The Effects of Layers in Dry Snow on Its Passive Microwave Emissions Using Dense Media Radiative Transfer Theory Based on the Quasicrystalline Approximation (QCA/DMRT)

Abstract

A model for the microwave emissions of multilayer dry snowpacks, based on dense media radiative transfer (DMRT) theory with the quasicrystalline approximation (QCA), provides more accurate results when compared to emissions determined by a homogeneous snowpack and other scattering models. The DMRT model accounts for adhesive aggregate effects, which leads to dense media Mie scattering by using a sticky particle model. With the multilayer model, we examined both the frequency and polarization dependence of brightness temperatures (Tb's) from representative snowpacks and compared them to results from a single-layer model and found that the multilayer model predicts higher polarization differences, twice as much, and weaker frequency dependence. We also studied the temporal evolution of Tb from multilayer snowpacks. The difference between Tb's at 18.7 and 36.5 GHz can be 5 K lower than the single-layer model prediction in this paper. By using the snowpack observations from the Cold Land Processes Field Experiment as input for both multiand single-layer models, it shows that the multilayer Tb's are in better agreement with the data than the single-layer model. With one set of physical parameters, the multilayer QCA/DMRT model matched all four channels of Tb observations simultaneously, whereas the single-layer model could only reproduce vertically polarized Tb's. Also, the polarization difference and frequency dependence were accurately matched by the multilayer model using the same set of physical parameters. Hence, algorithms for the retrieval of snowpack depth or water equivalent should be based on multilayer scattering models to achieve greater accuracy.Index Terms-Dense media, dense media radiative transfer (DMRT) theory, layered media, microwave remote sensing, snow.

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

“…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.…”

“…They also use a similar method to solve the radiative transfer equation, which explains the small rootmean-square difference in brightness temperature of about 0.03 K obtained at both polarizations for the angle range 0-60 • . In contrast, the comparison of SMRT to DMRT-QMS shows larger differences since the latter computes scattering by DMRT Mie QCA and implements a different connection of streams between layers in the interface conditions for solving the radiative transfer equation Liang et al, 2008). Nevertheless, the differences at both polarizations do not exceed 0.3 K RMS, which is acceptable considering the implementations are different and fully independent.…”

“…For the SHS microstructure, Liang et al (2008) suggest setting the stickiness parameter to "0.1 because it yields 2.8 for the frequency dependence of the extinction coefficient which corresponds to the experimental values" (Hallikainen et al, 1987). These experimental values are the basis of the extinction formulation in the HUT model .…”

“…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).…”

Microstructure representation of snow in coupled snowpack and microwave emission models

“…SNTHERM simulates a grain size that is closer in concept to the visual estimates of grain diameter than the other two models. The large spread when coupling snowpack evolution and microwave models, due to the differences in the modelling of snow microstructure, is consistent with the wide range of studies that have investigated how to link snowpack observations of microstructure to the microstructure parameter required in electromagnetic models (e.g Kendra et al, 1998;Du et al, 2005;Liang et al, 2008;The Cryosphere, 11, 229-246, 2017 www.the-cryosphere.net/11/229/2017/ Durand et al, 2008;Brucker et al, 2011;Xu et al, 2012;Montpetit et al, 2013;Roy et al, 2013;Rutter et al, 2014;Picard et al, 2014). Nevertheless there are differences between microwave emission models for a particular microstructure evolution model and even differences within the same family of emission models.…”