A Novel Approach for the Snow Water Equivalent Retrieval Using X-Band Polarimetric Synthetic Aperture Radar Data

In this article, an algorithm for snow depth (SD) and snow water equivalent (SWE) retrieval is proposed based on a polarimetric synthetic aperture radar (SAR) decomposition model and field measured snow data. The field campaigns were conducted at the Dhundi observatory (in the Indian Himalaya) in Ja...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2021-05, Vol.59 (5), p.3753-3763
Main Authors: Patil, Akshay, Singh, Gulab, Rudiger, Christoph, Mohanty, Shradha, Kumar, Sanjeev, Snehmani
Format: Article
Language:English
Subjects:
Algorithms
Backscatter
Data
Data acquisition
Data models
Equivalence
Estimation
Mathematical models
Parameters
Permittivity
Permittivity measurement
Radar
Radar data
Radar polarimetry
Retrieval
Root-mean-square errors
SAR (radar)
Snow
Snow density
Snow depth
Snow depth (SD)
snow water equivalent (SWE)
Snow-water equivalent
Superhigh frequencies
Synthetic aperture radar
synthetic aperture radar (SAR)
TerraSAR-X
Water depth
X-band
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Summary:In this article, an algorithm for snow depth (SD) and snow water equivalent (SWE) retrieval is proposed based on a polarimetric synthetic aperture radar (SAR) decomposition model and field measured snow data. The field campaigns were conducted at the Dhundi observatory (in the Indian Himalaya) in January 2016 and 2018. The field-measured data are used here to build a linear regression between wetness ( w ) and the imaginary part of the snow permittivity ( \boldsymbol {\epsilon }^{\prime \prime } ), and the validation of retrieved SD and SWE. The snow density ( \boldsymbol {\rho }_{s} ) and w are calculated with a generalized volume parameter derived using a theoretical model and SAR data (coherency matrix). These snow parameters and the field-based regression relating w and \boldsymbol {\epsilon }^{\prime \prime } are eventually used for the SD and SWE retrieval. Three TerraSAR-X scenes of the quad-polarization X-band data acquired in January 2016 are used to study the effect of the snow conditions on the accuracy of the proposed algorithm. The mean absolute error (MAE), root-mean-square error (RMSE), and index of agreement (IOA) for SD are 4.84 cm, 5.12 cm, and 0.71, respectively. On the other hand, for SWE, it is 1.42 cm, 1.53 cm, and 0.71, respectively.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2020.3016527