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Passive microwave algorithms for refractive index of Arctic sea ice: a comparison of two approaches and interpretations

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Authors

Lee, Sang-Moo

Issue Date
2021-11
Publisher
Taylor & Francis
Citation
International Journal of Remote Sensing, Vol.42 No.22, pp.8691-8708
Abstract
The refractive index of sea ice can be estimated using the Debye relaxation model. However, the practical use of this model is difficult because it requires a large amount of information as inputs. To resolve this issue, two different approaches have been developed to retrieve the Arctic basin-scale refractive index from satellite passive microwave measurements using the bulk radiative transfer concept. One is the simplified three-layer sea ice radiative transfer (3SRT) approach and the other is the Brewster's angle approximation (BAA) approach. Although these methods are different, both find polarized emissivities for the microwave emitting layer of sea ice to estimate the refractive index using a relationship between polarized emissivities and the refractive index. A typographical erroneous version of the equation for this relationship has been used in numerous studies without noting the errors. This study provides the correct equations for the relationship. The refractive index obtained using the 3SRT method is within a reasonable range of 1.2-1.8. However, the refractive index obtained using the BAA approach shows too high values ranging from 1.2 to 3.2. The sensitivity of the approaches to variation in the emissivity is analysed. The BAA method is sensitive to the surface emissivity because of the assumptions considered. The refractive index of sea ice obtained from the 3SRT approach is compared with that obtained from the Debye relaxation model. The results show that a plausible refractive index over Arctic sea ice can be estimated from satellite passive microwave measurements using the 3SRT method.
ISSN
0143-1161
URI
https://hdl.handle.net/10371/201042
DOI
https://doi.org/10.1080/01431161.2021.1985740
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  • College of Natural Sciences
  • Department of Earth and Environmental Sciences
Research Area Data Assimilation for Numerical Weather Prediction, Radiative Transfer Modeling, Satellite Remote Sensing

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