Application of Unmanned Aerial Vehicle (UAV) Sensing for Water Status Estimation in Vineyards under Different Pruning Strategies

Abstract

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 (Ψstem) 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 Ψstem, bunch and berry weight, number of bunches, and plant yield. Linear regression between the Ψstem and NDVI presented the tightest correlation at 12 h solar time (R2 = 0.58). The red and red-edge bands were included in a generalised multivariable linear regression and achieved higher accuracy (R2 = 0.74) in predicting the Ψstem. Using high-resolution multispectral imagery has proven useful in predicting the vine water status independently of the pruning management strategy.