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Effect of Black Carbon Concentration on the Reflection Property of Snow: A Comparison With Model Results.

Authors :
Lv, Yunfeng
Wu, Di
Sun, Zhongqiu
Source :
IEEE Transactions on Geoscience & Remote Sensing. Nov2018, Vol. 56 Issue 11, p6823-6840. 18p.
Publication Year :
2018

Abstract

Snow has a very high reflection property when compared with all other natural surfaces on Earth; thus, a small amount of contamination (30 ng/g) can dramatically reduce the reflectance of snow. To quantify the effect of black carbon (BC) concentrations on the directional reflectance factors of snow, we deposited BC concentrations onto snow surfaces at natural levels (98–4095 ng/g) and compared the measurement results with those from a theoretical model. It was found that increasing BC concentrations decreased the reflectance factor of snow and changed its distribution pattern. Moreover, our data provided valuable verification of the snow reflection model, which has previously been used to characterize the reflectance of snow. The model did not well match our measurements; for example, the model found fewer anisotropic results than those from observations. Subsequently, a specular kernel combined with two free parameters was proposed to counter the forward and backward scattering of the optical property of snow. The improved model successfully characterized the observed variability in the reflection measurements of snow with different BC concentration levels under field conditions, and its inverted parameter (M) had the potential to estimate BC concentrations. The improved model also had the ability to simulate the spectral reflectance factor of snow with low BC concentrations (i.e., smaller than 118 ng/g) over a wide range of viewing zenith angles. This paper provides an additional and effective method for studying the angular and spectral reflection properties of snow. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01962892
Volume :
56
Issue :
11
Database :
Academic Search Index
Journal :
IEEE Transactions on Geoscience & Remote Sensing
Publication Type :
Academic Journal
Accession number :
133667594
Full Text :
https://doi.org/10.1109/TGRS.2018.2843817