Mapping regional air temperature fields using satellite-derived surface skin temperatures

INTERNATIONAL JOURNAL OF CLIMATOLOGY, VOL. 17, 1559±1579 (1997)

MAPPING REGIONAL AIR TEMPERATURE FIELDS USINGSATELLITE-DERIVED SURFACE SKIN TEMPERATURES

JUÈ RGEN V. VOGT1,* ALAIN A. VIAU2 AND FRANCE PAQUET2

1Space Applications Institute, Joint Research Centre, TP440, 21020 Ispra (Va), Italy,email: [email protected]

2Centre de Recherche en GeÂomatique, UniversiteÂ Laval, Sainte-Foy, QueÂbec G1K 7P4, Canada

Received 13 September 1996Revised 22 May 1997

Accepted 29 May 1997

ABSTRACT

Screen air temperature is an important climatological variable and accurate mapping of its spatial and temporal distribution isof great interest for various scienti®c disciplines. The low spatial density of meteorological stations, however, results inrelatively large errors during data interpolation and makes it dif®cult to retrieve the spatial pattern of the temperature ®eld.Errors of the order of 1 to 3 K are mentioned in the literature.

The current study investigates the possibilities of mapping and monitoring the spatial distribution of daily maximum airtemperatures with the help of time series of NOAA-AVHRR images. The study has been performed for the Mediterraneanregion of Andalusia in southern Spain.

Data analysis included 31 meteorological stations and 148 AVHRR images from the year 1992. Regression analysisbetween the daily maximum air temperature (Tmax) and the mean surface skin temperature (Ts) retrieved for 11 km2 imagewindows centred over each station, suggests that Tmax is strongly linked to Ts in the given environment (mean R2� 0�823) andthat for individual stations Tmax can be retrieved from Ts with a mean error of about 2 K. The spatial representativity of thestation measurements as well as the in¯uence of altitude and land use on the results are discussed. Finally, the possibilities ofretrieving the spatial pattern of Tmax have been evaluated through a cross-validation approach. In this analysis Tmax has beenpredicted for each station and for all days of available image data based on a regression model retrieved from all other stations.Again the results indicate that we are able to reproduce the daily distribution of maximum air temperatures with a mean errorof the order of 2 to 2�5 K, using satellite-retrieved surface skin temperatures. In addition, the method allows for the detectionof stations with a low spatial representativity or a pronounced measurement bias.

Future research will aim at the inclusion of further physiographic data, the grouping of stations according to site-speci®ccharacteristics and an analysis according to seasons. # 1997 Royal Meteorological Society Int. J. Climatol., 17, 1559±1579(1997)

(No. of Figures: 10. No. of Tables: 7. No. of References: 38)

KEY WORDS: Andalusia (Spain); remote sensing; AVHRR; regression; regionalization; maximum air temperature; surface skin temperature;land use; altitude; local climate.

1. INTRODUCTION

Air temperature is an important climatological variable and plays a major role in applied meteorology. It is

measured at a large number of stations, many of them having long-term records of daily measurements (Jones et

al., 1986a,b). Minimum, maximum and mean daily, monthly and annual air temperatures have been analysed

extensively in order to characterize climatic and agroclimatic conditions or for evidence of large-scale climatic

change (e.g. Jones et al., 1986a,b; Bayer et al., 1994; SoÈderstroÈm and Magnusson, 1995). More locally, the

occurrence of frosts has been widely examined due to its importance for the cultivation of many economically

important crops (e.g. Tabony, 1985). These local studies, however, usually rely on speci®c networks or mobile

measuring devices available only for limited periods of time.

CCC 0899-8418/97/141559-21 $17.50

# 1997 Royal Meteorological Society

*Corresponding author: J. Vogt, Space Applications Institute, Joint Research Centre, TP440, 21020 Ispra (Va), Italy.

Despite the great absolute number of stations measuring air temperature, their spatial density is highly variable

and their distribution is not optimal for regional to local applications in most cases. The knowledge of the spatial

pattern of the air temperature ®eld, however, is of great importance at these scales. Differences of the order of 1

to 2 K, for example, may be crucial for the cultivation of a certain crop. The retrieval of the spatial pattern of the

air temperature ®eld, therefore, is a complex and dif®cult task. Shortcomings associated with this task stem from

the inadequate density of the station network, the limited intrinsic precision of the measurement and the

complexity of the terrain. This is especially true when dealing with instantaneous measurements, as compared

with daily, decadal or monthly means, for example.

Remote sensing, in contrast, allows us to retrieve the distribution of the surface skin temperature with a high

spatial sampling rate, on a repetitive basis and over extended regions. In the case of early afternoon images, taken

under relatively cloud-free conditions, the radiative heating of the surface and the subsequent heat ¯ux towards

the lower atmosphere through conduction and convection result in a strong relation between surface skin

temperature and air temperature in the boundary layer (Oke, 1987). The basic hypothesis of our work, therefore,

is that the variability in land surface response imposes a major forcing to the atmosphere and that it has a

considerable in¯uence on air temperature variability through the change in land surface temperature. This effect

will be pronounced during clear days when land surface characteristics have a major in¯uence on the surface

energy balance. Under such conditions, it should be possible to map the spatial pattern of the air temperature ®eld

with a high accuracy, using remotely sensed surface skin temperatures.

The goal of this paper is to investigate the possibility of using satellite-derived surface skin temperatures as a

means to improve the regionalization of air temperature measurements on a regional scale. More speci®cally, we

are analysing the statistical relation between both types of measurements in order to evaluate the accuracy to

which the air temperature ®eld can be mapped, using surface skin temperature as the independent variable.

Data analysis is performed for daily maximum air temperatures (Tmax) from a network of 31 stations in the

region of Andalusia in southern Spain and surface skin temperatures (Ts) derived from 148 NOAA-AVHRR

images. The time period covered is the year 1992.

Andalusia extends from 36�N to 39�N and from 7�300W to 1�300W. With a territory of 87 268 km2, Andalusia

is one of the largest regions of Spain and an important producer of agricultural goods (De TeraÂn et al., 1988). Its

climate is of a mediterranean type with dry and hot summers and mild and humid winters. The topography varies

from the low-lying plains of the Guadalquivir basin to the peaks of the Sierra Nevada, which reach 3481 m

altitude. The diverse topography as well as the aspect to either the Mediterranean Sea or the Atlantic Ocean give

rise to rapidly changing climatological conditions. The accurate spatial representation of meteorological and

agro-meteorological parameters poses considerable problems under such conditions.

Since the economy of the region is highly dependent on its agricultural production and given the serious

in¯uence of extreme events, such as the persistent drought after 1991, the Andalusian authorities are highly

interested in the accurate characterization of crop growing conditions. A ®rst step in this endeavour is the

accurate mapping of the various meteorological parameters such as air temperature and rainfall on a daily basis.

Section two presents a short review of the problems related to the accurate retrieval of the spatio-temporal

variability of the air temperature ®eld. Section three follows with an account of the data and methods used in this

study. In section four, the results of the study are presented and section ®ve summarizes the main conclusions.

2. LITERATURE REVIEW

Biases in long-term records of screen air temperatures from selected stations have received considerable attention

in the literature (e.g. Carlson et al., 1994). Air temperature, however, is highly variable in space as a function of

environmental conditions such as altitude, topography, soil humidity and land use. This particularly concerns

instantaneous and extreme values such as daily minima and maxima. Despite these well known facts, problems

related to the estimation of the spatial variability within the air temperature ®eld as well as problems related to

spatial averaging from station observations, have received relatively little attention over the last two decades

(Willmott and Robeson, 1995).

Inaccuracies in the spatio-temporal representation of the temperature ®eld mainly relate to the measurement

bias, the method used for spatial interpolation, and the representativity of the station network.

1560 J. V. VOGT, A. A. VIAU AND F. PAQUET

INT. J. CLIMATOL, VOL. 17: 1559±1579 (1997) # 1997 Royal Meteorological Society

(i) Measurement bias. The accuracy of air temperature measurements ®rst of all depends on the sensor

characteristics, its calibration and exposure (Mitchell, 1953; Quayle et al., 1991; Gall et al., 1992). Whereas

measurement biases resulting from poor sensor performance are generally small due to systematic

calibration and maintenance of the sensors, temperature data more frequently contain biases related to

changes of instrumentation and inhomogeneities related to the time of observation or to changes in station

location (Carlson et al., 1994). Several studies (e.g. Mitchell, 1958; Baker, 1975; Schaal and Dale, 1977;

Karl et al., 1986) investigate the in¯uence of the time-of-observation on the calculation of mean daily air

temperatures and show that the bias can be substantial. This is very important because practically all long-

term studies are based on mean daily temperatures and/or further aggregations of these values. Another

important bias may result from changes in the environment surrounding weather stations, such as

urbanization. It is well known that these changes can produce signi®cant temperature anomalies (e.g.

Landsberg, 1981; Balling and Idso, 1989; Jones et al., 1989). However, even small changes in the local

environment may result in relevant modi®cations of the microclimate around a station and as such can

introduce a bias in the long-term measurements from the station.

(ii) Spatial interpolation. Air temperature measurements from meteorological stations are interpolated and

averaged over relatively large distances. This interpolation currently represents the only way to obtain air

temperature estimates at any point of interest. Using interpolation techniques implies spatial averaging and

therefore may generate substantial errors and biases (Willmott et al., 1991; Robeson and Willmott, 1993).

Several investigations have shown that the interpolation of screen air temperature may result in large errors

and that spatial averages obtained from different station networks are not consistently related to the

corresponding air temperature ®elds (Willmott and Robeson, 1995).

Reported interpolation errors generally range from about 1 to 3 K. Willmott and Robeson (1995) report on

interpolation errors between 1.3 and 1.9 K for mean annual temperatures on a global scale. The magnitude

of the error depended mainly on the density of the station network. With a mean expected error of 0.8 K,

they could achieve better results with a technique called climatologically aided interpolation (CAI). This

technique, however, requires the availability of long-term climatological means.

On a regional scale and for a study area in Japan, Ishida and Kawashima (1993) report errors between 1

and 3 K for the interpolation of hourly data from January and August. They cokriged temperature and

elevation. Hudson and Wackernagel (1994) used kriging with elevation as an external drift factor for

January mean temperatures in Scotland. They point to the superior results when using the kriging technique

as compared with more simple interpolation algorithms, although no absolute error evaluation is given. At

the same time they underline the need for adequate kriging neighbourhoods. This is not a trivial problem

given the generally low density of stations.

On a local scale, SoÈderstroÈm and Magnusson (1995) report interpolation errors between 0�4 K and 1�6 K,

using a kriging approach on an 8 km2 study area in Sweden. They analysed the cold-air distribution (frost

risk) in clear, calm nights using a kriging approach based on measurements taken along the road network

with a mobile measurement unit.

Although kriging seems to be a promising technique for an optimal interpolation, it must be noted that its

results will depend on the station density and distribution. SoÈderstroÈm and Magnusson (1995) as well as

Ishida and Kawashima (1993) discuss the problem of the estimation of the variogram from a limited number

of stations, which may be particularly pronounced in rural regions.

With the exception of Ishida and Kawashima (1993), all studies cited refer to daily, monthly or even yearly

mean temperatures. Signi®cantly larger errors should, therefore, be expected when analysing instantaneous

measurements.

(iii) Network representativity. The question of the network representativity is directly linked to the problem of

interpolation accuracy. The con®dence placed in a meteorological parameter interpolated from a station

network re¯ects the ability of the network to represent the meteorological conditions in the area of interest.

Issues such as optimum station density and spatial variability of the parameter under investigation are highly

important in this context (Hubbard, 1994). Because air temperatures are controlled by factors strongly

related to location and topography (Hudson and Wackernagel, 1994; SoÈderstroÈm and Magnusson, 1995),

appropriate site selection is crucial to the representativity of a network. This is especially true for

MAPPING REGIONAL AIR TEMPERATURE FIELDS 1561

# 1997 Royal Meteorological Society INT. J. CLIMATOL, VOL. 17: 1559±1579 (1997)

agricultural purposes, where the knowledge of local climatic differences within relatively small areas is

important.

Willmott et al. (1991) discuss the problem. They underline that the question whether the available station

networks can faithfully represent areas, and in particular large areas such as regions and continents, has been

incompletely examined. So far, most efforts have been concentrated on the question of how accurately

instrumental records represent the climate at the station location. An early discussion of the problem has

been given by Zemel and Lomas (1976) for the local scale of a valley of about 700 km2 in Israel. They

investigate the smallest number of base stations necessary for the representation of the minimum

temperature distribution during radiation cooling nights in the valley and discuss the problem of the area of

representativity of each station.

On a more regional scale, Hubbard (1994) investigated the maximum distance between stations

acceptable to represent climate variability in the High Plains of Colorado and Nebraska in the USA. He

analysed the fraction of the temporal variability of various parameters explained by neighbouring stations as

a function of distance. His results suggest a maximum station distance from 20 to 60 km to characterize the

seasonal patterns in spatial variability of surface meteorological variables. Whereas 60 km seem to be

suf®cient for mean temperature, the adequate spatial representation of minimum temperature, relative

humidity, solar radiation and evapotranspiration, requires a maximum station distance of 30 km. Finally,

station distances should not exceed 10 to 20 km for wind and soil temperature, and 20 km for precipitation.

It should be noted that these values are strongly related to the study area, and in this sense can be taken only

as a ®rst indication. Hubbard further attributes the apparent lack of studies of the spatial variability of

meteorological variables to the fact that current networks do not ful®ll these requirements and consequently

are not well suited for such analysis.

The foregoing discussion shows that remotely sensed surface parameters have a high potential for the

improvement of the spatial representation of the screen air temperature, because on clear sky days they are able to

provide a complete spatial sampling of extended areas. Satellite sensors, however, can only measure radiation

intensities and the retrieval of relevant surface parameters is not a trivial task. Recent improvements in the remote

retrieval of surface skin temperatures over land (see Prata, 1993 and Vogt, 1996 for reviews) and ocean (e.g.

Topliss, 1995; Viau and Thomson, 1997) are crucial in this endeavour. The development of relevant tools for the

analysis and interpretation of satellite-measured radiation intensities and surface parameters derived thereof, is a

dif®cult but most important task. The determination of the link between remotely sensed surface parameters and

in situ meteorological measurements, as well as their integrated analysis, will be an important asset for the

characterization of the spatial variability of surface meteorological parameters (Viau et al., 1994; Paquet and

Viau, 1995). The improved availability of long-term satellite archives and the development of adequate analysis

tools will, therefore, open new possibilities for the characterization of regional climate patterns.

3. DATA AND METHODS

For 1992, daily minimum and maximum temperatures were available for a total of 31 meteorological stations

in the study area (see Figure 1 and Table 1). Out of them, six are synoptic stations and 24 are stations of

three regional networks. These temperatures could be compared with data derived from 148 satellite images

from the afternoon overpasses of NOAA-11. In addition, a digital terrain model (DTM) of 300 m spatial

resolution (Figure 2) and a digital land-cover/land-use map at a nominal scale of 1:100 000 (Figure 3) have been

available. Major data processing steps are brie¯y outlined in the following and schematically presented in

Figure 4.

The meteorological data from the synoptic station network have undergone a quality control with a dedicated

in-house software which checks for spatial and temporal consistency of the measurements. The retained data can,

therefore, be used with a high con®dence. Only limited quality checking, however, was performed for the data

from the regional networks.

The image data have been acquired by the Advanced Very High Resolution Radiometer (AVHRR) aboard the

NOAA series of satellites. This radiometer allows for a daily global coverage. This global coverage, however, can



Table I. Station information. Synoptic stations are marked by an asterisk (see also Figure 1)

Number Station LocationLatitude(degrees)

Longitude(degrees) Altitude (m)

Distance tocoast (km)

1 4258 Aldea de Cuenca 38�325 ÿ5�563 571 1712 4556 Valdezufre 37�865 ÿ6�493 611 853 4575 Valverde del Cam. 37�575 ÿ6�749 323 474 4602 Presa de Sancho 37�458 ÿ6�976 61 285 4620 El Guijo 37�425 ÿ6�608 193 376 5796 Moron de la Fron. 37�156 ÿ5�616 88 767 5910 Rota 36�623 ÿ6�341 5 08 5973 Cadiz 36�496 ÿ6�260 4 09 6001 Tarifa 36�009 ÿ5�609 36 0

10 6006 Algeciras 36�125 ÿ5�448 100 011 6139 Alozaina 36�728 ÿ4�857 380 2412 6201 Algarrobo la May. 36�774 ÿ4�041 20 313 6268 Motril 36�724 ÿ3�530 3 014 6295 Enix 36�878 ÿ2�602 723 715 6308 Laujar Monterrey 37�026 ÿ2�899 1280 3016 6364 Albox 37�556 ÿ2�148 420 46

*17 8391 Sevilla/San Pablo 37�420 ÿ5�880 34 76*18 8410 Cordoba 37�850 ÿ4�830 92 135*19 8419 Granada 37�180 ÿ3�770 567 52*20 8451 Jerez de la Frontera 36�730 ÿ6�070 27 26*21 8482 Malaga 36�670 ÿ4�480 16 2*22 8487 Almeria 36�850 ÿ2�380 15 423 42671 Hinojosa del D. 38�506 ÿ5�110 540 20724 46052 Huelva 37�258 ÿ6�954 26 825 46421 Huelva 37�278 ÿ6�910 20 1226 60762 Marbella 36�487 ÿ4�957 6 027 60972 Archidona 37�094 ÿ4�388 700 4128 61502 Antequera El Torcal 36�953 ÿ4�544 1218 2829 62772 Adra 36�749 ÿ3�031 31 030 62922 La Mojonera 36�792 ÿ2�705 100 831 63282 Cabo de Gata 36�815 ÿ2�203 50 5

Figure 1. Geographical location of the meteorological stations



be achieved only through a wide view angle. In order to limit problems related to the particular viewing

geometry, data from viewing angles larger than 30� off nadir have not been used, thus limiting the maximum size

of the instantaneous ®eld of view to 1�6961�39 km (IFOV� 1�41 mrad, satellite altitude� 833 km). For a more

detailed discussion of the AVHRR and the problems related to the analysis of AVHRR data the reader is referred

to Vogt (1995).

The AVHRR data have been pre-processed by the SPACE software developed by the AIS unit of SAI

(Editorial note: see Appendix for acronyms). This program package allows for the automatic processing of

AVHRR scenes into a daily AVHRR mosaic for the European Community with a grid cell size of 1�1 by 1�1 km.

SPACE performs tasks such as data calibration and atmospheric and geometric correction of images in a highly

automatic way (Vowles, 1992; Millot, 1995). The ®nal geometric accuracy of the images is better than 1 km.

Image data are stored as re¯ectances for the visible and near-infrared channels and as brightness temperatures for

the thermal infrared channels. Some of the major characteristics of the resulting mosaics have been further

discussed by Vogt (1995).

From the data base of European AVHRR mosaics, a window covering the whole of Andalusia (36�00�N to

38�75�N, 1�50�W to 7�75�W) has been extracted for the year 1992 as the basis for this study. During extraction a

pre-selection has been performed, such that only images with a maximum percentage cloud cover of 50 per cent

have been retained. The monthly distribution of the 148 images retained is given in Figure 5. This ®gure shows

that the images are well distributed over all seasons. For December 1992, no data have been available. In 1992

the local solar time of the satellite measurements is between 1415 hours and 1445 hours, which is in good

temporal correspondence with maximum air temperatures. Figure 6 shows the case for the station of Almeria (No.

22 in Figure 1). It illustrates the periodical ¯uctuations in the time of measurement which are due to the viewing

geometry of the sensor. It further shows a general trend towards the later afternoon. This general delay of about

30 minutes per year is due to the orbital characteristics of the satellite.

Figure 2. Digital terrain model for Andalusia with a grid-cell size of 300 m(source: ConsejerõÂa de Medio Ambiente, Junta de Andalucia, Sevilla)



From these images the normalized difference vegetation index (NDVI) and the surface skin temperature have

been calculated. The NDVI has been calculated from the re¯ectances in the visible (r1) and the near-infrared (r2)

spectral bands (channels 1 and 2, respectively):

NDVI � r2 ÿ r1

r2 � r1

�1�

Surface skin temperatures (Ts) have been retrieved using the split-window algorithm of Coll et al. (1994):

Ts � T4 � A�T4 ÿ T5� � B �2�

Figure 4. Flowchart of data processing



where:

A� 1�0� 0�58 (T47 T5)

B� 0�51� 40(17 e)7 bDe

e� (e4� e5)/2, De� e47 e5

T4 and T5 refer to the brightness temperatures in AVHRR channels 4 and 5, respectively. e4 and e5 are the relevant

spectral emissivities for these channels.

In this formula, A is the function of the difference of the brightness temperatures in channel 4 and 5, and

depends on both the atmospheric water vapour content and the sensor view angle. As a consequence, A will

increase with an increasing water vapour content of the atmosphere and/or increasing view angle. B is a function

of the mean surface emissivity in the 10�3±11�3 mm and 11�5±12�5 mm wavebands (e), the difference in the

Figure 5. Monthly distribution of AVHRR images used in this study

Figure 6. Mean local solar time of the satellite measurements. Example of station 22 (Almeria).Data are limited to a maximum scan angle of 30�



surface emissivities of these wavebands (De), and a coef®cient b, which decreases with increasing atmospheric

water vapour content. Mean climatological values may be used for b, with b� 50 K for a tropical atmosphere,

b� 75 K for a mid-latitude summer atmosphere and b� 150 K for a mid-latitude winter atmosphere.

Due to the lack of adequate information on b, e and De, B has been parameterized using the NDVI and

empirical values retrieved during the EFEDA ®eld campaign in central Spain in 1991 (MelõÂa et al., 1991):

B � 0�51� C �3�

where:

C �0�00; for water surfaces

1�16; for fully vegetated areas �NDVI � 0�60�2�98; for bare soils �NDVI � 0�15�:

8><>:These data are taken as adequate for our study area due to its proximity to the EFEDA test site. Values of C have

been linearly interpolated for NDVI values between 0�15 and 0�60, assuming that the NDVI is linearly related to

the percentage vegetation cover within these limits. The NDVI thresholds used for full vegetation cover and bare

soil have been determined by visual inspection of the minimum and maximum NDVI values occurring in the

study area during 1992.

The resulting error on the surface skin temperature (Ts) estimate depends on the actual percentage vegetation

cover as well as on the soil/surface cover type, with less con®dence in the estimate for areas with a large amount

of bare soil exposed and a high con®dence in the estimate for areas with good vegetation cover. The precision of

the temperature estimate may, therefore, change with season (e.g. agricultural areas) and location (e.g. coast, high

altitudes). In general, this empirical correction is expected to yield an accuracy of the order of 2 K for the derived

surface skin temperatures. A comprehensive discussion of the various possibilities to derive Ts from AVHRR data

as well as of the problems linked to the split-window approach is given in Vogt (1996).

Surface skin temperatures have then been extracted for small windows of 363 pixels (ca. 3�363�3 km)

centred over the meteorological stations. For stations located directly at the coast, the windows have been shifted

slightly in order to avoid the inclusion of the sea surface. During data extraction all windows have been checked

for cloud contamination, the occurrence of water pixels, and the sensor view angle (maximum 30�). Any window

which failed one or more of the tests was rejected in order to ensure a data set as clean as possible. This selection

resulted in 36 to 84 measurements per station. The mean value of all pixels in the window was then used for the

comparison with the maximum air temperature of the same day.

Data analysis then included linear regressions between Ts and Tmax for each station as well as the analysis of

the residuals of these regressions.

Further, a cross-validation was performed by a stepwise prediction of Tmax from Ts for each station and for all

days with available images, using the regression equation retrieved from all other stations excluding the one to be

predicted. Again the residuals of this prediction have been analysed in detail.

Finally, statistics of the altitude and land cover classes occurring within each window have been analysed in

order to explain some of the observed variation in the regression analysis. A visit to several stations, including

a systematic survey of the station environment, resulted in further valuable information allowing for the

explanation of inconsistencies encountered during data analysis.

4. RESULTS

The spatial and temporal correlation between the maximum screen air temperature (Tmax) and the surface skin

temperature extracted from the NOAA-AVHRR images (Ts) has been analysed for all 31 stations, based on the

mean surface skin temperature extracted for each 363 pixel window (11 km3) centred over the station locations.

Correlations could be calculated for an average of 60 observations per station, the actual value per station

depending on the limitations stemming from the daily cloud cover and the sensor viewing geometry. The actual



number of observations varied between 36 (station 28) and 84 (station 22), which corresponds to 24 to 57 per cent

of the images and 10 to 23 per cent of the days of the year.

Table II shows the summary of the results of this analysis. It includes the coef®cients of determination (R2) of

the regressions for each station as well as the root-mean-square error (RMS) and the mean absolute error (MAE)

of the regression model. In addition, the number of observations (n) is shown.

The RMS is de®ned as the root of the mean squared difference between Tmax measured at the station (Tmaxi)

and Tmax predicted with the regression model (TÃmaxi) for each day i.

RMS ��

1

nÿ 2

Pni�1

Tmaxiÿ T̂maxi

� �2

s�4�

The MAE corresponds to the mean absolute difference between Tmax measured on each day i and Tmax predicted

with the regression model:

MAE � 1

nÿ 2

Pni�1

jTmaxiÿ T̂maxi

j �5�

MAE is used as an additional measure of the quality of the regression, because the RMS is very sensitive to

outliers. A detailed analysis of selected data sets has shown that despite the extensive quality checking during Ts

Table II. Results of the regression between Tmax and Ts for each station

Station number R2 RMC (�C) MAE (�C) n

1 0�926 2�45 1�89 682 0�847 2�51 1�98 573 0�908 2�38 1�88 464 0�728 3�89 3�17 485 0�921 2�09 1�65 586 0�927 1�99 1�53 707 0�840 2�03 1�63 598 0�732 2�26 1�87 569 0�788 1�56 1�33 65

10 0�808 2�24 1�88 6311 0�819 2�71 2�13 6612 0�800 2�21 1�74 8013 0�655 2�52 2�10 7714 0�453 4�24 3�61 4015 0�864 2�24 1�75 5916 0�879 1�95 1�53 5617 0�924 1�89 1�47 5418 0�929 2�05 1�63 5019 0�949 1�99 1�64 5620 0�889 2�19 1�66 5421 0�798 2�43 1�90 7522 0�799 2�50 2�02 8423 0�947 1�97 1�49 7224 0�790 2�42 1�89 5625 0�866 2�15 1�64 5826 0�779 1�77 1�49 6927 0�899 2�57 1�95 6828 0�666 2�97 2�48 3629 0�756 2�27 1�92 7330 0�862 2�29 1�92 8131 0�757 2�76 2�43 74

Average 0�823 2�37 1�91 62

RMS, root-mean-square error; MAE, mean absolute error.



extraction, extreme outliers are occasionally to be expected. They are due mainly to residual cloud contamination

in the image (e.g. due to subpixel size clouds). Secondly, these cases frequently are related to coding errors in the

meteorological data base. Both types of error are dif®cult to check in an automatic procedure. We therefore need

to use a robust estimator of the accuracy of the prediction. The MAE is an adequate measure in this case, because

it will signi®cantly reduce the in¯uence of few but extreme outliers (Press et al., 1992).

The observed coef®cients of determination (R2) as given in Table II range from 0�453 to 0�949, with an average

value of 0�823. In more than 80 per cent of the cases, the images, therefore, allow for a good reproduction of the

maximum air temperature as measured at the station. Considering the fact that the AVHRR window represents a

surface of almost 11 km2, this result indicates that in most cases the stations are representative of the

environmental conditions in their immediate surroundings.

At the same time, we are able to identify stations having a weak spatial representativity concerning daily

maximum air temperature. Relatively low coef®cients of determination (< 0�8) generally pertain to stations

located directly at or near to the coast (e.g. stations 13 and 22) as well as to stations in mountainous areas (e.g.

stations 14 and 28). There is, however, no general rule because several mountainous and coastal stations are

characterized by values of R2 close to or above the average (e.g. stations 7, 15, and 19).

Various environmental characteristics are to be considered during data interpretation because they may have a

signi®cant in¯uence on the relationship. For the coastal stations, sea breezes prevailing during the early afternoon

may reduce the quality of the relationship through a strong advective component. In addition, the 363 pixel

window for these stations is not centred over the station, but shifted slightly inland in order to avoid the inclusion

of sea surface measurements. A reduced relationship between the two measurements should, therefore, be

expected. In mountainous terrain, a rapidly changing topography will in¯uence the relationship through changes

in altitude, slope and aspect. In addition, variations in land use and land cover will translate into varying energy

exchanges between the surface and the atmosphere. Finally, it is important to recognize that we compare a point

measurement of screen air temperature with an integrated measurement of a surface skin temperature over an area

of about 11 km2. The in¯uence of the various factors mentioned will be discussed in more detail below using

exemplary cases.

In addition to the coef®cient of determination, the expected error is an important measure of the strength of the

relationship between both variables. In our interpretation, we refer to the MAE. The RMS is shown in all tables

for consistency with error evaluations as given in many other publications.

The relationship between the coef®cient of determination and the MAE for all stations is shown in Figure 7. It

demonstrates that with an R2 above 0�75, the MAE falls within the range of 1�5 to 2�0 K. In the ®gure,

problematic cases with very low R2 or high values of MAE are marked by the station number as given in Table II

and discussed in the following.

Figure 7. Relationship between the coef®cient of determination (R2) and the mean absolute error (MAE) for all stations



A closer look at these cases may help in better understanding the factors in¯uencing the relationship between

Ts and Tmax. Station 14, for example, is characterized by an R2 of only 0�453 and an MAE of 3�61. A result

signi®cantly below the general mean. This station, however, is located at an altitude of 723 m at a distance of

only 7 km from the coast. Within the window, the altitude varies between 608 m and 1027 m, with a mean value

of 892 m and a standard deviation of 110 m. This indicates that the topography around the station is highly

variable and that the altitude of the station is signi®cantly different from the major part of the window. A closer

analysis of the DTM further showed that the station is located on a slope facing south towards the sea, whereas

the image window includes the ridge to the north of the station and part of the north-facing slope of the adjacent

valley of the Andarax river. The surface skin temperature measured over this window will not allow a

reproduction of the maximum air temperature as measured at this station. Nevertheless, it is most probably the

better source of data for mapping the temperature variability in the surroundings of the station.

That the altitude, however, is not necessarily the most important factor, is demonstrated by the case of station

15, located at an altitude of 1280 m at 30 km distance from the coast. With an R2 of 0�864 and an MAE of 1�75,

this station represents a case slightly better than the average. The altitude in the corresponding window varies

from 1000 to 1812 m, with a mean value of 1388 m and a standard deviation of 160 m. Although the altitude

range within this window is about 800 m, the station altitude is well within one standard deviation from the mean

altitude and probably more representative for the window than in the case of station 14.

Other variables which may signi®cantly in¯uence the relationship between Ts and Tmax are the distance to the

coast and the land cover type. It is obvious that station 15 is less in¯uenced by advective sea breezes as compared

with station 14. Table III gives the percentage surface cover for the main land cover classes to be found within the

3�363�3 km windows centred over each station. Whereas the window around station 15 is characterized by 69

per cent forest/woodland and 24 per cent sclerophyllous vegetation, the window around station 14 is

characterized by 50 per cent sclerophyllous vegetation and 42 per cent permanent crops. The large percentage of

forest in the surroundings of station 15 may especially contribute to the stable relationship.

This hypothesis is supported by the case of station 28, with environmental conditions similar to station 15

(station altitude 1218 m, 28 km distance to the coast, altitude variation 580 m). The land cover in the window

surrounding this station is, however, characterized by 67 per cent open and bare land. The exposure of large

amounts of bare soil and rock will cause a highly varying surface temperature related to the varying slope and

exposition within the window and may, therefore, lead to the reduced R2 (0�666) and increased MAE (2�48). This

hypothesis was con®rmed by a ®eld check of the station and its surroundings. It is located on an isolated karstic

plateau with more than 80 per cent of the surface covered by bare rock and a very speci®c climate. Part of the

image window includes the surrounding valleys, with distinctively different land cover types and altitudes.

Similar arguments (related to the land cover type) hold for the case of station 31 (station altitude 50 m, 5 km

distance to the coast, altitude variation 65 m), which is located in the driest zone of Andalusia and characterized

by 74 per cent open and bare land and 10 per cent of permanently irrigated agriculture.

A further case with a large MAE is station 4 (station altitude 61 m, distance to the coast 28 km, altitude

variation 70 m). In this case, the poor relationship is probably due to the fact that the window includes 11 per cent

water surfaces. Due to the different spatio-temporal behaviour of the water skin temperature and its in¯uence on

the energy exchange between the atmosphere and the surface, the relationship between Tmax and Ts within this

window will be more complex than can be expressed by a simple linear regression. The result is a large MAE

(3�17) and a relatively low R2 (0�728). Station 24 seems to contradict these assumptions. With 50 per cent built-up

areas and about 50 per cent wetland and water surfaces, this station is characterized by a very particular setting

and will require some further veri®cation.

Table IV gives a summary of the regression result for three groups of stations: (i) stations within 10 km from

the coast, (ii) stations in mountainous terrain (above 500 m altitude) and (iii) all other stations (within 0 and

500 m altitude and at least 10 km distant from the coast). The six most problematic cases as discussed above (4,

14, and 28) and later in the text (2, 9, and 16), have been excluded for the calculation of this table in order to

highlight the differences within the main body of stations. Table IV shows that the relationship is weakest for the

coastal stations with the highest expected error. The mountainous and other stations have about the same and very

high R2 and low expected error. This suggests that a speci®c treatment may only be necessary for the coastal



stations. All other stations can be treated as one group. It should, however, be noted that the stations above 500 m

are few and that altitudes above 1300 m are not represented.

Despite the differences mentioned, the results show a generally good relation for all three groups of stations,

with a mean expected error (MAE) of less than 2 K. This demonstrates that in Mediterranean environments and

Table III. Percentage surface for the main land cover classes to be found within the 3�363�3 km windows centred over eachstation.

Land cover classesa (percentage cover)Stationnumber 1 2 3 4 5 6 7 8 9 10 11 12 13 14 N/D

1 1 71 7 202 22 13 8 52 53 13 51 7 3 23 14 8 37 7 37 115 33 7 10 7 39 56 23 777 67 20 1 128 26 4 4 41 2 239 5 83 12

10 33 5 60 211 2 16 4 30 4712 1 41 6 5213 25 70 3 314 1 42 50 715 3 4 69 24 116 42 22 12 2417 19 21 6018 14 81 419 19 34 4720 7 4 69 2021 38 51 11 122 84 1623 99 124 50 20 2925 20 61 9 1026 43 10 15 30 1 127 6 3 8 59 3 2128 4 14 14 6729 2 3 30 2 6430 54 1 4531 2 14 10 74

a1, Built-up areas; 2, non-irrigated agriculture; 3, permanently irrigated agriculture; 4, rice; 5, permanent crops; 6, pastures; 7, heterogeneousagricultural land; 8, forest and other woodlands; 9, natural grasslands; 10, moors and heathland; 11, sclerophyllous vegetation; 12, open/bareland; 13, wetlands; 14, water; N/D not determined.

Table IV. Average regression results for three groups of stations (excluding all problematic stations)

Average class valuesNumber of Stations

Station class stations R2 RMS MAE excluded

coast(<10 km)

13 0�792 2�32 1�90 9, 14

mountain(>500 m)

4 0�915 2�19 1�71 2, 14, 28

others 8 0�898 2�18 1�70 4, 16



for relatively clear-sky conditions, surface skin temperatures as derived from NOAA-AVHRR images can be

used to characterize the surface conditions determining the maximum screen air temperature in the surroundings

of meteorological stations. The strong relationship between image and station data further suggests that the

images can provide valuable support for the regionalization of the screen air temperature and for investigations

concerning its spatio-temporal variability.

Based on these results, a regression model has been used to predict Tmax from Ts as measured by the satellite.

The accuracy of this prediction has been evaluated by a cross-validation procedure. One by one (with

replacement) each station has been removed from the station network. Tmax as measured at the station removed

has then been estimated through a regression model derived from Ts and Tmax at all other stations. Tmax has been

predicted for each station and for all days with available image data. Predicted maximum screen air temperatures

were then compared with the measured ones. The results of this procedure are given in Table 5. At this ®rst stage

of the analysis no stations have been excluded from the analysis in order to highlight problematic cases.

The results show an average coef®cient of determination of about 0�80 for the regression including 30 stations.

The MAE for the prediction of Tmax at the individual stations ranges from 1�56 to 5�44 K, with an average value

of 2�53. This error is comparable to the errors reported for various interpolation techniques (e.g. Ishida and

Kawashima, 1993; Willmott and Robeson, 1995). Taking into account that the stations have not been subdivided

into classes according to site characteristics (e.g. distance to the coast) and that problematic stations have not

been excluded, these results are encouraging.

In general, the expected error (RMS, MAE) is somewhat larger than for the regression of the individual

stations. The problematic cases, as described above, are again characterized by comparatively large errors, as was

to be expected (station 4, 3�17 MAE; station 14, 3�82 MAE; station 28, 5�44 MAE). Station 28 is characterized by

a particularly large increase in MAE, which underlines its speci®c climatological conditions. Three additional

stations are, however, characterized by relatively large errors (stations 2, 9 and 16 with MAEs of 4�50, 3�46 and

4�19, respectively). All of these stations show a signi®cant difference between the MAE of the station regression

(Table II) and the MAE of the prediction (Table V). Whereas for stations 4, 14 and 28, the site-speci®c

characteristics already discussed are the probable cause of the large errors, stations 2, 9 and 16 have shown good

to very good individual regression results. Apparently, their Ts\Tmax relationship is signi®cantly different from the

relationship revealed by all other stations.

Figure 8 gives a graphical demonstration of the different cases encountered during this step of the analysis. It

summarizes the results for four stations (2, 6, 9 and 14), each representing a different type of situation. The left

column of the ®gure shows the results of the individual regressions for each of the stations. The right column

gives the residuals of this regression (black) as well as the residuals of the prediction (light grey).

Station 6 (Moron de la Frontera) represents the large group of stations having good regression as well as good

prediction results. It is important to note that the distributions of the regression and prediction residuals are almost

identical, which implies that the station shows the mean Ts\Tmax relationship as given by our set of stations.

Station 14 (Enix), in contrast, represents the very problematic cases, where individual site characteristics do

not permit retrieval of Tmax from Ts in the 3 km environment of the station. Tests with a smaller window

(1�1 km61�1 km) centred over this station as well as with a 3�3 km63�3 km window shifted towards the south

(excluding the north-facing slopes of the Andarax valley) showed no signi®cant improvement of the results

(11 km: R2� 0�466, MAE� 3�67; 3�3 km shifted: R2� 0�454, MAE� 3�79). This leads to the conclusion that

this station is not representative of the Tmax conditions in its immediate surroundings. At the same time prediction

errors are large and follow the same distribution as the regression errors, which implies that similar arguments

hold for the case of the prediction. The station has consequently been excluded from further analysis.

Station 9 (Tarifa) represents a case with very good regression results. The prediction, however, failed. The

distribution of the prediction errors may help to explain the causes. Station 9 shows a signi®cant overestimation

of Tmax, especially for surface temperatures above 30�C. The explanation is to be found in the geographical

setting of the station, as identi®ed during a ®eld mission in December 1996. Station 9 is situated at an altitude of

36 m on top of a small tower located directly at the coast. Located at the southern edge of Andalusia, looking

over the Strait of Gibraltar, it is exposed to almost permanently blowing winds and represents extreme maritime

conditions. The station represents much more the weather conditions over the Strait of Gibraltar than in the

coastal zone of Andalusia. It has, therefore, been excluded from further analysis.



Station 2 (Valdezufre) represents a case where the regression gave acceptable results, whereas the prediction

resulted in a signi®cant and constant underestimation of the station temperature (bias). A visit of the station

allowed for an explanation. The station is located in the centre of a village in a narrow courtyard (about

6 m66 m) surrounded by two-story buildings, with the screen located under a large orange tree. The

measurements of this station are strongly biased and cannot represent the conditions of the oak tree woodlands

surrounding the village. The station measurements give systematically higher temperatures than to be expected in

the village surroundings. The station was consequently excluded from further analysis.

Station 16 (Albox) could not be visited. A careful check of the altitude values as retrieved within the image

window and their comparison with the altitude given for the station as well as a comparison between the

geographic co-ordinates given for the station and the location of the village of Albox, revealed a probable error in

the geographic co-ordinates as given in the data base. Tests with a window centred over the village of Albox gave

signi®cantly better results (MAE of 2�58 for the prediction). Because the exact location has, however, not yet

been con®rmed, the station has been removed from the analysis for the time being.

The relatively detailed discussion of the various analysis steps should highlight the possibilities for cross-

checking the representativity of individual stations within a set of stations of a given region. It should also

underline the need for a cross-validation in order to identify problematic stations that are not identi®ed by the

regression approach alone.

Table V. Results of the cross-validation with all stations

Regression PredictionStationexcluded R2 n RMS (�C) MAE (�C) n

1 0�791 1860 2�83 2�23 682 0�808 1871 5�07 4�50 573 0�795 1882 2�73 2�23 464 0�798 1880 3�96 3�17 485 0�793 1870 2�15 1�75 586 0�792 1858 2�02 1�56 707 0�798 1869 2�05 1�61 598 0�802 1872 3�13 2�46 569 0�805 1863 4�03 3�46 65

10 0�799 1865 2�40 1�91 6311 0�798 1862 3�00 2�46 6612 0�802 1848 3�01 2�60 8013 0�802 1851 2�85 2�35 7714 0�805 1888 4�73 3�82 4015 0�795 1869 2�28 1�86 5916 0�805 1872 4�56 4�19 5617 0�795 1874 2�52 2�03 5418 0�795 1878 3�07 2�54 5019 0�794 1872 2�95 2�52 5620 0�798 1874 3�14 2�48 5421 0�799 1853 2�50 1�91 7522 0�800 1844 2�82 2�27 8423 0�789 1856 2�08 1�60 7224 0�799 1872 2�50 1�96 5625 0�798 1870 2�63 1�97 5826 0�801 1859 2�63 2�13 6927 0�795 1860 2�90 2�35 6828 0�808 1892 5�82 5�44 3629 0�800 1855 2�68 2�30 7330 0�796 1847 2�39 2�05 8131 0�803 1854 3�34 2�81 74

Average 0�799 1866 3�06 2�53 62

RMS, root-mean-square error; MAE, mean absolute error.



Figure 8. Results of the regression per station (left) and the residuals of the regression and the prediction (right) for four selected stations



As a ®nal step, the prediction has been redone with the 25 remaining stations, excluding the stations with

identi®ed problems. The results of this procedure are shown in Table VI and Table VII.

With an average R2 of 0�836 the regression for 24 stations is signi®cantly better than for 30 stations. At the

same time, the error of the prediction has been reduced by 0�38�C and 0�36�C respectively for the RMS and the

MAE 9 (Tables V and VI). The values for the MAE range from 1�52�C to 2�73�C, with most values well below

2�5�C. This result is very encouraging, especially when considering that the stations have not been subdivided

according to their geographic location and that they represent a wide range of altitudes and landscape types. Still

we should note that high altitudes are not well presented and that above 1280 m no meteorological station is

available. This problem, however, pertains to any interpolation of the meteorological measurements.

In order to check whether the stations should be subdivided into distinct groups based on, for example, their

distance from the coast or their altitude, the difference between the MAE of the station regression (Table II) and

the MAE of the prediction (Table VI) has been analysed. The larger this difference, the more the Ts\Tmax

relationship for a given station deviates from the mean Ts\Tmax relationship found for the full set of 25 stations. If,

for example, high-altitude stations or stations close to the coast should behave distinctly different from the main

body of stations, their MAE differences should be relatively high. Figure 9 shows a plot of the MAE difference as

a function of the distance of the station from the coast. It demonstrates that the magnitude of the difference is not

related to the distance of the station from the coast. The same applies to the difference with regard to the altitude

(not shown). This result suggests that a reasonable accuracy can be expected, even without working on subgroups

of stations based on these two criteria. At the same time, Figure 9 demonstrates that the largest differences pertain

to stations located at the airports of the major cities (e.g. Jerez, Granada, Sevilla, Cordoba). A detailed analysis of

the error distribution shows that Tmax as measured at these stations is systematically underpredicted. This is most

Table VI. Results of the cross-validation with 25 stations (without stations 2, 4, 9,14, 16 and 28)

Regression PredictionStationexcluded R2 n RMS (�C) MAE (�C) n

1 0�831 1560 2�87 2�30 683 0�834 1582 2�86 2�37 465 0�831 1570 2�12 1�69 586 0�830 1558 2�00 1�52 707 0�836 1569 2�08 1�61 598 0�840 1572 3�00 2�38 56

10 0�838 1565 2�49 1�97 6311 0�838 1562 3�11 2�56 6612 0�841 1548 2�88 2�49 8013 0�844 1549 2�83 2�33 7715 0�834 1569 2�32 1�91 5917 0�834 1574 2�69 2�21 5418 0�835 1578 3�25 2�72 5019 0�833 1572 3�06 2�60 5620 0�838 1574 3�31 2�64 5421 0�838 1553 2�56 1�94 7522 0�839 1544 2�74 2�22 8423 0�828 1556 2�07 1�58 7224 0�840 1570 2�46 1�92 5625 0�837 1567 2�74 2�07 5826 0�840 1561 2�51 2�01 6927 0�834 1560 2�88 2�31 6829 0�838 1558 2�61 2�26 7330 0�835 1547 2�34 2�00 8131 0�842 1554 3�25 2�73 74

Average 0�836 1563 2�68 2�17 65



Table VII. Regression equations and correspondingR2 as used in the cross-validation analysis with 25stations (without stations 2, 4, 9, 14, 16 and 28)

Stationexcluded R2 Regression equation

1 0�831 y� 0�560 x� 5�0303 0�834 y� 0�564 x� 4�8615 0�831 y� 0�565 x� 4�8446 0�830 y� 0�567 x� 4�7747 0�836 y� 0�566 x� 4�7978 0�840 y� 0�569 x� 4�764

10 0�838 y� 0�567 x� 4�72611 0�838 y� 0�566 x� 4�76412 0�841 y� 0�571 x� 4�71313 0�844 y� 0�572 x� 4�66315 0�834 y� 0�566 x� 4�80317 0�834 y� 0�563 x� 4�85018 0�835 y� 0�561 x� 4�92119 0�833 y� 0�560 x� 4�98820 0�838 y� 0�562 x� 4�87621 0�838 y� 0�568 x� 4�72022 0�839 y� 0�570 x� 4�71123 0�828 y� 0�563 x� 4�92724 0�840 y� 0�569 x� 4�76725 0�837 y� 0�567 x� 4�78226 0�840 y� 0�569 x� 4�76127 0�834 y� 0�561 x� 5�00629 0�838 y� 0�570 x� 4�69630 0�835 y� 0�565 x� 4�87131 0�842 y� 0�574 x� 4�684

Average 0�836 y� 0�566 x� 4�808

Figure 9. Difference between the MAE obtained from the station regression (Table II) and the MAE obtained from the prediction (Table VI)for the 25 stations retained in 1992



probably related to the locally elevated temperature in the vicinity of the airports with their large asphalted

surfaces.

Figure 10 gives an example of Tmax retrieved from AVHRR data for the 25 October 1992, using the average

regression for 24 stations as given in Table VII. It clearly demonstrates the possibilities to map Tmax with a high

spatial resolution and to infer spatial patterns of the temperature ®eld with great detail. The latter is especially

striking for the relatively ¯at terrain of the Guadalquivir basin, where standard interpolation techniques will result

in a comparatively homogeneous temperature ®eld.

5. SUMMARY AND CONCLUSIONS

The results of this study demonstrate that in Mediterranean environments, the daily maximum air temperature

®eld can be retrieved with an accuracy of about 2�0 to 2�5 K, using AVHRR derived surface skin temperatures.

These results are well within the limits of accuracy as discussed in the literature regarding air temperature

interpolation on regional scales (e.g. Ishida and Kawashima, 1993; Willmott and Robeson, 1995).

In order to improve these results, other environmental factors will have to be considered. A detailed analysis of

the station surroundings (e.g. land cover, land use, topography) might reveal such distinctive in¯uences. This may

help to subdivide stations according to a set of site-speci®c characteristics or to improve the prediction through a

multiple regression approach including other environmental variables.

Further investigations should also consider a possible seasonal variability of the Ts\Tmax relation. Such an

approach needs, however, to be based on a multi-year data set with a large number of sample pairs covering

interannual variability. We are currently preparing such an analysis.

The reader should, however, be aware that the consideration of further parameters as well as the subdivision of

the stations according to site-speci®c characteristics or an analysis on a seasonal basis will signi®cantly

complicate the approach and the generation of results.

Improvements can further be expected from a better Ts estimation, which may result from a more sophisticated

emissivity parameterization or a regionally adapted split-window algorithm. The problem of comparing a point

measurement of a screen air temperature with an integrated measurement of a surface skin temperature will,

however, remain. Ts cannot explain all variation of Tmax. Still our research has shown that under relatively clear-

sky conditions and in the given Mediterranean environment, Ts apparently integrates most of the environmental

forcing on Tmax and that this information can be used for an improved regionalization of the air temperature

measurements as well as for an analysis of the spatial representativity of individual stations with regard to the air

temperature ®eld in their surroundings. Combined with the analysis of the spatial patterns of the retrieved air

temperature ®eld, this information will allow us to suggest an optimal station distribution within our study area.

Given the high spatial resolution of the AVHRR measurements, the spatial pattern of the air temperature ®eld

can be retrieved with a high precision from these data. The reader should, however, keep in mind that with the

AVHRR this is possible only under relatively clear-sky conditions and for speci®c hours during the day. Other

satellite-speci®c in¯uences, such as the local solar time of the measurements, will further have to be considered.

The launch of future satellites, such as the second generation of GOES and Meteosat satellites will certainly

improve on these drawbacks. Considering the amount of data to be retrieved by these future systems, the

development of advanced analysis tools will be a major contribution to their adequate use.

ACKNOWLEDGEMENTS

We kindly acknowledge the help of our colleagues from the Environmental Agency of Andalusia (Consejeria de

Medio Ambiente, Sevilla), especially Dr J. M. Moreira MaduenÄo and his collaborators, as well as from Professor

M. F. Pita LoÂpez and Monica Aguilar (Departamento de GeografõÂa Fisica, Universidad de Sevilla). They

provided most of the meteorological and environmental data and contributed to the study through many useful

discussions. France Paquet has been supported by a grant from the `Fonds pour la formation de chercheurs et

l'aide aÁ la recherche (FCAR) du QueÂbec'. Thanks are further due to Michel Verstraete and an anonymous

reviewer for their constructive criticism on earlier versions of the manuscript.



ACRONYMS

AIS Agriculture Information Systems unit (SAI)

AVHRR Advanced Very High Resolution Radiometer

DTM Digital terrain model

ECHIVAL European International Project on Climatic and Hydrological Interactions between the Vegetation,

the Atmosphere and the Land Surface

EFEDA ECHIVAL Field Experiment in a Deserti®cation-threatened area

HRPT High-resolution picture transmission

IFOV Instantaneous ®eld of view

JRC Joint Research Centre of the European Union

MAE Mean absolute error

NDVI Normalized difference vegetation index

NOAA US National Oceanographic and Atmospheric Administration

RMS Root-mean-square error

SAI JRC ± Space Applications Institute

SPACE Software for the Processing of AVHRR images for the Communities of Europe

Tmax Maximum screen air temperature measured at the meteorological station

Ts Surface skin temperature retrieved from NOAA-AVHRR images

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Mapping regional air temperature fields using satellite-derived surface skin temperatures