Saki et al. (2026) Graph-Transformer for Spatiotemporal Soil Moisture Forecasting Using Multimodal Remote Sensing Data

Identification

• Journal: IEEE Access
• Year: 2026
• Date: 2026-01-01
• Authors: Mahdi Saki, Daniel Franklin, Mehran Abolhasan, Justin Lipman, N. Shariati
• DOI: 10.1109/access.2026.3669499

Research Groups

• N/A

Short Summary

This paper proposes a Graph-Transformer model for spatiotemporal soil moisture forecasting, leveraging multimodal remote sensing data.

Objective

• To develop and apply a Graph-Transformer model for spatiotemporal soil moisture forecasting using multimodal remote sensing data.

Study Configuration

• Spatial Scale: N/A
• Temporal Scale: N/A

Methodology and Data

• Models used: Graph-Transformer
• Data sources: Multimodal remote sensing data

Main Results

• N/A

Contributions

• N/A

Funding

• N/A

Citation

@article{Saki2026GraphTransformer,
  author = {Saki, Mahdi and Franklin, Daniel and Abolhasan, Mehran and Lipman, Justin and Shariati, N.},
  title = {Graph-Transformer for Spatiotemporal Soil Moisture Forecasting Using Multimodal Remote Sensing Data},
  journal = {IEEE Access},
  year = {2026},
  doi = {10.1109/access.2026.3669499},
  url = {https://doi.org/10.1109/access.2026.3669499}
}