Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation

Remote Sensing of Environment 113 (2009) 2592–2605

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Remote Sensing of Environment j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / r s e

Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation Marina Stathopoulou ⁎, Constantinos Cartalis Department of Physics, Division of Environmental Physics and Meteorology, University of Athens, University Campus, Building PHYS-V, GR 157 84, Athens, Greece

a r t i c l e

i n f o

Article history: Received 19 March 2009 Received in revised form 23 June 2009 Accepted 20 July 2009 Keywords: AVHRR Downscaling LST Urban area SUHI intensity

a b s t r a c t Surface urban heat island (SUHI) is a phenomenon of both high spatial and temporal variability. In this context, studying and monitoring the SUHIs of urban areas through the satellite remote sensing technology, requires land surface temperature (LST) image data from satellite-borne thermal sensors of high spatial resolution as well as temporal resolution. However, due to technical constrains, satellite-borne thermal sensors yield a trade-off between their spatial and temporal resolution; a high spatial resolution is associated with a low temporal resolution and vice versa. To resolve this drawback, we applied in this study four downscaling techniques using different scaling factors to downscale 1-km LST image data provided by the Advanced Very High Resolution Radiometer (AVHRR) sensor, given that AVHRR can offer the highest temporal resolution currently available. The city of Athens in Greece was used as the application site. Downscaled 120-m AVHRR LSTs simulated by the downscaling techniques, were then used for SUHI intensity estimation based on LST differences observed between the main urban land covers of Athens and the city's rural background. For the needs of the study, land cover information for Athens was obtained from the Corine Land Cover (CLC) 2000 database for Greece. Validation of the downscaled 120-m AVHRR LSTs as well of the retrieved SUHI intensities was performed by comparative analysis with time-coincident observations of 120m LST and SUHI intensities generated from the band 6 of the Thermal Mapper (TM) sensor onboard the Landsat 5 platform. The spatial pattern of the downscaled AVHRR LST was found to be visually improved when compared to that of the original AVHRR LST and to resemble more that of TM6 LST. Statistical results indicated that, when compared to 120-m TM6 LST, the root mean square error (RMSE) in 120-m AVHRR LST generated by the downscaling techniques ranged from 4.9 to 5.3 °C. However, the accuracy in SUHI intensity was found to have significantly improved, with a RMSE value decreasing from 2.4 °C when the original AVHRR LST was utilized, down to 0.94 °C in case that downscaling was applied. © 2009 Elsevier Inc. All rights reserved.

1. Introduction When referring to land observation from space, LST is the physical parameter of prime importance to be estimated, as it is required for a wide variety of scientific studies. In urban climatology, LST is the key input parameter used in algorithms to make estimates of net radiation, sensible and latent heat flux, thermal inertia, SUHI intensity, precipitable water, evapotranspiration, urban-induced surface runoff and surface moisture. Related studies are reviewed in Voogt and Oke (2003), Arnfield (2003), Gamba et al. (2005) and Stathopoulou and Cartalis (2007a). Satellite observations of LST have also been used successfully for urban landscape management (Quattrochi et al., 2000), urban environmental quality monitoring (Nichol & Wong, 2005) and urban risk analysis (Dousset et al., 2007). To gain knowledge about LST over urban areas from space, satellitebased sensors operating in the Thermal InfraRed (TIR) spectral region

⁎ Corresponding author. Tel.: +30 210 7276843; fax: +30 210 7276774. E-mail address: [email protected] (M. Stathopoulou). 0034-4257/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2009.07.017

are used as they provide LST image data at various spatial and temporal scales. However, due to technical constrains, satellite thermal sensors are unable to supply both spatially and temporally dense LST image data. The reason for this is that the spatial and temporal resolutions of a satellite thermal sensor are anti-correlated, meaning that a high spatial resolution is related with a low temporal resolution and vise versa. Thus, while some of the current satellite-borne thermal sensors (Table 1), such as the Landsat 7 ETM+, the TERRA ASTER and the Landsat 5 TM, can provide LST at a high spatial resolution (≤120 m), their utilization in urban climate studies is restricted because of limited available nighttime image data and low temporal resolution. On the other hand, among the most commonly used operational satellite thermal sensors with a low spatial resolution (≥1 km), such as the AVHRR, the MODIS, the AATSR and the ATSR, AVHRR is the only one that offers the highest temporal resolution, providing temporally dense LST image data of an observed area twice daily. In fact, this revisit time can be further increased up to 4 times per day by acquiring AVHRR images from the pair of NOAA satellites that orbit the Earth. Due to resolution trade-off, the spatially dense LST observations needed for many urban applications can only be provided at large time

M. Stathopoulou, C. Cartalis / Remote Sensing of Environment 113 (2009) 2592–2605 Table 1 Technical characteristics of operational satellite thermal sensors. Sensor – platform

Spatial resolution

Spectral resolution (µm)

Temporal resolution

AVHRR – NOAA TM – Landsat 5 ETM+ – Landsat 7 ASTER – Terra AATSAR – Envisat ATSAR – ERS MODIS – Terra

1100 120 60 90 1000 1000 1000

band4: 10.3–11.3, band5: 11.5–12.5 band6: 10.4–12.5 band6: 10.4–12.5 band10 to band14: 8.125–11.65 11 µm band, 12 µm band 11 µm band, 12 µm band band31 to band36: 10.78–14.39

twice daily 16 days 16 days 16 days 35 days 35 days 1 to 2 days

m m m m m m m

intervals, most commonly once per month at the satellite over-pass time. This is critical if satellite LST image data are used for monitoring the state of the thermal environment of urban areas on a daily basis or for supporting urban environmental problems that are characterized by a high spatial variability and a high temporal variation such as the SUHI phenomenon, namely the relative warmth of urban surfaces compared to non-urbanized surfaces of their surrounding countryside (Voogt and Oke, 2003). SUHI phenomenon may be developed during both daytime and night-time hours. The replacement of natural surfaces by those characteristic of a city, radically alters the aerodynamic, radiative, thermal and moisture properties of an urban area (Oke & Maxwell, 1975). In cities, most of the materials used in construction (such as concrete and asphalt) are characterized by a high heat capacity, high heat conductivity, low albedo, low emissivity and low permeability. On the other hand, rural surfaces present totally different properties depending on the types of land they are composed. For instance, rural surfaces mostly occupied by dry bare soil are characterized by a low thermal inertia, meaning that they exhibit large changes in diurnal surface temperature. SUHIs are mainly developed due to the different daytime warming and night-time cooling between the urban and surrounding rural surfaces. To measure the strength of the SUHI phenomenon, we use the SUHI intensity (ΔΤ), generally defined as the difference between the maximum urban surface temperature and the background rural one (Oke, 1982). Under calm and clear weather conditions, daytime ΔT usually acquires its largest values as solar radiation affects LST. As stated by Roth et al. (1989), the nocturnal urban–rural differences in LST are much smaller than in daytime, as strong solar heating can lead to larger temperature differences between dry surfaces and wet, shaded, or vegetated surfaces. However, in many cities around the world, daytime ΔT may be weak after sunrise or even negative at midday, meaning that urban surfaces are cooler than those of the surrounding countryside. Such results have been reported for the city of Indianapolis (Carnahan & Larson, 1990), for Singapore (Nichol, 1996), for Paris (Dousset & Gourmelon, 2003) as well as for Athens (Stathopoulou & Cartalis, 2007b). The main reason for the development of a “cool island” over a city near the midday hours is a lag in urban surface warming due to the heat storage capacity of the building materials and the extensive shading of some parts of the city by tall buildings or other natural structures located in the surrounding fields (i.e. mountains) (Oke, 1982). On the other hand, SUHIs on calm, cloudless nights are considered most pronounced, with the effects of street canyon geometry on radiation and of thermal properties on heat storage release, been the primary and almost equal causes on most occasions (Oke et al., 1991). Satellite SUHI studies require the relatively high spatial resolution of the TM, ETM+ and ASTER thermal band data, so as to understand the spatial intra-urban variability of SUHI through LST mapping. This requirement is of particular importance in the case where the study area reflects an urban area of moderate size (Stathopoulou et al., 2004) or a region of particular concern within a city (Nichol, 1996). Moreover, satellite SUHI studies require the revisit time of the NOAA AVHRR sensor, so as to estimate SUHI intensities at a frequent basis as

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well as to retrieve and map the thermal inertia of the urban landscape, which helps to understand the roles that individual components of the city (such as parks, industrial complexes, etc) play in the thermal patterns of the city (Voogt & Oke, 1998). This requirement is particularly important if the link between SUHI intensity and air pollution is to be explored (Lo & Quattrochi, 2003) or if satellitederived LST is used to calculate energy parameters, such as the cooling degree days (Stathopoulou et al., 2006) or bioclimatic parameters, such as the thermal discomfort index (Stathopoulou et al., 2005). Improvement in the spatial resolution of AVHRR LST image data may address the spatial and temporal requirements for studying and most important, monitoring the SUHI from space. Practically, for capturing the complex LST changes of the urban environment, very high resolution (5–10 m) LST measurements from aircrafts are needed (Lo et al., 1997). Yet, satellite-derived LST image data at the scale of 102 m can be considered adequate for depicting most of the intra-urban variations of LST related to urban morphology (Nichol, 1996). Considering this, the aim of the present study is twofold. Firstly, to improve the original low spatial resolution of the AVHRR LST image data (1100 m) to that of TM band 6 (120 m) by applying different scaling factors in the pixel block intensity modulation (PBIM) method (Guo & Moore, 1998). Secondly, to evaluate the accuracy of each downscaling method based on comparative analysis with near-coincident TM LST image data as well as to measure the improvement in the SUHI intensity estimation accuracy in case that the downscaled 120-m AVHRR LST image data are utilized. A summertime AVHRR LST image acquired on July 10, 2004 at the midmorning over the metropolitan city of Athens, in Greece, was used for the downscaling application (Fig. 1). 2. Background Downscaling is the scaling process of converting from a low to a high spatial resolution. In the event of LST estimation, downscaling refers to the process of determining the subpixel LST values within the pixels composing a low-resolution LST image. In practice, to downscale a satellite image improving thus its spatial resolution, the approach of merging is used; a low-resolution image is merged with a highresolution image (from the same or different sensor), so that the lowresolution image obtains the spatial details of the high-resolution image (Liang, 2004). Munechika et al. (1993) separated downscaling into three categories: 1) downscaling for display enhancement 2) downscaling by separate manipulation of spatial and spectral information and 3) downscaling to maintain radiometry. If the resulting downscaled image is intended to be used in data processing and computing, it is vital that the downscaling process preserves the original image radiometry. Since the objective of this study is to downscale AVHRR LST image data in view of their applicability in SUHI intensity estimation, we used a downscaling process of the third category, namely the PBIM method. 2.1. The PBIM method The PBIM method is expressed in a general form as (Guo & Moore, 1998): BandðλÞsim =

BandðλÞlow Bandðγ Þhigh BandðγÞmean

ð1Þ

where Band(λ)sim is the simulated (downscaled) high-resolution image generated from the low-resolution image Band(λ)low; Band(γ)high is the high-resolution image and Band(γ)mean is a low-resolution image derived from the high-resolution image Band(γ)high by local averaging over the relevant pixel block of the low-resolution image Band(λ)low. As defined by Eq. (1), the PBIM method provides the following advantages: 1. It enables the merging of image data from sensors of differing spatial resolutions.

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Fig. 1. Map of Greece and an AVHRR image over the metropolitan Athens area (dotted polygon line).

2. It can be applied to individual bands. 3. The simulated high-resolution image retains the radiometric characteristics of the original low-resolution image. In particular, the average of the simulated high-resolution subpixel values is equal to the pixel value of the original low-resolution image. In case of LST downscaling, this is important, as it has been found that over heterogeneous flat areas, the LST at the low scale can be expressed as a simple areal average of the LSTs at the high scale (Liu et al., 2006). 4. There is no limitation regarding the high-resolution image used for scaling, because as shown in Eq. (1), the spectral properties of the simulated image (denoted with λ) are independent of those of the high-resolution image (denoted with γ). When applied to a TIR image, the method integrates the spatial details recorded in the high-resolution image within the thermal information recorded in the low-resolution TIR image. In this study, the PBIM method was applied so as to downscale an AVHRR LST image by merging it with high-resolution image data derived from the TM sensor. According to the scaling factor (Band (γ)high/Band(γ)mean) used so as to perform downscaling of the AVHRR LST image, four case methods were examined: ▪ Case method 1: Use of TM effective emissivity as a scaling factor. ▪ Case method 2: Use of TM LST (season-coincident with AVHRR LST) as a scaling factor. ▪ Case method 3: Combined use of TM effective emissivity and TM LST as a scaling factor. ▪ Case method 4: Use of a high-resolution estimate of AVHRR LST (extracted from near time-coincident TM LST) as a scaling factor. In fact, this case method is a slight modification of the Price (1987) merging method, developed for merging a low-resolution multispectral image with a high-resolution panchromatic image.

August 14, 2005 was used to produce a 120-m high-resolution LST image, which is season-coincident with the low-resolution AVHRR LST image. Furthermore, to produce a high-resolution estimate of AVHRR LST as well as to validate the downscaling results of the case methods examined, the near-coincident TM image acquired on July 10, 2004 was utilized. Note that, the difference in acquisition times between the two images was approximately 28 min (Table 2). Also, the AVHRR image was acquired under cloud-free conditions with a near-nadir sensor viewing angle which minimized the atmospheric effects and permitted comparison to the nadir-viewed TM image. Finally, the Corine Land Cover 2000 (CLC00) database for Greece was used to gain information on the spatial distribution of the different land types covering the metropolitan city of Athens. 3. Pre-processing of the satellite and land cover data Flow chart of Fig. 2 shows the pre-processing of the data applied so as to derive LST, effective emissivity and a thematic land cover/use map for metropolitan Athens. 3.1. Geometric correction As shown in Fig. 2, the first step in satellite image data processing was the georegistration procedure. Raw AVHRR and TM image data were georeferenced to the CLC00 vector data having a UTM/WGS84 coordinate reference system. Georeferencing was performed by collecting many dispersed ground control points (GCPs) throughout each image and applying a 1st-order polynomial transformation to match the image coordinates with the CLC00 coordinates using the nearest neighbor resampling method. The accuracy of the transformation was determined by the total RMSE, which was considered to be acceptable when not exceeding one half of the pixel size of the input

2.2. Datasets used For the application site of Athens, five satellite images were collected (Table 2). The AVHRR image acquired on July 10, 2004 was used to produce a 1080-m low-resolution LST image intended for downscaling. The three TM images acquired on multiple dates in 2005, from April to August, were used to calculate a warm season TM NDVI-composite image, from which a 120-m high-resolution effective emissivity image of Athens was derived. The TM image acquired on

Table 2 Characteristics of the satellite images used in this study. Image

Acquisition date

Acquisition time (UTC)

Satellite zenith angle (°)

AVHRR TM TM TM TM

10/07/2004 10/07/2004 08/04/2005 27/06/2005 14/08/2005

09:15 08:47 08:52 08:53 08:53

3.42 nadir view nadir view nadir view nadir view

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Fig. 2. Schematic representation of the satellite and land cover data pre-processing.

image according to the US National Map Accuracy Standards (NMAS). As a result, geometrically corrected images were produced having a pixel size of 1080-m for AVHRR, 120-m for TM6 and 30-m for TM1-5, 7. 3.2. Radiometric calibration Each geometrically corrected image was processed according to the calibration technique of the sensor involved, in order to convert from digital number (DN) values to at-sensor spectral radiance (Lλ) values. Therefore, for NOAA-17 AVHRR image calibration of the thermal bands (AVHRR-4/-5) was carried out by applying the non-linear radiance correction procedure described in the NOAA KLM User's Guide (Goodrum et al., 2000) using the band-specific thermal operational coefficients embedded in the AVHRR image header file. For Landsat images on July 10, 2004 and August 14, 2005 (Fig. 2), calibration of TM6 data was performed according to the post-calibration procedure proposed by the National Landsat Archive Production System (NLAPS) (Chander & Markham, 2003) using the band-specific slope and offset coefficients given in each TM image header file. At a next step, Lλ values of thermal bands from both sensors were converted to top-of-atmosphere (TOA) brightness temperatures BTλ by inverting the Planck's blackbody equation. Finally, for 2005 TM images on April 8, June 27 and August 14 (Fig. 2), atmospheric correction of their reflective-band data (TM1-5, TM7) and calibration to surface reflectance was carried out using the Chavez (1996) COST method. The accuracy for the retrieved surface reflectance values was tested by comparison with typical surface reflectance values measured in different parts of the spectrum for 5 targets: water, dense dark vegetation, green vegetation, agricultural soil and asphalt (Richter, 2002). 3.3. LST computation LST from AVHRR-4/-5 image data on July 10, 2004 as well as from TM6 image data taken on July 10, 2004 and August 14, 2005 was computed with the use of the generalized single-channel algorithm proposed by Jiménez-Munõz and Sobrino (2003). The main reason for selecting this algorithm to retrieve LST is that, it can be applied to different satellite thermal sensors using the same equation and coefficients, thus overpassing the limitation of many other published LST algorithms that are sensor-specific. Therefore, the error induced into the estimation of LST from using two different LST algorithms, one for AVHRR and one for TM, is eliminated. This is important,

considering that this study evaluates the downscaled AVHRR LSTs and resulting SUHI intensities based on comparison with relevant measurements derived from near-coincident TM image data. The single-channel LST algorithm was applied to the thermal band data of both sensors (AVHRR-4/-5 and TM6) and LST values were obtained by using an atmospheric water vapour content value of 2.474 g/cm2 for July 10, 2004 and 2.361 g/cm2 for August 14, 2005 (as measured from radiosonde data at 12:00 UTC on the observation date considered at the Athinai Airport) with a constant land emissivity of 0.95, which represents mean thermal emissivity of urbanized surfaces in Athens (Stathopoulou et al., 2007). To ensure spectral consistency required for comparison, 1080-m AVHRR LST was produced by averaging the AVHRR-4 LST (10.3–11.3 µm) and the AVHRR-5 LST (11.5–12.5 µm), so as its wavelength location is very close to that of TM LST (10.4–12.5 µm). In this study, TAVHRR,1080 will thereafter denote the band-averaged LST at 1080-m resolution from the AVHRR sensor. 3.4. Effective emissivity computation Normalized Difference Vegetation Index (NDVI) over metropolitan Athens was initially calculated by using the surface reflectance values measured from TM3 and TM4 bands of the 2005 TM images on April 8, June 27 and August 14 (Fig. 2). Then, a warm season TM NDVIcomposite image of Athens was produced by taking the maximum pixel value for NDVI from all three TM images. In the following, the TM NDVI-composite image was upscaled from the original 30-m to 120-m resolution by local averaging every 4 × 4 pixels within each 120-m pixel, so as to make its spatial resolution accordance with TM LST. From the upscaled TM NDVI-composite image statistics, the maximum NDVI (NDVImax) and the minimum NDVI (NDVImin) values for the study area were determined, which were further used for computing the fractional vegetation cover (fv) (Choudhury et al., 1994; Gillies & Carlson, 1995):
fv =

NDVI −NDVImin NDVImax −NDVImin

2

ð2Þ

Finally, the effective emissivity was computed from the following equation (Caselles et al., 1991): ε = fc εc + ð1 − fc Þεv = ð1 − fv Þεc + fv εv

ð3Þ

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Table 3 Original CLC00 land cover classes with codes and new classification. CLC00 code

CLC00 class name

New code

New class name

111 112 121 122

Continuous urban fabric Discontinuous urban fabric Industrial or commercial units Road and rail networks and associated land Port areas Airports Mineral extraction sites Dump sites Construction sites Green urban areas Sport and leisure facilities Agricultural areas Forests and semi-natural areas

1 2 3 4

Urban/densely built Suburban/medium built Industrial/commercial Urban use

4 4 4 4 4 2 2 5 6

Urban use Urban use Urban use Urban use Urban use Suburban/medium built Suburban/medium built Agriculture Forest

123 124 131 132 133 141 142 2 3

where, fc is the fractional city cover, εv is the emissivity of vegetation and εc is the emissivity of the city. By substituting Eq. (2) into Eq. (3) and using a mean emissivity value of 0.93 for city (considered to be composed by urban/densely build surfaces) and 0.98 for vegetation (Stathopoulou et al., 2007), the effective emissivity of metropolitan Athens was computed. The term εTM,120 will thereafter denote the TM pixel-based effective emissivity of metropolitan Athens at 120-m resolution derived from Eq. (3). 3.5. TM LST correction for effective emissivity As mentioned in Section 3.3, LST computation was performed by applying the single-channel LST algorithm considering a constant

emissivity value of 0.95. In order to account for the spatial variation of the land emissivity, TM LST values of Athens were corrected for εTM,120 according to the estimated impact of the emissivity error on the LST computed from the single-channel algorithm. As reported in JiménezMunõz and Sobrino (2003), the error in the LST is proportional to the error in the emissivity; specifically, a 0.01 error in emissivity may lead to a 0.6 K error in LST. Consequently, the TM LST image was corrected pixel by pixel for effective emissivity by using the following conditional equations:

T TM;120 V

8 TTM;120 > >   < = TTM;120 + 60  εTM;120 − 0:95 > > :T TM;120 − 60  0:95 − eTM;120

if εTM;120 = 0:95 if εTM;120 N 0:95

ð4Þ

if εTM;120 b0:95

where T′TM,120 is the TM LST image corrected for effective emissivity, TTM,120 is the 120-m TM LST image derived from the single-channel algorithm using a constant emissivity value of 0.95 and εTM,120 is the 120-m TM pixel-based effective emissivity image of metropolitan Athens. 3.6. Land cover and land use map production CLC00 is a detailed database describing land cover and use according to a nomenclature of 44 classes at the scale of 1:100 000 (Büttner et al., 2002). First, the CLC00 vector data covering metropolitan Athens were extracted using a mask in a GIS environment. Thus, the main land cover and use types comprising the study area were identified (Table 3). Secondly, to separate the residential from the urban use areas of the city, a new classification was performed; the original 15 CLC00 classes were regrouped according to

Fig. 3. Land cover and land use map of metropolitan Athens for the year 2000 as reproduced from the CLC00 database for Greece. A = Elaionas, B = Eleusina, C = Mt. Parnitha, D = Mt. Penteli, F = Mt. Imittos, G = Mt. Aigaleo, H = Mesogia and J = El. Venizelos international airport.

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mined and extracted from the images, covered by the urban agglomeration of Athens and its surroundings. 2. At a next step, the co-registered images were overlaid over one another in order to examine potential pixel misalignment caused by the different spatial resolutions of AVHRR and TM. Indeed, some misalignment was visually detected, which was further corrected by offsetting the low-resolution image based on the coordinates of the upper-left corner of the high-resolution image. Using the Swipe Overlay mode, the alignment between the two images was also visually verified.

Fig. 4. Scatter plot diagram and linear regression line for the pairs of LST values obtained from the AVHRR and TM images on July 10, 2004.

After co-registration, both low- and high-resolution images had the same reference system, spatial resolution and dimensions (the same minimum and maximum X and Y coordinates) according to the pre-downscaling requirements. 4.2. Modifications to PBIM method

similar landscape characteristics and reduced to form six new ones. In particular, the classes “discontinuous urban fabric”, “green urban areas” and “sport and leisure facilities” of CLC00 were grouped together forming a new class named as “suburban/medium built areas”. Likewise, the classes “road and rail networks and associated land”, “port areas” and “airports” were grouped with the classes “mineral extraction sites”, “dump sites” and “construction sites” into a new aggregated class labeled “urban use areas”. Therefore, after regrouping, the six land cover and use classes retained for further analysis were: urban/densely built areas, suburban/medium built areas, industrial/ commercial areas, urban use areas, agriculture, and forests. Their spatial distribution over metropolitan Athens is illustrated in Fig. 3. 4. Downscaling process Prior to downscaling satellite imagery with varying spatial resolution from different sensors, accurate geometric co-registration is required. Images need to be processed so as to have the same reference system and spatial resolution as well as to cover the same geographical area. 4.1. Image co-registration As described earlier, all images employed were initially georegistered to the same reference system of UTM/WGS84 prior to downscaling. However, georegistration process often incorporates residual geometric errors causing images to differ slightly in position and orientation. To reduce the georegistration error as much as possible and achieve pixelto-pixel matching, the low-resolution image of TAVHRR,1080 was coregistered to the high-resolution images of TTM,120 and εTM,120. In each case, the procedure involved two steps: 1. Each 1080-m AVHRR pixel was replicated into 81 120-m pixels to match the TM6 spatial resolution. Considering the high-resolution image as the reference image, the low-resolution image was registered to the high-resolution image using well-distributed GCPs and an over-defined 1st order polynomial transformation. The co-registration accuracy was evaluated by the X-, Υ-Residuals and total RMSE. Then, a common geographical area was deter-

Downscaling of AVHRR LST was performed by using four different scaling factors in the general form of the PBIM algorithm. Each PBIM modification method was applied to the AVHRR LST image of Athens taken on July 10, 2004 as follows: 4.2.1. Method 1: Effective emissivity as a scaling factor In this case method, the effective emissivity derived from the TM NDVI-composite image was used as the scaling factor to obtain AVHRR LST at higher spatial resolution. Specifically, the AVHRR LST was downscaled from 1080-m to 120-m, using the following equation: TAVHRR;120 =

TAVHRR;1080  εTM;120 εTM;120Y1080

ð5Þ

where TAVHRR,120 is the simulated 120-m AVHRR LST image, εTM,120 is the 120-m TM effective emissivity image and εTM,120→1080 is the average of the 9 × 9 pixel values in the εTM,120 image corresponding to the AVHRR LST pixel. 4.2.2. Method 2: Season-coincident LST as a scaling factor In this case method, a TM LST image taken on a date during the same seasonal period at which the AVHRR LST image was acquired was used as a scaling factor in the PBIM algorithm. For example, if downscaling is applied to a July AVHRR LST image (as occurs in this study), a TM LST image obtained during the summer period (June, July or August) should be used in order to derive AVHRR LST with higher spatial resolution. Given that the regional LST spatial distribution and variation is greatly related to heating from solar radiation and the physical properties of the land surface itself, the potential of using as a scaling factor a high-resolution LST image that is season-coincident and not necessarily time-coincident with the low-resolution LST image was explored. However, there are a number of prerequisites for selecting a proper TM LST image as a scaling factor: 1. No significant changes in land cover and land use should be observed over the test site during the period between the acquisition dates of the TM LST and AVHRR LST image. 2. Both TM LST and AVHRR LST images should correspond to similar weather conditions (cloudiness, mean daily air temperature and relative humidity, rainfall).

Table 4 Statistics of LST (in °C) for the test area. Image

N

Min

Max

Mean

Std. dev.

Median

Variance

Skewness

Kurtosis

TTM,1080 TAVHRR,1080 TTM,120 ̂ TAVHRR,120

2043 2043 165,483 165,483

35.00 35.07 26.69 29.85

62.24 55.26 73.22 62.80

49.86 46.28 49.89 46.28

5.01 3.76 5.77 4.08

50.43 46.28 50.59 46.78

25.09 14.13 33.28 16.68

− 0.30 − 0.34 − 0.26 − 0.26

− 0.24 − 0.28 − 0.13 − 0.13

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Fig. 5. a) Transect line (AD) on the superimposed image of 1080-m AVHRR LST with 1080-m TM LST passing through the Athens basin and b) spatial profile of LST for the two sensors.

3. The TM LST and AVHRR LST images should correspond to the same season period so that there are no significant variations in the solar height. For this method, a summertime TM LST image acquired on August 14, 2005 over the metropolitan Athens area was employed to downscale the AVHRR LST image from 1080-m to 120-m, as follows: 14 = 8 = 05

TAVHRR;120 =

TAVHRR;1080  TTM;120 14 = 8 = 05

TTM;120Y1080

ð6Þ

where TAVHRR,120 is the simulated 120-m AVHRR LST image, T14/8/05 TM,120 is the 120-m TM LST image acquired on August 14, 2005 and T14/8/05 TM,120→1080 is the average of the 9 × 9 pixel values in the T14/8/05 TM,120 image corresponding to the AVHRR LST pixel. Eq. (6) was also applied by using as a scaling factor the TM LST image on August 14, 2005 that was corrected pixel by pixel for effective emissivity. In this approach, Eq. (6) was modified as: 14 = 8 = 05

TAVHRR;120 =

TAVHRR;1080  T TM;120 V 14 = 8 = 05

T TM;120Y1080 V

14/8/05 T′TM,120 image corresponding to the AVHRR LST pixel. For convenience, downscaling Eqs. (6) and (7) will thereafter be referred to as Method 2a and Method 2b, respectively.

4.2.3. Method 3: Combined use of effective emissivity and LST as a scaling factor In this case method, the combined use of the effective emissivity and the season-coincident TM LST for defining the scaling factor in the PBIM method was examined. In particular, downscaling of the AVHRR LST image was performed by applying the following equation:

TAVHRR;120

  14 = 8 = 05 TAVHRR;1080  TTM;120 = εTM;120  =  14 = 8 = 05 TTM;120Y1080 = εTM;120Y1080

ð8Þ

where TAVHRR,120 is the simulated 120-m AVHRR LST image, T14/8/05 TM,120 is the 120-m TM LST image on August 14, 2005, εTM,120 is the 120-m TM effective emissivity image, whereas εTM,120→1080 and T14/8/05 TM,120→1080 are the averages of the 9 × 9 pixel values in the εTM,120 and T14/8/05 TM,120 images, respectively, that correspond to the AVHRR LST pixel.

ð7Þ

where TAVHRR,120 is the simulated 120-m AVHRR LST image, T′14/8/05 TM,120 is the 120-m effective emissivity-corrected TM LST image on August 14/8/05 14, 2005 and T′TM,120→1080 is the average of the 9 × 9 pixel values in the

4.2.4. Method 4: High-resolution estimate of AVHRR LST as a scaling factor This case method is based on the regression method proposed by Price (1987) to merge a multispectral image with a correlated panchromatic image, so as the multispectral image to have the spatial

Fig. 6. Diurnal fluctuations of Ts at 15-cm depth on 3 typical cloudless days using ground measurements from the Thissio weather station in Athens.

M. Stathopoulou, C. Cartalis / Remote Sensing of Environment 113 (2009) 2592–2605

details of the panchromatic image. Similar to the Price method, a highresolution estimate of the AVHRR LST image, derived from a near time-coincident TM LST image, was used here as a scaling factor in the PBIM algorithm. Specifically, the procedure consisted of four steps: 1. The TM LST image was aggregated by simple averaging to the 1080-m resolution of the AVHRR LST image. 2. The pixel values of the aggregated 1080-m TM LST image were plotted against the pixel values of the 1080-m AVHRR LST image and a linear regression model was established (TAVHRR,1080 = α·TTM,1080 + β). It should be noted that, for this application only pixels included in a certain area of interest were considered. This test area

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(2043 pixels ≅ 2382.96 km2) was manually selected on both images and was fully covered by land surfaces excluding coastline and water surfaces. The correlation between TTM,1080 and TAVHRR,1080 is shown in Fig. 4. Notice, that the model was rather good, explaining 89% of the total variation in TAVHRR,1080 (R2 = 0.89) and showing a positive relation between TAVHRR,1080 and TTM,1080. The correlation coefficient (0.94) was found significant at the 0.01 confidence level. 3. Considering the linear regression model to be valid at the 120-m resolution, the subpixel values of the AVHRR LST image were predicted by using the 120-m TM LST image resulting thus, to a high-resolution ̂ estimate of the AVHRR LST (T AVHRR,120 =α·TTM,120 +β). The descriptive statistics for the four images are presented in Table 4.

Fig. 7. The (a) original 120-m TM LST image, (b) original 1080-m AVHRR LST image, (c) 120-m predicted AVHRR LST image, (d) simulated 120-m AVHRR LST image by method 1, (e) simulated 120-m AVHRR LST image by method 2a, (f) simulated 120-m AVHRR LST image by method 2b, (g) simulated 120-m AVHRR LST image by method 3 and (h) simulated 120-m AVHRR LST image by method 4. LST values are given in °C.

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of the National Observatory of Athens (NOA). As shown in Fig. 6, the diurnal Ts variation follows a wave-like pattern with a maximum in the early evening (16:00–18:00 h local time) and a minimum in the early morning (07:00–09:00 h local time). The Ts wave amplitude is clearly dependent upon season as the climatic parameters affecting the overall thermal condition of the surface vary accordingly (solar radiation, soil moisture, rainfall, cloudiness). On July 10, 2004, between 10:30 am and 11:30 am local time, an increase of the order of 1 °C/h in Ts was measured from station data, resulting to a Ts difference of about 0.5 °C between the acquisition times of the TM and AVHRR LST images. Given that this difference is much smaller than the LST estimation error of the single-channel algorithm, it was regarded as negligible, thereby contributing minor to the MBE of 3.6 °C computed between TAVHRR,1080 and TTM,1080. However, this bias error may have arisen mainly from land surface heterogeneity and spatial resolution. In a previous study, Liu et al. (2006) found the 1-km MODIS LST to differ from the ASTER LST, after scaled to 1-km, by approximately 3 K on average. Other potential sources of error between TAVHRR,1080 and TTM,1080 involve the difference in the satellite altitude (TM: 705 km, AVHRR: 833 km), the relative spectral response functions of the two sensors as well as the pixel-to-pixel georegistration. Similar problems were identified in Goward et al. (2003) between IKONOS and ETM+ intercomparison NDVI measurements.

4. After the high-resolution estimate of the AVHRR LST image was computed (hereafter named predicted 120-m AVHRR LST), it was used to define the scaling factor in the PBIM algorithm:

TAVHRR;120 =

̂ TAVHRR;1080  T AVHRR;120

ð9Þ

̂ TAVHRR;120 Y1080

From the statistical data of Table 4, a mean bias error (MBE) of 3.6 °C between TTM,1080 and TAVHRR,1080 was computed. Specifically, the TTM,1080 values appeared to be higher than the TAVHRR,1080 values, a fact that was further supported by the spatial profile of LST for the two images. Fig. 5 illustrates the distribution of LST along the NW–SE transect drawn from the foot of Mt. Parnitha (A) to the Mesogia (D) along the Athens basin (BC) that was recorded by the two sensors. As shown in Fig. 5, the AVHRR LST pattern was found to be similar to that derived from the 1080-m TM LST image, in the sense that there was a consistency among the two sensors regarding the location where peaks, valleys and plateaus of LSTs occupied. However, the TTM,1080 values were systematically higher than the TAVHRR,1080 values exhibiting at the same time higher maximum and variance values (Table 4), attesting that the AVHRR scale cannot detect small-scale (∼120-m) “hot spots” and spatial variation in LST patterns. On the other hand, the spatial variation of the TM LST image was preserved after aggregation from 120-m to 1080-m. The reason for this is that, the data being averaged (neighboring pixels) are statistically related and not independent. As stated in Stein et al. (1999), the higher the spatial resolution, the more variation there is between the pixels in an image and the less variation that is lost through averaging and represented in the data. To investigate whether there was a systematic error in LST measurements obtained from the TM and AVHRR sensors due to difference in their radiometric calibration, an extended and homogeneous common sea sub-area was first extracted from both TAVHRR,1080 and TTM,1080 images and then, its mean surface temperature was calculated. In both cases, the mean surface temperature of the sea subarea was found to be about 26 °C (AVHRR: 298.93 ± 0.38 K, TM: 298.71 ± 0.57 K), implying that no significant contribution to TM AVHRR LST difference at 1080-m existed due to different calibration of the sensors. Additionally, the magnitude of error introduced due to time difference between the two images was examined. To achieve this, the diurnal variation in surface temperature (Ts) on July 10, 2004 was utilized (Fig. 6), which was based on hourly measurements at the depth of 15-cm from the Thissio weather station (37° 58′N, 23° 43′E)

5. Results and discussion The characteristics of the 120-m simulated AVHRR LST images generated from the four downscaling methods were compared with the characteristics of the original TTM,120 image that is near timecoincident with the original TAVHRR,1080 image as well as with the ̂ characteristics of the predicted TAVHRR,120 image, both serving as proxies for real observations at that resolution. The comparisons were performed on a visual and statistical basis using a sub-area (65,346 pixels ≅ 940.98 km2) of the LST images covered by the city of Athens. Given that the purpose of this study is to investigate the improvement in the SUHI intensity estimated from the downscaled AVHRR LST images, the accuracy of each method in the estimation of the SUHI intensity was further examined. 5.1. Visual comparison of downscaled images As can be seen by referring to Fig. 7, all downscaled AVHRR images are improved compared to the original AVHRR image in terms of LST spatial distribution mapping. A comparing of the original TAVHRR,1080

Table 5 Statistical measures of AVHRR LST downscaling methods. (a) Downscaling methods

Summary measures ̅ TTM,120

̅ TAVHRR,120

Method 1 51.48 47.59 Method 2a 51.48 47.63 Method 2b 51.48 47.98 Method 3 51.48 47.62 Method 4 51.48 47.68 ̅ 1080-m AVHRR LST: T AVHRR,1080 = 47.65 °C, s2 = 2.77

Difference measures

Correlation measures

s2TM,120

s2AVHRR,120

N

a

b

MAE

RMSE

RMSEs

RMSEu

d

R2

4.60 4.60 4.60 4.60 4.60 °C.

3.25 3.75 3.80 3.91 3.39

65346 65346 65346 65346 65346

22.23 18.41 18.85 18.99 20.42

0.49 0.57 0.57 0.56 0.53

4.31 4.28 4.03 4.39 4.19

5.10 5.09 4.89 5.27 4.96

4.51 4.31 4.02 4.37 4.35

2.34 2.69 2.77 2.96 2.36

0.679 0.699 0.712 0.684 0.697

0.484 0.485 0.468 0.428 0.516

(b) Downscaling methods

Summary measures P

Method Method Method Method Method

1 2a 2b 3 4

Difference measures

Correlation measures

̂ TAVHRR;120

̅ TAVHRR,120

̂ s2(TAVHRR,120 )

s2AVHRR,120

N

a

b

MAE

RMSE

RMSEs

RMSEu

d

R2

47.61 47.61 47.61 47.61 47.61

47.59 47.63 47.98 47.62 47.68

3.30 3.30 3.30 3.30 3.30

3.25 3.75 3.80 3.91 3.39

65346 65346 65346 65346 65346

14.98 6.04 3.99 4.08 3.06

0.69 0.87 0.92 0.91 0.94

1.97 1.84 1.75 1.93 0.98

2.56 2.43 2.32 2.50 1.41

1.04 0.42 0.45 0.28 0.22

2.34 2.40 2.28 2.48 1.39

0.831 0.871 0.886 0.869 0.954

0.482 0.591 0.642 0.596 0.831

̂ Comparison with (a) 120-m original TM LST values (TTM,120) and (b) 120-m predicted AVHRR LST values (TAVHRR,120 ).

M. Stathopoulou, C. Cartalis / Remote Sensing of Environment 113 (2009) 2592–2605

image with the original TTM,120 image, shows the deficiency of the AVHRR sensor to capture the LST variations not only within the urban regions, but also across the surroundings. On the other hand, the downscaled AVHRR LST images appear to have reproduced a significant amount of the spatial detail of the original TTM,120 image, presenting LST values that are slightly higher than those of the original TAVHRR,1080 image, yet preserving the mean value of it. As a result, LST differences among different land covers are sharper in the downscaled AVHRR images and can be clearly observed as the ones displayed in the original TTM,120 image. For example, in contrast to Fig. 7(b), Fig. 7(d)–(h) adequately depicts the high LST values of the industrial

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regions at Eleusina and Elaionas, the lower LST values of the N/NE suburbs of Athens and the higher LST values of the SW/W suburbs of Athens as well as the low LST values recorded at the surrounding mountains of the Athens basin (Imittos, Aigaleo, Penteli and Parnitha). ̂ With respect to the TAVHRR,120 image, the downscaled AVHRR LST images produce a spatial LST pattern that resembles greatly that of ̂ the predicted TAVHRR,120 image, both qualitative and quantitative. On the contrary, the downscaled AVHRR LST images appear to quantitative differ from the original TTM,120 image by about two LST classes. More quantitative information is given in the statistical analysis that follows.

Fig. 8. Scatter plots of original 120-m TM LST versus simulated 120-m AVHRR LST generated by the downscaling methods. The perfect agreement line (1:1) is also plotted.

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5.2. Statistical comparison of downscaled images The statistical analysis performed was two-fold: 1. To quantitatively evaluate the accuracy of the methods to downscale the 1080-m AVHRR LST values. This was accomplished by applying a set of evaluation statistics (Willmott, 1982) to original and simulated LST images. By original images we mean the TTM,120 and predicted ̂ TAVHRR,120 images, whereas by simulated images we mean the TAVHRR,120 images generated by the downscaling methods.

2. To quantitatively evaluate the accuracy of the SUHI intensity attained by using the downscaled AVHRR LST images. To achieve this, SUHI intensity values retrieved from the 120-m TM LST image corrected for effective emissivity (T′TM,120) were comparatively evaluated to those obtained from the downscaled TAVHRR,120 images based on statistical measures of accuracy. It should be noted that the SUHI intensity was defined here, as the difference in mean LST observed between the urban (urban/densely build area, suburban/ medium-build area, industrial/commercial area and urban use area) and rural (agricultural area, forest area) land covers of Athens

Fig. 9. Scatter plots of predicted 120-m AVHRR LST versus simulated 120-m AVHRR LST generated by the downscaling methods. The perfect agreement (1:1) and linear regression lines are also plotted.

M. Stathopoulou, C. Cartalis / Remote Sensing of Environment 113 (2009) 2592–2605

(Fig. 4). In particular, the downscaled TAVHRR,120 images were initially superimposed on the land cover map of Athens and the mean LST for each land cover type was then computed using a zonal summary GIS operation. Thereby, SUHI intensity was calculated from differences in mean LST observed between the different pairs of land cover types. The set of evaluation statistics included summary, difference and correlation measures. As a result, each downscaling method was evaluated based on the mean (T ),̅ standard deviation (s2) and variance (s) values, the regression coefficients (a : y-intercept, b : slope), the mean absolute error (MAE), the RMSE, the index of agreement (d) and the coefficient of determination (R2). Besides RMSE, its systematic (RMSEs) and unsystematic (RMSEu) components were additionally considered. All these quantitative measures were also accompanied by scatter plots, as they are often used to show the strength of the relationship between original

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and simulated variables. Statistical results of LST are given in Table 5, whereas scatter plots between TTM,120 and downscaled TAVHRR,120 values ̂ as well as between predicted TAVHRR,120 and downscaled TAVHRR,120 values are illustrated in Figs. 8 and 9. As shown in Table 5a, the mean value of the original TAVHRR,1080 image is well retained in all downscaled TAVHRR,120 images. However, their s2 values are found to be closer to that of the TTM,120 image, indicating that all downscaled TAVHRR,120 images can reproduce the spatial pattern of the TTM,120 image to a greater degree than the original TAVHRR,1080 image. Especially Method 2b and Method 3, perform substantially better in terms of capturing the variability in the TTM,120 image. On the other hand, the a and b coefficients of the linear regression lines indicate that all downscaled TAVHRR,120 values systematically underestimate the TTM,120 values; in particular, underestimation is less for Methods 2a and 2b. This is also supported by the scatter plot diagrams of Fig. 8. Nevertheless,

Fig. 10. Original 120-m SUHI intensities by effective emissivity-corrected TM versus simulated 120-m SUHI intensities by AVHRR downscaling methods. Units for both axes are °C.

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Table 6 ′ Statistical difference measures of 120-m original SUHI intensities by T TM,120 and 120-m simulated SUHI intensities by AVHRR downscaling methods. AVHRR downscaling method

MAE

RMSE

d

R2

Method 1 Method 2a Method 2b Method 3 Method 4 Original AVHRR (1080 × 1080 m)

1.37 0.98 0.79 0.80 0.74 2.04

1.60 1.13 0.94 0.96 0.89 2.35

0.864 0.937 0.958 0.959 0.964 0.671

0.749 0.943 0.975 0.923 0.970 0.287

Results are given in °C.

these underestimations were somewhat expected and are justified given that the downscaled TAVHRR,120 images have the same mean value with the original TAVHRR,1080 image. The examination of MAE indicates that Method 2b has the smallest MAE, implying that it can produce downscaled TAVHRR,120 values that differ to the original TTM,120 values by 4.03 °C, on the average. However, this average error is computed to be 4.89 °C according to RMSE. The discrepancy between MAE and RMSE is explained as it can be attributed to the fact that RMSE is more sensitive to extreme values than MAE (Fox, 1981). Among all downscaling methods, Method 2b contains the smaller systematic error RMSEs, having a potential accuracy of 2.77 °C as described by RMSEu. As shown in Table 5a, RMSE and MAE results are consistent with the results given by the index of agreement d. Therefore, Method 2b presents the highest d, suggesting that the downscaled TAVHRR,120 values simulated by this method agree with the original TTM,120 values up to 71.2%. The only contradiction in results comes from the R2, which indicates a rather modest correlation (0.47) between the TTM,120 and downscaled TAVHRR,120 values for Method 2b. Despite the fact that R2 is widely used, its interpretation was not given considerable weight for the reason that it is often a misleading measure of accuracy when used to compare original and simulated variables (Willmott, 1982). ̂ Comparison of downscaled TAVHRR,120 with predicted TAVHRR,120 values shows a higher agreement for all downscaling methods applied. For example, the summary measures of Table 5b show the mean and s2 values of the downscaled TAVHRR,120 images to approach very closely to ̂ those of the predicted TAVHRR,120 image, while the regression coefficients ̂ indicate the downscaled TAVHRR,120 values to fit the predicted TAVHRR,120 values very well. This is clearly depicted in the scatter plot diagrams of Fig. 9. Especially for Methods 2b and 3, the regression and the best fit lines do get much closer and nearly coincide. This is also observed for Method 4. However, Method 4 is excluded from comparison, as the downscaled image generated by it, is actually a linear combination of the ̂ predicted TAVHRR,120 image and thus, they are not independent of each other. Considering this, the downscaled TAVHRR,120 image displaying the least MAE and RMSE values is the one simulated by Method 2b, with a value of 1.75 °C and 2.32 °C, in respective. Except Method 1, all other downscaling methods yield RMSEs and RMSEu values that approach zero and RMSE respectively, thus indicating a good agreement between the ̂ downscaled TAVHRR,120 and predicted TAVHRR,120 values. This is further supported by the index of agreement d, as its value exceeds 0.80 for all downscaling methods. Again, the highest d value is computed for Method 2b (0.89), with Method 2a to follow on order (0.871) and Method 3 exhibiting a somewhat weaker agreement (0.869). The original 120-m SUHI intensities retrieved by the T′TM,120 image were plotted against the 120-m simulated SUHI intensities obtained from the downscaled TAVHRR,120 images (Fig. 10). Table 6 reports the accuracy of the 120-m SUHI intensities derived from the downscaled TAVHRR,120 images. It can be said that the improvement in SUHI intensity estimation is evident either graphically or statistically. As illustrated in Fig. 10, the simulated SUHI intensities approximate the original SUHI intensities better than the 1080-m SUHI intensities derived from the original TAVHRR,1080 image. Compared to diagrams for the downscaling methods, the diagram for the original AVHRR image depicts scatter points of 1080-m SUHI intensities that are clearly more

dispersed, thus defining a regression line that departs the most from the 1:1 line. However, after downscaling, the regression lines appear to approach more closely the 1:1 line, while the correlation between original and simulated SUHI intensities change from small (0.287) to very large (0.975). Improvement in the SUHI intensity results by the AVHRR downscaling methods is further verified by the statistical measures of Table 6. Noticeably, all downscaled methods result in smaller MAE and RMSE on SUHI intensity than the original TAVHRR,1080 image. In particular, MAE is reduced from 2.04 °C to less than 1.40 °C, while RMSE is decreased from 2.35 °C to less than 1.60 °C. The smaller MAE and RMSE values are attained first by Method 2b and secondly by Method 3. Finally, the index of agreement d is considerably increased from 0.67 to 0.86 for Method 1, 0.94 for Method 2a and 0.96 for Methods 2b, 3 and 4. 6. Conclusions In this study, four modifications of the PBIM method were examined for downscaling a low-resolution 1080-m AVHRR LST image to 120-m resolution for the purpose of SUHI intensity retrieval. The scaling factors used in the general form of the PBIM method were defined on the basis of 120-m high-resolution products generated from the TM sensor. Downscaled 120-m AVHRR LST values were validated against near timecoincident 120-m TM LST values and a 120-m estimate image values of AVHRR LST extracted from the later. Likewise, SUHI intensities retrieved from downscaled AVHRR LSTs were validated against SUHI intensities derived from 120-m TM LSTs corrected for pixel-based effective emissivity. Taking into account the visual and statistical results along with the simplicity of application, the downscaling method using a seasoncoincident TM LST image as a scaling factor was found to achieve better performance, as it substantially improved the spatial pattern of the 1080-m AVHRR LST and at the same time generated 120-m LST and SUHI intensity results with the higher accuracy. In the downscaled 120-m AVHRR LST image, boundaries between different land covers were sharper and linear features were highlighted, forming a pattern of LST that captured considerable spatial details of the original 120-m TM LST image. Moreover, the downscaled 120-m AVHRR LSTs generated by this method, achieved a high accuracy for SUHI intensity with an RMSE of 0.94 °C, thus providing a statistically significant improvement in SUHI intensity RMSE of the order of 40% when compared with SUHI intensities derived from the original 1080-m AVHRR LSTs. The good performance of the method was further supported by the increase in the index of agreement of about 29% over the original 1080-m AVHRR LST-derived SUHI intensities. As for the LST accuracy, an RMSE of 4.89 °C was reported after downscaling, which was regarded as satisfactory and reasonable, considering the change in the spatial resolution of the AVHRR LST (from 1080-m to 120-m, improved by an order of 9) and the potential sources of error due to different sensor properties. References Arnfield, A. J. (2003). Two decades of urban climate research: A review of turbulence, exchanges of energy and water, and the urban heat island. International Journal of Climatology, 23, 1−26. Büttner, G., Feranec, G., & Jaffrain, G. (2002). Corine land cover update 2000. Technical guidelinesEEA Technical report, Vol. 89. http://reports.eea.europa.eu/technical_report_ 2002_89/en Carnahan, W. H., & Larson, R. C. (1990). An analysis of an urban heat sink. Remote Sensing of Environment, 33, 65−71. Caselles, V., Lopez Garcia, M. J., Melia, J., & Perez Cueva, A. J. (1991). Analysis of the heatisland effect of the city of Valencia, Spain through air temperature transits and NOAA satellite data. Theoretical and Applied Climatology, 43, 195−203. Chander, G., & Markham, B. L. (2003). Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges. IEEE Transactions on Geoscience and Remote Sensing, 41, 2674−2677. Chavez, P. S. (1996). Image-based atmospheric corrections—Revisited and revised. Photogrammetric Engineering and Remote Sensing, 62, 1025−1036.

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Further reading Voogt, J. A. (2000). Image representations of complete urban surface temperatures. Geocarto International, 15, 19−30.