Yu et al. (2025) Optimizing Crop Maximum Carboxylation Rate Using Machine Learning to Improve Maize Yield Estimation Under Drought Conditions

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2025
• Date: 2025-12-09
• Authors: Liming Yu, Jiahua Zhang, Sha Zhang, Zhiyuan Ma, Xin Jiang, Yun Bai, Shanshan Yang
• DOI: 10.1109/tgrs.2025.3642031

## Research Groups -

Short Summary

This study focuses on optimizing the crop maximum carboxylation rate using machine learning to enhance maize yield estimation under drought conditions.

Objective

• To improve maize yield estimation under drought conditions by optimizing the crop maximum carboxylation rate (Vcmax) using machine learning.

Study Configuration

• Spatial Scale:
• Temporal Scale:

Methodology and Data

• Models used: Machine learning algorithms.
• Data sources:

## Main Results -

## Contributions -

## Funding -

Citation

@article{Yu2025Optimizing,
  author = {Yu, Liming and Zhang, Jiahua and Zhang, Sha and Ma, Zhiyuan and Jiang, Xin and Bai, Yun and Yang, Shanshan},
  title = {Optimizing Crop Maximum Carboxylation Rate Using Machine Learning to Improve Maize Yield Estimation Under Drought Conditions},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2025},
  doi = {10.1109/tgrs.2025.3642031},
  url = {https://doi.org/10.1109/tgrs.2025.3642031}
}