texture property, these image regions are classified into different snow zones. The higher level of knowledge about the areal size of and adjacency relationship between snow zones is incorporated into the algorithms to correct classification errors caused by the SAR image noise and relief-induced radiometric distortions. Mathematical morphology operations and a line-tracing algorithm are designed to extract a vector line representation of snow-zone boundaries. With the daily passive microwave Special Sensor Microwave/Imager (SSM/I) data, dry and melt snow zones were derived using a multiscale wavelet-transform-based method. The analysis results respectively derived from Radarsat SAR and SSM/I data were compared and correlated. The complementary nature and comparative advantages of frequently repeated passive microwave data and spatially detailed radar imagery for detecting and characterizing snow zones were demonstrated.     

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     

Investigations of the apparent temperature of snow cover in the submillimeter wavelength region

Equipment and methods used for measuring the apparent temperature of snow cover in high mountains is described. The spectrum of dry snowpack emission in the wavelength region 0.85÷1.3 mm is obtained. The dependence of the apparent temperature of snow cover on the atmospheric optical depth is determined. It is shown that in the first approximation the snow cover emission is described by the Lambert law and the snow albedo is within 0.6÷0.67.     

Sea ice concentration, ice temperature, and snow depth using AMSR-E data

A summary of the theoretical basis and initial performance of the algorithms that are used to derive sea ice concentration, ice temperature, and snow depth on sea ice from newly acquired Earth Observing System-Aqua/Advanced Microwave Scanning Radiometer-EOS (AMSR-E) radiances is presented. The algorithms have been developed and tested using historical satellite passive microwave data and are expected to provide more accurate products, since they are designed to take advantage of the wider range of frequencies and higher spatial resolution of the AMSR-E microwave instrument. Validation programs involving coordinated satellite, aircraft, and surface measurements to determine the accuracies of these sea ice products and to improve further our capability to monitor global sea ice are currently underway.     

Radar measurements of snow: experiment and analysis

This paper considers two specific types of experiments conducted to improve the authors' understanding of radar backscatter from snow-covered ground surfaces. The first experiment involves radar backscatter measurements at Cand X-band of artificial snow of varying depths. The relatively simple target characteristics, combined with an exhaustive ground truth effort, make the results of this experiment especially amenable to comparison with predictions based on theoretical methods for modeling volume-scattering media. It is shown that both conventional and dense-medium radiative transfer models fail to adequately explain the observed results. A direct polarimetric inversion approach is described by which the characteristics of the snow medium are extracted from the measured data. The second type of experiment examined in this study involves diurnal backscatter measurements that were made contemporaneously with detailed measurements of the snow-wetness depth profiles of the observed scene. These data are used to evaluate the capability of a recently proposed algorithm for snow wetness retrieval from polarimetric synthetic aperture radar (SAR) measurements, which has hithertofore been applied only to data from very complex and extended mountainous terrains     

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.     

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.     

Quantifying Uncertainty in Modeling Snow Microwave Radiance for a Mountain Snowpack at the Point-Scale, Including Stratigraphic Effects

Merging microwave radiances and modeled estimates of snowpack states in a data assimilation scheme is a potential method for snowpack characterization. A radiance assimilation scheme for snow requires a land surface model (LSM) coupled to a radiative transfer model (RTM). In this paper, we explore the degree of model fidelity required in order for radiance assimilation to yield benefits for snowpack characterization. Specifically, we characterize the uncertainty of Microwave Emission Model for Layered Snowpacks (MEMLS) radiance predictions by quantifying model accuracy and sensitivity to the following: (1) the LSM snowpack layering scheme and (2) the properties of the snow layers, including melt-refreeze ice layers. MEMLS was consistent with the measured brightness temperatures at 18.7 and 36.5 GHz with a bias (mean absolute error) of 0.1 K (3.1 K) for the vertical polarization and 3.4 K (9.3 K) for the horizontal polarization. An error in the predictions at horizontal polarization is due to uncertainty in ice-layer properties. It was found that in order for predicted brightness temperatures from the coupled LSM and RTM to be adequate for radiance assimilation purposes, the following must be satisfied: (1) the LSM snowpack layering scheme must accurately represent the stratigraphic snowpack layers; (2) dynamics of melt-refreeze ice layers must be modeled explicitly, and the predicted density of melt-refreeze layers must be accurate within ; and (3) the MEMLS correlation length must be predicted within 0.016 mm, or effective optical grain diameter must be predicted within 0.045 mm. Recommendations for future field measurements are made.     

Retrieval of dry-snow parameters from microwave radiometric data using a dense-medium model and genetic algorithms

A numerical technique based on genetic algorithms (GAs) is used to invert the equations of an electromagnetic model based on dense-medium radiative transfer theory (DMRT) to retrieve snow depth, mean grain size, and fractional volume from microwave brightness temperatures. In order to study the sensitivity of the GA to its parameters, the technique is initially tested on simulated microwave data with and without adding a random noise. A configuration of GA parameters is selected and used for the retrieval of snow parameters from both ground-based observations and brightness temperatures recorded by the Advanced Microwave Scanning Radiometer-EOS (AMSR-E). Retrieved snow parameters are then compared with those measured on ground. Although more investigation is required, results suggest that the proposed technique is able to retrieve snow parameters with good accuracy.     

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     

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     

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.     

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     

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     

Monitoring Snow Characteristics With Ground-Based Multifrequency Microwave Radiometry

Long-term microwave and infrared radiometric measurements of snowpack were carried out with ground-based sensors in winter 2006-2007 and 2007-2008, together with conventional measurements of snow-cover profiles. The first experiment focused on the behavior of snow emission during the destructive and constructive metamorphisms. The second involved a correlation analysis of the small fluctuations related to diurnal solar cycle in order to obtain the time delay of microwave brightness temperatures Tb with respect to the snow surface temperature. From this analysis, it was possible to estimate an effective (weighed average) temperature and the thickness of the layer that mostly contributed to microwave emission at 19 and 37 GHz. The ratio of the brightness temperature to the effective temperature can be assumed to be an equivalent emissivity of the snowpack. Data collected in both years have been compared with simulations carried out using the advanced Institute of Applied Physics (IFAC) Radiative Advanced Dry Snow Emission (IRIDE) model driven by data collected on ground. The model is based on the advanced integral equation method to represent soil, coupled to a layer of dry snow whose electromagnetic properties are described by the dense medium radiative transfer theory with quasi-crystalline approximation applied to a medium (air) filled with sticky particles. Simulations performed by using ground data as inputs to the model have been found to be well in agreement with experimental data. Moreover, the comparison of model simulations with experimental data allowed one to understand some peculiar characteristics of microwave emission from the snowpack related to its physical conditions.