The Evaluation of Small-Scale Field Maize Transpiration Rate from UAV Thermal Infrared Images Using Improved Three-Temperature Model.

Abstract

Transpiration is the dominant process driving water loss in crops, significantly influencing their growth, development, and yield. Efficient monitoring of transpiration rate (Tr) is crucial for evaluating crop physiological status and optimizing water management strategies. The three-temperature (3T) model has potential for rapid estimation of transpiration rates, but its application to low-altitude remote sensing has not yet been further investigated. To evaluate the performance of 3T model based on land surface temperature (LST) and canopy temperature (T C ) in estimating transpiration rate, this study utilized an unmanned aerial vehicle (UAV) equipped with a thermal infrared (TIR) camera to capture TIR images of summer maize during the nodulation-irrigation stage under four different moisture treatments, from which LST was extracted. The Gaussian Hidden Markov Random Field (GHMRF) model was applied to segment the TIR images, facilitating the extraction of T C . Finally, an improved 3T model incorporating fractional vegetation coverage (FVC) was proposed. The findings of the study demonstrate that: (1) The GHMRF model offers an effective approach for TIR image segmentation. The mechanism of thermal TIR segmentation implemented by the GHMRF model is explored. The results indicate that when the potential energy function parameter β value is 0.1, the optimal performance is provided. (2) The feasibility of utilizing UAV-based TIR remote sensing in conjunction with the 3T model for estimating Tr has been demonstrated, showing a significant correlation between the measured and the estimated transpiration rate (T r -3T C ), derived from T C data obtained through the segmentation and processing of TIR imagery. The correlation coefficients (r) were 0.946 in 2022 and 0.872 in 2023. (3) The improved 3T model has demonstrated its ability to enhance the estimation accuracy of crop Tr rapidly and effectively, exhibiting a robust correlation with T r -3T C . The correlation coefficients for the two observed years are 0.991 and 0.989, respectively, while the model maintains low RMSE of 0.756 mmol H 2 O m -2 s -1 and 0.555 mmol H 2 O m -2 s -1 for the respective years, indicating strong interannual stability.

Plant phenotyping relevance

UAV熱画像のセグメンテーションと改良3温度モデルにより、トウモロコシの蒸散速度という生理形質を推定する手法を開発・検証しており、フェノタイピング手法が中心である。

Code and data availability

The supplied blocks describe UAV thermal infrared data collection, GHMRF segmentation, and 3T model analysis for maize transpiration, but contain no public phenotype/trait dataset, image deposit, author analysis code, trained model, or supplement with an authors' public URL. No data or code availability statement with

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