Thermal remote sensing of urban climates
J.A. Voogta,*, T.R. Okeb
a
Department of Geography, University of Western Ontario, London, ON, Canada N6A 5C2
b
Atmospheric Science Program, Department of Geography, University of British Columbia, Vancouver, BC, Canada V6T 1Z2
Received 8 April 2002; received in revised form 20 September 2002; accepted 28 December 2002
Abstract
Thermal remote sensing has been used over urban areas to assess the urban heat island, to perform land cover classifications and as input
for models of urban surface atmosphere exchange. Here, we review the use of thermal remote sensing in the study of urban climates, focusing
primarily on the urban heat island effect and progress made towards answering the methodological questions posed by Roth et al.
[International Journal of Remote Sensing 10 (1989) 1699]. The review demonstrates that while some progress has been made, the thermal
remote sensing of urban areas has been slow to advance beyond qualitative description of thermal patterns and simple correlations. Part of the
difficulty lies in the tendency to use qualitatively based land use data to describe the urban surface rather than the use of more fundamental
surface descriptors. Advances in the application of thermal remote sensing to natural and agricultural surfaces suggest insight into possible
methods to advance techniques and capabilities over urban areas. Improvements in the spatial and spectral resolution of current and nextgeneration
satellite-based sensors, in more detailed surface representations of urban surfaces and in the availability of low cost, high
resolution portable thermal scanners are expected to allow progress in the application of urban thermal remote sensing to the study of the
climate of urban areas.
D 2003 Elsevier Inc. All rights reserved.
Keywords: Remote sensing; Urban climates; Rural
1. Introduction
The surface temperature is of prime importance to the
study of urban climatology. It modulates the air temperature
of the lowest layers of the urban atmosphere, is central to the
energy balance of the surface, helps to determine the
internal climates of buildings and affects the energy
exchanges that affect the comfort of city dwellers. Surface
and atmospheric modifications due to urbanization generally
lead to a modified thermal climate that is warmer than the
surrounding non-urbanized areas, particularly at night. This
phenomenon is the urban heat island (UHI). UHIs have long
been studied by ground-based observations taken from fixed
thermometer networks or by traverses with thermometers
mounted on vehicles. With the advent of thermal remote
sensing technology, remote observation of UHIs became
possible using satellite and aircraft platforms and has
provided new avenues for the observation of UHIs and
the study of their causation through the combination of
thermal remote sensing and urban micrometeorology. In
some ways, it has also complicated definitions of urban
heat islands and interpretations of the resulting observations.
The present review is prompted, in part, by the appearance
of new satellite-based sensors and the increasingly widespread
use of infrared sensors in the study of surface
climates in general. This has also increased opportunities
for studying the UHI and urban-modified climates more
generally.
We begin by emphasizing the importance of using proper
definitions in the application of thermal remote sensing to
the study of urban climates. This is followed by a brief
survey of literature appearing since Roth, Oke, and Emery
(1989) that highlights several recurrent themes. It then reexamines
the methodological questions raised by Roth et al.
(1989) concerning problems involved in the application of
remotely sensed thermal imagery to the study of urban
climates. It makes comment on the progress made on these
points by research conducted over urban and other surfaces.
The review concludes by commenting on future prospects
for progress in answering these questions.
0034-4257/03/$ - see front matter D 2003 Elsevier Inc. All rights reserved.
doi:10.1016/S0034-4257(03)00079-8
* Corresponding author. Fax: +1-519-661-3750.
E-mail address: javoogt@uwo.ca (J.A. Voogt).
www.elsevier.com/locate/rse
Remote Sensing of Environment 86 (2003) 370–384
1.1. Thermal remote sensing of urban surfaces: definitions
Proper definition of remotely sensed variables is important
in order to understand precisely the information content
of remotely sensed quantities and how they relate to
actual surface properties. Thermal remote sensing of urban
surface temperatures is a special case of observing land
surface temperature which varies in response to the surface
energy balance. The resultant surface temperature incorporates
the effects of surface radiative and thermodynamic
properties, including surface moisture, thermal admittance
and surface emissivity, the radiative input at the surface
from the sun and atmosphere, and the effects of the nearsurface
atmosphere and its relation to turbulent transfer
from the surface.
Becker and Li (1995), Norman and Becker (1995), Norman,
Divakarla, and Goel (1995) and Prata, Caselles, Coll,
and Sobrino (1995) have carefully examined the definitions
associated with thermal remote sensing of land surfaces, and
the reader is referred to them for details. Here, we use the
term directional brightness temperature to describe the
temperature derived from the inversion of Planck’s law for
a thermal sensor operating in a given waveband. Directional
brightness temperatures relate the detector-received radiance
to a temperature, without consideration of any processes
influencing the received radiation along the path from the
Fig. 1. Heat island transect across Vancouver BC for (a) nighttime (YD 238 1992; 9 h after sunset) and (b) daytime (YD 237 1992; 2 h following solar noon)
showing canopy level air temperature and remotely sensed surface temperature with various levels of correction applied. The plotted results represent values
normalized to a single time. Symbols are reduced to every third point to improve readability. The automobile traverse passed through a tunnel ( f 4 km) and
over bridges ( f 15 and 25 km) along the route. Panel (c) is the sky view factor for sites along the traverse route as determined from digital fish-eye lens
photographs, NDVI derived from AVHRR imagery during the traverse, emissivities applied during the conversion to obtain directional radiometric
temperatures, and wall and vegetation area weightings applied to take into account ‘‘unseen’’ surfaces from the nadir viewing position.
J.A. Voogt, T.R. Oke / Remote Sensing of Environment 86 (2003) 370–384 371
surface through the atmosphere to the sensor (with appropriate
sensor offsets and gains applied). Directional radiometric
temperatures are those that have been corrected for
atmospheric transmission and surface emissivity effects for
a particular sensor-viewing angle. These definitions apply
most strictly to a homogeneous surface, but techniques are
available to extend their use to heterogeneous surfaces
(Becker & Li, 1995; Norman & Becker, 1995).
Heat islands can be defined for different layers of the
urban atmosphere, and for various surfaces and even the
subsurface (Oke, 1995; Voogt & Oke, 1997). It is important
to distinguish between these different heat islands as their
underlying mechanisms are different (Oke, 1982; Roth et al.,
1989). Unless otherwise indicated, an urban heat island
refers to the excess warmth of the urban atmosphere compared
to the non-urbanized surroundings. Atmospheric heat
islands are best expressed under calm and clear conditions at
night when radiative cooling differences are maximized
between urban and surrounding rural locations (e.g. Fig. 1a).
Atmospheric heat islands may be defined for the urban
canopy layer (UCL), that layer of the urban atmosphere
extending upwards from the surface to approximately mean
building height, and the urban boundary layer (UBL), that
layer above the UCL that is influenced by the underlying
urban surface. Canopy layer UHI are typically detected by
in situ sensors at standard (screen-level) meteorological
height or from traverses of vehicle-mounted sensors, such
as that shown in Fig. 2. UBL heat island observations are
made from more specialized sensor platforms such as tall
towers, radiosonde or tethered balloon flights, or from
aircraft-mounted instruments. These direct, in situ measurements
require radiation shielding and aspiration to give
representative measurements and their setting relative to
surrounding features is important.
Measurements of atmospheric fluxes and scalar quantities
such as air temperature are influenced by turbulent or
scalar source areas that lie on the surface upwind of the
measurement site. The shapes of these areas are determined
by the sensor height and characteristics of atmospheric
turbulence and stability (Fig. 2). They may be defined in a
probabilistic sense using models (e.g. Schmid, 1997). Most
current source area models apply to sensors positioned well
above the surface roughness elements; the source areas of
sensors located within the UCL are less well known due to
the complexities of within canopy flows and are the subject
of current research.
On the other hand, thermal remote sensors observe the
surface urban heat island (SUHI), or, more specifically they
‘see’ the spatial patterns of upwelling thermal radiance
received by the remote sensor (most often directional radiometric
temperatures or directional brightness temperatures
corrected only for atmospheric transmission). The effective
radiometric source area for a remote thermal measurement
is the instantaneous field of view (IFOV) of the sensor
projected onto the surface. This geometrically defined
source area is significantly different than the source areas
of turbulent atmospheric fluxes and scalars (Fig. 2). The
actual combination of surfaces viewed within the effective
radiometric source area depends on the sensor viewing
geometry and surface structure; a significant portion of the
complete urban surface may not be viewed due to the threedimensional
structure of the surface.
In contrast to the direct in situ measurements made of
atmospheric heat islands, the remotely sensed SUHI is an
indirect measurement requiring consideration of the intervening
atmosphere and the surface radiative properties that
influence the emission and reflection of radiation within the
spectral wavelengths detected by the sensor. Fig. 1 shows
the surface temperatures along a cross-section of Vancouver,
BC at each stage of the correction process. Observations
were made from an airborne thermal scanner (8–14 Am)
with a 12j FOV, at an altitude of approximately 2100 m
(day) and 1500 m (night), yielding imaged areas on the
ground of 450 Â 450 m–320 Â 320 m for which the mean
temperatures are displayed. The original measured signal,
converted to a temperature is the directional brightness
temperature. The second stage is to apply correction for
atmospheric effects. This is accomplished in the case study
using locally launched balloon soundings of pressure, temperature
and humidity as input to the LOWTRAN 7 atmospheric
radiative transfer model (Kneizys et al., 1988) to
estimate the atmospheric transmission and emission in the
path between the sensor and the surface. Thirdly, the directional
radiometric temperature series is calculated using
Fig. 2. Conceptual source areas. The Instantaneous Field Of View (IFOV,
geometrically derived) defines the radiative source area for an aircraft or
satellite-based thermal remote sensor viewing an urban surface. Source
areas of a scalar, such as air temperature, for a sensor S located within the
urban canopy layer are indicated by the hatched areas. Two different
approximate scalar source areas are shown to illustrate that their location
and size changes with wind direction and atmospheric stability; the precise
shape of these source areas is approximate due to the complexities of flow
within the UCL.
J.A. Voogt, T.R. Oke / Remote Sensing of Environment 86 (2003) 370–384372
estimates of the down-welling radiance derived from LOWTRAN
and an estimate of the emissivity of the surface
materials; varying from 0.97 (0.98 when dew was noted) for
the vegetated agricultural surfaces to 0.95 for more highly
urbanized surfaces (Fig. 1c). Finally, a crude correction is
added to account for surfaces not viewed by the remote
sensor, thereby incorporating the three-dimensional roughness
of the surface (Fig. 1c; wall and vegetation weightings).
Further details are provided in the section How do
sensor detected radiant temperatures relate to the actual
urban surface temperature? This illustration shows how
significant such corrections can be. Indeed, if they are not
applied, or are incorrectly estimated, it is possible to alter
interpretations (e.g. the magnitude of the SUHI) or miscalculate
derived quantities (e.g. the surface–air turbulent
fluxes; note the change in the strength of the surface–air
temperature differences in Fig. 1). It is sobering to realize
that these corrections are relatively crude and can be
expected to change as our knowledge of urban radiation
properties and exchanges improves.
2. Literature review
The first SUHI observations (from satellite-based sensors)
were reported by Rao (1972). Since then, a variety of
sensor-platform combinations (satellite, aircraft, groundbased)
have been used to make remote observations of the
SUHI, or of urban surface temperatures that contribute to
SUHI over a range of scales. Table 1 lists studies, since the
review of Roth et al. (1989), that have used remote sensing
to examine urban thermal climates. Gallo, Tarpley, McNab,
and Karl (1995) also review procedures and prospects for
satellite identification of urban heat islands.
The research in Table 1 has addressed several main
themes. First, many studies have used thermal remote
sensing to examine the spatial structure of urban thermal
patterns and their relation to urban surface characteristics.
This type of research dates back to the time of Rao’s (1972)
study. Satellite-based studies have continued to use AVHRR
or Landsat thermal imagery combined with independent
land use (description of urban activities occurring on the
land surface), or sometimes land cover maps (with more
specific description of the types of materials or structure
present) to assess the spatial patterns of directional brightness
or radiometric surface temperature (Balling & Brazel,
1988; Carnahan & Larson, 1990; Lougeay, Brazel, &
Hubble, 1996). Multispectral techniques are now more
frequently used to perform land use or land cover assessments
at the same time as the thermal imagery is obtained
(Aniello, Morgan, Busbey, & Newland, 1995; Dousset,
1991; Gallo & Owen, 1998; Lougeay et al., 1996; Nichol,
1996). High spatial resolution imagery obtained primarily
from airborne remote sensing has been used to assess the
thermal behaviour of urban surfaces in relation to surface
characteristics such as sky view factors (Eliasson, 1992),
surface materials (Ben-Dor & Saaroni, 1997; Quattrochi &
Ridd, 1994) or NDVI (Lo, Quattrochi, & Luvall, 1997).
With increasing sensor resolution and low-altitude flights, it
is possible to extract temperatures from specific urban
surfaces for analysis (Quattrochi & Ridd, 1994; Shoshany,
Aminov, & Goldreich, 1994) or use in models (Iino &
Hoyano, 1996; Voogt & Grimmond, 2000). A modelling
study relevant to the study of remotely sensed urban surface
temperatures and the SUHI is the surface heat island model
(SHIM) presented by Johnson et al. (1991) that was used by
Oke, Johnson, Steyn and Watson (1991) to evaluate proposed
mechanisms for the genesis of the heat island. The
SHIM model results underscore the importance of surface
geometry and surface thermal properties (especially thermal
admittance) in the creation of the SUHI and the importance
of assessing these parameters in both the urban and rural
environments. Some recent work has begun to address
issues related to the three-dimensional roughness of cities
through combination of ground-based and remotely sensed
directional radiometric temperatures to generate more
areally representative urban radiometric temperature estimates
(Iino & Hoyano, 1996; Nichol, 1998; Voogt, 2000;
Voogt & Oke, 1997). These combine thermal remote sensing
with detailed urban surface morphology databases to
assess the directional effects inherent in directional radiometric
temperature observations made over urban areas
(Voogt & Oke, 1998; Voogt & Soux, 2000).
The second theme found in the studies of Table 1 is the
application of thermal remote sensing to the study of urban
surface energy balances. This is accomplished by coupling
urban climate models of the urban atmosphere with
remotely sensed observations. The most frequently applied
approach has been that of Carlson, Dodd, Benjamin, and
Cooper (1981). This approach couples remotely sensed
measurements of temperature with a 1-D atmospheric model
to estimate surface energy balance fluxes and estimates of
surface properties such as thermal admittance and surface
moisture availability based on regression equations relating
atmospheric model output versus remotely observed surface
brightness temperature (corrected for atmospheric influences).
The most recent application of this approach was to the
city of Atlanta by Hafner and Kidder (1999) wherein a more
detailed 3-D numerical model of the atmosphere was used.
Thermal data, as well as information from other spectral
bands, notably short-wave reflectance, to model surface
absorption of short-wave radiation and NDVI to help
parameterize the ground heat flux, have also been used
(Kim, 1992; Parlow, 1999). Another method relating NDVI
and directional brightness temperature has been used to
study urban climate modifications, and to monitor changes
in climate resulting from expansion of urban areas (Carlson
& Sanchez-Azofeifa, 1999; Owen, Carlson, & Gillies,
1998). Bulk heat transfer approaches based on the use of
remotely sensed directional radiometric temperatures, used
extensively over agricultural and vegetated surfaces, have
also been applied to urban areas (Voogt & Grimmond, 2000)
J.A. Voogt, T.R. Oke / Remote Sensing of Environment 86 (2003) 370–384 373
and at the scale of individual buildings (Hoyano, Asano, &
Kanamaru, 1999).
The third major theme in Table 1 is the application of
thermal remote sensing to study the relation between atmospheric
heat islands and SUHIs. Several studies combine
coincident remote and ground-based observations (Ben-Dor
& Saaroni, 1997; Caselles, Lopez Garcia, Melia, & Perez
Cueva, 1991; Dousset, 1989, 1991; Lee, 1993; Stoll &
Brazel, 1992) and some also with urban atmosphere models
(Hafner and Kidder, 1999; Henry, Dicks, Wetterqvist, &
Roguski, 1989) to study surface–air temperature relations,
although this is more generally addressed through empirical
models. Other studies have been motivated by the idea that
satellite observations may be able to detect and correct for
Table 1
Studies that have applied thermal remote sensing to the study of urban climates
Study Platform: sensor Application
Balling and Brazel (1988) Sat: AVHRR Relation between surface temperature patterns and land use and day-to-day
variability of spatial patterns.
Dousset (1989) Sat: AVHRR Surface and air temperature relationships over an urban area.
Henry et al. (1989) Sat: HCMM Urban heat island analysis using remote sensing, ground observations and
modelling.
Carnahan and Larson (1990) Sat: Landsat TM Urban–rural heating and cooling differences.
Caselles et al. (1991) Sat: AVHRR Satellite and ground-based heat island analysis.
Dousset (1991) Sat: AVHRR, SPOT Multispectral classification of urban land use areas and their relation to
surface temperature.
Johnson et al. (1991) Ground-based IRT Surface urban heat island model.
Eliasson (1992) Ac: AGEMA Correlation between ground surface temperature and sky view factor.
Kim (1992) Sat: Landsat TM Energy balance modelling of an urban area.
Stoll and Brazel (1992) Aircraft,
Ground-based/IRT
Detailed assessment of surface and air temperature relations for different
urban surface types.
Gallo et al. (1993a, 1993b) Sat: AVHRR Use of NDVI to assess the urban heat island.
Lee (1993) Sat: AVHRR Air and surface heat island assessment of Korean cities in relation to
urban development.
Johnson et al. (1994) Sat: TOVS Estimation of rural air temperatures from satellite sounding data for deriving
urban air temperature bias.
Quattrochi and Ridd (1994) Ac: TIMS Day and nighttime thermal response of individual urban surface types.
Shoshany et al. (1994) Ac: Thermal Scanner Extraction of roof top temperatures for heat island analysis.
Aniello et al. (1995) Sat: Landsat TM,
MSS
Spatial distribution of urban surface temperatures and tree cover.
Epperson et al. (1995) Sat: AVHRR, DMSP Estimating urban air temperature bias using NDVI and nighttime light data.
Gallo et al. (1995) Sat: AVHRR Review of procedures and future prospects for satellite assessment of urban
heat island effects.
Gallo and Tarpley (1996) Sat: AVHRR Effect of compositing on the use of NDVI for assessing heat island effect.
Iino and Hoyano (1996) Ac: MSS Urban energy balance modelling using remote sensing and GIS databases.
Lougeay et al. (1996) Sat: Landsat TM Temperature patterns associated with land use and land use change.
Nichol (1996) Sat: Landsat TM Spatial patterns of surface temperature in relation to urban morphology.
Ben-Dor and Saaroni (1997) Ac: TIrS Simultaneous surface and air temperature heat island analysis.
Lo et al. (1997) Ac: ATLAS Relation of thermal data to land cover and NDVI.
Voogt and Oke (1997) Ac: AGEMA Creation of areally representative urban surface temperatures.
Gallo and Owen (1998) Sat: AVHRR, DSMP/
Landsat MSS
Multispectral identification of urban areas for estimating heat island bias
in large scale temperature records.
Nichol (1998) Sat: Landsat TM Incorporation of wall surface temperatures with remote sensing to create
three-dimensional representation of urban temperatures.
Owen et al. (1998) Sat: AVHRR Use of thermal and NDVI data coupled with SVAT models for investigating
climate change associated with urbanization.
Voogt and Oke (1998) Ac: AGEMA Thermal anisotropy of urban surfaces.
Carlson & Sanchez-Azofeifa (1999) Sat: AVHRR Urban microclimate change associated with urbanization.
Hafner and Kidder (1999) Sat: AVHRR SUHI and UHI patterns associated with thermal inertia and moisture availability.
Hoyano et al. (1999) Ground-based
thermal scanner
Calculation of sensible heat flux from individual buildings.
Parlow (1999) Sat: Landsat TM Energy balance modelling of an urban area using multispectral methods.
Wald and Baleynaud (1999) Sat: Landsat TM Air quality assessment using thermal remote sensing.
Quattrochi et al. (2000) Ac: ATLAS Use of thermal remote sensing in a GIS framework to assess urban heat islands.
Soux et al., 2000 Tower/IRT Three-dimensional sensor view model of urban surfaces.
Voogt (2000) Ac: AGEMA Areally representative urban surface temperatures at different scales.
Voogt and Grimmond (2000) Ac: AGEMA Sensible heat flux modelling and estimation of surface thermal roughness
lengths of an urban area using thermal remote sensing and ground observations.
Voogt and Soux (2000) Tower/Thermal
Scanner, IRT
Local scale urban thermal anisotropy.
J.A. Voogt, T.R. Oke / Remote Sensing of Environment 86 (2003) 370–384374
any urban influence that may ‘contaminate’ screen-level air
temperature records (Epperson et al., 1995; Gallo et al.,
1993a, 1993b; Gallo & Owen, 1999; Gallo & Tarpley, 1996;
Johnson, 1994). Gallo et al. (1995) review procedures for
satellite assessment of the UHI effect and consider prospects
for future use of satellite remote sensing in the evaluation
and monitoring of UHI. Much emphasis has been placed on
the use of urban–rural differences in the vegetation index
(NDVI) as a measure of the difference in surface properties
such as heat storage capacity and evaporation, to estimate
urban and rural minimum air temperatures (e.g. Gallo &
Tarpley, 1996). More recently, satellite observed nighttime
light data have been found to help discriminate between
urban and rural areas (Gallo & Owen, 1998). NDVI-based
measures are found to be consistently slightly better than
measures based on satellite-derived surface temperature
differences, and to perform similarly to those based on
population (Gallo & Owen, 1998). However, measures
based on population are known to be less successful than
those that incorporate site specific measures of surface
properties known to be important to the establishment of
differential cooling rates (e.g. Oke, 1982).
2.1. Progress on questions raised by Roth et al. (1989)
In a paper now commonly used to frame research
proposals, Roth et al. (1989) raised four questions regarding
the limitations of applying satellite-derived thermal imagery
in urban climate studies. These were as follows.
(1) What are the characteristics of the urban surface as
viewed by thermal remote sensors?
(2) What is the relation between remotely observed
radiometric surface temperature and the actual temperature
of the urban-atmosphere interface?
(3) How can surface urban heat islands be related to
atmospheric urban heat islands?
(4) How can thermal remote sensing of urban surfaces
provide input into models of urban climate?
In the following, any progress made toward answering
each of these questions is reviewed in relation to the urban
studies in Table 1, as well as to related developments over
other rough, inhomogeneous surfaces such as agricultural
and natural vegetated areas.
2.1.1. What is the nature of the urban surface as seen by a
remote sensor?
Some progress has been made on the assessment of urban
surfaces, as viewed by a remote sensor through the use of
sensor view models (e.g. Soux, Voogt, & Oke, 2003) that
advance our understanding beyond the basic conceptual
description given by Roth et al. (1989) and illustrated in
Voogt and Oke (1997). Sample results are shown in Fig. 3
for a modelled urban surface described in Table 2, where
dimensions have been chosen to replicate the complete-toplan
area ratio of the study area as determined from GIS
analysis (Voogt & Oke, 1997). Such models typically
represent the buildings as block-like elements on a plane
surface and therefore constitute only a crude approximation
to the actual complexity of urban surfaces. These models
have their basis in simple two-dimensional models of
agricultural surfaces (e.g. Caselles, Sobrino, & Coll,
1992). Development and application of sensor view models
are more advanced for agricultural and forested than for
urban surfaces (Otterman, Brakke, Fuchs, Lakshmi, &
Cadeddu, 1999; Otterman et al., 1995; Smith & Goltz,
1994) where better information on the structural attributes
of vegetation canopies is available. Highly detailed canopy
radiative transfer models are available for these surfaces to
couple with sensor-viewing models (e.g. Myneni et al.,
1995) including examples that use ray-tracing (Govaerts
and Verstraete, 1998) and radiosity methods (Qin and
Gerstl, 2000).
The canopy architecture of vegetated surfaces has
received detailed attention (e.g. Fournier, Rich, & Landry,
1997) and techniques have been developed to extract surface
structural parameters using remote sensing (e.g. Jasinski
& Crago, 1999). The parameters are applied across a
range of models of processes over these surfaces including
models of thermal anisotropy (Otterman et al., 1995). In
Fig. 3. View factors of urban surface components for an airborne thermal
scanner sensor calculated using the S3mod model (Soux et al., 2000) for the
light industrial area of Vancouver BC (Table 2). YD 228 1992, 1030 LDT.
Table 2
Surface dimensions for the light industrial region of Vancouver, BC (see
Voogt and Grimmond 2000; Voogt, 2000) used for the model simulations
shown in Figs. 3, 5, and 6
Surface dimension Value (m)
Building height 7
Building width 30
Building length 23
Street width 22
Alley width 12
Building spacing 9
Number of buildings per street block 3
Roof-to-plan area ratio 0.4
Complete-to-plan area ratio 1.43
J.A. Voogt, T.R. Oke / Remote Sensing of Environment 86 (2003) 370–384 375
contrast, urban surface morphology, while subject to
detailed inventories of land cover (e.g. Quattrochi & Ridd,
1994), has only recently been assessed quantitatively (Bottema,
1997; Grimmond & Oke, 1999) in relation to questions
of urban climate. This has largely been through
morphometric analysis intended to characterize the aerodynamic
roughness of urban surfaces for use in air pollution
dispersion (Brown, 2000; Cionco & Ellefsen 1998) and
turbulent transfer. Even then, our ability to describe the
combination of built and vegetative elements of the urban
surface is lacking. Urban areas are typically represented at
small scales as regular combinations of rectangular bluffbody
elements, but this ignores significant small scale
complexity such as pitched roofs, variable building height
and it often completely ignores urban vegetation. Planeparallel
vegetation canopies and regular geometric urban
surfaces form the end-members of a spectrum of surface
types; more realistic urban surface representations need to
include elements of both.
At small scales, more advanced depiction of urban areas
in GIS models is becoming possible (Gruen & Wang, 1998;
Kim & Muller, 1998). Haala and Brenner (1999) combine
multispectral imagery and laser altimeter data to extract
buildings, trees and grassy areas that can be used to generate
3-D visualizations of urban landscapes that combine buildings
and trees. Other methods that combine laser altimeter
data with building footprint maps can be used to reconstruct
3-D building geometry. Object extraction of buildings, and
3-D city and building models are listed among the major
thematic topics published in the journal ISPRS (Baltsavias,
2000) and a future special issue on urban areas is planned to
include detection and 3-D object reconstruction (including
buildings and vegetation), generation of 3-D city models,
and the application of multisensor data techniques to urban
areas http://www.photogrammetry.ethz.ch/journal). In a
modelling context, application of computer graphics techniques
such as radiosity and ray tracing in complex structural
environments may provide avenues for advancement.
At larger scales, databases with detailed land cover
derived from field observation (Grimmond & Souch,
1994), or multispectral remote sensing, have contributed
to a better understanding of the urban surface in relation to
surface energy balances, but they have not been related
specifically to structural parameters that could be used to
better define the urban surface for use in sensor view
models. The ‘‘triangle’’ method of Gillies and Carlson
(1995) provides one approach to derive more physically
relevant surface characteristics to urban climate (Owen et
al., 1998).
Advances in our knowledge of urban surface structure
and other properties may come from increases in capability
afforded by digital hemispherical photography in the assessment
of view factors of urban surfaces (Grimmond, Potter,
Zutter & Souch, 2001) and the combined use of remote
sensing and GIS to better characterize the structure of the
urban surface. As noted, urban surface representation needs
to be improved to include the impacts of small-scale
structural features such as: roof geometry, variable building
height and vegetation geometry (including stand parameters
such as LAI, leaf angle distributions) as they contribute to
the overall urban surface structure. Here, the use of remotely
sensed parameters such as NDVI and other high-resolution
multispectral remote sensing information (e.g. IKONOS
imagery), or perhaps radar imagery, may contribute to better
characterization of the urban surface as it relates to understanding
remotely observed thermal imagery. Details of the
small-scale structure of the urban surface also need to be
parameterized, so it can be applied to remotely sensed
variables observed at coarser resolution, such as the widely
used AVHRR or Landsat TM instruments.
2.1.2. How do sensor detected radiant temperatures relate
to the true temperature of the urban-air interface?
Research on the use of thermal remote sensing to
determine land surface temperatures has been the subject
of several reviews, e.g. Carlson et al. (1995), Norman et al.
(1995), Prata et al. (1995), Qin and Karnieli (1999). Thermal
remote sensors estimate surface temperature from the
radiance received by a detector that has a narrow solid angle
of view. Measurements are subject to the effects of: (a)
atmospheric absorption and emission between the sensor
and the surface, and (b) the characteristics of the surface,
especially its emissivity and geometric form. Corrections for
atmospheric effects are relatively well established in remote
sensing practice, although there is little information on the
role played by the known spatial variations of atmospheric
transmissivity over urban areas that may influence accurate
retrieval of surface temperatures. These variations may be
particularly important to the determination of urban–rural
temperature differences when effects of the polluted ‘urban
plume’ are taken into account (e.g. Wald & Baleynaud,
1999).
The application of satellite sensors to the determination
of land surface temperature is complicated by any heterogeneity
or roughness of the land, therefore, this becomes a
significant issue when dealing with urban surfaces. The
three-dimensional nature of the urban surface, combined
with solar and sensor geometric considerations, implies that:
(a) urban surfaces contain strong microscale temperature
patterns that are influenced by the relative orientation of
urban surface facets to the sun (or to the sky at night), as
well as by the thermal properties of surfaces that usually
also vary with their location and orientation, e.g. roof
properties vs. wall properties;
(b) a biased view of the urban surface is ensured when
narrow IFOV sensors are used to view a threedimensionally
rough surface. Together, these properties
lead to an effective anisotropy of the upwelling longwave
radiation from the urban surface; i.e. directional
variations in the sensor-detected upwelling long-wave
radiance. The term ‘‘effective’’ anisotropy is used to
J.A. Voogt, T.R. Oke / Remote Sensing of Environment 86 (2003) 370–384376
indicate that it is a function of the surface structure as
distinct from the (assumed) near-Lambertian properties
of individual surface components. In some presentations,
anisotropic effects due to temperature differences
are not distinguished from emissivity effects due to
rough surfaces. An important discussion of the
terminology related to thermal remote sensing of
surfaces that underscores the difficulty in applying
thermal remote sensing to rough surfaces is given by
Norman and Becker (1995) and Norman et al. (1995).
Anisotropy is not unique to the study of urban surfaces.
Treatment of anisotropic effects was identified as a priority
item arising from the FIFE field campaign by Sellers and
Hall (1992) who recommended that surface anisotropic
effects be identified over a range of surface types. Here,
observations of effective thermal anisotropy and modelling
it for urban surfaces are considered separately.
2.1.2.1. Observations of effective thermal anisotropy. Thermal
anisotropy of rough Earth surfaces has been studied at
scales ranging from soil surface micro relief (Verbrugghe
& Cierniewski, 1998) upwards through plant canopies
(Chehbouni et al., 2001; Lagouarde, Kerr, & Brunet,
1995) and forests (e.g. Lagouarde, Ballans, Moreau,
Guyon, & Coraboeuf, 2000; McGuire, Balick, Smith, &
Hutchison, 1989) to mountainous terrain Lipton and Ward
(1997) and the assessment of large scale satellite studies
(Minnis & Khaiyer, 2000). Agricultural and natural vegetated
surfaces have been studied most extensively (Paw U,
1992) including observations (Lagouarde et al., 1995)
intended to validate models as well as to better represent
heat fluxes over rough surfaces (e.g. Brutsaert & Sugita,
1996).
Effective thermal anisotropy from selected urban land
use areas has been directly observed using both airborne
(Iino & Hoyano, 1996; Voogt & Oke, 1998) and towermounted
measurements (Voogt & Soux, 2000), as well as
through combinations of ground-level and remote observations
(Nichol, 1998). Asano and Hoyano (1996) tested a
specialized spherical thermography technique to better sample
the 3-D temperature structure of urban areas. To date,
satellite assessment of urban thermal anisotropy has not
been reported although satellite thermal anisotropy has been
used in larger scale studies (Lipton & Ward, 1997; Minnis &
Khaiyer, 2000).
The available observations indicate that urban areas show
significant effective thermal anisotropy that ranks them high
relative to other surfaces. Nadir remote views of the urban
surface may yield temperatures that are warmer or cooler
than off-nadir views, depending on the view direction
relative to solar position. The observations also indicate
that anisotropy remains surprisingly strong in residential
areas with only relatively low building heights and large
amounts of vegetation. In these areas, the microscale structure
of some urban surfaces, especially peaked roofs and the
anisotropy created due to shading patterns by a fairly sparse
canopy of trees may be influencing factors. Confirmation
awaits examination of a greater range of urban surfaces
incorporating a range of vegetative canopy structures and/or
modelling studies.
The daytime airborne traverse data displayed in Fig. 1b
includes anisotropic effects as evident from the duplicated
portion of the traverse over the residential area ( f 18 km).
This arises due to a slight off-nadir viewing angle of the
scanner and alternating directions of the airborne traverse so
that the scanner azimuth was reversed between the two
portions of the traverse (the temperature data has been
normalized to account for surface cooling during the time
of the traverse).
Thermal anisotropy is not limited to the daytime case.
The effects of the thermal and structural properties of cities,
as described by Roth et al. (1989), particularly the low
thermal admittance and large sky view factor of roofs
compared to building walls or other surfaces deeper within
urban canyons, generates nocturnal effective anisotropy,
such that near-nadir views generate lower directional radiometric
temperatures than do off-nadir views (Fig. 4).
As yet, no long-term observational studies of urban areas
have been conducted to assess the temporal nature of the
effective anisotropy as related to varying solar zenith angle;
however, the ongoing development of models is likely to
allow progress in this area. Ground-based observations of
hemispherical long-wave radiation that may be used to
develop relations between hemispheric and directional temperature
(e.g. Otterman et al., 1995) have not often been
made as part of urban climate studies, but more recent urban
field studies should correct this; ESCOMPTE, (Mestayer &
Durand, 2002), BUBBLE (Rotach, 2002). In these studies, it
will be necessary to assess the representativeness of the
ground-based measurements, but here again, models may be
useful.
Direct and indirect observations of urban thermal anisotropy
have been used to devise methods to estimate more
Fig. 4. Thermal image of downtown Vancouver, obtained from an airborne
thermal scanner illustrating nighttime temperature variations from a small
off-nadir viewing angle. Rooftops are cool relative to building walls and
streets. The image is corrected for atmospheric effects.
J.A. Voogt, T.R. Oke / Remote Sensing of Environment 86 (2003) 370–384 377
representative temperatures of the urban surface (Nichol,
1998; Voogt, 2000; Voogt & Oke, 1997). These methods
attempt to combine the temperatures of various component
surface types (e.g. vertical as well as horizontal surfaces) to
yield areally weighted temperatures that take into account
all urban surfaces. Such results represent a step towards
developing tools to correct or normalize for urban thermal
anisotropy. The results themselves need to be considered in
the context of the application intended; e.g. a fully areally
weighted urban temperature may not be representative of
surfaces that contribute to the urban sensible heat flux.
Other weighting schemes may be considered, for example,
based on the use of surface view factors. The application of
GIS techniques to detailed urban surface representations
also provides the ability to extract select surface components
for combination with temperature data to allow
various other surface combinations. Such techniques have
already been applied to the study of urban aerodynamic
roughness lengths (Bottema, 1997; Grimmond & Oke,
1999). The surface heat island traverses shown in Fig. 1
incorporate a crude correction for the wall and vegetated
surfaces that cannot be seen by a nadir-pointing remote
sensor. In this case, weighting for the wall and obscured
vegetated surfaces is derived for the downtown, residential
and light industrial areas studied by Voogt and Oke (1997)
and is then extrapolated to other portions of the traverse.
Average wall temperatures are derived from observations
made during the study period (Voogt, 2000) and nonviewed
vegetated surfaces are assumed to be at air temperature.
The application of these corrections can be seen to
have a substantial impact on the derived magnitude of the
SUHI and of the difference between canopy level air
temperatures and surface temperatures. It is also interesting
to note that within the urbanized portion of the transect, the
area weighting for unseen vegetation, calculated from
analysis of field observed vegetation structural parameters,
has a relatively good correspondence with the NDVI values
calculated from the AVHRR sensor. However, the generality
of this relationship is unknown due to the relative
importance of tree canopies to the contribution of unseen
vegetation area, and trees make up a large fraction of the
vegetated area in the residential areas of the city relative to
the agricultural area.
Extraction or inversion of component temperatures has
been accomplished over vegetated areas where the surface
can be generalized into two components, vegetation and
soil, by coupling multi-directional thermal remote observations
with either model results or ground observations (e.g.
Chehbouni et al., 2001; Francßois, 2002; Francßois, Ottle, &
Prevot, 1997). These results demonstrate that multi-directional
measurements hold promise for better detection of
soil moisture status using thermal remote sensing. It is not
clear whether this can be accomplished over the more
complex surfaces of urban areas, although there is some
evidence that some urban surface components can be
detected in the distribution of directional radiometric temperatures
(Voogt & Oke, 1998) and the day and nighttime
thermal response of individual urban surfaces has been
documented (Quattrochi & Ridd, 1994).
Directional effects of effective thermal anisotropy are
complicated by uncertainty in urban surface emissivities.
Emissivities applied to urban surfaces have ranged from
0.87 (Balling & Brazel, 1988) up to 0.97 (Dousset, 1989;
Henry et al., 1989), with most values in the range 0.92–
0.95. A few studies incorporate variable emissivity corrections
based on land use characteristics (Balling & Brazel,
1988; Caselles et al., 1991; Lougeay et al., 1996) with
emissivity values derived from tabled properties. Very few
direct observations of urban surface emissivity are available.
Notable exceptions are the roof emissivity study of Artis
and Carnahan (1982), and some field-based observations of
the emissivity of component urban surfaces (Verseghy &
Munro, 1989). Some newer compilations of spectral emissivities
for common urban materials have become available
(MODIS UCSB emissivity library: http://www.icess.ucsb.
edu/modis/EMIS/html/em.html ASTER spectral library:
http://speclib.jpl.nasa.gov/) based on laboratory measurements
of sample materials. Current estimates of ‘‘bulk’’
urban emissivity (i.e. an estimate that would apply to scales
larger than an individual component surface, and which
takes into account the trapping effect of rough surface
geometry, e.g. Sutherland & Bartholic, 1977) are limited
to model results (Arnfield, 1982). Advances in the separation
of land surface temperature and emissivity effects using
satellites (Gillespie et al., 1998; Schmugge, French, Ritchie,
Rango & Pelgrum, 2002; Sobrino, Raissouni, & Li, 2001)
may offer some ability to generate urban surface emissivities,
although the assumptions inherent in the methods may
be restrictive over cities where strong small-scale heterogeneity
is present. For example, Sobrino et al. (2001) note
that algorithms are available to retrieve surface emissivity
but under the restriction that the angular dependence of the
bidirectional reflectivity of the surface is known and atmospheric
corrections are applied. No studies have examined
the former issue over urban areas, and the effect of spatially
varying atmospheric transmission across urban areas has not
been well studied in relation to thermal remote sensing.
Newer generation satellites, such as ASTER, include multiple
thermal wavelength channels and have significantly
improved spatial resolution. This makes them ideal candidates
to assess the surface variability of urban surface
temperature and to extract surface emissivity estimates using
multispectral methods. Gillespie et al. (1998) suggest that
the emissivity algorithm developed for the new ASTER
sensor should work well with mixed pixels although there is
some dependence on the radiative trapping by rough surfaces
such as urban canyons and some assumptions regarding
the behaviour of individual surface component emissivities
with relation to the methodology employed that have not
been explicitly tested over urban areas. Work on the
application of ASTER over urban surfaces is underway
(Dousset, 2002).
J.A. Voogt, T.R. Oke / Remote Sensing of Environment 86 (2003) 370–384378
2.1.2.2. Modelling thermal anisotropy. Many models are
available to correct anisotropic short-wave radiation distributions
(e.g. Cabot & Dedieu, 1997; Privette, Eck, &
Deering, 1997). These models provide the ability to normalize
directional reflectance, predict directional behaviour and
calculate integrated hemispherical values for a surface
thereby supplementing the limited angular sampling of
remote sensors (Cabot & Dedieu, 1997). However, only a
few models are available for the thermal wavelengths and
none are used operationally due to the relative complexity of
the input data required (Minnis & Khaiyer, 2000).
Thermal models have been used over agricultural and
forested surfaces (Paw U, 1992) to estimate the magnitude
and timing of anisotropy. Some models allow inversion to
recover a limited number of component temperatures from
simple combinations of surface types (soil and vegetation)
but they require extension to include more surface components
(Smith, Ballard & Pedelty, 1997) that are characteristic
of both vegetated and urbanized surfaces. A few
models incorporate the canopy energy balance (e.g. Smith
& Goltz, 1994); most others use prescribed temperatures.
This limits their applicability to situations where observed
temperature distributions are available, or an assumption of
an isothermal or simple statistical temperature distribution
for the canopy can be made (Otterman et al., 1999).
Typically, models developed for vegetated surfaces portray
plane-parallel surfaces and require detailed canopy
structural parameters (LAI, leaf angle distribution and directional
gap fraction) to accurately model the anisotropy and/
or to recover the component leaf and soil temperatures
(Francßois et al., 1997). This will be difficult for cities until
characterization of urban surface morphology has received
more attention.
Simple urban surfaces have now been modelled (Soux et
al., 2000) by extending a 2-D orchard model (Caselles et al.,
1992) to three dimensions and using prescribed surface
temperatures (Figs. 3, 5 and 6). The model allows estimates
of the directional brightness temperature to be estimated for
any given view direction over a modelled urban surface.
Fig. 5 illustrates results for a range of sensor off-nadir and
azimuth view directions at mid-morning for the urban surface
modelled using the parameters given in Table 2 and
using observed component temperatures for sunlit and
shaded components of building roofs, walls and streets from
the study of Voogt and Oke (1997). Fig. 6 summarizes the
model-derived anisotropy for a 45j off-nadir view angle,
represented as the temperature range over all sensor azimuth
directions, and compares these to observations from a
helicopter mounted sensor. Observations are the average
difference between sensor view directions using sequences
of airborne thermal images along flight lines (Voogt & Oke,
1998). Modelled values use observed mean temperatures
from building walls, streets and roofs (see Voogt & Oke,
1997) coupled with a 3-D sensor view model (Soux et al.,
2003). The modelled sensor average is created by running
multiple model simulations for different sensor positions (x
and y location) over the modelled urban surface. The model
requires further validation and continued development by
coupling it to urban energy balance models and by incorporating
better representation of urban canopy structure.
Simple empirical models of thermal anisotropy are lacking,
but recent work over other surfaces (Minnis & Khaiyer,
2000; Suleiman & Crago, 2002) suggests that the development
of such models is feasible.
2.1.3. What is the relation between satellite-derived surface
urban heat islands and those measured in the air?
The brief review presented with Table 1 indicates that a
substantial body of work has been amassed on the subject of
relating SUHI to atmospheric heat islands. To date relations
remain empirical and no simple general relation has been
found. Direct comparison of radiometric surface temperature
with air temperature should consider the differing
source areas for the two measurements (Fig. 2). In addition,
Fig. 5. Modelled directional brightness temperatures for an airborne thermal
scanner (12j FOV) viewing a simple urban surface (Table 2) from an
elevation of 650 m for a mid-morning simulation (YD 228, 1992) using
S3mod (Soux et al., 2000). The image plots sensor-detected brightness
temperature interpolated from modelled values at 5j azimuthal and offnadir
steps.
Fig. 6. Observed and modelled thermal anisotropy for a 45j off-nadir
sensor (12j FOV) at 650 m during one summer day (YD 228 1992) over a
simple urban surface (Table 2). Error bars represent F 1 standard deviation.
J.A. Voogt, T.R. Oke / Remote Sensing of Environment 86 (2003) 370–384 379
urban air temperatures are also influenced by several other
processes in the UCL. Air temperatures are partly determined
by radiative divergence in the UCL air volume. This
topic has been little studied in the UCL since the work of
Nunez and Oke (1976). Advection (horizontal transport of
heat by wind) that arises due to the spatial configuration of
various components of the urban surface that have varying
properties of surface moisture, thermal admittance, and
aerodynamic roughness resulting in different energy balances
and surface temperatures is also important. In urban
areas, small-scale shading patterns can also be an important
influence on the UCL air temperature structure leading to
potential air quality problems due to altered atmospheric
mixing (Reisner, Smith, Bossert, & Winterkamp, 1998;
Ruffieux, Wolfe, & Russel, 1990).
The impact of microadvection was explored by Stoll and
Brazel (1992) who found that correlations between surface
and air temperatures measured from ground stations could
be explained largely through the atmospheric mixing, mean
wind velocity and thermal properties of surface materials.
When extending the analysis to larger scales by using
airborne observations of directional brightness temperature,
the correlations were poorer because of the mixture of
surfaces present within the sensor IFOV. At scales typical
of most satellite sensors, the IFOV of the thermal remote
sensor will view a substantial mix of surfaces incorporating
significant subgrid scale advection, so that correlations may
be expected to include substantial noise. To fully understand
relations, detailed, fully coupled models of surface and
atmospheric processes are required.
Correlations of surface temperature with air temperature
are improved at night, (Fig. 1a; see also Dousset, 1989)
when microscale advection is reduced. Predictive power at
satellite resolutions remains limited (Gallo & Owen, 1998)
although some improvements can be made through the use
of NDVI. This is largely due to the relatively close coupling
between surface and air temperatures that occur at night
over vegetated surfaces.
The addition of weightings to incorporate ‘‘unseen’’
surfaces from nadir-viewing positions does reduce the
difference between surface and air temperatures by overcoming
the anomolously cold (night) and warm (day)
surfaces viewed by near-nadir sensors (Fig. 1). These
corrections have applied detailed knowledge of surface
characteristics that are not routinely available for large-scale
satellite observations. Operational use requires further
research to determine their more general applicability and
methods by which the surface structure and characteristics
may be inferred from more routinely observed variables.
However, even then, we expect that simple surface and air
temperature correlations are likely to work well only in
certain limited situations constrained by atmospheric conditions
and surface properties. For the nighttime case of the
UHI, surface–air temperature differences are expected to be
minimized as winds increase, due to mixing and disruption
of any surface-based inversion layer. Under calm winds and
clear skies, when the UHI has its best expression, microscale
processes dependent on surface thermal properties, sky
view factor and microscale advection will be most apparent
thereby increasing differences between the UHI and SUHI.
We conclude that explanation of the air and surface temperature
differences remains rooted in detailed study of the
surface micrometeorology and geography and are only
likely to be predicted by the application of detailed, fully
coupled surface-atmosphere models.
2.1.4. How appropriate is thermal remote sensing data as
input to models of urban climate?
Roth et al. (1989) note the difficulties inherent in defining
the surface of observation and matching observations
made over rough, incompletely viewed surfaces with the
conceptual surfaces represented in models, and that this
presents a significant problem for urban climatology. For
progress in this area, we again refer to remote-sensing
studies over rough vegetated surfaces. A topic much studied
over the past decade is the ability to model sensible heat
fluxes from such surfaces utilizing thermal remote sensing
(ground, aircraft- or satellite-mounted sensors). For a recent
review of the approaches to thermal remote sensing of the
surface energy balance, see Friedl (2002).
A key difficulty in the estimation of turbulent sensible
heat flux using thermal remote sensing is the relation (or
difference) between the remotely observed surface temperature
and the required aerodynamic temperature (T0 aero)
that yields the correct surface sensible heat flux (Mahrt, Sun,
MacPherson, Jensen, & Desjardins, 1997). Over rough
surfaces, there are real differences in the effective levels
that act as momentum and heat sources, z0m and z0h
respectively, and kBÀ 1
=(ln z0m/z0h). While numerous studies
on the nature of kBÀ 1
and z0h are available for vegetated
surfaces, only one study has reported values for an urban
surface (Voogt & Grimmond, 2000). The results suggest
significant differences between urban and vegetated results
due to the increased importance of bluff elements and the
significant anisotropy of urban surfaces. Newer work over
vegetated surfaces has begun to examine the diurnal and
seasonal variations of kBÀ 1
and z0h that exist due to vertical
temperature distributions (also yielding anisotropy) in plant
canopies (Brutsaert & Sugita, 1996; Matsushima & Kondo,
1997; Crago, 1998; Qualls & Hopson, 1998). Relatively,
little is known about the diurnal variation of z0h, but it has
been suggested (Qualls & Hopson, 1998) that relations may
be developed between the surface temperature patterns and
solar elevation angle (the forcing for anisotropy) to correct
for these effects. Coupling radiative transfer schemes to
models of surface sensible heat flux (e.g. Smith & Goltz,
1994; Smith, Ballard et al., 1997; Smith, Chauhan et al.,
1997) to handle vertical canopy temperature variations is
also a possible solution (Qualls & Yates, 2001). Over urban
surfaces, canopy radiative transfer schemes have been used
extensively for assessing urban canopy layer climate (e.g.
Arnfield, 1982) but they have not been coupled to remote
J.A. Voogt, T.R. Oke / Remote Sensing of Environment 86 (2003) 370–384380
sensing applications. One approach to avoiding inconsistencies
arising from the use of dual- or single source heat
flux models is to model the aerodynamic temperature
(Mahrt et al., 1997; Sun, 1999). Another option is to use
other remotely sensed parameters (Mahrt et al., 1997) to
help determine the aerodynamic temperature, or the relation
between the radiatively determined surface temperature
(T0 rad) and T0 aero.
A promising approach termed the ‘‘triangular’’ method
that couples a soil–vegetation–atmosphere transfer (SVAT)
model to remotely sensed surface temperature and NDVI is
given by Gillies and Carlson (1995) and validated over
natural vegetated surfaces by Gillies, Carlson, Cui, Kustas
and Humes (1997). This approach utilizes relations between
temperature and NDVI to derive surface fractional vegetative
cover and surface soil water content, and also instantaneous
fluxes of sensible and latent heat. The approach can
be used over a range of spatial scales. The method has also
been applied to detect land cover alterations due to urbanization
and to provide estimates of local scale climate
change associated with those disturbances (Owen et al.,
1998). This method gets around the weakness of classifications
that provide land use descriptions rather than parameters
known to be physically linked to surface–atmosphere
exchange processes (Carlson & Sanchez-Azofeifa, 1999).
The surface moisture parameter derived in his way is an
important input to surface atmosphere models and has been
observed to be important in the ratio of sensible and latent
heat fluxes within urban areas (e.g. Grimmond & Oke,
1995, 2002).
3. Summary
This review suggests that progress on the questions raised
by Roth et al. (1989) has been slow to emerge in studies of
thermal remote sensing from urban areas, but that some first
steps have been made. By way of comparison, the surface
structure and anisotropic thermal behaviour of vegetated
surfaces is much better documented and modelled, although
in that field too, progress is noted to have lagged that in
handling canopy—radiation processes in the short-wave
region. Part of the lack of progress over urban surfaces can
be attributed to the greater difficulties in making observations
over the relatively tall urban surface, compared to
shorter vegetated surfaces, and the more complicated surface
structure that combines vegetation and buildings that has
been less well observed or modelled.
To enhance progress, research should examine the ability
to apply advances made in the study of thermal remote
sensing over vegetated surfaces to urban areas, and particularly
to provide progress in three key areas: (1) determine
appropriate surface radiative (e.g. emissivity) and structural
parameters from remote sensing to better describe the urban
surface, and to ensure they are appropriate for use in urban
atmospheric models; (2) couple canopy radiative transfer
models with both sensor view models and surface energy
balance models to better simulate and understand urban
thermal anisotropy and the link between surface temperatures,
the surface energy balance and air temperature in and
above the urban canopy layer; and (3) perform observational
studies with the goal of obtaining better independent validations
of the surface effective parameters derived from
remote thermal sensors.
Glossary
Ac Aircraft
ASTER Advanced Spaceborne Thermal Emission and
Reflection Radiometer
AVHRR Advanced Very High Resolution Radiometer
DMSP Defence Meteorological Satellite Program
IRT Infared Thermometer
IFOV Instantaneous Field of View
MSS Multispectral Scanner
NDVI Normalized Difference Vegetation Index
SUHI Surface Urban Heat Island.
TIMS Thermal Infrared Multispectral Scanner.
TIrS Thermal Infrared Scanner.
UHI Urban Heat Island
UCL Urban Canopy Layer
UBL Urban Boundary Layer
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