estimation of the vapour pressure deficit using noaa avhrr data

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Estimation of the vapour pressuredeficit using NOAA-AVHRR dataMehmet Şahin a , Bekir Yiğit Yıldız b , Ozan Şenkal c & Vedat

Peştemalci d

a Engineering Faculty, Siirt University, Siirt, Turkeyb Karaisalı Vocational School, Çukurova University, Adana, Turkeyc Faculty of Education, Department of Computer Education andInstructional Technology, Çukurova University, Adana, Turkeyd Department of Physics, Çukurova University, Adana, TurkeyVersion 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 RemoteSensing, 34:8, 2714-2729

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International Journal of Remote SensingVol. 34, No. 8, 20 April 2013, 2714–2729

Estimation of the vapour pressure deficit using NOAA-AVHRR data

Mehmet Sahina*, Bekir Yigit Yıldızb , Ozan Senkalc , and Vedat Pestemalcid

aEngineering Faculty, Siirt University, Siirt, Turkey; bKaraisalı Vocational School, ÇukurovaUniversity, Adana, Turkey; cFaculty of Education, Department of Computer Education and

Instructional Technology, Çukurova University, Adana, Turkey; dDepartment 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 NationalOceanic and Atmospheric Administration Advanced Very High Resolution Radiometer(NOAA/AVHRR) satellite data set is shown. Twenty-four NOAA/AVHRR data imageswere 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 obtainedfrom the Ulivieri et al. split-window algorithm with a root mean square error (RMSE) of2.7 K, whereas the VPD values were retrieved with an RMSE of 6 mb. Furthermore, theVPD value was calculated on an average monthly basis and its correlation coefficientwas 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 airand how much moisture it can hold when it is saturated at a given temperature (Shuttleworth1993). VPD is a convenient indicator of condensation potential because it quantifies howclose the air is to saturation. The air is saturated when it reaches a maximum water-holdingcapacity for a given temperature. Therefore, VPD is a measure of the lack of moistureequilibrium 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 regionaldistribution and abundance of arthropod disease vectors (Rogers et al. 1996; Hay andLennon 1999). Arthropods cause epidemic diseases. For example, malaria relates to theabundance of Anopheles Gambiae Sensu Stricto and Anopheles Arabiensis (Linsay, Parson,and Thomas 1998). Vector-borne disease distribution and transmission potentials areaffected 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 ofmalaria in human populations, and provide information that supports the proposed epi-demic early warning systems for malaria. Reliable assessments of the changing risk tohuman populations require accurate, timely, and extensive climate information; therefore,this information is of considerable interest to epidemiologists (Green and Hay 2002).

*Corresponding author. Email: [email protected]

ISSN 0143-1161 print/ISSN 1366-5901 online© 2013 Taylor & Francishttp://dx.doi.org/10.1080/01431161.2012.750021http://www.tandfonline.com

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mailto:[email protected]

International Journal of Remote Sensing 2715

VPD affects plants. For example, the water stress index was recently developed usingVPD by employing a radiometric measurement of foliage temperature and a psychometricmeasurement of air VPD. It is necessary to find the relationship between the foliage-airtemperature differential and the air VPD for plants (Idso 1982). Strawberry plants weremonitored for water stress by measuring the foliage temperature with a hand-held infraredthermometer. In addition to the foliage temperature measurement, weather variables, thedifference between leaf and air temperature, derived crop water stress index, soil matricpotential, 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 leafwater 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 thegrowth, transpiration, and quality of tomato fruits. In this study, plants were grown in twoglasshouse compartments under two VPD levels: a low VPD was obtained by increasingair humidity with a fogging system, and a high VPD was obtained during sunny hours ina greenhouse where the air humidity was not controlled. The mean value of the six driesthours of the day during the considered growing period of the fruits was 16 mb under lowVPD and 22 mb under high VPD conditions. The study showed that as VPD increases from16 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 plantsgrown under higher VPDs. The same trend was not observed at lower VPDs, where fruitgrowth varied more regularly during daylight hours. Habermann et al. (2003) found thathealthy sweet orange plants measured at a VPD of 25 mb showed a 50% decrease in thetranspiration rate and an 80% decrease in stomatal conductance when compared to mea-surements at 12 mb. The amount of proportion between photosynthesis and transpirationrates 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 tropicalrainforest trees. Therefore, large reductions in stomatal conductance and net photosyn-thesis were measured with increasing VPDs in tropical rainforest trees. Earlier studies didnot 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 toadequate soil moisture, increased nutrient uptake under ambient conditions due to highertranspiration rates, and other factors such as temperature and light, and limiting the growthof 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 importantdifference between temperate and tropical rainforest trees is the ability of the temperatespecies to tolerate a mild soil drought. Temperate rainforest trees may possess adaptationssuch as deeper, more extensive root systems and increased resistance to xylem cavitationand osmotic adjustments that allow them to maintain photosynthesis and growth for longerperiods 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 skiesduring midday. Temperature and VPD were determined at the time of measurement. Thehighest and lowest leaf water potential values measured on well-watered grapevines were51.102 and 115.102 mb, respectively. According to the results, the leaf and stem waterpotentials were linearly related to VPD. The coefficient of determination was greater forthe relationship between the leaf water potential and VPD (r2 = 0.74) than temperature andVPD (r2 = 0.58). The leaf water potential and stem water potential values as a function

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2716 M. Sahin et al.

of VPD or temperature could serve as baselines, indicating whether the grapevines arefully 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 elevatedatmospheric 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. Theirresults show that the beech sapling growth and development strongly depend not onlyon soil moisture but also on the prevailing VPD level. Glenn et al. (2008) found thattranspiration highly depended on the leaf area and was controlled by VPD in the atmo-sphere. Sarcobatus vermiculatus tended to have higher transpiration rates than Atriplexcanescens 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 ETestimation 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 andis thus an important force for ET, plant respiration, biomass production, and the uptake ofharmful pollutants such as ozone through the stomata (Andersson-Sköld, Simpsonb, andØdegaard 2008). To survive in adverse environments subject to drought, high-salt con-centration, or low temperature, some plants seem to be able to synthesize biochemicalcompounds, including proteins, in response to changes in water activity or osmotic pres-sure. Water activity or osmotic pressure measurements of simple aqueous solutions havebeen based on freezing point depression or VPD. Osmotic pressure measurements of plantsunder 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 regionsstudied. 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 satelliteobservations. This study presented a method for estimating the monthly mean VPD fromsatellite observations and then evaluated the accuracy of the estimated values by compar-ing them with globally distributed ground measurements. The square of the correlationcoefficient 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 valueswith NOAA/AVHRR (National Oceanic and Atmospheric Administration/Advanced VeryHigh Resolution Radiometer) and field observations. The comparison resulted in a VPDroot 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 Africaand compared remote sensing and climate spatial interpolation using VPD obtainedfrom the NOAA/AVHRR data set. According to the result, the mean accuracy for theyear was an RMSE of 6 mb (range 4.91–6.43) with a mean adjusted r2 = 0.63 (range0.40–0.71) 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 VPDusing saturated vapour pressure calculated from MODIS (Moderate Resolution ImagingSpectroradiometer) – LST (land surface temperature) at a number of different temporal andspatial resolutions. They estimated model parameters for VPD estimation both regionallyand globally with RMSE values ranging from 3.2 to 3.8 mb. VPD was overestimated alongcoastlines and underestimated in arid regions with low-vegetation cover. Additionally, theresiduals 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 andappeared useful for estimation within the range 0–25 mb.

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In this study, land surface temperature (T s), total precipitable water in the atmosphericcolumn (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 werestatistically compared.

2. Data

Two different data sets received from the Scientific and Technological Research Councilof 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 QuorumSoftware, and in the second step, the brightness temperatures of channel 4 and channel5 (range 10.3–11.3 µm and range 11.5–12.5 µm, respectively) were obtained from Level1b data by employing the Envi 4.3 image-processing program and data received from theScientific and Technological Research Council of Turkey during 2002.

Land meteorological values were necessary to determine whether the VPD estimatesobtained from the satellites are indeed adequate. To achieve this, air temperature and vapourpressure (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 canbe especially useful when determined by thermal infrared remote sensing (Seguin andItier 1983). An approach based on the differential absorption in two adjacent infraredchannels, 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 thisstudy, 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 basicsplit-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 empiricalformulations do not always hold. Hence, the water vapour dependence was subsequentlyincorporated into a non-linear quadratic equation (Coll et al. 1994; François and Ottle1996). Coefficient a in Equation (1) was given by a = ((a5/a4) – 1)−1, where a5/a4 wasdetermined from �T5 (�T4)−1 (the brightness temperature spatial variations in AVHRR

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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 arethe emissivities of channels 4 and 5; and T4 and T5 are the brightness temperatures ofNOAA/AVHRR channels 4 and 5, respectively (Dash et al. 2002). The final form of theequation 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 + PT4 + T5

2+ M

T4 − T5

2, (3)

where temperature is in K, and the coefficients P and M are given by

P = 1 + 0.156161 − ε

ε− 0.482

�ε

ε2, (4)

M = 6.26 + 3.981 − 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, andValor 1997; Chrysoulakis and Cartalis 2002). The coefficient 1.274 in Equation (3) wascalculated from numerical simulations and local atmospheric effects (Becker and Li 1990).

3.3. Vidal (1991) algorithm

Ts = T4 + 2.78 (T4 − T5) + 501 − ε

ε− 300

�ε

ε. (6)

The coefficients related to the emissivity in this algorithm were obtained from a study byBecker (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 ofsatellite 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, andsuperior performance in independent tests. Becker and Li (1995) and Vazquez, Reyes, andArboledas (1997) tested the algorithm with different data sets and different split-widowalgorithms. In all cases, the Ulivieri et al. (1994) algorithm performed well. The Ulivieriet al. algorithm can be written as

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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; Chrysoulakisand Cartalis 2002). This equation was developed for cases of column atmospheric watervapour less than 3 g cm−2, a reasonable condition for many of the semi-arid areas ofcontinental Africa (Pinheiro et al. 2006).

4. Estimation of VPD

Generally, researchers use two sources to find VPD: meteorological station data and satellitedata. 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. howmuch water in the gas phase is present in the air. The presence of more water vapour in theair 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 directlyrelated to temperature. Thus, the difference between the saturation vapour pressure andthe real air vapour pressure is called VPD. The magnitude of VPD gives an indicationof how close the air is to condensation (Choudhury 1998; Prenger and Ling 2000). Verysimply, VPD is a measure of the lack of moisture equilibrium between an object and thesurrounding 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

T2air

− 100000

T3air

, (9)

where T air is the air temperature in Kelvin.

4.2. Estimation of VPD using satellite data

Determining VPD, and the difference between saturated and actual atmospheric vapourpressures, involves estimating the precipitable water in the atmospheric column usingthe thermal infrared channels 4 and 5 of AVHRR, from which the surface humidity isderived. The total precipitable water in the atmospheric column, U (kg m−2), is estimatedas 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 precipitablewater 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 thedensity of water is 1 g cm−3. The estimated precipitable water content U is used to obtainthe surface dew temperature Td (◦F); surface dew temperature can be calculated using thefollowing equation (Smith 1966):

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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, amean value of λ = 2.99 was calculated from the annual mean λ presented by Smith forlocations between 0 and ±40 degrees of latitude. Then, the Td values should be convertedinto kelvin (Smith 1966).

Finally, VPD in kilopascals (kPa) can be calculated from Td and T s, as given byEquation (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 strengthof 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 aremean 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 referencevalues. The bias is the difference between the mean estimate e and the mean reference valuev. 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 abias (Kendall and Stuart 1963). The formula of RMSE is

RMSE =[

1

n

n∑i=1

(ei − vi)2

] 12

. (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 beingmodelled 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-windowalgorithms were used to obtain the land-surface temperature. The Price (1984) algorithmwas 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 throughimage-processing programs, the land-surface temperature from the Price (1984) algorithmwas measured at a minimum of 299 K, maximum of 310 K, and an average of 302.75 K.

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293.88

<273.57276.47279.37282.27285.18288.08290.98

296.78299.69302.59305.49308.39311.29314.20317.10320.00

N

Figure 1. Map of land-surface temperature obtained as based on the Price (1984) algorithm at06.51 local time on 6 July 2002 (K).

N

<273.00275.94

281.81284.75

290.63293.56296.50299.44302.38305.31308.25311.19314.13317.06320.00

287.69

278.88

Figure 2. Map of land-surface temperature obtained as based on the Becker and Li (1990) algorithmat 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 averagewas 302.84 K; in the Ulivieri et al. (1994) algorithm, the minimum value was calculated as297.56 K, the maximum was 307.47 K, and the average was 300.83 K.

Accordingly, the land-surface temperature calculation was completed using24 NOAA/AVHRR satellite images for each split-window algorithm. The values obtainedfrom the split-window algorithms had to be evaluated with the meteorological data fromchosen control point cities on Turkey’s map. Therefore, it was important to choose thecities on the map by taking into consideration Turkey’s different geographical regions andat least one city from each region had to be included. The cities were chosen according tothe 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

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N

<273.00

320.00

275.94

281.81284.75

290.63293.56296.50299.44302.38305.31308.25311.19314.13317.06

287.69

278.88

Figure 3. Map of land-surface temperature obtained as based on the Vidal (1991) algorithm at06.51 local time on 6 July 2002 (K).

N

<273.00

320.00

275.94

281.81284.75

290.63293.56296.50299.44302.38305.31308.25311.19314.13317.06

287.69

278.88

Figure 4. Map of land-surface temperature obtained as based on the Ulivieri et al. (1994) algorithmat 06.51 local time on 6 July 2002 (K).

determine land-surface temperature accuracy. The minimum, maximum, and averagevalues of land-surface temperature obtained from control points were compared tometeorological values on a monthly basis (see Table 1). Although the averages of themeteorological 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 ofthe meteorological values was 285.78 K, and the nearest estimate was 288.94 K, whichwas from Ulivieri et al. (1994). The four algorithms gave results that were somewhatsimilar to the meteorological values in March, October, and December. The average ofthe meteorological values in April was 283.95 K, and the closest estimates belonged tothe 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 resultsin terms of the algorithm average values belonged to Ulivieri et al. (1994) and Beckerand Li (1990) with 283.34 and 283.63 K, respectively. In June and July, the Ulivieri

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Kayseri

Samsun

Ankara

Antalya

Denizli

Afyonkarahisarizmir

Bahkesir

Istanbul

Konya

Adana

Malatya

Sivas

RizeArtvin

Van

KarsErzurum

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 Ulivieriet 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 deviationratio 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 correlationcoefficients (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 andmeteorological data.

The other statistical result was the RMSE values of the algorithms. The RMSE valuesranged from 2.7 K, which was calculated using the Ulivieri et al. (1994) algorithm, to nearly4 K, which was calculated from the Price (1984) algorithm. The algorithm with the smallestRMSE value was that of Ulivieri et al. (1994); thus, this algorithm is suggested to estimatethe 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 werecalculated 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 observedthat VPD values were in the ranges 0–10 mb and 10–20 mb, which were considerablylow. 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 regionsof Turkey: Central Anatolia, South-Eastern, Mediterranean, and the coastal lines of the

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Table 1. Min, max, and average temperature values of meteorological and algorithm data (K).

Month

Minimum/maximum/average (K)

Meteorologicalvalue

Ulivieri et al.(1994)

Becker andLi (1990)

Vidal(1991) Price (1984)

Min 271.40 268.70 269.69 271.10 270.00January Max 289.40 291.19 291.39 293.21 293.00

Ave 281.05 281.05 281.05 281.05 281.05Min 277.90 282.40 283.49 284.49 284.00

February Max 291.60 293.32 293.79 295.65 296.00Ave 285.78 288.94 289.15 290.85 290.69Min 270.20 267.50 267.63 268.85 268.00

March Max 289.30 289.77 289.58 291.14 290.00Ave 283.78 283.46 283.37 284.82 283.85Min 276.20 277.07 277.05 278.43 277.00

April Max 287.00 290.11 290.53 293.28 292.00Ave 283.95 286.59 286.85 287.93 288.21Min 270.20 267.50 267.63 268.85 268.00

May Max 291.60 293.32 293.79 295.65 296.00Ave 282.30 283.34 283.63 285.07 284.48Min 290.40 293.61 295.00 295.00 296.00

June Max 302.60 305.00 305.19 307.00 306.00Ave 298.26 300.51 301.17 302.30 301.38Min 293.60 297.56 296.91 299.06 299.00

July Max 303.50 307.47 308.92 309.84 310.00Ave 298.04 300.73 301.62 302.48 303.01Min 294.30 296.96 296.67 297.82 298.00

August Max 315.00 315.35 311.89 316.56 315.00Ave 300.49 302.68 302.19 304.34 304.04Min 283.40 284.26 284.22 285.03 285.00

September Max 308.00 304.38 304.22 307.47 306.00Ave 293.19 293.26 293.83 295.08 295.09Min 280.50 277.99 277.83 279.31 279.00

October Max 292.00 292.26 291.41 292.37 293.00Ave 285.66 285.14 284.84 286.10 286.48Min 273.40 269.70 260.92 271.00 260.00

November Max 284.00 285.81 282.67 284.70 285.00Ave 276.57 274.35 272.37 275.24 273.10Min 270.40 269.00 269.86 271.54 271.00

December Max 282.00 280.00 280.41 281.94 282.00Ave 275.93 274.20 274.90 276.52 276.17

Aegean Sea. The VPD values in Central Anatolia and South-Eastern being between 30 and40 mb was attributed to the heating weather and, because of that heat, deficit humidity inthe atmosphere. Although enough water was present in the regions of the Mediterraneanand Aegean Sea coastal line, the cause of the VPD values being between 30 and 40 mbwas the heating weather in the early times of the day and, in spite of the humidity holdingcapacity 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 overTurkey, these rates were observed frequently over Iraq and Syria. In a similar way, VPDvalues were calculated over all 24 satellite images.

Then, the cities of Adana, Ankara, Afyonkarahisar, Artvin, Antalya, Balıkesir, Denizli,Erzurum, Eskisehir, Istanbul, Izmir, Kars, Kayseri, Konya, Malatya, Samsun, Sivas,

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320

(a) (b)

(c) (d)

320

310

310

300

300

290

290

Meteorologic temperature (K)

280

280270

270

Tem

pera

ture

(K

)r = 0.958


320

310

300

290

280

270320310300290280270

Tem

pera

ture

(K

)

r = 0.961


320

310

300

290

280

270320310300290280270

Tem

pera

ture

(K

)

r = 0.967


320

310

300

290

280

270320310300290280270

Tem

pera

ture

(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 followingwere found.

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100

90

90

80

80

70

70

60

60

50

50

40

40

30

30

Meteorologic VPD (mb)

r = 0.957Sa

telli

te V

PD (

mb)

20

20

10

100

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 averagebasis was 0.991, the RMSE value was 2.67 mb. When the meteorological and satellite VPDvalues 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 RMSEwas 5.665 mb (see Figure 8). According to statistical rules, the VPD RMSE value can bewritten as 6 mb instead of 5.665 mb.

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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, andValor (1997) attempted to obtain LST at reasonable accuracies (RMSE of 1–3 K) fromcurrent operational and research NOAA/AVHRR satellite-borne visible/infrared radiome-ters. In our study, the accuracies of split-window algorithms resulted in an average RMSEvalue of 3 K (range 2.733–3.731 K). The Ulivieri et al. (1994) algorithm was found to bevery successful compared to studies from the literature. Because of this result, the Ulivieriet al. (1994) algorithm was used to estimate the VPD formula. The VPD accuracy wasdetermined by the RMSE value and the correlation coefficient, which were calculated tobe 6 mb and 0.957, respectively. Furthermore, on a monthly average basis, while the VPDcorrelation 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 and10.9 mb.

As a result, we conclude that the VPD values obtained using satellite data can be usedin studies related to plants (germination, growth, and harvest), outbreak control of illness,drought determination, and ET over wide areas in which the meteorological station networkdensity is normally not sufficient.

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