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Modelling the L-Band Snow-Covered Surface Emission in a Winter Canadian Prairie Environment.

Authors :
Roy, Alexandre
Leduc-Leballeur, Marion
Picard, Ghislain
Royer, Alain
Toose, Peter
Derksen, Chris
Lemmetyinen, Juha
Berg, Aaron
Rowlandson, Tracy
Schwank, Mike
Source :
Remote Sensing; Sep2018, Vol. 10 Issue 9, p1451, 1p
Publication Year :
2018

Abstract

Detailed angular ground-based L-band brightness temperature (T<subscript>B</subscript>) measurements over snow covered frozen soil in a prairie environment were used to parameterize and evaluate an electromagnetic model, the Wave Approach for LOw-frequency MIcrowave emission in Snow (WALOMIS), for seasonal snow. WALOMIS, initially developed for Antarctic applications, was extended with a soil interface model. A Gaussian noise on snow layer thickness was implemented to account for natural variability and thus improve the T<subscript>B</subscript> simulations compared to observations. The model performance was compared with two radiative transfer models, the Dense Media Radiative Transfer-Multi Layer incoherent model (DMRT-ML) and a version of the Microwave Emission Model for Layered Snowpacks (MEMLS) adapted specifically for use at L-band in the original one-layer configuration (LS-MEMLS-1L). Angular radiometer measurements (30°, 40°, 50°, and 60°) were acquired at six snow pits. The root-mean-square error (RMSE) between simulated and measured T<subscript>B</subscript> at vertical and horizontal polarizations were similar for the three models, with overall RMSE between 7.2 and 10.5 K. However, WALOMIS and DMRT-ML were able to better reproduce the observed T<subscript>B</subscript> at higher incidence angles (50° and 60°) and at horizontal polarization. The similar results obtained between WALOMIS and DMRT-ML suggests that the interference phenomena are weak in the case of shallow seasonal snow despite the presence of visible layers with thicknesses smaller than the wavelength, and the radiative transfer model can thus be used to compute L-band brightness temperature. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
10
Issue :
9
Database :
Complementary Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
131938721
Full Text :
https://doi.org/10.3390/rs10091451