Content uploaded by Márcia Matias
Author content
All content in this area was uploaded by Márcia Matias on Mar 12, 2024
Content may be subject to copyright.
Remote Sensing Applications: Society and Environment 34 (2024) 101163
Available online 13 February 2024
2352-9385/© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).
Contents lists available at ScienceDirect
Remote Sensing Applications: Society and
Environment
journal homepage: www.elsevier.com/locate/rsase
LCZ thermal and exoatmospheric albedo assessment using Landsat
8 land surface temperature and reflectance dataset: Case study of
Lisbon
Márcia Matias a,*, António Lopes a,b
aUniversity of Lisbon, Centre of Geographical Studies, Institute of Geography and Spatial Planning, Lisbon, Portugal
bAssociated Laboratory Terra, Portugal
ARTICLE INFO
Keywords:
Land surface temperature
Local climate zones
Landsat 8
Exoatmospheric albedo
Urban heat island
ABSTRACT
This study investigates microclimate changes induced by urbanization, with a focus on the Urban
Heat Island (UHI) phenomenon and the crucial role of Land Surface Temperature (LST). Using the
Landsat 8 Land Surface Temperature and Reflectance dataset obtained from the USGS Earth Ex-
plorer platform, the research evaluates LST and exoatmospheric albedo characteristics within Lis-
bon's Metropolitan Area across a defined set of thermal periods from 2015 to 2020. Among the
findings, the Bare Soil or Sand LCZ consistently exhibits heightened LST values, while the Large
Low Rise urban LCZ consistently records the highest temperatures, exceeding 54 °C and 55 °C in
spring and summer and 43.8 °C and 37.8 °C in autumn and winter, respectively. These insights,
derived from a meticulous examination of the Landsat 8 dataset and advanced processing meth-
ods, bear critical implications for climate change adaptation strategies, providing valuable in-
sights to mitigate the adverse effects of the Urban Heat Island and foster sustainable urban devel-
opment practices.
1. Introduction
Land Surface Temperature (LST) is a key parameter for determining the Urban Heat Island. LST refers to the temperature of the
Earth’s surface measured by its thermal radiation and is one of the most important physical parameters examined in modelling land
surface processes. Surface temperature influences human health directly and alters the biodiversity and productivity of the environ-
ment (Stewart and Kremer, 2022).
Ren et al. (2022) established a comprehensive nexus between LST and various factors, including the season, time of day, the per-
centage of impervious and vegetated covered areas, water body areas, and population density. Rasul et al. (2017) emphasize that
surface temperature significantly influences the thermal comfort of urban inhabitants by modifying air temperature in the lowest
layer of the urban atmosphere. Consequently, there is a growing interest in research dedicated to mitigating the rise in LST, aiming
to prevent further exacerbation of urban thermal environment challenges (Yang et al., 2021).
Despite the escalating attention to rising land surface temperatures and climate changes resulting from urbanization, studies ad-
dressing the intricacies of LST characteristics remain scarce (Ren et al., 2022). Current urban classification systems primarily serve to
identify spatial and temporal processes, such as LST in cities, attempting to surmount the challenge of urban heterogeneity. The Local
* Corresponding author.
E-mail address: marcia.a.matias@edu.ulisboa.pt (M. Matias).
https://doi.org/10.1016/j.rsase.2024.101163
Received 16 October 2023; Received in revised form 18 December 2023; Accepted 1 January 2024
Remote Sensing Applications: Society and Environment 34 (2024) 101163
2
M. Matias and A. Lopes
Climate Zones (LCZ), as developed by Stewart and Oke (2012), emerge as a fitting method in the literature for discerning spatial and
temporal thermal patterns.
Yang et al. (2021) findings underscore the variation in temperature among different LCZs, emphasizing the role of land surface
geometry in influencing LST distribution within cities. Stewart and Kremer (2022) call for attention to the fact that land surface
temperature variation is amplified and modified by vertical structures, where a high-rise building shadowing and cooling an adja-
cent parcel of land, normally is not considered in classification systems that are not compositional and that don’t integrate building
height.
The classification of land Cover/land use as Local Climate Zones has gained prominence, especially in research focused on urban
climate (Stewart and Oke, 2012;Oliveira et al., 2020). Demuzere et al. (2022) have recently compiled a world map of LCZs, further
consolidating the importance of this categorization. Variations in LST, assessed through satellite remote sensing in space and time,
not only aid in estimating various geophysical variables but, when correlated with LCZ, provide insights into the temporal and spatial
characteristics of LST (O'Malley and Kikumoto, 2022).
The integration of LST with remotely sensed images, classified by land types, has been instrumental in urban heat studies, as ex-
emplified by Oliveira et al. (2021). Most of the works that have been done using satellite images and LCZ to assess the LST in each
one, focuses of critical periods, such as heatwave days or critical summer periods. In their work, Oliveira et al. (2021) used satellite
imagery from Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) to characterize each LCZ class concerning LST
during heatwave days.
However, a crucial gap exists in characterizing the climatic behavior of local urban typologies, through the years, revealing how
surface characteristics changes can in fact influence the surface temperature. Some works, as the one from Choudhury et al. (2021),
do an assessment of the thermal behavior and patterns of the different LCZ. In their study, authors found higher LST in built up LCZs,
with particularly interest in near built-up industrial areas. They also found that there are variations across the LST values when com-
paring LCZ.
A recent study, by Das and Das (2020), showed that in Sriniketan- Santiniketan Planning Area (India), LST has increase more than
0,3 °C per year in winter and more than 0,5 °C per year in summer season. These authors have also documented variations across the
LST from LCZ to LCZ. A very recent study, by Rahmani and Sharifi (2023), where the authors performed a comparative analysis of
SUHI effects, between 200 and 2022, in two Japanese cities, found that although the LST increased in both cities, it was higher in Hi-
roshima than Sapporo. This shows that it is of utmost importance to study each cities characteristics to understand the evolution of
LST through the years.
Despite many studies examining the LST phenomenon using the LCZ scheme, there's still a significant gap in research when it
comes to comparing how LST changes over the years in Portuguese cities based on the LCZ classification scheme.
1.1. Study area
Lisbon is located in the western part of mainland Portugal (38° 42′N; 9° 10′W) and has hot and dry summers and mild winters
(Köppen-Geiger climate classification Csa –(Kottek et al., 2006). The average air temperature is about 17.4 °C, with minimum values
in January (11.5 °C) and the highest average values in August (23.5 °C), according to IPMA climate normals from 1981 to 2010
(https://www.ipma.pt/en/oclima/normais.clima/1981-2010/).
Lisbon's climate is primarily influenced by local geographic factors, such as its proximity to the Atlantic Ocean (Fig. 1), the size of
the Tagus River's estuary, and the topography. These factors combined, produce a special thermal amenity that is characterized by an
elevated number of days with north and northwest winds throughout the year. This north wind pattern is essential to ventilate the
city, more compact and denser to the south, where more intense urban heat islands nucleus (around 2/3 °C) can arouse (Lopes et al.,
2013;Oliveira et al., 2021). In summer, when extreme temperatures can overcome the “amenity”, south-to-southwest breezes can be
very important to amend thermal stress in the southern part of the city (Lopes et al., 2013). The city of Lisbon has a diverse urban
morphology as a result of numerous phases of urban growth throughout the years, often in an unplanned and inappropriate manner
without taking into account the potential effects on the environment (Lopes, 2003).
Considering the LCZ scheme for the study area, as said by Oliveira et al. (2020), Lisbon’s urban fabric is mostly mid-rise within
municipal boundaries where morphological parameters such as H/W or Z0 tend to have low spatial variations except for large public
or green spaces. In Fig. 2 a representation of Lisbon's LCZ is presented. It should be noted that according to Oliveira et al. (2020),
there are 12 different LCZs in Lisbon. Urban LCZs account for more than 645 m2and non-urban for more than 3468 m2, so 15,7% and
84,3%, respectively. Although Urban LCZs only represent 15.7% of the total Lisbon Metropolitan Area, it is where most of the popula-
tion lives and works. Lisbon is mostly characterized by mid-rise continuous fabric.
2. Materials and methods
2.1. Data collection
Recently, Landsat 8 Collection 2 Surface Temperature images were generated from Landsat Collection 2 Level-1 thermal infrared
bands and previous products from this satellite. These images allow one to calculate several thermal characteristics of surface urban
areas. Since the Landsat 8 scenes are acquired during the late morning, surfaces exposed to sunlight are already warm, storing heat
which will later be released, with a time delay that depends on the properties of the material, such as thermal inertia (Oliveira et al.,
2020). Table 1 presents the used images for all years divided by the thermal periods that were considered in this study.
Remote Sensing Applications: Society and Environment 34 (2024) 101163
3
M. Matias and A. Lopes
Fig. 1. Lisbon’s Metropolitan Area (LMA) location within Portugal (a); (b) LMA boundary.
2.2. LST and exoatmospheric albedo calculations
For the treatment of the collected satellite images, the presented steps in Fig. 3 were followed. First, Landsat 8 images were re-
trieved from the USGS platform Earth Explorer (https://earthexplorer.usgs.gov/). Only bands 2 to 6 and band 10 were collected.
According to Reis et al. (2020), the climatic seasons normally used to understand the behavior of different climatic parameters,
have changed for Lisbon. The authors suggested that new “thermal periods”are used for these calculations (see Table 2).
All the collected images were categorized according to the new thermal periods listed before. For each thermal period a minimum
number of images were collected. As for the autumn and winter period the number of images that could be used was lower than the
spring summer thermal periods, the values used in the calculations were all weighted by the number of available images.
For all the used images, the cloud cover had to be lower than 20%. Although some of the images have higher percentages than
that, the clouds were not covering any part of the study area. To use these satellite images a scale factor had to be applied to them, ac-
cording to the United States Geological Survey. The scale factor applied to the different images can be seen in Figure 3, 2nd step. After
that, Equation (1) (Olmedo et al., 2016) was used to calculate the exoatmospheric albedo, as follows:
Albedo =RSB2×0.246 +RSB3×0.146 +RSB4×0.191 +RSB5×0.304 +RSB6×0.105 +RSB7×0.008
(1)
where RS is Radiance Surface and BX = Band and X the number.
This means that each band, from 2 to 6, for all the collected dates needs to be multiplied by the respective value and all of them
need to be summed at the end. To calculate the Land Surface Temperature of the collected images, the scale factor was applied to
Bands 10, for all the collected dates. After that, as the LST is given in Kelvin the conversation with Celsius was done. These results al-
lowed us to understand the differences between different LCZ classification areas and to comprehend their evolution through the
years in each thermal period (Table 1).
3. Results
The results for land surface temperature and exoatmospheric albedo observed in each LCZ between 2015 and 2021 and in four dif-
ferent thermal periods are presented in this section.
Remote Sensing Applications: Society and Environment 34 (2024) 101163
4
M. Matias and A. Lopes
Fig. 2. Lisbon's Metropolitan Area LCZ classification made based on Oliveira et al. (2020) data.
Fig. 4 shows the surface temperature readings of the urban LCZs for the spring thermal period. The highest LST in an urban LCZ
was recorded in the Large Low Rise LCZ (54.26 °C). The highest minimum LST was recorded in the urban LCZ Heavy Industry
(22.2 °C).
The range of values from minimum to maximum LST is wider in summer than in any other thermal season (Fig. 5). The highest
minimum LST was recorded in the LCZ Heavy Industry with a temperature of 28.09 °C. The LCZ Large Low Rise was the LCZ with the
highest differences between the maximum and minimum LST recorded. The highest LST was recorded by LCZ Large Low Rise in the
fall, at 43.86 °C (Fig. 6). The LCZ Heavy industry (17.32 °C) registered the highest low temperatures. The maximum and minimum
LST variations were greater for LCZ Large Low Rise. Comparing this thermal period to the spring or summer thermal periods, it is real-
istic to observe that the thermic amplitudes are lower.
The highest LST in an urban LCZ during the winter thermal period (Fig. 7) was measured in the LCZ Large Low Rise (37.84 °C). We
discovered that the highest minimum LST was observed in the Heavy Industry LCZ (15.14 °C), just as in the other thermal periods.
The LCZ Large Low Rise has the greatest variation between maximum and minimum LST. Thermic amplitudes in this thermal period
are the lowest of all thermal periods. Even though it barely makes up 0.5% of the AML area, LCZ Heavy Industry consistently presents
the highest results for LST.
Figs. 8–11 show the variation for the average mean LST in each urban LCZ between 2015 and 2020 for each thermal period. Ex-
cept for the winter thermal period all differences were positive. The higher average differences were observed in Spring, reaching
up to 7.66 °C and 7.62 °C, for the Heavy Industry and Large Low-Rise LCZ, respectively. The maximum differences that where
found, were higher than 20 °C, for Heavy Industry. When compared to the other four LCZ, the difference is of almost 10 °C. This can
show that, although the average differences are relatively similar in all 5 urban LCZ (Fig. 8), when looking at the maximum differ-
ences the values can vary drastically. The same happens in the thermal period of Autumn, where LCZ Large Low Rise presents a
maximum difference of more than 15 °C, but the average is only 4.27 °C.
In winter, as said before, the average differences were always negative. This can be explained by the average air temperatures
recorded in 2020 that, in this thermal period, were lower than in 2015. Air temperature and surface temperature are closely related
through heat transfer. Higher air temperatures in urban areas can influence local surface temperatures. During the day, when the air
Remote Sensing Applications: Society and Environment 34 (2024) 101163
5
M. Matias and A. Lopes
Table 1
Satellite images used in the calculations.
Year Period Dates Year Period Dates
2015 Winter 1st January 2015 2018 Winter –
18th February 2015
03rd December 2015
Spring 06th March 2015 Spring 17th May 2018
24th April 2015
25th May 2015
Summer 26th June 2015 Summer 20th July 2018
28th July 2015 5th August 2018
30th September 2015 8th October 2018
Autumn –Autumn 24th October 2018
2016 Winter 21st February 2016 2019 Winter 12th January 2019
21st December 2016 12 February 2019
30th December 2019
Spring 8th March 2016 Spring 4th May 2019
24th March 2016
25th April 2016
Summer 12th June 2016 Summer 21st June 2019
28th June 2016 24th August 2019
16th September 2016
Autumn –Autumn –
2017 Winter –2020 Winter –
Spring 12th April 2017 Spring –
Summer 15th June 2017 Summer 25th July 2020
01st July 2017
2nd July 2017 26th August 2020
05th October 2017
Autumn 6th November 2017 Autumn 29th October 2020
Fig. 3. Workflow for the treatment of satellite images.
Table 2
Thermal periods.
Periods Duration
Winter 26/11 to 04/03
Spring 05/03 to 10/06
Summer 11/06 to 08/10
Autumn 9/10 to 25/11
Adapted from (Reis et al., 2020).
Remote Sensing Applications: Society and Environment 34 (2024) 101163
6
M. Matias and A. Lopes
Fig. 4. Land surface Temperature in Lisbon's Urban LCZ Spring thermal period.
Fig. 5. Land surface Temperature in Lisbon's Urban LCZ Summer thermal period.
Fig. 6. Land surface Temperature in Lisbon's Urban LCZ Autmn thermal period.
temperature is higher than the surface temperature, heat is transferred from the atmosphere to the surface, warming it. This is known
as convective heating. At night, when the air temperature drops, surface cooling can occur as the surface radiates heat to space. This
can explain, that, with lower temperatures in 2020 when compared to 2015, the differences can be negative.
For the urban LCZ, the observed behavior for the exoatmospheric albedo in spring (Fig. 12) is very comparable to the thermal pe-
riod of summer (Fig. 13). The minimum values for Heavy Industry and Compact Mix Rise LCZ are still the highest. Analysis of the
exoatmospheric albedo values during the fall thermal season (Fig. 14) reveals that the urban LCZs achieve their lowest minimum val-
Remote Sensing Applications: Society and Environment 34 (2024) 101163
7
M. Matias and A. Lopes
Fig. 7. Land surface Temperature in Lisbon's Urban LCZ Winter thermal period.
Fig. 8. Mean Difference in LST (2015–2020) in Spring - urban LCZ.
Fig. 9. Mean Difference in LST (2015–2020) in Summer - urban LCZ.
ues during this time. When compared to other thermal periods, wintertime exoatmospheric albedo values (Fig. 15) are relatively simi-
lar between LCZ.
For the non-urban LCZ, the highest LST was recorded in the LCZ Bare Rock Soil or Sand during the thermal season of Spring (Fig.
16), with 51.1 °C. The non-Urban LCZ Bare Rock or Paved had the highest minimum LST (18.84 °C). The LCZ Large Low Rise and Bare
Soil or Sand exhibit the greatest variations in maximum and lowest temperatures, according to the analysis of these discrepancies. We
Remote Sensing Applications: Society and Environment 34 (2024) 101163
8
M. Matias and A. Lopes
Fig. 10. Mean Difference in LST (2015–2020) in Autumn - urban LCZ.
Fig. 11. Mean Difference in LST (2015–2020) in Winter - urban LCZ.
Fig. 12. Exoatmospheric Albedo in Lisbon's Urban LCZ Spring thermal period.
can observe that the exoatmospheric albedo values for the non-urban LCZ vary between them. The LCZs that show greater values are
Bush Scrubs and bare soil or sand.
The non-urban LCZ Bare Soil or Sand had the highest LST recorded throughout the summer (Fig. 17) with 53.08 °C. The Bare Rock
or Paved LCZ had the highest minimum LST value at 23.5 °C. The LCZ Low plants was the LCZ that showed the largest variations be-
tween the highest and minimum reported LST.
Remote Sensing Applications: Society and Environment 34 (2024) 101163
9
M. Matias and A. Lopes
Fig. 13. Exoatmospheric Albedo in Lisbon's Urban LCZ Summer thermal period.
Fig. 14. Exoatmospheric Albedo in Lisbon's Urban LCZ Autmn thermal period.
Fig. 15. Exoatmospheric Albedo in Lisbon's Urban LCZ Winter thermal period.
The highest LST, 38.87 °C, was recorded by LCZ bare soil or sand during autumn (Fig. 18). The LCZ Bare Rock or Soil recorded the
highest low temperatures (16.71 °C). Higher variations between the highest and minimum LST were seen in LCZ Bare Soil or Sand.
The non-urban LCZ Bare Soil or Sand reported the highest LST for the winter thermal period (Fig. 19) at 33.30 °C. Similar to the other
thermal periods, the Bare Rock or Paved LCZ had the highest minimum LST (12.70 °C). The LCZ Bare Soil or Sand had the largest vari-
ation between the maximum and minimum LST values.
Except for the winter thermal period in LCZ Scattered Tres, Bush Scrubs, Bare Rock or Paved and Water, differences for the aver-
age mean LST were always positive in all non-urban LCZ (Figs. 20–23).
Remote Sensing Applications: Society and Environment 34 (2024) 101163
10
M. Matias and A. Lopes
Fig. 16. Land surface Temperature in Lisbon's Non-urban LCZ Spring thermal period.
Fig. 17. Land surface Temperature in Lisbon's Non-urban LCZ Summer thermal period.
Fig. 18. Land surface Temperature in Lisbon's Non-urban LCZ Autmn thermal period.
Remote Sensing Applications: Society and Environment 34 (2024) 101163
11
M. Matias and A. Lopes
Fig. 19. Land surface Temperature in Lisbon's Non-urban LCZ Winter thermal period.
Fig. 20. Mean Difference in LST (2015–2020) in Spring - non-urban LCZ.
Fig. 21. Mean Difference in LST (2015–2020) in Summer - non-urban LCZ.
Remote Sensing Applications: Society and Environment 34 (2024) 101163
12
M. Matias and A. Lopes
Fig. 22. Mean Difference in LST (2015–2020) in Autumn - non-urban LCZ.
Fig. 23. Mean Difference in LST (2015–2020) in Winter - non-urban LCZ.
The differences in the minimum mean LST for the non-urban LCZs were consistently positive over the spring and autumn thermal
periods. The biggest differences - always greater than 8 °C - occurred in the spring. Except for LCZ Water, all LCZ display negative dif-
ference values during the winter thermal season.
As seen in urban LCZ, spring is the thermal period were the average differences between 2015 and 2020 are higher, reaching up to
more than 8 °C in Scattered Trees LCZ.
Regarding the exoatmospheric albedo values for the non-urban LCZ (Figs. 24–27), there is not much variation between thermal
periods, being the obtained values very similar in all of them. For the Spring Thermal period, when compared to the Summer one it is
visible that, although very similar, some differences can be found. These can be seen in the maximum values that are obtained, for ex-
ample, for the LCZ of Bare Soil or Sand.
Exoatmospheric albedo values in the autumn and winter thermal periods are lower than in spring or summer. The bare soil or sand
LCZ consistently displays higher values for the non-urban LCZs when all the values for the four thermal periods are analyzed. Dense
Trees is the LCz that consistently exhibits the lowest values during each of the four temperature phases when the Water LCZ is ig-
nored.
The data show that bare rock or pavement and bare soil or sand had the greatest temperature values in the non-urban LCZ. This
can be explained, for instance, by the fact that this classification is given to areas that are entirely exposed, where there is nothing
covering the grounds and, because they lack infrastructure, absorb almost all of the solar energy that is received during the day and
emit it to the atmosphere at night in the form of heat fluxes that warm the atmosphere. Additionally, regardless of the thermal period
examined, the Bare Rock or Paved LCZ in non-urban LCZs always has the highest minimum temperatures.
4. Discussion
According to the results, Large Low Rise and Heavy Industry are the urban LCZs with the highest LST. The LCZ Bare Soil or Sand
and Bush Scrubs within the non-urban LCZ had the highest surface temperatures. The exoatmospheric measurements obtained indi-
cate that there are no obvious differences between the urban LCZs. However, there are greater differences between them when we
look at the non-urban LCZs.
Remote Sensing Applications: Society and Environment 34 (2024) 101163
13
M. Matias and A. Lopes
Fig. 24. Exoatmospheric Albedo in Lisbon's Non-urban LCZ Spring thermal period.
Fig. 25. Exoatmospheric Albedo in Lisbon's Non-urban LCZ Summer thermal period.
Fig. 26. Exoatmospheric Albedo in Lisbon's Non-urban LCZ Autmn thermal period.
Remote Sensing Applications: Society and Environment 34 (2024) 101163
14
M. Matias and A. Lopes
Fig. 27. Exoatmospheric Albedo in Lisbon's Non-urban LCZ Winter thermal period.
Looking at the evolution of the LCZs in Lisbon over this period (2015–2020), we can see that there are differences in the albedo
values between the first year (2015) and the last year analyzed (2020), with the values in 2020 being consistently lower in the urban
LCZs than in 2015. This may indicate that these surfaces appear darker than in 2015. The same occurs in the non-urban LCZ, where
measurements of exoatmospheric albedo in 2020 show lower values than in earlier years.
Comparing the LST in the summer of 2020, the most recent year of data, it is possible to see some differences between the LCZs
that exist in the city center and the classifications that exist more on the outskirts. For example, the Compact Mix-Rise LCZ has values
of 40.8 °C right in the city center. Compared to the periphery, e.g.in the Large Low Rise and Heavy Industry LCZs, the difference is
more than 4 °C, with the latter two being higher. Compared to the Sparsely Built LCZ, the difference is -6 °C (33.6 °C in the latter).
Compared to non-urban LCZs in the periphery of Lisbon, for example, the Bush Scrubs and Bare Soil or Sand LCZs show values of
32.9 °C and 37.1 °C respectively.
This shows that the highest LST values do not always occur in the city center. However, when looking at this city, the highest val-
ues always occur in the LCZ where most of the LMA population lives. Comparing the city center with its surroundings, we can see that
the LST is directly related to the classification and urban occupation, since the urban spaces always have higher surface temperature
values compared to the non-urban LCZs, whether they are in the city center or not.
When looking at the values for the exoatmospheric albedo, is noted that the exoatmospheric albedo behavior during spring in ur-
ban Local Climate Zones (urban LCZ) is very comparable to the thermal period of summer. Interestingly, the minimum values for
Heavy Industry and Compact Mix Rise LCZs still remain the highest among urban LCZ. This may indicate distinct thermal properties
in these categories. In non-urban LCZs, there’s little variation in exoatmospheric albedo values between different thermal periods, in-
dicating notable stability in the characteristics of these areas throughout the year. Seasonal differences between thermal periods are
more evident for the Bare Soil or Sand LCZ, especially when comparing spring to summer. Differences are present in the maximum
values obtained. During the autumn and winter thermal periods, exoatmospheric albedo values are consistently lower than in spring
or summer. The Bare Soil or Sand LCZ stands out with higher values among non-urban LCZs in all periods. LCZ with Dense Trees ap-
pears to show minimal variation in exoatmospheric albedo values during each thermal periods, excluding the Water LCZ, consistently
displaying the lowest values.
Urban Atlas (2022) states that in Lisbon, artificial surfaces made up 900 km2, or 20,48% of the total area, in 2012. With a total
area of 907 km2in 2018, this percentage increased to 20.64%, and some changes in the classification of industrial, commercial, pub-
lic, military, and private units were noted.
Despite the changes, there haven't been any significant ones to the Lisbon metropolitan area's urban fabric. However, understand-
ing these variations is crucial for understanding how Lisbon's land use has changed over the past few years and how this may be af-
fecting the LST values that can be discovered using this approach. Although the physical changes are not very significant, the changes
in surface temperature values found in the different LCZs show that there are changes in the type of building and covering that is used
in the different urban typologies within the city of Lisbon.
It should also be noted that even though the type of construction has not changed significantly, it is in the places classified as ur-
ban LCZs that most of the population lives. It is also in these areas, as shown above, where LST are higher and where the albedo is
lower, which can contribute to the strong warming of the urban atmosphere and to harmful effects on the health of the population.
5. Conclusion
In the context of global warming and climate change, variations in LST have a direct impact on human thermal comfort. Land sur-
face temperature, as mentioned above, is a crucial aspect in assessing how much the surface is warming. The study of LST and exoat-
mospheric albedo in Lisbon's LCZs is a novel approach that seems to improve our understanding of the thermal behavior of the city.
Remote Sensing Applications: Society and Environment 34 (2024) 101163
15
M. Matias and A. Lopes
The results of this study show that LCZ bare soil or sand almost always have higher LST values. LCZs representing vegetation and
water environments have lower land surface temperatures. Yang et al. (2021) found similar results. The changes in LST within the
city appear to be consistent with the spatial distribution of LCZs. LST values were higher during the summer thermal season and lower
during the winter, as expected.
With respect to the urban LCZ, the Large Low Rise LCZ consistently recorded the highest values in all thermal periods, with spring
and summer temperatures above 54 °C and 55 °C respectively. The recorded temperatures during the autumn and winter seasons, ex-
ceeded 43 °C and 37 °C, respectively. The non-urban LCZ bare soil or sand consistently had the highest LST values in summer, autumn
and winter (above 53 °C, 33 °C and 38.8 °C respectively). The bare rock or sand LCZ (>51 °C) had the highest values in spring.
As for the differences found between 2015 and 2020, which allowed us to understand whether the LCZs have higher temperature
values than in the first year analyzed, the LCZ Heavy Industry was the one with the highest values in spring and summer (<20.9 °C
and >5.9 °C, respectively). In the autumn and winter thermal periods, the highest differences were recorded in the LCZ Large Low
Rise, but in the winter period this difference was negative (−9.47 °C), meaning that the LST in 2020 were lower than in 2015.
In summary, the analysis of exoatmospheric albedo behavior in urban zones reveals a notable seasonal uniformity between spring
and summer, signifying thermal consistency in these metropolitan areas. Furthermore, the persistence of higher minimum values in
the Heavy Industry and Compact Mix Rise LCZs suggests distinctive thermal properties in these specific categories, which may have
significant implications for understanding and managing the urban thermal environment.
In non-urban LCZs, the analysis underscores remarkable stability throughout the year, as evidenced by the limited variation in
exoatmospheric albedo values across different thermal periods. However, seasonal differences become more pronounced in the Bare
Soil or Sand LCZ, especially when comparing spring to summer, indicating a differentiated response to thermal stimuli in these areas.
This study contributes to a more comprehensive understanding of thermal patterns in both urban and non-urban environments, pro-
viding valuable insights for environmental planning and management.
This work and its results provide insights into how LST and the exoatmospheric albedo varies in different urban and non-urban en-
vironments within Lisbon, its seasonal patterns, and how it has changed over a specific period, highlighting the significance of under-
standing these variations in the context of climate change and urban planning. Future studies could look into long-term monitoring,
urban planning strategies, microclimatic assessments, and the socio-economic impacts of varying Land Surface Temperature (LST) in
Lisbon. These investigations may aid in developing effective climate change adaptation measures, mitigating urban heat island ef-
fects, and promoting sustainable urban development for the city's residents and environment.
Ethical statement
The authors declare that all ethical practices have been followed in relation to the development, writing, and publication of the ar-
ticle.
CRediT authorship contribution statement
Márcia Matias: Conceptualization, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Writing –
original draft, Writing –review & editing. António Lopes: Conceptualization, Investigation, Methodology, Resources, Software,
Supervision, Validation, Visualization, Writing –original draft, Writing –review & editing.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to
influence the work reported in this paper.
Data availability
No data was used for the research described in the article.
Acknowledgements
This research was funded by Foundation for Science and Technology (FCT) with the grant nr. 2021.05248.BD, granted to Márcia
Matias and the ZEPHYRUS research group of the CEG/IGOT—Universidade de Lisboa (UIDB/00295/2020 and UIDP/00295/2020).
References
Choudhury, D., Das, A., Das, M., 2021. Investigating thermal behavior pattern (TBP) of local climatic zones (LCZs): a study on industrial cities of Asansol-Durgapur
development area (ADDA), eastern India. Urban Clim. 35 (May 2020), 100727. https://doi.org/10.1016/j.uclim.2020.100727.
Das, M., Das, A., 2020. Assessing the relationship between local climatic zones (LCZs) and land surface temperature (LST) –a case study of Sriniketan-Santiniketan
Planning Area (SSPA), West Bengal, India. Urban Clim. 32 (April 2019), 100591. https://doi.org/10.1016/j.uclim.2020.100591.
Demuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I.D., van Vliet, J., Bechtel, B., 2022. A global map of local climate zones to support earth system
modelling and urban-scale environmental science. Earth Syst. Sci. Data 14 (8), 3835–3873. https://doi.org/10.5194/essd-14-3835-2022.
Kottek, M., Grieser, J., Beck, C., Rudolf, B., Rubel, F., 2006. World map of the Köppen-Geiger climate classification updated. Meteorol. Z. 15 (3). https://doi.org/
10.1127/0941-2948/2006/0130.
Lopes, A., 2003. Ph.D. Thesis. Modificações no Clima de Lisboa Como Consequência do Crescimento Urbano. Vento, Ilha de Calor de Superfície e Balanço Energético. University
of Lisbon, Lisbon, Portugal. 2003.
Lopes, A., Alves, E., Alcoforado, M., Machete, R., 2013. Lisbon urban heat island updated: new highlights about the relationships between thermal patterns and wind
regimes. Adv. Meteorol. 11. https://doi.org/10.1155/2013/487695.
Remote Sensing Applications: Society and Environment 34 (2024) 101163
16
M. Matias and A. Lopes
O’Malley, C., Kikumoto, H., 2022. An investigation into heat storage by adopting local climate zones and nocturnal-diurnal urban heat island differences in the Tokyo
Prefecture. Sustain. Cities Soc. 83. https://doi.org/10.1016/j.scs.2022.103959.
Oliveira, A., Lopes, A., Correia, E., Niza, S., Soares, A., 2021. Heatwaves and summer urban heat islands: a daily cycle approach to unveil the urban thermal signal
changes in Lisbon, Portugal. Atmosphere 12 (3). https://doi.org/10.3390/atmos12030292.
Oliveira, A., Lopes, A., Niza, S., 2020. Local climate zones in five southern European cities: an improved GIS-based classification method based on Copernicus data.
Urban Clim. 33 (May 2019), 100631. https://doi.org/10.1016/j.uclim.2020.100631.
Olmedo, G.F., Ortega-Farías, S., de la Fuente-Sáiz, D., Fonseca-Luengo, D., Fuentes-Peñailillo, F., 2016. Water: tools and functions to estimate actual evapotranspiration
using land surface energy balance models in R. R J. 8 (2), 352–370. https://doi.org/10.32614/rj-2016-051.
Rahmani, N., Sharifi, A., 2023. Comparative analysis of the surface urban heat island (SUHI) effect based on the local climate zone (LCZ) classification scheme for two
Japanese cities, Hiroshima, and sapporo. Climate 11 (7). https://doi.org/10.3390/cli11070142.
Rasul, A., Balzter, H., Smith, C., Remedios, J., Adamu, B., Sobrino, J., Srivanit, M., Weng, Q., 2017. A review on remote sensing of urban heat and cool islands. Land 6
(2), 38. https://doi.org/10.3390/land6020038.
Reis, C., Lopes, A., Correia, E., Fragoso, M., 2020. Local weather types by thermal periods: deepening the knowledge about Lisbon’s urban climate. Atmosphere 11 (8).
https://doi.org/10.3390/ATMOS11080840.
Ren, J., Yang, J., Zhang, Y., Xiao, X., Xia, J.C., Li, X., Wang, S., 2022. Exploring thermal comfort of urban buildings based on local climate zones. J. Clean. Prod. 340
(February), 130744. https://doi.org/10.1016/j.jclepro.2022.130744.
Stewart, I.D., Oke, T.R., 2012. Local climate zones for urban temperature studies. Bull. Am. Meteorol. Soc. 93 (12), 1879–1900. https://doi.org/10.1175/BAMS-D-11-
00019.1.
Stewart, J.D., Kremer, P., 2022. Temporal change in relationships between urban structure and surface temperature. Environ. Plan. B Urban Anal. City Sci. https://
doi.org/10.1177/23998083221083677.April, 239980832210836. .
Yang, J., Ren, J., Sun, D., Xiao, X., Xia, J. (Cecilia), Jin, C., Li, X., 2021. Understanding land surface temperature impact factors based on local climate zones. Sustain.
Cities Soc. 69 (February), 102818. https://doi.org/10.1016/j.scs.2021.102818.
