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.     

Use of ground observations to simulate the seasonal changes in thebackscattering coefficient of the subarctic forest

RADARSAT synthetic aperture radar (SAR) data acquired at C Band, HH polarization, and for the 20°-27° and 45°-49° incidence angle ranges were available over northern Quebec, Canada, (54°N, 72°12'W), in the fall of 1996, the winter of 1997, and the spring of 1997. The main land occupation of this area is sparse black spruce (Picea mariana) forests. Vegetation characteristics are jointly used with snow and soil observations coinciding with the satellite overpasses to simulate the seasonal changes in the backscattering coefficient of the subarctic forest. The aim of this study is twofold. First to evaluate the effects of the seasonal changes in vegetation on the RADARSAT SAR data, and second to use backscattering models as a tool for a better interpretation and understanding of the RADARSAT SAR data over snow-covered forested areas. Simulations show the importance of the surface-vegetation interaction term and the wet snow surface roughness on the discrimination between open forest and denser forest, and on the contrast between wet snow and dry snow covers. When comparing the simulations to the RADARSAT SAR data, the poorest results are obtained in the spring for a rough wet snow. It is shown that they are mainly due to a crude evaluation of the vegetation dielectric constant rather than to uncertainties introduced by the spatial variability in the wet snow surface roughness     

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     

Retrieving snowpack properties and accumulation estimates from a combination of SAR and scatterometer measurements

This study combines two satellite radar techniques, low-resolution C-/Ku-band scatterometer and high-resolution C-band synthetic aperture radar (SAR) for glaciological studies, in particular mass-balance estimations. Three parameters expressing the mean backscattering and its dependency on azimuth and incidence angle are used to describe and classify the Antarctic ice sheets backscattering behavior. Simple linear regression analyses are carried out between ground truth accumulation data and the SAR backscattering coefficient along continuous profile lines. From this we parameterize the accumulation rate separately for certain snow pack regimes. We find that SAR data can be used to map mass-balance changes, however only within limited areas. Applying this method therefore generally requires accurate ground truth for regional calibration together with additional information regarding mean air temperature or elevation. This investigation focuses on the area of Dronning Maud Land, Antarctica. We present the first high-resolution accumulation map based on SAR data for the surrounding area of the EPICA deep ice core drilling site Kohnen, which is compared to reliable ground truth records as well as to a surface-mass-balance map interpolated from these at low resolution.     

Effect of Surface Profile Length on the Backscattering Coefficients of Bare Surfaces

The root mean square (rms) height s and autocorrelation length l are commonly used as the surface roughness input parameters to surface scattering models. Whereas it is well known that the surface roughness parameters of a natural soil surface are underestimated with a short surface profile, it is not clear how much the underestimated surface parameters affect the backscattering coefficients of the surface for various incidence angles and polarizations. In this paper, the backscattering coefficients of simulated and measured surface profiles are computed using the integral equation method and analyzed to answer this question. A 4000lmacr-long rough surface is generated numerically, where lmacr is the true correlation length of the surface, and the backscattering coefficients of the surface are computed and analyzed for various conditions. The rms error of the backscattering coefficient at a medium range of incidence angles is less than 1.5 dB for vv-polarization and 0.5 dB for hh-polarization if the profile length is larger than 5lmacr for a surface with ks=1.0, kl=10.0, and epsiv _r=(10.0,2.0). Similar results are obtained from numerous simulations with various roughness conditions and various wavelengths. It is also shown that the rms error of the backscattering coefficients between 5- and 1-m-long measured surface profiles is 1.7 dB for vv-polarization and 0.5 dB for hh-polarization at a medium range of incidence angle (15deglesthetasles70deg), whereas the surface roughness parameters are significantly reduced from 2.4 to 1.5 cm for the rms height s and from 35.1 to 10.0 cm for the autocorrelation length l     

Microwave Sea-Ice Signatures near the Onset of Melt

On June 22, 1982, the Canada Centre for Remote Sensing's Convair 580 aircraft (CCRS CV-580) made X-band SAR, Ku-band scatterometer, and K-band Radiometer measurements of the sea ice in Crozier Channel. Measurements of the physical properties of the ice and snow cover were in progress at a site in the southern portion of the CV-580 measurement area at the time of overflight. The CV-580 X-band SAR and Ku-band scatterometer were cross calibrated with the University of Kansas Heloscat to examine the frequency dependence of surface signatures. Analysis of the combined airborne and surface characterization data set shows that the microwave signatures of the surface, under the conditions present, were dominated by the snow cover and, in bare ice areas, by surface moisture. At frequencies above 9.35 GHz no scattering cross section/brightness temperature signatures could be uniquely related to ice type over the entire experiment area.     

Analysis of ERS Tandem SAR coherence from glaciers, valleys, andfjord ice on Svalbard

Interferometric satellite synthetic aperture radar (SAR) data was acquired from an area on Spitsbergen, Svalbard, during the European remote sensing satellite (ERS) Tandem mission in 1995-1996. Analyzing these data sets shows that the estimated SAR coherence is highly dependent on the satellite baseline length, and that corrections for this decorrelation effect is necessary if the baseline is a few hundred meters or more. Meteorological recordings are compared to SAR coherence estimates made at different seasons and surface categories: glaciers in motion, glacial forefields dominated by ice-cored moraines, lakes, rivers, and flat valleys with fine moraine materials like gravel and sand. It was found that temporal decorrelation effects are mainly due to changing surface conditions caused by precipitation and temperature variations around freezing, but that wind redistribution of snow also may play a role. Structures and cracks in the fjord ice as well as boundaries of lakes, rivers, and coastlines can be detected in SAR coherence images because of the contrast between high and low coherence areas. Low coherence is observed from those parts of moving glaciers that experience deformations shear, or zones of relative high velocity. The usefulness of 35-days interferometric SAR (e.g., the foreseen ENVISAT configuration) will be limited, even in sparsely vegetated areas like Svalbard, as compared to the ERS Tandem configuration     

Seasonal dynamics of C-band backscatter of boreal forests withapplications to biomass and soil moisture estimation

The seasonal changes of the C-band backscattering properties of boreal forests are investigated by applying 1) a semiempirical forest backscattering model and 2) multitemporal ERS-1 SAR data from two test areas in Finland. The semiempirical modeling of forest canopy volume backscattering and extinction properties is based on high-resolution data from the authors' ranging scatterometer HUTSCAT. The response of ERS-1 SAR to forest stem volume (biomass) and other forest characteristics is investigated by employing the National Forest Inventory sample plots, stand-wise forest inventory data and LANDSAT- and SPOT-based digital land use maps. The results show that the correlation between the backscattering coefficient and forest stem volume (biomass) varies from positive to negative depending on canopy and soil moisture. Additionally, the seasonal snow cover and soil freezing/thawing effects cause drastic changes in the radar response. A novel method for the estimation of forest stem volume (biomass) is introduced. This technique is based on the use of: 1) multitemporal ERS-1 SAR data; 2) reference sample plot information; and 3) the semiempirical backscattering model. It is shown that the multitemporal ERS-1 SAR images can be successfully used for estimating the forest stem volume. The effects of soil moisture variations to ERS-1 SAR results have been analyzed using hydrological soil moisture model and in situ data. The results indicate that the semiempirical model can he used for predicting the soil and canopy moisture variations in ERS-1 images     

Investigating of snow wetness parameter using a two-phase backscattering model

A two-phase backscattering model with nonsymmetrical inclusions is applied to calculate radar backscatter from a half-space of wet snow using strong fluctuation theory. Wet snow is assumed to consist of dry snow (host) and liquid water (inclusions). The shape and size of water inclusions are considered using an anisotropic and azimuth symmetric correlation function. The relationship between correlation lengths and snow wetness is presented by comparing strong fluctuation theory with the experimental data at 1.2, 8.6, 17, and 35.6 GHz. In the comparisons, correlation lengths are used as free fitting parameters. The effect of snow wetness on the backscattering coefficient is investigated. Numerical results of comparison between the two-phase backscattering model with nonsymmetrical inclusions and the experimental data are illustrated at 1.2, 8.6, 17, and 35.6 GHz. The effect of size and shape of water inclusions at different snow wetness values to backscatter level is shown. The comparison of angular response of backscattering coefficient (decibels) to wet snow between the model and the experimental data is presented at 2.6, 8.6, 17, and 35.6 GHz.     

The effect of inhomogeneous roughness on radar backscattering fromslightly deformed sea ice

This paper focuses on the spatially varying backscattering signature of an area of refrozen brash ice observed by a ship based scatterometer. The measurements were carried out as part of the Baltic Experiment for ERS-1 in 1994. The scatterometer was operated at 5.4 GHz at different incidence angles and polarizations. By analysing the scatterometer data over azimuth scans, it was found that the backscattering variabilities are not only due to fading but also contain a textural component. Surface height profiles were measured using a laser. The observed ice surface roughness was nonstationary over the measurement area. The ice surface can be approximated by adjacent patches of stationary roughness with patch dimensions of about 4.5 m. From the roughness spectra of different stationary patches, two roughness classes can be distinguished. The implications of estimating the roughness parameters from relatively short profile lengths is discussed and the effect on theoretical predictions of the backscattering coefficient is investigated. The texture variance is evaluated theoretically on the basis of the simulated backscattering coefficients of the two observed roughness classes and is found to compare with the scatterometer data     

基于双高斯拟合的目标激光偏振特性实验研究

对几种典型目标的后向散射光偏振度进行了测量和分析。基于探测光的Stokes 矢量描述,利用接收光路和发射光路的一体化设计,设计了典型目标偏振特性的激光探测实验装置;在不同偏振光入射角、不同粗糙度参数以及不同表面材质的条件下,对几种典型目标后向散射光的偏振度进行了测量;在实验的基础上对偏振度测量数据进行了双高斯拟合和分析,得出了目标后向散射光偏振度与双高斯函数基本吻合以及基于后向散射光偏振度可以区分不同属性目标的结论。     

95-GHz scattering by terrain at near-grazing incidence

This study, consisting of three complimentary topics, examines the millimeter-wave backscattering behavior of terrain at incidence angles extending between 70 and 90°, corresponding to grazing angles of 20° to 0°. The first topic addresses the character of the statistical variability of the radar backscattering cross section per unit area σ_A. Based on an evaluation of an extensive data set acquired at 95 GHz, it was determined that the Rayleigh fading model (which predicts that σ_A is exponentially distributed) provides an excellent fit to the measured data for various types of terrain covers, including bare surfaces, grasses, trees, dry snow, and wet snow. The second topic relates to the angular variability and dynamic range of the backscattering coefficient σ⁰, particularly near grazing incidence. We provide a summary of data reported to date for each of several types of terrain covers. The last topic focuses on bare surfaces. A semi-empirical model for σ⁰ is presented for vertical (VV), horizontal (HH), and cross (HV) polarizations. The model parameters include the incidence angle &thetas;, the surface relative dielectric constant ϵ, and the surface roughness ks, where k=2π/λ and s is the surface root mean square (RMS) height     

WindSat radio-frequency interference signature and its identification over land and ocean

Radio-frequency interference (RFI) in the spaceborne multichannel radiometer data of WindSat and the Advanced Microwave Scanning Radiometer-EOS is currently being detected using a spectral difference technique. Such a technique does not explicitly utilize multichannel correlations of radiometer data, which are key information in separating RFI from natural radiations. Furthermore, it is not optimal for radiometer data observed over ocean regions due to the inherent large natural variability of spectral difference over ocean. In this paper, we first analyzed multivariate WindSat and Scanning Multichannel Microwave Radiometer (SMMR) data in terms of channel correlation, information content, and principal components of WindSat and SMMR data. Then two methods based on channel correlation were developed for RFI detection over land and ocean. Over land, we extended the spectral difference technique using principal component analysis (PCA) of RFI indices, which integrates statistics of target emission/scattering characteristics (through RFI indices) and multivariate correlation of radiometer data into a single statistical framework of PCA. Over ocean, channel regression of X-band can account for nearly all of the natural variations in the WindSat data. Therefore, we use a channel regression-based model difference technique to directly predict RFI-free brightness temperature, and therefore RFI intensity. Although model difference technique is most desirable, it is more difficult to apply over land due to heterogeneity of land surfaces. Both methods improve our knowledge of RFI signatures in terms of channel correlations and explore potential RFI mitigation, and thus provide risk reductions for future satellite passive microwave missions such as the NPOESS Conical Scanning Microwave Imager/Sounder. The new RFI algorithms are effective in detecting RFI in the C- and X-band Windsat radiometer channels over land and ocean.     

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.     

Estimation of snow water equivalence using SIR-C/X-SAR. I. Inferring snow density and subsurface properties

Algorithms for estimating dry snow density and the dielectric constant and roughness of the underlying soil or rock use backscattering measurements with VV and HH polarization at L-band frequency (1.25 GHz). Comparison with field measurements of snow density during the first SIR-C/X-SAR overpass shows absolute accuracy of 42 kg m/sup -3/ (13% relative error). For the underlying soil, comparisons with the ground scatterometer measurements showed errors of 4% by volume for soil moisture estimation and 4 mm for the surface root mean square (RMS) height. Values of snow density and the properties of the underlying soil are necessary for the estimation of snow water equivalence.     

Surface-Based Polarimetric C-Band Microwave Scatterometer Measurements of Snow During a Chinook Event

This paper presents a case study of C-band backscatter observations of snow during a Chinook event. A surface-based C-band polarimetric data set collected in February 2006 is used to contrast the polarimetric response to sampled conditions of bare frozen ground, cold snow-covered ground, and snow during a Chinook event. Chinook activity is inherently spatially and temporally variable across the region in winter and produces considerable spatial variability of snow-cover physical properties associated with snow–water-equivalent (SWE) estimates. A temporal analysis of polarimetric backscatter sensed during a Chinook-induced ablation event on February 27, 2006 is used to describe the associated changes in snow conditions and scattering mechanisms. Analysis reveals that the polarimetric surface-based C-band scatterometer data respond to changes in snow parameters associated with the specific ground and snow conditions and to the temporal Chinook ablation event. Use of the copolarizations, cross-polarization, depolarization ratio, copolarization ratio, complex copolarization correlation coefficient, and the copolarized phase difference information show promise in describing changes in snow physical parameters, differing ground and snow conditions, and transitional ablation events, based on differing scattering mechanisms. This paper infers that an increase in volume scattering and fluctuations in surface scattering during the Chinook ablation event may be associated with specific physical changes such as density, crystal structure, and permittivity caused by wind speed. This paper has implications for remotely sensed estimations of snow-covered area (SCA) and SWE. Association of SCA and SWE with backscatter coefficients is not explicit in this paper; however, changes in SWE and snow properties are inferentially linked to changes in backscatter.      

SIR-C data quality and calibration results

The SIR-C/X-SAR imaging radar took its first flight on the Space Shuttle Endeavour in April 1994 and flew for a second time in October 1994. This multifrequency radar has fully polarimetric capability at L- and C-band, and a single polarization at X-band (X-SAR). The Endeavour missions were designated the Space Radar Laboratory-1 (SRL-1) and -2 (SRL-2). Calibration of polarimetric L- and C-band data for all the different modes SIR-C offers is an especially complicated problem. The solution involves extensive analysis of pre-flight test data to come up with a model of the system, analysis of in-flight test data to determine the antenna pattern and gains of the system during operation, and analysis of data from over fourteen calibration sites distributed around the SIR-C/X-SAR orbit track. The SRL missions were the first time a multifrequency polarimetric imaging radar employing a phased array antenna has been flown in space. Calibration of SIR-C data products involved some unique technical problems given the complexity of the radar system. In this paper, the approach adopted for calibration of SIR-C data is described and the calibration performance of the data products is presented     

Estimation of surface roughness parameters from dual-frequencymeasurements of radar backscattering coefficients

A simple model was developed for estimating the surface roughness parameters of a bare soil field. The model uses a set of dual-frequency measurements of the field's radar backscattering coefficients, which can be matched to calculated results obtained with assumed values for the surface roughness parameters, as represented by the surface height standard deviation σ and its correlation lengths. Scatter plots of measured and calculated radar backscattering coefficients at the C -band (4.25-GHz) frequency versus those at L-band (1.5 GHz) show that it is feasible to estimate the surface roughness parameters using this technique. The estimated values for σ are in excellent agreement with those of measurements. However, there are discrepancies between the estimated and measured values for the correlation length L. For a very rough field, the geometrical optics model could be more appropriate for modeling the C-band data     

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