Brightness Temperature Of Snow Research Articles

Understanding Uncertainty of Snow Radiative Transfer Modeling Within a Mixed Deciduous and Evergreen Forest

Satellite-based passive microwave observations provide the best available continuous observational estimates of global snow water storage due to their broad geographic footprint and low sensitivity to clouds and precipitation. However, these observations are subject to substantial uncertainty due to the complex radiative properties of snow and from interference in forested areas. Physical radiative transfer models can be leveraged to improve the fidelity of these observations and as a data-assimilation tool. In this article, the Dense Media Radiative Transfer model with Multiple Layers (DMRT-ML) is used to simulate snow brightness temperatures from data collected from snow pits excavated during a two-day-long field study performed a temperate forest in the Northeast United States. The simulations are evaluated against surface-based radiometer observations collected at the snow pits. The DMRT-ML is configured with varying complexity to determine the snowpack characteristics most essential toward simulating brightness temperature within a temperate forest with complicated snow stratigraphy. In general, the single-layer configurations were not sufficiently complex to accurately simulate snow brightness temperature without significant tuning. The most accurate simulation was a two-layer configuration with a prescribed ice layer separating the snow layers. This simulation had a root-mean-square error $<; $15 K for the 37-GHz frequency. More complicated snowpack stratigraphy configurations did not substantively improve the results over the two-layer model configuration. The DMRT-ML was also used to examine differences between redundant datasets of density and grain size. It was determined that similar snow data collection and radiative transfer model configuration techniques are critical to ensure cross-study comparability.

In-situ passive microwave emission model parameterization of sub-arctic frozen organic soils

Many passive microwave remote sensing applications such as land surface temperature, snow water equivalent and soil moisture retrievals need to take into account a soil parameterization to the overall surface signal emission. Soil emission modeling presents large uncertainties when the soil is frozen. In this paper, an empirical retrieval method is presented, specifically for rough frozen soil permittivity estimates at 10.7, 19 and 37GHz. The method was tested and validated using in-situ passive microwave measurements at incidence angles from 0 to 60° of sub-arctic frozen organic soils in Northeastern Canada. The retrieved permittivity values give an overall RMSE between the measured and simulated brightness temperatures of 4.6K for all frequencies combined. A sensitivity analysis was conducted on the different soil parameters optimized in this study. This analysis suggests that the accuracy of the retrieved parameters, using the method given here, is of ±1.00 for the permittivity and ±0.12cm for surface roughness. Also, a comparison was conducted between the parameterization used in this study and the one of Wegmüller and Mätzler (1999) to estimate the soil contribution to the emitted brightness temperature of snowpacks. An improvement of 66% of the RMSE between the modeled and measured snow brightness temperatures was observed when using the approach of this study compared to the previous work. The method shows great potential to improve the estimation of the frozen soil contribution to the measured passive microwave brightness temperature.

Comparison of commonly-used microwave radiative transfer models for snow remote sensing

This paper reviews four commonly-used microwave radiative transfer models that take different electromagnetic approaches to simulate snow brightness temperature (TB): the Dense Media Radiative Transfer - Multi-Layer model (DMRT-ML), the Dense Media Radiative Transfer - Quasi-Crystalline Approximation Mie scattering of Sticky spheres (DMRT-QMS), the Helsinki University of Technology n-Layers model (HUT-nlayers) and the Microwave Emission Model of Layered Snowpacks (MEMLS). Using the same extensively measured physical snowpack properties, we compared the simulated TB at 11, 19 and 37GHz from these four models. The analysis focuses on the impact of using different types of measured snow microstructure metrics in the simulations. In addition to density, snow microstructure is defined for each snow layer by grain optical diameter (Do) and stickiness for DMRT-ML and DMRT-QMS, mean grain geometrical maximum extent (Dmax) for HUT n-layers and the exponential correlation length for MEMLS. These metrics were derived from either in-situ measurements of snow specific surface area (SSA) or macrophotos of grain sizes (Dmax), assuming non-sticky spheres for the DMRT models. Simulated TB sensitivity analysis using the same inputs shows relatively consistent TB behavior as a function of Do and density variations for the vertical polarization (maximum deviation of 18K and 27K, respectively), while some divergences appear in simulated variations for the polarization ratio (PR). Comparisons with ground-based radiometric measurements show that the simulations based on snow SSA measurements have to be scaled with a model-specific factor of Do in order to minimize the root mean square error (RMSE) between measured and simulated TB. Results using in-situ grain size measurements (SSA or Dmax, depending on the model) give a mean TB RMSE (19 and 37GHz) of the order of 16–26K, which is similar for all models when the snow microstructure metrics are scaled. However, the MEMLS model converges to better results when driven by the correlation length estimated from in-situ SSA measurements rather than Dmax measurements. On a practical level, this paper shows that the SSA parameter, a snow property that is easy to retrieve in-situ, appears to be the most relevant parameter for characterizing snow microstructure, despite the need for a scaling factor.

Observation and Modeling of the Microwave Brightness Temperature of Snow-Covered Frozen Lakes and Wetlands

Small-scale variability in land cover influences both the snow cover and the microwave response of a snow-covered surface. Since low microwave frequencies penetrate below the snowpack, the differing dielectric properties of soil and water have a significant effect on passive microwave observations and therefore cause errors in the interpretation of snow parameters from satellite data. Here, the brightness temperature of snow- and ice-covered lakes and wetlands is studied using airborne and spaceborne microwave radiometer observations and modeling of brightness temperature from in situ measurements. We aim at assessing the validity of the multilayer Helsinki University of Technology (HUT) snow emission model on lake- and wetland-rich areas and at examining the error from omission of water bodies in the forward modeling of brightness temperature. The results indicate that the model can estimate brightness temperatures of lakes and wetlands with rms errors of 12-28 K and 9-16 K, respectively. The inclusion of lakes in the satellite-scale simulations reduces the simulation error in 52%-100% of the simulated areas at 18.7 and 36.5 GHz. The inclusion of wetlands further improves simulations, resulting in an rms error of satellite scenes of 4-5 K at 18.7 and 36.5 GHz (5-10 K without lakes and wetlands). However, the natural variability of brightness temperature over water bodies is not entirely captured particularly at 10.65 GHz. The inclusion of lakes and wetlands can be used to reduce errors in the forward model and thus increase the accuracy of snow parameters derived from satellite data.

Snow Microwave Emission Modeling of Ice Lenses Within a Snowpack Using the Microwave Emission Model for Layered Snowpacks

Ice lens formation, which follows rain on snow events or melt-refreeze cycles in winter and spring, is likely to become more frequent as a result of increasing mean winter temperatures at high latitudes. These ice lenses significantly affect the microwave scattering and emission properties, and hence snow brightness temperatures that are widely used to monitor snow cover properties from space. To understand and interpret the spaceborne microwave signal, the modeling of these phenomena needs improvement. This paper shows the effects and sensitivity of ice lenses on simulated brightness temperatures using the microwave emission model of layered snowpacks coupled to a soil emission model at 19 and 37 GHz in both horizontal and vertical polarizations. Results when considering pure ice lenses show an improvement of 20.5 K of the root mean square error between the simulated and measured brightness temperature (Tb) using several in situ data sets acquired during field campaigns across Canada. The modeled Tbs are found to be highly sensitive to the vertical location of ice lenses within the snowpack.

Improved Corrections of Forest Effects on Passive Microwave Satellite Remote Sensing of Snow Over Boreal and Subarctic Regions

Microwave radiometry has been extensively used in order to estimate snow water equivalent in northern regions. However, for boreal and taiga environments, the presence of forest causes important uncertainties in the estimates. Variations in snow cover and vegetation in northeastern Canada (north of the Québec province) were characterized in a transect from 50°N to 60 °N during the International Polar Year field campaign of February 2008. Forest properties show a strong latitudinal gradient in fraction and stem volume. A large database (>; 2000 points with a stem volume ranging between 0 and 700 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ·ha <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> ) showed that brightness temperatures ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tb</i> ) decrease as forest cover fraction decreases until a cover fraction of about 25% is reached. Furthermore, <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tb</i> values saturate at high stem volume, particularly at 37 GHz. We defined new relationships for the forest transmissivity as a function of stem volume and depending on the frequency/polarization. The proposed relationships give asymptotic transmissivity saturation levels of 0.51, 0.55, 0.53, and 0.53 for 19 GHz [vertical (V) polarization], 19 GHz [horizontal (H) polarization], 37 GHz (V polarization), and 37 GHz (H polarization), respectively. These relationships were used to estimate snow <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tb</i> from the Advanced Microwave Scanning Radiometer-Earth Observing System brightness temperatures at 18.7 and 36.5 GHz, and results show an estimated snow brightness temperature well correlated to the airborne snow brightness temperatures over vegetation-free areas.

Sensitivity Analysis on Snow Parameters Impacting Passive Microwave Brightness Temperature of Snow——An Study Based on MEMLS

There are various factors that impact passive microwave responses of snow.These factors include depth,crystal size,wetness,density and temperature of snow.In this paper,a sensitivity study was carried out based on Microwave Emission Model of Layered Snowpacks(MEMLS)model.The result showed that grain size made the most effect on the output of the model,while depth,wetness,density,and snow temperature are in the next place.The relation between snow depth and the difference between 19 and 37GHz brightness temperature can be described by a linear formula when the snow depth is less than 50cm,while the range of increase will slow down and tend to a saturation when the snow depth is large than 50cm.Snow density and grain size can greatly influence the coefficient of the linear formula.The passive microwave remote sensing data could not reflect snow depth and snow water equivalent when snow is wet because liquid water significantly changes the permittivity of dry snow.The difference of snow temperature at different channels can reduce the effect of brightness temperature on the estimation of snow depth.

Multimodel Estimation of Snow Microwave Emission during CLPX 2003 Using Operational Parameterization of Microphysical Snow Characteristics

Abstract Existing forward snow emission models (SEMs) are limited by knowledge of both the temporal and spatial variability of snow microphysical parameters, with grain size being the most difficult to measure or estimate. This is due to the sparseness of in situ data and the lack of simple operational parameterizations for the evolution of snowpack properties. This paper compares snow brightness temperatures predicted by three SEMs using, as inputs, predicted snowpack characteristics from the Variable Infiltration Capacity (VIC) model. The latter is augmented by a new parameterization for the evolution of snow grain morphology and density. The grain size dynamics are described using a crystal growth equation. The three SEMs used in the study are the Land Surface Microwave Emission Model (LSMEM), the Dense Media Radiative Transfer (DMRT) model, and the Microwave Emission Model of Layered Snowpacks (MEMLS). Estimated brightness temperature is validated against the satellite [Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E)] data at two sites from the Cold Land Processes Experiment (CLPX), conducted in Colorado in the winter of 2003. In addition, a merged multimodel estimate, based on Bayesian model averaging, is developed and compared to the measured brightness temperatures. The advantages of the Bayesian approach include the increase in the mean prediction accuracy as well as providing a nonparametric estimate of the error distributions for the brightness temperature estimates.

Influence of land-cover category on brightness temperature of snow

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

A Study of the Microwave Brightness Temperature of Snow from the Point of View of Strong Fluctuation Theory

An application of strong fluctuation theory to the computation of the brightness temperature of dry and wet isothermal snowpacks is made. The numerical problems associated with computations are analyzed for the case of plane boundaries but arbitrary structure in the vertical direction. Comparisons of computed effective dielectric constants as well as brightness temperatures with experimental observations are made. The generally good agreement that is achieved does not depend on the arbitrary introduction of correction factors as is necessary when using radiative transfer theory.

Effect of viewing angle on the infrared brightness temperature of snow

Remote sensing of snowpack temperatures from satellites requires knowledge of the spectral emissivity of snow. A model for spectral emissivity is combined with the Planck function to calculate brightness temperature of snow in thermal infrared wavelengths for a range of grain sizes and viewing angles. Emissivity variations caused by density, grain shape, liquid water, and grain size are apparently unimportant, but emissivity varies with viewing angle to produce differences between thermodynamic temperature and brightness temperature as large as 3 K at wavelengths 12 to 14 μm, within the major atmospheric infrared window. This difference is also verified by experimental measurements. An equation to convert brightness temperatures to thermodynamic temperatures is presented, and this is also combined with a dual‐wavelength atmospheric correction method. The spectral emissivity model is also used to calculate an ‘all‐wave’ emissivity of snow: 0.985–0.990 for all grain sizes.

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.