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Proceedings Paper

Interpreting snowpack radiometry using currently existing microwave radiative transfer models
Author(s): Do-Hyuk Kang; Shurun Tang; Edward J. Kim
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Paper Abstract

A radiative transfer model (RTM) to calculate the snow brightness temperatures (Tb) is a critical element in terrestrial snow parameter retrieval from microwave remote sensing observations. The RTM simulates the Tb based on a layered snow by solving a set of microwave radiative transfer equations. Even with the same snow physical inputs to drive the RTM, currently existing models such as Microwave Emission Model of Layered Snowpacks (MEMLS), Dense Media Radiative Transfer (DMRT-QMS), and Helsinki University of Technology (HUT) models produce different Tb responses. To backwardly invert snow physical properties from the Tb, differences from RTMs are first to be quantitatively explained. To this end, this initial investigation evaluates the sources of perturbations in these RTMs, and reveals the equations where the variations are made among the three models. Modelling experiments are conducted by providing the same but gradual changes in snow physical inputs such as snow grain size, and snow density to the 3 RTMs. Simulations are conducted with the frequencies consistent with the Advanced Microwave Scanning Radiometer- E (AMSR-E) at 6.9, 10.7, 18.7, 23.8, 36.5, and 89.0 GHz. For realistic simulations, the 3 RTMs are simultaneously driven by the same snow physics model with the meteorological forcing datasets and are validated against the snow insitu samplings from the CLPX (Cold Land Processes Field Experiment) 2002-2003, and NoSREx (Nordic Snow Radar Experiment) 2009-2010.

Paper Details

Date Published: 30 October 2015
PDF: 9 pages
Proc. SPIE 9637, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII, 96371B (30 October 2015); doi: 10.1117/12.2195502
Show Author Affiliations
Do-Hyuk Kang, NASA Goddard Space Flight Ctr. (United States)
Shurun Tang, Univ. of Michigan (United States)
Edward J. Kim, NASA Goddard Space Flight Ctr. (United States)


Published in SPIE Proceedings Vol. 9637:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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