Lu et al. (2026) A Thermal Infrared Remote Sensing Model for Diagnosing Winter Wheat Water (Triticum aestivum L.) Stress by Integrating Angular Effects and Kernel-Driven Models

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Identification

• Journal: Plants
• Year: 2026
• Date: 2026-07-18
• Authors: Xiaohan Lu, Guoqiang Hu, Xiaofei Yang, Hui Li, Hao Liu, Qi Xu, Yanfu Liu, Daoxu Fan, Z L Li, J B Chen, Xin Hui, Maosheng Ge, Zhang Zb
• DOI: 10.3390/plants15142201

Research Groups

Not specified in the provided text.

Short Summary

This study investigates the directional effects of canopy temperature in winter wheat using UAV thermal imagery and employs a kernel-driven model to retrieve isotropic temperature for more accurate Crop Water Stress Index (CWSI) estimation.

Objective

• To analyze the directional characteristics of winter wheat canopy temperature and evaluate how angular correction improves the sensitivity and reliability of CWSI models in diagnosing crop water stress.

Study Configuration

• Spatial Scale: Crop canopy level (winter wheat) monitored via Unmanned Aerial Vehicle (UAV).
• Temporal Scale: Not specified.

Methodology and Data

• Models used: Kernel-driven model (for directional component separation and isotropic temperature retrieval) and three different CWSI models (including an empirical CWSI model).
• Data sources: Multi-angular thermal infrared imagery from a UAV platform and soil moisture measurements at a depth of 30 cm.

Main Results

• Winter wheat canopy temperature exhibits significant directional effects, generally decreasing as the relative azimuth angle between the viewing direction and solar incident direction increases.
• Isotropic canopy temperature retrieved via the kernel-driven model shows an improved correlation with soil moisture at 30 cm depth ($R^2 = 0.54$).
• Angular correction enhances the sensitivity of all CWSI models to crop water variations and improves the discrimination between different irrigation treatments.
• The empirical CWSI model outperformed other approaches in diagnosing water stress, achieving $R^2 = 0.73$ and $\text{RMSE} = 1.59\%$.

Contributions

The research provides a theoretical and technical framework for reducing thermal directional effects in UAV-based remote sensing, thereby increasing the stability and reliability of CWSI for precision irrigation management.

Funding

Not specified in the provided text.

Citation

@article{Lu2026Thermal,
  author = {Lu, Xiaohan and Hu, Guoqiang and Yang, Xiaofei and Li, Hui and Liu, Hao and Xu, Qi and Liu, Yanfu and Fan, Daoxu and Li, Z L and Chen, J B and Hui, Xin and Ge, Maosheng and Zb, Zhang},
  title = {A Thermal Infrared Remote Sensing Model for Diagnosing Winter Wheat Water (Triticum aestivum L.) Stress by Integrating Angular Effects and Kernel-Driven Models},
  journal = {Plants},
  year = {2026},
  doi = {10.3390/plants15142201},
  url = {https://doi.org/10.3390/plants15142201}
}