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\begin{document}

\title{Natural water storage and aquifer recharge assessment in
Brazilian savanna wetland using Unmanned Aerial Vehicle and geophysical
survey}



\author[1]{Lucas Furlan}%
\author[2]{Vania Rosolen}%
\author[3]{Jepherson Sales}%
\author[2]{Cesar Moreira}%
\author[4]{Manuel Ferreira}%
\author[3]{Guilherme Bueno}%
\author[2]{Carla Coelho}%
\author[5]{Stéphane Mounier}%
\affil[1]{Universidade Estadual Paulista Julio de Mesquita Filho Instituto de Biociencias Campus de Rio Claro}%
\affil[2]{Universidade Estadual Paulista Julio de Mesquita Filho}%
\affil[3]{Universidade Federal Goiás }%
\affil[4]{Universidade Federal de Goias}%
\affil[5]{Universite de Toulon}%


\vspace{-1em}



  
  \date{May 03, 2024}


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\selectlanguage{english}
\begin{abstract}
Human pressure on the water resources provided by natural wetlands has
intensified in Brazil due to an increase in agricultural land equipped
with irrigation. However, the amount of water stored in these areas, and
its contribution to aquifer recharge is unknown. The objectives of this
study were to determine the amount of water that can be retained in a
natural wetland surrounded by farmland and to propose a model of
groundwater recharge. We used remote sensing techniques involving
Unmanned Aerial Vehicle (UAV) to map the area and highlight the wetland
internal morphology, using RGB orthomosaic and its respective digital
surface model. According to the topography of the study area three
compartments were defined. Compartment 1, corresponding to the external
border of the wetland, can store 313,121.00 m\selectlanguage{ngerman}³ of water; compartment 2
and 3 can store 85,923.20 m³ and 17,952.10 m³, respectively. The 2D
inversion and a pseudo-3D model produced from Electrical Resistivity
Tomography (ETR) data were used to visualize the subsurface geologic
structure and hydrologic flow paths. Our results showed the direct
interactions between groundwater (GW) and surface water (SW) in the
center of the wetland (compartment 3), which constitutes the aquifer
recharge zone with vertical infiltration. As the depth increases,
infiltration and water flow deviate laterally in the southwest and
northeast direction. The wetland is characterized as an unconfined
aquifer that plays an important role in the hydrogeological dynamics of
the catchment. Remotely sensed images allied with geophysical techniques
allow complete surface and subsurface imaging and offers visual tools
that contribute to understanding the hydrodynamic of an area.%
\end{abstract}\selectlanguage{ngerman}%



\sloppy


\textbf{}

\textbf{Introduction}

Worldwide, 324 million hectares of land, which contribute 40\% of the
total food production, are equipped for irrigation (FAO, 2016). At a
global scale, average annual water consumption by irrigation is 7,700 m3
per hectare. In Brazil, irrigation's water uptake is approximately 1000
m3/s, characterizing the major consumption in the territory. That
quantity is distributed through irrigations systems covering 7 million
ha with potential to expand by 43.3\% (ANA, 2017). In the countryside,
rivers and small dams are the major sources of irrigation, despite the
substantial volumetric impact on water resources during the dry season.
Considering that both urban and rural water supplies are provided by
superficial water, scarcity has been a critical concern. For example, in
the biennium 2014--2015, the reservoirs of S\selectlanguage{ngerman}ão Paulo metropolitan region
were reduced to single-digit percentages, leading to a profound water
crisis (Millington, 2018).

Almost 20\% of the Brazilian territory is covered by wetlands, and their
distribution has been dramatically reduced by land conversion, thus
affecting their natural water storage and ecological functions (Junk et
al., 2013; Rosolen, Oliveira \& Bueno, 2015). Around the world,
uncontrolled exploitation is damaging ecosystem services that depend on
wetlands (Verones, Bartl, Pfister, Vilchez \& Hellweg, 2012).

In the Cerrado region---the world's most threatened savanna environment
and Brazil's last remaining agricultural frontier areas under native
vegetation---the pronounced seasonality necessitates the use of
extensive irrigation for expanding agricultural land use (Rodrigues et
al., 2018). This biome is characterized by long dry periods and by the
presence of herbaceous plants and scattered woody plants and trees that
cover well-drained lateritic soils of low fertility. The Cerrado's
wetlands represent islands of natural water storage. In the southeast
portion of the ecosystem, our study area in this research, the water
dynamic and volume are closely related to rainfall seasonality and the
fluctuation of shallow groundwater, comprising a freshwater system
integrated with rivers in the catchments. Surrounding the study area,
large farms with center-pivot irrigation systems have been using the
wetland's water for grain cultivation. Although the allocation of water
is legally required by some farmers, the resource's conservation is
jeopardized by a lack of knowledge about the balance between input and
output of water in wetlands, and about the hydrological dynamic between
aquifers and soils with hydromorphic properties. Despite closed wetlands
store water and promote groundwater infiltration, influencing the
hydrologic cycle at the watershed scale, few studies have relied on
field data to further our understanding of the impacts, either
individual or cumulative, of natural landscape characteristics and
wetland drainage at the local scale (Haque \& Badiou, 2018).

Hydrodynamic models typically partition rainfall into: soil moisture
storage, groundwater recharge and surface runoff. This partitioning
largely depends not only on the hydrological conditions, but also on the
pedogeological conditions. Invasive data acquisition techniques such as
drilling or trench excavations are labor-intensive, costly and involve
extensive fieldwork sampling and laboratory analysis. They can be
efficiently complemented by non-invasive geophysical and remote sensing
methods which are time and cost effective, i.e. they succeed in covering
large areas of land cheaper and quicker than the soil sampling
techniques.

Unmanned Aerial Vehicles (UAVs), and in particular micro-UAVs (payload
less than 1.5--2 kg), represent the latest frontier in land and water
monitoring because of low-altitude flight and flexible payload design
(Anderson and Gaston, 2013). In recent years, miniaturized components
(GNSS receivers, inertial measurement units, autopilots) have advanced
(Watts et al., 2012), and UAVs have been used also for a wide range of
hydrological applications such as fluvial monitoring; river bathymetry
and photogrammetric DEM generation using very high resolution (VHR)
imagery (Lejot et al., 2007).

Innovative quantification techniques that use remote sensing, and
especially those using high spatio-temporal resolution imagery, have
been applied in non-invasive mapping of soil surface furthering our
understanding of the relationship between surface soil water and aquifer
in wetlands (Lin, 2012).

In this field, UAVs equipped with digital cameras had the capability to
acquire VHR and describe the distribution of soil moisture in wetlands.

Traditional techniques emphasized in pedological studies (e.g., grain
size analysis, mineralogy, chemical etc) carried out in a few meters
thick do not provide adequate information about soil-water relationships
as well the possibility to quantify the volume of water in soil. The use
of images generated by UAV allow obtain information not only from
surface features but also to provide excellent estimated values of,
e.g., the water volume that the wetland can storage.

Another technique is electrical resistivity tomography (ERT), which can
be successfully applied in waterlogged areas, since it is highly
sensitive to the presence of water and can provide valuable information
regarding the structure and volume of storage in rocks and soils
(Carrara, Pece \& Robertini, 1994; Zhang et al, 2016; Luo et al., 2019).
These techniques have been widely applied in resource exploration and
hydrogeology surveys, having the advantage of being rapid and
cost-effective means of generating information (Kearey \& Brooks, 1991;
Helaly, 2017).

Besides, geophysical ERT technique allow to assess soil architecture,
from surface to saprolite, highlighting zones with distinctive presence
of water in pore space. That information is a key to understand the soil
itself; soil-water transfers and to provide a model of water flow and
hydrological process.

Aerial imaging is a widely applied technique in the study of wetlands,
but digital aerial photogrammetry, i.e., the calculation trough UAV
images of the area and the potential volume of water that can be stocked
in the wetlands is a lack of knowledge, so that is one of our
propositions in this work. Still, the second aim of this work is to
propose an empirical model for estimating the spatial distribution of
water in a specific Cerrado wetland of Minas Gerais state, Brazil, using
UAV and ERT techniques to assess groundwater flow and aquifer recharge.

Combining both, UAV and ERT techniques, we employed noninvasively tools
to directly quantify soil architecture and hydrodynamic. The
methodological choice aimed to improve the needed linkage between
digital architecture soil mapping with hydropedology (Lin, 2012).

\textbf{2. Materials and Methods}

\textbf{2.1 Study site}

The studied area is located in the upper basin of the Rio Claro
(19°28'08''S, 47°48'20''W), at an altitude of 966 m a.s.l (above sea
level), in the municipality of Uberaba-MG (Figure 1). The wetland is a
distinct, upstream depression on a flat plateau, occupying a shallow
concavity covered with grassy savanna vegetation on hydric soil; it
forms the first headwater of the Rio Claro River. The seasonally flooded
area extends over 426,064.80 m². On the flat plateau, the upland soils
are well-drained ferruginous concretionary soils (Ferralsols by IUSS,
2014), which have been planted with soybean, corn, and sugarcane crops
since the 1970s. This wetland formation corresponds to a topographic
depression, the soils of which developed in an aquic moisture regime
(Gleysol by IUSS, 2014). The wetland areas have been drastically reduced
by agricultural land conversion. Distinct original vegetation covers
both Ferralsol and Gleysol soils; the Ferralsols are covered by tree
savanna. Wetlands are a grass-dominated ecosystem, with scattered woody
plants and small trees covering mounds (locally known as\emph{murundus}
), a microtopographic characteristic of the landscape. Mounds are found
exclusive in the waterlogged area, up to the border the plateau. The
entire area is part of the Cerrado\emph{,} which has been developed as
an agricultural frontier and is becoming a biodiversity extinction
hotspot (Anache, Flanagan \& Srivastava, 2018). The climate is
characterized by a pronounced dry season in the winter, from April to
October, followed by a rainy season during the summer months, from
November to March. The average annual rainfall and temperature are 1,516
mm and 23°C, respectively. Geologically, the area is characterized by
upper Cretaceous sandstones, with iron-rich silt and clay layers
belonging to the Serra da Galga Member of the Marília Formation
(Fernandes \& Ribeiro, 2015). The catchment has gentle slopes (declivity
\textless{} 5 \%) and a flat, wide and imperfectly drained valley.

\textbf{2.2 UAV Very High Resolution imagery acquisition and processing}

Wetlands are difficult features to map from the ground because of the
complexity of the landscape, especially during months when they are
flooded. In order to map a wetland with accuracy it is necessary to use
an aerial perspective (orthogonal projection, angle of 90º), preferably
three dimensional and combined with field experiments to fully
understanding the character and spatial extent of this phenomenon
(Madden et al., 2015). Considering the hydrodynamic characterization of
the wetland surface, we used remote sensing and geoprocessing tools in
the study area, including photogrammetry by Unmanned Aerial Vehicle
(UAV) very high resolution (VHR) images, providing satisfactory results
for the calculation of the area and the volume of water that the wetland
could, theoretically, store.

The images were obtained with fixed-wing UAV: model eBee Plus RTK/PPK;
wingspan 110 cm; weight 1.1 kg; powered by electricity; radio link range
\textasciitilde{} 3 km; cruising speed 40--110 km/h (24.8--68.3 mph);
wind resistance up to 45 km/h (72.45 mph); maximum flight time
\textasciitilde{} 50 minutes, with no need for ground control points
(GCP) in real-time kinematic (RTK) or post-processed kinematic (PPK)
mode; and absolute and relative accuracy of X, Y, Z coordinates
(RTK-/-PPK) of up to 3 cm, with a flight altitude of 120 meters. For
imaging, we used the optical sensor RGB SenseFly S.O.D.A. (Sensor
Optimised for Drone Applications), with 20 megapixels.

The flight plan and the correction of the images in PPK mode were done
in Emotion Software 3.0, by SenseFly (Figure 2 A). The predicted
cross-flight lateral and longitudinal overlap was 70\%, ensuring high
data accuracy by obtaining a large number of images and covering the
target with overlaps in two different directions (d'Oleire-Oltmanns,
Marzolff, Peter \& Ries, 2012), resulting in better digital surface
models (DSM) for the relief analysis. During processing, we were able to
overlap an average of nine images of the area where photogrammetry was
performed (Figure 2 B).

Next, we performed planialtimetric correction using GNSS data installed
in the field during flyovers, which allowed us to obtain post-processing
through the Precise Point Positioning service of the Brazilian Institute
of Geography and Statistics (IBGE-PPP, www.ibge.gov.br). This operation
performs a correction to PPP mode using GPS RINEX (Receiver Independent
Exchange Format) data (Matsuoka, de Azambuja, Souza \& Veronez, 2009).
Our fieldwork equipment was the GNSS Topcon Hiper PRO RTK (GPS/GLONASS
dual receiver), used only for the base unit in operation for 04 hours of
point recording. The rover unit was onboard eBee Plus RTK/PPK (Figure
3).

The coordinates corrected by GNSS have significantly greater accuracy
than the conventional GPS onboard the UAV. The PPK processing greatly
increased the model's reliability, resulting in a precision of 0.038 m,
against 6.220 m with the common GPS (Table 1).

The last step was to process all aerial photographs and GNSS data in the
Agisoft Metashape software and perform the digital photogrammetry. This
computational operation aligned all the images, creating an orthomosaic.
Processing proceeded with the tie point step (generated 5.716 points),
followed by the generation of dense cloud (15.699.250 points) and the
creation of the 3D model (1.046.616 faces). The digital surface model
(6677x5328) and the orthomosaic (19623x16048) were then processed and
readied for classification. Finally, after the classification of image
data, the contours of the wetland's compartments were created
(shapefile) and their area and volume calculated. In order to identify
each compartment of the wetland, the digital surface model and the
orthomosaic data were processed in a GIS platform. The processing
consisted in tests of percentage declivity of the surface and by visual
interpretation of the relief. Thus, the \emph{Minimum bounding geometry,
Erase, Multipart to singlepart} and \emph{dissolve} tools were applied
to define the exact declivity of the murundus, which enabled the
differentiation of three declivity-density distinct compartments.

To calculate the volume, Agisoft Metashape performs interpolation and
value integration using the Best Fit Plane (BFP) tool, by the generation
of a plane between each of the compartments and the calculation of the
volume below that plane. The plane is generated from automatic analysis
of the topographic perimeter profiles of the compartments (Figure 4).
The generated plane minimizes the root mean square (RMS) of the
orthogonal distance between each vertex of its polygon.

Considering that soil saturation and the elevation of the water level
above the land surface occur first in the center of the topographic
depression and the cumulative saturation increases towards the border,
we used the Best Fit Plane tool to establish an average water height
limit for each compartment based in field observations, in order to
produce a more reliable volume estimate.

The vegetation does not influence the estimated volume considerably,
since wetlands consist largely of grassy area. Therefore, it was not
necessary to generate a digital terrain model; a digital surface model
was adequate for all calculations. The volume of each compartment is
directly influenced by the elevations of the perimeter, by the number of
mounds, and by the terrain's unique morphology. In order to subtract the
space occupied by the mounds it was necessary to quantify them. The
processing is performed through the slope classes that better
represented the murundus contour of the study area. Percentage slope
tests were performed with indices of 10\%, 15\%, 20\% and 25\%. The
slope index that came closest to the correct mapping was 20\%.

\textbf{2.3 Electrical resistivity tomography (ERT) 2D and pseudo-3D}

The geophysical surveys were designed to provide the best possible
understanding of subsurface hydrodynamics, \emph{i.e.} , groundwater
behavior and infiltration zone architecture. This technique is rarely
applied in wetlands because the equipment is difficult to install in the
field (long power cables and batteries). Therefore, electrical
tomography was performed to estimate the flow direction of groundwater,
showing the direction of the superficial water flow and a horizontal
infiltration pattern in the center of the concavity (compartment 3).

The ERT technique involves the DC Resistivity method for geophysical
acquisition data, measuring the electric resistivity parameter using
electrodes fixed on the surface of the ground, connected to the
measuring instrument by cable assembly (Keller \& Frischknecht, 1966;
Telford, Geldart \& Sheriff, 2004).

The data was acquired using the Schlumberger arrangement, with 2 m
between each iron electrode. We assembled nine 80-meter lines in the
direction N45°W, spaced 10 meters apart, and one 80-m line perpendicular
to all the others (Figure 5).

For data acquisition, we used the Terrameter LS (ABEM Instrument,
Sweden), which can measure resistivity from a single pre-programmed
module for transmitting and receiving automated signals, with 250 W, 1
\selectlanguage{greek}µ\selectlanguage{english}V resolution, and a maximum current of 2.5 A (ABEM 2012). This
equipment was calibrated to the following specifications: transmission
of 200 mA for 1s for each measurement. During acquisition, the data were
stored in the equipment's internal memory, then exported via USB
interface as DAT file format compatible with the input format of word
processing software.

The data were processed and manipulated to obtain a two-dimensional
electrical profile and three-dimensional models of resistivity (\selectlanguage{greek}Ω.\selectlanguage{english}m).
All data were processed in the Res2Dinv software, which automatically
performs the following tasks: (1) determining a two-dimensional (2D)
model of resistivity of the subsurface, using data obtained through
electrical imaging surveys (Griffiths \& Barker, 1993); (2) quickly
generating 2D inversion models for DC Resistivity, using the algorithm
of smoothness constrained least-square method; and (3) performing
topographic modeling when reversing the data set, which incorporates the
topography within the mesh model (Geotomo Software 2003). The
topographic data was corrected by the GNSS in field.

The Geotomo Software applies the smooth inversion method, which is the
mathematical method of least squares that uses the optimization
Gauss--Newton and quasi-Newton, described by Loke \& Barker (1996). This
method is based on cells; the software recognizes the terrain surface as
rectangular blocks, with constant values for each parameter studied
(DeGroot-Hedlin \& Constable, 1990). The results are 2D models of
inversion of resistivity data, based on distance (length of the
acquisition lines) and depth, and presented in logarithmic graphic scale
and intervals of color interpolation values.

The numeric data of the 2D inversion for each compartment were assembled
in a single spreadsheet, compiling the positions of readings along the
lines (variable x), spacing between the lines (variable y), depth
modeled by inversion (variable z), and the value of electrical
resistivity (R), which is subsequently used to generate the pseudo-3D
visualization models. A pseudo-3D model is a method used for rendering
visual-data with a sense of depth (Z axis).

The pseudo-3D model was processed in the Oasis Montaj platform, where
the 2D data obtained in the Res2Dinv software were interpolated using
the minimum curvature method to generate pseudo-3D visualization models.
The models can be visualized as layers, according to the chosen depth.

Visualization of pseudo-3D models generated from geophysical data
contributes to our understanding of complex geological structures and
hydrological problems. This methodology has many environmental
applications and is described in other studies (Moreira et al., 2012,
2016a, b, 2017, 2018; Veloso, Moreira \& Cortes, 2015; Cortes, Moreira
\& Veloso, 2016; Vieira et al. 2016; Helene, Moreira \& Carrazza, 2016;
Carrazza, Moreira \& Helene, 2016).

\textbf{3. Results and Discussion}

\emph{3.1 Potential of UAV imagery to study soil moisture and stock of
water in wetland}

The selection of the modeling approach depends, besides the modelling
objectives, on both scientific and technical aspects as well as on the
resources available, which include the scale of the simulation domain,
topography, hydrological and topographical data available and the
complexity of the hydraulic regime (Wester et al., 2018). For these
reasons, to estimate water storage the studied wetland was subdivided
into three compartments, in order to facilitate the hydrodynamic
modeling. The compartments were defined through rigorous analysis of the
images (RGB and DMS) generated by UAV, which showed distinct
pixel-information about soil surface inside that vary according
topography. Thus, we considered the topography and terrain morphology:
the slope, from the border to the center of the topographic depression
that characterizes the wetland site, and the density of the
microtopography (mound fields), which is directly related to the level
of hydromorphism in the wetland (Rosolen et al., 2019).

Between the border and the center of the depression, compartment 1
corresponds to its boundary with the well-drained plateau; compartment 2
is the intermediate segment between compartments 1 (boundary) and 3
(center); and compartment 3 corresponds to the central part of the
topographic depression (Figure 2 C).

The data acquired by the UAV images allowed us to estimate the amount of
water that each compartment can support and, therefore, the total water
volume that can be stored on the surface of the studied wetland. Based
on the data of the altitude, geographical location and the slope in
relation to adjacent points of each pixel of the images we calculated
the volume by delineating a 3D polygon in each compartment. Because the
mounds (\emph{murundus} ) do not fill up with water, their volumes were
subtracted from the total volume of the compartments.

The result of semi-automatic extraction through slope and visual
interpretation of the orthomosaic obtained 1751 mounds features in the
research area. The biggest mound have 3643.40 m\selectlanguage{ngerman}² and the smallest mapped
shape was 1.01 m². Regarding the perimeter recorded in the extraction,
the smallest recorded form was 4.60 m and the largest with 476.63 m.
Figure 6 shows the histograms with murundus distribution by area and
perimeter in the research area. It is observed that the murundus of the
research area are mostly distributed with perimeters ranging from 24.48
m to 58.49 m and the area are distributed mostly between 67.41 m² and
167.37 m².

When each compartment is flooded, it displays a distinct water residence
time and dynamic associated with its topographic position. Water
flooding over the land surface reaches compartment 1, during the
rainfalls. In compartment 3 the water level remains high for a prolonged
period due the lateral water influx from compartment 2 and 1. Clearly,
there are variation in soil moisture pattern linked to topography, and
that variable has a major impact on the hydrology in landscape (Moore et
al., 1991). The largest amount of microtopography with little trees and
shrubs on their top occurs in Compartment 1 in agreement with lower soil
moisture. Wetland area also exhibit grassy cover in Compartment 3 in
agreement with the elevated soil moisture. These differences of soil
moisture observed in field are in accordance to features observed in the
aerial imaging.

Rainfall seasonality determines the wetland's water supply. In the
field, we observed that the wetland area is a natural seasonal water
reservoir, filling up in rainy months and drying up during dry months
(Figure 7 A, B). We performed the photogrammetry at dry months in order
to estimate the water stock during the rainy period.

Table 2 shows each compartment's total storage capacity, in cubic meters
and liters (Figure 2 D), its area in square meters, and the minimum and
maximum altitudes that were used in the calculations. The altitude
values \selectlanguage{english}\selectlanguage{english}were calculated for each pixel of the images of each
compartment.

In compartment 3, we conducted the electrical tomography tests to
provide an understanding of subsurface hydrodynamics and identify the
architecture of soil and rock, which acts as an infiltration zone.

\emph{3.2 Water flow paths by using 2D and pseudo-3D ERT data}

An overall analysis of all electrical tomography data allowed the
definition of a single scale of values, \selectlanguage{english}\selectlanguage{english}facilitating comparisons
between the sections. For the electrical resistivity data, we defined a
variable scale between 200 \selectlanguage{english}.m and 10000 \selectlanguage{english}.m, the lower and higher
values \selectlanguage{english}\selectlanguage{english}measured during the field data acquisition. The definition of
these values enabled the distinction of low-resistivity zones (cool
colors) and high-resistivity zones (hot colors) in the 2D lines and
pseudo-3D maps.

The images representing the inversion of electrical resistivity data
(Figure 8) show subsurface heterogeneity; the low resistivity values may
be associated with groundwater (blue colors), and in contrast, the high
resistivity (dark green, brown and red colors) values \selectlanguage{english}\selectlanguage{english}suggest the
presence of dry soil (Keller \& Frischknecht 1966; Telford et al. 2004;
Loke, 2004). In the analysis of the studied wetland, low values \selectlanguage{english}\selectlanguage{english}of
resistivity ranged from 200 to 612 \selectlanguage{english}.m; intermediate values from 1069 to
1870 \selectlanguage{english}.m; and high values \selectlanguage{english}\selectlanguage{english}of resistivity from 1870 to 10000 \selectlanguage{english}.m.

All ten lines showed clear contrasts in resistivity values, with both
low and high values. Lines 2, 3, 4, 5, 7 and 10 best represent the
wetland contrasts (Figure 8).

Stretches of lines 5 and 10, which ran perpendicular to each other, were
located exactly in the lower topographic level of compartment 3 of the
wetland. In these stretches, low-resistivity zones (blue colors) are
visible immediately below the soil surface, reaching the deep aquifer.
These zones are where surface water interacts with the groundwater and
the aquifer recharge occurs (McLachlan, Chambers, Uhlemann \& Binley,
2018). Similar features having low resistivity show a lateral continuity
in the distribution of the aquifer and are present in all parallel
lines. Deeper in the regolith, just below the low-resistivity features
(6--8 meters depth), lay sharply contrasting zones of high-resistivity
features (dark green), indicating a barrier to vertical water flow. At
this level, saprolite rock supports the aquifer and imposes a lateral
continuity of the water body. In the soil mantle and saprolite, layers
of increased saturation lead to more lateral hydraulic conductivity
(Appels et al., 2015). The dense and fine-textured layers in the bedrock
and soil mantle retain the water flow (Freer et al., 2002). Such
stratification is typical in the Serra da Galga Formation (Fernandes \&
Ribeiro, 2015).

The aquifer is shallow, because the low-resistivity zones begin at two
meters deep (964 m.a.s.l.). However, the aquifer has a vertical
extension of at least 12 meters in some places, which is most visible in
the lines 2, 3, 4 and 5. This demonstrates a connection between shallow
subsurface water and aquifer-water, in a genuine interaction.

Zones with intermediate values (1069--1870 \selectlanguage{english}.m) (light green) present
features that suggest transition zones, where water may be contained in
soil porosity, indicating that the aquifer is not confined. The fine
soil texture occurring here can also retain water and shows lower
resistivity (Greer et al., 2017).

The physical parameter of electrical conductivity is a response from the
water content or the contents and composition of the soil and rock
solutions. When rocks are saturated, the electrical conductivity of the
solutions usually increases, decreasing the electrical resistivity
values. Various studies indicate a positive relationship between
electrical conductivity and both porosity and hydraulic conductivity of
an aquifer; \emph{i.e.} , low resistivity reflects higher porosity and
higher hydraulic conductivity (Kelly, 1977; Kelly \& Frohlich, 1985;
Urish, 1981; Chandra, Ahmed, Ram \& Dewandel, 2008; Chukwudi, 2011;
Moreira, Cavalheiro, Pereira \& Sardinha, 2013).

High-resistivity zones in the central region (values between 1870 and
5719 \selectlanguage{greek}Ω.\selectlanguage{english}m) are a common feature of all lines and indicate the presence of
low-permeability materials, with no significant presence of stored
water.

Generally, the most superficial portions of the soil (0 to 2 meters
deep) present high-resistivity zones, sometimes throughout their entire
extent, such as in line 7. This occurs due to the presence of very
clayey soils with low hydraulic conductivity, making the studied wetland
an excellent natural reservoir.

In order to enhance the visualization and understanding of groundwater
behavior, pseudo-3D models were produced by interpolation of data
generated by the 2D inversion of electric tomography lines.

From the pseudo-3D models, resistivity maps were generated for every two
meters of depth, from 966 m a.s.l. (Surface) to 950 m a.s.l. (16 meters
deep). Thus, nine resistivity maps were generated (Figure 9).

The map representing the surface (966 m a.s.l.) shows that the central
region has lower resistivity (values below 3270 \selectlanguage{greek}Ω.\selectlanguage{english}m) than the rest of
the map, indicating zones of more permeable soil and, thus, of possible
aquifer recharge. At greater depths in the soil mantle, high-resistivity
materials are evident in the central region, oriented in the NW-SE
direction. Up to 6 meters deep (960 m a.s.l.), the water seems to flow
horizontally, while from 8 meters deep (958 m a.s.l.), the resistivity
in this zone intensifies, causing the water flow to move towards the
southwest and, in greater quantity, towards the northeast. Yet, in the
southeast region of the maps, the low-resistivity zones continue as the
depth increases. This trend suggests that the low and high-resistivity
zones must grow closer together at greater depths, indicating that the
aquifer is connected.

\emph{3.3 Revealing the integration of soil-aquifer in wetland combining
UAV-ERT tools}

Based on a joint analysis of the surface geoprocessing and on our
understanding of the groundwater dynamics, we were able to ascertain the
occurrence of a lateral water flow from compartment 1 that is,
theoretically, responsible for prolonged flooding in compartment 3.
Hillslope with saturated flow dominates lateral flow and reflects the
relative contribution to the lateral hydraulic potential gradient of the
relief of the lower-most impermeable boundary (Harman \& Kim, 2019).
During wet periods, for instance, when water tables are close to the
surface, shallow subsurface flow can occur (Brannen, Spence, \& Ireson,
2015) due to the formation of an effective transmission zone (van der
Kamp \& Hayashi, 2009) (Figure 10).

In the field, we observed that compartment 3 remains flooded for the
longest period. This observation, in addition to the analysis of the
electrical resistivity tomography, justifies the hypothesis that direct
aquifer recharge occurs in this compartment.

In the area, the soil water has been supplied by rainfall concentrated
during the summer seasons (October-March). In the end of that period,
the water level remains over the land surface conducting to long-term
soil saturation characterizing a typical wetlands hydrology. That
conditions are accept when the water table is within 30 cm of soil
surface or above the surface for period of 14 days or more in a year
(Vepraskas and Lindbo, 2012). The studied wetland is not connected with
any river by surficial channel, imposing the vertical flows towards the
shallow aquifer. That dynamic characterizes the hydrology in the
topographic depression where surface (water that enter in soil by rain)
and subsurface (shallow aquifer) waters are mixed. The soils in catena
allow the direct recharge of aquifer although the occurrence of
subsurface massive layers with higher bulk density could diminish the
velocity of vertical flow. That layer has light grey color, in the ERT
sections, due to iron-depletion and is continuous in the entire
extension of the catena.

Combining soil morphology and ERT images made it possible to propose a
broader hydrologic interpretation on wetland on the plateau. On the
plateau, the topographic depression that characterize wetlands with
aquic soils are zones of aquifer recharge characterized by preferential
downward movement of water from periodically unsaturated soil surface to
saturated groundwater. The vertical flows occurred by the opening of
leakage point in the center of the depression (compartment 3) which was
formed by geochemical erosion, particularly the iron-depletion from soil
matrices. The contemporary hydrology in the wetland is a result of soil
landscape changes closed linked to its past geological history. The flat
plateau is a remnant of South American Surface and the landform was
preserved by the presence of continuous ferricrete. Regional
environmental changes to more humid climatic conditions
(Salgado-Laboriau et al., 1998) enhanced the leaching of iron from the
oxidized phase in the ferricrete opening soft zones that act as
preferential flows path to water. The chemical process (deferrugination)
is followed by loss of pedality resulting in topographic lowering (Lucas
and Chauvel, (1992), Antonellini et al., 2019). The topographic
depression developed in flat surfaces retains water inducing to
endosaturation of soils (Vepraskas and Lindbo, 2012), which induces the
rejuvenation of the old lateritic materials. Ferricrete developed in
lateritic soils is an impermeable layer that can retains rainwater over
in a pershed watertable on the highest positions of the landscape
(Tardy, 1993). The excess of water in top soil intensify ferricrete
dissolution opening permeable points through which the soil water
percolates deep in the profile reaching the shallow aquifer
characterizing a recharge zone by leaching and redox depletions (Evans
and Freeland, 2000). That soil-water process corresponds to the
contemporaneously hydrology in the wetland developed in the plateau.

\textbf{4. Conclusion}

The study aimed to test the efficacy of remote sensing and geophysical
techniques to quantify and understand the water cycle in a savanna
wetland of Brazil. ERT and the digital aerial photogrammetry from RGB
sensors aboard UAV are non-destructive techniques for assessing water
cycling in wetlands and can be used to identify, quantify and promote
the protection of water resources. The studied wetland is a depressed
area in a well-drained lateritic environment in the Cerrado biome,
surrounded by agricultural areas. The wetland acts as a natural seasonal
rainwater reservoir, storing up to 416.996,3 m\selectlanguage{ngerman}³ of water on the land
surface during the rainy months. The distinct internal compartments that
characterize the wetland morphology present distinct water storage.
Furthermore, water is retained in soil porosity, feeding the aquifer by
vertical flow and permeable and impermeable layers in soil and rock
characterize the complex hydrology of this environment. Surface water
and aquifer are connected in the center of the wetland through direct
vertical flow, whereas the subsurface heterogeneity interrupts the flow
direction, driving the water laterally and expanding the flooded area.
Remotely sensed VHR images allied with geophysical techniques allowed
complete surface and subsurface imaging and offered visual tools that
significantly contribute to understanding hydrodynamics of the wetland.
Further studies will be focused on assessing water use conflict between
wetland water storage and agricultural consumption.

\textbf{Acknowledgement}

This work was financially supported by the FAPESP - Fundação de Amparo à
Pesquisa do Estado de São Paulo (Processes n° 2017/14168-1) and by the
CNPq -- Conselho Nacional de Desenvolvimento Científico e Tecnológico.

\textbf{Data Availability Statement}

The data that support the findings of this study are available from the
corresponding author upon reasonable request.

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\textbf{Table 1 -- Coordinates correction from GPS to GNSS. RTK (Real
Time Kinematic) error of 6.220 m and PPK (Post Processed Kinematic)
error of 0,038 m.}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
\textbf{Geotags (GPS)}\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\textbf{RTK}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0\%\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\selectlanguage{greek}σ \selectlanguage{english}= 0,000 m\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\textbf{Standalone}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
204\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
100\%\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\selectlanguage{greek}σ \selectlanguage{english}= 6.220 m\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\textbf{Overall}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
204\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
100\%\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\selectlanguage{greek}σ \selectlanguage{english}= 6.220 m\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\textbf{Geotags (GNSS)}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\textbf{PPK}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
204\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
100\%\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\selectlanguage{greek}σ \selectlanguage{english}= 0.038 m\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\textbf{Standalone}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
0\%\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\selectlanguage{greek}σ \selectlanguage{english}= 0.000 m\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\textbf{Overall}\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
204\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
100\%\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\selectlanguage{greek}σ \selectlanguage{english}= 0.038 m\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2 - Volume, area and minimum and maximum altitudes of
internal}

\textbf{wetland compartments 1, 2 and 3.}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
\textbf{Compartment 1}\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
\textbf{Compartment 2}\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
\textbf{Compartment 3}\strut
\end{minipage} & \begin{minipage}[b]{0.19\columnwidth}\raggedright\strut
\textbf{Total}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Volume} (m\selectlanguage{ngerman}³)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
313.121,00\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
85.923,20\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
17.952,10\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
416.996,3\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\textbf{Volume} (Liters)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
313.121,000\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
85.923,200\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
17.952,100\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
416.996,300\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Area} (m\selectlanguage{ngerman}²)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
141.239,30\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
150.481,20\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
134.344,30\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
426.064,8\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Max. Altitude} (m)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
976.39\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
974.91\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
973.01\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
--\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Min. Altitude}\\
(m)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
968.7\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
968.7\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
968.7\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
--\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure legends}

\textbf{Figure 1 -- Geographic location of the studied wetland.}

\textbf{Figure 2 -- (A) Predicted cross-flight performed in the field by
the UAV for image acquisition.} \textbf{B) Graphic showing the spatial
overlap of the images, indicating high overlap in the region of interest
(average of 9 longitudinally and laterally overlapping images). (C)
Compartmentalization identified through aerial image processing: from
border to center, compartments 1, 2 and 3. (D) Volume of stored water,
in cubic meters, calculated for each compartment. In compartment 3, the
water level remains higher for a longer period.}

\textbf{Figure 3 -- Description of eBee Plus RTK/PPK UAV, and GNSS
(base) support mode.}

\textbf{Figure 4 - Topographic profile of the perimeter of each
compartment, used by the software to define the \emph{best fit plane}, a
plane that limits the polygon and calculates the area and volume of
water that each compartment can store.}

\textbf{Figure 5 -} \textbf{Position of the electrical tomography (ERT)
lines in the study area.}

\textbf{Figure 6 - Histogram with \emph{mounds} distribution by (A)
perimeter (B) and area.}

\textbf{Figure 7 -- Field observations of rainfall seasonality. (A)
August 2018, the month with the lowest precipitation, when compartment 3
was dried up. (B) December 2018, the month with the highest
precipitation, when compartment 3 was already flooded.}

\textbf{Figure 8 - Inversion models of resistivity for the high-contrast
sections of the set obtained in the field. The dashed line marks the
areas of low resistivity (aquifer), indicating its spatial disposition.
The arrows indicate the water flow, which migrates from vertical to
lateral in the subsurface.}

\textbf{Figure 9 - Maps of resistivity between the shallower and deeper
positions of investigation. Dashed lines indicate the location of
horizontal infiltration (recharge zone of the aquifer) and flow change
(lower resistivity as the depth increases).}

\textbf{Figure 10 - Schematic model of the wetland surface and
subsurface hydrodynamic during the dry and rainy seasons.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2-A-B-C-D/Figure-2-A-B-C-D}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-3/Figure-3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-4/Figure-4}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-5/Figure-5}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-6/Figure-6}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-7--A-B/Figure-7--A-B}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-8/Figure-8}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-9/Figure-9}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-10/Figure-10}

\end{center}
\end{figure}

\selectlanguage{english}
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\end{document}

