Yan et al. (2026) Reconstructing all-weather remotely sensed air temperature via a kernel-based temporal filling and bias correction (KTF-BC) framework

Identification

• Journal: Remote Sensing of Environment
• Year: 2026
• Date: 2026-01-20
• Authors: Xin Yan, Yongming Xu, Xudong Tong, Meng Ji, Yaping Mo, Yonghong Liu, Shanyou ZHU
• DOI: 10.1016/j.rse.2026.115253

Research Groups

• School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing, China
• School of the Environment, Geography and Geosciences, University of Portsmouth, Portsmouth, United Kingdom
• CMA Earth System Modeling and Prediction Centre, Beijing, China

Short Summary

This study developed a Kernel-based Temporal Filling and Bias Correction (KTF-BC) framework to reconstruct all-weather, spatially complete daily mean near-surface air temperature (Ta) from thermal infrared remote sensing, demonstrating high accuracy across China.

Objective

• To develop and apply a Kernel-based Temporal Filling and Bias Correction (KTF-BC) framework for reconstructing all-weather, spatially complete near-surface air temperature (Ta) from thermal infrared remote sensing data.

Study Configuration

• Spatial Scale: China, with a resolution of 1 kilometer.
• Temporal Scale: Daily mean temperature from 2019 to 2023.

Methodology and Data

• Models used: Kernel-based Temporal Filling and Bias Correction (KTF-BC) framework, comprising a kernel-based temporal filling method and a bias correction model.
• Data sources: Thermal infrared (TIR) remote sensing data (satellite observations) for Ta reconstruction, and meteorological station observations for validation.

Main Results

• The KTF-BC framework achieved high accuracy in reconstructing all-weather Ta under cloudy conditions.
• Validation against meteorological stations showed R² values up to 0.99.
• Mean Absolute Errors (MAEs) ranged from 0.91 to 0.95 °C.
• Root Mean Square Errors (RMSEs) ranged from 1.21 to 1.26 °C.
• Biases were close to 0 °C.
• The method effectively captured fine-scale thermal heterogeneity and demonstrated robust performance across varying cloud conditions and surface environments.

Contributions

• Developed a novel two-step Kernel-based Temporal Filling and Bias Correction (KTF-BC) framework to address cloud-induced data gaps in remotely sensed near-surface air temperature (Ta).
• Provided a practical and reliable solution for generating all-weather, spatially complete, high-resolution (1 km) daily mean Ta datasets from satellite observations.
• Demonstrated robust performance and high accuracy across diverse environments and cloud conditions over a large geographical region (China).

Funding

• Not specified in the provided text.

Citation

@article{Yan2026Reconstructing,
  author = {Yan, Xin and Xu, Yongming and Tong, Xudong and Ji, Meng and Mo, Yaping and Liu, Yonghong and ZHU, Shanyou},
  title = {Reconstructing all-weather remotely sensed air temperature via a kernel-based temporal filling and bias correction (KTF-BC) framework},
  journal = {Remote Sensing of Environment},
  year = {2026},
  doi = {10.1016/j.rse.2026.115253},
  url = {https://doi.org/10.1016/j.rse.2026.115253}
}