Li et al. (2026) UAV-Based Thermal Inversion for Canopy Temperature Retrieval and Precision Irrigation

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Sensors
• Year: 2026
• Date: 2026-08-07
• Authors: Haoming Li, Wei Li, Chenchen Liu, Leilei Ji, Zhenbo Liu, Ramesh K. Agarwal
• DOI: 10.3390/s26165023

Research Groups

Not specified

Short Summary

This study develops a UAV-based thermal infrared framework for high-resolution canopy temperature retrieval and irrigation decision support in tea plantations using the Crop Water Stress Index (CWSI).

Objective

• To develop a practical technical workflow for high-resolution canopy temperature retrieval and quantitative irrigation decision-making in tea plantations using UAV thermal imagery.

Study Configuration

• Spatial Scale: Tea plantation (field scale)
• Temporal Scale: Not specified

Methodology and Data

• Models used: Gray–temperature calibration model, Crop Water Stress Index (CWSI), and a threshold-based irrigation strategy.
• Data sources: UAV thermal infrared imagery and measured stomatal conductance.

Main Results

• The linear gray-temperature calibration model achieved a maximum absolute error of less than 0.3 °C.
• The calculated CWSI and estimated irrigation requirements demonstrated a strong correlation with measured stomatal conductance, with $R^2$ reaching 0.91.

Contributions

• Provides a comprehensive technical workflow that bridges the gap between UAV thermal data acquisition and quantitative precision irrigation management specifically for tea plantations.

Funding

Not specified

Citation

@article{Li2026UAVBased,
  author = {Li, Haoming and Li, Wei and Liu, Chenchen and Ji, Leilei and Liu, Zhenbo and Agarwal, Ramesh K.},
  title = {UAV-Based Thermal Inversion for Canopy Temperature Retrieval and Precision Irrigation},
  journal = {Sensors},
  year = {2026},
  doi = {10.3390/s26165023},
  url = {https://doi.org/10.3390/s26165023}
}