Remote sensing is now a valued solution for more accurately budgeting water supply by identifying spectral and spatial information. A study was put in place in a Vitis vinifera L. cv. Cabernet-Sauvignon vineyard in the San Joaquin Valley, CA, USA, where a variable rate automated irrigation system was installed to irrigate vines with twelve different water regimes in four randomized replicates, totaling 48 experimental zones. The purpose of this experimental design was to create variability in grapevine water status, in order to produce a robust dataset for modeling purposes. Throughout the growing season, spectral data within these zones was gathered using a Near InfraRed (NIR) - Short Wavelength Infrared (SWIR) hyperspectral camera (900 to 1700 nm) mounted on an Unmanned Aircraft Vehicle (UAV). Given the high water-absorption in this spectral domain, this sensor was deployed to assess grapevine stem water potential, Ψ_stem, a standard reference for water status assessment in plants, from pure grapevine pixels in hyperspectral images. The Ψ_stem was acquired simultaneously in the field from bunch closure to harvest and modeled via machine-learning methods using the remotely sensed NIR-SWIR data as predictors in regression and classification modes (classes consisted of physiologically different water stress levels). Hyperspectral images were converted to bottom of atmosphere reflectance using standard panels on the ground and through the Quick Atmospheric Correction Method (QUAC) and the results were compared. The best models used data obtained with standard panels on the ground and allowed predicting Ψ_stem values with an R² of 0.54 and an RMSE of 0.11 MPa as estimated in cross-validation, and the best classification reached an accuracy of 74%. This project aims to develop new methods for precisely monitoring and managing irrigation in vineyards while providing useful information about plant physiology response to deficit irrigation.

遥感现在是一种有价值的解决方案，可以通过识别光谱和空间信息来更准确地预算供水。对Vitis vinifera L. cv. 进行了一项研究。美国加利福尼亚州圣华金谷的赤霞珠葡萄园安装了可变流量自动灌溉系统，以 12 种不同的水况进行 4 次随机重复，总共 48 个实验区。该实验设计的目的是创造葡萄树水分状况的变化，以便为建模目的生成可靠的数据集。在整个生长季节，使用安装在无人机 (UAV) 上的近红外 (NIR) - 短波红外 (SWIR) 高光谱相机（900 至 1700 nm）收集这些区域内的光谱数据。鉴于该光谱域中的高吸水性，该传感器被部署用于从高光谱图像中的纯葡萄树像素评估葡萄树茎水势 Ψ_茎，这是植物水分状况评估的标准参考。 Ψ_茎是在田间从束闭合到收获的过程中同时采集的，并使用遥感 NIR-SWIR 数据作为回归和分类模式的预测因子（类别由生理上不同的水分胁迫水平组成）通过机器学习方法进行建模。使用地面上的标准面板并通过快速大气校正方法（QUAC）将高光谱图像转换为大气底部反射率，并对结果进行比较。最佳模型使用地面标准面板获得的数据，并允许预测 Ψ_茎值，R ²为 0.54，RMSE 为 0。交叉验证估计为11 MPa，最佳分类准确率达到74%。该项目旨在开发精确监测和管理葡萄园灌溉的新方法，同时提供有关植物生理学对缺水灌溉的反应的有用信息。