Pang et al. (2026) Improved Transformer Model With Meteorological Constraints Enhances Time Series Prediction of Evapotranspiration in Arid Regions

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Zijie Pang, Lei Li, Li Lu, Y.-J. LIU, Feihu Xue, Haohao Zhao
• DOI: 10.1109/tgrs.2026.3652947

Research Groups

[Not available in the provided text.]

Short Summary

This paper introduces an improved Transformer model, incorporating meteorological constraints, to enhance the accuracy of evapotranspiration time series prediction specifically in arid regions.

Objective

• To develop and evaluate an improved Transformer model with meteorological constraints for enhanced time series prediction of evapotranspiration in arid regions.

Study Configuration

• Spatial Scale: Arid regions (specific scale not available).
• Temporal Scale: Time series prediction (specific resolution not available).

Methodology and Data

• Models used: Transformer model (an improved version with meteorological constraints).
• Data sources: [Implied: Meteorological data, evapotranspiration data. Specific sources not available.]

Main Results

[Not available in the provided text, but the title suggests improved prediction performance.]

Contributions

[Not available in the provided text, but likely the novel integration of meteorological constraints into a Transformer model for evapotranspiration prediction in arid regions.]

Funding

[Not available in the provided text.]

Citation

@article{Pang2026Improved,
  author = {Pang, Zijie and Li, Lei and Lu, Li and LIU, Y.-J. and Xue, Feihu and Zhao, Haohao},
  title = {Improved Transformer Model With Meteorological Constraints Enhances Time Series Prediction of Evapotranspiration in Arid Regions},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3652947},
  url = {https://doi.org/10.1109/tgrs.2026.3652947}
}