Purpose

High resolution imagery from unmanned aerial vehicles (UAVs) has been established as an important source of information to perform precise irrigation practices, notably relevant for high value crops often present in semi-arid regions such as vineyards. Many studies have shown the utility of thermal infrared (TIR) sensors to estimate canopy temperature to inform on vine physiological status, while visible-near infrared (VNIR) imagery and 3D point clouds derived from red–green–blue (RGB) photogrammetry have also shown great promise to better monitor within-field canopy traits to support agronomic practices. Indeed, grapevines react to water stress through a series of physiological and growth responses, which may occur at different spatio-temporal scales. As such, this study aimed to evaluate the application of TIR, VNIR and RGB sensors onboard UAVs to track vine water stress over various phenological periods in an experimental vineyard imposed with three different irrigation regimes.

Methods

A total of twelve UAV overpasses were performed in 2022 and 2023 where in situ physiological proxies, such as stomatal conductance (g_s), leaf (Ψ_leaf) and stem (Ψ_stem) water potential, and canopy traits, such as LAI, were collected during each UAV overpass. Linear and non-linear models were trained and evaluated against in-situ measurements.

Results

Results revealed the importance of TIR variables to estimate physiological proxies (g_s, Ψ_leaf, Ψ_stem) while VNIR and 3D variables were critical to estimate LAI. Both VNIR and 3D variables were largely uncorrelated to water stress proxies and demonstrated less importance in the trained empirical models. However, models using all three variable types (TIR, VNIR, 3D) were consistently the most effective to track water stress, highlighting the advantage of combining vine characteristics related to physiology, structure and growth to monitor vegetation water status throughout the vine growth period.

Conclusion

This study highlights the utility of combining such UAV-based variables to establish empirical models that correlated well with field-level water stress proxies, demonstrating large potential to support agronomic practices or even to be ingested in physically-based models to estimate vine water demand and transpiration.

中文翻译：

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评估结合无人机高分辨率热图像、多光谱图像和 3D 图像来监测葡萄园水分压力的效用

 目的

无人机 (UAV) 的高分辨率图像已成为执行精确灌溉实践的重要信息来源，特别是与葡萄园等半干旱地区常见的高价值作物相关。许多研究表明，热红外 (TIR) 传感器可用于估计树冠温度，从而了解藤蔓的生理状态，而来自红绿蓝 (RGB) 摄影测量的可见光-近红外 (VNIR) 图像和 3D 点云也已被证明是可行的。在更好地监测田间冠层性状以支持农艺实践方面表现出了巨大的希望。事实上，葡萄树通过一系列生理和生长反应来应对水分胁迫，这些反应可能发生在不同的时空尺度上。因此，本研究旨在评估无人机上 TIR、VNIR 和 RGB 传感器的应用，以跟踪采用三种不同灌溉方式的实验葡萄园中不同物候期的葡萄树水分胁迫。

 方法

2022 年和 2023 年总共进行了 12 次无人机立交桥，其中对气孔导度 ( _gs )、叶 (Ψ _leaf ) 和茎 (Ψ _tube ) 水势以及冠层性状 (例如 LAI) 等原位生理指标进行了分析在每个无人机立交桥期间收集。根据现场测量对线性和非线性模型进行训练和评估。

 结果

结果揭示了 TIR 变量对于估计生理代理（g _s 、 Ψ_叶、 Ψ_茎）的重要性，而 VNIR 和 3D 变量对于估计 LAI 至关重要。 VNIR 和 3D 变量在很大程度上与水分胁迫代理不相关，并且在经过训练的经验模型中表现出不太重要。然而，使用所有三种变量类型（TIR、VNIR、3D）的模型始终是追踪水分胁迫的最有效方法，突显了结合与生理、结构和生长相关的葡萄树特征来监测整个葡萄树生长期间植被水分状况的优势。

 结论

这项研究强调了结合此类基于无人机的变量来建立与田间水分胁迫代理良好相关的经验模型的效用，展示了支持农艺实践的巨大潜力，甚至可以纳入基于物理的模型来估计葡萄藤需水量和蒸腾作用。