Article

Soil Moisture Spatial Variability and Water Conditions of

Coffee Plantation
Sthéfany Airane dos Santos Silva 1 , Gabriel Araújo e Silva Ferraz 1, * , Vanessa Castro Figueiredo 2 ,
Gislayne Farias Valente 1 , Margarete Marin Lordelo Volpato 2 and Marley Lamounier Machado 2

1 Department of Agricultural Engineering, School of Engineering, Federal University of Lavras (UFLA),

Lavras 37200-900, Brazil; sthefany.santos1@estudante.ufla.br (S.A.d.S.S.); gislayne.valente@ufla.br (G.F.V.)
2 Agricultural Research Company of Minas Gerais, Belo Horizonte 31170-495, Brazil;
vcfigueiredo@epamig.br (V.C.F.); margarete@epamig.br (M.M.L.V.); marley@epamig.br (M.L.M.)
* Correspondence: gabriel.ferraz@ufla.br

Abstract: Remotely piloted aircraft (RPA) are essential in precision coffee farming due to
their capability for continuous monitoring, rapid data acquisition, operational flexibility
at various altitudes and resolutions, and adaptability to diverse terrain conditions. This
study evaluated the soil water conditions in a coffee plantation using remotely piloted
aircraft to obtain multispectral images and vegetation indices. Fifteen vegetation indices
were chosen to evaluate the vigor, water stress, and health of the crop. Soil samples
were collected to measure gravimetric and volumetric moisture at depths of 0–10 cm and
10–20 cm. Data were collected at thirty georeferenced sampling points within a 1.2 ha area
using GNSS RTK during the dry season (August 2020) and the rainy season (January 2021).
The highest correlation (51.57%) was observed between the green spectral band and the
0–10 cm volumetric moisture in the dry season. Geostatistical analysis was applied to
map the spatial variability of soil moisture, and the correlation between vegetation indices
and soil moisture was evaluated. The results revealed a strong spatial dependence of
soil moisture and significant correlations between vegetation indices and soil moisture,
highlighting the effectiveness of RPA and geostatistics in assessing water conditions in
coffee plantations. In addition to soil moisture, vegetation indices provided information
Academic Editors: Simone Pascuzzi about plant vigor, water stress, and general crop health.
and Francesco Marinello

Received: 26 December 2024 Keywords: precision coffee farming; kriging interpolation; water status monitoring
Revised: 20 March 2025
Accepted: 31 March 2025
Published: 8 April 2025

Citation: Airane dos Santos Silva, S.; 1. Introduction

Ferraz, G.A.e.S.; Figueiredo, V.C.;
Coffee is one of the most widely consumed beverages worldwide. Brazil is the largest
Valente, G.F.; Volpato, M.M.L.;
Machado, M.L. Soil Moisture Spatial
global producer and exporter and the second largest consumer [1]. The estimate from
Variability and Water Conditions of Conab [2] for the Brazilian coffee harvest in 2025 is 51.8 million bags, representing a 4.4%
Coffee Plantation. AgriEngineering reduction compared with 2024. This decline is attributed to adverse climatic conditions,
2025, 7, 110. https://doi.org/ such as water scarcity and high temperatures, which affected flowering and consequently
10.3390/agriengineering7040110
reduced productivity. Compared with the 2023 harvest, also a low biennial year, the drop is
Copyright: © 2025 by the authors. 5.9%. The total area allocated to coffee cultivation will grow by 0.5%, totaling 2.25 million
Licensee MDPI, Basel, Switzerland. hectares. However, the area in production will decrease by 1.5%, reaching 1.85 million
This article is an open access article
hectares, while the fields in formation will increase by 10.7%, reaching 391.46 thousand
distributed under the terms and
hectares [2].
conditions of the Creative Commons
Attribution (CC BY) license
Coffee production is highly sensitive to climatic variations, which directly affect
(https://creativecommons.org/ its productivity and quality [3–5]. Between 2021 and 2024, South American production
licenses/by/4.0/). fell by 7.6%, the largest reduction since 2004/05, according to the International Coffee

AgriEngineering 2025, 7, 110 https://doi.org/10.3390/agriengineering7040110

AgriEngineering 2025, 7, 110 2 of 22

Organization (ICO) [6]. In Minas Gerais, the main state producing arabica coffee, the
scarcity and irregularity of rainfall have compromised biennial productivity. According
to Conab [2], the 2025 harvest was estimated at 34.68 million bags, a reduction of 12.4%
compared with the previous year. Productivity also dropped by 11.0%, reaching 23.4 bags
per hectare.
Given this scenario, soil attributes, such as moisture, are fundamental for coffee culti-
vation, directly influencing growth and productivity [7]. Studies on the effects of irrigation
on coffee production and development indicate that water application can either accelerate
or delay fruit maturation [8,9]. Beverage quality is associated with uniform maturation,
which depends on flowering synchronization and proper irrigation management, avoiding
water stress during critical grain formation phases [10]. Soil water scarcity can reduce the
number of reproductive nodes, compromising flower and fruit formation.
However, water movement in the soil is heterogeneous and influenced by factors such
as climate, soil type, topography, and vegetation cover, resulting in significant spatial and
temporal variability [11,12]. Studies by [13] demonstrate that creating soil moisture distribu-
tion maps using interpolation techniques allows for more efficient irrigation management,
increasing productivity and reducing waste. Additionally, according to [14], spatial and
temporal variations in soil moisture can be integrated into precision irrigation management.
A comprehensive understanding of the spatial and temporal variability of soil moisture
is crucial for optimizing water management in coffee cultivation. To quantify coffee
plants’ water needs, adopting precision agriculture (PA) technologies has proven to be an
effective strategy. The application of precision agriculture (PA) techniques and tools in
coffee cultivation remains relatively recent, particularly in the assessment of soil and plant
heterogeneity [15]. Studies such as those by [16], which analyzed the spatial variability of
soil moisture through gravimetric moisture, and studies by [15], which investigated the
spatial variability of soil fertility and productivity in coffee crops, exemplify this application.
Remote sensing and remotely piloted aircraft (RPA) have been extensively utilized
to evaluate soil and plant attribute variability, offering advantages such as ease of data
acquisition, enhanced operational flexibility, and greater reliability compared with tradi-
tional methods [17,18]. These technologies employ reflectance spectroscopy-based sensors
to measure electromagnetic radiation reflected after interacting with various surfaces at
specific wavelengths, such as near-infrared and mid-infrared [19]. This reflection gener-
ates spectral bands, allowing the extraction of vegetation indices, which are essential for
agricultural monitoring [20,21].
In agricultural fields, each surface uniquely reflects electromagnetic radiation, present-
ing a specific spectral signature. Water and soil have reflectance peaks in the near-infrared
wavelength, while vegetation emits radiation in this range and absorbs radiation in the
visible spectrum band. In the mid-infrared wavelength, spectral behavior is influenced by
water presence in the leaves [22]. Plant chlorophylls exhibit absorption peaks in the blue
and red regions of the spectrum, making the red spectral band a key component of remote
sensing applications This facilitates the application of indices such as NDVI (normalized
difference vegetation index), described by Rouse et al. (1973) [23], and the CIrededge index,
described by Gitelson [24]. Additionally, the normalized difference water index (NDWI),
introduced by McFeeters [25], is extensively utilized for soil moisture estimation.
Numerous studies emphasize the application of vegetation indices in coffee cultiva-
tion [26,27]. Despite the large volume of research on using multispectral images obtained
by RPA in coffee plantations, only [28] has evaluated plant water conditions through the
leaf water potential attribute.
AgriEngineering 2025, 7, x FOR PEER REVIEW 3 of 23

AgriEngineering 2025, 7, 110

by RPA in coffee plantations, only [28] has evaluated plant water conditions through 3 ofthe
22

leaf water potential attribute.

Thus, this study aims to evaluate the spatial variability of soil moisture and explore
Thus, this study aims to evaluate the spatial variability of soil moisture and explore
possible correlations between this attribute and vegetation indices obtained from high-
possible correlations between this attribute and vegetation indices obtained from high-
resolution images to assess the water status of coffee plants.
resolution images to assess the water status of coffee plants.

2. Materials
2. and Methods
Materials and Methods
The research workflow (Figure 1) was divided into four stages. The first stage in-
The research workflow (Figure 1) was divided into four stages. The first stage involved
volved the construction of the sampling mesh and the georeferencing of the area and col-
the construction of the sampling mesh and the georeferencing of the area and collection
lection points. The second stage involved collecting soil samples from georeferenced
points. The second stage involved collecting soil samples from georeferenced plants to
plants to determine soil moisture (gravimetric and volumetric moisture). The third stage
determine soil moisture (gravimetric and volumetric moisture). The third stage involved
involved conducting
conducting a flight
a flight with with a multispectral
a multispectral sensor mounted
sensor mounted on the on theThe
RPA. RPA. Thestage
final final
stage involved
involved carrying
carrying out correlation
out correlation and regression
and linear linear regression
analysisanalysis
betweenbetween soil mois-
soil moisture and
ture and vegetation indices calculated using multispectral
vegetation indices calculated using multispectral bands. bands.

Figure 1.
Figure 1. Work
Work sequence.
sequence.

2.1.
2.1. Crop
Crop Characterization
Characterization
The
The study
study site
site (Figure
(Figure 2)2) consisted
consisted of
of aa 1.2
1.2 ha
ha coffee
coffee plantation
plantation of
of the
the species
species Coffea
Coffea
arabica,
arabica, specifically the Topázio MG1190 cultivar. The spacing between coffee rows was
specifically the Topázio MG1190 cultivar. The spacing between coffee rows was
3.70
3.70 m
m and
andbetween
betweenplants
plants0.70
0.70m.m.The
Thearea is is
area located in in
located thethe
municipality
municipalityof Três Pontas,
of Três the
Pontas,
southern region
the southern of the
region of state of Minas
the state Gerais,
of Minas at 905
Gerais, at m
905altitude and with
m altitude UTMUTM
and with coordinates
coordi-
S7640030.4 and E449531.5, zone 23K.
nates S7640030.4 and E449531.5, zone 23K.
According to the Köppen–Geiger climate classification, the region has a Cwb climate.
According to the Köppen–Geiger climate classification, the region has a Cwb climate.
This climate type is characterized as a high-altitude temperate or tropical highland climate,
This climate type is characterized as a high-altitude temperate or tropical highland cli-
with dry winters (temperatures below 18 ◦ C) and mild summers (temperatures below
mate, with dry winters (temperatures below 18 °C) and mild summers (temperatures be-
23 ◦ C). It generally occurs in regions with altitudes above 900 m. Therefore, the Cwb
low 23 °C). It generally occurs in regions with altitudes above 900 m. Therefore, the Cwb
classification reflects climatic conditions marked by wetter summers and dry winters,
classification reflects climatic conditions marked by wetter summers and dry winters, typ-
typical of higher altitude areas in Brazil.
ical of higher altitude areas in Brazil.
The same study area was evaluated in the works of [15,29]. The authors also aimed
to analyze the correlation between vegetation indices and attributes related to coffee trees.
However, none of these studies explored the application of soil moisture in assessing the
water status of coffee plants.
AgriEngineering 2025, 7,
AgriEngineering2025, 7, x110
FOR PEER REVIEW 4 of 23
4 of 22

Figure 2. Location of the study area.

The same study area was evaluated in the works of [29,15]. The authors also aimed
to analyze the correlation between vegetation indices and attributes related to coffee trees.
However, none of these studies explored the application of soil moisture in assessing the
water status of coffee plants.

2.2. Georeferencing and Sampling

Using QGIS software version 3.4.8, a sampling grid was created with 30 points (Fig-
ure 3) at a density of 25 points per hectare. The area and sampling points were subse-
quently georeferenced using GNSS RTK. The sampling neighborhood affects interpola-
tion accuracy and should be considered when developing maps, as highlighted by [30].
Figure2.2.Location
Figure Locationofofthe
thestudy
study area.
area.
Soil moisture, being an attribute susceptible to changes due to anthropogenic activities,
requiresThea same
sampling
2.2. Georeferencing
studydensity
and Sampling
area was thatevaluated
adequately represents
in the works ofthe variability
[29,15]. of the soil
The authors alsoproperty.
aimed
However,UsingtheQGISfeasibility
softwareofversion
adopting a
3.4.8, high sampling
a sampling griddensity across
was created the
with
to analyze the correlation between vegetation indices and attributes related to coffee trees. entire
30 pointsarea was
limited,
(Figure
However, as none
3) the sample
at a
of thesecollection
density of 25 points processthe
per
studies explored
hectare. is labor-intensive
The area and and
sampling
application of soil laboratory
points
moisture
were analysestheare
subse-
in assessing
quently georeferenced using GNSS RTK. The sampling neighborhood affects interpolation
costly.
water status of coffee plants.
accuracy and should be considered when developing maps, as highlighted by [30]. Soil
The equidistant sampling grid methodology for precision coffee farming, as pro-
moisture, being an attribute susceptible to changes due to anthropogenic activities, requires
2.2.
posed Georeferencing
by [30], was and Sampling
employed in the construction of the sampling grid. This method aimed
a sampling density that adequately represents the variability of the soil property. However,
totheoptimize
feasibilitymovements
Using QGIS software
of adopting awithin
version
high the study
3.4.8,
sampling area
a sampling
density by grid
across defining walking
was created
the entire routes.
area waswith asInthe
30 points
limited, total,
(Fig-30
points were
ure 3) at
sample sampled,
a density
collection with distances
of 25ispoints
process ranging
per hectare.
labor-intensive from 6 m
The area and
and laboratory (minimum)
sampling
analyses to 175 m (maximum).
points were subse-
are costly.
quently georeferenced using GNSS RTK. The sampling neighborhood affects interpola-
tion accuracy and should be considered when developing maps, as highlighted by [30].
Soil moisture, being an attribute susceptible to changes due to anthropogenic activities,
requires a sampling density that adequately represents the variability of the soil property.
However, the feasibility of adopting a high sampling density across the entire area was
limited, as the sample collection process is labor-intensive and laboratory analyses are
costly.
The equidistant sampling grid methodology for precision coffee farming, as pro-
posed by [30], was employed in the construction of the sampling grid. This method aimed
to optimize movements within the study area by defining walking routes. In total, 30
points were sampled, with distances ranging from 6 m (minimum) to 175 m (maximum).

Figure 3. Sampling grid for the collection of 30 points from this study.

The equidistant sampling grid methodology for precision coffee farming, as proposed
by [30], was employed in the construction of the sampling grid. This method aimed to
optimize movements within the study area by defining walking routes. In total, 30 points
were sampled, with distances ranging from 6 m (minimum) to 175 m (maximum).
 In this experiment, each of the 30 sampling points corresponded to an individual
plant, which was numerically identified. To assess the water status of the coffee trees, two
AgriEngineering 2025, 7, 110
sampling campaigns were conducted:
5 of 22
• August 2020 (dry season).
• January 2021 (rainy season).
In this experiment, each of the 30 sampling points corresponded to an individual
Table 1 presents climatological data for August 2020 and January 2021, correspond-
plant, which was numerically identified. To assess the water status of the coffee trees,
ing to the sampling period, along with water balance information. The table was con-
two sampling campaigns were conducted:
structed based on data from the National Institute of Meteorology (INMET is the acronym
• August 2020 (dry season).
in Portuguese) and the Phytosanitary Bulletins of the Procafé Foundation. Table 2 repre-
• January 2021 (rainy season).
sents the altitude range among the sampling points and the soil properties of the study
area. Table 1 presents climatological data for August 2020 and January 2021, corresponding
to the sampling period, along with water balance information. The table was constructed
based1.on
Table data from
Climatic the National
conditions and waterInstitute
balanceof Meteorology
during the dry and(INMET is the acronym in
rainy seasons.
Portuguese) and the Phytosanitary Bulletins of the Procafé Foundation. Table 2 represents
theMonthly
altitude range Monthly Accumu-
Monthly Mean Mean among
Rel- the sampling points and Wind
Mean the soil properties of the study area.
Season lated Precipitation Water Balance
Temperature (°C) ative Moisture (%) Speed (m/s)
(mm)balance during the dry and rainy seasons.
Table 1. Climatic conditions and water
PET SWS EXC WD
Monthly Monthly Monthly
Dry (Aug/2020) 18.3*
Mean 59.3*
Mean 17.6*
Accumulated 2.0*
Mean Wind 53.8* 0.0* 0.0* 94.1*
Season Water Balance
Rainy (Jan/2021) 23.0*
Temperature 70.9*
Relative 270.6*
Precipitation 1.7*
Speed (m/s) 110.0* 39.9* 212.9* 0.0*
(◦ C) Moisture (%) (mm)
PET—potential evapotranspiration, SWS—soil water storage, EXC—excess water and WD—water
PET SWS EXC WD
Dry (Aug/2020) 18.3 * deficit. * Source:
59.3 * INMET (2020,17.6
2021);
* Foundation PROCAFE
2.0 * (2020,
53.82021).
* 0.0 * 0.0 * 94.1 *
Rainy (Jan/2021) 23.0 * 70.9 * 270.6 * 1.7 * 110.0 * 39.9 * 212.9 * 0.0 *
Table 2. Altitude
PET—potential and soil properties.
evapotranspiration, SWS—soil water storage, EXC—excess water and WD—water deficit.
* Source: INMET (2020, 2021); Foundation PROCAFE (2020, 2021).
Parameter Value
Altitude range (m)
Table 2. Altitude and soil properties. 917–935
Elevation variation (m) 18
Parameter Value
Soil texture clayey
Altitude range (m) 917–935
Clay (%) 36–38
Elevation variation (m) 18
Silt
Soil(%)texture 32–33 clayey
Sand
Clay(%) (%) 29–32 36–38
Silt (%)
Organic matter (%) 2.08–3.38 32–33
Sand (%) 29–32
PhOrganic
(KCl) matter (%) 6.23–8.11 2.08–3.38
Cation
Ph (KCl)exchange capacity (cmol/dm3) 6.0 6.23–8.11
Base saturation
Cation (%) capacity (cmol/dm3 )
exchange 69.56–74.16 6.0
Base saturation (%) 69.56–74.16
Figure 4 illustrates soil water storage levels, highlighting different absorption and
waterFigure
deficit4 zones for plants.
illustrates soil water storage levels, highlighting different absorption and
water deficit zones for plants.

Figure 4. Soil water storage from water balance. Source: adapted from FUNDAÇÃO PROCAFÉ
(2020, 2021). Own translation.
AgriEngineering 2025, 7, 110 6 of 22

2.3. Obtaining Soil Moisture

Soil samples were collected from 30 sampling points (Figure 3). At each point, samples
were taken at depths of 0–10 cm and 10–20 cm. The samples were removed from the
soil using volumetric rings (metal rings with a known volume), which were penetrated
into the soil with the aid of an auger. These samples were properly labeled, wrapped in
plastic film, and stored in a thermal box to prevent moisture loss before being sent to a soil
analysis laboratory.
In the laboratory, following the NBR 6457/2016 [31] protocol, samples were weighed
on a precision scale to determine wet mass, then placed in an oven at 105 ◦ C for 24 h.
After this period, the samples were weighed again. After determining the dry mass, it
was possible to obtain the values of gravimetric moisture (GM) and soil density (SD) using
Equations (1) and (2), respectively.

moist soil − dry soil

GM g/g = (1)
dry soil

dry soil
SD g = (2)
cm3 volumetric ring
The volumetric moisture (VM) of the soil is defined as the volume of water contained
in a given volume of soil sample. It can also be related to gravimetric moisture. According
to [32], a mathematical equation (Equation (3)) is applied to convert soil moisture from
a mass-based to a volume-based measurement. The product of the multiplication of the
gravimetric moisture values by the soil density, divided by water density, gives rise to
moisture based on volume, expressed in m3 of water/m3 of soil, cm3 /cm3 , or mm3 /mm3 .

GM × SD
Vm cm3 = (3)
cm3 WD

where GM is the gravimetric moisture (g/g), SD is the soil density (g/cm3 ), and WD is the
water density (g/cm3 ).

2.4. Statistical Analysis

After collecting soil moisture data in the field and conducting laboratory analyses, the
samples were evaluated in two stages: descriptive statistics and statistical analysis.

2.4.1. Descriptive Statistics

Before performing the geostatistical analysis, the data underwent descriptive analysis
to characterize the variability of soil moisture samples. Descriptive statistics, including the
minimum (Min), maximum (Max), median (Md), mean, variance (Var), standard deviation
(SD), and coefficient of variation (CV), were employed to analyze the data. The CV was
classified according to [33], considering variability as low when below 12%, moderate
between 12% and 60%, and high when above 60%. This classification allowed for a more
precise assessment of attribute variability, essential for understanding soil heterogeneity
in the study area. The analyses were conducted using the open-access software R version
4.3.1 [34].

2.4.2. Geostatistical Assessment

Semivariograms were employed to assess the spatial dependence of gravimetric and
volumetric moisture. Semivariance was estimated by Equation (4), according to [35]:

Ni =(h)
1
γ̂(h) =
2 N (h) ∑ [ Z ( xi ) − Z ( xi + h)]2 (4)
i =1
AgriEngineering 2025, 7, 110 7 of 22

where N (h) is the number of experimental pairs of observations Z(xi ) and Z (xi + h)
separated by a distance h. The semivariogram is represented by the graph as a function of h.
By fitting a mathematical model to the calculated values, the coefficients of the theoretical
semivariogram model were estimated. These coefficients include the nugget effect (C0), sill
(C0 + C), and range (a), as described by [36].
In this study, the ordinary least squares (OLS) method was applied alongside spherical,
exponential, and Gaussian models. The selection of the most suitable model was based on
the leave-one-out cross-validation (LOO-CV) method, using the lowest mean error (ME) as
the criterion. To minimize model bias, the mean error should be as close to zero as possible.
To ensure that the models met cross-validation requirements, the mean error (ME) was
calculated following [37], and should be as close to zero as possible. With the adjustment
of semivariograms, after detection of the spatial variability, the data were interpolated by
ordinary kriging.
The degree of spatial dependence (DSD) of the variables was calculated according to
the classification proposed by [38]. This classification indicated strong spatial dependence
when the semivariogram’s nugget effect was equal to or less than 25% of the threshold,
moderate spatial dependence when it was between 25% and 75%, and weak spatial de-
pendence when it exceeded 75%. Geostatistical analysis was performed using the freely
distributed RStudio software version 1.3 [34] and the geoR package [39], while the isoline
map was created in QGis software version 3.4.8.

2.5. Image Acquisition and Processing

Images were obtained using an RPA eBee-SQ Sensefly, a fixed-wing model, with an
average flight time of 55 min, reaching an average speed of 12 m/s. The aircraft was
equipped with a multispectral sensor (Parrot Sequoia model), which includes a high-
resolution RGB sensor and four monochromatic sensors for the spectral bands: green
(550 ± 40 nm), red (660 ± 40 nm), near-infrared (790 ± 40 nm), and red edge (735 ± 40 nm),
with a resolution of 4.71 cm/px. In addition to these sensors, Sequoia has a sensor for
luminosity correction, thus obtaining data with radiometric correction. A flight plan was
created using a base station and eMotion software 3.5.0 version, provided by the RPA
manufacturer. The flight plan presented the following characteristics:
• Focal Length: 3.98 mm.
• Vertical Coverage: 70%.
• Horizontal Cover: 70%.
• Flight Altitude: 50 m.
• Speed: 12 m/s.
The flight was carried out between 11:00 a.m. and 1:00 p.m. to avoid and minimize
the influence of lighting geometry (zenithal and azimuthal solar angle).
Before the flight, the multispectral sensor was directed at a calibration reflectance
panel, which has a known and uniform reflectance. This procedure allows the system to
adjust the measured values, compensating for variations in ambient light and ensuring
standardized reflectance readings of the crops. Additionally, the Sequoia features an irra-
diance light sensor mounted on the top of the drone, which monitors lighting conditions
in real time during the flight. This sensor automatically adjusts the captured values to
correct variations in sunlight intensity, such as changes in the sun’s angle or the passage
of clouds. With the data collected from the calibration panel and the light sensor, the pro-
cessing software applies corrections to the acquired data, ensuring that the measurements
accurately represent the actual conditions of the vegetation and soil, without interference
from fluctuations in ambient light.
AgriEngineering 2025, 7, 110 8 of 22

After completing the flights for each sampling period, the images were processed
in Pix4D software 4.5.2 version, generating 5 orthomosaics, 1 being RGB and 4 for the
multispectral bands (RED, NIR, RED EDGE, and GREEN).

2.6. Vegetation Indices

The vegetation indices were selected based on the study by [30], which evaluated and
identified indices suitable for assessing the spectral response of coffee plants. The vegetation
indices calculated for this research are represented in Table 3, containing their respective
mathematical formulas, along with references from the authors who developed them.

Table 3. Vegetation indices of images obtained by the multispectral sensor coupled to RPA.

Index Acronym Equation Reference

N IR− RED
Normalized Difference Vegetation Index NDVI N IR+ RED [23]
G − N IR
Normalized Difference Water Index NDWI G + N IR [25]
N IR− RED
Enhanced Vegetation Index 2 EVI2 2.5 × ( N IR+2.4× RED +1) [40]
N IR− RED Edge
Normalized Difference Red Edge NDRE N IR+ RED Edge [41]
Chlorophyll Vegetation Index CVI N IR RED [42]
GREEN × GREEN
Green Normalized Difference Red Edge GNDVI N IR− GREEN [43]
N IR+ GREEN
Canopy Chlorophyll Content Index CCCI NDRE [41]
NDV I
Green Ratio of Vegetation Index GRVI N IR [44]
GREEN
( N IR )−1
qRED 
Modified Simple Ratio MSR N IR [45]
RED +1

Infrared Percentage Vegetation Index IPVI N IR [46]

N IR+ RED
(1+ L)( N IR− RED )
Soil-Adjusted Vegetation Index SAVI L+ N IR+ RED [47]
0.5
i
2
Modified Soil-Adjusted Vegetation Index 2 MSAVI [2N IR+1−((2N IR+1) −8( N IR− RED )) [48]
2
( N IR− RED )
Optimized Soil-Adjusted Vegetation Index OSAVI ( N IR+ RED +0.16)
[49]
 
CIgreen N IR
Green Chlorophyll Index −1
GREEN [24]
 
CIrededge N IR
Red Edge Chlorophyll Index RED EDGE − 1 [24]

2.7. Correlation Analysis

To determine the relationship between the calculated vegetation indices and the
gravimetric and volumetric moisture attributes, a correlation analysis was performed.
Basically, correlation analysis summarizes the degree of relationship that exists between
two or more variables (x and y, for example).
For the correlation analysis, a shapefile was created containing 30 polygons, each with
a diameter of 0.20 m (20 cm). These polygons were precisely positioned at the center of
the canopy of each sampled plant. This approach aimed to ensure that the calculations
considered only the plant’s foliage area, minimizing interference from other elements such
as bare soil and shadows.
Vegetation index values were extracted using the zonal statistics tool in QGIS software.
This tool calculates descriptive statistics for the pixel values contained within each defined
polygon. In this study, the average pixel value within each polygon corresponding to a
sampled plant was computed.
AgriEngineering 2025, 7, 110 9 of 22

As a result, for each vegetation index calculated, 30 average values were obtained,
corresponding to the 30 georeferenced sampled plants. This procedure guaranteed that the
values utilized in the correlation analysis accurately reflected the actual canopy conditions
of each plant, minimizing the influence of external variations.
The average values of each vegetation index, along with the gravimetric and volu-
metric moisture values, were exported to a table for calculating the Pearson correlation
coefficient (R), as expressed in Equation (5). The R value consistently ranged from −1 to
1. The interpretation of the coefficient (R) followed the criteria established in the study
by [50].
n∑in=1 xi yi −∑in=1 xi ∑in=1 yi
R= p n (5)
n∑i=1 xi 2 − (∑in=1 xi )2 n∑in=1 yi 2(∑in=1 yi )2
p

3. Results
3.1. Statistical Evaluation
3.1.1. Statistical Summary
Table 4 compares the gravimetric moisture (Gm) and volumetric moisture (Vm) of the
soil at different depths (0–10 cm and 10–20 cm) during the dry and rainy seasons.

Table 4. Gravimetric moisture (Gm) and volumetric moisture (Vm) in dry season (Dry) and rainy
season (Rainy).

Season Variables (%) Min Max Md Mean Var SD CV (%)

Dry Gm (0–10 cm) 12.50 20.64 17.67 17.22 3.65 1.91 0.11
Dry Gm (10–20 cm) 11.75 19.33 17.93 17.61 2.63 1.62 0.09
Rainy Gm (0–10 cm) 19.52 29.54 25.52 24.83 6.00 2.45 0.09
Rainy Gm (10–20 cm) 18.48 27.97 25.69 25.26 4.94 2.22 0.08
Dry Vm (0–10 cm) 18.92 31.19 23.06 23.57 7.34 2.68 0.11
Dry Vm (10–20 cm) 15.36 36.19 23.23 22.74 13.93 3.73 0.16
Rainy Vm (0–10 cm) 26.24 45.45 34.39 35.02 8.08 2.84 0.07
Rainy Vm (10–20 cm) 22.79 48.85 34.68 33.87 25.37 5.03 0.14
Min—minimum value; Max—maximum value; Md—median; Mean—mean; Var—variance; SD—standard
deviation; CV—coefficient of variation (%).

The results indicate that the moisture content for both Gm and Vm was significantly
higher during the rainy season compared with the dry season. Soil moisture tended to be
slightly lower at a depth of 10–20 cm, where the average values were 15.36% for volumetric
moisture and 18.48% for gravimetric moisture, compared with the 0–10 cm layer, which
presented 18.92% and 12.50%, respectively. This difference was more evident during the
dry season. The variability of volumetric moisture (Vm) and gravimetric moisture (Gm)
was considered low (<12%) under all conditions, except for Vm during the rainy season,
which was classified as moderate.
The monthly accumulated precipitation data indicate a significant difference between
the analyzed periods. During the dry season in August 2020, precipitation was only
17.6 mm, whereas in the rainy season of January 2021, it reached 270.6 mm. Consequently,
soil water availability was impacted, reflecting in the gravimetric Gm and volumetric Vm
moisture values. The water balance also illustrates this difference, with a water deficit of
94.1 mm in the dry season, while, in the rainy season, there was a surplus of 212.9 mm,
indicating a greater availability of water in the soil for plant absorption.
The study area’s altitude ranged from 917 to 935 m, with an altimetric difference of
18 m. The soil texture was classified as clayey 36 to 38 percent clay, 32 to 33 percent silt,
and 29 to 32 percent sand, which provides a high-water retention capacity. Clayey soils,
such as those observed in this area, tend to retain more water, especially during rainy
seasons, reducing surface runoff and increasing moisture storage. However, this same
AgriEngineering 2025, 7, 110 10 of 22

characteristic can lead to slower drainage, and under excess water conditions, it may hinder
water absorption by plants due to low porosity.
Based on soil water storage through the water balance shown in Figure 4, it can be
stated that, during the dry season, volumetric moisture was closer to water deficit zones,
making absorption by plants more difficult. In the rainy season, the higher moisture
values remained close to or above the maximum storage capacity, ensuring greater water
availability for the roots.
The results corroborate the statements of [51], who highlighted the influence of climatic
factors, especially precipitation and temperature, on the phenological phases of coffee
plants. The variation in soil moisture throughout the year can directly impact coffee
productivity and quality, as periods of water deficiency may compromise vegetative growth
and fruiting.
Additionally, the analyzed data align with the observations of [52], who emphasized
the need for adequate moisture between October and May to ensure fruit growth and
filling, while a dry season after May favors uniform grain ripening and flowering induction.
The water balance evidenced in this study reinforces this necessity, demonstrating that
water deficiency during the dry season can hinder crop development, while the greater
availability of water in the rainy season is essential for the coffee plant’s productive cycle.

3.1.2. Geostatistical Analysis

The semivariogram adjustment parameters—nugget effect (C0), contribution (C1),
threshold (C0 + C1), and range (A)—were determined using the ordinary least squares
(OLS) method with spherical and exponential models. The values for the degree of spatial
dependence (DSD) and mean error (ME) are presented in Table 5.

Table 5. Parameters used in modeling the geostatistical semivariogram of gravimetric moisture (Gm)
and volumetric moisture (Vm) during the dry season (Dry) and the rainy season (Rainy).

Season Variable Mod. C0 C1 C0 + C1 A (m) DSD ME

Gm (0–10 cm) Sph 0.01 3.50 3.51 70.00 0.28 strong −0.00
Dry Gm (10–20 cm) Sph 0.10 2.50 2.60 40.00 3.84 strong 0.01
Vm (0–10 cm) Exp 0.25 3.80 4.05 35.00 6.17 strong −0.02
Vm (10–20 cm) Exp 0.01 4.00 4.01 50.00 0.24 strong 0.00
Gm (0–10 cm) Sph 0.00 8.00 8.00 45.00 0.00 strong 0.00
Rainy Gm (10–20 cm) Exp 0.00 15.00 15.10 40.00 0.66 strong 0.00
Vm (0–10 cm) Sph 0.01 22.00 22.01 20.00 0.04 strong −0.02
Vm (0–10 cm) Sph 0.01 28 28.01 20.00 0.00 strong 0.07
nugget effect: C0; contribution: C1; sill: C0 + C1; range: A; degree of spatial dependence (DSD); mean error (ME);
model used (Mod); spherical (Sph); exponential (Exp).

Geostatistical analysis of soil moisture, both gravimetric (Gm) and volumetric (Vm),
was conducted using spherical (Sph) and exponential (Exp) models for different depths
(0–10 cm and 10–20 cm) and seasons (dry and rainy).
The results revealed that all variables exhibited strong spatial dependence (SSD),
indicating that soil moisture follows a defined spatial pattern. The range varied from 45 to
20 m in the rainy season and from 70 to 35 m in the dry season.
The mean error (ME) was low for all variables, confirming the accuracy of the geostatis-
tical models applied. Variations in the semivariogram parameters were observed between
the dry and rainy seasons, reflecting changes in the spatial structure of soil moisture
throughout the year.
Analysis of the four semivariograms (Figures 5 and 6) provided an overview of the
spatial dependence of soil moisture (gravimetric and volumetric) at both depths (0–10 cm
and 10–20 cm) during the dry season.
 tween
tween thethe dry
dry andand rainy
rainy seasons,
seasons, reflecting
reflecting changes
changes in in
thethe spatial
spatial structure
structure of of soil
soil moisture
moisture
throughout
throughout thethe year.
year.
Analysis
Analysis of of
thethe four
four semivariograms
semivariograms (Figures
(Figures 5 and
5 and 6) 6) provided
provided anan overview
overview of of
thethe
AgriEngineering 2025, 7, 110 spatial dependence of soil moisture (gravimetric and volumetric) at both depths (0–10 cmcm 11 of 22
spatial dependence of soil moisture (gravimetric and volumetric) at both depths (0–10
and
and 10–20
10–20 cm)cm) during
during thethedrydry season.
season.

(a)(a) (b)(b)
Figure
Figure
Figure 5. 5.Semivariograms
Semivariograms
5.Semivariograms fitted
fitted byby
fitted the
the
by spherical
spherical
the and
and
spherical exponential
exponential
and models
models
exponential forfor
thethe
models variables
variables (a) Gm
0– 0– (a) Gm
for the(a)variables
Gm
10 10
0–10 cm
cmcmand and
and(b)(b)
Gm
(b)Gm 10–20
10–20
Gm cmcm
10–20 collected
collected
cm during
during
collected thethe
during drydry season.
season.
the dry season.

(a)(a) (b)(b)
Figure 6. Semivariograms fitted
byby
Figure
Figure 6.
6.Semivariograms
Semivariograms fitted
fitted bythe
the spherical
spherical
the andand
spherical exponential
exponential
and models
models
exponential forfor
thethe
models variables
variables
for the(c)(c)
Vm Vm
variables (a) Vm
0–10
0–10 cmcm and (d) Vm 10–20 cm collected during the dry season.
0–10 cm and
and(d)(b)Vm
Vm10–20 cmcm
10–20 collected during
collected the dry
during theseason.
dry season.

All four
All graphs indicate spatial dependence, with semivariance increasing with dis-
All four
fourgraphs
graphsindicate
indicatespatial dependence,
spatial dependence,with semivariance increasing
with semivariance with
increasingdis-with dis-
tance (20–40 m) until reaching
tance (20–40 m) until reaching a sill. a sill.
tance (20–40 m) until reaching a sill.
Figures
Figures 7a–d
7a–d and
and 8a–d
8a–d represent
represent kriging
kriging maps
maps of of soil
soil gravimetric
gravimetric moisture
moisture in in per-
per-
Figures
centage (%)
7a–d and 8a–d represent kriging maps of soil gravimetric moisture in percent-
centage (%) at at depths
depths of of 0–10
0–10 cmcm andand 10–20
10–20 cmcm during
during thethe dry
dry andand rainy
rainy seasons,
seasons, respec-
respec-
age (%)These
tively.
tively.
at depths
These maps
maps
ofwere
0–10
were
cm andto10–20
generated
generated to cm during
visualize
visualize thethe
thedistribution
spatial
spatial
dry and rainy
distribution
of of
seasons,
soil
soil
respectively.
moisture
moisture in in
thethe
These study
maps
study area.
were
area. generated to visualize the spatial distribution of soil moisture in the
study area.
The values in Figures 7 and 8 exhibit high variability. This variation highlights
how relying solely on the means of field observations can lead to management errors, as
discussed in studies by [51,52]. These findings underscore the significance of precision
agriculture (PA) in coffee production management and highlight the relevance of statistical
tools that incorporate spatial relationships.
In Figure 8c, Vm 10–20 cm (dry season) presents a relatively homogeneous spatial
distribution of soil moisture in the study area. A slight variation is observed, with some
areas appearing wetter (18% to 23%) and others drier (27%). The color gradient indicates
that cooler colors (green shades) represent areas with lower moisture, while warmer colors
(yellow shades) indicate areas with higher moisture.
In Figure 8d, Vm 10–20 cm (rainy season), the map indicates greater variability in soil
moisture compared with the dry season. High-moisture areas are depicted in warm colors,
whereas low-moisture areas appear in cool colors. A more distinct moisture distribution
pattern is evident, with high-moisture zones concentrated in specific regions of the map.
This suggests that rainfall may have contributed to a heterogeneous distribution of moisture
across the study area.
AgriEngineering 2025, 7, x FOR PEER REVIEW 12 of 23
AgriEngineering 2025, 7, 110 12 of 22

Figure 7. Kriging map for the variables (a) Gm 0–10 cm, (b) Gm 0–10 cm, (c) Gm 10–20 cm, and (d)
Gm 10–20
Figure cm in themap
7. Kriging dry for
andthe
rainy seasons,
variables (a)respectively.
Gm 0–10 cm, (b) Gm 0–10 cm, (c) Gm 10–20 cm, and
(d) Gm 10–20 cm in the dry and rainy seasons, respectively.
Figure 7. Kriging map for the variables (a) Gm 0–10 cm, (b) Gm 0–10 cm, (c) Gm 10–20 cm, and (d)
Gm 10–20 cm in the dry and rainy seasons, respectively.

Figure 8. Cont.
AgriEngineering 2025, 7, x FOR PEER REVIEW 13 of 23
AgriEngineering 2025, 7, 110 13 of 22

Figure 8. Kriging map for the variables (a) Vm 0–10 cm, (b) Vm 0–10 cm, (c) Vm 10–20 cm, and
Figure
(d) 8. Kriging
Vm 10–20 cm inmap
the for
drythe
andvariables (a) Vmrespectively.
rainy seasons, 0–10 cm, (b) Vm 0–10 cm, (c) Vm 10–20 cm, and (d)
Vm 10–20 cm in the dry and rainy seasons, respectively.
3.2. Statistical Correlation Between Vegetation Indices and Soil Moisture
Theassess
To values thein potential
Figures 7 of and 8 exhibit high images
high-resolution variability. This variation
for detecting highlights
the water statushowof
relying
coffee solely
trees, on the means
correlation of field
analysis observations
was conducted can lead
between to management
field-collected errors, asdata
soil moisture dis-
cussed
and in studies
vegetation by [54,55].
indices (TableThese
3). findings underscore the significance of precision agri-
culture
Table (PA) in coffee
6 presents theproduction management
R values (Pearson and coefficients)
correlation highlight the forrelevance
the studied of variables,
statistical
tools that
spectral incorporate
bands, spatial relationships.
and vegetation indices. An F-test was performed to evaluate the significance
of theIncorrelations
Figure 8c, at Vm the10–20 cm (dry
0.05 (5%) season) level.
significance presents a relatively homogeneous spatial
distribution of soil moisture in the study area. A slight variation is observed, with some
Table 6. Correlation analysis between vegetation indices and soil gravimetric moisture (Gm) and
areas appearing wetter (18% to 23%) and others drier (27%). The color gradient indicates
volumetric moisture (Vm) in dry season (Dry) and rainy season (Rainy).
that cooler colors (green shades) represent areas with lower moisture, while warmer col-
ors (yellow shades)
Gm (0–10 cm) indicate
Gm (10–20 cm)areas with higher Vm moisture.
(0–10 cm) Vm (10–20 cm)
Index
Dry RainyIn Figure Dry8d, Vm 10–20 cm (rainy season),
Rainy Dry the map indicates greater
Rainy Dry variabilityRainyin soil
RED 0.3005 ns moisture compared
0.2767 ns 0.1235 with 0.2781
ns the dry season.
ns High-moisture
0.4637 * 0.4163areas
* are depicted in 0.4368
0.2668 ns warm*col-
NIR 0.1991 ns 0.0185 ns 0.0938 ns 0.0110 ns 0.2213 ns 0.1155 ns 0.2210 ns 0.1020 ns
RED EDGE 0.1782 ns ors,
0.0878
whereas
ns low-moisture
0.0355 ns areas
0.1732 nsappear in cool
0.2608 ns colors. A
0.0506
more
ns distinct
0.2289
moisture
ns distribu-
0.0197 ns
GREEN 0.2840 ns tion pattern 0.1093
0.0618 ns is evident, with
ns 0.3141high-moisture
ns 0.5157zones
* concentrated
0.1692 ns in specific regions
0.3601 ns 0.2597of
ns the
NDVI 0.1328 ns 0.1791 ns 0.0463 ns 0.1604 ns 0.2421 ns 0.3275 ns 0.0871 ns 0.3329 ns
NDWI 0.0129 ns
map. This suggests
0.0841 ns 0.0299 ns
that rainfall
0.2558 ns
may have contributed
0.1079 ns
to a heterogeneous
0.2815 ns 0.0011 ns
distribution
0.3418 ns
of
EVI2 0.0627 ns moisture across
0.0584 ns the study0.0330
0.0375 ns area. ns 0.0177 ns 0.1738 ns 0.0950 ns 0.1652 ns
NDRE 0.1202 ns 0.0853 ns 0.1854 ns 0.2312 ns 0.0156 ns 0.1809 ns 0.0786 ns 0.2458 ns
CVI 0.4363 * 0.0277 ns 0.2487 ns 0.2798 ns 0.3791 * 0.1283 ns 0.2602 ns 0.2195 ns
GNDVI 0.0129 ns 3.2. Statistical
0.0841 ns Correlation
0.0299 ns Between
0.2558 nsVegetation
0.1079Indices
ns and SoilnsMoisture
0.2815 0.0011 ns 0.3418 ns
CCCI 0.0431 ns 0.0277 ns 0.1035 ns 0.0325 ns 0.0881 ns 0.1454 ns 0.0302 ns 0.2664 ns
GVI 0.0409 ns 0.1141 To
ns assess thenspotential
0.0287 of ns
0.2642 high-resolution
0.0833 ns images forns detecting
0.3033 0.0129the
ns water status
0.3587 ns of
MSR 0.1328 ns coffee trees, 0.0463
0.1791 ns
correlation analysis
ns 0.1604 was conducted
ns 0.2421 ns
between
0.3275field-collected
ns 0.0871 soil moisture
ns 0.3329 nsdata
IPVI 0.1328 ns 0.1791 ns 0.0463 ns 0.1604 ns 0.2421 ns 0.3275 ns 0.0871 ns 0.3329 ns
SAVI 0.0827 ns and
0.0655vegetation
ns indices
0.0480 ns (Table
0.04113).
ns 0.0457 ns 0.1848 ns 0.1171 ns 0.1774 ns
MSAVI 0.0158 ns 0.0542 Table
ns
6 presents
0.0200 ns
the R values
0.0301 ns
(Pearson
0.0490correlation
ns
coefficients)
0.1696 ns
for
0.0536 the
ns
studied
0.1613varia-
ns
OSAVI 0.0076 ns 0.1002 ns 0.0098 ns 0.0778 ns 0.0799 ns 0.2321 ns 0.0331 ns 0.2287 ns
CIgreen 0.0409 ns bles, ns
0.1141 spectral0.0287
bands,
ns and vegetation
0.2642 ns indices.
0.0833 An
ns F-test wasnsperformed
0.3033 0.0129to
ns evaluate thenssig-
0.3587
CIrededge 0.1360 ns nificance of the
0.0876 ns
correlations
0.1941 ns
at the 0.05 0.0005
0.2377 ns
(5%) significance
ns 0.1751level.
ns 0.0908 ns 0.2424 ns
ns—not significant, * = significant at 5%.
Table 6. Correlation analysis between vegetation indices and soil gravimetric moisture (Gm) and
volumetric
The REDmoisture (Vm)
spectral in dry
band season
(dry and(Dry)
rainyand rainy season
seasons) (Rainy).
and the GREEN spectral band (rainy
season), along with the chlorophyll vegetation index (CVI) (dry and rainy seasons), exhib-
Gm (0–10 cm) Gm (10–20 cm) Vm (0–10 cm) Vm (10–20 cm)
Index ited significant correlations at the 5% significance level, according to Pearson’s correlation
Dry Rainy Dry Rainy Dry Rainy Dry Rainy
RED 0.3005 ns
analysis (Figures0.1235
0.2767 ns
9–11).nsThe CVI exhibited0.4637
0.2781 ns
the strongest
*
statistically
0.4163 *
significant0.4368
0.2668 ns
positive
*
NIR 0.1991 ns correlation
0.0185 with
ns Gm (0–10
0.0938 ns cm) (r =
0.0110 0.4363).
ns 0.2213 ns 0.1155 ns 0.2210 ns 0.1020 ns
RED EDGE 0.1782 ns 0.0878 ns 0.0355 ns 0.1732 ns 0.2608 ns 0.0506 ns 0.2289 ns 0.0197 ns
GREEN 0.2840 ns 0.0618 ns 0.1093 ns 0.3141 ns 0.5157 * 0.1692 ns 0.3601 ns 0.2597 ns
 tion with Vm (10–20 cm) (r = 0.4368).
Figures 9–11 illustrate the spectral analysis across different seasons. Figure 9 depicts
the RED band for the dry season (a) and the rainy season (b), Figure 10 represents the
AgriEngineering 2025, 7, 110 GREEN band for both seasons, and Figure 11 illustrates the CVI vegetation index for the 14 of 22
dry season (a) and the rainy season (b).

griEngineering 2025, 7, x FOR PEER REVIEW 15 of 23

AgriEngineering 2025, 7, x FOR PEER REVIEW 15 of 23

Figure 9. RED spectral bands for the dry season (a) and rainy season (b).

Figure 10. GREEN spectral band for the dry season (a) and rainy season (b).
Figure 10. GREEN spectral band for the dry season (a) and rainy season (b).
Figure 10. GREEN spectral band for the dry season (a) and rainy season (b).

AgriEngineering 2025, 7, x https://doi.org/10.3390/xxxxx

Figure 11. CVI vegetation index for the dry season (a) and rainy season (b).
Figure 11. CVI vegetation index for the dry season (a) and rainy season (b).

Figure
The11.RED
CVI band
vegetation index values
exhibited for the dry seasonfrom
ranging (a) and rainy
0.01 season
to 0.33, (b). no noticeable visual
with
differences between the two periods (Table 1). The GREEN spectral band ranged from
The RED band exhibited values ranging from 0.01 to 0.33, with no noticeable visual
0.01 to 0.24, with predominant values between 0.01 and 0.08 during the dry season and
AgriEngineering 2025, 7, 110 15 of 22

The GREEN index exhibited the strongest significant positive correlation with Vm
(0–10 cm) (r = 0.5157). The RED index exhibited the strongest significant positive correlation
with Vm (10–20 cm) (r = 0.4368).
Figures 9–11 illustrate the spectral analysis across different seasons. Figure 9 depicts
the RED band for the dry season (a) and the rainy season (b), Figure 10 represents the
GREEN band for both seasons, and Figure 11 illustrates the CVI vegetation index for the
dry season (a) and the rainy season (b).
The RED band exhibited values ranging from 0.01 to 0.33, with no noticeable visual
differences between the two periods (Table 1). The GREEN spectral band ranged from 0.01
to 0.24, with predominant values between 0.01 and 0.08 during the dry season and between
0.08 and 0.16 during the rainy season. The CVI vegetation index displayed distinct visual
differences between the two periods, ranging from 5.76 to 8.17 in the dry season and from
0.94 to 3.35 in the rainy season.

4. Discussion
4.1. Descriptive Statistics
During the dry season, the variables Gm (10–20 cm) and Vm (0–10 cm and 10–20 cm)
exhibited CV values below 10%. The CV value for Gm at 0–10 cm was 11%, indicating
moderate variability. In the rainy season, only Vm (0–10 cm) exhibited low heterogeneity
(CV < 10%), while the other variables presented moderate heterogeneity (10% < CV < 20%).
The lowest gravimetric and volumetric moisture values during the dry season were
recorded at depths of 10–20 cm, at 11.75% and 18.48%, respectively. A similar pattern was
observed during the rainy season, with the lowest gravimetric and volumetric moisture
values recorded at depths of 10–20 cm (15.36% and 22.79%). The highest moisture values
for both the dry and rainy seasons were recorded at depths of 0–10 cm for both gravimetric
and volumetric measurements.
The gravimetric moisture levels observed in coffee cultivation soils in this study were
consistent with those reported by [53], who recorded a mean value of 17.82%, and [54],
who reported values of 18.20% at a depth of 0–20 cm and 20.00% at 20–40 cm. Another
similarity between this study and [54] is that the gravimetric moisture values did not vary
significantly with sampling depth.
The mean gravimetric moisture values recorded were 17.67% for samples collected at a
depth of 0–10 cm and 17.93% for samples collected at a depth of 10–20 cm in the dry season.
In the rainy season, the values were 23.06% and 23.23% for samples collected at depths of
0–10 cm and 10–20 cm, respectively. The mean volumetric moisture values recorded were
24.83% and 25.26% at depths of 0–10 cm and 10–20 cm, respectively, during the dry season.
For the rainy season, the values were 35.02% for the 0–10 cm sampling depth and 33.87%
for the 0–20 cm depth.
Ref. [55] assessed soil volumetric moisture at three depths (0–5 cm, 5–10 cm, and
10–20 cm), reporting values of 45.00%, 41.00%, and 38.00%, respectively. The volumetric
moisture at a depth of 0–10 cm in this study (35.02%) closely matched the value reported
by [55], who recorded 38.00% at a depth of 10–20 cm. Another common finding was the
decline in volumetric moisture as sampling depth increased.
Refs. [51,53] analyzed the physical attributes of a coffee plantation located in the
municipality of Três Pontas. Both studies determined gravimetric moisture values from
samples collected at six depth layers within a 0–60 cm soil profile. Ref. [51] reported an
average moisture content of 22.33%, while [53] reported an average moisture range of
20.57% to 24.58%. These authors examined five coffee strains of different heights. The
average soil moisture values reported by these authors closely resembled those observed in
AgriEngineering 2025, 7, 110 16 of 22

this study. Although the sampling depth differed, the crop and the region studied remained
the same.
Gravimetric and volumetric moisture levels increased during the dry season (August
2020) and the wet season (January 2021). One of the main factors influencing this condition
was rainfall during these periods. According to the precipitation data in Table 1, the last
recorded rainfall before data collection in August 2020 occurred 74 days earlier (20 mm on
31 May 2020, which was likely associated with a period of water scarcity for the crop. In
contrast, a total of 115 mm of rainfall was recorded seven days before the second collection
in January 2021 (Table 1), representing a precipitation volume six times greater than that
observed prior to data collection in the dry season.
Analyzing the mean, maximum, minimum, and CV values presented in Table 5
revealed variations between measurements. However, simply knowing these values was
insufficient to fully express variable variability. Therefore, a geostatistical analysis was
necessary to evaluate the spatial distribution of the attributes assessed in this study. In
addition to the geostatistical assessment, thematic maps were generated to facilitate the
visual interpretation of the spatial distribution of attributes within the study area.

4.2. Climatic Conditions, Water Balance, Altitude, and Soil Properties

Climatic data further highlighted the impact of seasonal variations on soil moisture.
The accumulated monthly precipitation data revealed a significant difference between the
analyzed periods. In August 2020 (dry season), precipitation totaled only 17.6 mm, whereas,
in January 2021 (rainy season), it reached 270.6 mm. Consequently, soil water availability
was affected, reflected in the gravimetric (Gm) and volumetric (Vm) moisture values. The
water balance further illustrated this contrast, with a water deficit of 94.1 mm during the
dry season, while, in the rainy season, there was a surplus of 212.9 mm, indicating greater
water availability in the soil for plant absorption.
Temperature and relative humidity also played significant roles in soil moisture
variation. During the dry season, the monthly mean temperature was 18.3 ◦ C, with a lower
relative humidity of 59.3%. The reduced atmospheric moisture content, combined with
a higher potential evapotranspiration (PET) of 53.8 mm, contributed to soil water loss,
exacerbating the water deficit. In contrast, the rainy season recorded a higher monthly
mean temperature of 23.0 ◦ C but with an increased relative humidity of 70.9%. The elevated
humidity reduced evaporative demand, preserving soil moisture. Additionally, the lower
PET value of 110.0 mm in the rainy season, combined with high precipitation, resulted in
greater soil moisture retention and water surplus.
Wind speed also affected soil moisture conditions. During the dry season, the average
wind speed was 2.0 m/s, slightly higher than the 1.7 m/s recorded in the rainy season.
Stronger winds can accelerate soil moisture loss by increasing evapotranspiration, further
exacerbating water scarcity in the dry season. Conversely, lower wind speeds in the rainy
season contributed to reduced evaporation rates, promoting moisture retention.
The study area’s altitude ranged from 917 to 935 m, with an elevation difference of
18 m. The soil texture was classified as clayey (36–38% clay, 32–33% silt, and 29–32% sand),
providing high water retention capacity. Clayey soils, such as those in this area, tend to
retain more water, especially during rainy seasons, reducing surface runoff and increasing
moisture storage. However, this characteristic can also lead to slower drainage, and, under
excess water conditions, it may hinder plant water absorption due to low porosity.
Based on the soil water storage derived from the water balance, it can be concluded
that, during the dry season, volumetric moisture was closer to water deficit zones, making
plant absorption more difficult. In the rainy season, higher moisture values remained near
or above the maximum storage capacity, ensuring greater water availability for the roots.
AgriEngineering 2025, 7, 110 17 of 22

The results corroborated the findings of [56], who highlighted the impact of climatic
factors, particularly precipitation and temperature, on the phenological stages of coffee
plants. Variations in soil moisture throughout the year can directly impact coffee produc-
tivity and quality, as periods of water deficiency may compromise vegetative growth and
fruit development.
Furthermore, the analyzed data were consistent with the observations of [57], who
stressed the importance of adequate moisture between October and May to ensure fruit
growth and filling, while a dry season after May promotes uniform grain ripening and
flowering induction. The water balance observed in this study reinforced this requirement,
demonstrating that water deficiency during the dry season can hinder crop development,
whereas greater water availability in the rainy season is essential for the coffee plant’s
productive cycle.

4.3. Geostatistical Analysis

Ref. [58] reported that variables related to water retention exhibited greater continuity
and spatial dependence. Conversely, [59] indicated that soil moisture varied significantly
within the 0–20 cm sampling interval. These findings were validated in this study, as soil
moisture measured at a depth of 0–20 cm demonstrated spatial dependence, as evidenced
by the geostatistical data (Table 6 and Figures 3 and 4).
The spherical and exponential models were suitable for data modeling, as the mean
error (ME) values were very close to zero, thus meeting the cross-validation criteria. The
attribute Vm 10–20 cm had the highest mean error value (0.0661) during the rainy season,
while the lowest mean error value was observed for Gm 10–20 cm (ME = 0.0010), also
during the rainy season.
In this study, all evaluated variables exhibited a strong degree of spatial dependence
(DSD). A similar pattern was observed by [51,55], who analyzed the spatial variability of
soil physical attributes in coffee cultivation areas and identified a strong degree of spatial
dependence (DSD) for soil moisture.
Ref. [55] reported that, in geostatistical modeling for soil science studies, the spherical
model is the most frequently applied. However, semivariogram adjustments related to soil
properties are often observed for both spherical and exponential models.
When analyzing the range values during the dry season, it was observed that the shal-
lower the sampling depth (0–10 cm), the greater the range for gravimetric and volumetric
moisture values. In contrast, this phenomenon was not observed in the rainy season, that
is, despite variations in sampling depth, the range values remained equal (Vm) or very
close (Gm: A = 45 and Gm: A = 40).
The kriging map indicated that gravimetric moisture at a depth of 0–10 cm in the dry
season (Figure 7a) ranged from 12% to 20%, with predominant values between 16% and
20%. In the rainy season, gravimetric moisture at the same depth (Figure 7b) ranged from
20% to 28%. At a depth of 10–20 cm (Figure 7c), gravimetric moisture varied from 12% to
18%, with predominant values between 12% and 14%. The kriging map of Vm (10–20 cm)
in the rainy season (Figure 7d) revealed a moisture variation between 20% and 35%, with
values above 25% being predominant.
The kriging maps for volumetric moisture at a depth of 0–10 cm (Figure 8a,b) predom-
inantly varied from 20% to 27% in the dry season and from 30% to 42% in the rainy season.
The spatial map of volumetric moisture at a depth of 10–20 cm (Figure 8c,d) indicated a
predominant range of 23% to 27% in the dry season and 32% to 36% in the rainy season.
The maps demonstrated that locations with higher and lower soil moisture concentra-
tions remained consistent between the two periods, though a general increasing trend was
AgriEngineering 2025, 7, 110 18 of 22

observed. This pattern was attributed to the sampling times, which were conducted in two
distinct climatic seasons (dry and rainy).

4.4. Analysis of Correlation Between Field Data and Vegetation Indices

The correlations described in Table 5 do not imply causality, as the observed relation-
ships may be influenced by other factors not considered in this study. Vegetation indices
are affected by multiple variables, including vegetation type, soil cover, and atmospheric
conditions, all of which can impact their relationship with soil moisture.
Figure 9a illustrates the reflectance of the red band during the dry season, where
lighter tones indicate exposed soil or sparse vegetation, while darker tones suggest denser
vegetation cover or higher soil moisture. In Figure 9b, representing the rainy season, an
overall reduction in reflectance was observed, indicating increased vegetation density or
soil moisture.
According to [22], vegetation absorbs most of the radiation in the visible spectrum,
with absorption peaks in the blue and red regions due to chlorophyll activity. Greater
vegetation activity results in higher absorption in the red band, leading to lower reflectance.
Seasonal variations in reflectance reflect changes in chlorophyll activity influenced by water
availability, while spatial differences within each season may be associated with leaf cellular
structure, soil texture, and topography.
Ref. [60] investigated the water conditions of a coffee crop by evaluating potential
correlations between leaf water potential and multispectral bands, along with vegetation
indices derived from high-resolution images captured by RPA. The authors identified a
significant correlation of 39.93% between the red spectral band and leaf water potential.
Ref. [29] examined the correlation between chlorophyll and NDVI obtained using
passive and active sensors. The study was conducted in a coffee plantation, where the
authors established sampling grids of 30, 60, 90, and 120 points to assess the correlation
between attributes. The results indicated a significant correlation between NDVI obtained
via active sensors and chlorophyll (31%) for the 90-point grid and (48%) for the 120-point
grid. NDVI continues to be one of the most extensively utilized indices due to its integration
of near-infrared and red spectral bands [22].
Figure 10 illustrates the reflectance of the green band during the rainy season, following
the same pattern as the red band. Lighter tones indicate higher reflectance, while darker
tones represent lower reflectance. Figure 11a displays the CVI index during the dry season,
where lower values indicate reduced chlorophyll content and possible plant stress, whereas
higher values suggest greater vigor. In Figure 11b, corresponding to the rainy season,
a general increase in CVI is observed, represented by redder tones, indicating higher
chlorophyll content and greater plant vigor. These Figures highlight the seasonal variations
in spectral reflectance and the CVI vegetation index. During the rainy season, lower
reflectance is observed in both the red and green bands, accompanied by an increase in CVI,
suggesting greater vegetation cover, higher chlorophyll content, and potentially higher soil
moisture.
The green coloration of plants results from chlorophylls preferentially absorbing light
in the blue and red regions of the electromagnetic spectrum while reflecting light in the
green region. The more active and healthier the vegetation, the greater the absorption of
red light by chlorophyll, consequently leading to lower reflectance in this spectral range.
Conversely, in stressed plants or those with lower chlorophyll content, red light absorption
decreases, resulting in higher reflectance. This characteristic forms the basis for using
vegetation indices, such as CVI, which assess vegetation health by comparing red light
absorption with reflectance in the near-infrared region.
AgriEngineering 2025, 7, 110 19 of 22

Studies by [42] indicate that CVI exhibits high sensitivity to leaf chlorophyll concen-
tration. Moreover, even in soils with significant reflectance variability due to moisture, CVI
maintains a stable linear relationship with chlorophyll concentration, particularly under
extreme conditions of completely dry or wet soil. This result highlights the robustness of
the index in adapting to soil moisture variations.
The aforementioned authors emphasize in their studies the applications of RPA in
coffee farming, along with the evaluation of possible correlations between coffee tree
attributes and vegetation indices. However, to date, no studies have been identified that
assess the water conditions of coffee trees using high-resolution images while incorporating
soil moisture variability analysis.

5. Conclusions
Gravimetric and volumetric moisture data collected at different depths and during
different periods (dry and rainy seasons) served as a basis for evaluating the spatial
variability of these attributes through geostatistical analysis. By adjusting semivariograms,
a mathematical function was fitted to express the spatial dependence structure of soil
moisture. Additionally, kriging interpolation enabled the estimation of this attribute’s
values in unsampled locations.
Spatial variability in the studied variables was assessed by calculating the coefficient
of variation, which indicated strong spatial dependence for all evaluated variables. The
semivariograms were best fitted to the spherical and exponential models. The kriging maps
illustrate the spatial distribution of the studied variables, allowing the identification of
areas with higher and lower intensities for each variable.
Conversely, vegetation indices revealed the extent to which a crop’s reflectance values
fluctuate between different periods (i.e., dry and rainy seasons). These methods allowed
the evaluation of multiple parameters, including plant health, chlorophyll content, and
leaf water content. Therefore, correlation and regression analyses were conducted to assess
whether soil moisture was correlated with vegetation indices. The results indicated that the
RED and GREEN spectral bands, along with the CVI index, were significantly correlated
with soil moisture.
The application of geostatistical tools and vegetation indices derived from high-resolution
images proved to be effective in this study. However, future research could enhance the
efficiency of these tools for monitoring and detecting water variability in coffee plantations,
providing valuable support for producers in making informed management decisions.

Author Contributions: Conceptualization, S.A.d.S.S., G.A.e.S.F. and V.C.F.; methodology, S.A.d.S.S.

and G.A.e.S.F.; software, S.A.d.S.S. and M.L.M.; validation, G.A.e.S.F., V.C.F. and M.M.L.V.; formal
analysis, G.A.e.S.F.; investigation, M.M.L.V. and M.L.M.; resources, V.C.F.; data curation, S.A.d.S.S.,
G.F.V. and M.L.M.; writing—original draft preparation, S.A.d.S.S. and G.F.V.; writing—review and
editing, G.A.e.S.F. and M.M.L.V.; visualization, M.L.M.; supervision, G.A.e.S.F. and V.C.F.; project
administration, G.A.e.S.F. and V.C.F.; funding acquisition, G.A.e.S.F. and V.C.F. All authors have read
and agreed to the published version of the manuscript.

Funding: This work was funded by the Consórcio Pesquisa Café (10.18.20.023.00.00 and 10.18.20.041.00.00),
the National Council for Scientific and Technological Development (CNPq) (project 310186/2023-4),
and the Minas Gerais Research Support Foundation (FAPEMIG) (project APQ-00661-22).

Data Availability Statement: All relevant data are included in the manuscript.
AgriEngineering 2025, 7, 110 20 of 22

Acknowledgments: The authors would like to thank the Agricultural Research Corporation of Minas
Gerais (EPAMIG), especially the Consórcio Pesquisa Café project, and also the Federal University of
Lavras (UFLA), the Department of Agricultural Engineering (DEA), Conselho Nacional de Desen-
volvimento Científico e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível
Superior (CAPES), and Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG), for
funding the project and granting scholarships.

Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or
in the decision to publish the results.

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