Xu et al. (2025) Spatiotemporal Reconstruction of FY-3B Soil Moisture Using a Hybrid Attention and Partial Convolution Neural Network

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2025
• Date: 2025-11-21
• Authors: Renjiong Xu, Zushuai Wei, Shiliang Fu, Linguang Miao, Hui Wang, Jixiang Kou
• DOI: 10.1109/jstars.2025.3632561

Research Groups

Not available in the provided text.

Short Summary

This paper focuses on the spatiotemporal reconstruction of FY-3B satellite soil moisture data using a novel deep learning architecture.

Objective

• To achieve spatiotemporal reconstruction of FY-3B soil moisture using advanced neural network techniques.

Study Configuration

• Spatial Scale: Not specified in the provided text, but implied to be regional or global given the use of satellite data.
• Temporal Scale: Not specified in the provided text, but implied to be continuous over time given "spatiotemporal reconstruction".

Methodology and Data

• Models used: Hybrid Attention and Partial Convolution Neural Network.
• Data sources: FY-3B satellite soil moisture data.

Main Results

Not available in the provided text.

Contributions

Not available in the provided text.

Funding

Not available in the provided text.

Citation

@article{Xu2025Spatiotemporal,
  author = {Xu, Renjiong and Wei, Zushuai and Fu, Shiliang and Miao, Linguang and Wang, Hui and Kou, Jixiang},
  title = {Spatiotemporal Reconstruction of FY-3B Soil Moisture Using a Hybrid Attention and Partial Convolution Neural Network},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2025},
  doi = {10.1109/jstars.2025.3632561},
  url = {https://doi.org/10.1109/jstars.2025.3632561}
}