Yu et al. (2026) Metamodel-Accelerated High-Resolution Maize Yield Mapping via Sentinel-2 Assimilation and Random Forest

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Haiwei Yu, Huapeng Li, Jian Lü, Tongtong Zhao, Baoqi Liu
• DOI: 10.1109/jstars.2026.3655376

Research Groups

Not specified in the provided text.

Short Summary

This paper introduces a novel approach for high-resolution maize yield mapping, integrating metamodel acceleration, Sentinel-2 satellite data assimilation, and Random Forest algorithms to enhance efficiency and accuracy.

Objective

• To develop and apply a metamodel-accelerated method for high-resolution maize yield mapping, leveraging Sentinel-2 data assimilation and Random Forest algorithms.

Study Configuration

• Spatial Scale: High-resolution, specific scale not detailed in provided text.
• Temporal Scale: Not specified in the provided text.

Methodology and Data

• Models used: Metamodel, Random Forest
• Data sources: Sentinel-2 satellite data (via assimilation)

Main Results

Not specified in the provided text.

Contributions

Not specified in the provided text.

Funding

Not specified in the provided text.

Citation

@article{Yu2026MetamodelAccelerated,
  author = {Yu, Haiwei and Li, Huapeng and Lü, Jian and Zhao, Tongtong and Liu, Baoqi},
  title = {Metamodel-Accelerated High-Resolution Maize Yield Mapping via Sentinel-2 Assimilation and Random Forest},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2026},
  doi = {10.1109/jstars.2026.3655376},
  url = {https://doi.org/10.1109/jstars.2026.3655376}
}