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 legacy
in many regions. Lower Franconia is one of Germany’s largest wine-producing areas with more
than 6,000 ha of vineyards and a production of 450,000 hectolitres of wine. In the context of climate
change, strategies to adapt to changing precipitation and temperature patterns and to mitigate risks
from drought and grape diseases is of utter importance for sustainable viticulture. Water deficit
produces diverse effects, such as reduced berry size or the failure of fruit maturation, depending on the
plant’s growth stage [1]. Severe water stress triggers partial or complete stomatal closure, resulting in a
reduction of photosynthetic activity [3]. Thus, ideal soil moisture is key to sustainable viticulture and
monitoring plant development, soil moisture and climate variables is crucial for precision viticulture
[2]. Due to the typical trellis systems, satellite remote sensing using deca-meter resolutions (such
as Landsat or Sentinel-2 series) is not well-suited for plant monitoring as pixel information at this
resolution consists of vines and ground information of the space between plant rows that might be
grass, other plants or soil. Thus, we investigate time-series of very-high-resolution multispectral
data derived from a UAV-based system, hyperspectral data from in-situ measurements as well as
sensor data for soil moisture and evaluate results with respect to different irrigation patterns. Such
multi-sensor and multi-temporal approaches contribute to a better understanding of the vineyard
as a dynamic system and, thus, lead to better monitoring and management options and allow to
build a GIS-based Digital Twin of the vineyard. We will present first results from daily to weekly
measurements, evaluate different vegetation indices and highlight temporal patterns and reaction
times between soil moisture data and spectral measurements.

[1] WJ Hardie and JA Considine. “Response of grapes to water-deficit stress in particular stages of
development”. In: American Journal of Enology and Viticulture 27.2 (1976), pp. 55–61.
[2] Margareth A Oliver. “An overview of geostatistics and precision agriculture”. In: Geostatistical
applications for precision agriculture (2010), pp. 1–34.
[3] Maria Romero et al. “Vineyard water status estimation using multispectral imagery from an UAV
platform and machine learning algorithms for irrigation scheduling management”. In: Computers
and Electronics in Agriculture 147 (Apr. 2018), pp. 109–117. (Visited on 10/10/2023).