Wan et al. (2026) GREAT-Net: A Deep Spatiotemporal Fusion Network for High-Resolution Multisatellite GNSS-REflectometry Soil Moisture Retrieval With ScATterometer Constraints

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: X. Wan, W. Wan, Z. Guo, X. Hu, B. Liu, J. Zhang, C. Yin, T. Zhang, Zhi Gong
• DOI: 10.1109/tgrs.2026.3662188

Research Groups

[Not available in the provided text.]

Short Summary

This paper introduces GREAT-Net, a deep spatiotemporal fusion network designed for high-resolution soil moisture retrieval by integrating multisatellite GNSS-reflectometry data with scatterometer constraints.

Objective

• To develop and evaluate a deep spatiotemporal fusion network (GREAT-Net) for high-resolution soil moisture retrieval, leveraging multisatellite GNSS-reflectometry and scatterometer data.

Study Configuration

• Spatial Scale: High-resolution (specific resolution not available in the provided text).
• Temporal Scale: Spatiotemporal fusion (specific temporal resolution not available in the provided text).

Methodology and Data

• Models used: GREAT-Net (a deep spatiotemporal fusion network).
• Data sources: Multisatellite GNSS-reflectometry (GNSS-R) data, Scatterometer data.

Main Results

[Not available in the provided text.]

Contributions

• Introduction of GREAT-Net, a novel deep spatiotemporal fusion network for soil moisture retrieval.
• Integration of multisatellite GNSS-R and scatterometer data for enhanced high-resolution soil moisture mapping.

Funding

[Not available in the provided text.]

Citation

@article{Wan2026GREATNet,
  author = {Wan, X. and Wan, W. and Guo, Z. and Hu, X. and Liu, B. and Zhang, J. and Yin, C. and Zhang, T. and Gong, Zhi},
  title = {GREAT-Net: A Deep Spatiotemporal Fusion Network for High-Resolution Multisatellite GNSS-REflectometry Soil Moisture Retrieval With ScATterometer Constraints},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3662188},
  url = {https://doi.org/10.1109/tgrs.2026.3662188}
}