Intra-vineyard spatial variability explored over multiple seasons by sensor-based techniques in the Valpolicella area

Abstract

The identification and management of intra-vineyard variability are key to precision viticulture, and sensors have been proven to be highly efficient tools for detecting these variations. Vineyard variability often manifests as differences in vine vigor, which, in turn, can influence fruit development. However, this variation stems from complex interactions among multiple factors (e.g., topography, soil, water availability, microclimate, and climate), that may vary from season to season. Given the dynamic nature of this system, it is important to define the vine and berry vigor responses and their correlation with sensor-derived data over multiple seasons.

To this end, a vineyard in the Valpolicella viticultural area (north-east Italy) was monitored over three consecutive years (2021–2023) employing various sensor platforms. NDVI was calculated using Sentinel-2 and UAV multispectral images, as well as a handheld NDVI sensor. The UAV orthomosaic was further used to estimate the canopy dimension (2D Canopy). A thermal camera was used to assess the crop water stress (CWSI), and a TDR moisture meter was used to measure soil volumetric water content (VWC). Ground truthing across 11 dispersed blocks included bud fruitfulness, shoot development, yield, and vine-balance traits. Berries were analyzed for their weight, skin thickness, and quality compounds.

The spatial variability of each parameter was assessed for its temporal stability, and the relationships between sensor-derived data and direct vine and berry measurements were established across seasons. Correlation analyses and clustering identified several key, tightly related vigor parameters (e.g., NDVIs, 2D Canopy, shoot length, yield, berry weight, and acidity), which were consistently negatively correlated with the CWSI. The latter maintained positive associations with the pH, TSS, anthocyanins, and polyphenols.

This study explores inter- and intra-annual dynamics linking vine agronomic parameters and berry quality traits to sensor-derived data to enhance the informative potential of the latter and help sensor-assisted vineyard management.