Snow-Cover Parameters Retrieved from Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR) Data

The Nimbus-7 satellite launched on October 24, 1978, carries a multifrequency, dual-polarized microwave imager. The instrument is designed to sense the ocean surface, the atmosphere, and land surfaces remotely. From previous ground-based and satellite-based microwave experiments, it is well known, that snow cover over land has a very distinct effect on the microwave signatures of the earth surface. It was the goal of this study to show that the three snow-cover parameters: extent, snow water equivalent, and onset of snow melt can be determined using scanning multichannel microwave radiometer (SMMR) data. Our analysis has shown, that the three snow parameters mentioned above are retrievable with sufficient accuracy to be of great value in climatology, meteorology, and hydrology. Snow extent is determined for dry snow cover with depth ?5 cm, snow water equivalent can be determined on a regional basis with ?2 g/cm2 rms accuracy, and the onset of snow melt is clearly visible by the detection of melt and refreeze cycles prior to snow runoff. The algorithms derived are simple enough to be incorporated in fully automated operational data analysis schemes.     

Average areal water equivalent of snow in a mountain basin using microwave and visible satellite data

Satellite microwave and millimeter-wave data have been used to evaluate the average areal water equivalent of snow cover in the mountainous Rio Grande basin in Colorado. Areal water equivalent data for the basin were obtained from contoured values of point measurements and from elevation-zone water volume values generated by a reliable snowmelt-runoff model using data on visible snow-cover extent. A significant relationship between the difference in brightness temperature at two different frequencies (37 and 18 GHz) and a basin-wide average snow-water equivalent value was obtained. This relationship and microwave observations were used to estimate the average areal water equivalent of the snow cover.<>     

Theoretical and Experimental Studies of Microwave Emission Signatures of Snow

The brightness temperature of snow in Finland has been studied theoretically and experimentally at 5,12, and 37 GHz for satellite remote sensing applications. A snow model consisting of ice spheres covered by a water shell has been used in theoretical calculations taking into account scattering and absorption. The brightness temperature of a natural snow field on the bare ground and on the ground covered with aluminum sheets has been measured from a tower. The experimental brightness temperatures are compared with calculated ones and show a reasonably good agreement. Experimental results also show that relatively small changes in the snow conditions cause large changes in the brightness temperature. Possible methods for using satellite observations in the remote sensing of snow are suggested.     

Comparison of algorithms for retrieval of snow water equivalentfrom Nimbus-7 SMMR data in Finland

The correlation between the brightness temperature of snow-covered terrain (dry snow) and the snow-water equivalent in Finland was investigated using Nimbus-7 SMMR data. The satellite data set covers the winters of 1978-9 through 1981-2. The correlation analysis was performed for 17 different brightness temperature functions, each involving one or several frequencies and polarizations. The highest correlation coefficients between the satellite-derived brightness temperature functions and the manually measured snow-water equivalent values were obtained by using the brightness temperature difference between 37 GHz and either 18 GHz or 10.7 GHz, vertical polarization     

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     

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.     

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.     

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.     

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     

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.     

Airborne retrievals of snow and ice surface emissivity atmillimeter wavelengths

Passive microwave radiometers (24-157 GHz) have been flown over Baltic Sea ice and snow sites in April 1995 and on March 15, 1997. Data from these instruments are analyzed with reference to ground measurements of snow and ice conditions, and emissivity spectra are presented for 12 classifications of surface type. A simple model based on dielectric permittivity can accurately represent the microwave spectra of sea ice, but cannot be extended to the behavior of dry snow above 100 GHz without the addition of an extra term to represent volume scattering. The parameterization presented is intended to provide a background for temperature and humidity retrievals from satellite sounders, but the results will be of interest to the snow and ice remote-sensing communities     

Passive microwave measurements of snow-covered forest areas inEMAC'95

Airborne passive microwave signatures collected in Northern Finland during EMAC-95 are analyzed with the emphasis on forested areas and dry snow conditions. The microwave signatures cover the 6.8-18.7-GHz frequency range and were acquired at both vertical and horizontal polarizations. The analysis is carried out with respect to the forest-stem volume data and comprises three different snow-depth situations. Emissivities approach saturation limit with the increasing stem volume. At 10.65 GHz, the saturation level was found to be linearly related to the snow-water equivalent. On the basis of passive-microwave measurements, an empirical forest transmissivity model is developed. The model is valid at vertical polarization 50° incidence angle, and it accounts for microwave frequency and forest-stem volume effects in the range of 6.8-94 GHz and 0-150 m³/ha, respectively     

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.     

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.     

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     

Towards the Definition of Optimum Sensor Specifications for Microwave Remote Sensing of Snow

The interaction of nicrowaves with snow strongly depends on parameters such as snow wetness and the size and structure of snow grains. Therefore microwave radiometry and scatterometry are excellent tools for remote sensing of the snowcover. Multifrequency radiometry can be used to classify snow as was shown with ground-based measurements of the period April-June 1977 at a high altitude Alpine test site. The continuation of the measurement program yielded data of 3 additional snow seasons with widely varying snow conditions, therefore the present information has become representative for alpine regions. Relationships between the brightness temperature and the water equivalents show a similar variation with snow type as in other snow regions, so that the range of validity of our data set is not restricted to the Alps. The problem of discriminating regions of wet snow from snow-free land is found to be solvable with microwave scatterometry. Two cluster analyses in factorial spaces of both the ground truth and the microwave data sets demonstrate the potential of microwave sensors to classify snow which is a prerequisite for snow algorithms retrieving hydrologic parameters. The results are used to define sensor specifications with optimum sensitivity for microwave remote sensing of snow.     

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.     

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     

Observations of Snow Water Equivalent Change on Landfast First-Year Sea Ice in Winter Using Synthetic Aperture Radar Data

In this paper, we examine the utility of synthetic aperture radar (SAR) backscatter data to detect a change in snow water equivalent (SWE) over landfast first-year sea ice during winter at relatively cold temperatures. We begin by reviewing the theoretical framework for linking microwave scattering from SAR to the thermodynamic and electrical properties of first-year sea ice. Previous research has demonstrated that for a given ice thickness and air-temperature change, a thick snow cover will result in a smaller change in the snow-ice interface temperature than will a thin snow cover. This small change in the interface temperature will result in a relatively small change in the brine volume at the interface and the resulting complex permittivity, thereby producing a relatively small change in scattering. A thin snow cover produces the opposite effect-a greater change in interface temperature, brine volume, permittivity, and scattering. This work is extended here to illustrate a variation of this effect over landfast first-year sea ice using in situ measurements of physical snow properties and RADARSAT-1 SAR imagery acquired during the winter of 1999 in the central Canadian Archipelago at cold (~-26degC) and moderately cold (~-14degC) snow-sea-ice interface temperatures. We utilize in situ data from five validation sites to demonstrate how the change in microwave scattering covaries and is inversely proportional with the change in the magnitude of SWE. These changes are shown to be detectable over both short (2 days) and longer (45 days) time durations     

InSAR for estimation of changes in snow water equivalent of drysnow

This paper describes the theoretical relation between interferometric phase and changes in snow water equivalent (SWE) and show results from experiments using ERS-½ tandem data. The main scattering contribution from a dry snow cover is from the snow-ground interface. However, the radar wave will be refracted in the snow. Thus, only small changes in the snow properties between two interferometric synthetic aperture radar (SAR) images will change the interferometric phase. This phase change is shown to introduce a significantly increase in the digital elevation model (DEM) height error, although no effects are observed on the degree of coherence. The phase change is also shown to affect the differential interferometric results and may wrongly be interpreted as range displacement. The presented theory and results implies that light snowfall and/or small changes in snow properties between interferometric SAR (InSAR) image acquisitions, may introduce significant height errors in DEM derived from glaciers, ice sheets, or bare ground, even in the case of high degree of coherence. Thus, meteorological observations in addition to degree of coherence must be considered when generating DEM in areas covered with snow or where snow fall is likely to have occurred