Alpine Snowpack Refractive Index Retrieval Based on Tomograms Distortion Calculation

This letter introduces an iterative technique for estimating the refractive indices of a multilayered snowpack. It is based on minimizing the difference between the tilt observed in a TomoSAR image, assuming free-space propagation, and the one obtained using a developed analytical modeling approach....

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2025-01, Vol.22, p.1-5
Main Authors: Harkati, Lekhmissi, Khengaoui, Ikram
Format: Article
Language:English
Subjects:
Algorithms
Analytical models
Electromagnetic radiation
Ice
Iterative methods
Mathematical models
Media
Modelling
Particle swarm optimization
Particle swarm optimization (PSO)
Penetration depth
Permittivity
Polynomials
Propagation velocity
Refractive index
Refractivity
Snow
Snowpack
snowpack characterization
Synthetic aperture radar
synthetic aperture radar tomography
Wave propagation
Wave velocity
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Summary:This letter introduces an iterative technique for estimating the refractive indices of a multilayered snowpack. It is based on minimizing the difference between the tilt observed in a TomoSAR image, assuming free-space propagation, and the one obtained using a developed analytical modeling approach. This minimization is carried out using particle swarm optimization (PSO) algorithm, and the subsequent approach applies an analytical modeling method that combines geometric principles and Snell-Descartes law to relate the apparent positions of scatterers to their real positions. In addition, it takes also into account the transmitting and receiving antenna coordinates and medium properties such as thickness and wave propagation velocity. This study is further enhanced by calculating cumulative and normalized intensities, coherence, and phase center. This analysis offers valuable insights into the depth of penetration and the layers that prevent the electromagnetic waves propagation from penetrating them.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2024.3523444