Sensitivity of Snow NDSI to Simulated Snow Grain Shape Characteristics

The normalized difference snow index (NDSI) is a fundamental spectral indicator of snow/ice in visible and shortwave-infrared imagery. The complex grain shapes in nature have well-known significant effects on the single-scattering properties (SSPs) and subsequently the bidirectional reflectance of s...

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Bibliographic Details
Published inIEEE geoscience and remote sensing letters Vol. 20; pp. 1 - 5
Main Authors Wang, Gongxue, Jiang, Lingmei, Pan, Fangbo, Weng, Haiteng, Zhang, Yongsheng
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN1545-598X
1558-0571
DOI10.1109/LGRS.2022.3233379

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Summary:The normalized difference snow index (NDSI) is a fundamental spectral indicator of snow/ice in visible and shortwave-infrared imagery. The complex grain shapes in nature have well-known significant effects on the single-scattering properties (SSPs) and subsequently the bidirectional reflectance of snow. The shape effects on snow NDSI need to be further characterized as NDSI is a nonlinear combination of two reflectance bands. Considering the common snow grain shapes represented by sphere, spheroid, hexagonal plate, and Koch snowflake, we use the ray-tracing approach to simulate the SSPs of ice particles and the discrete ordinate algorithm to solve the bidirectional reflectance function and calculate NDSI of snow. According to simulating results, the angular pattern of snow NDSI is subject to snow grain shape, whereas the shape effects can be significantly weakened by the increasing surface roughness of ice particles. The shape of Koch snowflake causes an NDSI habit different from other three shapes for large snow grain size. Moreover, snow NDSI also has complex responses to aspect ratio (AR) for spheroid and hexagonal prism. The theoretical characterization of the snow NDSI responses to various grain shapes would enrich the knowledge of NDSI variation mechanism in snow-covered area mapping applications.
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2022.3233379