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Article

A Methodology for Comparing the Surface Urban Heat Island in Selected Urban Agglomerations Around the World from Sentinel-3 SLSTR Data

1
Global Change Unit (UCG), Image Processing Laboratory (IPL), University of Valencia (UVEG); 46980 Paterna, Spain
2
Land and Atmosphere Remote Sensing group (LARS), Centro de Tecnologías Físicas, Universitat Politècnica de València (UPV), 46022 Valencia, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(12), 2052; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12122052
Submission received: 17 May 2020 / Revised: 17 June 2020 / Accepted: 24 June 2020 / Published: 25 June 2020
(This article belongs to the Special Issue Advancement of Urban Heat Island Studies with Remote Sensing)

Abstract

:
Retrieval of land surface temperature (LST) from satellite data allows to estimate the surface urban heat island (SUHI) as the difference between the LST obtained in the urban area and the LST of its surroundings. However, this definition depends on the selection of the urban and surroundings references, which translates into greater difficulty in comparing SUHI values in different urban agglomerations across the world. In order to avoid this problem, a methodology is proposed that allows reliable quantification of the SUHI. The urban reference is obtained from the European Space Agency Climate Change Initiative Land Cover and three surroundings references are considered; that is, the urban adjacent (Su), the future adjacent (Sf), and the peri-urban (Sp), which are obtained from mathematical expressions that depend exclusively on the urban area. In addition, two formulations of SUHI are considered: SUHIMAX and SUHIMEAN, which evaluate the maximum and average SUHI of the urban area for each of the three surrounding references. As the urban population growth phenomenon is a world-scale problem, this methodology has been applied to 71 urban agglomerations around the world using LST data obtained from the sea and land surface temperature radiometer (SLSTR) on board Sentinel-3A. The results show average values of SUHIMEAN of (1.8 ± 0.9) °C, (2.6 ± 1.3) °C, and (3.1 ± 1.7) °C for Su, Sf, and Sp, respectively, and an average difference between SUHIMAX and SUHIMEAN of (3.1 ± 1.1) °C. To complete the study, two additional indices have been considered: the Urban Thermal Field Variation Index (UFTVI) and the Discomfort Index (DI), which proved to be essential for understanding the SUHI phenomenon and its consequences on the quality of life of the inhabitants.

Graphical Abstract

1. Introduction

By 2050, the world’s population is estimated to increase to nine billion, 70% of whom will live in urban areas [1]. The rapid increase of these areas without adequate prior planning is an increasingly worrying problem that seriously threatens the environment and the health and well-being of the population [2].
One of the most problematic consequences of rapid urbanization is the increase of the urban heat island (UHI) [3,4], which is defined as the difference between the air temperature (AT) within the urban area and the AT of its surroundings [5]. Generally, the temperature in urban areas is higher than in rural areas, especially at night [5]. This phenomenon, which will be reinforced by the effects of climate change, not only affects people psychologically and physiologically, but also controls daily behaviours and economic activities [6] and can lead to a drastic increase in morbidity and mortality [7], increased energy consumption [8], and even violent behaviours [9] within urban areas [10]. UHI was shown to also have a positive effect on heating-dominated buildings owing to the lower heating demands during winter [11]. When the UHI is monitored with remote sensing data, we have to talk about surface urban heat island (SUHI), as the parameter studied is no longer AT, but the land surface temperature (LST) [12].
Many authors have written on obtaining LST and urban heat islands on spatial, spectral, and temporal scales [3,4,13,14,15,16,17,18,19,20,21,22,23,24], in addition to examining satellite sensors that may be more or less suitable for SUHI detection and calculation [25]. Other authors have documented the importance of different satellite images in detecting the relationship between LST and land use change [26,27], but most of the studies carried out to date focus on a single city or urban agglomeration where the definition of rural and urban areas is adapted to each study case [10], which complicates the comparison between cities in different parts of the world. On the other hand, there is no concrete methodology to define the non-urban area. Proper selection of this area is crucial to make a correct estimation of the SUHI. If we consider areas too close to the city, the effect can be underestimated, owing to the human activity that persists outside the urban core, and in highly urbanized metropolitan areas, it is likely that external reference areas are influenced by the SUHI of nearby cities [28]; in addition, too external areas may suppose too extreme a change of scenario to use as a reference.
In this paper, we propose a new methodology to calculate and compare SUHI values of urban agglomerations around the world. SUHI will be calculated for three surrounding areas using LST acquired during the night-time according to the recommendations given by [12]. The proposed methodology is applied to 71 selected urban agglomerations around the world. The LST product used was the provided by the European Space Agency (ESA) from the land and sea surface temperature radiometer (SLSRT) aboard the Sentinel-3A (SLSTR Level-2 LST) [29] (for more information, visit the site https://earth.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2/lst-processing). Finally, to complement SUHI analysis, two additional indices have been considered, the Urban Thermal Field Variance Index (UTFVI), which quantifies the SUHI at the district level [6,18,30], and the Discomfort Index (DI), which estimates the impact of temperature on human health [31].

2. Methodology

2.1. SUHI Selection of the Urban and Surrounding References

The main objective of this work is to present a methodology that allows the analysis and comparison of SUHI in urban agglomerations around the world. For this purpose, night-time LST obtained from satellite data has been used [12] to estimate SUHIMAX and SUHIMEAN, which are defined as the thermal differences between the maximum and average LST of the urban area and the LST of its surroundings, respectively, according to
SUHI MAX = LST URB MAX LST SUR
SUHI MEAN = LST URB MEAN LST SUR
where LST URB MAX is the maximum LST of the urban area (hottest pixel), LSTURB-MEAN is the average temperature of the pixels that define the urban area, and LST SUR is the average temperature of the pixels that compose the surrounding area.
The main problem in estimating SUHIMAX and SUHIMEAN is the difficulty in identifying urban and surrounding references. There is no clear definition in the literature of how to select these areas [32], which makes it extremely difficult to compare SUHI between different urban agglomerations. To address this problem, we propose the following approach:
(a)
Urban:
To define the urban reference, a land cover map with explicit representation of urban areas is the most operational solution. In our case, among the large number of free land cover products available today, we use the global land cover map produced by the European Space Agency (ESA) Climate Change Initiative (CCI) [33] (for more information, visit the website https://maps.elie.ucl.ac.be/CCI/viewer/), where the urban reference is obtained from those classified as “urban areas”. From this class, a polygon is generated that is identified with the area of the urban agglomeration selected (A).
  • (b) Surroundings:
As for the surrounding, three different reference areas were defined, the urban adjacent (SU), the future urban adjacent (Sf), and the peri-urban (SP). The width (WU, Wf, and WP) of the buffer for each surrounding is calculated as follows:
WU = 0.25 A1/2
Wf = 0.25 AWu ½
WP = 1.5 A1/2 − Wf − WU
where Awu is the sum of A and Su areas (see Figure 1). Similar expressions for Wu and Wp can be found in [34] and [35], respectively, while Wf is introduced in this paper assuming future expansion of the urban area to include the urban adjacent surrounding Su. With this approach, the extent of the surrounding areas is clearly defined and depends only on A (the area of the urban agglomeration). As an example, Figure 2 shows the application of the methodology to the Paris urban agglomeration.

2.2. UTFVI and DI Indices

To complement the analysis of SUHI, two additional indices were considered. That is, the Urban Thermal Field Variance Index (UTFVI) [18,31] and the Discomfort Index (DI) [36]. The UTFVI is the most widely used index for the ecological evaluation of urban environment owing to its direct relation to LST and considers the thermal impact of the different sub-areas (district level) in the urban agglomeration area (A), according to
UFTVI = 1 − (LSTURB-MEAN/LSTURB-PIXEL)
where LSTURB-PIXEL is the LST in K, obtained from satellite data, of a given pixel of A and LSTURB-MEAN is the average LST of the whole urban area (A). Note that SUHIMAX and SUHIMEAN describe the SUHI between the whole urban area and the surroundings, while UFTVI is used for evaluating the effect for each pixel located within the urban area with respect to the whole urban area. UTFVI is divided into six levels by six specific ecological evaluation indices. The thresholds at the six UFTVI levels are shown in Table 1, from no SUHI (excellent) if LSTURB-PIXEL < LSTURB-MEAN to strongest (worst) with UFTVI > 0.02, a situation that occurs when the value of LSTURB-PIXEL is several degrees higher than LSTURB-MEAN, for example, 302 K and 295 K, respectively.
It is well known that one of the consequences of SUHI is the influence on human health. The Discomfort Index (DI), also known as the Thom’s discomfort index [37], is a measure of the reaction of the human body to a combination of heat and humidity. DI can be estimated according to Sobrino et al. [5] at night-time from satellite measurements according to
DI = LST − (0.55 − 0.055 RH) (LST − 14.5)
where LST is the land surface temperature in ˚C, obtained from satellite data, for a given pixel of A and RH is the relative humidity in %. RH can be obtained from in situ or satellite data. Our objective is to propose an operational methodology and, for this purpose, RH is obtained from the atmospheric infrared sounder (AIRS), L3 surface relative humidity product on board NASA’s AQUA satellite [38]. DI is divided into ten categories, which are shown in Table 2.

2.3. Criteria for Urban Agglomerations Selection

In order to apply the methodology developed, 71 urban agglomerations were selected around the world: 7 in Africa, 19 in America, 24 in Asia, 18 in Europe, and 3 in Oceania (more information on the characteristics of the selected agglomerations can be found in Appendix A). The criteria used for selection (see Figure 3a–c) were as follows
(a)
urban agglomeration areas, which cover the globe as extensively and widely as possible at different latitudes and longitudes, in different climatic zones and with different population and density of habitants, giving priority to those that are experiencing a large increase in population [39,40] or are considered particularly vulnerable to climate change [41];
(b)
urban areas at different altitudes (e.g., from Perth at 0 m above sea level to Lhasa at 3650 m);
(c)
coastal and inland agglomerations (e.g., Rio de Janeiro, Moscow);
(d)
urban agglomerations with high levels of NO2 [42] and night-time light pollution (e.g., Shanghai, New York);
(e)
urban agglomerations with an area greater than 50 km2 in order to have a number of pixels representative at the spatial resolution of the satellite.

2.4. Satellite Data

So far, the most used satellites for SUHI estimation have been TERRA, AQUA, and Landsat [11], with few studies using data from the ESA’s Sentinel 3 satellites owing to the short period of operation. In this paper, SUHI is analysed using the LST product obtained from the SLSTR sensor onboard Sentinel-3A (Level-2 LST) [29] during the period June 2018 to May 2019. Night images were selected following Sobrino et al. [5], when the SUHI effect is most notable. For each urban agglomeration, the month with the warmest temperature records was searched, and for that month, a warm and clear night.
Level-2 LST products have been validated against in situ observations from twelve “gold standard” stations spread thoughout the Earth that are installed with well-calibrated instrumentation: seven from the Surface Radiation Budget Network (SURFRAD) in Bondville, Illinois; Desert Rock, Nevada; Fort Peck, Montana; Goodwin Creek, Mississippi; Penn State University, Pennsylvania; Sioux Fall, South Dakota; Table Mountain, and Colorado; two from the Atmospheric Radiation Measurement (ARM) network in Southern Great Plains, Oklahoma; Barrow, and Alaska; and three from the U.S. Clima Reference Network (USCRN) in Williams, Arizona; Des Moines, Iowa; Manhatten, and Kansas. The average absolute accuracy is within the 1 K requirement (better than 1 K) [43].

3. Results and Discussion

In this section, values of SUHI, UTFVI, and DI in 71 urban agglomerations around the world are given. It is important to note that whether a given agglomeration has high or low values of SUHI, or can be classified according to the values of UFTVI and Di, should be interpreted as these are the results for the day and time of the selected Sentinel 3A image (more information, including the numerical values of the indices and the Level-2 LST for each agglomeration, can be found in Appendix A).
We also want to point out that the main objective of the present work is to propose an operational methodology that allows a systematic and effective assessment of SUHI, UTFVI, and DI in order to detect warning situations and identify the vulnerabilities of the urban area. This is particularly necessary in the current context of global warming, but even more so if we consider future scenarios, for example, Sobrino et al. [44] shows a linear warming trend of the surface temperature of the planet of 0.18 K per decade. In that sense, the inhabitants of the urban area, especially those who live or develop their activities in urban districts with high UTFVI values, are already intensely suffering the effects of the increase in temperature.

3.1. SUHI

Figure 4 and Figure 5 show the SUHIMAX and SUHIMEAN values, respectively, which vary according to the agglomeration and the surrounding area considered. The proposed methodology has the potential to reflect the differences in a quantitative way. For example, the European agglomerations show less dispersion than the American and Asian agglomerations, among which there were those that present greater differences with respect to the selected surrounding.
In general, the highest differences are for the peri-urban areas (Sp), and the smallest are for the urban adjacent (Su). We only identified three cases (San Diego-Tijuana, Los Angeles, and Taskent) that show a different pattern to the other 68 cities with greater temperature differences in the adjacent urban surroundings (Su) and smaller in Sf and Sp. In the case of San Diego-Tijuana and Los Angeles, the temperature of the most remote areas is higher owing to the proximity of desert and other urban agglomerations that emit heat. Taskent, on the other hand, has an adjacent urban area of crops and irrigated land that cools the surface, while the farthest areas are covered with dry vegetation or bare soil. Other particular cases are agglomerations that show similar SUHI values for Su, Sf, and Sp (e.g., Dammam, Calcutta, Shanghai, or Athens). In most cases, this corresponds to urban areas whose surroundings present similar characteristics to the area close to the urban nucleus. Characteristics that are not very common in the rest of the selected agglomerations. In the case of Lhasa, the values for Sf and Sp are much higher than for Su. This is because the city of Lhasa is built in the valley of the Brahmaputra river, surrounded by the Himalayan mountains that take altitudes immediately higher than those of the city in very short distances, which contributes to the maintenance of higher temperatures in the urban area. In addition, the dams built in the river regulate the temperature of the city holding part of the heat accumulated during the day. In some cases, there is a big difference in LST in the hottest area within the urban area compared with the peri-urban area (Sp). Figure 4 (blue column) shows values above 8 °C in Vancouver, New York, Tokyo, Lhasa, Ürümqi, Las Vegas, Ciudad de Mexico, Rio de Janeiro, Jakarta, Buenos Aires, San José, and Moscow.
A relevant aspect to highlight is that the average difference between SUHIMAX and SUHIMEAN for all cases is (3.1 ± 1.1) ⁰C (see numerical values in Table A2 of Appendix A). This implies that, on average for the agglomerations selected, the inhabitants of the urban zone where the maximum temperature occurs experience up to 3.1 degrees higher LST than the rest of the inhabitants of the urban agglomerations. Note that a difference of zero between SUHIMAX and SUHIMEAN would imply a value of UFTVI lower than 0 (i.e., an excellent ecological evaluation index, see Table 1).
As a complement to Figure 5, Table 3 shows the SUHIMEAN values for the three surrounding areas (Su, Sf, and Sp) ordered according to the following criteria: by continent, for populations above 20 million inhabitants, with an urban area higher than 1000 km2, at a minimum distance of 1000 km from the coast, at an altitude higher than 1 km above sea level, by climate classification according to the five Köppen vegetation groups [45] and including the 71 agglomerations.
Taking these last values as reference, the average heat island effect of 1.8 °C for the adjacent surrounding area increases by 0.8 °C for Sf and 1.3 °C for Sp, which means that, considering this average as a representative on a global scale and assuming a linear warming trend of 0.18 K per decade [44], the inhabitants of the urban agglomerations in the world are already suffering the effects of the warming that will be reached by the inhabitants of the adjacent surrounding area in the next century.
It is also noted that Europe is the continent with the highest values of SUHI in the three reference areas, with Africa being the continent with the lowest values. High values are also observed in the seven agglomerations with more than 20 million inhabitants, being 0.3 °C higher than those obtained for agglomerations with surfaces above 1000 km2. With regard to the agglomerations situated at a distance of more than 1000 km from the coast, values similar to the world average are obtained, with the elevation producing a slight decrease of 0.3 °C compared with the average in Su. Finally, in terms of climate, the highest values are found in warm temperate and snow climates, and the lowest in the equatorial and arid climates.

3.2. UTFVI

UTFVI is a complementary index to SUHIMEAN that allows to detect areas affected by heat accumulation within the urban agglomeration. In Figure 6, we present the maximum values of the UFTVI index for each urban agglomeration. The highest values are obtained for the urban agglomerations of San José and Ürümqi, followed by Mexico City, New York, Los Angeles, Toronto, Jakarta, Kuala Lumpur, and Buenos Aires.

3.3. DI

With regard to the DI index, the values are correlated to climate, geographical location, and altitude, so that, in some cases, the accumulation of temperature in urban areas allows a transition from cold to comfortable categories (e.g., Vancouver). Figure 7 shows the map of maximum DI values for the 71 urban areas considered. The highest values are obtained for the urban agglomerations of Wuham, Karachi, Shanghai, Manila, Asunción, and Tokyo. Note that, with the exception of Irkutsk, Addis Abeba, and Lhasa, all agglomerations have values above 20 °C.
Figure 8 shows the maximum and average values of the DI versus the LSTURB-MEAN for the selected agglomerations. As can be seen, 89% of the 71 agglomerations have a DIMEAN above 20 °C (hot) and 10% are very hot (see Table 2). As for the DIMAX, 37% of the urban agglomeration are very hot and 9% are torrid.
Finally, to facilitate the comparison, a ranking of the 20 urban agglomerations with highest values of SUHIMEAN, DIMEAN, and UTFVIMAX is presented in Table 4.

4. Conclusions

Retrieval of land surface temperature (LST) from satellite data allows the estimation of the surface urban heat island (SUHI) as the difference between the LST obtained in the urban area and the LST of its surroundings. However, this definition depends on the selection of the urban and its surrounding. So far, there is no clear definition in the literature of how to select these reference areas, and thus this makes it extremely difficult to compare the SUHI between different urban agglomerations.
In this work, a methodology was proposed to estimate the SUHI in a precise and simple way in which the urban reference is obtained from the urban area class of the ESA CCI land cover map and three surroundings references are defined: the urban adjacent (SU), the future adjacent (SF), and the peri-urban (SP), which are obtained from mathematical expressions that depend exclusively on the total urban area (A). In addition, two formulations of SUHI are considered: SUHIMAX and SUHIMEAN, which evaluate the maximum and average SUHI of the urban area for each of the three surrounding references.
The proposed methodology was applied to the LST level-2 data product obtained from the SLSTR sensor on board the Sentinel-3A satellite in 71 urban agglomerations worldwide. To complete the study, two additional indices were considered: the Urban Thermal Field Variation Index (UTFVI) and the Discomfort Index (DI), which proved to be complementary to the SUHI phenomenon.
Once the methodology was presented and applied, future work will require a systematic evaluation of SUHIMAX and SUHIMEAN in urban agglomerations around the world in order to analyse the impact of latitude, longitude, morphology of the urban area, season, distance from ocean, as well as the impact of global warming, which will make necessary to take preventive measures against episodes of heat waves that will be increasingly intense and frequent.

Author Contributions

Conceptualization, J.A.S.; methodology, J.A.S.;; software, J.A.S.; and I.I.; validation, J.A.S.; and I.I.;; formal analysis, J.A.S.; and I.I.; investigation, J.A.S.; and I.I.; resources, José Antonio Sobrino; data curation, J.A.S.; and I.I.; writing—original draft, J.A.S.; and I.I.; writing—review and editing, J.A.S.; visualization, J.A.S.; and I.I.; supervision, J.A.S.; project administration, J.A.S.; funding acquisition, J.A.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Ministerio de Ciencia, Innovación y Universidades project ESP2017-85770-R.

Acknowledgments

The authors thank the European Space Agency (ESA) Sentinel 3 SLSTR Level-2 LST product, the ESA Climate Change Initiative Land Cover 2015, the NASA AIRS L3 product, and the UN Department of Economic and Social Affairs, Population Division (2018) for making data freely available and to M.J. Rubio-Lara for the artistic view of Figure 1.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Appendix A

Characteristics of the Urban Agglomerations Selected (see Table A1)

Table A1. Urban agglomerations selected in the present paper. CITY is the name of the urban agglomeration. LAT and LON are the latitude and longitude. HEIGHT is the average elevation above sea level in meters. CLIMATE indicates the climatic zone according to Köppen–Geiger classification, see Table 1 of [45]. AREA is the extension in km2 of the urban agglomeration according to [33]. POPULATION is the number of inhabitants in thousands, referring to 2018 [46].
Table A1. Urban agglomerations selected in the present paper. CITY is the name of the urban agglomeration. LAT and LON are the latitude and longitude. HEIGHT is the average elevation above sea level in meters. CLIMATE indicates the climatic zone according to Köppen–Geiger classification, see Table 1 of [45]. AREA is the extension in km2 of the urban agglomeration according to [33]. POPULATION is the number of inhabitants in thousands, referring to 2018 [46].
CITY.LATLONHEIGHT (m)CLIMATEAREA (km2)POPULATION (Thousands)
Adís Abeba9.0338.742355.00Cwb2054400
Antananarivo−18.9447.521435.00Cwb, Cfb1433058
Cairo30.0631.2423.00BWh69020,076
Lagos6.523.3841.00Aw79813,463
Niamey13.512.11207.00BSh651214
Tripoli32.8913.1981.00BSh2821158
Yamena12.1215.07298.00BSh921323
Chicago41.90−87.65182.00Dfa48608864
Las Vegas36.17−115.14610.00BWk7032541
Los Angeles34.05−118.2471.00Csb579112,458
New York40.67−73.9410.00Cfa576518,819
Oklahoma City35.48−97.54366.00Cfa613969
San Diego-Tijuana32.72−117.1622.00Csa15415270
Toronto43.67−79.3976.00Dfb13786082
Vancouver49.25−122.983.00Cfb6002531
Habanna23.14−82.3659.00Aw1922136
México City19.42−99.152250.00Cwb138221,581
Monterrey25.67−100.31530.00BSh4684712
San José9.93−84.081300.00Am, Cfb3101358
Asunción−25.28−57.6489.00Cfa4083222
Buenos Aires−34.60−58.3825.00Cfa203214,967
Caracas10.50−66.931000.00Aw1762935
Manaus−3.10−60.0292.00Am2252171
Río de Janeiro−22.91−43.2011.00Aw151313,293
Santiago de Chile−33.45−70.67520.00Csb5526680
São Paulo−23.55−46.63760.00Cfa152621,650
Aktobe50.3057.17225.00Dfa69420
Alepo36.2037.15379.00Csa1331754
Bangkok13.75100.521.50Aw123110,156
Beijing39.91116.3943.00Dwa195119,618
Dammam26.2850.2010.00BWh3911197
Ho Chi Minh10.82106.6319.00Aw6328145
Hyderabad17.3778.48505.00Aw4989482
Irkutsk52.28104.30440.00Dwc109633
Jakarta−6.21106.854.00Af448110,517
Jeddah21.5439.1712.00BWh3604433
Karachi24.8667.018.00BWh41515,400
Kolkata22.5488.349.00Aw79514,681
Kuala Lumpur3.15101.7066.00Af9637564
Lhasa29.6591.103650.00ET58330
Manila14.58121.005.00Am79413,482
New Delhi28.6777.22239.00Cwa, BSh118128,514
Riyad24.6546.71612.00BWh8796907
Shanghai31.17121.474.00Cfa173925,582
Taskent41.3069.27455.00Csa3902464
Tokyo 35.68139.686.00Cfa502837,468
Ürümqi43.8387.60830.00Bsk2414011
Wuhan30.57114.2837.00Cfa5468176
Yakutsk62.03129.7395.00Dfd80318
Yinchuan38.48106.231100.00BWk1391483
Atenas37.9823.72170.00Csa4243156
Berlin52.5213.3834.00Cfb6043552
Bilbao43.26−2.956.00Cfb70352
Catania37.5015.097.00Csa143586
Istambul41.0128.9640.00Csa88114,751
Lisbon38.72−9.172.00Csa4262927
London51.51−0.1335.00Cfb12339046
Madrid40.42−3.69657.00Csa3186497
Milán45.469.19120.00Cfa6883132
Moscow55.7637.62156.00Dfb111112,410
Paris48.862.3533.00Cfb145710,901
Roma41.8912.4821.00Csa2934210
Ruhr region51.477.5545.00Cfb10065119
Saint Petersburg59.9530.323.00Dfb4255383
Sevilla37.39−5.98200.00Csa113707
Toulouse43.601.44141.00Cfb252997
Valencia39.47−0.3816.00Csa197830
Warsaw52.2221.03100.00Cfb3601768
Melbourne−37.82144.9631.00Cfb16564771
Perth−31.95115.860.00Csa9511991
Sydney−33.87151.203.00Cfa13254792
Table A2. The results for the urban agglomerations. LST is the average temperature of the urban area in K (LSTURBAN-MEAN). Columns 3 to 8 give the values for the surroundings Su, Sf, and Sp of SUHIMAX and SUHIMEAN, respectively. UTFVIMAX and DIMAX are the maximum values of the indices and DATE is the day of the Sentinel 3A image.
Table A2. The results for the urban agglomerations. LST is the average temperature of the urban area in K (LSTURBAN-MEAN). Columns 3 to 8 give the values for the surroundings Su, Sf, and Sp of SUHIMAX and SUHIMEAN, respectively. UTFVIMAX and DIMAX are the maximum values of the indices and DATE is the day of the Sentinel 3A image.
CITYLST (K)SUHI MAX Su Sf Sp SUHI MEAN Su Sf Sp UTFVI (MAX)DI (MAX)DATE
Adís Abeba286.403.464.483.790.851.871.180.00915.572019/01/16
Antananarivo294.732.514.134.960.021.652.480.00823.382019/01/16
Cairo302.643.544.394.921.442.282.810.00727.842018/08/13
Lagos297.053.124.716.171.342.934.380.00624.762019/04/03
Niamey300.464.234.985.271.532.272.570.00927.792018/08/04
Tripoli302.394.364.885.031.291.811.970.01026.032018/07/19
Yamena301.562.402.813.170.911.311.670.00523.132019/04/03
Chicago301.363.946.266.191.714.023.950.00727.382018/06/30
Las Vegas307.725.387.749.182.384.746.170.01029.322018/08/04
Los Angeles298.136.786.753.481.351.32−1.950.01825.252018/08/04
New York294.887.838.9010.462.033.104.660.01926.092018/07/19
Oklahoma City302.704.755.625.641.872.742.760.00927.802018/07/22
San Diego-Tijuana297.495.544.914.231.180.56−0.130.01424.792018/08/04
Toronto292.136.817.087.481.331.601.990.01823.262018/07/22
Vancouver291.006.239.8612.182.436.078.380.01321.092018/08/04
Habanna297.854.405.405.391.832.832.820.00925.522019/04/26
México City288.549.1110.289.153.504.673.540.01920.682018/07/22
Monterrey304.333.855.426.872.313.885.330.00529.922018/07/22
San José290.566.647.648.460.761.762.580.02022.862019/04/26
Asunción302.793.964.434.881.381.852.310.00830.072019/01/23
Buenos Aires297.926.366.738.931.872.234.440.01528.042019/01/23
Caracas293.745.065.026.262.232.193.420.01022.892018/07/19
Manaus300.364.163.544.400.23−0.390.470.01329.032018/07/19
Río de Janeiro301.575.698.339.032.445.085.770.01128.752019/01/29
Santiago de Chile295.233.734.546.920.931.744.120.00922.312019/01/26
São Paulo298.655.235.514.722.753.022.240.00825.692019/01/29
Aktobe297.513.273.703.910.761.191.400.00825.712019/07/19
Alepo299.103.624.074.261.211.661.850.00824.852018/08/14
Bangkok300.612.623.373.770.251.001.400.00828.152019/03/11
Beijing301.995.313.643.412.801.481.250.00829.262018/07/28
Dammam309.291.912.112.10−0.040.160.150.00629.792018/08/13
Ho Chi Minh298.951.702.162.690.380.831.370.00425.742019/03/11
Hyderabad306.043.494.395.071.692.583.270.00629.912019/05/16
Irkutsk291.323.383.604.081.151.371.840.00819.772018/07/14
Jakarta297.526.939.138.982.144.344.190.01627.752018/07/16
Jeddah305.331.982.462.840.430.911.290.00528.442018/08/14
Karachi303.972.663.173.12−0.220.290.240.00931.382019/05/23
Kolkata303.592.222.342.230.931.050.930.00429.372019/05/16
Kuala Lumpur298.627.909.057.573.044.182.700.01628.592018/07/16
Lhasa282.714.478.709.911.826.057.260.00912.322019/05/16
Manila302.803.104.245.661.152.293.710.00630.332019/04/30
New Delhi300.765.035.815.942.152.933.060.00926.182019/05/16
Riyad308.554.565.756.572.563.754.570.00628.212018/07/31
Shanghai303.643.433.643.411.271.481.250.00731.352018/07/19
Taskent300.236.255.914.792.562.221.100.01224.952018/07/09
Tokyo301.595.828.2610.022.805.246.990.01030.062018/07/31
Ürümqi298.676.396.989.370.411.003.400.02027.472018/08/11
Wuhan306.604.064.374.711.932.242.570.00731.872018/07/19
Yakutsk294.194.755.356.121.271.872.640.01224.082018/07/07
Yinchuan297.734.325.185.631.542.402.840.00923.822018/07/25
Atenas298.136.857.226.823.143.513.120.01226.892018/08/13
Berlin296.976.787.347.492.903.473.620.01326.392018/08/03
Bilbao297.373.664.064.211.932.332.470.00625.392018/08/02
Catania298.924.415.526.671.272.383.540.01026.602018/08/03
Istambul297.625.506.146.931.632.273.060.01326.592018/08/14
Lisbon302.015.015.836.661.802.623.450.01125.892018/08/04
London296.974.845.927.062.493.564.710.00825.212018/08/02
Madrid303.036.096.526.982.703.133.580.01128.212018/08/02
Milán299.855.786.617.222.503.333.940.01127.002018/07/19
Moscow293.056.477.378.033.063.964.620.01222.592018/07/31
Paris300.635.076.046.762.573.544.260.00828.112018/08/03
Roma297.475.336.287.082.303.264.050.01025.722018/07/19
Ruhr region297.174.565.646.141.292.372.880.01126.162018/08/02
Saint Petersburg295.906.536.987.373.894.344.730.00925.022018/07/31
Sevilla302.355.085.646.102.603.173.630.00826.152018/08/01
Toulouse300.275.335.946.192.212.823.070.01027.902018/08/02
Valencia301.594.444.765.221.752.072.530.00927.832018/08/02
Warsaw295.394.695.405.832.062.773.200.00923.732018/08/13
Melbourne294.675.946.536.862.292.873.200.01223.242019/01/03
Perth293.835.266.325.961.112.171.810.01424.382019/01/15
Sydney295.905.095.886.341.712.502.960.01125.042019/01/03
Land surface temperature images (Sentinel-3A SLSTR Level-2 LST product) of the urban agglomerations selected. The images cover the peri-urban area. The polygon is the urban area obtained from ESA CCI [33].
Figure A1. Land surface temperature images (Sentinel-3A SLSTR Level-2 LST product) of the urban agglomerations selected. The images cover the peri-urban area. The polygon is the urban area obtained from ESA CCI [33].
Figure A1. Land surface temperature images (Sentinel-3A SLSTR Level-2 LST product) of the urban agglomerations selected. The images cover the peri-urban area. The polygon is the urban area obtained from ESA CCI [33].
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Figure 1. Boundaries definition concept.
Figure 1. Boundaries definition concept.
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Figure 2. Surrounding areas of the Paris agglomeration. The white colour is the urban agglomeration polygon generated from the land cover map produced by European Space Agency (ESA) Climate Change Initiative (CCI) [33], whose value is 1457 km2. The yellow, orange, and burgundy colours are those of the surrounding areas Su, Sf, and Sp, respectively, which are obtained from QGIS with the buffer tool (WU = 9.5 km, Wf = 15.7 km, and WP = 32.1 km).
Figure 2. Surrounding areas of the Paris agglomeration. The white colour is the urban agglomeration polygon generated from the land cover map produced by European Space Agency (ESA) Climate Change Initiative (CCI) [33], whose value is 1457 km2. The yellow, orange, and burgundy colours are those of the surrounding areas Su, Sf, and Sp, respectively, which are obtained from QGIS with the buffer tool (WU = 9.5 km, Wf = 15.7 km, and WP = 32.1 km).
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Figure 3. (a) Geographical location of the 71 selected urban agglomerations (red points), (b) population (in thousands of inhabitants) versus urban extension (km2), and (c) detail for agglomerations with less than 5550 thousand inhabitants and 750 km2 of surface.
Figure 3. (a) Geographical location of the 71 selected urban agglomerations (red points), (b) population (in thousands of inhabitants) versus urban extension (km2), and (c) detail for agglomerations with less than 5550 thousand inhabitants and 750 km2 of surface.
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Figure 4. Surface urban heat island (SUHI)MAX values (height of the column) in °C for the adjacent urban (red), the future adjacent urban (orange), and the peri-urban (blue) surroundings of the 71 agglomerations selected.
Figure 4. Surface urban heat island (SUHI)MAX values (height of the column) in °C for the adjacent urban (red), the future adjacent urban (orange), and the peri-urban (blue) surroundings of the 71 agglomerations selected.
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Figure 5. SUHIMEAN values (height of the column) in °C for the adjacent urban (red), the future adjacent urban (orange), and the peri-urban (blue) surroundings of the 71 urban agglomerations selected.
Figure 5. SUHIMEAN values (height of the column) in °C for the adjacent urban (red), the future adjacent urban (orange), and the peri-urban (blue) surroundings of the 71 urban agglomerations selected.
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Figure 6. Map of the maximum values (see Appendix A) of the UTFVI for the urban agglomeration areas (A) and the sea and land surface temperature radiometer (SLSTR) Sentinel 3A images selected in this paper. The colors indicate the specific ecological evaluation according to Table 1.
Figure 6. Map of the maximum values (see Appendix A) of the UTFVI for the urban agglomeration areas (A) and the sea and land surface temperature radiometer (SLSTR) Sentinel 3A images selected in this paper. The colors indicate the specific ecological evaluation according to Table 1.
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Figure 7. Map of the maximum values (see Table A2 of Appendix A) of the Discomfort Index (DIMAX) for the urban agglomeration areas selected in this paper. The colors indicated the DI categories according to Table 2.
Figure 7. Map of the maximum values (see Table A2 of Appendix A) of the Discomfort Index (DIMAX) for the urban agglomeration areas selected in this paper. The colors indicated the DI categories according to Table 2.
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Figure 8. Maximum (red points) and mean (purple points) values of Discomfort Index (DI) versus LSTURB-MEAN obtained from the SLSTR Level-2 land surface temperature (LST) product.
Figure 8. Maximum (red points) and mean (purple points) values of Discomfort Index (DI) versus LSTURB-MEAN obtained from the SLSTR Level-2 land surface temperature (LST) product.
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Table 1. Threshold values of Urban Thermal Field Variance Index (UTFVI) and ecological evaluation index [18].
Table 1. Threshold values of Urban Thermal Field Variance Index (UTFVI) and ecological evaluation index [18].
Urban Thermal Field Variation IndexUrban Heat Island PhenomenonEcological Evaluation Index
<0NoneExcellent
0–0.005WeakGood
0.005–0.010MiddleNormal
0.010–0.015StrongBad
0.015–0.020StrongerWorse
>0.020StrongestWorst
Table 2. Threshold values of the Discomfort Index (DI) categories [36].
Table 2. Threshold values of the Discomfort Index (DI) categories [36].
DI CategoriesDI temperature (°C)
Hyperglacial<−40
Glacial−39.9 to −20
Extremely cold−19.9 to −10
Very cold−9.9 to −1.8
Cold−1.7 to +12.9
Cool+13 to +14.9
Comfortable+15 to +19.9
Hot+20 to +26.4
Very hot+26.5 to +29.9
Torrid>+30
Table 3. Values of surface urban heat island (SUHI)MEAN in (°C) for the urban adjacent (Su), the future adjacent (Sf), and the periurban (Sp) considering different criteria.
Table 3. Values of surface urban heat island (SUHI)MEAN in (°C) for the urban adjacent (Su), the future adjacent (Sf), and the periurban (Sp) considering different criteria.
CriteriaSUHIMEAN (Su) (°C)SUHIMEAN (Sf) (°C)SUHIMEAN (Sp) (°C)
Africa1.1 ± 0.52.0 ± 0.52.4 ± 1.0
America1.9 ± 0.82.8 ± 1.63.3 ± 2.4
Asia1.4 ± 1.02.3 ± 1.62.7 ± 1.9
Europe2.4 ± 0.63.1 ± 0.63.6 ± 0.7
Oceania1.7 ± 0.62.5 ± 0.32.7 ± 0.7
(1)Population > 20 millions2.4 ± 0.83.3 ± 1.33.5 ± 1.8
(2)Urban surface > 1000 km22.1 ± 0.83.0 ± 1.33.3 ± 2.0
(3)Coast distance > 100 km1.9 ± 0.92.7 ± 1.33.2 ± 1.5
(4)Elevation > 1 km1.5 ± 1.12.9 ± 1.73.3 ± 1.9
(5)Equatorial1.4 ± 0.92.4 ± 1.62.8 ± 1.5
Arid1.2 ± 0.92.1 ± 1.52.8 ± 1.9
Warm Temperate2.0 ± 0.72.7 ± 1.13.1 ± 1.7
Snow2.1 ± 1.1
All (71 agglomerations)1.8 ± 0.92.6 ± 1.33.1 ± 1.7
(1) Tokyo, New Delhi, Shanghai, Sao Paulo, Ciudad de México, El Cairo, Beijing. (2) Los Angeles, New York, Tokyo, Chicago, Jakarta, Buenos Aires, Beijing, Shanghai, Melbourne, San Diego-Tijuana, Sao Paulo, Rio de Janeiro, Paris, Ciudad de Mexico, Toronto, Sydney, London, Bangkok, New Delhi, Moscow, Ruhr región. (3) Milan, Toulouse, Paris, Antananarivo, Beijing, Berlin, Ruhr región, Ciudad de Mexico, Warsaw, Monterrey, Madrid, Hyderabad, Riyad, Las Vegas, Aktobe, Adis Abeba, Wuhan, Mosocw, Oklahoma City, Yakutsk, Lhasa, Niamey, Asunción, New Delhi, Yinchuan, Manaus, Yamena, Taskent, Ürümqi. (4) Lhasa, Adis Abeba, Ciudad de Mexico, Antananarivo, San José, Yinchun, Caracas. (5) Urban agglomerations are classified in Appendix A according to the five vegetation groups of Köppen [45]: equatorial (13 agglomerations), arid (12 agglomerations), warm temperate (37 agglomerations), and snow (6 agglomerations).
Table 4. Urban agglomerations with highest SUHIMEAN, with respect to the urban adjacent surrounding (Su), UTFVIMAX, and DIMEAN for the Sentinel 3A images selected.
Table 4. Urban agglomerations with highest SUHIMEAN, with respect to the urban adjacent surrounding (Su), UTFVIMAX, and DIMEAN for the Sentinel 3A images selected.
RANKINGSUHIMEAN (Su) (°C)UTFVIMAXDIMEAN (°C)
1Saint Petersburg3.9San José0.020Wuhan30.1
2Ciudad de México3.5Ürümqi0.020Shanghai29.3
3Athens3.1Nueva York0.019Karachi28.9
4Moscow3.1Ciudad México0.019Monterrey28.6
5Kuala Lumpur 3.0Toronto0.018Hyderabad28.5
6Berlin2.9Los Ángeles0.018Manila 28.5
7Beijing2.8Kuala Lumpur0.016Kolkata28.3
8Tokyo2.8Yakarta0.016Asunción27.8
9Sao Paulo2.8Buenos Aires0.015Las Vegas27.4
10Chicago2.8San Diego0.014Jeddah27.3
11Madrid2.7Perth0.014Tokyo27.3
12Sevilla2.6Manaus0.013Beijing27.1
13Paris2.6Berlín0.013Riyad27.0
14Taskent2.6Vancouver0.013El Cairo26.2
15Riyad2.6Estambul0.013Rio de Janeiro26.1
16Milan2.5Athens0.012Bangkok26.0
17London2.5Melbourne0-012Paris25.9
18Rio de Janeiro2.4Taskent0.012Madrid25.7
19Vancouver2.4Yakuts0.012Valencia25.7
20Las Vegas2.4Moscow0.011Oklahoma City25.7

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MDPI and ACS Style

Sobrino, J.A.; Irakulis, I. A Methodology for Comparing the Surface Urban Heat Island in Selected Urban Agglomerations Around the World from Sentinel-3 SLSTR Data. Remote Sens. 2020, 12, 2052. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12122052

AMA Style

Sobrino JA, Irakulis I. A Methodology for Comparing the Surface Urban Heat Island in Selected Urban Agglomerations Around the World from Sentinel-3 SLSTR Data. Remote Sensing. 2020; 12(12):2052. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12122052

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Sobrino, José Antonio, and Itziar Irakulis. 2020. "A Methodology for Comparing the Surface Urban Heat Island in Selected Urban Agglomerations Around the World from Sentinel-3 SLSTR Data" Remote Sensing 12, no. 12: 2052. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12122052

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