International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 20 (2017) pp.

9679-9687
© Research India Publications. http://www.ripublication.com

Land Surface Temperature Retrieval from LANDSAT data using Emissivity

Estimation

Jeevalakshmi. D 1
Research Scholar, Department of Electronics and Communication Engineering,
Sri Venkateswara University College of Engineering, Sri Venkateswara University,
Tirupati, Andhra Pradesh, India.
Orcid Id: 0000-0001-8647-9769

Dr. S. Narayana Reddy 2

Professor, Department of Electronics and Communication Engineering,
Sri Venkateswara University College of Engineering, Sri Venkateswara University,
Tirupati, Andhra Pradesh, India.

Dr. B. Manikiam 3
Sir MV - ISRO Chair Professor, Department of Physics,
Bangalore University, Jnanabharathi Campus,
Bangalore, Karnataka, India.

Abstract the skin temperature of the land surface. Worldwide

urbanization has significantly reshaped the landscape, which
Land surface temperature (LST) is an essential factor in many
has important climatic implications across all scales due to the
areas like global climate change studies, urban land use/land
simultaneous transformation of natural land cover and
cover, geo-/biophysical and also a key input for climate
introduction of urban materials i.e. anthropogenic surfaces.
models. LANDSAT 8, the latest satellite from LANDSAT
Identification and characterization of Urban Heat Island (UHI)
series, has given lot of possibilities to study the land processes
is typically based on LST that varies spatially, due to the non-
using remote sensing. In this study an attempt has been made
homogeneity of land surface cover and other atmospheric
to estimate LST over Chittoor district, Andhra Pradesh, India,
factors [1]. Ground surveys would permit a highly accurate
using LANDSAT 8 – Operational Line Imager & Thermal
Land Use Land Cover (LULC) classification, but they are
Infrared Sensor (OLI & TIRS) satellite data. The variability of
time-consuming, burdensome and expensive, which highlights
retrieved LSTs has been investigated with respect to
remote sensing an evident and preferred alternative. Medium
Normalized Difference Vegetation Index (NDVI) values for
spatial resolution data, such as that from the LANDSAT and
different land use/land cover (LU/LC) types determined from
SPOT are suitable for land cover or vegetation mapping at
the Landsat visible and NIR channels. The Land Surface
regional local scale. LANDSAT 8 carries two sensors, i.e., the
Emissivity (LSE) values needed in order to apply the method
Operational Land Imager (OLI) and the Thermal Infrared
have been estimated from a procedure that uses the visible and
Sensor (TIRS). OLI collects data at a 30m spatial resolution
near infrared bands. The present study focuses on developing
with eight bands located in the visible and near-infrared and
an ERDAS IMAGINE image processing method using the
the shortwave infrared regions of the electromagnetic
LANDSAT 8 thermal imagery of band 10 data. The difference
spectrum, and an additional panchromatic band of 15m spatial
between retrieved LST and Automatic Weather Station
resolution. TIRS senses the TIR radiance at a spatial
(AWS) data indicates that the technique works by giving an
resolution of 100m using two bands located in the
error of ±3C.
atmospheric window between 10 and 12 μm [2,3].
Keywords: Land Surface Temperature (LST), Land Surface
Various techniques have been developed to estimate LST for
Emissivity (LSE), Normalized Difference Vegetation Index
Urban Heat analysis, Meteorology and Climatology, Land
(NDVI), Operational Line Imager & Thermal Infrared Sensor
Cover Dynamic monitoring using brightness temperature [1];
(OLI & TIRS), Remote sensing.
Split Window Technique and Single Channel Technique
[2,4,5,6,7,8,9]. The technique used for the study area is
developed in ERDAS IMAGINE 2016, with the model maker.
INTRODUCTION
The idea behind this technique was probably first suggested
Land Surface Temperature (LST) is the temperature of the by Ugur Avdan et al., 2016 [10]. The technique presented in
surface which can be measured when the land surface is in this paper is used for estimating the LST of a given
direct contact to the measuring instrument. LST is nothing but LANDSAT 8 image with the input of the red band (0.64–

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0.67m), near infrared band (NIR) (0.85–0.88m), and Table 1: Landsat 8 OLI and TIRS
thermal infrared band10 (TIR) (10.60– 11.19m). Following
the instructions of USGS vide January 6, 2014, of not using Band Designations Wavelength Resolution
TIRS band 11 due to its larger calibration unreliability, only (m)
(m)
band 10 was considered in the technique.
Band 1 (Coastal Aerosol) 0.43 - 0.45 30

DESCRIPTION OF STUDY AREA AND DATA

Band 2 (Blue) 0.45 - 0.51 30
Chittoor district is a part of Rayalaseema region of Andhra
Pradesh. The district occupies an area of 15,359 square Band 3 (Green) 0.53 - 0.59 30
kilometers (5,930 sq mi). The district lies extreme south of the
Andhra Pradesh state approximately between 12°37′ - 14°8′N Band 4 (Red) 0.64 - 0.67 30
and 78°3′ - 79°55′E (Lat/Long respectively). 30% of the total
land is covered by forests in the district. The district Band 5 (Infrared) 0.85 - 0.88 30
constitutes of red loamy soil 57%, red sandy soil 34% and the
remaining 9% is covered by black clay, black loamy, black
Band 6 (Short wave infrared) 1.57 - 1.65 30
sandy and red clay soils. Because of the higher altitude of the
western parts compared to the eastern parts of the district, the
Band 7 (Short wave infrared) 2.11 - 2.29 30
temperature in the western parts, like Punganur, Madanapalle
Horsley Hills are relatively lower than the eastern parts. The
summer temperature reaches up to 46 °C in the eastern parts Band 8 (Panchromatic) 0.50 - 0.68 15
whereas in the western parts it ranges around 36° to 38 °C.
Similarly the winter temperatures of the western parts are Band 9 (Cirrus) 1.36 - 1.39 30
relatively in low range around 12 °C to 14 °C and in eastern
parts it is about 16 °C to 18 °C. It receives an annual rainfall Band 10 (Thermal infrared) 10.6 - 11.19 100
of 918.1 mm. The South West Monsoon and North East
Monsoon are the prime sources of rainfall for the district Band 11 (Thermal infrared) 11.50 - 12.51 100
[Source: Regional Agricultural Research Station].
The multispectral remote sensing images of Chittoor region of
two dates were collected from USGS. Landsat 8 satellite The steps involved in the proposed work are detailed in the
images the entire earth once in 16 days. Band designations of following literature.
Landsat8 are as given in Table 1 [11]. Satellite data over Step1:
Chittoor region of 30th May and 21st October of 2015 (day
time, level-1G product, path/row 143/51) have been used in The satellite data products were geometrically corrected data
this study. Satellite images of two dates of same region were set. The metadata of the satellite images is presented in Table
downloaded from USGS website. The study area chosen 2. The first step of the proposed work is to convert the DN
includes water, bare soil, vegetation cover and built-up area. (Digital Number) values of band10 to at-sensor spectral
The images were resampled using nearest neighbor method. radiance using the following equation [11,12,13,14]
All the data are re-projected to a Universal Transverse
Mercator (UTM) coordinate system, datum WGS84, zone 44.
Lλ = - Oi (1)

METHODOLOGY
The approach to the proposed work to estimate LST is shown Where,
in the Figure 1. This technique can only be used to process Lmax is the maximum radiance (Wm-2sr-1m-1)
LANDSAT 8 data. In this study, band 10 is used to estimate
brightness temperature and bands 4 and 5 are used to calculate Lmin is the minimum radiance (Wm-2sr-1m-1)
NDVI. Qcal is the DN value of pixel
Qcalmax is the maximum DN value of pixels
Qcalmin is the minimum DN value of pixels
Oi is the correction value for band 10

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Step2: gives the estimation of area under each land cover type. The
vegetation and bare soil proportions are acquired from the
After converting DN values to at- sensor spectral radiance, the
NDVI of pure pixels. Values of NDVIv = 0.5 and NDVIs =
TIRS band data should be converted to brightness temperature
0.2 were proposed to apply in global conditions While the
(BT) using the thermal constants given in metadata file and
value for vegetated surfaces (NDVIv = 0.5) may be too low in
some cases, for higher resolution data over agricultural sites,
NDVIv can reach 0.8 or 0.9 [14]. Pv can be calculated using
Table 2: Metadata of the satellite image
the equation (4).
Variable Description Value
Pv = (4)
K1 Thermal constants, Band 10 774.8853
K2 1321.0789

Lmax Maximum and Minimum values of 22.00180

Radiance, Band 10
Lmin 0.10033

Qcalmax Maximum and Minimum values of 65535

Quantize Calibration, Band 10
Qcalmin 1

Oi Correction value, Band 10 0.29

the following equation

BT = (2)

Where K1 and K2 are the thermal constants of TIR band 10

which can be identified in the metadata file associated with
the satellite image.
To have the results in Celsius, it is necessary to revise by Figure 1: Flow diagram for LST retrieval
adding absolute zero which is approximately equal to -273.15.
Step 5:
Since the atmosphere in our research area is comparitively dry
and therefore, the range of water vapor values is relatively Calculation of land surface emissivity (LSE) is required to
small, the atmospheric effect is not taken into consideration in estimate LST since, LSE is a proportionality factor that scales
retrieving the LST. the black body radiance (Plank’s law) to measure emitted
radiance and it is the ability of transmitting thermal energy
Step 3: across the surface into the atmosphere [10]. At the pixel scale,
Normalized Difference Vegetation Index (NDVI) is essential natural surfaces are heterogeneous in terms of variation in
to identify different land cover types of the study area. NDVI LSE. In addition, the LSE is largely dependent on the surface
ranges between -1.0 to +1.0. NDVI is calculated on per-pixel roughness, nature of vegetation cover etc.[15].
basis as the normalized difference between the red band (0.64 ԑλ =ԑvλPv + ԑsλ (1 - Pv) + Cλ (5)
- 0.67m) and near infrared band (0.85-0.88m) of the images
using the formula. where ԑv and ԑs are the vegetation and soil emissivities
respectively, and C is the surface roughness taken as a
NDVI = (3) constant value of 0.005[16]. The emissivity of water bodies is
utmost stable in comparison with land surfaces. Since the
Where NIR is the near infrared band value of a pixel and RED emissivity depends on the wavelength, the NDVI threshold
is the red band value of the same pixel. Calculation of NDVI method (NTM) [17] can be used to estimate the emissivity of
is necessary to further calculate proportional vegetation (Pv) different land surfaces in the 10-12 μm range.
and emissivity (ԑ).
= (6)
Step 4:
Next step is to calculate proportional vegetation (P v) from
NDVI values obtained in step 3. This proportional vegetation

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The average emissivity of four major land cover types can be for the dates 30.05.2015 and 21.10.2015 (path/row: 143/51)
considered in Band 10 as, when the NDVI is less than 0, it is were used for the present study. The Visible bands and Near
classified as water, and the emissivity value of 0.991 is given, Infrared bands are combined together to form a False Color
for NDVI values between 0 and 0.2, it is considered that the Composite (FCC) image. The images were resampled using
land cover type is soil, and the emissivity value of 0.966 is nearest neighbor method. All the data were re-projected to a
assigned, values between 0.2 and 0.5 are considered as Universal Transverse Mercator (UTM) coordinate system,
mixture of soil and vegetation cover and equation (6) is datum WGS84, zone 44. The FCC images of two data sets are
applied to calculate the emissivity. In the last case, when the shown in Figure 3. The FCC images were created by layer
NDVI value is greater than 0.5, it is considered as vegetation stacking band 4, band 3 and band 2 of each data set
cover, and the value of 0.973 is assigned. correspondingly.
Step 6: After conversion of DN values to spectral radiances, NDVI of
each dataset is calculated. The NDVI images are shown in
The final step is to calculate LST using brightness temperature
Figure 4. The NDVI values range between -1.0 to +1.0. An
(BT) of band 10 and LSE derived from Pv and NDVI [18].
improvement in NDVI for vegetated land covers can be seen
LST can be retrieved using the equation (7)
for the date 21.10.2015 when compared to the NDVI of
(7) 30.05.2015. That means, vegetation has been increased which
shows an impact on the surface temperature. But, whereas,
there is no much change in NDVI for the land cover types like
built up area and bare land. LSE were estimated to retrieve
Where, Ts is the LST in Celsius (o C), BT is at- sensor BT
LST for the acquired satellite data.
(oC), λ is the average wavelength of band 10, ԑ λ is the
emissivity calculated from equation (6) and ρ is (h x Since the atmosphere in our research area is comparitively dry
and therefore, the range of water vapor values are relatively
which is equal to 1.438 x 10-2 mK in which, σ is the
small. We are convinced with the results achieved and hence
Boltzmann constant (1.38 x 10-23 J/K), h is Plank’s constant
atmospheric effect is not taken into account in estimating the
(6.626 x 10-34) and c is the velocity of light (3 x 108 m/s).
LST.
Automatic Weather Station (AWS) hourly data were collected
RESULTS from Andhra Pradesh State Development Planning Society
(APSDPS), Andhra Pradesh, India and are used for
The study area Chittoor district, India, is shown in Figure 2.
comparison with the retrieved LST. The near surface air
Satellite images of two dates of same region were downloaded
temperature of AWS is used for validating the retrieved LST
from USGS website. The study area chosen includes water,
of satellite images for 14 representative points.
bare soil, vegetation cover and built-up area. Landsat 8 data

Figure 2: Geographic location of Chittoor District in India

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other thing that has to be taken into consideration is the

location of the thermal sensor in AWS which is at a height of
2 meters.
Comparison of retrieved LST and AWS surface air
temperature
LSTs were retrieved for the downloaded satellite images using
the mono window technique in ERDAS Imagine 2016. For the
selected study area, 14 points were choosen in keeping view
about the cloud pixels and other unwanted events for
(a) (b) assessment of accuracy. The difference between retrieved
LST and the AWS data for the two dates are shown in Table
Figure 3: FCC images of (a) 30.05.2015 and (b) 21.10.2015
3. The corresponding images of TIR band10 for both the
datasets are shown in Figure 5. Plots showing AWS data and
retrieved LST for the region of interest are shown in Figure 6.
The comparison was made with air temperature of AWS
From the plots it can be observed that the retrieved LST and
station, which is not same and can sometimes result in big
AWS data for the two datasets follows the same pattern for
differences since the resolution of LANDSAT 8 is 100m for
the selected 14 meteorological stations in the study area. LST
the thermal band and 30m for the optical bands. The LST was
maps of two datasets witht the meteorological stations and
calculated and taken for the pixel in which the AWS is
LST scale in oC are shown in Figure 7 and Figure 8
located. The differences may also be due to some weather
respectively.
conditions and sensor characteristics of the AWS. And the

Table 3: Retrieved LST and AWS data for 30.05.2015 and 21.10.2015

S.No. Location Latitude Longitude Land 30.05.2015 21.10.2015

Cover NDVI AWS LST Error NDVI AWS Data LST Error
types Data at 5 Retrieved (C) at 5 A.M Retrieved (C)
A.M (C) (C) (C) (C)
1 Puttur 13.443777 79.556068 Built up 0.119 28.2 25.58 2.62 0.108 25.53 25.4 0.13
2 Chowdepalle 13.434461 78.692558 Bare land 0.042 24.41 26.83 -2.42 0.003 23.96 24.52 -0.56
3 Gudipala 13.078956 79.125069 Dense 0.335 23.52 21.64 1.88 0.529 23.01 24.5 -1.49
vegetation
4 Aranyakandriga 13.402667 79.63726 Dense 0.305 27.18 25.67 1.51 0.574 24.63 25.83 -1.2
vegetation
5 Nindra 13.376643 79.699702 Vegetation 0.261 28.53 30.68 -2.15 0.457 24.25 25.24 -0.99
6 Thavanampalle 13.262898 79.012993 Vegetation 0.046 22.95 23.54 -0.59 0.215 22.06 24.9 -2.84
7 Vijayapuram 13.267402 79.698624 Vegetation 0.346 25.46 27.82 -2.36 0.429 23.6 23.41 0.19
8 Yerpedu 13.693141 79.593695 Built up -0.045 29.46 30.96 -1.5 0.05 24.78 27.66 -2.88
9 KVB Puram 13.53205 79.73996 Vegetation 0.044 31.23 31.94 -0.71 0.242 24.89 25.65 -0.76
10 Shivaramapuram 13.430556 78.409444 Vegetation 0.146 24.06 25.15 -1.09 0.377 21.18 22.49 -1.31
11 Tirupati 13.6175 79.403333 Built up 0.153 30.07 27.62 2.45 0.191 27.04 28.53 -1.49
12 Etavakili 13.374444 78.526667 Built up 0.015 21.15 23.74 -2.59 0.093 21.55 24.01 -2.46
13 Bandapalli 13.307083 79.121117 Bare land 0.105 25.98 26.17 -0.19 0.1 22.19 21.6 0.59
14 Sathravada 13.32085 79.542483 Vegetation 0.163 25.27 26.82 -1.55 0.215 25.01 26.03 -1.02

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LST). This paper proposes an ERDAS image processing

method to estimate LST and can be used to understand the
urban development impacts on environment. This tool has
proved as a dynamic tool to estimate LST using brightness
temperature information of TIR sensor and Land surface
emissivity (LSE) from proportional vegetation cover of
optical bands of OLI sensor of LANDSAT 8.

(a) (b)
Figure 4: NDVI images for (a) 30.05.2015 and (b)
21.10.2015

Conclusions & Future Scope

The model created in ERDAS Imagine 2016, estimated the
LST for the selected datasets over the study area. The
algorithm was created using the brightness temperature of (a) (b)
TIRS band 10 and emissivity of different land covers types,
derived from visible and near infrared bands of LANDSAT 8. Figure 5: TIR band 10 images for (a) 30.05.2015 and (b)
The retrieved LSTs were verified using the near surface 21.10.2015
temperature of AWS data. From the comparison, it has been In future studies, the technique to estimate LST can be altered
concluded based on the 14 meteorological stations that the by considering atmospheric effect and weather conditions of
standard deviation calculated for the first case was 1.79 oC and seasonal variations by processing the time series data over
that for the second case it was 1.02oC. The presented our region of interest. And also correlation between NDVI
technique estimated LST with a smallest absolute difference and LST can be demonstrated which helps specifically in
of 0.19oC and 0.56oC for the two datasets respectively. These urban heat analysis.
differences can be due to the difference between the
resolutions of thermal band 10, which is of 100m and visible
& NIR bands, which are of 30m. And also the comparison
was done between the point measurement (AWS data) that is
2m above the surface and surface temperature (retrieved

(a) (b)
Figure 6: Plots showing AWS & LST for (a) 30.05.2015 and (b) 21.10.2015

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Figure: 7 LST map for 30.05.2015

Figure 8: LST map for 21.10.2015

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ACKNOWLEDGEMENT 9852; doi: 10.3390/ rs6109829, ISSN 2072- 4292,

2014.
The authors are extremely grateful to the Centre of Excellence
on "Atmospheric Remote Sensing and Advanced Signal [8] Meijun Jin, Junming Li, Caili Wang, & Ruilan
Processing", at Department of ECE, S V University College Shang. “A Practical Split-Window Algorithm for
of Engineering, Tirupati, Andhra Pradesh, India, for providing Retrieving Land Surface Temperature from Landsat-
necessary resources to carry out the present work. The 8 Data and a Case Study of an Urban Area in China”,
LANDSAT 8 data from US Geological Survey (USGS), the Remote Sens., 7, 4371- 4390; doi: 10.3390/
AWS data from Andhra Pradesh State Development Planning rs70404371, ISSN 2072 – 4292, 2015.
Society (APSDPS) are acknowledged in this research.
[9] Offer Rozenstei, Zhihao Qin, Yevgeny Derimian, &
Arnon Karnieli. “Derivation of Land Surface
Temperature for Landsat-8 TIRS Using a Split
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