Estimation of the vapour pressure deficit using noaa avhrr data

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Estimation of the vapour pressure
deficit using NOAA-AVHRR data
Mehmet Şahin
a
, Bekir Yiğit Yıldız
b
, Ozan Şenkal
c
& Vedat
Peştemalci
d
a
Engineering Faculty, Siirt University, Siirt, Turkey
b
Karaisalı Vocational School, Çukurova University, Adana, Turkey
c
Faculty of Education, Department of Computer Education and
Instructional Technology, Çukurova University, Adana, Turkey
d
Department of Physics, Çukurova University, Adana, Turkey
Version of record first published: 15 Jan 2013.
To cite this article: Mehmet Şahin , Bekir Yiğit Yıldız , Ozan Şenkal & Vedat Peştemalci (2013):
Estimation of the vapour pressure deficit using NOAA-AVHRR data, International Journal of Remote
Sensing, 34:8, 2714-2729
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International Journal of Remote Sensing
Vol. 34, No. 8, 20 April 2013, 2714–2729
Estimation of the vapour pressure deficit using NOAA-AVHRR data
Mehmet ¸Sahina
*, Bekir Yi˘git Yıldızb
, Ozan ¸Senkalc
, and Vedat Pe¸stemalcid
a
Engineering Faculty, Siirt University, Siirt, Turkey; b
Karaisalı Vocational School, Çukurova
University, Adana, Turkey; c
Faculty of Education, Department of Computer Education and
Instructional Technology, Çukurova University, Adana, Turkey; d
Department of Physics,
Çukurova University, Adana, Turkey
(Received 31 October 2010; accepted 11 November 2012)
In this study, the calculation of vapour pressure deficit (VPD) using the National
Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer
(NOAA/AVHRR) satellite data set is shown. Twenty-four NOAA/AVHRR data images
were arranged and turned to account for both VPD and land surface temperature (LST),
which was necessary to calculate the VPD. The most accurate LST values were obtained
from the Ulivieri et al. split-window algorithm with a root mean square error (RMSE) of
2.7 K, whereas the VPD values were retrieved with an RMSE of 6 mb. Furthermore, the
VPD value was calculated on an average monthly basis and its correlation coefficient
was found to be 0.991, while the RMSE value was calculated to be 2.67 mb. As a result,
VPD can be used in studies that examine plants (germination, growth, and harvest),
controlling illness outbreak, drought determination, and evapotranspiration.
1. Introduction
Vapour pressure deficit (VPD) is the difference between the amount of moisture in the air
and how much moisture it can hold when it is saturated at a given temperature (Shuttleworth
1993). VPD is a convenient indicator of condensation potential because it quantifies how
close the air is to saturation. The air is saturated when it reaches a maximum water-holding
capacity for a given temperature. Therefore, VPD is a measure of the lack of moisture
equilibrium between an object and its surrounding atmosphere (Hay and Lennon 1999;
Prenger and Ling 2000).
VPD is used in many fields, such as in investigating methods that predict the regional
distribution and abundance of arthropod disease vectors (Rogers et al. 1996; Hay and
Lennon 1999). Arthropods cause epidemic diseases. For example, malaria relates to the
abundance of Anopheles Gambiae Sensu Stricto and Anopheles Arabiensis (Linsay, Parson,
and Thomas 1998). Vector-borne disease distribution and transmission potentials are
affected by climatic changes (Bouma and Van der Kaay 1994, 1996). Spatial and tem-
poral fluctuations in climatic conditions also influence disease risk and the spread of
malaria in human populations, and provide information that supports the proposed epi-
demic early warning systems for malaria. Reliable assessments of the changing risk to
human populations require accurate, timely, and extensive climate information; therefore,
this information is of considerable interest to epidemiologists (Green and Hay 2002).
*Corresponding author. Email: sahanmehmet2000@yahoo.com
ISSN 0143-1161 print/ISSN 1366-5901 online
© 2013 Taylor & Francis
http://dx.doi.org/10.1080/01431161.2012.750021
http://www.tandfonline.com
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International Journal of Remote Sensing 2715
VPD affects plants. For example, the water stress index was recently developed using
VPD by employing a radiometric measurement of foliage temperature and a psychometric
measurement of air VPD. It is necessary to find the relationship between the foliage-air
temperature differential and the air VPD for plants (Idso 1982). Strawberry plants were
monitored for water stress by measuring the foliage temperature with a hand-held infrared
thermometer. In addition to the foliage temperature measurement, weather variables, the
difference between leaf and air temperature, derived crop water stress index, soil matric
potential, leaf water potential, photosynthetic gas exchange rates, transpiration rates, photo-
synthetic pigments, sugars, starch, canopy structure, and accumulated yield were measured.
A regression analysis showed that the air VPD contributed significantly to variations in leaf
water potential under very mild water stress conditions tested in a wet treatment. However,
the contribution was not significant under mild water stress of dry treatment (Peñuelas et al.
1992). Leonardi, Guichard, and Bertin (2000) investigated how a high VPD influences the
growth, transpiration, and quality of tomato fruits. In this study, plants were grown in two
glasshouse compartments under two VPD levels: a low VPD was obtained by increasing
air humidity with a fogging system, and a high VPD was obtained during sunny hours in
a greenhouse where the air humidity was not controlled. The mean value of the six driest
hours of the day during the considered growing period of the fruits was 16 mb under low
VPD and 22 mb under high VPD conditions. The study showed that as VPD increases from
16 to 22 mb during summer, effects can be observed on both tomato growth and quality.
During daylight hours, the relative fruit growth rate was significantly reduced for plants
grown under higher VPDs. The same trend was not observed at lower VPDs, where fruit
growth varied more regularly during daylight hours. Habermann et al. (2003) found that
healthy sweet orange plants measured at a VPD of 25 mb showed a 50% decrease in the
transpiration rate and an 80% decrease in stomatal conductance when compared to mea-
surements at 12 mb. The amount of proportion between photosynthesis and transpiration
rates and stomatal conductance of leaves from healthy plants was measured at both VPDs.
Reducing VPD has no long-term effect on the growth of temperate and tropical
rainforest trees. Therefore, large reductions in stomatal conductance and net photosyn-
thesis were measured with increasing VPDs in tropical rainforest trees. Earlier studies did
not show significant reductions in growth (Cunningham 2004, 2005). Possible explana-
tions for the lack of effect of VPD on growth include avoidance of water stress due to
adequate soil moisture, increased nutrient uptake under ambient conditions due to higher
transpiration rates, and other factors such as temperature and light, and limiting the growth
of tropical species. In temperate rainforests, unlike in this experiment, higher VPD val-
ues in summer were associated with a limited water supply. Therefore, a more important
difference between temperate and tropical rainforest trees is the ability of the temperate
species to tolerate a mild soil drought. Temperate rainforest trees may possess adaptations
such as deeper, more extensive root systems and increased resistance to xylem cavitation
and osmotic adjustments that allow them to maintain photosynthesis and growth for longer
periods under low-soil moisture than wet, tropical rainforest trees (Cunningham 2006).
Williams and Baeza (2007) studied Vitis vinifera grown at different locations to deter-
mine the relationship among temperature, VPD, and leaf water potential under clear skies
during midday. Temperature and VPD were determined at the time of measurement. The
highest and lowest leaf water potential values measured on well-watered grapevines were
51.102
and 115.102
mb, respectively. According to the results, the leaf and stem water
potentials were linearly related to VPD. The coefficient of determination was greater for
the relationship between the leaf water potential and VPD (r2
= 0.74) than temperature and
VPD (r2
= 0.58). The leaf water potential and stem water potential values as a function

2716 M. ¸Sahin et al.
of VPD or temperature could serve as baselines, indicating whether the grapevines are
fully irrigated or are not water stressed under the environmental conditions found in semi-
arid grape-growing regions. Lendzion and Leuschner (2008) studied the effects of elevated
atmospheric water VPD on the European beech. They tested the hypothesis that an increas-
ing VPD negatively affects the growth and development of European beech saplings. Their
results show that the beech sapling growth and development strongly depend not only
on soil moisture but also on the prevailing VPD level. Glenn et al. (2008) found that
transpiration highly depended on the leaf area and was controlled by VPD in the atmo-
sphere. Sarcobatus vermiculatus tended to have higher transpiration rates than Atriplex
canescens and had a steeper response to VPD.
VPD is one of the critical variables that drives evapotranspiration (ET) and is funda-
mentally important to many models. The estimation of VPD can be used in several ET
estimation equations that are used to estimate regional ET patterns in which only tem-
perature, precipitation, and insolation measurements are available (Castellvi et al. 1996,
1997). Furthermore, VPD is one of the key controls in opening the stomata in plants and
is thus an important force for ET, plant respiration, biomass production, and the uptake of
harmful pollutants such as ozone through the stomata (Andersson-Sköld, Simpsonb, and
Ødegaard 2008). To survive in adverse environments subject to drought, high-salt con-
centration, or low temperature, some plants seem to be able to synthesize biochemical
compounds, including proteins, in response to changes in water activity or osmotic pres-
sure. Water activity or osmotic pressure measurements of simple aqueous solutions have
been based on freezing point depression or VPD. Osmotic pressure measurements of plants
under water stress have been mainly based on VPD (Kiyosawa 2003).
As observed in recent studies, VPD plays an important role in various fields.
Additionally, it was estimated using meteorological stations that are limited in the regions
studied. For these reasons, new methods to estimate VPD are necessary. One of these meth-
ods uses satellite data. Choudhury (1998) estimated VPD over land surfaces from satellite
observations. This study presented a method for estimating the monthly mean VPD from
satellite observations and then evaluated the accuracy of the estimated values by compar-
ing them with globally distributed ground measurements. The square of the correlation
coefficient and standard error of estimation were found to be 0.85 and 4 mb, respec-
tively. Prince et al. (1998) conducted a study to obtain VPD and then compared the values
with NOAA/AVHRR (National Oceanic and Atmospheric Administration/Advanced Very
High Resolution Radiometer) and field observations. The comparison resulted in a VPD
root mean square error (RMSE) of 10.9 mb over a range of 58 mb. Hay and Lennon
(1999) studied meteorological variables and control of vector-borne disease across Africa
and compared remote sensing and climate spatial interpolation using VPD obtained
from the NOAA/AVHRR data set. According to the result, the mean accuracy for the
year was an RMSE of 6 mb (range 4.91–6.43) with a mean adjusted r2
= 0.63 (range
0.40–0.71) and an RMSE of 5.3 mb (range 3.65–7.64) with a mean adjusted r2
= 0.78
(range 0.67–0.86). Hashimoto et al. (2008) developed simple linear models to predict VPD
using saturated vapour pressure calculated from MODIS (Moderate Resolution Imaging
Spectroradiometer) – LST (land surface temperature) at a number of different temporal and
spatial resolutions. They estimated model parameters for VPD estimation both regionally
and globally with RMSE values ranging from 3.2 to 3.8 mb. VPD was overestimated along
coastlines and underestimated in arid regions with low-vegetation cover. Additionally, the
residuals were larger with higher VPDs because of the non-linear relationship between sat-
uration vapour pressure and LST. Linear relationships were observed at multiple scales and
appeared useful for estimation within the range 0–25 mb.

In this study, land surface temperature (Ts), total precipitable water in the atmospheric
column (U), and dew point temperature (Td) were calculated to estimate VPD using satel-
lite data. Meanwhile, VPD was estimated using meteorological data. Then, the results were
statistically compared.
2. Data
Two different data sets received from the Scientific and Technological Research Council
of Turkey and the Turkish State Meteorological Service were used to obtain VPD. First,
raw NOAA12-14-15/AVHRR data were translated into a Level 1b format using Quorum
Software, and in the second step, the brightness temperatures of channel 4 and channel
5 (range 10.3–11.3 µm and range 11.5–12.5 µm, respectively) were obtained from Level
1b data by employing the Envi 4.3 image-processing program and data received from the
Scientific and Technological Research Council of Turkey during 2002.
Land meteorological values were necessary to determine whether the VPD estimates
obtained from the satellites are indeed adequate. To achieve this, air temperature and vapour
pressure (VPair) values were received from the Turkish State Meteorological Service.
3. Estimation of land-surface temperature
Land-surface temperature is important because it is one of the key factors in determin-
ing the exchange of energy and matter between the Earth’s surface and atmosphere.
Simultaneously, it is an important measurement for energy-balance applications and can
be especially useful when determined by thermal infrared remote sensing (Seguin and
Itier 1983). An approach based on the differential absorption in two adjacent infrared
channels, called the ‘Split-Window’ technique, is used for determining the surface temper-
ature. The AVHRR channels 4 and 5 are widely used for deriving the surface temperature
(Kant and Badarinath 2000). Many algorithms have been proposed by Price (1984), Becker
(1987), Becker and Li (1990), Vidal (1991), Prata (1993, 1994), Sobrino et al. (1994,
1996), Coll et al. (1994), Becker and Li (1995), Coll and Caselles (1997), Ouaidrari et al.
(2002), Pinheiro et al. (2006), and Katsiabani, Adaktilou, and Cartalis (2009). These stud-
ies 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. In this
study, the Price (1984), Becker and Li (1990), Vidal (1991), and Ulivieri et al. (1994)
split-window algorithm techniques were used to obtain land-surface temperatures.
3.1. Price (1984) algorithm
By reducing the effect of atmosphere and using radioactive transfer theory, Price (1984)
developed a split-window algorithm technique that has been used extensively. The basic
split-window algorithm can be written as
Ts = T4 + a(T4 − T5) + b, (1)
where coefficients a and b account for atmospheric conditions (related to spectral radi-
ance and transmission) and surface emissivity, respectively. However, linear empirical
formulations do not always hold. Hence, the water vapour dependence was subsequently
incorporated into a non-linear quadratic equation (Coll et al. 1994; François and Ottle
1996). Coefficient a in Equation (1) was given by a = ((a5/a4) – 1)−1
, where a5/a4 was
determined from T5 ( T4)−1
(the brightness temperature spatial variations in AVHRR

channels 4 and 5) for the small study area. The a5/a4 value was calculated to be 1.30,
a = 3.33, and b was linked to the emissivity difference. The emissivity difference, ε =
ε4 − ε5 = –0.005, and ε depend on ε4 and ε5 as in the relation, ε =
ε4 + ε5
2
= 0.975
(Caselles, Coll, and Valor 1997; Chrysoulakis and Cartalis 2002), where ε4 and ε5 are
the emissivities of channels 4 and 5; and T4 and T5 are the brightness temperatures of
NOAA/AVHRR channels 4 and 5, respectively (Dash et al. 2002). The final form of the
equation is
Ts = [T4 + 3.33 (T4 − T5)]
5.5 − ε4
4.5
− 0.75T5 ε. (2)
3.2. Becker and Li (1990) algorithm
Based on radioactive transfer theory and numerical simulations, Becker and Li (1990)
proposed a local split-window algorithm for AVHRR channels 4 and 5:
Ts = 1.274 + P
T4 + T5
2
+ M
T4 − T5
2
, (3)
where temperature is in K, and the coefficients P and M are given by
P = 1 + 0.15616
1 − ε
ε
− 0.482
ε
ε2
, (4)
M = 6.26 + 3.98
1 − ε
ε
+ 38.33
ε
ε2
, (5)
where P and M are local coefficients that depend on the surface emissivity, but are indepen-
dent of atmospheric effects. ε = ε4 − ε5 = –0.005, and ε = 0.975 (Caselles, Coll, and
Valor 1997; Chrysoulakis and Cartalis 2002). The coefficient 1.274 in Equation (3) was
calculated from numerical simulations and local atmospheric effects (Becker and Li 1990).
3.3. Vidal (1991) algorithm
Ts = T4 + 2.78 (T4 − T5) + 50
1 − ε
ε
− 300
ε
ε
. (6)
The coefficients related to the emissivity in this algorithm were obtained from a study by
Becker (1987). ε = ε4 − ε5 = –0.005, and ε = 0.975 (Caselles, Coll, and Valor 1997;
Chrysoulakis and Cartalis 2002). This algorithm was generated from a large number of
satellite data and land-surface temperature calculations (Vidal 1991).
3.4. Ulivieri et al. (1994) algorithm
This algorithm was developed by Ulivieri et al. (1994) for its simplicity, robustness, and
superior performance in independent tests. Becker and Li (1995) and Vazquez, Reyes, and
Arboledas (1997) tested the algorithm with different data sets and different split-widow
algorithms. In all cases, the Ulivieri et al. (1994) algorithm performed well. The Ulivieri
et al. algorithm can be written as

Ts = T4 + 1.8 (T4 − T5) + 48(1 − ε) − 75 ε, (7)
where ε = ε4 – ε5 = –0.005 and ε = 0.975 (Caselles, Coll, and Valor 1997; Chrysoulakis
and Cartalis 2002). This equation was developed for cases of column atmospheric water
vapour less than 3 g cm−2
, a reasonable condition for many of the semi-arid areas of
continental Africa (Pinheiro et al. 2006).
4. Estimation of VPD
Generally, researchers use two sources to ﬁnd VPD: meteorological station data and satellite
data. Therefore, these two sources were used in this study to perform comparison.
4.1. Estimation of VPD using meteorological station data
The vapour pressure (VPair) is a measure of how much water vapour is in the air, i.e. how
much water in the gas phase is present in the air. The presence of more water vapour in the
air leads to a greater water vapour pressure. When the air reaches its maximum water con-
tent, the vapour pressure is called the saturation vapour pressure (VPsat), which is directly
related to temperature. Thus, the difference between the saturation vapour pressure and
the real air vapour pressure is called VPD. The magnitude of VPD gives an indication
of how close the air is to condensation (Choudhury 1998; Prenger and Ling 2000). Very
simply, VPD is a measure of the lack of moisture equilibrium between an object and the
surrounding atmosphere (Hay and Lennon 1999). VPD is given by Unwin (1980) as
VPD = VPsat − VPair, (8)
where the saturation vapour pressure, VPsat (mb), is given by
log10 VPsat = 9.24349 −
2305
Tair
−
500
T2
air
−
100000
T3
air
, (9)
where Tair is the air temperature in Kelvin.
4.2. Estimation of VPD using satellite data
Determining VPD, and the difference between saturated and actual atmospheric vapour
pressures, involves estimating the precipitable water in the atmospheric column using
the thermal infrared channels 4 and 5 of AVHRR, from which the surface humidity is
derived. The total precipitable water in the atmospheric column, U (kg m−2
), is estimated
as Equation (10) (Eck and Holben 1994):
U = A + B (T4 − T5) , (10)
where A and B are constants equal to 1.337 and 0.837, respectively. The total precipitable
water is expressed in units of pressure and is converted to the amount of water in centime-
tres that would be precipitated from the atmospheric column by dividing by 10, because the
density of water is 1 g cm−3
. The estimated precipitable water content U is used to obtain
the surface dew temperature Td (◦
F); surface dew temperature can be calculated using the
following equation (Smith 1966):

Td(◦
F) =
ln U − (0.113 − ln(λ + 1))
0.0393
. (11)
The λ values given by Smith for different latitudinal zones were used. In this analysis, a
mean value of λ = 2.99 was calculated from the annual mean λ presented by Smith for
locations between 0 and ±40 degrees of latitude. Then, the Td values should be converted
into kelvin (Smith 1966).
Finally, VPD in kilopascals (kPa) can be calculated from Td and Ts, as given by
Equation (12), following Prince and Goward (1995):
VPD = 0.611 exp 17.27
Ts − 273
Ts − 36
− exp 17.27
Td − 273
Td − 36
. (12)
5. Evaluation of the estimation results
The choice of the relevant criteria allowing the estimation methods’ performance evalua-
tion is an important issue. Various statistical parameters can be used to measure the strength
of the statistical relationship between the estimated and reference values. We assume that vi
(i = 1, n) is the set of n reference values and ei (i = 1, n) is the set of estimates; ¯v and ¯e are
mean reference and estimate value, respectively. The bias, linear correlation coefficient (r),
and RMSE can be calculated using the standard deviations of the reference (σv) and esti-
mate (σe) values, means of the reference and estimate values, and estimated and reference
values. The bias is the difference between the mean estimate ¯e and the mean reference value
¯v. The statistical criterion formula of the linear correlation coefficient r is the following:
r =
n
i=1 (vi − ¯v) (ei − ¯e)
nσvσe
, (13)
where r measures the proximity between estimate and reference and is not sensitive to a
bias (Kendall and Stuart 1963). The formula of RMSE is
RMSE =
1
n
n
i=1
(ei − vi)2
1
2
. (14)
In statistics, RMSE is a frequently used measure of the differences between values pre-
dicted by a model or an estimator and the values actually observed from the subject being
modelled or estimated (Laurent, Jobard, and Toma 1998).
6. Results
6.1. Land-surface temperature
After obtaining brightness temperatures of NOAA/AVHRR channels 4 and 5, split-window
algorithms were used to obtain the land-surface temperature. The Price (1984) algorithm
was calculated first using Equation (2) (see Figure 1). Then, the Becker and Li (1990), Vidal
(1991), and Ulivieri et al. (1994) algorithms were calculated using Equations (3), (6), and
(7), respectively (see Figures 2–4). When the maps in Figures 1–4 were examined through
image-processing programs, the land-surface temperature from the Price (1984) algorithm
was measured at a minimum of 299 K, maximum of 310 K, and an average of 302.75 K.

293.88
<273.57
276.47
279.37
282.27
285.18
288.08
290.98
296.78
299.69
302.59
305.49
308.39
311.29
314.20
317.10
320.00
N
Figure 1. Map of land-surface temperature obtained as based on the Price (1984) algorithm at
06.51 local time on 6 July 2002 (K).
N
<273.00
275.94
281.81
284.75
290.63
293.56
296.50
299.44
302.38
305.31
308.25
311.19
314.13
317.06
320.00
287.69
278.88
Figure 2. Map of land-surface temperature obtained as based on the Becker and Li (1990) algorithm
at 06.51 local time on 6 July 2002 (K).
In the Becker and Li (1990) algorithm, the minimum value was determined to be 296.91 K,
the maximum was 307.78 K, and the average was 301.16 K; in the Vidal (1991) algorithm,
the minimum value was found to be 299.28 K, the maximum was 309.30 K, and the average
was 302.84 K; in the Ulivieri et al. (1994) algorithm, the minimum value was calculated as
297.56 K, the maximum was 307.47 K, and the average was 300.83 K.
Accordingly, the land-surface temperature calculation was completed using
24 NOAA/AVHRR satellite images for each split-window algorithm. The values obtained
from the split-window algorithms had to be evaluated with the meteorological data from
chosen control point cities on Turkey’s map. Therefore, it was important to choose the
cities on the map by taking into consideration Turkey’s different geographical regions and
at least one city from each region had to be included. The cities were chosen according to
the geographical regions of Turkey and the city locations on the map (see Figure 5).
As observed on the map, Adana, Ankara, Antalya, Balıkesir, Denizli, Erzurum, ˙Izmir,
Kayseri, Malatya, Samsun, Sivas, Rize, and Van were used as the control points to

N
<273.00
320.00
275.94
281.81
284.75
290.63
293.56
296.50
299.44
302.38
305.31
308.25
311.19
314.13
317.06
287.69
278.88
Figure 3. Map of land-surface temperature obtained as based on the Vidal (1991) algorithm at
06.51 local time on 6 July 2002 (K).
N
<273.00
320.00
275.94
281.81
284.75
290.63
293.56
296.50
299.44
302.38
305.31
308.25
311.19
314.13
317.06
287.69
278.88
Figure 4. Map of land-surface temperature obtained as based on the Ulivieri et al. (1994) algorithm
at 06.51 local time on 6 July 2002 (K).
determine land-surface temperature accuracy. The minimum, maximum, and average
values of land-surface temperature obtained from control points were compared to
meteorological values on a monthly basis (see Table 1). Although the averages of the
meteorological and algorithmic values in January were the same (281.05 K), the land-
surface temperature values ranged from 268.70 to 293.21 K. In February, the average of
the meteorological values was 285.78 K, and the nearest estimate was 288.94 K, which
was from Ulivieri et al. (1994). The four algorithms gave results that were somewhat
similar to the meteorological values in March, October, and December. The average of
the meteorological values in April was 283.95 K, and the closest estimates belonged to
the Becker and Li (1990) and Ulivieri et al. (1994) algorithms. In May, the meteoro-
logical values were 270.20–282.30 K, and the average was 291.60 K. The best results
in terms of the algorithm average values belonged to Ulivieri et al. (1994) and Becker
and Li (1990) with 283.34 and 283.63 K, respectively. In June and July, the Ulivieri

Kayseri
Samsun
Ankara
Antalya
Denizli
Afyonkarahisar
izmir
Bahkesir
Istanbul
Konya
Adana
Malatya
Sivas
Rize
Artvin
Van
Kars
Erzurum
Figure 5. Locations of the cities used to estimate land-surface temperature and VPD.
et al. (1994) algorithm gave the results most consistent with the meteorological val-
ues. In August, the average error values of the Becker and Li (1990) and Ulivieri
et al. (1994) algorithms were approximately 2 K compared to the meteorological val-
ues. In September, the average error temperature values of all algorithms had a deviation
ratio with the meteorological values in the range of 0.07–1.9 K. In November, the clos-
est value to the meteorological value was from the Vidal (1991) and Ulivieri et al. (1994)
algorithms.
Additionally, statistical evaluation was performed by considering satellite and meteoro-
logical data with Equations (13) and (14). According to the evaluation result, the correlation
coefficients (r) were found to be 0.958, 0.961, 0.967, and 0.970 according to Price (1984),
Becker and Li (1990), Ulivieri et al. (1994), and Vidal (1991), respectively (see Figure 6).
The correlation coefficient results show a strong relationship between the satellite and
meteorological data.
The other statistical result was the RMSE values of the algorithms. The RMSE values
ranged from 2.7 K, which was calculated using the Ulivieri et al. (1994) algorithm, to nearly
4 K, which was calculated from the Price (1984) algorithm. The algorithm with the smallest
RMSE value was that of Ulivieri et al. (1994); thus, this algorithm is suggested to estimate
the land-surface temperature among the Price (1984), Becker and Li (1990), Vidal (1991),
and Ulivieri et al. (1994) algorithms.
6.2. Vapour pressure deficit
Two approaches (the first for the meteorological data, and the second for the satellite data)
were followed to estimate VPD. The VPD values for the meteorological data were cal-
culated using Equations (8) and (9), whereas the VPD values for the satellite data were
calculated using Equations (7), (10), (11), and (12) over the satellite images (see Figure 7).
When Figure 7 was examined through an image-processing program, it was observed
that VPD values were in the ranges 0–10 mb and 10–20 mb, which were considerably
low. Moreover, the VPD values in the range 20–30 mb were rather frequent over Turkey.
It was found that the VPD values were between 30 and 40 mb over the following regions
of Turkey: Central Anatolia, South-Eastern, Mediterranean, and the coastal lines of the

Table 1. Min, max, and average temperature values of meteorological and algorithm data (K).
Month
Minimum/
maximum/
average (K)
Meteorological
value
Ulivieri et al.
(1994)
Becker and
Li (1990)
Vidal
(1991) Price (1984)
Min 271.40 268.70 269.69 271.10 270.00
January Max 289.40 291.19 291.39 293.21 293.00
Ave 281.05 281.05 281.05 281.05 281.05
Min 277.90 282.40 283.49 284.49 284.00
February Max 291.60 293.32 293.79 295.65 296.00
Ave 285.78 288.94 289.15 290.85 290.69
Min 270.20 267.50 267.63 268.85 268.00
March Max 289.30 289.77 289.58 291.14 290.00
Ave 283.78 283.46 283.37 284.82 283.85
Min 276.20 277.07 277.05 278.43 277.00
April Max 287.00 290.11 290.53 293.28 292.00
Ave 283.95 286.59 286.85 287.93 288.21
Min 270.20 267.50 267.63 268.85 268.00
May Max 291.60 293.32 293.79 295.65 296.00
Ave 282.30 283.34 283.63 285.07 284.48
Min 290.40 293.61 295.00 295.00 296.00
June Max 302.60 305.00 305.19 307.00 306.00
Ave 298.26 300.51 301.17 302.30 301.38
Min 293.60 297.56 296.91 299.06 299.00
July Max 303.50 307.47 308.92 309.84 310.00
Ave 298.04 300.73 301.62 302.48 303.01
Min 294.30 296.96 296.67 297.82 298.00
August Max 315.00 315.35 311.89 316.56 315.00
Ave 300.49 302.68 302.19 304.34 304.04
Min 283.40 284.26 284.22 285.03 285.00
September Max 308.00 304.38 304.22 307.47 306.00
Ave 293.19 293.26 293.83 295.08 295.09
Min 280.50 277.99 277.83 279.31 279.00
October Max 292.00 292.26 291.41 292.37 293.00
Ave 285.66 285.14 284.84 286.10 286.48
Min 273.40 269.70 260.92 271.00 260.00
November Max 284.00 285.81 282.67 284.70 285.00
Ave 276.57 274.35 272.37 275.24 273.10
Min 270.40 269.00 269.86 271.54 271.00
December Max 282.00 280.00 280.41 281.94 282.00
Ave 275.93 274.20 274.90 276.52 276.17
Aegean Sea. The VPD values in Central Anatolia and South-Eastern being between 30 and
40 mb was attributed to the heating weather and, because of that heat, deﬁcit humidity in
the atmosphere. Although enough water was present in the regions of the Mediterranean
and Aegean Sea coastal line, the cause of the VPD values being between 30 and 40 mb
was the heating weather in the early times of the day and, in spite of the humidity holding
capacity increase, there was not enough evaporation due to the lack of warming of the sea-
water. Even if the VPD values between 50 and 80 mb were not frequently observed over
Turkey, these rates were observed frequently over Iraq and Syria. In a similar way, VPD
values were calculated over all 24 satellite images.
Then, the cities of Adana, Ankara, Afyonkarahisar, Artvin, Antalya, Balıkesir, Denizli,
Erzurum, Eski¸sehir, ˙Istanbul, ˙Izmir, Kars, Kayseri, Konya, Malatya, Samsun, Sivas,

320
(a) (b)
(c) (d)
320
310
310
300
300
290
290
Meteorologic temperature (K)
280
280
270
270
Temperature(K)
r = 0.958
320
310
300
290
280
270
320310300290280270
Temperature(K)
r = 0.961
320
310
300
290
280
270
320310300290280270
Temperature(K)
r = 0.967
320
310
300
290
280
270
320310300290280270
Temperature(K)
r = 0.970
Figure 6. Correlation coefﬁcients of the algorithms. (a) Price (1984), (b) Becker and Li (1990),
(c) Vidal (1991), and (d) Ulivieri et al. (1994) algorithms.
0 10 20 30 40 50 60 70 80 (mb)
Figure 7. Map of VPD obtained at 06.51 local time on 6 July 2002 (mb).
¸Sanlıurfa, Rize, and Van were chosen as control points for the satellite prediction accuracy
(see Figure 5).
Upon examining the monthly average VPD values at the control points, the following
were found.

100
90
90
80
80
70
70
60
60
50
50
40
40
30
30
Meteorologic VPD (mb)
r = 0.957
SatelliteVPD(mb)
20
20
10
10
0
0
Figure 8. Correlation coefficient of VPD.
• In January, the meteorological and satellite values were 1.9 and 4.2 mb, respectively.
• In February, the meteorological and satellite values were 6.35 and 8.72 mb,
respectively.
• In March, the meteorological and satellite values were 3.16 and 8.23 mb, respectively.
• In April, the meteorological and satellite values were 17 and 20.01 mb, respectively.
• In May, the meteorological and satellite values were 28.33 and 30.91 mb, respectively.
• In June, the meteorological and satellite values were 52.73 and 53.62 mb, respectively.
• In July, the meteorological and satellite values were 50.64 and 50.16 mb, respectively.
• In August, the meteorological and satellite values were 43.96 and 46.89 mb,
respectively.
• In September, the meteorological and satellite values were 26.19 and 28.29 mb,
respectively.
• In October, the meteorological and satellite values were 11.95 and 15.21 mb,
respectively.
• In November, the meteorological and satellite values were 2.49 and 5.45 mb,
respectively.
• In December, the meteorological and satellite values were 3.22 and 3.38 mb,
respectively.
Equations (13) and (14) tested the satellite prediction accuracy by calculating the cor-
relation coefficient (r) and RMSE. While the correlation coefficient on a monthly average
basis was 0.991, the RMSE value was 2.67 mb. When the meteorological and satellite VPD
values were not measured on a monthly average basis but were directly compared, the cor-
relation coefficient amongst the VPD values was found to be 0.957, and the value of RMSE
was 5.665 mb (see Figure 8). According to statistical rules, the VPD RMSE value can be
written as 6 mb instead of 5.665 mb.

7. Discussion and conclusion
In the literature, Price (1984), Becker (1987), Becker and Li (1990, 1995), Vidal (1991),
Prata (1993, 1994), Sobrino et al. (1994, 1996), Coll et al. (1994), and Caselles, Coll, and
Valor (1997) attempted to obtain LST at reasonable accuracies (RMSE of 1–3 K) from
current operational and research NOAA/AVHRR satellite-borne visible/infrared radiome-
ters. In our study, the accuracies of split-window algorithms resulted in an average RMSE
value of 3 K (range 2.733–3.731 K). The Ulivieri et al. (1994) algorithm was found to be
very successful compared to studies from the literature. Because of this result, the Ulivieri
et al. (1994) algorithm was used to estimate the VPD formula. The VPD accuracy was
determined by the RMSE value and the correlation coefficient, which were calculated to
be 6 mb and 0.957, respectively. Furthermore, on a monthly average basis, while the VPD
correlation coefficient was found to be 0.991, RMSE was found to be 2.67 mb. These val-
ues are rather compatible with studies from the literature, which range between 3.2 mb and
10.9 mb.
As a result, we conclude that the VPD values obtained using satellite data can be used
in studies related to plants (germination, growth, and harvest), outbreak control of illness,
drought determination, and ET over wide areas in which the meteorological station network
density is normally not sufficient.
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