Yin et al. (2026) A Season-Adaptive Machine Learning Framework for Estimating Ground Surface Temperature in Northeast China

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Zhiqiang Yin, M. Liu, Mengyao Chen, Jiao Wang, Dianfan Guo, Shuying Zang
• DOI: 10.1109/jstars.2026.3665722

Research Groups

Not specified in the provided text.

Short Summary

This paper introduces a season-adaptive machine learning framework designed for estimating ground surface temperature in Northeast China.

Objective

• To develop and apply a season-adaptive machine learning framework for estimating ground surface temperature (GST) in Northeast China.

Study Configuration

• Spatial Scale: Northeast China (regional scale).
• Temporal Scale: Season-adaptive, implying analysis across different seasons; specific duration not detailed.

Methodology and Data

• Models used: A season-adaptive machine learning framework (specific algorithms not detailed).
• Data sources: Not specified in the provided text.

Main Results

Not specified in the provided text.

Contributions

Not specified in the provided text.

Funding

Not specified in the provided text.

Citation

@article{Yin2026SeasonAdaptive,
  author = {Yin, Zhiqiang and Liu, M. and Chen, Mengyao and Wang, Jiao and Guo, Dianfan and Zang, Shuying},
  title = {A Season-Adaptive Machine Learning Framework for Estimating Ground Surface Temperature in Northeast China},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2026},
  doi = {10.1109/jstars.2026.3665722},
  url = {https://doi.org/10.1109/jstars.2026.3665722}
}