Nowack Juan C, Atencia-Payares Luz K, Tarquis Ana M, Gomez-Del-Campo M
CEIGRAM, ETSIAAB, Universidad Politécnica de Madrid (UPM), 28040 Madrid, Spain.
Departamento de Producción Agraria, ETSIAAB, Universidad Politécnica de Madrid (UPM), Av. Puerta de Hierro, n° 2-4, 28040 Madrid, Spain.
Plants (Basel). 2024 May 13;13(10):1350. doi: 10.3390/plants13101350.
Pruning determines the plant water status due to its effects on the leaf area and thus the irrigation management. The primary aim of this study was to assess the use of high-resolution multispectral imagery to estimate the plant water status through different bands and vegetation indexes (VIs) and to evaluate which is most suitable under different pruning management strategies. This work was carried out in 2021 and 2022 in a commercial Merlot vineyard in an arid area of central Spain. Two different pruning strategies were carried out: mechanical pruning and no pruning. The stem water potential was measured with a pressure chamber (Ψ) at two different solar times (9 h and 12 h). Multispectral information from unmanned aerial vehicles (UAVs) was obtained at the same time as the field Ψstem measurements and different vegetation indexes (VIs) were calculated. Pruning management significantly determined the Ψ, bunch and berry weight, number of bunches, and plant yield. Linear regression between the Ψ and NDVI presented the tightest correlation at 12 h solar time (R = 0.58). The red and red-edge bands were included in a generalised multivariable linear regression and achieved higher accuracy (R = 0.74) in predicting the Ψ. Using high-resolution multispectral imagery has proven useful in predicting the vine water status independently of the pruning management strategy.
修剪通过影响叶面积进而决定植物水分状况，从而影响灌溉管理。本研究的主要目的是评估利用高分辨率多光谱图像，通过不同波段和植被指数（VIs）来估计植物水分状况，并评估在不同修剪管理策略下哪种最为合适。这项工作于2021年和2022年在西班牙中部干旱地区的一个商业梅洛葡萄园进行。实施了两种不同的修剪策略：机械修剪和不修剪。在两个不同的太阳时间（9时和12时）用压力室测量茎水势（Ψ）。在进行田间Ψ茎测量的同时，获取来自无人机（UAVs）的多光谱信息，并计算不同的植被指数（VIs）。修剪管理显著决定了Ψ、果穗和浆果重量、果穗数量以及植株产量。Ψ与归一化植被指数（NDVI）之间的线性回归在太阳时间12时呈现出最紧密的相关性（R = 0.58）。红色和红边波段被纳入广义多变量线性回归，在预测Ψ时达到了更高的准确性（R = 0.74）。事实证明，使用高分辨率多光谱图像有助于独立于修剪管理策略来预测葡萄藤的水分状况。
