Time-series analysis on multi-modal data for precisionviticulture to support adaptation to changing climate patternsin Franconia

The cultivation of vine is an important economic sector in agriculture as well as a cultural legacyin many regions. Lower Franconia is one of Germanys largest wine-producing areas with morethan 6,000 ha of vineyards and a production of 450,000 hectolitres of wine. In the context of climatechange, strategies to adapt to changing precipitation and temperature patterns and to mitigate risksfrom drought and grape diseases is of utter importance for sustainable viticulture. Water deficitproduces diverse effects, such as reduced berry size or the failure of fruit maturation, depending on theplants growth stage 1. Severe water stress triggers partial or complete stomatal closure, resulting in areduction of photosynthetic activity 3. Thus, ideal soil moisture is key to sustainable viticulture andmonitoring plant development, soil moisture and climate variables is crucial for precision viticulture2. Due to the typical trellis systems, satellite remote sensing using deca-meter resolutions (suchas Landsat or Sentinel-2 series) is not well-suited for plant monitoring as pixel information at thisresolution consists of vines and ground information of the space between plant rows that might begrass, other plants or soil. Thus, we investigate time-series of very-high-resolution multispectraldata derived from a UAV-based system, hyperspectral data from in-situ measurements as well assensor data for soil moisture and evaluate results with respect to different irrigation patterns. Suchmulti-sensor and multi-temporal approaches contribute to a better understanding of the vineyardas a dynamic system and, thus, lead to better monitoring and management options and allow tobuild a GIS-based Digital Twin of the vineyard. We will present first results from daily to weeklymeasurements, evaluate different vegetation indices and highlight temporal patterns and reactiontimes between soil moisture data and spectral measurements. 1 WJ Hardie and JA Considine. Response of grapes to water-deficit stress in particular stages ofdevelopment. In: American Journal of Enology and Viticulture 27.2 (1976), pp. 5561.2 Margareth A Oliver. An overview of geostatistics and precision agriculture. In: Geostatisticalapplications for precision agriculture (2010), pp. 134.3 Maria Romero et al. Vineyard water status estimation using multispectral imagery from an UAVplatform and machine learning algorithms for irrigation scheduling management. In: Computersand Electronics in Agriculture 147 (Apr. 2018), pp. 109117. (Visited on 10/10/2023).