Remote Assessment of Soil Temperature on the Example of a Carbon

Landfill Site of the Republic of Bashkortostan (Yangan-Tau

Geopark)

Ekaterina Bogdan

1,2 a

, Alexander Volkov

1,3 b

, Larisa Belan

1,2 c

Rita Kamalova

1,2 d

and Iren Tuktarova

Ufa State Petroleum Technological University, Ufa, Russia

Bashkir State University, Ufa, Russia

Vector LLC, Ufa, Russia

Keywords: Soil temperature, thermodynamic temperature, data loggers, Landsat, Semi-automatic classification plugin,

Yangan-Tau Geopark.

Abstract: The article discusses approaches to remote assessment of the Earth's surface temperature (on the example of

the territory of the Yangan-Tau geopark, Republic of Bashkortostan, Russia). The relevance of remote

assessment of soil temperature is confirmed by studies demonstrating the high ecosystem role of this indicator.

The comparison of mathematical calculations of thermodynamic temperature and data generated by the Semi-

automatic classification plugin module of the Q-GIS program is carried out. The interrelations between the

results of ground-based studies of soil temperature obtained using data loggers and data from the thermal

channels of the Landsat 8 satellite are evaluated. The absence of a relationship between data loggers and

satellite imagery data in winter is determined, which is explained by the presence of snow cover. The greatest

correlation was found in the autumn period. A regression analysis was carried out, on the basis of which a

model of the relationship between data from Landsat 8 satellite images and the results of measurements by

data loggers of soil temperature was formed.

1 INTRODUCTION

Many studies show a significant influence of soil

temperature on ecosystem processes. In particular,

the impact of extreme soil temperature events can

affect the levels of biological organization (Jentsch,

Beierkuhnlein, 2008; Larjavaara et al., 2021) and

interact with other driving climatic variables,

changing the range and stability of many ecosystems

(Thuiller et al., 2008). The influence of temperature

on microbiological activity, nutrient mineralization

and soil respiration is noted (Yuste et al., 2007;

Hamdi et al., 2013), including short-term (12 hours)

near-surface extremes of soil temperature - 30-35°C

(Yuste et al., 2007), as well as soil warming from 15

https://orcid.org/0000-0003-0566-2639

https://orcid.org/0000-0001-5691-6438

https://orcid.org/0000-0003-3098-7881

https://orcid.org/0000-0002-8964-7622

https://orcid.org/0000-0003-4731-1394

up to 25°C (Kravchenko et al., 2019) significantly

reduce microbial activity and soil respiration in

ecosystems.

Thus, monitoring the temperature regime of the

soil cover is one of the necessary conditions for

measures to adapt to climate change and prepare

forecasts of the dynamics of vegetation productivity.

Convenience and efficiency are demonstrated by

remote methods for estimating the temperature of the

Earth's surface using Landsat satellite data. In their

study (El Garouani et al., 2021) showed that the

surface temperature has a high correlation with the air

temperature and differs only by a few degrees. In the

work (Mamash et al., 2021) for the city of

Novosibirsk, data obtained at meteorological stations

and from Landsat satellites were compared. The

Bogdan, E., Volkov, A., Belan, L., Kamalova, R. and Tuktarova, I.

Remote Assessment of Soil Temperature on the Example of a Carbon Landﬁll Site of the Republic of Bashkortostan (Yangan-Tau Geopark).

DOI: 10.5220/0011568800003524

In Proceedings of the 1st International Conference on Methods, Models, Technologies for Sustainable Development (MMTGE 2022) - Agroclimatic Projects and Carbon Neutrality, pages

215-221

ISBN: 978-989-758-608-8

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

215

standard deviation between the temperature values

ranged from 0.5 to 1.9°C. For the Republic of Tyva,

according to the Landsat 8 satellite, in the winter of

2014-2017, the value of the surface temperature

differs from the air temperature by an average of – 1.9

°C (Kuular, 2018).

2 MATERIALS AND METHODS

The research is carried out on the territory of the

Yangan-Tau UNESCO global geopark. One of the

key positions in the geopark's activities is the

implementation of Sustainable Development Goal

No. 13 "Taking urgent measures to combat climate

change and its consequences". In 2021, the geopark

entered the program of the carbon landfill of the

Republic of Bashkortostan, as one of the sites.

To identify current climatic trends occurring in

the territory of the Yangan-Tau Geopark, we

analyzed data from long-term observations of the

Duvan meteorological station in the period 1966-

2019. The basic characteristics of climatic values

were calculated: average (climatic norms of 1966-

2019 and 1981-2010), standard deviation for

temperature and coefficient of variation for

precipitation, anomalies of values (the basic norm of

the period 1981-2010 was used in the calculations).

The assessment of regional climate changes was

obtained using trend analysis. The angular coefficient

of the slope of the trend line is characterized by the

rate of change of the value, and the positive sign of

the coefficient indicates an increase in the value of a

quantity, the negative sign indicates its decrease. The

value of the determination coefficient was used to

assess the contribution of the linear trend to the

overall variability of the indicator and its statistical

significance (Kamalova et al., 2021).

A time analysis of the thermal regime revealed

that in almost all months there is an increase in air

temperature. Statistically significant trends were

found in January, October and for the year, as well as

in March of the period 1981-2010.

The increase in air temperature also confirms the

long-term course of its anomalies. Their distribution

shows that the frequency of warm years has been

increasing since the early 2000s. Changes are

observed not only in the thermal regime of the air, but

also in the moisture regime. Trend analysis shows that

the annual precipitation tends to increase in the period

1966-2019 (12 mm/10 years). At the same time,

considering the period of the basic norm of 1981-

2010, the opposite trend is found – a decrease in the

amount of precipitation at a rate of -11.6 mm/10

years. In general, the greatest contribution to this

trend is made by the trendline slope coefficient of the

amount of precipitation of the warm period (-24.6

mm/10 years). The amount of precipitation during the

cold period has positive statistically significant trends

(21.6 mm/10 years). The revealed trends of the main

climatic indicators collectively affect changes in the

hydrothermal conditions of the geopark territory. One

of the widely used indicators is the aridity index of D.

A. Pedya (Perevedentsev et al., 2011). In the work

(Kamalova et al., 2021), it was found that the long-

term dynamics of the aridity index shows a tendency

to increase (0.26 units/10 years). Thus, summer

conditions shift towards greater aridity, which, in

turn, affects the hydrothermal conditions of the soil.

Ground-based studies were conducted using

CEM-DT-171, Testo and VerigoPod temperature and

humidity data loggers. Packed in a container, the data

logger got into the soil to a depth of 15-20 cm (Figure

1).

Figure 1: Placement of data loggers at the research site in

the Yangan-Tau UNESCO global Geopark.

5 sites were selected on the territory of the

geopark, where data loggers were placed (Figure 2).

2 data loggers of different brands were placed on

each site to adjust the results. Studies have not shown

significant discrepancies in the measurement results

of data loggers from different manufacturers. The

study began in November 2020. Every six months,

data is read from data loggers and batteries are

replaced.

Remote methods. The basis for remote analysis

was Landsat 8 images, including thermal channels

B10 (10.60-11.19 microns) and B11 (11.50-12.51

microns). To obtain data on the thermodynamic

temperature, Formula 1 was used:

𝑇=

𝐾



𝑙𝑛



𝐾



𝑅



 273,15

(1)

MMTGE 2022 - I International Conference "Methods, models, technologies for sustainable development: agroclimatic projects and carbon

neutrality", Kadyrov Chechen State University Chechen Republic, Grozny, st. Sher

216

where T is the thermodynamic temperature, ˚C; K

and K

are calibration constants, the values of which

are presented in Table 1.

Table 1: Calibration coefficients for Landsat 8 images.

Calibration

coefficient

Channel B10 Channel B11

774.9 480.9

1321.1 1201.1

3,3420E-0,4 3,3420E-0,4

0.1 0.1

Further, according to the Formula (2), the R –

intensity of the radiation of the object is calculated:

𝑅=𝑀



∗𝑄+

𝐴



(2)

where M

and A

are the calibration coefficients, the

value of which is shown in Table 1; Q is the discrete

calibrated pixel value.

The value of the calibration coefficients is

presented in a meta-data file named "*_mtl.txt ",

included in the snapshot archive.

16 cloudless images were selected for analysis in

the period from November 2020 to November 2021.

For the pixels corresponding to the location of the

data loggers, the thermodynamic temperature values

were calculated based on the formulas presented

above. At the same time, the Q-GIS program has a

module for semi-automatic classification of the

Earth's surface (Semi-Automatic Classification

Plugin), which automatically recalculates the

thermodynamic temperature. A comparison of the

results obtained by calculation and the results

obtained using the semi-automatic classification

module showed their identity (Figure 3).

The correlation coefficient R for both channels

was 0.99, R

was 0.99, and the standard error was

0.55.

The temperature values determined by channels

B10 and B11 (they differ in the covered intervals of

the thermal range) of Landsat 8 differ from each other

by 1.5-3 °C. In a number of publications, they are

offered to average

(Silkin, 2015).

3 RESULTS AND DISCUSSION

A regression analysis was carried out based on the

data obtained from the images and data loggers. Both

the entire range of annual results and seasonal results

are analyzed. As can be seen in Figure 4 and Table 2,

a correlation is observed during the year between the

results obtained by ground measurements and data

from Landsat 8 images.

Figure 2: Locations of data loggers on the territory of the Yangan-Tau UNESCO global Geopark.

Remote Assessment of Soil Temperature on the Example of a Carbon Landﬁll Site of the Republic of Bashkortostan (Yangan-Tau Geopark)

217

Figure 3: Comparison of calculation results and processing data in the Semi-Automatic Classification Plugin module, where

B10-SCP and B11SCP are data obtained by processing in the Semi-Automatic Classification Plugin module for channels B10

and B11; B10-Math and B11-Math are data obtained by mathematical calculations.

Figure 4: Temperature values from data loggers and Landsat 8 images.

At the same time, the analysis of seasonal data

showed that there is no relationship between logger

data and winter images (Figure 5), which is explained

by the presence of snow cover (Table 2). The greatest

correlation was observed in the autumn period

(Figure 6).

As can be seen from Table 2, there are no

significant differences in the correlation relationships

between the results of ground measurements and the

data of channels B10, B11 and their average values.

In further studies, we used the average value of these

channels B10 and B11.

MMTGE 2022 - I International Conference "Methods, models, technologies for sustainable development: agroclimatic projects and carbon

neutrality", Kadyrov Chechen State University Chechen Republic, Grozny, st. Sher

218

Based on the data obtained, a linear predictive

model of the relationship between data from Landsat

8 satellite images and the results of measurements by

data loggers of soil temperature (Formula 3) is

formulated:

𝑇



= 3,06 + 0,28𝑇







(3)

where T

soil

is the soil temperature; T

image

is the value

of the thermodynamic temperature obtained from

Landsat 8 images.

More accurate values can be obtained in the

autumn period using other values of correction

coefficients (Formula 4):

𝑇



= 3,60 + 0,35𝑇







(4)

4 CONCLUSIONS

1. To obtain the values of the thermodynamic

surface temperature, you can use the Semi-

automatic classification plugin of the QGIS

program.

2. The use of satellite data for the winter period to

assess soil temperature is not advisable,

because snow cover has a significant impact on

the temperature regime of the soil.

3. The greatest correlation between the satellite

survey data and the results of ground

Table 2: Relationships between ground-based research results and satellite data.

Perio

Indicato

R R

Standard erro

Year

B10 0.84 0.71 3.80

B11 0.84 0.70 3.80

AVG 0.84 0.71 3.80

Winter

B10 0.02 4,00E-04 1.68

B11 0.02 4,00E-04 1.68

AVG 0.02 4,00E-04 1.68

Spring

B10 0.76 0.58 4.10

B11 0.77 0.59 4.00

AVG 0.77 0.59 4.00

Summer

B10 0.55 0.31 1.77

B11 0.57 0.33 1.75

AVG 0.57 0.32 1.76

Autumn

B10 0.96 0.93 1.15

B11 0.96 0.92 1.18

AVG 0.96 0.93 1.16

Figure 5: Temperature values from data loggers and Landsat 8 images in winter.

Remote Assessment of Soil Temperature on the Example of a Carbon Landﬁll Site of the Republic of Bashkortostan (Yangan-Tau Geopark)

219

measurements is observed in the autumn

period.

4. Remote data can be obtained as a result of

processing channels B10, B11 or their average

value. There are no differences in the

relationship with ground data.

ACKNOWLEDGEMENTS

The research was started with the assistance of the

Russian Geographical Society, continued with the

support of a grant from the Republic of

Bashkortostan, the internal cipher of the scientific

topic is ENOC–GVU-01-22.

REFERENCES

Jentsch, A., Beierkuhnlein, C., 2008. Research frontiers in

climate change: Effects of extreme meteorological

events on ecosystems. Geoscience. 340. 9-10. pp. 621-

628.

Larjavaara, M., Lu, X., Chen, X., Vastaranta, M., 2021.

Impact of rising temperatures on the biomass of humid

old ‑ growth forests of the world. Carbon Balance and

Management. Springer International Publishing. 31, 16.

pp. 1-9.

Thuiller, W. Albert, C., Araujo, M., Berry, P., Cabeza, M.,

Guisan, A., Hickler, T., Midgely, G., Paterson, J.,

Schurr, F., Sykes, M., Zimmermann, N., 2008.

Predicting global change impacts on plant species’

distributions: future challenges. Perspectives in Plant

Ecology, Evolution and Systematics. 9, 3-4. pp. 137-

152.

Yuste, J., Baldocchi, D., Gershenson, A., Goldstein, A.,

Misson, L., Wong, S., 2007. Microbial soil respiration

and its dependency on carbon inputs, soil temperature

and moisture. Global Change Biology. 13. pp. 2018-

2035.

Hamdi, S., Moyano, F., Sall, S., Bernoux, M., Chevallier,

T., 2013. Synthesis analysis of the temperature

sensitivity of soil respiration from laboratory studies in

relation to incubation methods and soil conditions. Soil

Biology and Biochemistry. 58. pp. 115-126.

Kravchenko, I. K., Tikhonova, E. N., Ulanova, R. V.,

Menko, E. V., Sukhacheva, M. V., 2019. Effect of

temperature on litter decomposition, soil microbial

community structure and biomass in a mixed-wood

forest in European Russia. Current Science. 116, 5. pp.

765-772.

El Garouani, M., Amyay, Mh., Lahrach, A., Oulidi, H. J.,

2021. Land Surface Temperature in Response to Land

Use/Cover Change Based on Remote Sensing Data and

GIS Techniques: Application to Saïss Plain, Morocco.

Journal of Ecological Engineering. 22, 7. pp. 100-112.

Mamash, E. A., Pestunov, I. A., Chubarov, D. L., 2021.

Construction of temperature maps of the city of

Novosibirsk based on Landsat 8 satellite data. Interexpo

Geo-Siberia. 4, 1. pp. 52-59.

Kuular, H. B., 2018. The temperature of the landscape

surface of the Republic of Tyva according to the

Landsat-8 satellite in winter 2014-2017. Modern

problems of remote sensing of the Earth from space. 15.

7. pp. 67-77.

Kamalova, R. G., Belan, L. N., Bogdan, E. A., 2021. The

climate of the Yangan-Tau Geopark and its modern

changes. Dynamics and interaction of the Earth's

geospheres. II. pp. 134-137.

Figure 6: Temperature values from data loggers and Landsat 8 images in autumn.

MMTGE 2022 - I International Conference "Methods, models, technologies for sustainable development: agroclimatic projects and carbon

neutrality", Kadyrov Chechen State University Chechen Republic, Grozny, st. Sher

220

Perevedentsev, Yu. P., Vereshchagin, M. A., Shantalinsky,

K. M., Naumov, E. P., Khabutdinov, Yu. G., 2011.

Changes in climatic conditions and resources of the

Middle Volga region: a textbook on regional

climatology. p. 296.

Silkin, K., 2015. Correction of Landsat materials. p. 5

https://gis-lab.info/qa/landsat-data-correction.html.

Remote Assessment of Soil Temperature on the Example of a Carbon Landﬁll Site of the Republic of Bashkortostan (Yangan-Tau Geopark)

221