Comparison of Commonly-Used Microwave Radiative Transfer Models for Snow Remote Sensing

Alain Royer; Alexandre Roy; Benoit Montpetit; Olivier Saint-Jean-Rondeau; Ghislain Picard; Ludovic Brucker; Alexandre Langlois

This paper reviews four commonly-used microwave radiative transfer models that take different electromagnetic approaches to simulate snow brightness temperature (T(sub 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 snowpack properties, we compared the simulated T(sub 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 (D(sub max)) 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(sub max)), assuming non-sticky spheres for the DMRT models. Simulated T(sub B) sensitivity analysis using the same inputs shows relatively consistent T(sub 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(sub B). Results using in-situ grain size measurements (SSA or D(sub max), depending on the model) give a mean T(sub 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(sub 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.

Document ID

20170003577

Acquisition Source

Goddard Space Flight Center

Document Type

Accepted Manuscript (Version with final changes)

External Source(s)

Authors

Date Acquired

April 17, 2017

Publication Date

January 8, 2017

Publication Information

Publication: Remote Sensing of Environment

Publisher: Elsevier

Volume: 190

Issue Publication Date: March 1, 2017

ISSN: 0034-4257

e-ISSN: 1879-0704

DOI: doi:10.1016/j.rse.2016.12.020

Subject Category

Report/Patent Number

Funding Number(s)

Distribution Limits

Public

Public Use Permitted.

Keywords

Available Downloads

Name

Type

20170003577.pdf

STI

No Preview Available

NTRS

NTRS - NASA Technical Reports Server

Available Downloads

Related Records

pFad - (p)hone/(F)rame/(a)nonymizer/(d)eclutterfier! Saves Data!