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This is a Quantitative Study study.

Monitoring water-stressed spring tea canopy temperature using UAV-derived indices and clustering models

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A strong correlation (0.74) exists between canopy-air temperature difference bounds and crop water stress index values, enhancing irrigation insights.
Using machine learning techniques like random forest and OPTICS clustering, effective predictions for canopy temperature based on vegetation indices were achieved.
Ground-based measurements complemented UAV multispectral imaging to explore the critical relationships between soil moisture and canopy temperature.
The study paves the way for advancements in UAV-based remote sensing applications, providing rapid and accurate water stress monitoring in tea trees.

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This study demonstrates using UAV-derived indices to monitor water stress, suggesting effective precision irrigation management.

Xie et al. (2025) studied this question.

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