Modeling Both Active and Passive Microwave Remote Sensing of Snow Using Dense Media Radiative Transfer (DMRT) Theory With Multiple Scattering and Backscattering Enhancement

Tan, Shurun; Chang, Wenmo; Tsang, Leung; Lemmetyinen, Juha; Proksch, Martin

doi:10.1109/jstars.2015.2469290

Cited by 63 publications

(50 citation statements)

References 20 publications

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“…Data collected during NoSREx have found use in numerous recent studies exploring the modelling of microwave signatures of snow-covered terrain (Proksch et al, 2015b;Tan et al, 2015;Chang et al, 2015;Pan et al, 2016). Here, we provide an overview of the instrumentation and data acquisition protocols used and of the collected microwave signatures.…”

Section: Introductionmentioning

confidence: 99%

Nordic Snow Radar Experiment

Lemmetyinen

Kontu

Pulliainen

et al. 2016

Geosci. Instrum. Method. Data Syst.

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Abstract. The objective of the Nordic Snow Radar Experiment (NoSREx) campaign was to provide a continuous time series of active and passive microwave observations of snow cover at a representative location of the Arctic boreal forest area, covering a whole winter season. The activity was a part of Phase A studies for the ESA Earth Explorer 7 candidate mission CoReH2O (Cold Regions Hydrology Highresolution Observatory).The NoSREx campaign, conducted at the Finnish Meteorological Institute Arctic Research Centre (FMI-ARC) in Sodankylä, Finland, hosted a frequency scanning scatterometer operating at frequencies from X-to Ku-band. The radar observations were complemented by a microwave dualpolarization radiometer system operating from X-to Wbands. In situ measurements consisted of manual snow pit measurements at the main test site as well as extensive automated measurements on snow, ground and meteorological parameters.This study provides a summary of the obtained data, detailing measurement protocols for each microwave instrument and in situ reference data. A first analysis of the microwave signatures against snow parameters is given, also comparing observed radar backscattering and microwave emission to predictions of an active/passive forward model.All data, including the raw data observations, are available for research purposes through the European Space Agency and the Finnish Meteorological Institute. A consolidated dataset of observations, comprising the key microwave and in situ observations, is provided through the ESA campaign data portal to enable easy access to the data.

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Section: Introductionmentioning

confidence: 99%

Nordic Snow Radar Experiment

Lemmetyinen

Kontu

Pulliainen

et al. 2016

Geosci. Instrum. Method. Data Syst.

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“…This obviously prevents interferometric calculations and may be restrictive when the layer thickness is smaller than the wavelength of the microwaves, that is, at low frequencies (e.g., at L band; Tan et al, 2015a;LeducLeballeur et al, 2015) or in the case of thin ice lenses in the snowpack (Mätzler and Wegmüller, 1987). In some cases, ad hoc corrections of the radiative transfer solution can be implemented.…”

Section: Limitations and Perspectivesmentioning

confidence: 99%

“…As a result, the backscatter for these bounces is increased by 3 dB compared to what is predicted by the incoherent radiative transfer theory. This phenomenon called backscattering enhancement has recently been taken into account by developing a specific solver of the radiative transfer equation able to distinguish the noncyclical and cyclical trajectories, and to apply a correction of 3 dB to the latter group (Tan et al, 2015b).…”

Section: Limitations and Perspectivesmentioning

confidence: 99%

“…The actual modeling challenge lies in the snowpack and the underlying surface (soil, ice, or water) where the coupling of various ingredients needs to be understood with sufficient accuracy to build efficient forward models. Examples comprise scattering by snow microstructure, liquid water, salinity, ice lenses , coherent effects (Mätzler and Wegmüller, 1987;Leduc-Leballeur et al, 2015;Tan et al, 2015a), the underlying surface, and especially its roughness. All of these effects have to be taken into account by physically based snow microwave models.…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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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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An InSAR scattering model for multi-layer snow based on Quasi-Crystalline Approximation (QCA) theory

Tian

et al. 2018

Sci. China Earth Sci.

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Modeling Both Active and Passive Microwave Remote Sensing of Snow Using Dense Media Radiative Transfer (DMRT) Theory With Multiple Scattering and Backscattering Enhancement

Cited by 63 publications

References 20 publications

Nordic Snow Radar Experiment

Nordic Snow Radar Experiment

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

An InSAR scattering model for multi-layer snow based on Quasi-Crystalline Approximation (QCA) theory

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