Model for microwave emission of a snow-covered ground with focus on L band

Schwank, Mike; Rautiainen, Kimmo; Mätzler, Christian; Stähli, Manfred; Lemmetyinen, Juha; Pulliainen, Jouni; Vehviläinen, Juho; Kontu, Anna; Ikonen, Jaakko; Ménard, Cécile B.; Drusch, Matthias; Wiesmann, Andreas; Wegmüller, U.

doi:10.1016/j.rse.2014.08.029

Cited by 68 publications

(48 citation statements)

References 27 publications

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“…In accordance with earlier work [13,14], the volume reflectivities of snow layers are assumed to be r S,j = 0, indicating the fact that volume scattering caused by snow microstructures is negligible at L-band. Clearly, neglecting snow volume scattering is applicable only in absence of prominent macrostructures (such as ice lenses) with dimensions of the order of λ.…”

Section: Methodology For the Analysis Of L-band Brightness Temperaturmentioning

confidence: 78%

“…Red lines are B-splines fitted to estimate temporal variations of h S and ρ S in between the times of measurements. The reason for showing snow bottom-layer density ρ S is that the snowpack parameter is expected to be the most influential on L-band emission for conditions as explained in [13,14]. Observed h S and ρ S reveal the expected increasing trend throughout the snow season, while short-term variations are mostly explained by snow metamorphism, settlement, and melting.…”

Section: Snow Density Profilesmentioning

confidence: 99%

“…However, throughout the main period of interest (i.e., cold winter and early spring periods), the ground is homogeneously frozen. Thus, using measurements T is that a surface-thawed snowpack refreezes overnight, and thus snow emission during cold nights is almost always zero [13,22,23]. As a consequence, during the cold nights throughout our main periods of interest (cold winter and early spring), T p B,R (θ) correspond with downwelling sky brightness temperatures T sky (θ) reflected by the metal-mash reflector towards the antenna aperture.…”

Section: Brightness Temperatures Of "Reflector Areas"mentioning

confidence: 99%

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Davos-Laret Remote Sensing Field Laboratory: 2016/2017 Winter Season L-Band Measurements Data-Processing and Analysis

2017

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Abstract:The L-band radiometry data and in-situ ground and snow measurements performed during the 2016/2017 winter campaign at the Davos-Laret remote sensing field laboratory are presented and discussed. An improved version of the procedure for the computation of L-band brightness temperatures from ELBARA radiometer raw data is introduced. This procedure includes a thorough explanation of the calibration and filtering including a refined radio frequency interference (RFI) mitigation approach. This new mitigation approach not only performs better than conventional "normality" tests (kurtosis and skewness) but also allows for the quantification of measurement uncertainty introduced by non-thermal noise contributions. The brightness temperatures of natural snow covered areas and areas with a reflector beneath the snow are simulated for varying amounts of snow liquid water content distributed across the snow profile. Both measured and simulated brightness temperatures emanating from natural snow covered areas and areas with a reflector beneath the snow reveal noticeable sensitivity with respect to snow liquid water. This indicates the possibility of estimating snow liquid water using L-band radiometry. It is also shown that distinct daily increases in brightness temperatures measured over the areas with the reflector placed on the ground indicate the onset of the snow melting season, also known as "early-spring snow".

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Section: Methodology For the Analysis Of L-band Brightness Temperaturmentioning

confidence: 78%

Section: Snow Density Profilesmentioning

confidence: 99%

Section: Brightness Temperatures Of "Reflector Areas"mentioning

confidence: 99%

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Davos-Laret Remote Sensing Field Laboratory: 2016/2017 Winter Season L-Band Measurements Data-Processing and Analysis

2017

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“…Hannula et al, 2016;Lemmetyinen et al, 2015;Kontu et al, 2014;Kontu and Pulliainen, 2010). The data set is also important as a reference for the development of remote sensing instruments (Lemmetyinen et al, 2016b) and interpretation algorithms and models (Leinss et al, 2015;Schwank et al, 2014;Rautiainen et al, 2014).…”

Section: Leppänen Et Al: Sodankylä Manual Snow Survey Programmentioning

confidence: 99%

Sodankylä manual snow survey program

Leppänen

Kontu

Hannula

et al. 2016

Geosci. Instrum. Method. Data Syst.

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Abstract. The manual snow survey program of the Arctic Research Centre of the Finnish Meteorological Institute (FMI-ARC) consists of numerous observations of natural seasonal taiga snowpack in Sodankylä, northern Finland. The easily accessible measurement areas represent the typical forest and soil types in the boreal forest zone. Systematic snow measurements began in 1909 with snow depth (HS) and snow water equivalent (SWE). In 2006 the manual snow survey program expanded to cover snow macro-and microstructure from regular snow pits at several sites using both traditional and novel measurement techniques. Present-day snow pit measurements include observations of HS, SWE, temperature, density, stratigraphy, grain size, specific surface area (SSA) and liquid water content (LWC). Regular snow pit measurements are performed weekly during the snow season. Extensive time series of manual snow measurements are important for the monitoring of temporal and spatial changes in seasonal snowpack. This snow survey program is an excellent base for the future research of snow properties.

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“…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). Early models for active microwave observations include only single scattering mechanisms (Bingham and Drinkwater, 2000;Flach et al, 2005;Longepe et al, 2009;Lacroix et al, 2008), which is generally sufficient at low frequencies at which scattering is weak compared to absorption.…”

Section: Introductionmentioning

confidence: 99%

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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Model for microwave emission of a snow-covered ground with focus on L band

Cited by 68 publications

References 27 publications

Davos-Laret Remote Sensing Field Laboratory: 2016/2017 Winter Season L-Band Measurements Data-Processing and Analysis

Davos-Laret Remote Sensing Field Laboratory: 2016/2017 Winter Season L-Band Measurements Data-Processing and Analysis

Sodankylä manual snow survey program

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

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