Chen et al. (2026) Atmospheric Correction for HY-3A CZI: A Deep Learning Method Applied Across Open Ocean, Coastal, and Turbid Inland Waters

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Xi Chen, Chen Zhang, Chaofei Ma, Xiaomin Ye, Liqiao Tian
• DOI: 10.1109/tgrs.2026.3665003

Research Groups

Not specified in the provided text.

Short Summary

This paper proposes and applies a deep learning method for atmospheric correction of HY-3A Coastal Zone Imager (CZI) data, demonstrating its utility across a diverse range of aquatic environments, including open ocean, coastal, and turbid inland waters.

Objective

• To develop and evaluate a deep learning method for atmospheric correction of HY-3A Coastal Zone Imager (CZI) data, ensuring its applicability and robustness across open ocean, coastal, and turbid inland water environments.

Study Configuration

• Spatial Scale: Open ocean, coastal waters, and turbid inland waters.
• Temporal Scale: Not specified in the provided text.

Methodology and Data

• Models used: Deep learning method.
• Data sources: HY-3A Coastal Zone Imager (CZI) satellite data.

Main Results

Not specified in the provided text.

Contributions

Not specified in the provided text.

Funding

Not specified in the provided text.

Citation

@article{Chen2026Atmospheric,
  author = {Chen, Xi and Zhang, Chen and Ma, Chaofei and Li, Wenkai and Ye, Xiaomin and Tian, Liqiao},
  title = {Atmospheric Correction for HY-3A CZI: A Deep Learning Method Applied Across Open Ocean, Coastal, and Turbid Inland Waters},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3665003},
  url = {https://doi.org/10.1109/tgrs.2026.3665003}
}