MultiScience - XXXIII. microCAD International Multidisciplinary Scientific Conference

University of Miskolc, 23-24 May, 2019, ISBN 978-963-358-177-3

TEMPORAL SOIL MOISTURE DATA ESTIMATION USING CITI-

ZEN OBSERVATORY DATA.

Daniel Kibirige1, Endre Dobos1, Kárloy Kovács1, Lajos Gál-Szabó1, András

Dobos1, Balint Pinezits1, Drew Hemment2

PhD Student

1Univerisity of Miskolc, Department of Physical Geography and Environmental

Sciences, Institute of Geography and Geoinformatics

2University of Dundee, Duncan of Jordanstone College of Art & Design

ABSTRACT

The paper examines the spatial and temporal distribution of soil moisture conditions in ag-

ricultural areas of Tard. The monitoring network was developed within the framework of the

“EU” funded “Grow” Horizon 2020 project. The soil moisture sensors in the sample area

were planted at the depths of 0 - 10 cm, 10 - 20 cm, from the plough layer most exposed to

meteorological and anthropogenic influences. The data measured by the sensors was logged

every 15 minutes and recorded in a geospatial database based on the exact coordinates of the

devices. The data were evaluated weekly and in chronological order. Continuous files were

generated from the point data obtained, allowing continuous observation of the examined

areas and their representation in the map file. In the course of the study, soil moisture content

was analysed at the data collection times, taking into account the terrain and soil parameters

as well as looking for changes and anomalies. Finally, complex finding were made on the

water balance of the examined genetic soil level. The University of Miskolc has carried out

several types of research on various aspects and topics in the field, the data of which were

integrated into our soil moisture estimation algorithms. Soil, water and local morphological

features had a close statistical relationship with each other, so they explain the rate and trends

of spatial and temporal changes in soil moisture. With long-term studies, we can identify the

phenomena of groundwater fluctuations, so that the periodic changes in the moisture content

of the soils can be mapped and traced.

INTRODUCTION

Recognition of the need for spatial soil information continues to grow as the advancement

in technology increases (Brevik et al., 2016). Where soil maps had once been seen as pri-

marily serving agricultural and cadastral purposes, increased awareness about the im-

portance of soil's role in environmental systems is driving new demands for better-digitised

soil maps (Miller, 2012). The technology already in existence today makes it possible to

DOI: 10.26649/musci.2019.006

create soil maps with greater global coverage, higher accuracy for specific soil properties

(e.g. soil moisture), and finer spatial resolution (Omuto, et al., 2013).

Soil Moisture (SM) is a key variable in the climate system and a key parameter in earth

surface processes; particularly in water and energy cycles, as it impacts the total volume of

water following on the earth’s surface and beneath the surface (Bablet, et al., 2018). SM is

defined as the volume of water content (units (vol/vol) at a particular time within a particular

space in the unsaturated layer (ideally 5-10 cm of soil surface) of the soil profile (Entekhabi,

et al., 2010). Therefore the variance between different soil profiles is critical in

understanding the spatial and temporal soil moisture patterns.

The spatial and temporal distribution of SM is regarded as a vital factor in various, hydro-

logical, meteorological (Kornelsen & Coulibaly, 2013) and remote sensing applications

(Purdy, et al., 2018). In hydrological studies, SM is critical in highlighting the relationship

between infiltration and runoff whereby infiltration is a determinant in verifying the amount

of water available for vegetation growth. From a meteorological perspective, SM and the

related flux between soil and the atmosphere have an important impact on the earth’s cli-

matic changes. Due to the advancement of remotely sensed images over the past decade,

remote sensing has been used as a tool in mapping soils of the world at global, national and

regional scales. In particular, the delivery of soil survey data has improved through the de-

velopment of Digital Soil Mapping (DSM) (Al Yaari et al., 2016, Zhang, et al., 2017).

DSM has allowed researchers to map soil properties quantitatively with the help of different

data sources and on a range of different spatial scales (McBratney et al., 2003, Boettinger,

et al., 2010). DSM requires a smaller soil sampling effort, compared to more traditional

mapping methods (McBratney et al., 2003; Bakker, 2012). The smaller amount of sampled

soil data is harmonised with environmental covariates from different sources, which are di-

rectly related to the soil. The various environmental covariates used in DSM are expected to

be related to the soil through the soil forming functions of Jenny (1941) equation.

To date, a large proportion of satellite imagery focuses on larger spatial scales (global and

national), and there seems to be a lack of methods for soil mapping at local scales (Rossiter,

2004, Malone, et al., 2018). In particular, mapping of SM at a local scale is limited, and as

such, this study focused on testing a method to estimate SM spatially and temporally for

local areas. Emphasis was placed on the terrain parameters that have a direct impact on soil

moisture. SM was selected as the soil property of investigation for two reasons, (1) moisture

is a principal soil property that affects and the physical, chemical and biological character-

istics of soil such as water retention capacity, temperature, nutrient transport and plant

growth (Montanarella, et al., 2016); (Colliander, et al., 2017) of which are vital in the agri-

cultural industry. (2) Several studies have predicted soil moisture at national and regional

scales which means there is a need for predicting soil moisture at a local scale which could

be of greater significance for local farmers and communities.

STUDY AREA

The study area is located on the southern tip of the mountain range of the North-Western

Carpathians, on the southern tip of the Bükk Mountains (Figure 1). Bükkalja is located in

the South to South East part of the Bükk Mountains, 8-10km wide, stretching from Miskolc

to Demjén in the north-western direction. The Tard village is located in the Tardinian Basin

between the two south-facing plateaus of the mountain range. The study is split by the Tardi

stream, the middle section and on the western part. The study area is surrounded by hills

with a height of 190, and 200 meters above mean sea level.

Figure 1 Site locality

A moderately warm-dry climate characterises Egri-Bükkalja. The latitude difference be-

tween the area is negligible, and only the terrain factors influence the differences between

the climatic factors. The average annual temperature in the higher areas is 8°C and in the

lower areas is 9-10°C (Dovenyi, 2010). From mid-April to mid-October, the number of frost-

free days is approximately 185 days, but the terrain greatly influences them. The maximum

average summer temperature is 34°C, and the mean winter minimum is -17°C. The amount

of precipitation is 640 mm while the amount of precipitation during the planting season pe-

riod is 340-380 mm. The average number of days of snow covering the surface is around 45

days within an approximate thickness of 16-18 cm. The dominant wind direction is NWN,

the formation of which is facilitated by the terrain.

SCORPAN FRAMEWORK

Some researchers have compared and contrasted suites of models for the spatial prediction

of soil properties, classes and attributes to identify the optimal model for a particular situa-

tion and region (Malone et al., 2018). This section is to present the Scorpan framework which

is more formally known as scorpan-SSPFe (soil spatial prediction function with spatially

auto-correlated errors) framework (McBratney et al., 2003, McBratney, et al., 2018). Within

this framework, a spatial soil inference system (SSINFERS) will be described which

incorporates the Scorpan equation as a fundamental guideline in soil spatial analysis

(McBratney et al., 2003, Lagacherie & McBratney, 2007).

𝑆𝑎 = (𝑠, 𝑐, 𝑜, 𝑟, 𝑝, 𝑎, 𝑛) + 𝜀 or 𝑆𝑐 = 𝑓(𝑠, 𝑐, 𝑜, 𝑟, 𝑝, 𝑎, 𝑛) + 𝜀 Equation 1

Whereby Sa refers to a soil attribute and Sc refers to soil classes

As defined by McBratney, et al., 2002, “a soil inference system takes information on what

we more-or-less know with a given level of uncertainty and infers data that we do not know

with minimal inaccuracy, using properly and logically conjoined functions.”

Spatial Soil Information System

The Spatial Soil Information System (SSINFOS) includes two components, soil and auxil-

iary databases (Lagacherie & McBratney, 2007) (Figure 2). The soil database comprises of

various soil information such as soil observations, basic soil properties, e.g. soil classes

(sand, silt or clay), pH, temperature as well as infiltration properties, for example, soil mois-

ture and field capacity. Then, within the SSINFOS, there shall be a link to the auxiliary

database which comprises of environmental covariates. These covariates are based on the

Scorpan equation which is a conceptual model of soil spatial inference. In this study, of the

Scorpan variables, the emphasis was placed on terrain-related variables. Regarding the Scor-

pan variables, two terrain variables were analysed; vegetation & land use (o) and relief (r).

Figure 2 Spatial soil inference system. Adapted from Lagacherie & McBratney, 2007 p. 12

In support of McBratney, et al., 2003; Dobos et al., 2006 initially proposed a Spatial

Inference System (SIS) to be used as DSM steps for decision making and policies

management within the European Union (EU) primarily based on both spatial and

environmental modelling. This proposal was then also supported by Hartemink &

McBratney, 2008 and more recently by Arrouays, et al., 2017. The end goal of DSM should

be to achieve change in policies. The SIS approach, in combination with the Scorpan

equation, was used to predict soil variables at each location within the study area. A common

spatial prediction technique that can be used to apply the Scorpan equation is the regression-

kriging (Hengl, et al., 2007) which was used to illustrate the general flow of data through

the system to estimate soil moisture and its associated parameters (Dobos, et al., 2006).

These models assumed that there was a stochastic relationship between various predictors

and target soil variables, although it can also be used to improve the deterministic models of

soil genesis (Hengl, et al., 2007).

1. Sampling design 2. Select a method for modelling soil

moisture 3. Select Covariates

4. Preprocessing of data

5. Soil moisture estimation

Figure 3 Data-flow used to interpolate soil variables from profile observations using auxil-

iary information (the regression-kriging model); adapted from Dobos et al., 2006, p 18

Figure 3 incorporates the Scorpan equation variables in a systematic flow diagram that

makes use of soil profile information and remote sensing indices in order to create layers of

predictive soil components. For this study, only a Digital Elevation Model (DEM) was used

for prediction. From the DEM terrain parameters that had the highest correlation with the

dependent (soil moisture) variable was derived using ArcGIS software. These soil predictive

components together with soil profile observation data were run through multiple linear re-

gression model in order to analyse the relationship between predictors and soil moisture.

METHODOLOGY

This study was based on a quantitative approach and aimed at developing a method for map-

ping soil moisture on a local scale with the use of primary soil moisture data. DSM and the

Scorpan-SSPFe framework were the backbone conceptual framework that guided the study

through a five-step approach.

1. Sampling design

The study area comprises of 1700 Hectares (Ha) of agricultural land. The land-use on these

parcels range from wheat, barley and canola from September to June and sunflower and corn

from April to June. It is important to note that soil sensors were placed on parcels that contain

wheat, barley and canola as this is a more extended period to analyse the change in the soil

moisture across the different crop (Figures 4, 5 & 6).

Figure 4 Landuse - March 2018

Figure 5 Landuse - April 2018

Figure 6 Landuse May 2018

The total area where in-situ soil moisture sensors were installed was approximately 1200 ha.

The reason for this was to show that more than 70% of the study area was covered during

the study indicating a good variance in soil types and land use. They were purposively placed

at upper, middle and lower elevations to get an appropriate spatial distribution of sensors

across the study area. A total of seventy-eight (78) soil sensors were installed at thirty-nine

(39) locations. At each location two soil sensors were installed at depths, 0 - 10 cm and 10

– 20 cm below the surface of the ground. The soil sensors recorded a comprehensive dataset

(temperature, light, soil moisture and fertiliser) at 15-minute logging intervals.

2. Select a method for modelling soil moisture

The next step was to select a modelling method for the prediction of soil properties. Based

on the literature, multiple linear regression was selected because it is a suitable method for

digital terrain modelling analysis (Bakker, 2012).

Multiple linear regression (MLR) is adept at predicting soil properties with multispectral

images and has a significant advantage as it can handle nominal covariates. MLR involved

testing the relationship between the individual covariates and the target variable by calculat-

ing the correlation coefficient. In other words, this means that the environmental covariates

were used as exploratory variables and soil moisture variables were used a dependent or

sometimes called ‘target variables’.

3. Select Covariates

A combination of literature and statistical processing was used to select several covariates.

Based on the literature, covariates had to meet two criteria; firstly, they had to represent the

soil forming factors and secondly, they had to have a direct relationship with associated

terrain parameters that can be derived from a DEM. In this study, covariates were derived

from DEM. As there are a large number of covariates that can be derived from a DEM, only

a few were selected that had a direct relationship with soil moisture. The covariates were

statistically tested through a Pearson Correlation to determine which covariates have the

highest (ideally .90 or .95) significance with soil moisture. The following covariates were

selected (Table 1).

Table 1. Selected covariates derived from DEM

Covariate Source Resolution Scorpan factor Slope EEA - HydroDEM 25m Relief Aspect EEA - HydroDEM 25m Relief Relief EEA - HydroDEM 25m Relief

Potential Drainage Density (PDD)

EEA - HydroDEM 25m Relief

4. Pre-processing of data

The DEM was projected to Hungarian 1972 Egyseges Orszagos Vetuleti, and in ArcGIS, a

clipping tool was run to select the DEM that only covered the entire study area. The most

important parameter of this study was the DEM because all other terrain parameters were

derived from it and as the terrain was selected as one of the most influential soil-forming

factors in this study. The study area varied in elevation from 110 m to 290 m (Figure 7)

above mean sea level. After that, each of the covariates was derived as listed in the steps

below.

Figure 7 Map of Digital Terrain Model of the area

Terrain Parameters

a. Slope

The first parameter derived from the DEM was the slope. The slope had an impact on

the runoff of surface water, infiltration rates and even the rate of erosion; all of which

are key to the soil moisture analysis (Figure 8). In ArcGIS, the slope was calculated using

the Spatial Analyst Tools/Surface/Slope using the DEM as the input raster. The tool

identifies the slope (gradient, or rate of maximum change in z-value) from each cell of a

raster surface.

Figure 8 Slope map of the area

b. Aspect

The next parameter to be derived was the aspect ratio (Figure 9). The direction of the

slopes affects the water retention capacity of some soils. For example, areas with south-

ern slopes are mostly exposed to the sun's radiation energy, making their surface level

drier, while the northern slopes may be colder, damper and more humid. The aspect was

derived using the Spatial Analyst Tools/Surface/Aspect.

Figure 9 Slope ratio map of the area

c. Relief Intensity

“Relief intensity” “RI” is one of the commonly used variables of surface characterisation,

which gives the differences between the highest and lowest points within a given unit of

𝐼

territory, that is, the amount of potential energy. Its value is usually expressed in m/km2.

Due to the small size of the area under investigation, we calculated a circle of 500 m

diameter with the help of the “focalrange” command (in Focal Statistics toolbox within

ArcGIS), around approach and a 10-pixel radius coming out of the 25-meter origin of a

DEM resolution of 250 meters (Figure 10).

Figure 10 Relief map of the area

d. PDD30

The Potential Drainage Density (PDD) is a measure of landscape dissection. Dissection

is difficult to measure. A potential approach to characterise the degree of landscape dis-

section is to measure the total lengths of valleys or drainage lines (Dobos & Daroussin,

2005). Drainage density (DD) is the total length of the permanent and seasonal streams

and rivers divided by a unit size of area (A) and is expressed by the following equation:

𝐷𝐷 = ∑

𝑛 𝐿

𝐴

Whereby, “L” is the length and “n” is the number of rivers. The degree of surface dis-

section is determined by the surface runoff and the permeability of the soils and the un-

derlying rock strata. The less the water infiltrates into the soil, the higher is the water

runoff on the surface, causing erosion and dissecting the land surface (Dobos &

Daroussin, 2005). Dobos & Daroussin, 2005, developed a methodology for the deriva-

tion of PDD Index. The first step in ArcGIS (using a DEM as an input raster) was to

create a DEM that is depressionless or hydrologically corrected. This step was completed

using FILL command in ArcGIS that fills all the sinks and ensures all chances of differ-

entiation on the plain areas are minimised, however, instead of filling all sinks, it is im-

portant to introduce a sink depth limit that will be used as the limit value. Any sink deeper

than the specified limit will be kept untouched. The second step was to create the flow

direction which was created by FLOWDIRECTION function to create a grid/image of

flow. It was calculated based on the following formula:

𝐷𝑟𝑜𝑝 = 𝐶ℎ𝑎𝑛𝑔𝑒 𝑖𝑛 𝑒𝑙𝑒𝑣𝑎𝑡𝑖𝑜𝑛 𝑣𝑎𝑙𝑢𝑒

𝐶𝑒𝑙𝑙 𝑐𝑒𝑛𝑡𝑟𝑒𝑠 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 ∗ 100

Then based on the flow direction grid, the size of the contributing area to each grid cell was

calculated with the FLOWACCUMULATION function of which the output grid file repre-

sented the catchment area size of each grid cell expressed as cell counts. The third step in-

volved applying a threshold value to the flow accumulation grid to create a stream network.

All cells, which had a contributing area higher than a particular threshold value, were as-

signed a value of 1, representing the drainage path. All other cells, which had a flow accu-

mulation value below the threshold were assigned “no data” and became background cells.

The fourth and final step was creating the PDD image which was run using the FOCALSUM

function. The FOCALSUM function was run on the drainage network layer and the function

summed up the cell values that fall within a predefined shaped and sized neighbourhood and

assigned the sum to the centre cell. In our case, summing was equivalent to a simple count

of the cells representing drainage lines as they all had a value of 1. The neighbourhood win-

dow then moved through the entire image and calculated the PDD value for every pixel. For

computing the PDD, a circle shape neighbourhood was chosen for which a radius expressed

as several cells were to be specified. The resultant image was then generated based on the

cells having a value referring to the length of drainage ways in a specified neighbourhood of

each cell as a raster file (Figure 11).

Figure 11 Map of PDD of the area

5. Soil Moisture estimation

The first step in the estimation of soil moisture is to link the soil moisture variable with the

covariates mentioned above. This is done within the ArcGIS software using the “Extract

Multi Values to Points” function in the Spatial Analyst toolbox. We chose the dependent

variable of soil moisture and selected five covariates (DEM, Slope, Relief, Aspect &

PDD30). The multi-values were then extracted to each point location resulting in a soil mois-

ture value for each point.

A B

The second step was to run Multiple Linear Regression (MLR) using the “Ordinary Least

Square” function within the Spatial Statistics toolbox. The input point feature class was one

of three (March, April & May) selected dates for soil moisture estimation. Then a unique ID

field was selected, the dependent variable of soil moisture was chosen, and finally, the ex-

ploratory/independent variables were selected from the five covariates. These steps were

then repeated for the other two selected dates.

𝑌 = 0 + 𝛽1𝑋1 + 𝛽2𝑋2 + ⋯ + 𝛽𝑛𝑋𝑛 + 𝑒 Equation 2

Whereby, Y is the target variable, 𝛽0 is the intercept and 𝑋1/𝑛 are explanatory variables,

𝛽1/𝑛 are the associated regression coefficients and 𝑒 is the error term.

The point output file together with the coefficient table was used to run the regression equa-

tion using the “Raster Calculator” in ArcGIS. 𝛽0 intercept value was taken from the coeffi- cient table as described above. The output raster was the soil moisture estimation for the

particular chosen time (March) frame. This estimation procedure was then repeated for April

and May.

RESULTS AND DISCUSSION

The results consisted of three scenarios; one at the end of late winter on 7th March, two in

early spring in 4th April and three towards the start of summer in 2nd May.

Scenario 1 –7th of March 2018

Figure 12 Soil moisture (%) estimation on 7th March 2018

When looking at the maps (A & B) (Figure 12), it is immediately apparent that there was a

high difference in soil moisture at 0 cm – 10 cm and 10 cm – 20 cm. This may seem surpris-

ing at first, but we need to pay attention to the fact that it was the beginning of spring and

that there may still have been frost on the ground. At that time, from the sensor data, it is

observed that the frost effect does not play a role near the surface. Additionally, at 8 a.m.,

the temperature values were above the freezing point.

An immediate flow with melting proves that a certain percentage of the pore water particles

were still frozen. Thus hardening of the surface makes infiltration almost impossible. The

runoff direction was generally in a North North East and South South East. Thus, the north-

ern areas were drier, and the southern areas were wetter due to the water flowing from the

north. From the measurements, it turns out that the deeper areas of 10 cm started to melt due

to the tensile drying of the northern area.

Moreover, because the moisture values were so high, we can assume that this melting was

already significant, but the slope-derived flow had not yet started in the lower 10 cm layer.

Emphasis was placed on that clay layer in the north-western part of our map which promoted

water retention capacity of the area, but it was seen in comparison that its influence was just

below the surface. In the southern areas, there was also a complete discrepancy between

surface and below the ground data. The run-off on the surface took longer because we were

already at the border of the Great Plain. The moisture content protects the deeper layers on

the surface from melting, so there were smaller amounts of particles in the molten state.

On several map surfaces, it clearly showed the topographical effect of the valleys on the soil

moisture regime. Most areas in the western part of our study area, demonstrated the effect

of the topography of soil moisture because most sensor pairs were located within that region.

However, it is not detectable in the Tardi valley due to the unavailability of sensors. As such,

in the present case, we were unable to present the soil moisture processes at the bottom of

the valley because we did not have sensors in the cavity. However, the naturally occurring

processes on the sides of the valley accurately reflect reality. There was a transverse erosion

perpendicular to the valley. Therefore, the increase and decrease of the slope were directly

proportional to the increase and decrease of the moisture content.

Conversely, we could not ignore the precipitation factor. Data from Szent István Agricultural

Cooperative showed on the 3rd of March, there was no precipitation, and before that, only a

small 4 mm was measured on 12th of February, but it was not significant in the days before

it (the most significant rain event in February was 20 mm).

Differential map

This section illustrates the merging of the two maps by subtracting 10 cm data from 0-cm

data to show the difference (differential) between the two data files. Out of the 39 sensor

pairs, only 30 pairs satisfied this test on this particular day.

The values of the differential map (Figure 13) come from the extraction of the two values

10 cm – 0 cm). On the map, one can see that the merging of the two maps created an inter-

mediate state. Where a 10 cm (Figure 12B) moisture content dominates, the water in the soil

has melted, or most of it was molten. Where a 0 cm (Figure 12A) moisture content domi-

nates, surface water and pore water freezing was still significant. In the soil (10 cm), melting

was the most significant where the surface was the warmest and the smallest where the sur-

face was the coolest. The alternation of these states accurately reflected the change in height

differences, as the runoff was dominant.

A B

Figure 13 Differential map of 7th March 2018

Scenario 2 – 4th of April 2018

Figure 14 Soil moisture (%) estimation on 4th April 2018

Our next date was the 4th of April, and the two maps show a more uniform picture (Figure

14). The reasons for the uniformity was that there was no frost effect and thus, the rate of

infiltration was much more consistent since the hardness of the frost layer on soil had melted

and in turn, contributed to the moisture readings. As a result of sprouting vegetation; run-off

was arguably lower than the recorded value in the previous scenario.

The course of the run-off was naturally followed by the most substantial amount of moisture

in the valley region. Therefore, the values of the deepest points cannot be treated as a bench-

mark in the map representation. Ideally, much higher moisture values should be obtained.

The Szent István Agricultural Cooperative recorded that on the 1st of April there was a sig-

nificant amount of precipitation (18 mm). Between the rainfall event and the time of the test,

there were three days. Most regions of the maps (Figure 14A & 14 B) show that the surface

and deeper layers were both drier than in the previous scenario (March). We may assume

that the infiltration was still in its initial phase. As such, lateral and transverse (West-East

and East-West) erosion within the valleys was observable, with the 0 cm and 10 cm layers

wetter in the region of erosion beds.

Differential map

Data of 28 pairs of sensors were compared out of the maximum of 39 pairs of sensors. The

difference (differentiation) of the data file showed that the difference between the 10 cm and

the 0 cm data was small. So our map has become much clearer as middle classes were a

much larger area is mapped than the extremes (Figure 15). This possibly can be attributed to

the difference in humidity between those levels of soil as being low. In the northern part of

the valley, the moisture content of the lower layer was again higher than expected, because

surface runoff dominates on steep terrain, and clay soils also played a role in the north-

western area because clay has a better water retention capacity. Moving in a southerly

direction, the surplus of surface moisture gradually increased until it became more pro-

nounced in the southern areas.

Figure 15 Differential map of 4th April 2018

A B

Scenario 3 – 2nd May 2018

Figure 16 Soil moisture (%) estimation on 2nd May 2018

Data in May consisted of 37 sensors at 0 cm and 36 sensors of 10 cm. A reason for this

difference between the maps (Figure 16A & 16B) was that the surface (0 cm) was drier than

the underground layer (20 cm) (Figure 16), resulting the 37th sensor (in Figure 12B) being

immersed in the soil and giving an anomaly of readings. This drying at the surface was evi-

dent for our entire territory except for the northern hills where it was the opposite. The cause

of the surface layer dryness was attributed to energy applied to the unit surface by the solar

radiation which was high in the given area, and there was only precipitation (27 mm) on 23rd

April.

It was assumed that evaporation played a more significant role than infiltration because the

recorded temperature data showed morning values of 20°C. It was the opposite in the

northern territories, and this could be attributed to soil parameters because the clay content

was higher than before due to the erosion and physical weathering. The erosion caused

deeper layers than surface layers of the soil to be drier whereas, in other areas, the runoff

was almost negligible. In the main valley network (Tardi valley basin) there were also anom-

alies arising from the lack of sensors so the valleys did not truly reflect reality and future

studies will have to ensure installing sensors in the valley. As such, the data of this scenario

needs to be handled with its reservations.

Differential map

The difference between the two data series (10 cm and 0 cm) did not yield a reliable result

(Figure 17). This was due to more than half of the sensor pairs were not available, which

meant that only 15 pairs of 39 pairs could be compared. On the other hand, the map shows

that in the deeper areas of the south and the valleys, the humidity is higher at a deeper 10 cm

level. Moving to the higher areas of the north and the terrain, the ratio of the moisture content

of the lower layer decreases more and more. At some point, it turns over, and the ratio of

surface moisture was dominant. Finally, the morphological diversity of the area was not

detectable from soil moisture.

Figure 17 Differential map of 2nd May 2018

CONCLUSION AND FUTURE WORK

This investigation tested a statistical method suitable for the detection of soil moisture and

using terrain covariates. This methodology is still in its initial phase, and in this case, the

statistical parameters did not produce highly significant results; however, it showed some

level of confidence in reality. However, our results were decisive in selecting and applying

accurate models, which can be used later with soil parameters, radiation models, and NDVI

data to find additional soil property layers that will enhance the statistical significance of the

findings. This study was unable to determine to what extent evaporation, precipitation (sig-

nificance of specific changes in time) and vegetation play. On the maps, however, it became

evident that temperature, temperature parameters, the variability of terrain and terrain con-

ditions, the degree of irradiation, and the temporal change of these parameters significantly

influence soil moisture.

Future work involves the addition of new sensors to be placed in the agricultural area of the

Tard Szent István Agricultural Cooperative. There will be a more extensive distribution of

sensors to cover the entire study area fully, different geomorphological-soil units, as well as

in the valleys so accurate estimation of soil moisture can be achieved.

Finally, satellite imagery will be downloaded from the European Space Agency (ESA), Sen-

tinel 2 multi-spectral imaging mission. Imagery will be downloaded for the period of the

pilot study, and after that refined to specific time intervals in the visible and near-infrared

(VINR) and shortwave infrared (SWIR). With ESA- Sentinel multispectral instrument, sev-

eral soil properties will be determined using spectral analysis. Therefore all ESA-Sentinel

bands of them are seen as potential covariates for soil moisture modelling (Mulder et al.,

2011). The ESA -Sentinel data comprises of a mosaic of images that were taken in the same

season; of which different images were used to prevent cloud cover in the final image and

each wavelength domain (VNIR & SWIR) has separate files. For image processing, ENVI

version 5.4 will be used as ENVI is software is used for analysis and visualisation of different

types of hyperspectral digital imagery (Bakker, 2012). A mosaic will be made out all the

files, after this, the files will be stacked in one raster file, and a soil moisture algorithm will

be applied resulting in a soil moisture map.

ACKNOWLEDGEMENTS

I want to thank European Commission-supported “Grow” H2020 project (Project ID:

690199) for the materials used in this study, expert advice on soil science and making this

knowledge available to many people from different facets of life. Additionally, the research

was also supported by the GINOP-2.3.2-15-2016- 00031 “Innovative solutions for

sustainable groundwater resource management” project of the Faculty of Earth Science and

Engineering at the University of Miskolc within the framework of the Széchenyi 2020 Plan,

funded by the European Union.

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