Comparison of commonly-used microwave radiative transfer models for snow remote sensing

REMOTE SENSING OF ENVIRONMENT

卷:190

Article

Royer, Alain^1,2  Roy, Alexandre^1,2  Montpetit, Benoit^1,6  Saint-Jean-Rondeau, Olivier^1,2  Picard, Ghislain³  Brucker, Ludovic^4,5  Langlois, Alexandre^1,2 

[1] Univ Sherbrooke, Ctr Applicat & Rech Teledetect CARTEL, 2500 Boul Univ, Sherbrooke, PQ J1K 2R1, Canada

[2] Ctr & Etud Nord, Quebec City, PQ, Canada

[3] Univ Grenoble Alpes, CNRS, LGGE UMR5183, F-38041 Grenoble, France

[4] NASA, Goddard Space Flight Ctr, Cryospher Sci Lab, Code 615, Greenbelt, MD 20771 USA

[5] Univ Space Res Assoc, Goddard Earth Sci Technol & Res, Columbia, MD 21046 USA

[6] Environm & Climate Change Canada, Canadian Ice Serv, Ottawa, ON, Canada

关键词: Snow microwave-emission model;    Snow microstructure;    Radiative transfer model;    Canada;    Ground-based measurements;    Brightness temperature;   

DOI  :  10.1016/j.rse.2016.12.020

来源: Elsevier

【 摘 要 】

This paper reviews four commonly-used microwave radiative transfer models that take different electromagnetic approaches to simulate snow brightness temperature (T-B): the Dense Media Radiative Transfer - Multi-Layer model (DMRT-ML), the Dense Media Radiative Transfer - Quasi-Crystalline Approximation Mie scattering of Sticky spheres (DMRT-QMS), the Helsinki University of Technology n-Layers model (HUT-nlayers) and the Microwave Emission Model of Layered Snowpacks (MEMLS). Using the same extensively measured physical snow pack properties, we compared the simulated T-B at 11, 19 and 37 GHz from these four models. The analysis focuses on the impact of using different types of measured snow microstructure metrics in the simulations. In addition to density, snow microstructure is defined for each snow layer by grain optical diameter (Do) and stickiness for DMRT-ML and DMRT-QMS, mean grain geometrical maximum extent (Dmax) for HUT n-layers and the exponential correlation length for MEMLS. These metrics were derived from either in-situ measurements of snow specific surface area (SSA) or macrophotos of grain sizes (D-max), assuming non-sticky spheres for the DMRT models. Simulated T-B sensitivity analysis using the same inputs shows relatively consistent T-B behavior as a function of Do and density variations for the vertical polarization (maximum deviation of 18 K and 27 K, respectively), while some divergences appear in simulated variations for the polarization ratio (PR). Comparisons with ground based radiometric measurements show that the simulations based on snow SSA measurements have to be scaled with a model-specific factor of Do in order to minimize the root mean square error (RMSE) between measured and simulated T-B. Results using in-situ grain size measurements (SSA or D-max, depending on the model) give a mean T-B RMSE (19 and 37 GHz) of the order of 16-26 K, which is similar for all models when the snow microstructure metrics are scaled. However, the MEMLS model converges to better results when driven by the correlation length estimated from in-situ SSA measurements rather than D-max measurements. On a practical level, this paper shows that the SSA parameter, a snow property that is easy to retrieve in-situ, appears to be the most relevant parameter for characterizing snow microstructure, despite the need for a scaling factor. (C) 2017 Elsevier Inc. All rights reserved.

【 授权许可】

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