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Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data

  • Eskisehir Technical University, Eskisehir, Turkey

Abstract and Figures

Land surface temperature is an important factor in many areas, such as global climate change, hydrological, geo-/biophysical, and urban land use/land cover. As the latest launched satellite from the LANDSAT family, LANDSAT 8 has opened new possibilities for understanding the events on the Earth with remote sensing. This study presents an algorithm for the automatic mapping of land surface temperature from LANDSAT 8 data. The tool was developed using the LANDSAT 8 thermal infrared sensor Band 10 data. Different methods and formulas were used in the algorithm that successfully retrieves the land surface temperature to help us study the thermal environment of the ground surface. To verify the algorithm, the land surface temperature and the near-air temperature were compared. The results showed that, for the first case, the standard deviation was 2.4°C, and for the second case, it was 2.7°C. For future studies, the tool should be refined with in situ measurements of land surface temperature.
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Research Article
Algorithm for Automated Mapping of Land Surface Temperature
Using LANDSAT 8 Satellite Data
Ugur Avdan and Gordana Jovanovska
Research Institute of Earth and Space Sciences, Anadolu University, Iki Eylul Campus, 26555 Eskisehir, Turkey
Correspondence should be addressed to Ugur Avdan;
Received  November ; Revised  January ; Accepted  February 
Academic Editor: Guiyun Tian
Copyright ©  U. Avdan and G. Jovanovska. is is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
Land surface temperature is an important factor in many areas, such as global climate change, hydrological, geo-/biophysical, and
urban land use/land cover. As the latest launched satellite from the LANDSAT family, LANDSAT  has opened new possibilities
for understanding the events on the Earth with remote sensing. is study presents an algorithm for the automatic mapping of land
surface temperature from LANDSAT  data. e tool was developed using the LANDSAT  thermal infrared sensor Band  data.
Dierent methods and formulas were used in the algorithm that successfully retrieves the land surface temperature to help us study
the thermal environment of the ground surface. To verify the algorithm, the land surface temperature and the near-air temperature
were compared. e results showed that, for the rst case, the standard deviation was .C, and for the second case, it was .C.
For future studies, the tool should be rened with in situ measurements of land surface temperature.
1. Introduction
Land surface temperature (LST) is dened as the temperature
the skin temperature of the ground []. As one of the most
important aspects of the land surface, LST has been a main
topic for developing methodologies to be measured from
space. LST is an important factor in many areas of studies,
such as global climate change, hydrological and agricultural
processes, and urban land use/land cover. Calculating LST
from remote sensed images is needed since it is an important
factor controlling most physical, chemical, and biological
processes of the Earth []. ere is a growing awareness
among environmental scientists that remote sensing can and
must play a role in providing the data needed to assess
ecosystems conditions and to monitor change at all special
scales []. e tool developed in this paper is simple and does
not require any background knowledge so scientists can use
it very easy in their researches.
oped using ERDAS IMAGINE , with the Model Maker
allowing us to create a model that will repeat the process
for doing pixel calculations. Without the tool, the process
of retrieving LST is very long, and it is prone to many
supporting pixel calculations from a given image, following
step by step this paper. Although an LST retrieval method for
LANDSAT  has been developed [, ], a tool is needed for
the complicated process of obtaining the LST. A similar study
for retrieving LST in ERDAS IMAGINE has been conducted
presented in this paper is used for calculating the LST of
a given LANDSAT  image with the input of the fourth
(red wavelength/micrometres, .–.), h (near infrared
(NIR) wavelength/micrometres, .–.), and tenth (ther-
mal infrared sensor (TIRS) wavelength/micrometres, .–
.) bands. Following January , , recommendations of
USGS of not using TIRS Band  due to its larger calibration
uncertainty, only Band  was included in the algorithm.
2. Data and Methods
e algorithm was created in ERDAS IMAGINE , and
of the data complexity. e LST of any Landsat  satellite
Hindawi Publishing Corporation
Journal of Sensors
Volume 2016, Article ID 1480307, 8 pages
Journal of Sensors
Input Band 10 Input Band 4 Input Band 5
Top of atmo sp heri c
spectral radiance
(see Section 2.1)
Calculating NDVI
(see Section 2.3.1)
Determination of
ground emissivity
(see Section 2.3.3)
Calculating LST
(see (7))
LST result
Conversions of
radians to at-sensor
(see Section 2.2)
proportion of
vegetation P
(see Section 2.3.2)
F : Flowchart for LST retrieval.
image can be retrieved following the steps of Figure . e
data of Landsat  is available at the Earth Explorer website
free of charge. In this study, the TIR band  was used to
estimate brightness temperature and bands  and  were used
for calculating the NDVI. e metadata of the satellite images
used in the algorithm is presented in Table .
2.1. Top of Atmospheric Spectral Radiance. e rst step of the
algorithm is the input of Band . Aer inputting band , in
the background, the tool uses formulas taken from the USGS
radiance ():
= 𝐿∗cal +𝐿−𝑖,()
where 𝐿represents the band-specic multiplicative rescal-
ing factor, cal is the Band  image, 𝐿is the band-specic
additive rescaling factor, and 𝑖is the correction for Band 
2.2. Conversion of Radiance to At-Sensor Temperature. Aer
TIRS band data should be converted from spectral radiance
to brightness temperature (BT) using the thermal constants
provided in the metadata le. e following equation is
T : Metadata of the satellite images.
ermal constant, Band 
Rescaling factor, Band 
Correction, Band 
used in the tool’s algorithm to convert reectance to BT
BT =2
ln 1/+1 273.15, ()
where 1and 2stand for the band-specic thermal conver-
sion constants from the metadata.
For obtaining the results in Celsius, the radiant tem-
.C) [].
Journal of Sensors
2.3. NDVI Method for Emissivity Correction
2.3.1. Calculating NDVI. Landsat visible and near-infrared
Vegetation Index (NDVI). e importance of estimating the
NDVI is essential since the amount of vegetation present is
an important factor and NDVI can be used to infer general
vegetation condition []. e calculation of the NDVI is
important because, aerward, the proportion of the vegeta-
tion (V)shouldbecalculated,andtheyarehighlyrelatedwith
theNDVI,andemissivity() should be calculated, which is
related to the V:
NDVI =NIR (band 5)−(band 4)
NIR (band 5)+(band 4),()
where NIR represents the near-infrared band (Band ) and
represents the red band (Band ).
2.3.2. Calculating the Proportion of Vegetation. Vis cal-
culated according to (). A method for calculating V[]
suggests using the NDVI values for vegetation and soil
(NDVIV=0.5and NDVI𝑠=0.2) to apply in global conditions
However, since the NDVI values dier for every area, the
since NDVIVand NDVI𝑠will depend on the atmospheric
conditions [].
2.3.3. Calculating Land Surface Emissivity. e land surface
emissivity (LSE ()) must be known in order to estimate LST,
since the LSE is a proportionality factor that scales blackbody
radiance (Planck’s law) to predict emitted radiance, and it
is the eciency of transmitting thermal energy across the
surface into the atmosphere []. e determination of the
ground emissivity is calculated conditionally as suggested in
V𝜆V+𝑠𝜆 1−
where Vand 𝑠are the vegetation and soil emissivities,
respectively, and represents the surface roughness (=
for homogenous and at surfaces) taken as a constant value
of . []. e condition can be represented with the
following formula and the emissivity constant values shown
in Table  []:
V𝜆V+𝑠𝜆 1V+, NDVI𝑠NDVI NDVIV,
𝑠𝜆 +, NDVI >NDVIV.
When the NDVI is less than , it is classied as water, and
the emissivity value of . is assigned. For NDVI values
with soil, and the emissivity value of . is assigned. Values
between . and . are considered mixtures of soil and
vegetation cover and () is applied to retrieve the emissivity.
In the last case, when the NDVI value is greater than ., it
is considered to be covered with vegetation, and the value of
. is assigned.
e last step of retrieving the LST or the emissivity-
correctedlandsurfacetemperature𝑠is computed as follows
1+BT/ln 𝜆,()
where 𝑠is the LST in Celsius (C, ()), BT is at-sensor BT
(C), is the wavelength of emitted radiance (for which the
peak response and the average of the limiting wavelength (=
=1.438×10−2 mK,()
where is the Boltzmann constant (. ×−23 J/K), is
Planck’s constant (. ×−34 J s), and is the velocity of
light (. ×8m/s) [].
3. LST Validation
e two major LST validation models are through ground
measurements or near-surface air temperature [, ]. e
LST results comparing with the ground measurements results
accuracy of the results in some area showed dierence of ±C
with actual ground temperature measurements. According to
Liu and Zhang, another method using the mean near-surface
air temperature to verify the retrieved LST results showed that
the LST retrieving error is about .C. For the validation, six
representative points have been used.
For the validation of the nal retrieved LST results in the
presented tool, the mean near-surface air temperature was
used [] but with bigger amount of data and taking not only
given pixel at the moment of the satellite passing over the area
for  representative points.
e comparison was made with air temperature, which
is dierent and can sometimes result in big dierences
since the resolution of LANDSAT  for the used bands
is  m for the thermal band and  m for the red and
NIR bands. e LST was calculated and taken for the
pixel in which the meteorological station fell. Sometimes,
the dierences can be very big depending on the weather
condition and other factors []. It should also be taken
into consideration that there is . to  meters’ dier-
ence between the LST and the air temperature, which
means that dierences in the temperatures are normal and
Journal of Sensors
F : Application of algorithm in Ontario and Quebec, Canada. (a) Geographic location of Ontario in Canada; (b) frames of satellite
images of study areas; (c) rst case located between Toronto and Huntsville; (d) second case located in surroundingarea of the city of Moncton.
3.1. Application of the Algorithm to Ontario and Quebec,
Canada. Hourly data were collected from the Canadian
Weather and Meteorology website (
.ca/) and used for comparison with the retrieved LST for
which, according to the available data, satellite images were
downloaded for // (Toronto area) and //
(Moncton area) for the areas shown in Figure .
e study area was the Canadian provinces of Ontario
and Quebec (Figure ). One satellite image was downloaded
from each of the two provinces. ese areas were chosen
because of their specications. at is, both study areas
included water, urban areas, and green areas.
3.2. Comparison of LST Validation Results. To c o m p a r e t h e
results, two dierent satellite images from two dierent
dates in two dierent areas were chosen according to the
available data. Aer downloading the satellite images from, LSTs were retrieved in ERDAS
using the algorithm presented in this paper. In the rst case,
the satellite image was located between Toronto and the city
of Huntsville near Lake Simcoe in Ontario, Canada. For this
area,  meteorological stations were found, but only  of
them were used for the accuracy assessment because of the
presence of clouds or other unwanted events. e dierences
between the retrieved LSTs and the air temperatures and
details on the stations are presented in Table  and Figure .
area surrounding the city of Moncton and included part of
New Brunswick, Prince Edward Island, and Nova Scotia in
Canada. For this area, we found  meteorological stations,
and all of them were used for the accuracy assessment. e
details are presented in Table  and Figure .
T : Emissivity of representative ter restrial materials for LAND-
SAT  TIRS Band .
Terrestrial material Water Building Soil Vegetation
Emissivity . . . .
4. Conclusion
is paper presented a new LST soware tool and its
algorithm created in ERDAS for calculating the LST from
LANDSAT  TIRS. e algorithm was derived using the
observed thermal radiance of the TIRS Band  of LANDSAT
 TIRS. To verify the nal retrieved LST results, the near-
surface air temperature method was used. From the anal-
ysis of the two areas in Canada from two dierent dates,
the standard deviation calculated for the rst case based
on  meteorological stations was .C, and that for the
second case based on  stations was .C. It should be
mentioned that sometimes, the dierence between the near-
surface temperature and the LST can be drastic since we
are comparing two dierent temperatures in dierent places
(ground temperature and . to . m o the ground). It
of the LANDSAT  TIRS data is  m for the thermal band
and  m for the red and NIR bands. Values smaller than
C in the two cases were considered to be clouds or other
unwanted events on the satellite images since the data were
from springtime; it was not expected. From Tables  and 
and Figures  and , it can be concluded that, for the rst
case, the smallest dierence between the LST retrieved from
Journal of Sensors
Lagoon City
Pa Udora Strong
Barrie Oro
Mono Centre
Pa Atmos Erin
Borden Awos
Uxbridge West
Pa Uxbridge Taris
Pa Claremont Silo Farm
Pa Atmos Claremont
Toronto Button Ville A
Egbert CS
Pa Tornto
Pa Atmos Vaughan
Pa Hardwood
Mountain Bike Park
Pa Caledon E Park
Pa Atmos Bran.
Pa Atmos Vaughan
Pa Markham North
Meteorological stations
<−5 C
Toy o t a
0 5 10 20
F : Retrieved LST image and meteorological stations from rst study area used in accuracy assessment.
Journal of Sensors
0 5 10 20
Doaktown Auto RCS
Buctouche CDA Cs
Mechan ic
Gagetown Awos
Pa Atmos Erin
Gagetown A
Napan Auto
Fundy Park (Alma) Cs
Saint Jhon A
Moncton Intl A
Meteorological stations
<−5 C
F : Retrieved LST image and meteorological stations from second study area used in accuracy assessment.
Journal of Sensors
T : Details and dierences of station from rst study case.
ST name Data pm LST Dierence Latitude Longitude
Barrie-Oro . . 1.0 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
Pa Hardwood Mountain Bike Park . . 5.7 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
Borden Awos . . 0.7 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
Pa Udora Strong . . 2.5 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
Lagoon City . . 1.5 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
Collingwood . . 4.9 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
Mono Centre . . 0.7 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
Uxbridge West . . 2.9 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
Pa Uxbridge Taris . . 3.5 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
Pa Atmos Vaughan . . 3.1 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
Pa Angus Glen Golf Club . . 2.9 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
Toronto Buttonville A . . 2.1 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
Pa Claremont Silo Farm . . 2.0 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
Pa Markham North Toyota . . 5.8 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
Pa Atmos Claremont . . 2.0 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
Pa Atmos Erin . . 1.8 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
T : Details and dierences of station from second study case.
ST name Data pm LST Dierence Latitude Longitude
Mechanic Settlement . . 0.2 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
Moncton Intl A . . 1.0 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
Fundy Park (Alma) Cs . . 2.2 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
Buctouche Cda Cs . . 0.2 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
Nappan Auto . . 2.1 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
Doaktown Auto Rcs . . 4.4 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
Saint John A . . 2.6 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
Parrsboro . . 7. 8 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
Gagetown A . . 4.8 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
Gagetown Awos A . . 2.3 󸀠.󸀠󸀠 󸀠.󸀠󸀠 /
Summerside . . 2.2 󸀠.󸀠󸀠 󸀠.󸀠󸀠 . m
the biggest was .C. In the second study case, the smallest
dierence was .Candthebiggestwas.
e tool can be signicant since it is opening an opportu-
nity to many researchers to get to the LST values easy and they
in this paper produced quite good results considering that the
accuracy assessment was made with near-air temperatures
and it can be used for dierent types of research. For future
studies,thetoolshouldberenedwithin situ measurements
of LST.
Conflict of Interests
e authors declare that there is no conict of interests
regarding the publication of this paper.
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... En el primer caso, la estrategia es tomar mediciones de la temperatura del aire aproximadamente a 1.3 -1.5 m de altura dentro de los límites de una determinada área vegetada y compararlas con las lecturas obtenidas en los alrededores, usualmente dentro de un radio de 500 m (Brown et al., 2015;Chang et al., 2007). En el segundo caso, las mediciones pueden realizarse con sistemas de sensibilidad remota (Avdan y Jovanovska, 2016;Zhanga et al., 2013). En este sentido, destaca el Satélite ...
... Precisamente, una de estas manifestaciones se refiere al estudio del ambiente térmico, específicamente a la temperatura en superficie, cuya estimación es posible gracias al cálculo basado en pixeles (Avdan y Jovanovska, 2016). La literatura es reiterativa al afirmar que el ambiente térmico y el confort están íntimamente relacionados y que es posible construir un puente entre el confort térmico y el uso intensivo de los EVU, al que se apareja un gran beneficio social. ...
... El algoritmo para determinar la estimación de la temperatura superficial fue desarrollado por la firma ERDAS IMAGINE 14.0 y es posible utilizarlo para procesar datos del satélite Landsat 8, gracias a la capacidad de tratar con la complejidad de sus datos (Avdan y Jovanovska, 2016). En esta investigación se utilizó ArcMap 10.8.1 para procesar el mismo modelo de estimación de temperatura en superficie siguiendo los pasos del flujo de trabajo de la Figura 4. ...
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El objetivo de este artículo fue explorar una conformación preliminar para la estimación del fenómeno Isla de Enfriamiento del Parque en Ciudad Juárez, México. También se desarrolló un mapa autómata de superficies para mostrar, mediante sensibilidad remota, el efecto de los espacios verdes urbanos en el ambiente térmico en superficie. Lo anterior, mediante la identificación de las zonas en las que la Isla de Enfriamiento del Parque tiene mayor relevancia. El área de estudio fueron los espacios verdes urbanos de Ciudad Juárez, México. Se utilizaron las imágenes satelitales de Landsat 8y un algoritmo para determinar la estimación de la temperatura superficial desarrollado por ERDAS IMAGINE 14,para procesar datos del satélite; además, se utilizó ArcMap 10.8.1 para la estimación de temperatura. Los resultados revelaron que dos espacios: el Parque Central y el Club Campestre logran regular la temperatura hasta en13°C, con relación a la de sus alrededores. Los resultados permiten concluir acerca dela importancia de la regulación térmica de la ciudad y de la importancia de los parques urbanos y otras áreas verdes como prestadores de servicios de regulación térmica.
... The model identifies vegetated areas where there are multispectral remote sensing data. NDVI presents higher sensitivity corresponding with crown density change than other vegetation index (Pettorelli et al., 2011;Avdan & Jovanovska, 2016;Zaitunah et al., 2018). ...
... Whereas, build up areas, barren rock, sand, or snow usually show very low NDVI values (Pettorelli et al., 2011;Zaitunah et al., 2018). For calculating NDVI, the user also needs to proceed a previous mathematical treatment of raw satellite data (Avdan & Jovanovska, 2016); for calculating UTFVI, this study based on Zhang (2006) and Santos et al. (2017). ...
... The USGS website for extracting top-of-atmosphere (TOA) spectral radiation is where the LST retrieval formulae were obtained from [60]. Using the radiance rescaling factors provided in the metadata file, the thermal band data DN were transformed to TOA spectral radiance [61] according to Equation (4). ...
... Using Equation (5), the thermal constants in the MTL file can be used to transform thermal band data from spectral radiance to top-of-atmosphere brightness temperature [61]. ...
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It is essential to assess the soil organic carbon pool (SOCP) in dry environments to apply appropriate management techniques that address sustainable development. A significant opportunity for sustaining agricultural output and reducing climate change is the storage of soil organic carbon in agricultural soil. The goal of this study was to measure the spatial variability of SOCP content, and determine the effects of soil texture, changes in land use, and land cover on SOCP in surface soil samples. The study additionally investigated the relationships between SOCP and other characteristics, including the normalized vegetation index (NDVI) and land surface temperature (LST), as well as the effects of increasing soil organic carbon on the amount of greenhouse gases. To accomplish this goal, 45 soil surface samples were collected to a depth of 30 cm at the Fayoum depression in Egypt, and analyzed. The soil samples were representative of various soil textures and land uses. The average SOCP concentration in cultivated regions is 32.1 and in bare soils it is 6.5 Mg ha −1 , with areas of 157,112.94 and 16,073.27 ha, respectively. According to variances in soil textures, sandy soils have the lowest SOCP (1.8 Mg ha −1) and clay loam soils have the highest concentrations (49 Mg ha −1). Additionally, fruit-growing regions have the greatest SOCP values and may therefore be better suited for carbon sequestration. The overall average SOCP showed 32.12 Mg C ha −1 for cultivated areas. A rise in arable land was accompanied by a 112,870.09 Mg C rise in SOCP. With an increase in soil organic carbon, stored carbon dioxide emissions (greenhouse gases) would be reduced by 414,233.24 Mg CO 2. We should consider improving fertilization, irrigation methods, the use of the multiple cropping index, decreasing desertion rates, appropriate crop rotation, and crop variety selection. The research highlights the significance of expanding cultivated areas towards sustainable carbon sequestration and the climate-change-mitigation potential.
... The analysis of the NDVI and NDBI indices in relation to land cover and urban morphology reveals distinct patterns within the study area. Firstly, it is evident that the NDVI index, which is associated with vegetation, exhibits higher values in rural areas [6,[41][42][43][44][45] in other cities and territories allowing to validate the data obtained in this research. These studies report results that mainly relate low NDVI values with high NDBI index values in compact areas of cities. Conversely, high NDVI values are related to low NDBI values in rural areas. ...
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Global warming is precipitating an amplification of severe meteorological occurrences such as prolonged dry spells and episodes of elevated temperatures. These phenomena are instigating substantial elevations in environmental warmth, with metropolitan regions bearing the brunt of these impacts. Currently, extreme heat is already impacting 30% of the global populace, and forecasts suggest that this figure will escalate to 74% in the forthcoming years. One of the objectives outlined in the United Nations 2030 agenda, specifically within Sustainable Development Goal 11 (SDG11), is the attainment of sustainable urban development. To achieve this, it is imperative to scrutinize and delve into urban environmental conditions in order to understand their dynamics comprehensively. This understanding serves as the foundation for implementing mitigation and resilience strategies against climate change, ultimately enhancing the well-being of city residents. In this context, the field of remote sensing and geographic information systems has made substantial advancements. Notably, the UrbClim model, developed by the European Space Agency, facilitates the assessment of environmental conditions within numerous European urban centers. This research, utilizing data from UrbClim, examines the evolution of the heat stress index (Hi) during extreme heat conditions in Barcelona during the summer of 2017. Leveraging Landsat 8 satellite imagery, we derived the following variables: the normalized difference vegetation index and the normalized building difference index. Our findings reveal that during extreme heat conditions, the Hi index experiences an escalation, with areas characterized by a higher population density and industrial zones displaying lower resistance in contrast to regions with a lower population density and rural areas, which exhibit greater resilience to Hi. This disparity can be attributed to higher vegetation coverage and reduced building density in the latter areas. In this way, Hi increases more quickly and intensely and decreases more slowly when using high temperatures compared to average temperatures. This is of utmost importance for the future planning of new urban developments.
... LST is defined as the temperature felt when the land surface is touched with the hands or the skin temperature of the ground (Rajeshwari and Mani, 2014).LST is an important issue in many areas, such as global climate change, hydrological, geo-/biophysical and urban land use/land cover (Avdan et al, 2016). LTS range is found more (21.21-40.17°C) in 2018 compared to the previous year (1995 and 2008) (Fig 11 and 12). ...
Conference Paper
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Hetauda sub-metropolitan city is undergoing rapid land use land cover change (LULC) since 1990s due to increased anthropogenic activities. LULC change leads to negative impact on urban environment affecting the quality life of urban people. However, limited studies have been carried out in this research area especially in developing countries like Nepal. The research aims to find out the response of land surface temperature (LST) to LULC dynamics in Hetauda sub-metropolitan city through the integration of remote sensing, geographical information system (GIS) and statistical analysis. The Landsat images of the year 1995, 2008 and 2018 were used for analyzing LULC dynamics and LST estimation. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built up Index (NDBI) and Normalized difference water index (NDWI) were used to analyze the relationship between LST and LULC. The results showed that forest and urban/built up areas were found to be increasing in study periods while cultivated land was decreasing. Forest area of Hetauda was found to have slightly increased due to implementation and promotion of community forestry program throughout country. Urban/built up area was considerably increased (10%) within 23 years period. The rapid temporal urbanization and industrialization in Hetauda contribute to significant increase in urban/ built up area. Increment in mean LST for forest class and urban/built up was found different for same time period. This shows that urban/built up area significantly contributes to increasing local warming effect. The relationship between LST and NDBI was found to be positive whereas negative relationship was observed between LST and vegetation index. The multiple regression analysis was done between LST and LULC indices. It demonstrates that industrial, urban and built up areas are responsible for increasing LST while green infrastructure significantly contributes to minimizing local warming effects. Overall, green infrastructure is vital component for sustainable management of urban area.
... For a precise LST estimate, the land surface emissivity (LSE) is required. The LSE is a proportionality factor that scales the black body radiance (Ugur and Gordana, 2016) to quantify the emitted radiance and the ability to transmit thermal energy from the surface into the atmosphere. It was calculated using Equation 14: ...
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In this study, the spatiotemporal dynamics of the urban environment and thermal environment of Benin City are analysed. The maximum likelihood algorithm for land use and land cover (LULC) analysis was used to categorise Landsat images. The relative transfer equation (RTE) and land surface emissivity (LSE) approaches were used to retrieve the land surface temperature (LST), whereas the Cellular Automata-Markov (CA-Markov) algorithm was used to forecast the LULC for 2030. The findings reveal evolving LULC patterns over time. Built-up areas made up 19.66% of the total area in 1990, bare ground made up 9.25%, and vegetation made up 71.08%. Built-up areas reached 23.40% in 2000, bare land reached 12%, and the vegetation cover dropped to 64.16%. In 2010, there was an increase in the proportion of built-up areas to 44.38%, the proportion of bare land increased to 22.20%, and the proportion of vegetation decreased to 33.42%. Built-up areas reached 61.79% in 2020, compared to 22.29% for bare land and 61.79% for vegetation. Regarding the relationship between the fractional vegetation cover (FVC) and LST, for the years 1992, 2002, 2012, and 2022, R2 is equal to 0.87097, 0.84598, 0.83957, and 0.71838, respectively. Conversely, for the LST and the normalised difference built-up index (NDBI), the R2 values were 0.5975, 0.73876, 0.86615, and 0.90368 for 1992, 2002, 2012, and 2022 respectively. In conclusion, this study evaluates Benin City's metropolitan setting and thermal environment. According to the LULC study, there are more built-up areas and less vegetation. The impact of the changing land cover on urban thermal features is shown through correlation analysis, which links more built-up regions to higher LSTs. These results can support urban design efforts to lessen the effects of climate change. Examining the distribution of the LST and its associations with particular land cover types was the major goal of this study. Future research will undoubtedly use this study as a useful reference when modelling urban terrain and temperature variations.
... This study investigates the thermal and ecological conditions of Lahore district for the years 2000, 2010, and 2020, using multi-temporal Landsat satellite imagery as previously adapted in research across the world Amiri et al., 2009;Avdan & Jovanovska, 2016;Gazi & Mondal, 2018;Mokarram et al., 2023). To assess the ecological and thermal conditions of urban areas, particularly in metropolitan cities like Lahore, understanding various city components and their spatiotemporal patterns, such as land use (LU), surface temperature variations, impervious surfaces, and urban thermal field variance index (UTFVI), is essential (Verma et al., 2020;Dilawar et al., 2021;Kafy et al., 2021a;Malah & Bahi, 2022). ...
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Lahore is the second major metropolitan city in Pakistan in terms of urban population and built-up area, making the city a more ideal place to form the surface urban heat island (SUHI) effects. In the last two decades, the considerable land-use conversion from a natural surface (vegetation) and permeable (waterbody) surface into an impervious (built-up area) surface has lead to an increase in land surface temperature (LST) in Lahore. The human thermal comfort (HTC) of the residents is also impacted by the higher LST. The present study uses multi-temporal Landsat (5&8) satellite imageries to examine the ecological and thermal conditions of Lahore between 2000 and 2020. The ecological and thermal conditions of Lahore are assessed by calculating the urban heat islands and UTFVI (urban thermal field variance index), based on LST data which quantitatively assessed the UHI effect and the quality of human life. The outcomes establish that the urban built-up area has increased by 18%, while urban vegetation , vacant land, and waterbody decreased by 13%, 4%, and 0.04%, respectively. In the last 20 years, the mean LST of the study region has risen by about 3.67 °C. The UHI intensity map shows intensification and a rise in surface temperature variation from 4.5 °C (2000) to 5.9 °C (2020). Furthermore, the finding shows that the ecological and thermal conditions are worse in construction sites, transition zones, and urban areas in comparison to nearby rural areas. The lower UTFVI was observed in dense vegetation cover areas while a hot spot of higher UTFVI was predominantly observed in the areas of transition zones and built-up area expansion. Those areas with higher hot spots are more vulnerable to the urban heat island effect. The main conclusions of this study are essential for educating city officials and urban planners in developing a sustainable urban land development plan to reduce urban heat island effects by investing in open green spaces for urban areas of cities.
... C and D are the intermediate variables; a and b are the linear regression coefficient. Landsat LST is not readily available as a pre-processed product, and it can be computed based on data from the TIR bands at a spatial resolution of 30 m [60,61]. GEE offers Landsat images that have undergone atmospheric correction, geometric correction, and radiometric Remote Sens. 2023, 15, 4473 6 of 20 calibration. ...
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Geothermal energy is an eco-friendly, renewable source of underground thermal energy that exists in the interior of the earth. By tapping into these formations, fluids can be channeled to heat the rock formations above, resulting in a significantly higher land surface temperature (LST). However, LST readings are influenced by various factors such as sun radiation, cyclical variations, and precipitation, which can mask the temperature anomalies caused by geothermal heat. To address these issues and highlight the LST anomalies caused by geothermal heat, this paper proposes a methodology to efficiently and quickly calculate the multi-temporal LST leveraging of the Google Earth Engine (GEE) in the Damxung–Yangbajain basin, Qinghai–Tibet Plateau. This method incorporates terrain correction, altitude correction, and multi-temporal series comparison to extract thermal anomaly signals. The existing geothermal manifestations are used as a benchmark to further refine the methodology. The results indicate that the annual mean winter LST is a sensitive indicator of geothermal anomaly signals. The annual mean winter LST between 2015 and 2020 varied from −14.7 °C to 26.7 °C, with an average of 8.6 °C in the study area. After altitude correction and water body removal, the annual mean winter LST varied from −22.1 °C to 23.3 °C, with an average of 6.2 °C. When combining the distribution of faults with the results of the annual mean winter LST, this study delineated the geothermal potential areas that are located predominantly around the fault zone at the southern foot of the Nyainqentanglha Mountains. Geothermal potential areas exhibited a higher LST, ranging from 12.6 °C to 23.3 °C. These potential areas extend to the northeast, and the thermal anomaly range reaches as high as 19.6%. The geothermal potential area makes up 8.2% of the entire study area. The results demonstrate that the approach successfully identified parts of known geothermal fields and indicates sweet spots for future research. This study highlights that utilizing the multi-temporal winter LST is an efficient and cost-effective method for prospecting geothermal resources in plateau environments.
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Çalışma, yer yüzeyi sıcaklıklarındaki (YYS) değişimlerin kent sınırları ve arazi kullanımı ile ilişkisinin Güneydoğu Anadolu Bölgesi’ndeki Şanlıurfa, Diyarbakır ve Mardin kentlerinde ortaya konmasını amaçlamıştır. YYS’de en belirgin artış kent yüzeylerinde meydana gelmektedir. Bu nedenle kentler çevresine göre daha sıcak ortamlar (ısı adası) olarak belirmektedir. Kentlerde farklı arazi kullanımlarına göre YYS de değişmektedir. Bu çalışmada kullanılan veriler 2019 yılı için Landsat 8 (OLI-TIRS), 1990 yılı için Landsat TM 5 uydu görüntüleri, kentsel alan sınırları, CORINE ve kentsel arazi örtüsü/arazi kullanım (AÖ/AK) sınıflarıdır. Literatürde önerilen formüller kullanılarak YYS haritaları oluşturulmuştur. 1990-2019 yılları arasında YYS’de meydana gelen değişim üretilen fark haritaları ile bulunmuştur. YYS değerleri ile arazi kullanım sınıfları örneklem noktaları kullanılarak karşılaştırılmış, meydana gelen YYS değişiminin nedenleri irdelenmiştir. Diyarbakır kentsel alanının %50’sinde, Şanlıurfa’nın %36’sında, Mardin’in %54’ünde kente özgü YYS fark ortalamasının üzerinde YYS değerleri tespit edilmiştir. Ayrıca üç kentte de 1990 yılı kent sınırının genel olarak YYS fark ortalamasının üstünde kaldığı saptanmıştır. Kente ve bölgeye özgü önerilerin yanı sıra aktif ve nitelikli yeşil altyapı çalışmaları ile kent çekirdeklerinde koruma-kullanma dengesi gözetilerek iklim projeksiyonlarına uygun ve yenilikçi çözümlerin uygulanması tavsiye edilmektedir.
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Launched in February 2013, the Landsat-8 carries on-board the Thermal Infrared Sensor (TIRS), a two-band thermal pushbroom imager, to maintain the thermal imaging capability of the Landsat program. The TIRS bands are centered at roughly 10.9 and 12 μm (Bands 10 and 11 respectively). They have 100 m spatial resolution and image coincidently with the Operational Land Imager (OLI), also on-board Landsat-8. The TIRS instrument has an internal calibration system consisting of a variable temperature blackbody and a special viewport with which it can see deep space; a two point calibration can be performed twice an orbit. Immediately after launch, a rigorous vicarious calibration program was started to validate the absolute calibration of the system. The two vicarious calibration teams, NASA/Jet Propulsion Laboratory (JPL) and the Rochester Institute of Technology (RIT), both make use of buoys deployed on large water bodies as the primary monitoring technique. RIT took advantage of cross-calibration opportunity soon after launch when Landsat-8 and Landsat-7 were imaging the same targets within a few minutes of each other to perform a validation of the absolute calibration. Terra MODIS is also being used for regular monitoring of the TIRS absolute calibration. The buoy initial results showed a large error in both bands, 0.29 and 0.51 W/m2·sr·μm or -2.1 K and -4.4 K at 300 K in Band 10 and 11 respectively, where TIRS data was too hot. A calibration update was recommended for both bands to correct for a bias error and was implemented on 3 February 2014 in the USGS/EROS processing system, but the residual variability is still larger than desired for both bands (0.12 and 0.2 W/m2·sr·μm or 0.87 and 1.67 K at 300 K). Additional work has uncovered the source of the calibration error: out-of-field stray light. While analysis continues to characterize the stray light contribution, the vicarious calibration work proceeds. The additional data have not changed the statistical assessment but indicate that the correction (particularly in band 11) is probably only valid for a subset of data. While the stray light effect is small enough in Band 10 to make the data useful across a wide array of applications, the effect in Band 11 is larger and the vicarious results suggest that Band 11 data should not be used where absolute calibration is required.
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The successful launch of the Landsat 8 satellite with two thermal infrared bands on February 11, 2013, for continuous Earth observation provided another opportunity for remote sensing of land surface temperature (LST). However, calibration notices issued by the United States Geological Survey (USGS) indicated that data from the Landsat 8 Thermal Infrared Sensor (TIRS) Band 11 have large uncertainty and suggested using TIRS Band 10 data as a single spectral band for LST estimation. In this study, we presented an improved mono-window (IMW) algorithm for LST retrieval from the Landsat 8 TIRS Band 10 data. Three essential parameters (ground emissivity, atmospheric transmittance and effective mean atmospheric temperature) were required for the IMW algorithm to retrieve LST. A new method was proposed to estimate the parameter of effective mean atmospheric temperature from local meteorological data. The other two essential parameters could be both estimated through the so-called land cover approach. Sensitivity analysis conducted for the IMW algorithm revealed that the possible error in estimating the required atmospheric water vapor content has the most significant impact on the probable LST estimation error. Under moderate errors in both water vapor content and ground emissivity, the algorithm had an accuracy of ~1.4 K for LST retrieval. Validation of the IMW algorithm using the simulated datasets for various situations indicated that the LST difference between the retrieved and the simulated ones was 0.67 K on average, with an RMSE of 0.43 K. Comparison of our IMW algorithm with the single-channel (SC) algorithm for three main atmosphere profiles indicated that the average error and RMSE of the IMW algorithm were -0.05 K and 0.84 K, respectively, which were less than the -2.86 K and 1.05 K of the SC algorithm. Application of the IMW algorithm to Nanjing and its vicinity in east China resulted in a reasonable LST estimation for the region. Spatial variation of the extremely hot weather, a frequently-occurring phenomenon of an abnormal heat flux process in summer along the Yangtze River Basin, had been thoroughly analyzed. This successful application suggested that the IMW algorithm presented in the study could be used as an efficient method for LST retrieval from the Landsat 8 TIRS Band 10 data.
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Land surface temperature (LST) is an important factor in global change studies, heat balance and as control for climate change. A comparative study of LST over parts of the Singhbhum Shear Zone in India was undertaken using various emissivity and temperature retrieval algorithms applied on visible and near infrared (VNIR), and thermal infrared (TIR) bands of high resolution Landsat-7 ETM+ imagery. LST results obtained from satellite data of October 26, 2001 and November 2, 2001 through various algorithms were validated with ground measurements collected during satellite overpass. In addition, LST products of MODIS and ASTER were compared with Landsat-7 ETM+ and ground truth data to explore the possibility of using multi-sensor approach in LST monitoring. An image-based dark object subtraction (DOS3) algorithm, which is yet to be tested for LST retrieval, was applied on VNIR bands to obtain atmospheric corrected surface reflectance images. Normalized difference vegetation index (NDVI) was estimated from VNIR reflectance image. Various surface emissivity retrieval algorithms based on NDVI and vegetation proportion were applied to ascertain emissivities of the various land cover categories in the study area in the spectral range of 10.4–12.5 μm. A minimum emissivity value of about 0.95 was observed over the reflective rock body with a maximum of about 0.99 over dense forest. A strong correlation was established between Landsat ETM+ reflectance band 3 and emissivity. Single channel based algorithms were adopted for surface radiance and brightness temperature. Finally, emissivity correction was applied on ‘brightness temperature’ to obtain LST. Estimated LST values obtained from various algorithms were compared with field ground measurements for different land cover categories. LST values obtained after using Valor’s emissivity and single channel equations were best correlated with ground truth temperature. Minimum LST is observed over dense forest as about 26 °C and maximum LST is observed over rock body of about 38 °C. The estimated LST showed that rock bodies, bare soils and built-up areas exhibit higher surface temperatures, while water bodies, agricultural croplands and dense vegetations have lower surface temperatures during the daytime. The accuracy of the estimated LST was within ±2 °C. LST comparison of ASTER and MODIS with Landsat has a maximum difference of 2 °C. Strong correlation was found between LST and spectral radiance of band 6 of Landsat-7 ETM+. Result corroborates the fact that surface temperatures over land use/land cover types are greatly influenced by the amount of vegetation present.
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The split window method is successfully being used to retrieve the temperature over sea surfaces from satellite radiances in clear sky and has the great advantage of simplicity. However, such a method does not work over land surfaces, mainly because the emissivity is not equal to 1 and depends on the channel. An extension of this method to apply to land surfaces requires one to take account of emissivity—such an extension is presented in this paper. First, using Lowtran 6, the accuracies of the various linearizations of the radiative transfer equation leading to the split window are checked. This implies that the retrieved surface temperature depends linearly on emissivities and brightness temperatures. Such behaviour has been checked on actual examples. Theoretical equations are then derived which show that the actual surface temperature can again be expressed as a linear combination of the brightness temperatures measured in two adjacent channels with coefficients depending on spectral emissivities but not on atmospheric conditions. Using Lowtran 6 these properties have been verified and the dependence of these coefficients has been explicitly computed leading to a local split window method for the NOAA-9 Advanced Very High Resolution Radiometer. Finally, we show that accurate surface temperatures can be retrieved using this local split window method once emissivities in two adjacent channels are known with sufficient accuracy.
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Land surface temperature (LST) is one of the key parameters in the physics of land surface processes from local through global scales. The importance of LST is being increasingly recognized and there is a strong interest in developing methodologies to measure LST from space. However, retrieving LST is still a challenging task since the LST retrieval problemis ill-posed. This paper reviews the current status of selected remote sensing algorithms for estimating LST from thermal infrared (TIR) data. A brief theoretical background of the subject is presented along with a survey of the algorithms employed for obtaining LST from space-based TIR measurements. The discussion focuses on TIR data acquired from polar-orbiting satellites because of their widespread use, global applicability and higher spatial resolution compared to geostationary satellites. The theoretical framework and methodologies used to derive the LST from the data are reviewed followed by the methodologies for validating satellite-derived LST. Directions for future research to improve the accuracy of satellite-derived LST are then suggested.
Land Surface Temperature (LST) is an important phenomenon in global climate change. As the green house gases in the atmosphere increases, the LST will also increase. This will result in melting of glaciers and ice sheets and affects the vegetation of that area. Its impact will be more in the monsoon areas, because the rainfall is unpredictable, failure of monsoon and there will be heavy down pour of rainfall. LST can be estimated through many algorithms viz., Split-Window (SW), Dual-Angle (DA), Single-Channel (SC), Sobrino and Mao. With the advent of satellite images and digital image processing software, now it is possible to calculate LST. In this study, LST for Dindigul District, Tamil Nadu, India, was derived using SW algorithm with the use of Landsat 8 Optical Land Imager (OLI) of 30 m resolution and Thermal Infrared Sensor (TIR) data of 100 m resolution. SW algorithm needs spectral radiance and emissivity of two TIR bands as input for deriving LST. The spectral radiance was estimated using TIR bands 10 and 11. Emissivity was derived with the help of NDVI threshold technique for which OLI bands 2, 3, 4 and 5 were used. The output revealed that LST was high in the barren regions whereas it was low in the hilly regions because of vegetative cover. As the SW algorithm uses both the TIR bands (10 and 11) and OLI bands 2, 3, 4 and 5, the LST generated using them were more reliable and accurate.
An extensive remotely sensed dataset recently available to the scientific community, The Global Land 1-km AVHRR Project, has been used to examine the possibilities of multi-temporal imagery for mapping and monitoring changes in the biophysical characteristics of land cover. Our goal was to investigate the regional response of the soil-vegetation system to climate in arid zones. We addressed this problemby applying theoretical models to obtain parameters such as Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) from satellite data and by analysing the spatial-temporal dynamics of these parameters. Morocco was selected as the area of study due to its high environmental diversity. This area is also clearly affected by the risk of the advance of the desert. Using The Global Land 1-km AVHRR Project dataset, two methodologies are proposed for the monitoring of land cover dynamics in different areas of interest defined using as mapping criteria the Annual Average of NDVI (AANDVI): (1) The Method of the Area of the Triangle (MAT), based on a form described by the annual evolution of LST and NDVI in each area; (2) the Method of the Slope, which analyses the slope of the line defined by the months of the maximum NDVI and the minimum LST.
Clear and cloudy daytime comparisons of land surface temperature (LST) and air temperature (Tair) were made for 14 stations included in the U. S. Climate Reference Network (USCRN) of stations from observations made from 2003 through 2008. Generally, LST was greater than Tair for both the clear and cloudy conditions; however, the differences between LST and Tair were significantly less for the cloudy-sky conditions. In addition, the relationships between LST and Tair displayed less variability under the cloudy-sky conditions than under clear-sky conditions. Wind speed, time of the observation of Tair and LST, season, the occurrence of precipitation at the time of observation, and normalized difference vegetation index values were all considered in the evaluation of the relationship between Tair and LST. Mean differences between LST and Tair of less than 2 degrees C were observed under cloudy conditions for the stations, as compared with a minimum difference of greater than 2 degrees C(and as great as 7+degrees C) for the clear-sky conditions. Under cloudy conditions, Tair alone explained over 94%-and as great as 98%-of the variance observed in LST for the stations included in this analysis, as compared with a range of 81%-93% for clear-sky conditions. Because of the relatively homogeneous land surface characteristics encouraged in the immediate vicinity of USCRN stations, and potential regional differences in surface features that might influence the observed relationships, additional analyses of the relationships between LST and Tair for additional regions and land surface conditions are recommended.
Remote sensing of urban heat islands (UHIs) has traditionally used the Normalized Difference Vegetation Index (NDVI) as the indicator of vegetation abundance to estimate the land surface temperature (LST) – vegetation relationship. This study investigates the applicability of vegetation fraction derived from a spectral mixture model as an alternative indicator of vegetation abundance. This is based on examination of a Landsat Enhanced Thematic Mapper Plus (ETM+) image of Indianapolis City, IN, USA, acquired on June 22, 2002. The transformed ETM+ image was unmixed into three fraction images (green vegetation, dry soil, and shade) with a constrained least-square solution. These fraction images were then used for land cover classification based on a hybrid classification procedure that combined maximum likelihood and decision tree algorithms. Results demonstrate that LST possessed a slightly stronger negative correlation with the unmixed vegetation fraction than with NDVI for all land cover types across the spatial resolution (30 to 960 m). Correlations reached their strongest at the 120-m resolution, which is believed to be the operational scale of LST, NDVI, and vegetation fraction images. Fractal analysis of image texture shows that the complexity of these images increased initially with pixel aggregation and peaked around 120 m, but decreased with further aggregation. The spatial variability of texture in LST was positively correlated with those in NDVI and in vegetation fraction. The interplay between thermal and vegetation dynamics in the context of different land cover types leads to the variations in spectral radiance and texture in LST. These variations are also present in the other imagery, and are responsible for the spatial patterns of urban heat islands. It is suggested that the areal measure of vegetation abundance by unmixed vegetation fraction has a more direct correspondence with the radiative, thermal, and moisture properties of the Earth's surface that determine LST.