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Citation: Brandmeier, M.; Heßdörfer,
D.; Siebenlist, P.; Meyer-Spelbrink, A.;
Kraus, A. Time Series Analysis of
Multisensor Data for Precision
Viticulture—Assessing Microscale
Variations in Plant Development with
Respect to Irrigation and Topography.
Remote Sens. 2024,16, 1419.
https://doi.org/10.3390/rs16081419
Academic Editor: Annamaria
Castrignanò
Received: 17 March 2024
Revised: 11 April 2024
Accepted: 12 April 2024
Published: 17 April 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
remote sensing
Article
Time Series Analysis of Multisensor Data for Precision
Viticulture—Assessing Microscale Variations in Plant
Development with Respect to Irrigation and Topography
Melanie Brandmeier 1,* , Daniel Heßdörfer 2, Philipp Siebenlist 1, Adrian Meyer-Spelbrink 1and Anja Kraus 1
1Faculty of Plastics Engineering and Surveying, Technical University of Applied Sciences
Würzburg-Schweinfurt (THWS), 97070 Würzburg, Germany; philipp.siebenlist@study.thws.de (P.S.);
adrian.meyer-spelbrink@study.thws.de (A.M.-S.); anja.kraus@study.thws.de (A.K.)
2Bavarian State Institute for Viticulture and Horticulture (LWG), 97209 Veitshöchheim, Germany;
daniel.hessdoerfer@lwg.bayern.de
*Correspondence: melanie.brandmeier@thws.de
Abstract: In the context of climate change, vineyard monitoring to better understand spatiotemporal
patterns of grapevine development is of utter importance for precision viticulture. We present a
time series analysis of hyperspectral in situ and multispectral UAV data for different irrigation
systems in Lower Franconia and correlate results with sensor data for soil moisture, temperature,
and precipitation. Analysis of Variance (ANOVA) and a Tukey’s HSD test were performed to see
whether Vegetation Indices (VIs) are significantly different with respect to irrigation systems as well
as topographic position in the vineyard. Correlation between in situ measurements and UAV data for
selected VIs is also investigated for upscaling analysis. We find significant differences with respect
to irrigation, as well as for topographic position for most of the VIs investigated, highlighting the
importance of adapted water management. Correlation between in situ and UAV data is significant
only for some indices (NDVI and CIRedEdge,
r2
of 0.33 and 0.49, respectively), while shallow soil
moisture patterns correlate well with in situ-derived VIs such as the CIRedEdge and RG index (
r2
of
0.34 and 0.46).
Keywords: irrigation systems; vegetation indices; UAVs; multispectral; hyperspectral; time series;
viticulture
1. Introduction
The cultivation of grapes is an important economic sector in global agriculture, as well
as a cultural legacy in many regions [
1
]. The German wine-growing region of Franconia is
known for the production of quality wine. The region in northern Bavaria has more than
6000 hectares of vineyards and a production of around 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 [
2
,
3
]. Water deficit produces diverse effects, such as reduced
berry size or the failure of fruit maturation, depending on the plant’s growth stage [
4
].
Severe water stress triggers partial or complete stomatal closure, resulting in a reduction
in photosynthetic activity [
5
]. Extended periods of excessive drought stress can lead to
vine losses in both the present and upcoming year and lead to bud break in the following
year. Thus, irrigation is an important measure for enhanced vine growth and productivity,
even though a slight hydric deficit is essential for a high quality of vine [
6
], as too much
water might have negative effects on factors such as excessive growth of the vines, low
sugar content, high acidity, and reduced pigment synthesis. Thus, ideal soil moisture is
key to sustainable viticulture and monitoring plant development; soil moisture and climate
variables are crucial for precision viticulture [7,8].
Remote Sens. 2024,16, 1419. https://doi.org/10.3390/rs16081419 https://www.mdpi.com/journal/remotesensing
Remote Sens. 2024,16, 1419 2 of 18
In this study, our objectives are as follows: (1) We evaluate multitemporal data derived
in situ hyperspectral plant measurements and UAV-based multispectral times series with
respect to different irrigation systems and topography. (2) Several Vegetation Indices (VIs)
are calculated, and a correlation between UAV data and in situ measurements are analyzed
together with data from soil moisture sensors and a climate station. (3) Patterns of soil
moisture at different depth levels are investigated and correlated with patterns of VIs over
time. As drought events are becoming more frequent due to climate change, investigating
spatial and temporal patterns of soil moisture and plant development with respect to
different irrigation strategies can contribute to improving water management in vineyards.
In the following, we first give a brief overview of remote sensing and sensor data for
precision viticulture and then proceed to the methods used in this study.
1.1. State-of-the-Art Remote Sensing for Precision Viticulture
Due to the typical trellis pattern of grapevines, satellite remote sensing using decameter
resolutions (such as Landsat or Sentinel-2 series) is not well-suited for plant monitoring, as
pixel information at this resolution consists of the spectral signatures of vines, as well as
of the space between plant rows that might be cover crops, other plants, or soil. There are
several studies comparing satellite imagery and spatially high-resolution UAV or airborne
data using different vegetation indices (e.g., [
9
,
10
]). Due to their flexibility and high spatial
resolution, UAVs show a high potential for agricultural monitoring tasks. The use of UAVs
is therefore a feasible tool for precision viticulture (PV) and has been studied in various
agricultural applications such as olive groves [
11
–
13
], almond plantations [
14
–
17
] , citrus
fruits [
14
,
18
,
19
], and as vineyards [
20
–
22
]. In viticulture, multi- and hypterspectral data
from UAVs (or other airborne data) are investigated for early detection of grape diseases,
such as Flavescene dorée [
23
], Phylloxera [
24
], leafroll disease [
25
],
Esca [26]
, and water
stress [22,27–29]
, as it offers a low-cost and highly flexible tool for monitoring. The basis
for many applications in agriculture and viticulture are Vegetation Indices (VIs) derived
from multi- or hypserspectral systems and are investigated with respect to structural
aspects of the grapevines, biochemical composition, physiological processes, and foliar
symptoms [29–31].
In the present study, we investigate the time series of multispectral
data derived from a UAV-based system with hyperspectral data from i -situ measurements
and derived vegetation indices, as well as sensor data, and evaluate results with respect to
different irrigation patterns. Such multisensor and multitemporal approaches contribute
to a better understanding of the vineyard as a dynamic system and, thus, lead to better
monitoring and management options.
1.2. Additional Sensor Systems for Precision Viticulture
With precision agriculture and, in particular, precision viticulture (PV) as evolving
fields in science, the use of sensor systems and wireless technology is increasingly investi-
gated for vineyards [
32
]. Sensor systems can be used for early detection of diseases and
pests, for aquiring temperature and precipitation data (climate stations), and for monitoring
soil moisture [
33
] and temperature. The latter can be performed using regular field mea-
surement approaches such as Time-Domain Reflectometry (TDR) or Frequency-Domain
Reflectometry (FDR), as in the present study with a Diviner 2000 system, or by using
low-cost wireless IoT devices, as in the study by [
32
]. Constant monitoring contributes to a
better understanding of plant development in changing conditions and the development of
low-cost monitoring approaches. Thus, correlating multisensor data is crucially important
for advancing methods in PV.
2. Materials and Methods
In this section, we first give details about the study area, the flight campaign, and
fieldwork, and subsequently, the analytical workflow applied on the collected data. The
flowchart in Figure 1shows the overall workflow of this study.
Remote Sens. 2024,16, 1419 3 of 18
Figure 1. Overall workflow of this study. For more information, refer to the text.
2.1. Study Area
The study area is located near Himmelstadt (Franconia, Germany; see Figure 2) at ca.
200 m above sea level with a southwest orientation. It is an experimental vineyard managed
by the Bavarian State Institute for Viticulture and Horticulture (LWG) and encompasses
approximately 1000 m
2
. Franconia has a humid cool temperate transitional climate with
average monthly temperatures varying depending on the area between about
−
1 and
−
2 °C
in January and 17 and 19 °C in August. The climate is generally sunny and relatively warm
and therefore well-suited for viticulture. The average annual rainfall in the study area is
about 425 mm. The soil’s grain size is described as loamy sands, and the underlying geology
is the Upper Muschelkalk formation. The cultivated grape variety is Müller-Thurgau,
grafted on SO4 rootstock that was planted in 2015 at a row spacing of 2 m and a vine spacing
of 1.3 m (equivalent to approximately 3850 vines per hectare). To investigate the impact of
drought stress on grapevines, the vineyard is divided into experimental plots with different
irrigation systems: (1) moderate irrigation, (2) intensive irrigation, and (3) no irrigation as
control plots. Moderate irrigation was started as required three weeks after flowering at an
predawn water potential of
−
0.25 megapascals. Within the intensive irrigation treatment,
it was ensured that the vines did not fall below the
−0.20 megapascals
predawn water
potential during the entire vegetation period. All water potential determinations were
carried out using a Scholander chamber [
34
]. The vines were irrigated using drip irrigation
with a flow rate of 1.6 liters per dripper and a weekly water application of 8 L per vine as
required. Additionally, we distinguish between upper
(ca. 211 m asl.)
, middle (ca 206 m
asl.), and lower parts (ca. 199 m asl.) of the vineyard to take into account the relief and
therefore waterflow of the area (compare Figure 2).
2.2. UAV Flight Campaign and In Situ Measurements
The flight campaign was conducted during the vegetation period of 2023 using a DJI
M300 RTK (DJI, Shenzhen, China) equipped with an Agrowing Alpha 7Riv multispectral
system (Agrowing Ltd., Rishon LeZion, Israel) with 10 bands in the visible and near-
infrared part of the electromagnetic spectrum. The wavelengths are centered at 405 nm,
430 nm,
450 nm
, 550 nm, 560 nm, 570 nm, 650 nm, 685 nm, 710 nm, and 850 nm with a
spectral bandwidth of 30 nm. The average ground sampling distance at a flight altitude of
100 m
is 1.5 cm. All flights were conducted at an elevation of 50 m with longitudinal and
transverse overlaps of 90% and 80%. The resulting spatial resolution was 2.38 cm, and the
total area covered 19,183 m
2
. As GCPs, 12 checkerboard targets were placed in the vineyard.
Calibration panels were used after takeoff for spectral calibration of the sensor system
of the UAV. Our goal was to conduct one flight in fair weather conditions around noon
each week to minimize the effect of shadows. Table 1shows the date, weather conditions,
and related in situ spectrometer measurement campaigns. Especially in July/August,
weather conditions in the study area were quite rainy, and mixtures of sunshine and
clouds dominated for weeks. The two flights in September were conducted after the grape
Remote Sens. 2024,16, 1419 4 of 18
harvest to examine plant reaction after harvest and without application of pesticides. In
situ hyperspectral measurements were conducted using a Spectral Evolution PSR+ 3500
spectroradiometer (Spectral Evolution, Haverhill, MA, USA) equipped with a contact
probe that was tailored to work as leafclip. The instrument measures the electromagenetic
spectrum from
350 nm
to 2500 nm at a spectral resolution of 1 nm. A spectralon white
reference was used for calibration. We took measurements on 44 individual plants (Figure 2),
representative for all irrigation types and the topographic location within the vineyard.
Each measurement consisted of averaging five individual measurements (setup of the
spectroradiometer), as well as averaging five separate measurements on a plant level.
Table 1. Flight campaign, weather conditions, and field measurements. Note that due to weather
conditions, plant development, and logistics, timing of measurements has some shift. Refer to text
for more information.
Flight Campaign Weather Conditions Spectrometer Measurement
31 May 2023 Sunny 23 May 2023
none Sunny 31 May 2023
06 June 2023 Slightly overcast 6 June 2023
none Slightly overcast 13 June 2023
20 June 2023 Cloudy 20 June 2023
28 June 2023 Mostly sunny/scattered clouds 28 June 2023
6 July 2023 Mostly sunny/scattered clouds 6 July 2023
13 July 2023 Cloudy 14 July 2023
20 July 2023 Cloudy 18 July 2023
26 July 2023 Cloudy 26 July 2023
none Sunny 2 August 2023
11 August 2023 Sunny 10 August 2023
18 August 2023 Sunny 16 August 2023
21 August 2023 Sunny 23 August 2023
none Cloudy 3 September 2023
7 September 2023 Sunny 7 September 2023
27 September 2023 Cloudy 14 September 2023
In addition to spectral measurements, soil moisture and climate variables (mean daily
temperature, precipitation) were collected from sensor data. Soil moisture measurements
were collected by the Bavarian State Institute for Viticulture and Horticulture (LWG) using
a Diviner 2000 portable soil moisture monitoring system (Sentek Technologies, Stepney,
Australia) designed to assess volumetric soil moisture by Frequency Domain Reflectometry
from eight PVC access tubes at different depth levels (max.
−
120 cm, depending on regolith
depth). All tubes are located in the non-irrigated parts of the vineyard to assess drought
stress. Measurements were collected 1–2 times per week.
Daily temperature and precipitation measurements were downloaded from the Bavar-
ian AgrarMeteology website (www.wetter-by.de (accessed on 7 July 2023)) from the climate
station located next to the vineyard (weather station WB-Himmelstadt).
Remote Sens. 2024,16, 1419 5 of 18
Figure 2. Study area near Himmelstadt, Bavaria. Different irrigation systems are shown in red, green,
and blue. Locations for hyperspectral measurements on plants are shown depending on their location
in the vineyard (yellow outline). Locations of tubes for soil moisture readings are shown in blue.
RGB of the flight on 13 July as basemap. The climate station is visible north of the vineyard.
2.3. Data Preprocessing and Calculation of Vegetation Indices
The multispectral UAV data were processed using the Agrowing Basic software
(Agrowing Ltd., Rishon LeZion, Israel) for calibration and tile export for each spectral band.
Pix4D Software (Pix4D S.A., Prilly, Switzerland) was then used to calculate orthophotos
for all spectral bands by applying bundle block adjustment and alignment of the images.
The accuracy of georeferencing of all bands was checked, comparing the coordinates of
the GCP panels showing high spatial accuracies with only slight vertical and horizontal
misplacement in the range of 0.3 cm to max. 2 cm.
Vegetation indices (see Table 2) were then calculated from (1) the UAV data (compare
Figure 3) and (2) from the in situ measurements of all 44 plants using python scripts to
automate the process. This was performed for all timestamps listed in Table 1.
Table 2. List of VIs used in this study.
Vegetation Index Equation Source
Normalized Difference Vegetation Index NDVI =N IR−Red
NI R+Red [35]
Green Normalized Difference Vegetation Index G NDV I =N I R−Green
NI R+Green [36]
R/G Index RG =Red
Green [37]
Normalized Green Red Difference Index NGRD I =Green+Red
Green−Red [38]
Chlorophyll Index Rededge CIRedEdge =N I R
RedEdge −1 [39]
Green Leaf Index GL I =(Green−Red)(Green+Blue)
2∗Green+Blue+Red [40]
Moisture Stress Index MSI =R1600
R820 [41]
Remote Sens. 2024,16, 1419 6 of 18
VIs from in situ data were calculated by using resampled bands for RGB, NIR, and
RedEdge bands to match the Sentinel 2 bands for better comparison with other studies and
to UAV spectral resolution for correlation analysis. For the CIRedEdge and the CIGreen
index, however, the narrow spectral bands defined by the indices were used. For the UAV
data, the following bands were used: G: 550, B: 450, R: 650, NIR: 850, Red edge: 710.
Figure 3. Example of one NDVI calculation (17 July 2023) showing the plant locations and zonal
statistics areas used for correlation with spectroradiometer measurements. As vines are aligned along
wires, it is important to automatically derive the maximum VI value locations (for some indices
minimum) and avoid sampling erroneous pixels (such as bare soil or stem areas), as can be seen in
the zoomed-in location.
2.4. Time Series Analysis of In Situ-Derived VIs with Respect to Different Irrigation Patterns
and Topography
To assess the differences in soil moisture and plant development during the course of
the year, nonirrigated parcels were compared with the two different irrigation systems, and
time series of the different vegetation indices calculated from in situ measurements were
analyzed. For each irrigation type, we calculate the mean and standard deviation. Next, we
calculate test statistics to see whether differences are significant using ANOVA analysis on
all samples from the respective classes (nonirrigated, moderate, and intensive irrigation),
followed by the Tukey’s HSD (Honestly Significant Difference) test. HSD is a post hoc test
commonly used after performing ANOVA to determine which specific groups differ from
each other significantly. The same was performed for different topographic classes (compare
Figure 3). For interpretation, climate information was also added to the data in order to
investigate differences and/or similarities between the parcels. A better understanding
of the impact of topology and amount of irrigation is crucial for better planning to save
resources in viticulture. We used python to automatically calculate and plot the indices for
the different irrigation classes (nonirrigated, moderate irrigation, and strong irrigation) and
topographical classes (upper, middle, and lower vineyard) to calculate statistical tests, as
well as to add temperature and precipitation data for comparison and interpretation. The
same workflow was also applied to UAV-derived indices for comparison. In the following
section, we describe the analytical workflow for statistical comparison.
Remote Sens. 2024,16, 1419 7 of 18
2.5. Correlation of UAV-Derived VIs and Hyperspectral In Situ Measurements
In situ spectral measurements provide highly accurate spectral information but are
not a practical way for vineyard monitoring. Thus, deriving accurate information from
UAV-based systems is very important for large-scale monitoring tasks. Due to variability
in atmospheric conditions, as well as leaf orientation and movement, correlating UAV or
even satellite-based measurements with in situ measurements is challenging. Sampling
good pixels that represent well-oriented and illuminated leaves is required for comparison
with in situ measurements. With respect to decameter satellite data, this is impossible, as
pixels always represent mixtures. The spectral resolution of our satellite and UAV-based
sensors is also different from the hyperspectral measurements. Thus, in order to compare
UAV-derived with in situ spectral measurements, spectral resampling of hyperspectral
data was conducted to match the spectral bands of the UAV data. Furthermore, as vines
are aligned along wires, and most of the area consists of other spectral classes such as soil
or grass, it is important to automatically derive the correct pixels of the vine leaves for
VI calculation. This was performed by spatial statistics (searching the neighborhood of
the central coordinate) in a GIS environment at the plant locations used for in situ data
collection (compare
Figure 3
). We derived the location of the maximum (minimum for the
RG index, CIRedEdge) VI values, indicating the leaf area of the plant. This was performed
for all images automatically using python scripts, including the calculation of different
VIs listed in Table 2for further evaluation. In situ measurements were then correlated
with UAV data in order to assess how well airborne data match highly accurate data from
leaf-level measurements. Note that data collection was not always on exactly the same date
and was sometimes delayed by a day or two.
2.6. Analysis of Soil Moisture Patterns During the Vegetation Period for Nonirrigated Plots and
Changes in VIs
Soil moisture data from different depth levels was available from the sensor system
as .csv files. We used python to automatically read the files for all measurement dates
and visualized the development of soil moisture at different depth levels with respect to
precipitation and vegetation indices from plants located at the measurement tube locations.
Linear correlations were calculated for the GNDVI, RG index, and CIRededge index for
shallow (30–40 cm) and deep levels (80–100 cm) to investigate whether there is a direct link
between plant development as seen in VIs and soil moisture levels.
3. Results
3.1. Time Series Analysis of In Situ-Derived VIs for Different Irrigation Patterns and Topography
Results are summarized in Figures 4–6. We first present results of in situ-derived
time series for selected VIs with respect to irrigation, then with respect to topography, and
finally, in a sense upscaling to UAV-derived results in Section 3.2. The time series of mean
values and standard deviation for selected VIs derived from hyperspectral measurements
for different irrigation patterns are shown in Figure 4.
The NDVI starts to increase during May with a decline during high temperatures at the
beginning of June. There are no significant differences with respect to irrigation. This is also
the case for the CIRededge and GNDVI indices. However, during the high-temperature
period in August, we see a significant drop for the nonirrigated class. For the RG, GLI
(Appendix A, additional graphs for Figure 4), and NGRDI indices, statistical analysis
shows significant differences for the whole vegetation period (see Figure 6: patterns of
lower (higher for the RG index)), and values for nonirrigated plants, especially in July and
August, are clearly visible. For all three indices, there are significant differences between
the irrigated classes and the nonirrigated class. There are no significant differences between
the two irrigation classes (compare Figure 6). The moisture stress index, on the contrary,
has lower values for irrigated plants after rainfall, though differences are not statistically
significant for the whole vegetation period (not shown in the table as this index was only
used once).
Remote Sens. 2024,16, 1419 8 of 18
Figure 4. Time series of (A) NDVI, (B) RG Index, (C) NGRDI, (D) MSI. NDVI and NGRDI show lower
values for nonirrigated plants from June through to September (NGRDI) and are more pronounced
in August for the NDVI. The RG index follows the same pattern with higher values for nonirrigated
plants. The moisture stress index, MSI, on the other hand, shows slightly higher values for irrigated
plants, especially after strong rainfall in August. Additional plots for VIs not shown here can be
found in Appendix A.
As topography and aspect are also important factors for soil moisture and plant
development, we also analyzed the time series of VIs with respect to location in the
vineyard (compare Figure 1). Results for four selected indices are shown in Figure 5
(additional plots for VIs not shown here can be found in Appendix A), and statistical
significance test results are shown in Figure 6.
For the RG, NGRDI, and GLI indices, we see a pronounced lower/higher index
through the whole vegetation period for the lower vineyard, indicating differences in
plant development with respect to topography. Statistical test results indicate significant
differences between all classes for the RG index and NGRDI index, as well as between the
middle vineyard and lower and middle classes for GLI and CIRedEdge indices, but only
for the CIRedEdge for lower and upper locations. This pattern is absent for the NDVI. The
middle part of the vineyard exhibits the best overall values for all indices, which is also
reflected in the test statistics. For the chlorophyll-sensitive indices, we observe a steady
increase until August, with a slight decrease in September.
Remote Sens. 2024,16, 1419 9 of 18
Legend:
Upper vineyard (mean, stdv)
Lower vineyard (mean, stdv)
Middle vineyard (mean, stdv)
Precipitation (mm)
Mean daiyly temperature (°C)
A B
CD
Figure 5. Time series of (A) NDVI, (B) CIRedEdge Index, (C) RG Index, (D) GLI index. For NDVI,
there are no significant differences with respect to topography, while the CIRedEdge index is less
favorable for the lower part of the vineyard. The RG index is lower for the lower part of the vineyard,
while the GLI is higher for most of the vegetation period. Additional plots for VIs not shown here
can be found in Appendix A.
Remote Sens. 2024,16, 1419 10 of 18
Index Group 1-2* Group 2-3* Group 3-1* overall p-value F-statistic
NDVI False False False 0.0940 2.3718
GNDVI False False False 0.1966 1.6302
RG index True False True 0.0000 13.5246
NGRDI True False True 0.0000 13.3013
CIRedEdge False False False 0.4022 0.9119
GLI True False True 0.0000 11.5334
NDVI False False False 0.7598 0.2748
GNDVI True False False 0.0032 5.7952
RG index True True True 0.0000 15.8611
NGRDI True True True 0.0000 15.3604
CIRedEdge True True False 0.0011 6.9182
GLI True False True 0.0000 12.5110
NDVI True False True 0.0002 8.7795
GNDVI True False True 0.0018 6.3664
RG index True False True 0.0022 6.1714
NGRDI True False True 0.0108 4.5611
CIRedEdge True False False 0.0001 9.1933
GLI True False False 0.0068 5.0349
NDVI False False False 0.0740 2.6184
GNDVI False True False 0.0250 3.7178
RG index False True True 0.0003 8.2937
NGRDI False True True 0.0002 8.4804
CIRedEdge False False False 0.8789 0.1291
GLI False False False 0.4771 0.7412
*Groups Irrigation: 1: non-irrigated; 2: moderat irrigation; 3: intense irrigation
*Groups Topography: 1: lower vineyard; 2: middle vineyard; 3: upper vineyard
Spectrometer
UAV
Irrigation
Topography
Multiple Comparison of Means - Tukey HSD
Irrigation
Topography
Figure 6. Matrix showing results of the Tukey HSD test after performing ANOVA analysis for all
indices with respect to topographic and irrigation classes. Test results are shown for in situ data as
well as UAV-derived indices. Significant test results are highlighted in green.
3.2. Correlation of UAV-Derived VIs and Hyperspectral In Situ Measurements
Time series for UAV-derived data are shown in Figure 7(additional plots for VIs
not shown here can be found in Appendix A), and statistical test results with respect to
irrigation and topography are summarized in Figure 6. Interestingly, for all indices, we see
significant differences between irrigated classes and the nonirrigated class, while this was
only partially the case for the in situ measurements. On the other hand, with respect to
topographic position, we only see differences between the middle and upper and lower
and upper positions for the RG index and the NGRDI, while in situ data were significant
for all indices (except for the NDVI) with respect to lower and middle positions, in addition
to the results from UAV data.
Correlation analysis for the NDVI, GNDVI, RG index, and GLI index resulted in a
positive correlation with an
r2
of 0.49 and 0.33 for CIRedEdge and NDVI, respectively
(compare Figure 8). The other investigated indices showed no significant correlation.
This observation indicates that indices that include NIR information are better suited for
correlation, while indices only based on the red and green part of the spectrum do not
exhibit correlation.
Remote Sens. 2024,16, 1419 11 of 18
Legend:
Plot with no irrigation (mean, stdv)
Plot with intense irrigation (mean, stdv)
Plot with moderate irrigation (mean, stdv)
Precipitation (mm)
Mean daiyly temperature (°C)
CD
B
A
Figure 7. Time series of (A) NDVI, (B) RG index, (C) NGRD index, (D) GNDVI index. Significant
differences for nonirrigated plots are clearly pronounced for all indices shown. The steep drop (NDVI,
NGRDI, GNDVI) at the end of September is not clearly visible in the spectrometer data, as the last
measurement took place on the 14th of September. Additional plots for VIs not shown here can be
found in Appendix A.
B
A
Figure 8. Correlation analysis of in situ data with UAV-derived VIs. (A) CIRedEdge, (B) NDVI.
3.3. Soil Moisture Patterns During the Vegetation Period for Nonirrigated Plots and Changes in VIs
Mean values and standard deviation of soil moisture at different depth levels are
shown in Figure 9. While shallow soil levels react dynamically to rainfall events and show
Remote Sens. 2024,16, 1419 12 of 18
high fluctuations, depth levels greater than 80–90 cm are more stable until prolonged
rainfall in July/August. During the whole vegetation period, we did not observe prolonged
drought, only a relatively dry period during May and the beginning of June. In
Figure 9B
,
we show the GNDVI mean value and standard deviation for the plants located at the
measurement tubes. Patterns for NDVI, CIRedEdge, and RG index are similar and not
shown. After a drop in values during rainy weather, we observe an increase, and at the end
of July, a steep increase in GNDVI values. The
r2
for GNDVI is 0.27 when correlating the
respective values (soil moisture at 20 to 40 cm), for CIRededge 0.34, and for the RG index
an even 0.46, though some measurement dates do not match perfectly. At deeper levels,
this correlation is not visible. Whether correlation is likely to be causation will be discussed
in the following section.
Mean
Rainfall
Soil moisture [vol. %]
GNDVI
A
B
Figure 9. (A) Mean soil moisture at different depth levels over the vegetation period. Stdv. is plotted
in shaded colors. Rainfall events are shown in blue. We observe high fluctuations in shallow soil
levels, while deeper levels mainly react to prolonged rainfall in July to August. (B) GNDVI mean
values for plants located at soil moisture measurement tubes. Stdv. is shown in shaded colors. We
observe a steep rise in GNDVI values after prolonged rainfall at the end of June and again in August.
4. Discussion
In this section, we first discuss results from the time series analysis with respect to
irrigation systems and topographic position and then from correlating airborne data with
in situ measurements. We finish with a comparison with other studies and an outlook for
further investigations.
Remote Sens. 2024,16, 1419 13 of 18
4.1. What Do We Learn from Time Series of VIs with Respect to Irrigation, Topography, and
Climate—And What Remains to Debate?
The analysis of the time series of VIs with respect to irrigation showed a high potential
of using VIs with information form the RedEdge region of the electromagnetic spectrum to
monitor plant development. We see statistically significant differences between irrigated
and nonirrigated patches for all investigated indices in UAV data and GLI, NGRDI, and RG
index for in situ data. UAV data proved to be more suitable for distinguishing these groups,
which might be due to our statistical approach of sampling the best pixel values from the
plants. The UAV only sees the top leaves, while in situ measurements take place in lower
plant levels, which might be less affected by lack of water. We also see that differences
become more pronounced during prolonged dry and warm weather in June/July and late
August for the RG, NGRDI, and CIRedEdge indices, making these indices suitable for
monitoring potential drought stress. As water uptake from vines can be from rather deep
levels, soil moisture development during 2023 was not critical with respect to drought, as
only shallow levels show significant fluctuations during warm periods in May/June. These
results indicate that UAV-based vineyard monitoring based on VIs could be leveraged for
adapted irrigation. The correlation between VIs and shallow-level soil moisture levels
shown in Section 3.3 might not be causal with respect to plant development (the vines show
steady development over the vegetation period with some boost after rainfall paired with
warm weather conditions) but might be related to the effect of rain on the surface of the
leaves. Our analysis of the effect of the topographic position within the vineyard showed
significant differences mostly between the lower part of the vineyard and the middle or
upper part for in situ-derived VIs and middle and upper parts for UAV data (compare
Figure 6). These results indicate that adapted treatment/irrigation of plants according to
location in the vineyard might be a tool to save water.
4.2. Scaling Up from In Situ to UAV—A Critical Assessment
Upscaling from in situ measurements to UAV data or even satellite sensors is a
challenge in remote sensing. This is due to different spatial, spectral, and—for satellite
systems—temporal resolutions, as well as atmospheric interference. To find best possible
correlations, we applied spatial statistics to extract good pixel values from the same plants
that were sampled in the field and resampled hyperspectral data to the UAV spectral reso-
lution prior to calculating VIs. Our findings show a significant correlation for CIRededge,
as well as for NDVI, but none for the other investigated indices. There are several reasons
for mismatching observations: (1) sometimes measurements were taken with a temporal
delay of one or two days, (2) the UAV sensor gets information of the top leaves while in situ
measurements are taken from side leaves that might have slightly different characteristics
due to different exposure to the sun, and (3) atmospheric conditions were not always ideal,
with some intervening clouds (compare Table 1) that altered the UAV-derived data. Still,
the correlation for two of the investigated indices is significant and indicates that the sensor
used for this study is well-suited for monitoring tasks.
4.3. Comparison with Other Works
The use of VIs for detecting drought or other plant stress is widely investigated for in
situ hyperspectral data, as well as from UAVs and satellites, as discussed in
Section 1.1 [6]
investigated VIs derived from hyperspectral measurements and stem water potential in
detail for test sites in France and concluded that the main spectral domains for water
deficiency are in the SWIR, NIR, and Red-Edge region of the electromagnetic spectrum.
This agrees with our findings for VIs that include NIR and Red Edge bands showing
significant differences with respect to irrigation systems for in situ but also for UAV data,
even though in our study the RG index that only uses the red and green bands also showed
significant differences with respect to irrigation and topography. Ref. [
42
] successfully
used airborne NDVI information for correlation with vine size, anthocyanins, and color,
as well as soil moisture, with similar results for the latter, as presented in this study
Remote Sens. 2024,16, 1419 14 of 18
(
r2
of
−
0.89). For Australia, Gautam et al. [
29
] showed the use of multispectral UAV
data for assessing grapevine crop coefficients by correlating in situ data with VIs. The
authors also use the NDVI and an RG indices, among others, and show high correlation
until véraison time point; otherwise, structural features were needed for good models.
These findings agree somewhat with our findings of spectral indices from UAVs being a
suitable, though somewhat limited, tool for optimizing irrigation within vineyards. With
respect to the discrepancies between UAV and in situ-derived VIs, other authors also found
similar problems (e.g., [
43
–
45
]) that might be related to the sensor, distance, or atmospheric
influence, as well as different leaves sampled. A positive correlation between NDVI and
soil moisture was also reported by [
46
], even though on a very different scale, between
MODIS/TERRA satellite data. Pagay and Kidman (2019) [
27
] used several spectral indices
calculated from thermal infrared for assessing vine water status and showed the potential of
several indices, such as CWIS on a regional scale. Combining multispectral data, as shown
in the present study, with data from a thermal sensor might, thus, be an interesting future
line of research with respect to quantitatively assessing water stress response in grapevines.
5. Conclusions and Outlook
The presented results highlight the potential of multispectral UAV data for monitoring
tasks for precision viticulture and thus the possibility of replacing time-consuming field
measurements for plant water supply. In our controlled environment with different irriga-
tion systems and soil moisture sensors, we were able to show significant differences in VIs
over the vegetation period with respect to (1) irrigation and (2) topography. Further inves-
tigation is needed to find, for example, critical thresholds in VIs with respect to drought
or water stress that was not observed in the wet vegetation period in 2023. Our findings
show the potential for adapted irrigation strategies based on detailed knowledge of spa-
tiotemporal variability that deliver just the right amount of water to each plant at the right
time or stage of development. In our study, there were no significant differences between
moderate and intense irrigation, indicating that water could be saved by monitoring plant
health and adapting irrigation accordingly. As phases of prolonged heat will become more
frequent due to climate change, such strategies are of utter importance for regions with
water scarcity.
Author Contributions: Conceptualization, M.B. and D.H.; methodology, M.B., A.M.-S., P.S. and A.K.;
formal analysis, M.B., A.M.-S., P.S. and A.K.; investigation, M.B. and D.H.; resources, M.B. and D.H.;
data curation, M.B. and D.H.; writing—original draft preparation, M.B.; writing—review and editing,
M.B., P.S. and D.H.; supervision, M.B. and D.H.; project administration, M.B.; funding acquisition,
M.B. All authors have read and agreed to the published version of the manuscript.
Funding: Supported by the publication fund of the Technical University of Applied Sciences
Würzburg-Schweinfurt
Data Availability Statement: Data are not provided due to ongoing research.
Acknowledgments: We want to thank the IHK Würzburg-Schweinfurt for awarding this research
with the “TH Advancement Award of the Mainfranken Economy”, and, above all, Wolfgang Probst
from the AllTerra Deutschland GmbH for providing the UAV used in the project.
Conflicts of Interest: The authors declare no conflicts of interest.
Remote Sens. 2024,16, 1419 15 of 18
Abbreviations
The following abbreviations are used in this manuscript:
VI Vegetation Index
UAV Unmanned Aereal Vehicle
NDVI Normalized Difference Vegetation Index
GNDVI Green Normalized Difference Vegetation Index
RE index Red/Green Index
CIRedEdge Chlorophyll Index Rededge
GLI Green Leaf Index
MSI Moisture Stress Index
HSD Honesty Significant Difference
ANOVA Analysis of Variance
Appendix A
Fig. 1: Additional plots for Fig. 4 in the paper: Timeseries of spectrometer data with respect to irrigation
systems for GLI and CIRedEdge.
Figure A1. Additional plots for Figure 4in the paper: Time series of spectrometer data with respect
to irrigation systems for GLI and CIRedEdge.
Remote Sens. 2024,16, 1419 16 of 18
Fig. 2: Additional plots for Fig. 5 in the paper: Timeseries of spectrometer data with respect to
topography for MSI and NGRDI.
Figure A2. Additional plots for Figure 5in the paper: Time series of spectrometer data with respect
to topography for MSI and NGRDI.
Fig. 3: Additional plot for Fig. 6 in the paper: Timeseries of UAV data with respect to irrigation for the
CIRedEdge (MSI not calculated).
Figure A3. Additional plot for Figure 7in the paper: Time series of UAV data with respect to irrigation
for the CIRedEdge (MSI not calculated).
Remote Sens. 2024,16, 1419 17 of 18
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