Monitoring Snow Characteristics With Ground-Based Multifrequency Microwave Radiometry

Brogioni, Marco; Macelloni, Giovanni; Palchetti, Enrico; Paloscia, S.; Pampaloni, P.; Pettinato, S.; Santi, Emanuele; Cagnati, Anselmo; Crepaz, A.

doi:10.1109/tgrs.2009.2030791

Cited by 33 publications

(10 citation statements)

References 30 publications

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“…For instance φ SHS = 2.1 (used by Picard et al, 2014) is equivalent to a stickiness value of around 0.13. Higher values of φ SHS up to 3.5 were used in the other studies (Brucker et al, 2010;Roy et al, 2013Roy et al, , 2016Dupont et al, 2013), corresponding to lower stickiness values approaching 0.1 as suggested by Liang et al (2008). This confirms that despite using different approaches, these studies converge towards stickiness values in the range 0.1-0.2, in agreement with Löwe and Picard (2015), who retrieved the stickiness from µCT of snow samples.…”

Section: On the Equivalence Of Microstructure Modelssupporting

confidence: 79%

“…However setting stickiness to 0.1 is insufficient to strictly determine the power dependence as it also depends on the grain size and density, i.e., very small scatterers always show a dependence of a power of 4 (Rayleigh scattering). Another approach was elaborated in a series of empirical studies (Brucker et al, 2010;Picard et al, 2014;Roy et al, 2013Roy et al, , 2016Dupont et al, 2013). It consists of using non-sticky spheres (i.e., infinite stickiness parameter) and scaling the radius a computed from SSA by an empirical factor φ SHS (called "grain size scaling factor").…”

Section: On the Equivalence Of Microstructure Modelsmentioning

confidence: 99%

“…Due to the demand for global monitoring of the cryosphere and its change, numerous algorithms have been developed to retrieve geophysical information on snow cover extent (Grody and Basist, 1996;Nghiem and Tsai, 2001), snow depth, and snow water equivalent on both land (Josberger and Mognard, 2002;Kelly and Chang, 2003;Derksen et al, 2003) and sea ice (Comiso et al, 2003;Cavalieri et al, 2012), snow accumulation on ice sheets (Abdalati and Steffen, 1998;Vaughan et al, 1999;Drinkwater et al, 2001;Winebrenner et al, 2001;Flach et al, 2005;Arthern et al, 2006;Dierking et al, 2012) wet snow (Zwally, 1977;Shi and Dozier, 1995;Abdalati and Steffen, 1997;Nagler and Rott, 2000;Steffen, 2004;Picard et al, 2007), snow temperature (Shuman et al, 1995;Schneider and Steig, 2002;Schneider et al, 2004), snow grain size (Brucker et al, 2010;Picard et al, 2012), and snow density Champollion et al, 2013). Even though many applications still rely on empirical approaches to relate snowpack properties (e.g., snow water equivalent, SWE) and measured signals, it is generally accepted that a physical understanding of the interaction between snow and electromagnetic waves is necessary to improve the accuracy and overcome inherent difficulties of the retrieval as an underdetermined problem.…”

Section: Introductionmentioning

confidence: 99%

“…Several physically based models have been developed previously mainly for passive microwave remote sensing, including HUT (Lemmetyinen et al, 2010), MEMLS , DMRT-QMS (Tsang et al, 2006;Liang et al, 2008), DMRT-ML , and other ones based on dense media radiative transfer (DMRT) (Macelloni et al, 2001;Grody, 2008;Brogioni et al, 2009). In addition, several models were tailored to low frequencies (i.e., up to a few gigahertz), such as 2S (Schwank et al, 2014), CMES (Drusch et al, 2009), WALOMIS (Leduc-Leballeur et al, 2015), and others (Tan et al, 2015a), triggered by the inception of spaceborne L-band radiometry (Barre et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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SMRT: an active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1.0)

Picard

Sandells²,

Löwe³

2018

Geosci. Model Dev.

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Abstract. The Snow Microwave Radiative Transfer (SMRT) thermal emission and backscatter model was developed to determine uncertainties in forward modeling through intercomparison of different model ingredients. The model differs from established models by the high degree of flexibility in switching between different electromagnetic theories, representations of snow microstructure, and other modules involved in various calculation steps. SMRT v1.0 includes the dense media radiative transfer theory (DMRT), the improved Born approximation (IBA), and independent Rayleigh scatterers to compute the intrinsic electromagnetic properties of a snow layer. In the case of IBA, five different formulations of the autocorrelation function to describe the snow microstructure characteristics are available, including the sticky hard sphere model, for which close equivalence between the IBA and DMRT theories has been shown here. Validation is demonstrated against established theories and models. SMRT was used to identify that several former studies conducting simulations with in situ measured snow properties are now comparable and moreover appear to be quantitatively nearly equivalent. This study also proves that a third parameter is needed in addition to density and specific surface area to characterize the microstructure. The paper provides a comprehensive description of the mathematical basis of SMRT and its numerical implementation in Python. Modularity supports model extensions foreseen in future versions comprising other media (e.g., sea ice, frozen lakes), different scattering theories, rough surface models, or new microstructure models.

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Section: On the Equivalence Of Microstructure Modelssupporting

confidence: 79%

Section: On the Equivalence Of Microstructure Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

SMRT: an active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1.0)

Picard

Sandells²,

Löwe³

2018

Geosci. Model Dev.

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“…These theories have been implemented in several models: the SFT in Surdyk and Fily (1995), the IBA in MEMLS and the DMRT-ML in Macelloni et al (2001); Tedesco and Kim (2006); Liang et al (2008); Grody (2008); Brogioni et al (2009). In addition to the underlying theory, the models differ by many aspects, including the methods for solving the radiative transfer, the presence of smooth or rough interfaces, the possible number of layers, etc.…”

Section: G Picard Et Al: Simulation Of the Microwave Emission Of Mumentioning

confidence: 99%

Simulation of the microwave emission of multi-layered snowpacks using the Dense Media Radiative transfer theory: the DMRT-ML model

et al. 2013

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Abstract. DMRT-ML is a physically based numerical model designed to compute the thermal microwave emission of a given snowpack. Its main application is the simulation of brightness temperatures at frequencies in the range 1-200 GHz similar to those acquired routinely by spacebased microwave radiometers. The model is based on the Dense Media Radiative Transfer (DMRT) theory for the computation of the snow scattering and extinction coefficients and on the Discrete Ordinate Method (DISORT) to numerically solve the radiative transfer equation. The snowpack is modeled as a stack of multiple horizontal snow layers and an optional underlying interface representing the soil or the bottom ice. The model handles both dry and wet snow conditions. Such a general design allows the model to account for a wide range of snow conditions. Hitherto, the model has been used to simulate the thermal emission of the deep firn on ice sheets, shallow snowpacks overlying soil in Arctic and Alpine regions, and overlying ice on the large icesheet margins and glaciers. DMRT-ML has thus been validated in three very different conditions: Antarctica, Barnes Ice Cap (Canada) and Canadian tundra. It has been recently used in conjunction with inverse methods to retrieve snow grain size from remote sensing data. The model is written in Fortran90 and available to the snow remote sensing community as an open-source software. A convenient user interface is provided in Python.

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Snow stratigraphic heterogeneity within ground‐based passive microwave radiometer footprints: Implications for emission modeling

Rutter

Sandells

Derksen

et al. 2014

J. Geophys. Res. Earth Surf.

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Two-dimensional measurements of snowpack properties (stratigraphic layering, density, grain size, and temperature) were used as inputs to the multilayer Helsinki University of Technology (HUT) microwave emission model at a centimeter-scale horizontal resolution, across a 4.5 m transect of ground-based passive microwave radiometer footprints near Churchill, Manitoba, Canada. Snowpack stratigraphy was complex (between six and eight layers) with only three layers extending continuously throughout the length of the transect. Distributions of one-dimensional simulations, accurately representing complex stratigraphic layering, were evaluated using measured brightness temperatures. Large biases (36 to 68 K) between simulated and measured brightness temperatures were minimized (À0.5 to 0.6 K), within measurement accuracy, through application of grain scaling factors (2.6 to 5.3) at different combinations of frequencies, polarizations, and model extinction coefficients. Grain scaling factors compensated for uncertainty relating optical specific surface area to HUT effective grain size inputs and quantified relative differences in scattering and absorption properties of various extinction coefficients. The HUT model required accurate representation of ice lenses, particularly at horizontal polarization, and large grain scaling factors highlighted the need to consider microstructure beyond the size of individual grains. As variability of extinction coefficients was strongly influenced by the proportion of large (hoar) grains in a vertical profile, it is important to consider simulations from distributions of one-dimensional profiles rather than single profiles, especially in sub-Arctic snowpacks where stratigraphic variability can be high. Model sensitivity experiments suggested that the level of error in field measurements and the new methodological framework used to apply them in a snow emission model were satisfactory. Layer amalgamation showed that a three-layer representation of snowpack stratigraphy reduced the bias of a one-layer representation by about 50%.

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Monitoring Snow Characteristics With Ground-Based Multifrequency Microwave Radiometry

Cited by 33 publications

References 30 publications

SMRT: an active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1.0)

SMRT: an active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1.0)

Simulation of the microwave emission of multi-layered snowpacks using the Dense Media Radiative transfer theory: the DMRT-ML model

Snow stratigraphic heterogeneity within ground‐based passive microwave radiometer footprints: Implications for emission modeling

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