Improvement of GNSS-R snow depth retrieval using SVM-based snow detection

Yuan, xintai

doi:10.5281/zenodo.13970314

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Improvement of GNSS-R snow depth retrieval using SVM-based snow detection

Yuan, xintai

Year: 2024 Journal: Zenodo (CERN European Organization for Nuclear Research) Publisher: European Organization for Nuclear Research

DOI: 10.5281/zenodo.13970314

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Abstract

In this study, we focus on the GNSS-R retrieval error when the snow-free state. As the existing studies indicate, there is a difference between the SNR arcs collected in snow-free state and snow state. We propose to introduce support vector machine (SVM) and use SNR arcs as input samples to detect the ground truth state.

Keywords:

Snow Focus (optics) Ground truth Snow cover Error analysis Support vector machine

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Soil Moisture and Remote Sensing

Physical Sciences → Environmental Science → Environmental Engineering

Cryospheric studies and observations

Physical Sciences → Earth and Planetary Sciences → Atmospheric Science

Synthetic Aperture Radar (SAR) Applications and Techniques

Physical Sciences → Engineering → Aerospace Engineering

Improvement of GNSS-R snow depth retrieval using SVM-based snow detection

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