Zhu et al. (2025) SRSDNet: Super-Resolution Snow Depth Retrieval and Mapping Over the Qinghai-Tibet Plateau

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2025
• Date: 2025-12-26
• Authors: Linglong Zhu, Zhou Zhou, Yonghong Zhang, Renliang Xu, Xu Liu, Xiaojun Niu, Xi Kan, Jiangeng Wang
• DOI: 10.1109/jstars.2025.3648297

Research Groups

Not available from the provided text.

Short Summary

This paper introduces SRSDNet, a novel method for super-resolution retrieval and mapping of snow depth, specifically applied to the Qinghai-Tibet Plateau.

Objective

• To develop and apply a super-resolution deep learning network (SRSDNet) for accurate snow depth retrieval and mapping over the Qinghai-Tibet Plateau.

Study Configuration

• Spatial Scale: Qinghai-Tibet Plateau (regional scale).
• Temporal Scale: Not available from the provided text.

Methodology and Data

• Models used: SRSDNet (Super-Resolution Snow Depth Network).
• Data sources: Not available from the provided text, but likely involves remote sensing data for snow depth.

Main Results

Not available from the provided text.

Contributions

• Introduction of SRSDNet, a novel super-resolution deep learning model for snow depth retrieval.
• Application of super-resolution techniques to enhance snow depth mapping over the Qinghai-Tibet Plateau.

Funding

Not available from the provided text.

Citation

@article{Zhu2025SRSDNet,
  author = {Zhu, Linglong and Zhou, Zhou and Zhang, Yonghong and Xu, Renliang and Liu, Xu and Niu, Xiaojun and Kan, Xi and Wang, Jiangeng},
  title = {SRSDNet: Super-Resolution Snow Depth Retrieval and Mapping Over the Qinghai-Tibet Plateau},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2025},
  doi = {10.1109/jstars.2025.3648297},
  url = {https://doi.org/10.1109/jstars.2025.3648297}
}