=Paper=
{{Paper
|id=Vol-3006/48_short_paper
|storemode=property
|title=A tool for analysis of the influence of the Earth surface soil layer temperature on the inhomogeneity of grain crops development by the Earth remote sensing data
|pdfUrl=https://ceur-ws.org/Vol-3006/48_short_paper.pdf
|volume=Vol-3006
|authors=Ruslan V. Brezhnev,Yuriy A. Maglinets,Ksenia V. Raevich,Vardui G. Margaryan
}}
==A tool for analysis of the influence of the Earth surface soil layer temperature on the inhomogeneity of grain crops development by the Earth remote sensing data==
https://ceur-ws.org/Vol-3006/48_short_paper.pdf
A tool for analysis of the influence of the Earth surface
soil layer temperature on the inhomogeneity of grain
crops development by the Earth remote sensing data
Ruslan V. Brezhnev1 , Yuriy A. Maglinets1 , Ksenia V. Raevich1 and
Vardui G. Margaryan2
1
Institute of Space and Information Technology of Siberian Federal University, Krasnoyarsk, Russia
2
Yerevan State University, Yerevan, Armenia
Abstract
The work is devoted to the analysis of the influence of the earth surface temperature on the inhomogeneity
of the agricultural crops development. The aim of the work is to expand the object-relational model for
describing the inhomogeneous spatial structure of a spatial object by including surface temperature as
one of the key features that allow determining the cause of vegetation heterogeneity, along with relief
features, differences in the soil chemical composition and other significant characteristics.
Experimental studies are carried out at sites located in Sukhobuzimsky district of Krasnoyarsk
Territory, for which agricultural crops (grains) and the their sowing dates are known a priori, which
allows stating any facts of the vegetation development deviation from the normative trajectory with
reference to the sequence and timing norms of phenological phase changing.
Landsat-8 OLI (Operational Land Imager) TIRS (Thermal Infrared Sensor) data are used as initial data
for temperature measurements. Objects of research are presented in the form of a polygon map in SHP
format.
The temperature values are calculated using the algorithm for estimating the earth temperature
developed by Weng Q., Lu D. and Schubring J. The surface reflectance values are the NDVI vegetation
index values also obtained from the Landsat-8 OLI data that underwent atmospheric correction by the
DOS method.
The research results are implemented in the form of a software module and integrated into the Earth
remote monitoring (ERM) system of SFU Space and Information Technologies Institute (SITI). The results
are used within the concept of object-oriented monitoring of spatial objects developed by the team of
authors, and represent index images of the surface temperature of objects, as well as vector schematic
maps.
Keywords
Earth remote sensing, Earth surface temperature, spatial object monitoring, agricultural crops, inhomo-
geneity, inhomogeneity structure of spatial object, dynamic model of spatial object.
1. Introduction
As an agromonitoring task, quantitative assessment of the state of crops becomes more compli-
cated as the dispersion of the phytocenosis structure appears in a separate field contour due to
inhomogeneity of its development. Uniform development of vegetation depends on a number of
SDM-2021: All-Russian conference, August 24β27, 2021, Novosibirsk, Russia
" brejnev.ruslan@gmail.com (R. V. Brezhnev)
Β© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)
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Ruslan V. Brezhnev et al. CEUR Workshop Proceedings 414β420
natural climatic conditions, such as chemical composition of soils, soil and air temperature and
humidity, wind speed and direction, level of environmental radiation, intensity of precipitation,
etc. In addition, the homogeneity is influenced by anthropogenic conditions, which change the
natural conditions either purposefully, thereby affecting the course of vegetation development,
like agro-technological measures, or indirectly, through the influence of industrial facilities,
energy, transport, pipeline communications, etc.
From the standpoint of aerospace monitoring of the earth surface, uniformity (or homogeneity)
of vegetation development is manifested in the same or close values of spectral or textural
properties measured at many points of the image fragment corresponding to the agricultural
contour. Such state of a spatial object, recorded at the k moment of time, can be considered
a reference if it corresponds to a phenological phase inherent to a given time interval in the
totality of the properties analyzed.
However, long-term remote and field observations of the grain crops development in Sukhobuz-
imsky district of Krasnoyarsk Territory have shown that only 30% of the objects remained
homogeneous throughout the growing season. Vegetation within the contours of most fields
was ahead and lagging behind the normative growth rates, thus requiring to consider the field
contour as an object with an inhomogeneous spatial structure. Therefore, the values of spectral
or textural features for such objects are distributed unevenly and form separate homogeneous
areas within the object contour.
To assess the state of vegetation in inhomogeneity conditions, it is necessary to have the
means to localize the inhomogeneous structure, on the one hand, a model that would allow
adequate description of the dependence of each inhomogeneous area within a spatial object on
environmental conditions. Previously, the authors developed the ππ model [1], which helps
represent the inhomogeneous structure of some π spatial object ππ as its decomposition into
its component parts (1). Each structural element is the π inhomogeneous region of the object
ππ β π€π , π = 1, π . In this case, each π€π region is described by a set of features that are compared
in the form of an object-relational structure, which make it possible to determine the current
state ππ΄π for π€π :
π
(1)
{οΈ π }οΈ
πππ = β¨πΌπ·, πΞ π , π πππ , π πππ , πππ , π π , π π, πππ , πππ , πππ , ππ΄π
π
β©,
{οΈ }οΈ
where πΌπ· is identifier of object ππ (field number), πΞ π π is a set of coordinates of region π€π ,
π
π πππ is area, π πππ is perimeter, πππ is thickness, π π is mean NDVI value, π π is time of image
acquisition and localization of the object structure.
Thus, the object-relational model (2) includes spectral, metric features and a number of
semantic descriptions associated with specific values or ranges of values of the listed features.
The model enables to describe the shape, size and position of an object in space, and to ascertain
a subset of the actual states (ππ΄π ) of the object regions. However, this model does not contain a
block describing probable causes of the inhomogeneity, which would be important for timely
decision-making on the impact on the regions that are lagging behind the norm. Among the
main reasons for the occurrence of the inhomogeneity are non-uniformity of the soil chemical
composition, as well as its moisture content πΛπ πππ and temperature π‘π πππ [2].
There are a number of objective restrictions that preclude determining the soil chemical
composition and surface moisture using Earth remote sensing (ERS) methods with acceptable
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Ruslan V. Brezhnev et al. CEUR Workshop Proceedings 414β420
accuracy. In case with the chemical composition, this is due to the lack of suitable open ERS
data, and the lack of up-to-date data from agrochemical services or access to the same, while
detailed determination of moisture is difficult due to high generalization of information in one
pixel of open low resolution data (for example, MODIS or MetOp).
Thus, this work addresses determination of the earth surface temperature as a feature that
expands the model for describing the inhomogeneous structure of an object and as a factor
influencing the occurrence of vegetation inhomogeneity.
2. Modeling the temperature distribution over the surface of a
spatial object with an inhomogeneous dynamically changing
structure
Landsat-8 OLI (Operational Land Imager) TIRS (Thermal Infrared Sensor) data is used as a
baseline for temperature measurements. The 10th channel of TIRS1 (10.6β11.19 πm) was taken
as a basis. The objects of research are identified on the territory of Sukhobuzimsky district of
Krasnoyarsk Territory and are presented in the Earth remote monitoring system (ERMS ISIT)
in the form of vector polygonal objects. A plain object without significant elevation differences,
containing forest outliers, is considered as an example. The coordinates of the object center are
56.4261 N 92.9590 E.
During the growing season of 2018, the state of vegetation at the site was monitored according
to Landsat-8 and Sentinel-2 data, as a result of which schematic maps of inhomogeneity were
obtained (Table 1) by reference to the homogeneity criteria (2) obtained through identifying the
correlation dependence of NDVI spectral index values, field spectrometric measurements and
phenological phases of grain crops development [3]:
β§
βͺ
βͺ π0 ππ‘ β1 β€ π (π₯, π¦) β€ 0.025,
βͺ
βͺ
βͺ
βͺ π1 ππ‘ 0.025 β€ π (π₯, π¦) β€ 0.26,
β¨ 2 ππ‘ 0.26 β€ π (π₯, π¦) β€ 0.43,
π
βͺ
βͺ
π (π₯, π¦) = π3 ππ‘ 0.43 β€ π (π₯, π¦) β€ 0.57, (2)
π ππ‘ 0.57 β€ π (π₯, π¦) β€ 0.65,
βͺ
4
βͺ
βͺ
βͺ
π ππ‘ 0.65 β€ π (π₯, π¦) β€ 0.72,
βͺ
βͺ
β© 5
βͺ
βͺ
π6 ππ‘ 0.72 β€ π (π₯, π¦) β€ 0.85
where π (π₯, π¦) is NDVI value, π (π₯, π¦) β segmented image, π are π€π region labels interpreting the
region status. Thus, π0 is a background, π1 is dragging or ploughing, π2 is sowing or harvesting,
π3 is sprouting and upgrowing, yellow ripeness, π4 is tillering, π5 is booting, π6 is earing and
blooming.
The NDVI values are calculated with regard to atmospheric correction performed by the DOS
(Dark-Object Subtraction) method:
π Β· π2 Β· ππππ β πΏβππ§π
ππ = , (3)
πΈπ π’π Β· cos2 π
where π is the value of the Earth to Sun distance at the shooting point during the survey; π is
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Ruslan V. Brezhnev et al. CEUR Workshop Proceedings 414β420
Table 1
Results of the study of the vegetation intensity dependence on the surface temperature.
Temperature Air temperature
Shooting
Surface Vegetation distribution according to the
Date time,
temperature, β C intensity over the object meteorological station
GMT+7
surface, β C at the shooting time, β C
2018-04-20 2.8β6.8 11:52
2018-05-31 23.7β27.1 11:45
2018-06-07 21.3β23.6 11:51
2018-06-23 27.5β30.0 11:51
2018-07-02 27.7β31.4 11:45
2018-07-09 18.3β24.6 11:51
2018-07-25 18.3β21.9 11:52
2018-08-10 23.2β29.3 11:52
2108-08-26 20.9β26.9 11:52
2018-09-20 4.8β14.5 11:46
2018-10-13 6.9β17.8 11:52
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Ruslan V. Brezhnev et al. CEUR Workshop Proceedings 414β420
a β vegetation state b β surface temperature
Figure 1: Legend.
zenith distance value (taken from metafiles); πΈπ π’π is solar spectral radiation coefficient; ππ ππ is
π·π (π, π) pixel value of the π image channel.
The method is based on subtracting the pixel (π·π ) spectral brightness value, which corre-
sponds to a completely black object or region in the image, from the entire image. The idea of
the method is based on finding the π·π value of an absolutely black object, the total spectral
pixel brightness of which should not exceed 0.01, i.e. no more than 1% of the total spectral
brightness of all image pixels.
The temperature values are calculated using the Earth surface temperature estimation algo-
rithm developed by Weng Q., Lu D. and Schubring J. [4]:
β‘ β€
π π΅
πΏππ = β£ (οΈ )οΈ β¦ β π, (4)
πΒ·π π΅
1 + π2 Β· ln(π π·π πΌ)
where πΏππ is the surface temperature in degrees Celsius; π π΅ is the brightness temperature
of the atmospheric surface (K); π is a constant equal to one degree Kelvin (273.15 K); π is the
wavelength of the emitted radiation for the tenth TIRS1 Landsat-8 channel equal to 10.8; and π2
is a constant equal to 14388 πmΒ·K, obtained from the expression:
π2 = β Β· π/π , (5)
where β is Planckβs constant, π is light velocity, and π is Boltzmann constant.
Data of the surface types classification, the constant or the vegetation indices values can
be used as the values of the surface emissivity in the formula (4) [4]. In this work, the NDVI
values, also obtained from the Landsat-8 OLI data, are used as the emissivity values subject to
atmospheric correction.
3. Results and discussion
A number of experimental measurements of surface temperature were carried out during the
growing season from April 20 to October 13, 2018, when the state of grain crops vegetation
was monitored, in particular, at the selected object. Each time point Tm for determining the
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Ruslan V. Brezhnev et al. CEUR Workshop Proceedings 414β420
state of vegetation corresponds to a temperature map presented in the form of multi-temporal
fragments of a pseudo-color temperature image (Table 1, column 2 β βSurface temperatureβ) and
schematic maps of vegetation states (Table 1, column 3 β βIntensity of vegetationβ), designated
by numbers from 0 to 6 in accordance with (2). Interpretation of the vegetation state values and
temperature is presented in the legend (Fig. 1 a, b).
For clarity, column 4 shows the temperature distribution over the observed object surface,
while column 5 shows the air temperature readings obtained from the nearest meteorological
station located 21 km to the northeast (Sukhobuzimskoye village) for the time interval closest
to the shooting time (from 10:00 to 13:00) as shown in column 6.
When summarizing the surface temperature readings in relation to the time points π ππ
(π = 1, πΎ, πΎ = 11) of the spatial object localization and its structure, it should be noted that
the surface heating does not always correlate with the air temperature. Thus, at lower values of
air temperature, higher values of surface temperature are observed and vice versa. This is due
to the direct effect of solar radiation, which means that in a relatively transparent atmosphere,
the surface heats up more than air and gives off thermal energy more slowly.
High intensity of vegetation was observed in those regions of the object where the surface
temperature indicators during the growing season were predominantly in the range from 15
to 31 β C. As a rule, at the moments of localization such regions were in a normative state in
accordance with the current phenological phases. Low intensity of vegetation was observed in
regions with high temperatures β above 31 β C. The state of vegetation in such regions lagged
behind the norm. There was no significant advance in the development of vegetation. In most
cases, a slight advance in time corresponds to an earlier transition from the current phenological
phase to the next one.
It is also noted that areas with open soil are warming up more than those covered with
vegetation, and the denser the vegetation, the lower the temperature. Thus, high density of
vegetation has a beneficial effect on the intensity of growth.
Figure 2 shows a graph summarizing the behavior of vegetation in the dominant region of
the spatial object under study, when the surface temperature changes.
Figure 2: Intensity of vegetation of the object dominant region depending on the surface temperature.
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According to the NDVI values, the graph describes the normal trajectory of vegetation
development, corresponding to the timing norms of the phenological phase changes.
4. Conclusion
For the model (2) of a spatial object with an inhomogeneous dynamically changing spatial
structure, a block for estimating the surface temperature was developed, which made it possible
to expand the description of wi regions of the object, including one of the key features that
help partially analyze the causes of vegetation inhomogeneity. The block is implemented as
an independent software component, has passed practical testing and is built into the Earth
Remote Sensing Monitoring System of Institute of Space and Information Technology.
Further development of the model involves inclusion of the moisture parameter πΛπ πππ , which
can be reconstructed from the surface temperature π‘π πππ and the NDVI values using the triangle
or parallelepiped method [5]. It is also worth considering that πΛπ πππ and π‘π πππ are very dependent
on the object surface relief, on the location proximity to water bodies, forest belts or other
objects shading part of the surface, therefore, for an objective assessment of inhomogeneity
occurrence, the model should include difference in heights, relief and other parameters.
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