This document describes a study that used artificial neural networks and remote sensing to model and estimate land surface temperature in Turkey. Specifically, it used a generalized regression neural network with meteorological and geographic data from 10 stations as inputs to estimate monthly mean land surface temperature as the output. It also used the Becker and Li method to estimate land surface temperature values from NOAA satellite data. The results found low errors between the estimated and measured temperatures, showing these methods can accurately predict land surface temperature.

1. 1 23
Journal of the Indian Society of
Remote Sensing
ISSN 0255-660X
Volume 40
Number 3
J Indian Soc Remote Sens (2012)
40:399-409
DOI 10.1007/s12524-011-0158-3
Modelling and Remote Sensing of Land
Surface Temperature in Turkey
Mehmet Şahin, B. Yiğit Yıldız, Ozan
Şenkal & Vedat Peştemalcı

2. 1 23
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3. RESEARCH ARTICLE
Modelling and Remote Sensing of Land Surface
Temperature in Turkey
Mehmet Şahin & B. Yiğit Yıldız & Ozan Şenkal &
Vedat Peştemalcı
Received: 28 September 2010 /Accepted: 15 August 2011 /Published online: 14 September 2011
# Indian Society of Remote Sensing 2011
Abstract This study introduces artificial neural net-
works (ANNs) for the estimation of land surface
temperature (LST) using meteorological and geo-
graphical data in Turkey (26–45°E and 36–42°N). A
generalized regression neural network (GRNN) was
used in the network. In order to train the neural
network, meteorological and geographical data for the
period from January 2002 to December 2002 for 10
stations (Adana, Afyon, Ankara, Eskişehir, İstanbul,
İzmir, Konya, Malatya, Rize, Sivas) spread over
Turkey were used as training (six stations) and testing
(four stations) data. Latitude, longitude, elevation and
mean air temperature are used in the input layer of the
network. Land surface temperature is the output.
However, land surface temperature has been estimated
as monthly mean by using NOAA-AVHRR satellite
data in the thermal range over 10 stations in Turkey.
The RMSE between the estimated and ground values
for monthly mean with ANN temperature(LSTANN)
and Becker and Li temperature(LSTB-L) method values
have been found as 0.077 K and 0.091 K (training
stations), 0.045 K and 0.003 K (testing stations),
respectively.
Keywords Generalized regression neural network .
Land surface temperature . Satellite data
Introduction
Land surface temperature (LST) is an important factor
controlling most physical, chemical, and biological
processes on Earth. Knowledge of land surface temper-
ature is necessary for many environmental studies and
management activities of the Earth’s resources (Li and
Becker 1993). In order to monitor macro-scale spatial
changes in surface temperature, scanners designed for
sensing in the thermal bands are placed onboard
platforms for remote sensing of the Earth’s resources
from space (Sabins 1997). The extensive application
and significant importance of temperature in environ-
mental studies and management is the main force
driving the study of LST in remote sensing. With the
availability of thermal sensing data, such as channels 4
and 5 of Advanced Very High Resolution Radiometer
(AVHRR) data as well as Landsat Thematic Mapper 6
(TM6), the study of LST has become one of the hottest
topics in remote sensing during the last two decades
(Vogt 1996). Thus, two approaches have been devel-
J Indian Soc Remote Sens (September 2012) 40(3):399–409
DOI 10.1007/s12524-011-0158-3
M. Şahin (*)
Siirt Vocational School, Siirt University,
56100 Siirt, Turkey
e-mail: sahanmehmet2000@yahoo.com
B. Y. Yıldız
Karaisalı Vocational School, Çukurova University,
01770 Karaisalı, Adana, Turkey
O. Şenkal
Faculty of Education Department of Computer Education
and Instructional Technology, Çukurova University,
01330 Sarıçam, Adana, Turkey
V. Peştemalcı
Physics Department, Çukurova University,
01330 Sarıçam, Adana, Turkey
Author's personal copy

4. oped to recover land surface temperature from multi-
spectral thermal infrared (TIR) imagery (Schmugge et
al. 1998). The first approach utilizes a radiative transfer
equation to correct the at-sensor radiance to surface
radiance, followed by an emissivity model to separate
the surface radiance into temperature and emissivity
(Schmugge et al. 1998). The second approach applies
the split-window technique for sea surfaces to land
surfaces, assuming that the emissivity in the channels
used for the split window is similar (Dash et al. 2002).
Land surface brightness temperatures are then calcu-
lated as a linear combination of the two channels.
A study was undertaken to retrieve land (soil–
vegetation complex) surface temperature (LST) over a
area in India using thermal bands (channel 4 and 5) of
AVHRR. The LST values were compared with near
synchronous soil and air temperature measurements.
The estimated LST of a semi-arid mixed agricultural
barren values were near midway between air temper-
ature (AT) and soil surface temperature (ST) with
mean bias of −2.9 K and 7.0 K respectively in winter.
However, in the summer, the LST values were found
to be closer to ST, which may be due to lower
fractional vegetation cover and NDVI (Bhattacharya
and Dadhwal 2003). So this study showed that there
is a relationship between land surface temperature,
soil temperature and air temperature.
Although land surface temperature (LST) can be
generally estimated with reasonable accuracy by using
satellite data, it is not possible to get same successful in
cloudy days. So, a new method is necessary to retrieve
land surface temperature. For this purpose, Artificial
Neural Networks (ANN) was applied as a new method
which used latitude, longitude, elevation and monthly
mean air temperature.
Artificial Neural Networks (ANN) have been
applied to many different environmental sectors,
especially in meteorological forecast (Gardner and
Dorling 1998; Aires et al. 2002). Govindaraju
(2000a, b) reports a number of studies which have
used ANNs to forecast rainfall over a short time
interval. It is clear from many studies that usage of
ANN method is suitable and applicable for estimating
global solar radiation especially for regions where
very large distances exist between meteorological
stations and also having abundant solar energy
(Kalogirou 2001; Bechrakis and Sparis 2004).
The objective of the present study is to apply the
LSTANN, LSTB-L methods in collaboration with each
other for the prediction of the land surface tempera-
ture of the target station using neighboring measuring
stations. This will show that these methods can be
applied to predict the land surface temperature for
any location around sampled measuring stations.
The studies which have used LSTANN, LSTB-L
method show a general perspective of land surface
temperature in Turkey.
Methodology and Data Sources
In this study, the ANN is used to model the LST from
near-surface air temperature and geographic information.
In addition, the LSTB-L method is used to retrieve the
LST from National Oceanic and Atmospheric Admin-
istration/Advanced Very High Resolution Radiometer
(NOAA/AVHRR) data. The LSTB-L method is pre-
sented for the determination of monthly global land
surface temperature from the NOAA/AVHRR satellite
data, which provide wide coverage together with
adequate spatial resolution (around 1.1 km at the nadir).
For application, ten stations (Adana, Afyon,
Ankara, Eskişehir, İstanbul, İzmir, Konya, Malatya,
Rize, and Sivas) have been selected from different
regions of Turkey. The geographical locations of these
land surface temperature stations are shown in Fig. 1.
The stations selected can give a general idea about the
land surface temperature values in Turkey. The
estimation of land surface temperature in Turkey
was based on meteorological and geographical data
(latitude, longitude, elevation, and monthly mean air
temperature). These selected locations in Turkey have
different values, as seen in Table 1. Generalized
regression neural network (GRNN) is used in the
ANN. Meteorological and geographical data are used
as input, and land surface temperature is the output.
Fig. 1 Land surface temperature measuring stations in Turkey
400 J Indian Soc Remote Sens (September 2012) 40(3):399–409
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5. Split-Window Method
The temporal resolution of NOAA-12-14-15
images is at the same location which is scanned
by the satellite 5 cloudless days of every month.
The satellite images were received in a raw image
and zipped data format. They were unzipped and
processed with the software ‘Quorum to Level1B’
in order to convert this raw image data to Level1B
format, so that remote sensing software can be
applied for processing. Radiometric and geometric
calibrations were applied first to the images to
correct the deficiencies and flaws that could result
from the imaging sensor in the platform (satellite).
Then, land surface temperature is predicted with
LSTB-L method. Usage of these methods is more
suitable for large places. Both methods are applica-
ble in any region for the very large distances
between the stations.
Split-window algorithms for the retrieval of LST
from NOAA-AVHRR data have been proposed by
different scholars in the last decades (Vogt 1996). The
algorithm given by Becker and Li (1990) is worthy of
detailed description since it has been used in many
studies (Franca and Cracknell 1994) on the study of
LST. The algorithm requires
– brightness temperatures in channels 4 (T4) and
5 (T5)
– the mean emissivity in these channels, ε=(ε 4+ ε 5)/
2=0.975
– the spectral emissivity difference, Δε=ε 4 -ε 5=
−0.005 (Caselles et al. 1997; Chrysoulakis et al.
2001).
Becker and Li (1990) presented a local split-
window algorithm for viewing angles of up to 46°
from nadir, given as follows:
TBec ker ÀLiÀ1990 ¼ A0 þ P
T4 þ T5
2
þ M
T4 À T5
2
ð1Þ
where A0, P, and M are coefficients influenced by a
number of factors in the process of radiance trans-
mission from the ground to the sensor. For NOAA/
AVHRR data, coefficient A0=1.274 and A2=0.15616,
A3=−0.482, B1=6.26, B2=3.98, B3=38.33. Later, Li
and Becker (1993) modified their algorithm into a
general one keeping the form of Eq. 1. The only
difference in the modified algorithm is that the
coefficients are determined in terms of water content
calculated from a radiance simulation using the LOW-
TRAN 7 program (Caselles et al. 1997). Based on
Becker and Li (1990), a generalized split-window
algorithm has been developed by Wan and Dozier
(1996). The form of the algorithm is the same as Eq. 2,
but the coefficients P and M are given as follows:
P ¼ A1 þ A2
1 À "
"
þ A3
$"
"2
ð2Þ
M ¼ B1 þ B2
1 À "
"
þ B3
$"
"2
ð3Þ
where A1~A3 and B1~B3 are parameters estimated
by the method given by Li and Becker (1993). The
difference is that Wan and Dozier (1996) defined A1
in their model as a variable while Becker and Li
(1990) defined it as a constant equal to 1. Similarly,
the parameters are determined by regression analysis
of the brightness temperature data to be simulated
ground data of standard atmosphere given by LOW-
Stations Latitude (°N) Longitude (°E) Elevation (m) The mean air
temperature (K)
The mean land surface
temperature (K)
Adana 36.59 35.21 27 290.31 291.07
Afyon 38.44 30.35 1001 280.84 282.10
Ankara 39.57 32.53 891 283.60 283.08
Eskişehir 39.47 30.34 786 278.79 282.30
İstanbul 41.01 28.59 0 287.33 287.10
İzmir 38.26 27.10 29 289.83 289.60
Konya 37.58 32.33 1031 282.13 283.57
Malatya 38.21 38.19 948 283.08 284.43
Rize 41.02 40.31 9 286.06 287.45
Sivas 39.45 37.01 1600 277.69 280.53
Table 1 Geographical and
meteorological parameters
for the stations
J Indian Soc Remote Sens (September 2012) 40(3):399–409 401
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6. TRAN 7 program, based on many assumptions on
such variables as atmospheric state and viewing
angles. (Qin and Karnieli 1999). The mean land
surface temperature for ten stations over Turkey has
been determined using the LSTB-L method.
Artificial Neural Networks
Artificial neural networks (ANNs) are information
processing systems that are non-algorithmic, non digital,
and intensely parallel (Dinçer et al. 1996). The use of
ANNs for modeling and prediction purposes has
become increasingly popular in recent decades (Çam
et al. 2005). Researchers have been applying the ANN
method successfully in various fields such as mathe-
matics, engineering, medicine, economics, meteorology,
psychology and neurology as well as in the prediction
of mineral exploration sites, in electrical and thermal
load predictions, in adaptive and robotic control. ANNs
have been trained to overcome the limitations of
conventional approaches to solving complex problems.
This method learns from given examples by construct-
ing an input–output mapping in order to perform
predictions (Kalogirou 2000).
Generalized Regression Neural Network (GRNN)
proposed by Speckt (1991) does not require an
iterative training procedure as in back propagation
method. It approximates any arbitrary function
between input and output vectors, drawing the function
estimate directly from the training data. Furthermore, it is
consistent; that is, as the training set size becomes large,
the estimation error approaches zero, with only mild
restrictions on the function. The GRNN is used for
estimation of continuous variables, as in standard
regression techniques. It is related to the radial basis
function network and is based on a standard statistical
technique called kernel regression. By definition, the
regression of a dependent variable y on an independent x
estimates the most probable value for y, given x and a
training set. The regression method will produce the
estimated value of y which minimizes the mean-squared
error. GRNN is a method for estimating the joint
probability density function (pdf) of x and y, given only
a training set. Because the pdf is derived from the data
with no preconceptions about its form, the system is
perfectly general. If f(x,y) represents the known joint
continuous probability density function of a vector
random variable, x, and a scalar random variable, y, the
conditional mean of y given X (also called the
regression of y on X) is given by:
E y Xj½ Š ¼
R1
À1
yf X; yð Þdy
R1
À1
f X; yð Þdy
ð4Þ
When the density f(x,y) is not known, it must
usually be estimated from a sample of observations of
x and y. The Probability estimator f(X,Y) is based
upon sample values X and Y of the random variables
x and y, where n is the number of sample observations
and p is the dimension of the vector variable x:
f X; Yð Þ ¼
1
2pð Þðpþ1Þ=2
sðpþ1Þ
1
n
Â
Xn
i¼1
exp
ÀðX À Xi
Þ
T
X À Xi
ð Þ
2s2
" #
exp
ÀðY À Yi
Þ
2
2s2
" #
ð5Þ
Land surface temperature
Pattern layer Output layerInput layer
Latitude
Longitude
Elevation
Air temperature
Summation layerFig. 2 Topology of a
generalized regression
neural network (GRNN)
artificial neural
networks (ANN)
402 J Indian Soc Remote Sens (September 2012) 40(3):399–409
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7. A physical interpretation of the probability esti-
mate f(X, Y) is that it assigns sample probability of
width σ for each sample Xi
and Yi
, and the probability
estimate is the sum of those sample probabilities
(Speckt 1991). Defining the scalar function Di
2
D2
i ¼ X À Xi
À ÁT
X À Xi
À Á
ð6Þ
and performing the indicated integrations yields the
following:
Y Xð Þ ¼
Pn
i¼1
Yi
exp
ÀD 2
i
2s2
Pn
i¼1
exp
ÀD 2
i
2s2
ð7Þ
The resulting Eq. 7 is directly applicable to
problems involving numerical data. When the
smoothing parameter σ is made large, the estimated
density is forced to be smooth and in the limit
becomes a multivariate Gaussian with covariance σ2
I.
On the other hand, a smaller value of σ allows the
estimated density to assume non-Gaussian shapes, but
with the hazard that wild points may have too great an
effect on the estimate (Speckt 1991). The GRNN
consists of four layers: input layer, pattern layer,
summation layer and output layer. The input units are
in the first layer. The second layer has the pattern
units and the outputs of this layer are passed on to the
summation units in the third layer. The final layer
covers the output units (Cigizoğlu and Alp 2006).
Mohandes et al. (2004) state that during the
training procedure, the weights of the connections
between neurons are adjusted in order to achieve the
desired input/output relation of the network. This
procedure goes on until the difference between the
actual output of the network and the desired output is
equal with a specified remainder value. Here, the
criterion is put forward as the network output which
should be closer to the value of desired output. This
training procedure has to be repeated for the rest of
the input–output pairs existing in the training data.
Input variables have been used to validate the ANN.
Results and Discussions
ANN is used for modeling land surface temperature in
Turkey. MATLAB software has been used to train the
LSTANN on a personal computer. A GRNN structure
GRNN (4, 1.0, 1) corresponds to 4 input nodes, a
spread value equal to 1.0 and a single output node.
The selected LSTANN structure is shown in Fig. 2.
This network consists of input layer, pattern layer,
summation layer and output layer. The stations used
Fig. 3 Comparison between measured and estimated monthly
mean land surface temperature using LSTB-L method in training
stations
Fig. 4 Comparison between measured and estimated monthly
mean land surface temperature using LSTB-L method in testing
stations
Fig. 5 Comparison between measured and estimated monthly
mean land surface temperature using LSTANN method in
training stations
J Indian Soc Remote Sens (September 2012) 40(3):399–409 403
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8. for training data are at Adana, Afyon, Ankara,
Eskişehir, İzmir, Sivas. In test data, stations, İstanbul,
Konya, Malatya and Rize are used. In order to train
the neural network, meteorological and geographical
data measured by the Turkish State Meteorological
Service for the period from January 2002 to December
2002 in Turkey from the above ten stations were used as
training and testing data. Inputs for the network are
latitude, longitude, elevation and monthly mean air
temperature; output is land surface temperature. In
addition, the monthly mean land surface temperature
data were measured with land thermometer from Turkish
State Meteorological Service. The LSTANN structure and
weights of neurons could help to understand the
relationship between air and surface temperature.
Separately, the method of LSTB-L was proposed for
the estimation of monthly global land surface temper-
ature values from meteorological satellite data. Land
surface temperature (LST) was estimated as a monthly
mean by using the LSTB-L method over ten stations in
Turkey. It is suggested in this study that both methods
describing the LST throughout a diurnal cycle very
well. Data of Turkish State Meteorological Service are
deficient throughout a nocturnal cycle than a diurnal
cycle in Turkey.
In the current study, ground data of the ten national
stations were used. These stations were selected in
such a way as to represent the widely changing
climatic conditions of Turkey. The monthly mean
global land surface temperature over Turkey was
determined to be a correlation coefficient 94.08% and
RMSE 0.091 K (Fig. 3), 91.13% and RMSE 0.003 K
(Fig. 4) for LSTB-L values (training and testing
stations). In the case of monthly mean correlation
coefficient, RMSE was found to be 98.06% and
0.077 K (Fig. 5), 98.01% and 0.045 K (Fig. 6) for
LSTANN values (training and testing stations).
Estimation of LSTB-L method values and LSTANN
method values (training, testing) were evaluated accord-
ing to statistical rules by using root mean square error
(RMSE) and mean bias error (MBE). The results of
evaluation are given in Table 2. The RMSE values,
ranging from 0.014 K to 9.318 K, differ from the actual
value for all stations. The maximum RMSE was found
to be 9.318 K for the Rize station (testing) in the LSTB-
L method values, while the best result was found to be
0.014 K for the Afyon station (training) in the LSTANN
values. The maximum MBE was found to be −2.690 for
LSTB-L method values of Rize station, while the
minimum MBE was found as 0.004 for Afyon and
Stations (cities) LSTANN LSTB-L
RMSE (K) MBE (K) RMSE (K) MBE (K)
Stations used in training Adana 0.0346 0.0100 0.2252 −0.0650
Afyon 0.0144 0.0042 1.2038 −0.3475
Ankara 0.0318 0.0092 2.4104 0.6958
Eskişehir 0.0433 −0.0958 0.7130 0.2058
İzmir 0.0144 0.0042 2.8204 −0.8142
Sivas 0.0491 0.0142 0.9873 0.2850
Stations used in testing İstanbul 0.1126 −0.0325 3.7470 1.0817
Konya 0.0895 −0.0258 5.1326 1.4817
Malatya 0.0837 0.0242 1.3221 0.3817
Rize 0.0289 0.0083 9.3184 −2.6900
Table 2 Error values of
the LSTANN and LSTB-L
method approach
Fig. 6 Comparison between measured and estimated monthly
mean land surface temperature using LSTANN method in testing
stations
404 J Indian Soc Remote Sens (September 2012) 40(3):399–409
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9. İzmir stations. Moreover, another significant point in
this table, the performance values of the training are
generally better than the performance values of the
testing. Figures 7 and 8 shows a comparison between
measured, LSTANN and LSTB-L values for the ten
stations (training and testing stations).
In literature, lots of algorithms have been pro-
pounded by Price(1984), Becker(1987), Vidal (1991),
Prata(1994, 1993), Sobrino et al. (1996, 1994), Coll et
al.(1994), Becker and Li(1995), Coll and Caselles
(1997). These studies indicated that it is possible to
retrieve LST at a reasonable accuracy (RMSE of 1–
3 K) from current operational and research satellite-
borne visible/infrared radiometers. This study has
showed that LSTANN and LSTB-L methods are more
successful than lots of algorithms which were used in
literature to get land surface temperature.
Using the LSTANN and LSTB-L methods is a cheap
and effective way to estimate monthly global land
surface temperature and construct a land surface
Adana
260
270
280
290
300
310
41 2 3 5 6 7 8 9 10 11 12
Months
41 2 3 5 6 7 8 9 10 11 12
Months
41 2 3 5 6 7 8 9 10 11 12
Months
MeanLandSurfaceTemperature(K)
260
250
270
280
290
300
310
MeanLandSurfaceTemperature(K)
260
250
270
280
290
300
310
MeanLandSurfaceTemperature(K)
Measured
Backer and L
ANN
Afyon
Ankara
Fig. 7 Comparison for the
land surface temperature
between the LSTANN,
LSTB-L and measured
values (training stations)
J Indian Soc Remote Sens (September 2012) 40(3):399–409 405
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10. temperature database. The LSTANN model, which needs
no satellite data, was used to estimate the monthly mean
at ground level. On the other hand, since the other
method needs no ground data and presents valuable
results, it can be applied to any region. The application
of these methods is suitable particularly for places
where the distances between the stations are very large.
Conclusion
A generalized regression neural network (GRNN) was
used to estimate monthly land surface temperature by
using meteorological and geographical data. The
generation of a typical land surface temperature is
significant for the calculations concerning many land
surface temperature methods. By using the LSTANN
and LSTB-L methods, a production of land surface
temperature was used at over ten stations in Turkey.
The monthly mean values were found as 0.077 K and
0.091 K (training stations), 0.045 K and 0.003 K
(testing stations), respectively. According to the
results of these ten locations, correlation values
indicate a relatively good agreement between the
observed LSTANN values and the predicted satellite
values. So, LSTANN and LSTB-L methods are sug-
Eski ehir
250
260
270
280
290
300
MeanLandSurfaceTemperature(K)
zmir
250
260
270
280
290
300
310
MeanLandSurface
Temperature(K)
Sivas
240
250
260
270
280
290
300
MeanLandSurface
Temperature(K)
41 2 3 5 6 7 8 9 10 11 12
Months
41 2 3 5 6 7 8 9 10 11 12
Months
41 2 3 5 6 7 8 9 10 11 12
Months
Fig. 7 (continued)
406 J Indian Soc Remote Sens (September 2012) 40(3):399–409
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11. gested to researchers who study on topics which are
related to land surface processes, combining surface-
atmosphere interactions, energy fluxes between the
atmosphere and the ground, estimation of radiation
budgets in heat balance studies, climatology, human-
environment interactions, hydrology systems, ecolo-
gy, greenhouse effect, biogeology, agricultural appli-
cations such as evaluating water requirements for
wheat and determining frost areas for citruses in the
future.
stanbul
260
270
280
290
300
310
1 2 3 4 5 6 7 8 9 10 11 12
Months
MeanLandSurface
Temperature(K)
Measured
Backer and Li
ANN
Konya
250
260
270
280
290
300
310
1 2 3 4 5 6 7 8 9 10 11 12
Months
1 2 3 4 5 6 7 8 9 10 11 12
Months
1 2 3 4 5 6 7 8 9 10 11 12
Months
MeanLandSurface
Temperature(K)
Malatya
250
260
270
280
290
300
310
MeanLandSurface
Temperature(K)
250
260
270
280
290
300
310
MeanLandSurface
Temperature(K)
Rize
Fig. 8 Comparison for the
land surface temperature
between the LSTANN,
LSTB-L and measured
values (testing stations)
J Indian Soc Remote Sens (September 2012) 40(3):399–409 407
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