Kamangir et al. (2026) CMAViT: Integrating Climate, Management, and Remote Sensing Data for Crop Yield Prediction With Multimodel Vision Transformers

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Hamid Kamangir, B. Sams, Nick Dokoozlian, Luis Sanchez, J. Mason Earles
• DOI: 10.1109/jstars.2026.3669511

Research Groups

[Not available from provided text]

Short Summary

This paper introduces CMAViT, a Multimodel Vision Transformer, for predicting crop yield by integrating climate, management, and remote sensing data.

Objective

• To develop and apply CMAViT, a Multimodel Vision Transformer, to enhance crop yield prediction through the integration of climate, management, and remote sensing data.

Study Configuration

• Spatial Scale: [Not available from provided text, likely regional or field-specific]
• Temporal Scale: [Not available from provided text, likely annual or seasonal]

Methodology and Data

• Models used: CMAViT (Multimodel Vision Transformers)
• Data sources: Climate data, Management data, Remote sensing data

Main Results

[Not available from provided text]

Contributions

[Not available from provided text]

Funding

[Not available from provided text]

Citation

@article{Kamangir2026CMAViT,
  author = {Kamangir, Hamid and Sams, B. and Dokoozlian, Nick and Sanchez, Luis and Earles, J. Mason},
  title = {CMAViT: Integrating Climate, Management, and Remote Sensing Data for Crop Yield Prediction With Multimodel Vision Transformers},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2026},
  doi = {10.1109/jstars.2026.3669511},
  url = {https://doi.org/10.1109/jstars.2026.3669511}
}