Retrieval of the Water Equivalent of Snow Cover in Finland by Satellite Microwave Radiometry

The use of satellite microwave radiometry to retrieve the water equivalent of seasonal snow cover in Finland was investigated. Data from the Scanning Multichannel Microwave Radiometer (SMMR) on board Nimbus-7 were employed to examine the feasibility of several water equivalent algorithms. The satellite data set covers the winters of 1978-1979 through 1981-1982. The ground truth consists of snow water equivalent maps of Finland, compiled by the National Board of Waters. The microwave response to the water equivalent of dry snow cover was observed to depend substantially on frequency. Only algorithms including the 37-GHz channel gave adequate agreement with the manually measured water equivalent values. The algorithm involving the brightness temperature difference between 18 and 37 GHz, vertical polarization, provided the highest overall linear correlation coefficient. Two distinct categories were observed in the microwave behavior of snow-covered terrain for dry snow conditions: 1) early and mid-winter (usually about three months in Finland), and 2) late winter (a few weeks, starts after several melt-freeze cycles). Short-term variations in the microwave response to snow water equivalent can be related to variations in the surface structure and, to some degree, the temperature of the snow cover. Especially for small snow depths, the microwave response is also affected by the temperature of the underlying ground. Annual variations were observed to correlate, in addition to snow parameters, with the water content and state (frozen or thawed) of the underlying ground. The microwave response to snow water equivalent was found to depend substantially on the land-cover category.     

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.<>     

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.     

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.     

Impact of horizontal and vertical heterogeneities on retrievals using multiangle microwave brightness temperature data

This paper investigates the impact of heterogeneity at the land surface on geophysical parameters retrieved from multiangle microwave brightness temperature data, such as would be obtained from the Soil Moisture and Ocean Salinity (SMOS) mission. Synthetic brightness temperature data were created using the Common Land (land surface) Model, coupled with a microwave emission model and set within the framework of the North American Land Data Assimilation System (NLDAS). Soil moisture, vegetation optical depth, and effective physical temperature were retrieved using a multiobjective calibration routine similar to the proposed SMOS retrieval algorithm for a typical on-axis range of look angles. The impact of heterogeneity both in the near-surface profiles of soil moisture and temperature and in the land cover on the accuracy of the retrievals was examined. There are significant errors in the retrieved parameters over regions with steep gradients in the near-surface soil moisture profile. These errors are approximately proportional to the difference in the soil water content between the top (at 0.7 cm) and second layer (at 2.7 cm) of the land surface model. The errors resulting from heterogeneity in the land cover are smaller and increase nonlinearly with increasing land-surface heterogeneity (represented by the standard deviation of the optical depth within the pixel). The most likely use of retrieved soil moisture is through assimilation into an LDAS for improved initiation of weather and climate models. Given that information on the soil moisture profile is already available within the LDAS, the error in the retrieved soil moisture as a result of the near-surface profile can be corrected for. The potential errors as a result of land-surface heterogeneity can also be assessed for use in the assimilation process.     

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.     

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     

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.     

A recurrent neural network classifier for improved retrievals ofareal extent of snow cover

Accurate detection of areal extent of snow in mountainous regions is important. Areal extent of snow is a useful climatic indicator. Moreover, snow melt is a major source of water supply for many arid regions (e.g., western United States, Morocco) and affects regional ecosystems. Unfortunately, accurate satellite retrievals of areal extent of snow have been difficult to achieve. Two approaches to effectively and accurately detect clear land, cloud, and areal extent of snow in satellite data are developed. A feed-forward neural network (FFNN) is used to classify individual images, and a recurrent NN is used to classify sequences of images. The continuous outputs of the NN, combined with a linear mixing model, provide support for mixed-pixel classification. Validation with independent in situ data confirms the classification accuracy (94% for feed-forward NN, 97% for recurrent NN). The combination of rapid temporal sampling (e.g., GOES) and a recurrent NN classifier is recommended (relative to an isolated scene (e.g., AVHRR) and a feed-forward NN classifier)     

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.     

A physical model to determine snowfall over land by microwave radiometry

Falling snow is an important component of global precipitation in extratropical regions. This paper describes the methodology and results of physically based retrievals of snow falling over land surfaces. Because microwave brightness temperatures emitted by snow-covered surfaces are highly variable, precipitating snow above such surfaces is difficult to observe using window channels that occur at low frequencies (/spl nu/<100 GHz). Furthermore, at frequencies /spl nu//spl les/37 GHz, sensitivity to liquid hydrometeors is dominant. These problems are mitigated at high frequencies (/spl nu/>100 GHz) where water vapor screens the surface emission, and sensitivity to frozen hydrometeors is significant. However, the scattering effect of snowfall in the atmosphere at those higher frequencies is also impacted by water vapor in the upper atmosphere. The theory of scattering by randomly oriented dry snow particles at high microwave frequencies appears to be better described by regarding snow as a concatenation of "equivalent" ice spheres rather than as a sphere with the effective dielectric constant of an air-ice mixture. An equivalent sphere snow scattering model was validated against high-frequency attenuation measurements. Satellite-based high-frequency observations from an Advanced Microwave Sounding Unit (AMSU-B) instrument during the March 5-6, 2001 New England blizzard were used to retrieve snowfall over land. Vertical distributions of snow, temperature, and relative humidity profiles were derived from the Mesoscale Model (MM5) cloud model. Those data were applied and modified in a radiative transfer model that derived brightness temperatures consistent with the AMSU-B observations. The retrieved snowfall distribution was validated with radar reflectivity measurements obtained from a ground-based radar network.     

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.     

Seasonal and Regional Variations of Active/Passive Microwave Signatures of Sea Ice

Ku-band (13.3 GHz) scatterometer and K-band (19.4 GHz) radiometer data acquired by the CCRS CV-580 aircraft over the period from 1979 to 1982 in Canadian and Danish (Greenland) coastal waters have been analyzed to determine the seasonal and regional variations of microwave sea-ice signatures. A clustering analysis of the like and cross-polarized scattering cross sections, ?HHo and ?HVo, and the H polarized emissivity ?H, has been used to identify distinct microwave sea-ice signatures for each ice type and to trace the evolution of these signatures with region and season. Ice-type signatures in the high Arctic under cold conditions are quite stable, and major ice classes are readily identified from microwave measurements. Under warmer conditions the signatures change with the structure, moisture content of the snow pack, and with the free water in the surface layers of the underlying ice. An attempt is made to create a consistent picture of the microwave signature transformation by grouping the data into " seaice seasons" (snow and ice surface transformation stages). The separation between microwave ice-class signatures reaches a minimum at the peak of the summer melt.     

Development of a technique to assess snow-cover mapping errors fromspace

Following the December 18, 1999, launch of the Earth Observing System (EOS) Terra satellite, daily snow-cover mapping is performed automatically at a spatial resolution of 500 m, cloud-cover permitting, using moderate resolution imaging spectroradiometer (MODIS) data. This paper describes a technique for calculating global-scale snow mapping errors and provides estimates of Northern Hemisphere snow mapping errors based on prototype MODIS snow mapping algorithms. Field studies demonstrate that under cloud-free conditions, when snow cover is complete, snow mapping errors are small (<1%) in all land covers studied except forests, where errors are often greater and more variable. Thus, the accuracy of Northern Hemisphere snow-cover maps is largely determined by percent of forest cover north of the snowline. From the 17-class International Geosphere-Biosphere Program (IGBP) land-cover maps of North America and Eurasia, the authors classify the Northern Hemisphere into seven land-cover classes and water. Estimated snow mapping errors in each of the land-cover classes are extrapolated to the entire Northern Hemisphere for areas north of the average continental snowline for each month. The resulting average monthly errors are expected to vary, ranging from about 5-10%, with the larger errors occurring during the months when snow covers the boreal forest in the Northern Hemisphere. As determined using prototype MODIS data, the annual average estimated error of the future Northern Hemisphere snow-cover maps is approximately 8% in the absence of cloud cover, assuming complete snow cover. Preliminary error estimates will be refined after MODIS data have been available for about one year     

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     

WindSat Passive Microwave Polarimetric Signatures of the Greenland Ice Sheet

WindSat has systematically collected the first global fully polarimetric passive microwave data over both land and ocean. As the first spaceborne polarimetric microwave radiometer, it was designed to measure ocean surface wind speed and direction by including the third and fourth Stokes parameters, which are mostly related to the asymmetric structures of the ocean surface roughness. Although designed for wind vector retrieval, WindSat data are also collected over land and ice, and this new data has revealed, for the first time, significant land signals in the third and fourth Stokes parameter channels, particularly over Greenland and the Antarctic ice sheets. The third and fourth Stokes parameters show well-defined large azimuth modulations that appear to be correlated with geophysical variations, particularly snow structure, melting, and metamorphism, and have distinct seasonal variation. The polarimetric signatures are relatively weak in the summer and are strongest around spring. This corresponds well with the formation and erosion of the sastrugi in the dry snow zone and snowmelt in the soaked zone. In this paper, we present the full polarimetric signatures obtained from WindSat over Greenland, and use a simple empirical observation model to quantify the azimuthal variations of the signatures in space and time.      

A semiempirical model for interpreting microwave emission fromsemiarid land surfaces as seen from space

A radiative transfer model for simulating microwave brightness temperatures over land surfaces is described. The model takes into account sensor viewing conditions (spacecraft altitude, viewing angle, frequency, polarization) and atmospheric parameters over a soil surface characterized by its moisture, roughness, and temperature and covered with a layer of vegetation characterized by its temperature, water content, single scattering albedo, structure and percent coverage. In order to reduce the influence of atmospheric and surface temperature effects, the brightness temperatures are expressed as polarization ratios that depend primarily on the soil moisture and roughness, canopy water content, and percentage of cover. The approach used is described, and the sensitivity of the polarization ratio to these parameters is investigated. Simulation of the temporal evolution of the microwave signal over semiarid areas in the African Sahel is presented and compared to actual satellite data from the SMMR instrument on Nimbus-7     

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     

Nimbus 7 SMMR Investigation of Snowpack Properties in the Northern Great Plains for the Winter of 1978-1979

An investigation of the capabilities of remote sensing of snowpack properties was conducted with brightness temperatures from the Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) and climatological data for the northern Great Plains for the winter of 1978-1979. The radiometer data included horizontally and vertically polarized brightness temperatures at the 0.81-, 1.66-, and 2.80-, and 4.54-cm wavelengths for both day and night overpasses, with a repeat coverage on the average of every two to three days. The brightness temperatures in each channel and the daily surface climatological elements of maximum and minimum air temperature, precipitation, snowfall, and snow depth were objectively analyzed to a 20-km grid with 35 rows and 42 columns. The analysis concentrated on temporal analyses of selected grid cells. Characteristic signatures were observed for initial snow accumulation, snow depth to about 20 cm, beginning of snow melting in the surface layers, and snow melt. The process of snow ripening was evident in the thawing and refreezing cycles of the snow surface layers. Discrimination of dry soil, wet soil, snow amount to 15 cm, and liquid water at the soil surface before runoff occurred was present with the use of both polarizations at the 0.81- and 1.66-cm wavelengths, although the longer wavelengths contained additional information on the state of the surface underlying the snow pack.     

Analysis of interannual changes in northern vegetation activityobserved in AVHRR data from 1981 to 1994

This paper reports on the analysis of Pathfinder AVHRR land (PAL) data set that spans the period July 1981 to September 1994. The time series of normalized difference vegetation index (NDVI) data for land areas north of 45° N assembled by correcting the PAL data with spectral methods confirms the northerly greening trend and extension of the photosynthetically active growing season. Analysis of the channel reflectance data indicates that the interannual changes in red and near-infrared reflectances are similar to seasonal changes in the spring time period when green leaf area increases and photosynthetic activity ramps up. Model calculations and theoretical analysis of the sensitivity of NDVI to background reflectance variations confirm the hypothesis that warming driven reductions in snow cover extent and earlier onset of greening are responsible for the observed changes in spectral reflectances over vegetated land areas