Modelling of air temperature using ann and remote sensing

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Modelling of air temperature using remote sensing and artificial
neural network in Turkey
Mehmet Sßahin ⇑
Engineering Faculty, Siirt University, 56100, Siirt, Turkey
Received 17 March 2012; received in revised form 14 June 2012; accepted 16 June 2012
Available online 26 June 2012
Abstract
The aim of this research was to forecast monthly mean air temperature based on remote sensing and artificial neural network (ANN)
data by using twenty cities over Turkey. ANN contained an input layer, hidden layer and an output layer. While city, month, altitude,
latitude, longitude, monthly mean land surface temperatures were chosen as inputs, and monthly mean air temperature was chosen as
output for network. Levenberg–Marquardt (LM) learning algorithms and tansig, logsig and linear transfer functions were used in the
network. The data of Turkish State Meteorological Service (TSMS) and Technological Research Council of Turkey–Bilten for the period
from 1995 to 2004 were chosen as training when the data of 2005 year were being used as test. Result of research was evaluated according
to statistical rules. The best linear correlation coefficient (R), and root mean squared error (RMSE) between the estimated and measured
values for monthly mean air temperature with ANN and remote sensing method were found to be 0.991–1.254 K, respectively.
Ó 2012 COSPAR. Published by Elsevier Ltd. All rights reserved.
Keywords: Air temperature; Artificial neural network; NOAA; AVHRR; Remote sensing; Satellite
1. Introduction
Air temperature is a measure of how hot or cold the air
is. It is the most commonly measured by weather parame-
ter. More specifically, air temperature describes the kinetic
energy or energy of motion of the gases that make up air. If
gas molecules move more quickly, air temperature
increases otherwise it decreases. The estimation of air tem-
perature is useful for lots of applications including study of
vector-borne diseases (Thomson et al., 1996; Goetz et al.,
2000), epidemic forecasting (Bian et al., 2006), weather
forecasting, veterinary uses, climate change (Kucharik
et al., 2010; Bocchiola and Diolaiuti, 2010; Kittel et al.,
2011), determination of various heat and radiation fluxes
(Brunsell et al., 2011), vapour pressure deficit, water poten-
tial (Aasamaa and So˜ber, 2011), urban land use and urban
heat island (Cheval et al., 2009), shortwave and longwave
radiation (Stanelle et al., 2010), stomatal resistance (Lee
et al., 2011), ecology (Myint et al., 2010; Smith, 2011; Hed-
ing et al., 2011), hydrology (Jain et al., 2011) and atmo-
spheric sciences. And also knowledge of air temperature
is necessary for the health of human being (Elwood et al.,
1993; Kunst et al., 1993; Ballester et al., 1997; Analitis
et al., 2008; Michelozzi et al., 2009; Almeida et al., 2010)
Generally, air temperature which is very important is the
frequently observed and recorded by weather meteorologi-
cal stations with high accuracy. But density of the station
network is normally not sufficient when air temperature
is employed in regional numerical models for climate or
evapotranspiration. So, a new method is necessary to get
air temperature over the wide fields by using satellites data.
The data of Advanced Very High Resolution Radiometer
(AVHRR), Geostationary Operational Environmental
Satellite (GOES), Meteosat, TIROS Operational Vertical
Sounder (TOVS), LANDSAT/TM and Moderate Resolu-
tion Imaging Spectroradiometer (MODIS) have been used
0273-1177/$36.00 Ó 2012 COSPAR. Published by Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.asr.2012.06.021
⇑ Tel.: +90 (484) 223 12 24; fax: +90 (484) 223 66 31.
E-mail addresses: msahin@siirt.edu.tr, sahanmehmet2000@yahoo.
com.
www.elsevier.com/locate/asr
Available online at www.sciencedirect.com
Advances in Space Research 50 (2012) 973–985

in historical term. The air temperatures have been
estimated from land surface temperatures retrieved from
satellite images with 1 km Â 1 km or 4 km Â 4 km resolu-
tion by using thermal infrared radiation emitted from the
surface by researchers. Generally, land surface temperature
has been retrieved from two thermal infrared bands (chan-
nels 4 and 5 of AVHRR of NOAA) located at 11 lm and
12 lm by using split-window equations. The split-window
algorithms are belonged to the difference in the brightness
temperatures of thermal infrared bands. Furthermore, land
surface temperature depends on the magnitude of the dif-
ference between the two grounds emissivity in the bands
(Becker, 1987).
In the literature, Kawamura and Edamatsu (1993) used
AVHRR thermal infrared data and estimated the air-tem-
perature (Ta) with an rms error 2.5–2.8 K. Prihodko and
Goward (1997) explored a methodology for estimating
air temperature directly from remotely sensed observations
using the correlation between a spectral vegetation index
and surface temperature (temperature-vegetation index).
These air temperature estimates were compared with coin-
cident ground-measured air temperatures recorded at stan-
dard meteorological stations. A strong correlation
(R = 0.93) was found between the satellite estimates and
measured air temperatures with a mean error of 2.92 K.
The correlation between surface temperature and spectral
vegetation index has been obtained to estimate air temper-
ature from satellite images that was called the Surface Tem-
perature/Spectral Vegetation Index (TVX) by Czajkowski
et al. (1997), Prihodko and Goward (1997). The TVX con-
cept and the empirical method have provided estimated air
temperatures with an rms error of 3 K (Cresswell et al.,
1999). Lakshmi et al. (2001) evaluated the ability of satel-
lites to map air temperature over the large land surface
areas. Then, they developed an algorithm that derives sur-
face air temperature by using observations from the TOVS
suite of instruments and also from the AVHRR. The result
of their study showed that the average bias over the 3-
months period compared with ground-based observations
was approximately 2 K or less for the three times of day
with TOVS having lower biases than AVHRR. Riddering
and Queen (2006) presented a technique for producing esti-
mates of near surface air temperature in complex terrain
based on composite data from the NOAA-AVHRR.
Results were tested against meteorological data. The corre-
lation coefficient approximately 0.742 and standard errors
of the estimate of 2.73 K were observed in the final model
implementation. Stisen et al.(2007) obtained air tempera-
ture belonging to a model that was including satellite data
with high temporal resolution that were desired for several
modelling applications by exploiting the thermal split-win-
dow channels in combination with the red and near infra-
red channels of the geostationary MSG SEVIRI sensor.
The research results showed that accuracy of estimation
of the air temperature was changing from 2.55 K to
2.99 K by root mean squared error. Vancutsem et al.
(2010) studied to explore the possibility of retrieving
high-resolution near surface air temperature (Ta) data from
the MODIS connected to land surface temperature
(LST) over different ecosystems in Africa. The comparisons
between night MODIS-LST data with minimum Ta
showed that MODIS night-time products have
provided a good estimation of minimum Ta (standard
deviation = 2.4 K).
The other way of estimation air temperature is ANN
which has been used for various purposes in remote sens-
ing. Jang et al. (2004) employed multilayer feed-forward
neural networks to estimate air temperatures in Southern
Quebec (Canada) using AVHRR images. The input vari-
ables for the networks were the five bands of the AVHRR
image, surface altitude, solar zenith angle, and Julian day.
The estimation was carried out using a dataset collected
during the growing season from June to September 2000.
Levenberg–Marquardt back-propagation (LM-BP) was
used to train the networks. The early stopping method
was applied to improve the LM-BP and to generalize the
networks. The network using all five bands, Julian day,
altitude, and solar zenith angle provided the best results,
with 22 nodes in the hidden layer. The difference between
estimated and station air temperatures was obtained within
1.79 K by RMSE.
In this study, ANN was applied to estimate air temper-
atures of Adana, Afyonkarahisar, Ankara, Antalya, Art-
vin, Balıkesir, Denizli, Erzurum, Eskisßehir, _Istanbul-
Go¨ztepe, _Izmir, Kars, Kayseri, Konya, Malatya, Rize,
Samsun, Sivas, Sßanlıurfa and Van cities. For this purpose,
raw data of NOAA-AVHRR were converted into Level 1B
data set by using “Quorum to Level1B” software. Then all
data sets were corrected radiometrically, geometrically and
atmospherically and used these data sets to get brightness
temperatures of band 4 and band 5 of NOAA-AVHRR
images. In continuance, brightness temperatures were
employed to estimate land surface temperature. The city,
month, altitude, latitude, longitude, monthly mean land
surface temperature were chosen as input data while
monthly mean air temperature was chosen as output data
in artificial neural network. The LM learning algorithms
and tansig, logsig and linear transfer functions were used
in the network. Results of the network have indicated that
monthly mean air temperature can be estimated correctly
in Turkey by using ANN which is including meteorological
and satellite data. At the same time, the study showed that
the model M8/6-14-1 which developed over Turkey has
provided more accurate outcomes than other studies in
the literature.
2. Study areas and their characteristics, data sources
In the study, cities of Adana, Afyonkarahisar, Ankara,
Antalya, Artvin, Balıkesir, Denizli, Erzurum, Eskisßehir,
_Istanbul-Go¨ztepe, _Izmir, Kars, Kayseri, Konya, Malatya,
Rize, Samsun, Sivas, Sßanlıurfa and Van were chosen as
study areas (see Fig. 1 and see Table 1). The mentioned cit-
ies have various climatic conditions from one another. The
974 M. Sßahin / Advances in Space Research 50 (2012) 973–985

climate of Adana, Antalya carries the properties of
Mediterranean climate; too hot and arid in summers while
warm and rainy in winters. The highlands have a mixed
property of Mediterranean climate and continental climate.
Precipitation is generally in the form of rainfall. The 29%
of the city lands is covered with forests. The forests are
on the highlands. The flora is composed of Mediterranean
plants; with scrubs up to 700–800 m altitude on mountain
slopes while with larches and cedars on the uplands.
Ankara, Eskisßehir, Kayseri, Konya, Sivas have terres-
trial climate that is the most pluvial season in spring. The
climate conditions and topographic structure have enabled
the growth of two plant associations around cities; steppe
and forest. The most prevalent plant association in the
regions is steppe. The steppe is common on plateaus and
in valleys where drop little precipitation. There is almost
no tree in that plant association. For the most part, thorny
bushes are seen. In addition, angustifolia, willow, and
populous trees in rows all along the stream are in steppe
climate.
Since Balıkesir and _Istanbul-Go¨ztepe are under the
impact of Marmara, Mediterranean and terrestrial climate,
a plant association at a quarter of the cities is not seen in
other part of the region. The 30% of the surface is sylvan
whereas 32% of the city lands is pasture area, and more,
23% arable lands; 15% olive groves, orchards, and vegeta-
ble gardens.
Mediterranean climate is dominant in Denizli and _Izmir
whereas Afyonkarahisar has terrestrial climate; hot and
arid in summers while rainy and warm in winters. The hot-
test months are July and August while the coldest months
are January and February. There is almost no snowfall.
Nearly 50% of the city land is scrub and sylvan while
33% planted area, and 15% pasture area.
The climate of Artvin, Rize and Samsun has variations
at coastal lines and inlands. Though there is a dominant
Mediterranean climate at coastal lines, due to mountains,
the inlands are under the influence of terrestrial climate.
Although it is not too hot in summers, the rate of humidity
is rather high on account of dam reservoirs as well as sea-
water evaporation. The winter months are slightly cold and
fairly rainy. The precipitation is mostly rainfall at coastal
areas while the inlands experience more snowy days, and
under the effect of continental climate. The land surface
is encased in plains, orchards, gardens, pasture areas and
planted fields. The mountains are brimming with forests.
Terrestrial climate dominates in Malatya and Sßanlıurfa.
Summers are long lasting and hot while winters are very
cold. Temperature difference between night and day is
high. The 60% of the city lands is planted while 38% is pas-
ture area. Field of forest and scrub is rare; 0.6%. The city
lands are in the form of steppe.
Erzurum is one of the highest and coldest cities of Tur-
key. Harsh continental climate rules over the city. The win-
ters are very cold whereas very hot and arid in summers.
Visual appearance of the city is green in springs, white in
winters, yellow (steppe) in falls and summers. The area
with forest and scrub covers 9%. Scotch pine and oak
Fig. 1. Air temperature measuring stations in Turkey.
Table 1
Geographical parameters for the stations.
Stations Latitude (°N) Longitude (°E) Altitude (m)
Adana 37.03 35.21 27
Afyonkarahisar 38.44 30.33 1035
Ankara 39.57 32.53 891
Antalya 36.42 30.44 64
Artvin 41.11 41.49 628
Balıkesir 40.06 27.39 37
Denizli 37.47 29.05 425.29
Erzurum 39.57 41.4 1758.18
Eskisßehir 39.45 30.33 805
_Istanbul-Go¨ztepe 40.58 29.05 32.98
_Izmir 38.23 27.04 28.55
Kars 40.37 43.06 1775
Kayseri 38.43 35.29 1092
Konya 37.52 32.28 1030
Malatya 38.21 38.13 947.87
Rize 41.02 40.30 8
Samsun 41.21 36.15 4
Sivas 39.45 37.01 1285
Sßanlıurfa 37.09 38.47 547.18
Van 38.28 43.21 1670.58
M. Sßahin / Advances in Space Research 50 (2012) 973–985 975

consist of the area at 1900–2000 m altitude. Pasture area
takes place 68% of the city land while 18% planted area.
Kars and Van are another cities dominating terrestrial
climate. The winters are harsh and long lasting. The Lake
Van provides less harsh winter conditions on very high
zones of Van. The summers are slightly rainy and very
hot. Even if myriad plantation associations are seen around
Van and Kars; plains, the mountains are generally treeless.
The 70% of the city lands is pasture area while 23% is
planted and 2% is covered with forest and scrub (http://
www.cografya.gen.tr/).
As known, air temperature is measured 2 m above from
the ground as World Meteorological Organization has
determined and made it a guideline. In the study; altitude,
latitude, longitude and air temperature data were taken
from TSMS for the period from 1995 to 2005 while satellite
data were obtained from Scientific and Technological
Research Council of Turkey-Bilten, simultaneously.
3. Methodology
3.1. Split-window method
To retrieve LST from the AVHRR top-of-atmosphere
brightness temperature measurements, it was chosen as
split window algorithm. This approach is mainly based
on adjacent thermal channels of satellite and an empirical
inverse model, and it is derived from a first order expansion
of the Taylor series applied to the Planck function. In the
case of the AVHRR sensors, channels 4 and 5 are used.
The algorithm assumes that atmospheric attenuation (due
mostly to atmospheric water vapour) is greater in channel
5 than in channel 4, and the difference in measured radi-
ance between the two channels increases with increasing
water vapour (Pinheiro et al., 2006).
Firstly raw data of NOAA12-14-15/AVHRR which had
no cloud, were translated into Level-1B format by using
Quorum Software and in second step, brightness tempera-
ture of channel-4 and channel-5 (range 10.3–11.3 lm and
range11.5–12.5 lm, respectively) were obtained from
Level-1B data by making use of Envi 4.3 image-processing
programmer. Then, radiometric and geometric calibrations
were applied to the images to correct the deficiencies and
flaws in the imaging sensors of the satellite. The images
were then turned into brightness temperatures which are
necessary for the retrieval of LST from AVHRR data.
In this study, split window LST algorithm developed by
Ulivieri et al. (1994) was used due to its simplicity, robust-
ness and superior performance in independent tests (Becker
and Li, 1995; Vazquez et al., 1997; Yu et al., 2008).
Ulivieri’s algorithm can be written as,
T ¼ T4 þ 1:8ðT 4 À T 5Þ þ 48ð1 À eÞ À 75De ð1Þ
e ¼
e4 þ e5
2
ð2Þ
De ¼ e4 À e5 ð3Þ
where T4 and T5 are the brightness temperatures of
AVHRR channels 4 and 5 respectively, and, Eq. (1) was
developed for cases of column atmospheric water vapour
less than 3.0 g/cm2
, a reasonable condition for much of
the semi-arid portions of continental Africa (Pinheiro
et al. 2006). e and De are mean spectral emission coefficient
and difference of the emission coefficients for channels 4
and 5, respectively.e4 and e5 are the surface emission coef-
ficients which were estimated from atmospherically cor-
rected NDVI (Normalized Difference Vegetation Index),
using the equations given by Valor and Casselles (1995)
for channel 4 and channel 5, respectively .
e4 ¼ 0:9897 þ 0:029 lnðNDVIÞ ð4Þ
e4 À e5 ¼ 0:01019 þ 0:01344 lnðNDVIÞ ð5Þ
where NDVI is a simple graphical indicator that can be
used to analyse remote sensing measurements and assess
whether the target being observed contains live green veg-
etation or not. Live green plants absorb solar radiation
in the photosynthetically active radiation spectral region,
which they use as a source of energy in the process of pho-
tosynthesis. Leaf cells have also evolved to scatter (i.e., re-
flect and transmit) solar radiation in the near-infrared
spectral region (which carries approximately half of the to-
tal incoming solar energy) because the energy level per pho-
ton in that domain (wavelengths longer than about
700 nm) is not sufficient to be useful to synthesize organic
molecules. A strong absorption at these wavelengths would
only result in over-heating the plant and possibly damaging
the tissues. Hence, live green plants appear relatively dark
in the photosynthetically active radiation and relatively
bright in the near-infrared (Gates, 1980). By contrast,
clouds and snow tend to be rather bright in the red (as well
as other visible wavelengths) and quite dark in the near-
infrared. The pigment in plant leaves, chlorophyll, strongly
absorbs visible light (from 0.4 to 0.7 lm) for use in photo-
synthesis. The cell structure of the leaves, on the other
hand, strongly reflects near-infrared light (from 0.7 to
1.1 lm). The more leaves a plant has, the more these wave-
lengths of light are affected. Since early instruments of
earth observation, such as NOAA’s AVHRR, acquired
data in visible and near-infrared, it was natural to exploit
the strong differences in plant reflectance to determine their
spatial distribution in this satellite images. The NDVI is
calculated from these individual measurements as follows:
NDVI ¼
NIR À VIS
NIR þ VIS
ð6Þ
where VIS and NIR stand for the spectral reflectance mea-
surements acquired in the visible (red) and near-infrared re-
gions, respectively (Goward et al., 1991; Santos and Negri,
1997).
3.2. Artificial neural network
ANN is a branch of artificial intelligence which was
developed in 1950s in order to imitate the biological

structure of the human brain (Viotti et al., 2002). The
ANN models work like a black box without requiring the
detailed information of a system. Instead of requiring this
information, they learn the relation between the input
parameters and the controlled and uncontrolled variables
by studying the previously recorded data like non-linear
regression. One more advantage of using ANN is the capa-
bility of managing large and complex systems with a vast
number of interrelated parameters. On the other hand they
don’t take into account the excess data which is very
important (Kalogirou, 2001).
The use of the ANNs for modelling and prediction pur-
poses is increasingly becoming popular in the last decades
(Ç am et al., 2005). Researchers have been applying the
ANN method successfully in various fields of mathematics,
engineering, medicine, economics, meteorology, psychol-
ogy, neurology, as well as in the prediction of mineral
exploration sites, in electrical and thermal load predictions,
in adaptive and robotic control, and many other subjects.
ANNs have been trained to overcome the limitations of
the conventional approaches to solve complex problems.
This method learns from given examples by constructing
an input-output mapping in order to perform predictions
(Kalogirou, 2000). Fundamentals processing element of a
neural network is a neuron. Each neuron computes a
weighted sum of its input signals.
The neuronal model of Fig. 2 includes an externally
applied bias, denoted by bk. The bias bk has the effect of
increasing or lowering the net input of the activation func-
tions, depending on whether it is positive or negative,
respectively.
In mathematical terms, a neuron k may be described by
writing the following pair of equations:
uk ¼
Xm
j¼1
wkjxj ð7Þ
yk ¼ uðuk þ bkÞ ð8Þ
where x1,x2, . . .,xm are the inputs signals; wk1, wk2, . . ., wkm
are the synaptic weights of neuron k; uk is the linear com-
biner output due to the input signals; bk is the bias; /(Á) is
the activation function and yk is the output signal of the
neuron. The use of bias bk has the effect of applying an
affine transformation to the output uk of the linear com-
biner in the model of Fig. 2, as shown by
vk ¼ uk þ bk ð9Þ
In particular, depending on whether the bias bk is posi-
tive or negative, the relationship between the induced local
field or activation potential vk of neuron k and the linear
combiner output uk is modified in the manner illustrated
in Fig. 3; hereafter the term “induced local field” is used.
Note that as a result of this affine transformation, the
graph of vk versus uk no longer passes through the origin.
The bias bk is an external parameter of artificial neuron
k. It may be accounted for its presence as in Eq. (8). Equiv-
alently, the combination of Eqs. (7) and (8) may be formu-
lated as follows, respectively (Haykin, 1999):
vk ¼
Xm
j¼0
wkjxj ð10Þ
yk ¼ uðvkÞ ð11Þ
The tangent sigmoid transfer function Eq. (12), log-sig-
moid transfer function Eq. (13) and linear transfer function
Eq. (14) are described with the following equation respec-
tively (Vogl et al.,1988).
uðxÞ ¼
2
1 þ eÀ2x
ð12Þ
uðxÞ ¼
1
1 þ eÀx
ð13Þ
uðxÞ ¼ linearðxÞ ¼ x ð14Þ
An ANN is organized as layers of neurons. Each neuron
in a layer is connected to all neurons in the previous layer.
An example of this type of arrangement, which is used also
in this study, is shown in Fig. 4. The network consists of an
input layer, one hidden layer and an output layer.
When Fig. 4 examined, it can be seen that the parame-
ters of city, month, altitude, latitude, longitude, and
monthly mean land surface temperatures in the input layer
x1
x2
xn
∑
wk1
wk2
wkn
Synaptic
weights
Inputs
yk
Activation
function
Summing
junction
Output
(.)ϕ
Bias
bk
vk
.
.
.
.
.
.
Fig. 2. Nonlinear model of a neuron.
Linear conbiner’s
output, uk
Induced local
field, vk
0
Bias bk>0
bk=0
bk<0
Fig. 3. Affine transformation produced by the presence of a bias; note that
vk = bk at uk = 0.

were employed to able to get monthly air temperature in
the output layer. Even if the values of land surface
temperature and air temperatures differ from one another,
both are always in the case of thermal interaction. The land
surface that is heated by solar radiation warms up the air;
heat lost to the air causes land surface temperature dimin-
ishing. So, ANN is an indispensable parameter in estima-
tion of air temperature. Further, because atmosphere is
heated by the radiations reflected from the earth ground,
lower atmosphere is hot while upper atmosphere is cold.
There is a progressive cooling of 1 °C for every 200 m rose
in the atmosphere. So, the altitude where the point of air
temperature estimation exists is crucial.
The angle at which solar radiation strikes the land sur-
face is the most important factor that affects temperature
pattern on the earth. The more the angle of solar radiation
increases the direction at a right angle, the more the point
where it strikes gets warmer. As the strike angle contracts,
the heating lessens. As known, the angle of incoming solar
radiation changes with latitude. The sun shines straight
down like near the equator whereas inclined around the
poles. The temperature diminishes when moved from the
equator towards the poles. That law put forwards that lat-
itude is salient in calculating air temperature. Moreover,
the hitting angle of solar radiation shifts throughout the
year depending on axial tilt and earth annual move. That
increases the importance of temporal resolution in estima-
tion of air temperature accurately. In the study, monthly
temporal resolution followed and month was used as one
of the input parameters.
The longitude is an angular measurement of any point
from starting meridian. The only impact of longitude is
the building local time differences. As known, each pixel
in the satellite image represents for a different point. Tem-
perature values scattered from thousands of different
points reach to satellite sensors depending on different
angles. The angles were linked with longitude angles, so
longitude was employed as input parameter in the study.
The other input parameter in the study is city. In sunny
and hot days, cities, in which tall buildings exist and which
are densely populated, is hotter when compared to their
surroundings because of urban heat island (UHI) effect.
UHI effects come into existence in cities, and the tempera-
ture values in these kinds of cities are higher because they
spread more heating energy. UHI effect changes from city
to city. In the study UHI effect is stated as city.
3.3. Evaluation of the estimation results
The choice of the relevant criteria allowing performance
evaluation of the estimation methods is an important issue.
Various statistical parameters can be used to measure the
strength of the statistical relationship between the esti-
mated values and the reference values. I assume that vi,
(i = 1, n) is the set of n reference values and ei, (i = 1, n)
is the set of the estimates. v and e are mean of reference
and estimates values respectively. The bias, R and RMSE
can be calculated by using standard deviations of reference
(rv) and estimate (re) values, mean of reference and esti-
mates values, estimated values and the reference values.
The bias which is the difference between the mean estimate
e and the mean reference value v. The statistical criteria for-
mula of the linear correlation coefficient R is the following,
R ¼
Pn
i¼1ðvi À vÞðei À eÞ
nrvre
ð15Þ
where R measures the proximity between estimate and ref-
erence. It is not sensitive to a bias (Kendall and Stuart,
1963). The formula of the RMSE is;
RMSE ¼
1
n
Xn
i¼1
ðei À viÞ
2
#1
2
ð16Þ
In statistics, RMSE is a frequently used measure of the
differences between values predicted by a model or an esti-
mator and the values actually observed from the thing
being modelled or estimated (Laurent et al., 1998).
4. Results and discussions
4.1. Estimation of land surface temperature
First, with the help of Quorum software, the data of
NOAA 12-14-15/AVHRR was converted to Level-1B for-
mat which image processing software can recognize easily.
Then, radiometric and geometric arrangements of the
images were done through Envi 4.3 and Idrisi Andes image
processing software. The primary factor that is crucial in
calculating land surface temperature is brightness tempera-
ture values. The brightness temperatures of the images for
4th and 5th channels were obtained also through Envi 4.3
and Idrisi Andes image processing software. Another fac-
tor that is necessary in calculating land surface temperature
is NDVI values. To acquire the stated values; 1(VIS). and
2(NIR). channels of NOAA 12-14-15/AVHRR and Eq.
(6) were used, and their NDVI images were obtained.
ln(NDVI) function should be used to calculate the values
of e4 and e5 in Eq. (4) and Eq. (5). The values in the range
of À1 and 0 will lead mathematically to trouble for
Altitude
Latitude
Longtitude
Months
Montly mean
LST temperature
Monthly mean
air temperature
City
Input layer Hidden layer Output layer
Fig. 4. The ANN model used in the study.

ln(NDVI) function. To overcome the trouble, the stated
ranges were deactivated in the images. The last form of
NDVI images were employed in Eq. (4) and (5), hence,
emissivity values of 4th
and 5th
channels were acquired.
By adding pre-calculated e4 and e5 values to the formulas
of De = (e4 À e5) and e = (e4 + e5)/2, respectively the emis-
sivity diﬀerence and mean of emissivity of 4th and 5th
channels were obtained.
In Eq. (1) land surface temperature maps were acquired
based on Ulivieri et al. (1994) algorithm via the use of
brightness temperature of 4th (T4) and 5th (T5) channels,
De, e. In Fig. 5, land surface temperature map was shown
as based on Ulivieri et al. (1994) algorithm at 06.56 a.m
local time on 10th June 2002.
Having an overall examination over the map, it is seen
that land surface temperature on coastal lines of Black
Sea Region in north of Turkey, is in the range of
293–302 K whereas in internal parts of Black Sea Region
and in Eastern Regions it is within the range of
281À299 K. The temperature in Eastern Region varies,
even if just a bit, between 299 and 308 K. Furthermore, it
is taken in from the map that the coastal lines of Aegean
and Mediterranean Regions, the majority of Central Ana-
tolia Region and inlands of Mediterranean Region take
temperature range of 296–305 K. Still, it can overtly be
viewed in the map that the temperature range in South-
eastern Region is mostly amongst 308–319 K. The temper-
ature range in most locations of the Thrace region i.e.
North-western is between 296 K and 299 K. And also,
the change of temperature is seen between 278 K and
287 K in cloud-covered areas. Furthermore, it is under-
stood from the map that land surface temperatures of
South and West regions of Turkey are more than North’s
and East’s regions.
At certain parts of Turkey’s neighbouring countries Iraq
and Syria, the temperature map has taken the black colour.
Because the stated countries have quite a weak vegetation
cover and desert climate, the algorithm developed on the
Fig. 5. Land surface temperature map based on Ulivieri et al. (1994) algorithm at 06.56 a.m local time on 10th June 2002 (K).
y = 0.9991x
R = 0.979
RMSE=1.778K
N=3055
240
250
260
270
280
290
300
310
320
330
255 265 275 285 295 305 315
Meteorological value(K)
Satellitevalue(K)
Fig. 6. The comparison of LST values obtained through Ulivieri et al. (1994) algorithm with meteorological values.

basis of the emissivity of vegetation was not able to have
had measurements with sufficient precision. But, because
there is enough vegetation cover in the study areas, the
temperature measure developed based on vegetation index
algorithm, does not bear any hinder for the study.
Similarly, the total 147 LST satellite images were derived
providing at least one image in each month with the same
method on the basis of algorithm developed by Ulivieri
et al.(1994), between 1995 and 2005 years. Land surface
temperature values were obtained from over the derived
images by using coordination of 20 cities specified in Table
1. By using Eqs. (15), (16), 3055 land surface temperature
values obtained through satellite, were compared with the
ones of TSMS values. At the end of the comparison, R
and RMSE were found to be 0.979 and 1.778 K, respec-
tively (see Fig. 6).
Land surface temperature in various points of the world
was calculated via satellite data. On examining the studies
in the literature, it was seen that rms error range in all stud-
ies occurred between 1 K and 3 K (Price, 1984; Becker and
Li, 1990; Vidal, 1991; Sobrino et al., 1996; Coll et al., 1994;
Ouaidrari et al., 2002; Pinheiro et al., 2006; Katsiabani
et al., 2009; Sßahin and Kandırmaz, 2010). Because the
RMSE value in the study was found as 1.778 K, the study
is in tune with the literature. So, there is no inconvenience
to use the algorithm developed by Ulivieri et al. (1994) to
acquire LST values based on NOAA/AVHRR data in Tur-
key. More, it is suggested researchers to use the stated algo-
rithm in theirs studies.
4.2. Estimation of air temperature
In this study, ANN was employed to calculate monthly
mean air temperature. The network used in the study is
composed of input layer, hidden layer and one output
layer. While month, altitude, latitude, longitude, city and
monthly mean land surface temperature were used as
input, monthly mean air temperature was acquired from
the output layer. Whereas the data from the period of
1995–2004 were used for the training of the network, the
data of 2005 were used to test the accuracy of the trained
network.
There is not a mathematical formula to determine the
number of neuron in hidden layer of ANN. The number
of neuron in hidden layer is decided in the result of net-
work training. Neurons between 1 and 50 in the hidden
layer were tested to determine the optimum artificial net-
work model employed in the study. In the meantime,
because the starting weights of ANNs were composed
randomly, the appropriate ANN model was decided after
the trails.
As consequence of the trails, the values of transfer func-
tions, correlation coefficients and root mean squared error
of models which were used in hidden and output layers of
the most accurate fourteen networks were calculated (see
Table 2).
High correlation coefficient and small RMSE value is a
statistical rule in the model which was developed to use in
comparison of coefficient values of calculated correlation
coefficient and error mean square. After having examined
Table 2 and when air temperature estimation results were
compared to statistical criteria, it was seen that the opti-
mum ANNs are 6-14-1 and 6-24-1 models which are called
M8 and M9. The transfer function in the hidden layer of
the model 6-14-1 is tansig whereas linear in output layer.
In addition, 6 neurons exist in the input layer while 14 in
the hidden layer, and 1 in the output layer. Similarly, the
transfer function in the hidden layer of the model 6-24-1,
called as M9, is tansig whereas it is linear in the output
layer. The 6 neurons exist in the input layer of the Model
M9 while 24 in the hidden layer, and 1 in the output layer.
Correlation coefficients of M8 and M9 models were found
equal to one another; 0.991. That means input variables in
ANN got a success up to 99.1% in estimating monthly
mean air temperature. But the values of RMSE are differ-
ent from one another. Whereas the RMSE value of M8 is
Table 2
The R and RMSE statistics of different ANN models.
Name Model Transfer
function
hidden
Transfer
function
output
R RMSE (K)
M1 6-05-1 Logsig Logsig 0.990 1.368
M2 6-25-1 Logsig Logsig 0.989 1.471
M3 6-25-1 Logsig Linear 0.987 1.562
M4 6-44-1 Logsig Linear 0.968 2.403
M5 6-24-1 Logsig Tansig 0.987 1.562
M6 6-44-1 Logsig Tansig 0.988 1.517
M7 6-50-1 Logsig Tansig 0.989 1.433
M8 6-14-1 Tansig Linear 0.991 1.254
M9 6-24-1 Tansig Linear 0.991 1.263
M10 6-44-1 Tansig Linear 0.990 1.373
M11 6-14-1 Tansig Tansig 0.989 1.430
M12 6-05-1 Tansig Tansig 0.991 1.268
M13 6-16-1 Tansig Logsig 0.989 1.399
M14 6-34-1 Tansig Logsig 0.976 2.118

1.254 K, it is 1.263 K with M9. In that case, the model M8
is the most accurate one developed in the study. More, by
using the model M8, city based correlation coefficient and
RMSE values were calculated (see Table 3). Along with
having rather high values on city based correlation coeffi-
cient in the range of 0.966–0.997, the highest value belongs
to Adana. On the other hand, the smallest value is of Kay-
seri. Its RMSE values are between 0.705 K and 2.600 K. In
estimation study of monthly mean air temperature, with
0.705 K, the smallest error was of Afyonkarahisar while
the highest is of Kayseri; 2.600 K. When the study is com-
pared to the other studies in the literature, it is seen that the
error ranges of the studies in the literature, for monthly
mean air temperature, fluctuate between 1.79 K and 3 K
(Kawamura and Edamatsu, 1993; Prihodko and Goward,
1997; Czajkowski et al., 1997; Cresswell et al., 1999;
Lakshmi et al., 2001; Jang et al., 2004; Riddering and
Queen, 2006; Stisen et al., 2007; Vancutsem et al., 2010)
whereas it is 0.705 K and 2.600 K in this study. The study
is accordant with the literature. Even, the general outcomes
of the study is more accurate than the ones in the literature
(R = 0.991; RMSE = 1.254 K).
Moreover in the study, estimated monthly mean air tem-
perature values and city based graphical figures of monthly
meteorological values were formed (see Fig. 7).
When Fig. 7 examined, it can be seen that ANN and
meteorological values which were estimated monthly in
Adana are rather close to one another. A difference
occurred between meteorological and ANN values in
September and November, but through the calculations
made, it was understood that the difference was not as
much as thought. Whereas the error between both values
in September was 1.629 K, it was 1.002 K in November.
In other months, the errors were in the range of
0.020–0.671 K. When the figure examined, it will be seen
that the errors in Ankara were quite high in February
and December. In consequence of a statistical study, it
was emerged that an error of 1.418 K was made in the
study carried in January for Ankara. The error reached
up to 5.825 K in February while 4.020 K in December.
Similarly, the errors got values between 0.005–0.995 K in
other months. It is understood from the graphic on the fig-
ure that the error in August in Balıkesir was higher than
other months. As a result of calculations, it was seen that
errors of 1.264 K in May and 2.338 K in August were
made. In other months the error range got a value between
0.180–0.871 K. When the figure examined for _Izmir, it can
be seen that the amount of error in August is more than
other months. In consequence of a statistical study, it
was emerged that the error in August was found as
2.903 K while in other months between 0.106 and
0.965 K. As can be seen from the figure, the amount of
error in Samsun was high in February, October and
December; 1.514 K in February, 1.729 K in October and
2.231 K in December. In other months, the error values
were in the range of 0.013–0.913 K. It was seen that the
error value for Sßanlıurfa was rather high in June whereas
low in October and December. The error was found as
3.990 K in June while 1.216 K in October, and 1.012 K in
December. The error range acquired in other months was
between 0.022 K and 0.848 K. It can be understood from
the figure that the amount of error in Van was rather high
in January, February, November, and December. The
error ranges were 3.303 K in January; 2.275 K in February;
1.533 K in November; and 1.122 K in December while
between 0.226 and 0.665 K in other months.
Generally describing, it is understood that the cities of
Afyonkarahisar, Antalya, Artvin, Erzurum, Eskisßehir,
Kars, and Sivas have rather close meteorological and
monthly air temperature values with one another. Error
ranges of the cities resulted in below 1.000 K. It can be
understood from the figure that error values of Denizli
increased in May and September. The error value found
2.100 K in May while 1.400 K in September. The error
value in _Istanbul-Go¨ztepe found between 1.200–1.800 K
in May, June, September, October, November, and
December whereas the value stayed under 1.000 K in other
months. In Kayseri, although meteorological and esti-
mated values happened fairly close, the error values were
in the range of 1.200–8.800 K in the months of June and
July, respectively. The error value of Konya fluctuated
between 1.300 and 3.500K in January, February, July,
August, and November whereas the value stayed under
1.000 K in the other months. Although the error values
got values below 1 K in Malatya in March, October,
November, and December, the error values in other
months were between 1.000 and 2.400 K. The error value
of 0.200–0.900 K prevailed in March, April, November,
Table 3
The correlation coefficient and RMSE values of cities for monthly mean
air temperature.
Stations Correlation
coefficient (R)
RMSE (K)
Adana 0.997 0.767
Afyonkarahisar 0.995 0.705
Ankara 0.983 2.142
Antalya 0.993 0.904
Artvin 0.995 0.779
Balıkesir 0.994 0.991
Denizli 0.990 1.060
Erzurum 0.996 0.961
Eskisßehir 0.995 0.826
_Istanbul-Go¨ztepe 0.991 1.134
Izmir 0.992 0.993
Kars 0.996 0.733
Kayseri 0.966 2.600
Konya 0.992 1.520
Malatya 0.995 1.477
Rize 0.990 1.189
Samsun 0.990 0.987
Sivas 0.994 0.880
Sßanlıurfa 0.993 1.309
Van 0.991 1.343

and December in the city of Rize while the values changed
from 1.000 to 2.100 K in the other months.
5. Conclusion
In the study, ANN and remote sensing methods were
used to estimate monthly mean air temperature in Adana,
Afyonkarahisar, Ankara, Antalya, Artvin, Balıkesir, Den-
izli, Erzurum, Eskisßehir, _Istanbul-Go¨ztepe, _Izmir, Kars,
Kayseri, Konya, Malatya, Rize, Samsun, Sivas, Sßanlıurfa
and Van. While month, latitude, longitude, altitude, city
and monthly mean land surface temperature obtained
through satellite data were used as input in ANN, monthly
mean air temperature were used as output. Satellite based
Adana
270
280
290
300
310
1 2 3 4 5 6 7 8 9 10 11 12
Months
Airtemperature(K)
Meteorological value(K)
Estimated value(K)
Afyonkarahisar
260
270
280
290
300
1 2 3 4 5 6 7 8 9 10 11 12
Months
Airtemperature(K)
Ankara
250
260
270
280
290
300
1 2 3 4 5 6 7 8 9 10 11 12
Months
Airtemperature(K)
Antalya
270
280
290
300
310
1 2 3 4 5 6 7 8 9 10 11 12
Months
Airtemperature(K)
Artvin
260
270
280
290
300
1 2 3 4 5 6 7 8 9 10 11 12
Months
Airtemperature(K)
Balıkesir
260
270
280
290
300
1 2 3 4 5 6 7 8 9 10 11 12
Months
Airtemperature(K)
Denizli
270
280
290
300
1 2 3 4 5 6 7 8 9 10 11 12
Months
Airtemperature(K)
Erzurum
240
250
260
270
280
290
300
1 2 3 4 5 6 7 8 9 10 11 12
Months
Airtemperature(K)
Eskişehir
260
270
280
290
300
1 2 3 4 5 6 7 8 9 10 11 12
Months
Airtemperature(K)
İzmir
270
280
290
300
310
1 2 3 4 5 6 7 8 9 10 11 12
Months
Airtemperature(K)
Fig. 7. Comparison of monthly mean air temperature between ANN and meteorological values.

land surface temperature values were obtained through the
use of NOAA-AVHRR satellite data together with Ulivieri
et al. (1994) algorithm. The values were compared statically
to the meteorological values. In accordance with the litera-
ture, R and RMSE values were found 0.979 and 1.778 K,
respectively.
Various ANN models were employed in monthly mean
air temperature estimation. The data from the period of
1995–2004 were used to develop the models while the
2005 data were used to test the trained models. The opti-
mum result was obtained through the model 6-14-1. Six
neurons exist in the input layer of this model while four-
İstanbul-Göztepe
260
270
280
290
300
310
1 2 3 4 5 6 7 8 9 10 11 12
Months
Airtemperature(K)
Kars
250
260
270
280
290
300
1 2 3 4 5 6 7 8 9 10 11 12
Months
Airtemperature(K)
Kayseri
260
270
280
290
300
1 2 3 4 5 6 7 8 9 10 11 12
Months
Airtemperature(K)
Konya
260
270
280
290
300
1 2 3 4 5 6 7 8 9 10 11 12
Months
Airtemperature(K)
Malatya
260
270
280
290
300
310
1 2 3 4 5 6 7 8 9 10 11 12
Months
Airtemperature(K)
Rize
260
270
280
290
300
310
1 2 3 4 5 6 7 8 9 10 11 12
Months
Airtemperature(K)
Samsun
260
270
280
290
300
1 2 3 4 5 6 7 8 9 10 11 12
Months
Airtemperature(K)
Sivas
260
270
280
290
300
1 2 3 4 5 6 7 8 9 10 11 12
Months
Airtemperature(K)
Şanlıurfa
260
270
280
290
300
310
1 2 3 4 5 6 7 8 9 10 11 12
Months
Airtemperature(K)
Van
260
270
280
290
300
1 2 3 4 5 6 7 8 9 10 11 12
Months
Airtemperature(K)
Fig 7. (continued)

teen in the hidden layer, and one in the output layer.
More, R and RMSE values were found as 0.991 and
1.254 K, respectively when estimation results of the
model were compared to meteorological values. The out-
comes obtained through the study shows that the present
study is more accurate than other studies in the
literature.
To develop a successful model is very important like in
study belongs to remote sensing and ANN. Because, even if
it is possible for the same study to be successful only based
on meteorological data, inadequacy of meteorological sta-
tions, financial burden, and inconvenient geographic distri-
bution of meteorological stations due to climate conditions
cause remote sensing method to be unavoidable. As
known, meteorological satellites employed in remote sens-
ing studies are able to commit easier and cheaper data
transmitting by scanning the land surface. Thus, ANN
technique and remote sensing can be used as an alternate
method in air temperature estimations which are useful
for lots of applications including study of vector-borne dis-
eases, epidemic forecasting, weather forecasting, veterinary
uses, climate change, determination of various heat and
radiation fluxes, vapour pressure deficit, water potential,
urban land use and urban heat island, shortwave and long-
wave radiation, stomatal resistance, ecology, hydrology
and atmospheric sciences. And also, this method is offered
to researchers who study for the health of human being,
especially about high blood pressure, ischemic heart dis-
ease, respiratory infections and system, heat related mor-
tality, influenza, ambient temperature.
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