Li et al. (2026) Retrieval of snow depth using synthetic aperture radar: past, current, and future

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-02-07
• Authors: Zhen Li, Haiwei Qiao, Ping Zhang, Yanan Bai, Huadong Hu
• DOI: 10.1016/j.jhydrol.2026.135103

Research Groups

• Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
• International Research Center of Big Data for Sustainable Development Goals, Beijing, China
• University of Chinese Academy of Sciences, Beijing, China

Short Summary

This paper provides a comprehensive review of snow depth retrieval using synthetic aperture radar (SAR) techniques, detailing the interaction between SAR signals and snow, various backscattering models, and methods like PolSAR, InSAR, PolInSAR, and TomoSAR, to offer a future outlook on this critical parameter.

Objective

• To comprehensively review the principles, methodologies, and applications of Synthetic Aperture Radar (SAR) for snow depth retrieval over the past two decades, and to identify future opportunities and challenges in this field.

Study Configuration

• Spatial Scale: Global (reviewing methods applicable to various snow-covered regions worldwide)
• Temporal Scale: Two decades (reviewing methods developed and applied over the last 20 years, with a scope covering past, current, and future perspectives)

Methodology and Data

• Models used: Snow backscattering models, surface scattering models (general categories, specific model names not detailed in the abstract/introduction provided)
• Data sources: Synthetic Aperture Radar (SAR) data, including techniques such as Polarimetric SAR (PolSAR), Interferometric SAR (InSAR), Polarimetric Interferometric SAR (PolInSAR), and Tomographic SAR (TomoSAR).

Main Results

• SAR offers a viable solution for superior precision and fine-scale retrieval of spatiotemporally continuous snow depth, addressing limitations of ground observations and passive microwave remote sensing.
• The paper systematically reviews the interaction between SAR signals and snow, categorizing different types of snow backscattering and surface scattering models.
• A comprehensive summary of snow depth retrieval methods based on PolSAR, InSAR, PolInSAR, and TomoSAR techniques developed over the last two decades is provided.
• Current opportunities and challenges in SAR-based snow depth retrieval are envisioned, offering reliable references for future research.

Contributions

• Provides a comprehensive and structured review of SAR-based snow depth retrieval techniques, synthesizing two decades of research.
• Outlines the underlying physics of SAR signal-snow interaction, various modeling approaches, and advanced SAR methodologies (PolSAR, InSAR, PolInSAR, TomoSAR).
• Identifies and discusses current opportunities and future challenges, offering a valuable roadmap for advancing snow depth monitoring using SAR.

Funding

• No funding information was provided in the excerpt.

Citation

@article{Li2026Retrieval,
  author = {Li, Zhen and Qiao, Haiwei and Zhang, Ping and Bai, Yanan and Hu, Huadong},
  title = {Retrieval of snow depth using synthetic aperture radar: past, current, and future},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.135103},
  url = {https://doi.org/10.1016/j.jhydrol.2026.135103}
}