A Methodology for Comparing the Surface Urban Heat Island in Selected Urban Agglomerations Around the World from Sentinel-3 SLSTR Data
Abstract
:1. Introduction
2. Methodology
2.1. SUHI Selection of the Urban and Surrounding References
- (a)
- Urban:
- (b) Surroundings:
2.2. UTFVI and DI Indices
2.3. Criteria for Urban Agglomerations Selection
- (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
3. Results and Discussion
3.1. SUHI
3.2. UTFVI
3.3. DI
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Characteristics of the Urban Agglomerations Selected (see Table A1)
CITY. | LAT | LON | HEIGHT (m) | CLIMATE | AREA (km2) | POPULATION (Thousands) |
---|---|---|---|---|---|---|
Adís Abeba | 9.03 | 38.74 | 2355.00 | Cwb | 205 | 4400 |
Antananarivo | −18.94 | 47.52 | 1435.00 | Cwb, Cfb | 143 | 3058 |
Cairo | 30.06 | 31.24 | 23.00 | BWh | 690 | 20,076 |
Lagos | 6.52 | 3.38 | 41.00 | Aw | 798 | 13,463 |
Niamey | 13.51 | 2.11 | 207.00 | BSh | 65 | 1214 |
Tripoli | 32.89 | 13.19 | 81.00 | BSh | 282 | 1158 |
Yamena | 12.12 | 15.07 | 298.00 | BSh | 92 | 1323 |
Chicago | 41.90 | −87.65 | 182.00 | Dfa | 4860 | 8864 |
Las Vegas | 36.17 | −115.14 | 610.00 | BWk | 703 | 2541 |
Los Angeles | 34.05 | −118.24 | 71.00 | Csb | 5791 | 12,458 |
New York | 40.67 | −73.94 | 10.00 | Cfa | 5765 | 18,819 |
Oklahoma City | 35.48 | −97.54 | 366.00 | Cfa | 613 | 969 |
San Diego-Tijuana | 32.72 | −117.16 | 22.00 | Csa | 1541 | 5270 |
Toronto | 43.67 | −79.39 | 76.00 | Dfb | 1378 | 6082 |
Vancouver | 49.25 | −122.98 | 3.00 | Cfb | 600 | 2531 |
Habanna | 23.14 | −82.36 | 59.00 | Aw | 192 | 2136 |
México City | 19.42 | −99.15 | 2250.00 | Cwb | 1382 | 21,581 |
Monterrey | 25.67 | −100.31 | 530.00 | BSh | 468 | 4712 |
San José | 9.93 | −84.08 | 1300.00 | Am, Cfb | 310 | 1358 |
Asunción | −25.28 | −57.64 | 89.00 | Cfa | 408 | 3222 |
Buenos Aires | −34.60 | −58.38 | 25.00 | Cfa | 2032 | 14,967 |
Caracas | 10.50 | −66.93 | 1000.00 | Aw | 176 | 2935 |
Manaus | −3.10 | −60.02 | 92.00 | Am | 225 | 2171 |
Río de Janeiro | −22.91 | −43.20 | 11.00 | Aw | 1513 | 13,293 |
Santiago de Chile | −33.45 | −70.67 | 520.00 | Csb | 552 | 6680 |
São Paulo | −23.55 | −46.63 | 760.00 | Cfa | 1526 | 21,650 |
Aktobe | 50.30 | 57.17 | 225.00 | Dfa | 69 | 420 |
Alepo | 36.20 | 37.15 | 379.00 | Csa | 133 | 1754 |
Bangkok | 13.75 | 100.52 | 1.50 | Aw | 1231 | 10,156 |
Beijing | 39.91 | 116.39 | 43.00 | Dwa | 1951 | 19,618 |
Dammam | 26.28 | 50.20 | 10.00 | BWh | 391 | 1197 |
Ho Chi Minh | 10.82 | 106.63 | 19.00 | Aw | 632 | 8145 |
Hyderabad | 17.37 | 78.48 | 505.00 | Aw | 498 | 9482 |
Irkutsk | 52.28 | 104.30 | 440.00 | Dwc | 109 | 633 |
Jakarta | −6.21 | 106.85 | 4.00 | Af | 4481 | 10,517 |
Jeddah | 21.54 | 39.17 | 12.00 | BWh | 360 | 4433 |
Karachi | 24.86 | 67.01 | 8.00 | BWh | 415 | 15,400 |
Kolkata | 22.54 | 88.34 | 9.00 | Aw | 795 | 14,681 |
Kuala Lumpur | 3.15 | 101.70 | 66.00 | Af | 963 | 7564 |
Lhasa | 29.65 | 91.10 | 3650.00 | ET | 58 | 330 |
Manila | 14.58 | 121.00 | 5.00 | Am | 794 | 13,482 |
New Delhi | 28.67 | 77.22 | 239.00 | Cwa, BSh | 1181 | 28,514 |
Riyad | 24.65 | 46.71 | 612.00 | BWh | 879 | 6907 |
Shanghai | 31.17 | 121.47 | 4.00 | Cfa | 1739 | 25,582 |
Taskent | 41.30 | 69.27 | 455.00 | Csa | 390 | 2464 |
Tokyo | 35.68 | 139.68 | 6.00 | Cfa | 5028 | 37,468 |
Ürümqi | 43.83 | 87.60 | 830.00 | Bsk | 241 | 4011 |
Wuhan | 30.57 | 114.28 | 37.00 | Cfa | 546 | 8176 |
Yakutsk | 62.03 | 129.73 | 95.00 | Dfd | 80 | 318 |
Yinchuan | 38.48 | 106.23 | 1100.00 | BWk | 139 | 1483 |
Atenas | 37.98 | 23.72 | 170.00 | Csa | 424 | 3156 |
Berlin | 52.52 | 13.38 | 34.00 | Cfb | 604 | 3552 |
Bilbao | 43.26 | −2.95 | 6.00 | Cfb | 70 | 352 |
Catania | 37.50 | 15.09 | 7.00 | Csa | 143 | 586 |
Istambul | 41.01 | 28.96 | 40.00 | Csa | 881 | 14,751 |
Lisbon | 38.72 | −9.17 | 2.00 | Csa | 426 | 2927 |
London | 51.51 | −0.13 | 35.00 | Cfb | 1233 | 9046 |
Madrid | 40.42 | −3.69 | 657.00 | Csa | 318 | 6497 |
Milán | 45.46 | 9.19 | 120.00 | Cfa | 688 | 3132 |
Moscow | 55.76 | 37.62 | 156.00 | Dfb | 1111 | 12,410 |
Paris | 48.86 | 2.35 | 33.00 | Cfb | 1457 | 10,901 |
Roma | 41.89 | 12.48 | 21.00 | Csa | 293 | 4210 |
Ruhr region | 51.47 | 7.55 | 45.00 | Cfb | 1006 | 5119 |
Saint Petersburg | 59.95 | 30.32 | 3.00 | Dfb | 425 | 5383 |
Sevilla | 37.39 | −5.98 | 200.00 | Csa | 113 | 707 |
Toulouse | 43.60 | 1.44 | 141.00 | Cfb | 252 | 997 |
Valencia | 39.47 | −0.38 | 16.00 | Csa | 197 | 830 |
Warsaw | 52.22 | 21.03 | 100.00 | Cfb | 360 | 1768 |
Melbourne | −37.82 | 144.96 | 31.00 | Cfb | 1656 | 4771 |
Perth | −31.95 | 115.86 | 0.00 | Csa | 951 | 1991 |
Sydney | −33.87 | 151.20 | 3.00 | Cfa | 1325 | 4792 |
CITY | LST (K) | SUHI MAX Su | Sf | Sp | SUHI MEAN Su | Sf | Sp | UTFVI (MAX) | DI (MAX) | DATE |
---|---|---|---|---|---|---|---|---|---|---|
Adís Abeba | 286.40 | 3.46 | 4.48 | 3.79 | 0.85 | 1.87 | 1.18 | 0.009 | 15.57 | 2019/01/16 |
Antananarivo | 294.73 | 2.51 | 4.13 | 4.96 | 0.02 | 1.65 | 2.48 | 0.008 | 23.38 | 2019/01/16 |
Cairo | 302.64 | 3.54 | 4.39 | 4.92 | 1.44 | 2.28 | 2.81 | 0.007 | 27.84 | 2018/08/13 |
Lagos | 297.05 | 3.12 | 4.71 | 6.17 | 1.34 | 2.93 | 4.38 | 0.006 | 24.76 | 2019/04/03 |
Niamey | 300.46 | 4.23 | 4.98 | 5.27 | 1.53 | 2.27 | 2.57 | 0.009 | 27.79 | 2018/08/04 |
Tripoli | 302.39 | 4.36 | 4.88 | 5.03 | 1.29 | 1.81 | 1.97 | 0.010 | 26.03 | 2018/07/19 |
Yamena | 301.56 | 2.40 | 2.81 | 3.17 | 0.91 | 1.31 | 1.67 | 0.005 | 23.13 | 2019/04/03 |
Chicago | 301.36 | 3.94 | 6.26 | 6.19 | 1.71 | 4.02 | 3.95 | 0.007 | 27.38 | 2018/06/30 |
Las Vegas | 307.72 | 5.38 | 7.74 | 9.18 | 2.38 | 4.74 | 6.17 | 0.010 | 29.32 | 2018/08/04 |
Los Angeles | 298.13 | 6.78 | 6.75 | 3.48 | 1.35 | 1.32 | −1.95 | 0.018 | 25.25 | 2018/08/04 |
New York | 294.88 | 7.83 | 8.90 | 10.46 | 2.03 | 3.10 | 4.66 | 0.019 | 26.09 | 2018/07/19 |
Oklahoma City | 302.70 | 4.75 | 5.62 | 5.64 | 1.87 | 2.74 | 2.76 | 0.009 | 27.80 | 2018/07/22 |
San Diego-Tijuana | 297.49 | 5.54 | 4.91 | 4.23 | 1.18 | 0.56 | −0.13 | 0.014 | 24.79 | 2018/08/04 |
Toronto | 292.13 | 6.81 | 7.08 | 7.48 | 1.33 | 1.60 | 1.99 | 0.018 | 23.26 | 2018/07/22 |
Vancouver | 291.00 | 6.23 | 9.86 | 12.18 | 2.43 | 6.07 | 8.38 | 0.013 | 21.09 | 2018/08/04 |
Habanna | 297.85 | 4.40 | 5.40 | 5.39 | 1.83 | 2.83 | 2.82 | 0.009 | 25.52 | 2019/04/26 |
México City | 288.54 | 9.11 | 10.28 | 9.15 | 3.50 | 4.67 | 3.54 | 0.019 | 20.68 | 2018/07/22 |
Monterrey | 304.33 | 3.85 | 5.42 | 6.87 | 2.31 | 3.88 | 5.33 | 0.005 | 29.92 | 2018/07/22 |
San José | 290.56 | 6.64 | 7.64 | 8.46 | 0.76 | 1.76 | 2.58 | 0.020 | 22.86 | 2019/04/26 |
Asunción | 302.79 | 3.96 | 4.43 | 4.88 | 1.38 | 1.85 | 2.31 | 0.008 | 30.07 | 2019/01/23 |
Buenos Aires | 297.92 | 6.36 | 6.73 | 8.93 | 1.87 | 2.23 | 4.44 | 0.015 | 28.04 | 2019/01/23 |
Caracas | 293.74 | 5.06 | 5.02 | 6.26 | 2.23 | 2.19 | 3.42 | 0.010 | 22.89 | 2018/07/19 |
Manaus | 300.36 | 4.16 | 3.54 | 4.40 | 0.23 | −0.39 | 0.47 | 0.013 | 29.03 | 2018/07/19 |
Río de Janeiro | 301.57 | 5.69 | 8.33 | 9.03 | 2.44 | 5.08 | 5.77 | 0.011 | 28.75 | 2019/01/29 |
Santiago de Chile | 295.23 | 3.73 | 4.54 | 6.92 | 0.93 | 1.74 | 4.12 | 0.009 | 22.31 | 2019/01/26 |
São Paulo | 298.65 | 5.23 | 5.51 | 4.72 | 2.75 | 3.02 | 2.24 | 0.008 | 25.69 | 2019/01/29 |
Aktobe | 297.51 | 3.27 | 3.70 | 3.91 | 0.76 | 1.19 | 1.40 | 0.008 | 25.71 | 2019/07/19 |
Alepo | 299.10 | 3.62 | 4.07 | 4.26 | 1.21 | 1.66 | 1.85 | 0.008 | 24.85 | 2018/08/14 |
Bangkok | 300.61 | 2.62 | 3.37 | 3.77 | 0.25 | 1.00 | 1.40 | 0.008 | 28.15 | 2019/03/11 |
Beijing | 301.99 | 5.31 | 3.64 | 3.41 | 2.80 | 1.48 | 1.25 | 0.008 | 29.26 | 2018/07/28 |
Dammam | 309.29 | 1.91 | 2.11 | 2.10 | −0.04 | 0.16 | 0.15 | 0.006 | 29.79 | 2018/08/13 |
Ho Chi Minh | 298.95 | 1.70 | 2.16 | 2.69 | 0.38 | 0.83 | 1.37 | 0.004 | 25.74 | 2019/03/11 |
Hyderabad | 306.04 | 3.49 | 4.39 | 5.07 | 1.69 | 2.58 | 3.27 | 0.006 | 29.91 | 2019/05/16 |
Irkutsk | 291.32 | 3.38 | 3.60 | 4.08 | 1.15 | 1.37 | 1.84 | 0.008 | 19.77 | 2018/07/14 |
Jakarta | 297.52 | 6.93 | 9.13 | 8.98 | 2.14 | 4.34 | 4.19 | 0.016 | 27.75 | 2018/07/16 |
Jeddah | 305.33 | 1.98 | 2.46 | 2.84 | 0.43 | 0.91 | 1.29 | 0.005 | 28.44 | 2018/08/14 |
Karachi | 303.97 | 2.66 | 3.17 | 3.12 | −0.22 | 0.29 | 0.24 | 0.009 | 31.38 | 2019/05/23 |
Kolkata | 303.59 | 2.22 | 2.34 | 2.23 | 0.93 | 1.05 | 0.93 | 0.004 | 29.37 | 2019/05/16 |
Kuala Lumpur | 298.62 | 7.90 | 9.05 | 7.57 | 3.04 | 4.18 | 2.70 | 0.016 | 28.59 | 2018/07/16 |
Lhasa | 282.71 | 4.47 | 8.70 | 9.91 | 1.82 | 6.05 | 7.26 | 0.009 | 12.32 | 2019/05/16 |
Manila | 302.80 | 3.10 | 4.24 | 5.66 | 1.15 | 2.29 | 3.71 | 0.006 | 30.33 | 2019/04/30 |
New Delhi | 300.76 | 5.03 | 5.81 | 5.94 | 2.15 | 2.93 | 3.06 | 0.009 | 26.18 | 2019/05/16 |
Riyad | 308.55 | 4.56 | 5.75 | 6.57 | 2.56 | 3.75 | 4.57 | 0.006 | 28.21 | 2018/07/31 |
Shanghai | 303.64 | 3.43 | 3.64 | 3.41 | 1.27 | 1.48 | 1.25 | 0.007 | 31.35 | 2018/07/19 |
Taskent | 300.23 | 6.25 | 5.91 | 4.79 | 2.56 | 2.22 | 1.10 | 0.012 | 24.95 | 2018/07/09 |
Tokyo | 301.59 | 5.82 | 8.26 | 10.02 | 2.80 | 5.24 | 6.99 | 0.010 | 30.06 | 2018/07/31 |
Ürümqi | 298.67 | 6.39 | 6.98 | 9.37 | 0.41 | 1.00 | 3.40 | 0.020 | 27.47 | 2018/08/11 |
Wuhan | 306.60 | 4.06 | 4.37 | 4.71 | 1.93 | 2.24 | 2.57 | 0.007 | 31.87 | 2018/07/19 |
Yakutsk | 294.19 | 4.75 | 5.35 | 6.12 | 1.27 | 1.87 | 2.64 | 0.012 | 24.08 | 2018/07/07 |
Yinchuan | 297.73 | 4.32 | 5.18 | 5.63 | 1.54 | 2.40 | 2.84 | 0.009 | 23.82 | 2018/07/25 |
Atenas | 298.13 | 6.85 | 7.22 | 6.82 | 3.14 | 3.51 | 3.12 | 0.012 | 26.89 | 2018/08/13 |
Berlin | 296.97 | 6.78 | 7.34 | 7.49 | 2.90 | 3.47 | 3.62 | 0.013 | 26.39 | 2018/08/03 |
Bilbao | 297.37 | 3.66 | 4.06 | 4.21 | 1.93 | 2.33 | 2.47 | 0.006 | 25.39 | 2018/08/02 |
Catania | 298.92 | 4.41 | 5.52 | 6.67 | 1.27 | 2.38 | 3.54 | 0.010 | 26.60 | 2018/08/03 |
Istambul | 297.62 | 5.50 | 6.14 | 6.93 | 1.63 | 2.27 | 3.06 | 0.013 | 26.59 | 2018/08/14 |
Lisbon | 302.01 | 5.01 | 5.83 | 6.66 | 1.80 | 2.62 | 3.45 | 0.011 | 25.89 | 2018/08/04 |
London | 296.97 | 4.84 | 5.92 | 7.06 | 2.49 | 3.56 | 4.71 | 0.008 | 25.21 | 2018/08/02 |
Madrid | 303.03 | 6.09 | 6.52 | 6.98 | 2.70 | 3.13 | 3.58 | 0.011 | 28.21 | 2018/08/02 |
Milán | 299.85 | 5.78 | 6.61 | 7.22 | 2.50 | 3.33 | 3.94 | 0.011 | 27.00 | 2018/07/19 |
Moscow | 293.05 | 6.47 | 7.37 | 8.03 | 3.06 | 3.96 | 4.62 | 0.012 | 22.59 | 2018/07/31 |
Paris | 300.63 | 5.07 | 6.04 | 6.76 | 2.57 | 3.54 | 4.26 | 0.008 | 28.11 | 2018/08/03 |
Roma | 297.47 | 5.33 | 6.28 | 7.08 | 2.30 | 3.26 | 4.05 | 0.010 | 25.72 | 2018/07/19 |
Ruhr region | 297.17 | 4.56 | 5.64 | 6.14 | 1.29 | 2.37 | 2.88 | 0.011 | 26.16 | 2018/08/02 |
Saint Petersburg | 295.90 | 6.53 | 6.98 | 7.37 | 3.89 | 4.34 | 4.73 | 0.009 | 25.02 | 2018/07/31 |
Sevilla | 302.35 | 5.08 | 5.64 | 6.10 | 2.60 | 3.17 | 3.63 | 0.008 | 26.15 | 2018/08/01 |
Toulouse | 300.27 | 5.33 | 5.94 | 6.19 | 2.21 | 2.82 | 3.07 | 0.010 | 27.90 | 2018/08/02 |
Valencia | 301.59 | 4.44 | 4.76 | 5.22 | 1.75 | 2.07 | 2.53 | 0.009 | 27.83 | 2018/08/02 |
Warsaw | 295.39 | 4.69 | 5.40 | 5.83 | 2.06 | 2.77 | 3.20 | 0.009 | 23.73 | 2018/08/13 |
Melbourne | 294.67 | 5.94 | 6.53 | 6.86 | 2.29 | 2.87 | 3.20 | 0.012 | 23.24 | 2019/01/03 |
Perth | 293.83 | 5.26 | 6.32 | 5.96 | 1.11 | 2.17 | 1.81 | 0.014 | 24.38 | 2019/01/15 |
Sydney | 295.90 | 5.09 | 5.88 | 6.34 | 1.71 | 2.50 | 2.96 | 0.011 | 25.04 | 2019/01/03 |
References
- European Space Agency—ESA. Mapping our global human footprint. 2019. Available online: http://www.esa.int/Our_Activities/Observing_the_Earth/Mapping_our_global_human_footprint (accessed on 10 July 2019).
- Anbar, M. The Climate impact on human comfort in the eastern Nile Delta. J. Fac. Art Cairo Univ. 2012, 72, 267–319. (In Arabic) [Google Scholar]
- Kim, H.H. Urban heat island. Int. J. Remote Sens. 1992, 13, 2319–2336. [Google Scholar] [CrossRef]
- Streutker, D.R. A remote sensing study of the urban heat island of Houston, Texas. Int. J. Remote Sens. 2002, 23, 2595–2608. [Google Scholar] [CrossRef]
- Sobrino, J.A.; Oltra-Carrió, R.; Sòria, G.; Jiménez-Muñoz, J.C.; Franch, B.; Hidalgo, V.; Mattar, C.; Julien, Y.; Cuenca, J.; Romaguera, M.; et al. Evaluation of the surface urban heat island effect in the city of Madrid by thermal remote sensing. Int. J. Remote Sens. 2012, 34, 3177–3192. [Google Scholar] [CrossRef]
- Ahmed, S. Assessment of urban heat islands and impact of climate change on socioeconomic over Suez Governorate using remote sensing and GIS techniques. Egypt. J. Remote. Sens. Space Sci. 2018, 21, 15–25. [Google Scholar] [CrossRef]
- Patz, J.A.; Campbell-Lendrum, D.; Holloway, T.; Foley, J.A. Impact of regional climate change on human health. Nature 2005, 438, 310–317. [Google Scholar] [CrossRef]
- Santamouris, M.; Cartalis, C.; Synnefa, A.; Kolokotsa, D. On the impact of urban heat island and global warming on the power demand and electricity consumption of buildings—A review. Energy Build. 2015, 98, 119–124. [Google Scholar] [CrossRef]
- O’Loughlin, J.; Witmer, F.D.W.; Linke, A.M.; Laing, A.; Gettelman, A.; Dudhia, J. Climate variability and conflict risk in East Africa, 1990–2009. Proc. Natl. Acad. Sci. USA 2012, 109, 18344–18349. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhou, D.; Xiao, J.; Bonafoni, S.; Berger, C.; Deilami, K.; Zhou, Y.; Frolking, S.; Yao, R.; Qiao, Z.; Sobrino, J.A. Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives. Remote Sens. 2018, 11, 48. [Google Scholar] [CrossRef] [Green Version]
- Mavrogianni, A.; Davies, M.; Batty, M.; Belcher, S.; Bohnenstengel, S.; Carruthers, D.; Chalabi, Z.; Croxford, B.; Demanuele, C.; Evans, S.; et al. The comfort, energy and health implications of London’s urban heat island. Build. Serv. Eng. Res. Technol. 2011, 32, 35–52. [Google Scholar] [CrossRef]
- Sobrino, J.A.; Oltra-Carrió, R.; Soria, G.; Bianchi, R.; Paganini, M. Impact of spatial resolution and satellite overpass time on evaluation of the surface urban heat island effects. Remote Sens. Environ. 2012, 117, 50–56. [Google Scholar] [CrossRef]
- Pal, S.; Ziaul, S. Detection of land use and land cover change and land surface temperature in English Bazar urban centre. Egypt. J. Remote Sens. Space Sci. 2017, 20, 125–145. [Google Scholar] [CrossRef] [Green Version]
- Weng, Q.; Larson, R.C. Satellite Remote Sensing of Urban Heat Islands: Current Practice and Prospects. In Geo-Spatial Technologies in Urban Environments; Springer: Berlin/Heidelberg, Germany, 2005; pp. 91–111. [Google Scholar]
- Pu, R.; Gong, P.; Michishita, R.; Sasagawa, T. Assessment of multi-resolution and multi-sensor data for urban surface temperature retrieval. Remote Sens. Environ. 2006, 104, 211–225. [Google Scholar] [CrossRef]
- Giridharan, R.; Kolokotroni, M. Urban heat island characteristics in London during winter. Sol. Energy 2009, 83, 1668–1682. [Google Scholar] [CrossRef]
- Bhatta, B. Analysis of Urban Growth and Sprawl from Remote Sensing Data; Springer: Berlin/Heidelberg, Germany, 2010. [Google Scholar]
- Liu, L.; Zhang, Y. Urban Heat Island Analysis Using the Landsat TM Data and ASTER Data: A Case Study in Hong Kong. Remote Sens. 2011, 3, 1535–1552. [Google Scholar] [CrossRef] [Green Version]
- Weng, Q. Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends. Remote Sens. Environ. 2012, 117, 34–49. [Google Scholar] [CrossRef]
- Xiong, Y.; Huang, S.; Chen, F.; Ye, H.; Wang, C.; Zhu, C. The Impacts of Rapid Urbanization on the Thermal Environment: A Remote Sensing Study of Guangzhou, South China. Remote Sens. 2012, 4, 2033–2056. [Google Scholar] [CrossRef] [Green Version]
- Abutaleb, K.; Ngie, A.; Darwish, A.; Ahmed, M.; Arafat, S. Assessment of Urban Heat Island Using Remotely Sensed Imagery over Greater Cairo, Egypt. Adv. Remote Sens. 2015, 4, 35–47. [Google Scholar] [CrossRef] [Green Version]
- Li, X.; Li, W.; Middel, A.; Harlan, S.; Brazel, A.; Turner, B. Remote sensing of the surface urban heat island and land architecture in Phoenix, Arizona: Combined effects of land composition and configuration and cadastral–demographic–economic factors. Remote Sens. Environ. 2016, 174, 233–243. [Google Scholar] [CrossRef] [Green Version]
- Zhou, D.; Zhao, S.; Liu, S.; Zhang, L.; Zhu, C. Surface urban heat island in China’s 32 major cities: Spatial patterns and drivers. Remote. Sens. Environ. 2014, 152, 51–61. [Google Scholar] [CrossRef]
- Khandelwal, S.; Goyal, R.; Kaul, N.; Mathew, A. Assessment of land surface temperature variation due to change in elevation of area surrounding Jaipur, India. Egypt. J. Remote Sens. Space Sci. 2018, 21, 87–94. [Google Scholar] [CrossRef]
- Al Kuwary, N.Y.; Ahmed, S.; Kaiser, M.F. Optimal Satellite Sensor Selection Utilized to Monitor the Impact of Urban Sprawl on the Thermal Environment in Doha City, Qatar. J. Earth Sci. Clim. Chang. 2015, 7, 1. [Google Scholar] [CrossRef] [Green Version]
- Voogt, J.A.; Oke, T. Thermal remote sensing of urban climates. Remote. Sens. Environ. 2003, 86, 370–384. [Google Scholar] [CrossRef]
- Chen, X.; Zhao, H.-M.; Li, P.-X.; Yin, Z.-Y. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sens. Environ. 2006, 104, 133–146. [Google Scholar] [CrossRef]
- Zhou, D.; Bonafoni, S.; Zhang, L.; Wang, R. Remote sensing of the urban heat island effect in a highly populated urban agglomeration area in East China. Sci. Total Environ. 2018, 415–429. [Google Scholar] [CrossRef]
- European Space Agency. Sentinel 3 SLSTR Level-2 LST. Available online: https://earth.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/level-2-algorithms-products (accessed on 16 January 2020).
- Zhang, Y. Land surface temperature retrieval from CBERS-02 IRMSS thermal infrared data and its applications in quantitative analysis of urban heat island effect. J. Remote Sens. 2006, 10, 789–797. [Google Scholar]
- Toy, S.; Yılmaz, S.; Yilmaz, H. Determination of bioclimatic comfort in three different land uses in the city of Erzurum, Turkey. Build. Environ. 2007, 42, 1315–1318. [Google Scholar] [CrossRef]
- Stewart, I.D.; Oke, T.R. Local Climate Zones for Urban Temperature Studies. Bull. Am. Meteorol. Soc. 2012, 93, 1879–1900. [Google Scholar] [CrossRef]
- European Space Agency—ESA. Climate Change Initiative Land Cover 2015. Available online: https://maps.elie.ucl.ac.be/CCI/viewer/ (accessed on 16 January 2020).
- Kasanko, M.; Barredo, J.I.; LaValle, C.; McCormick, N.; Demicheli, L.; Sagris, V.; Brezger, A. Are European cities becoming dispersed? Landsc. Urban Plan. 2006, 77, 111–130. [Google Scholar] [CrossRef]
- García-Nieto, A.P.; Geijzendorffer, I.R.; Baró, F.; Roche, P.; Bondeau, A.; Cramer, W. Impacts of urbanization around Mediterranean cities: Changes in ecosystem service supply. Ecol. Indic. 2018, 91, 589–606. [Google Scholar] [CrossRef] [Green Version]
- Giles, B.D.; Balafoutis, C.; Maheras, P. Too hot for comfort: The heatwaves in Greece in 1987 and 1988. Int. J. Biometeorol. 1990, 34, 98–104. [Google Scholar] [CrossRef]
- Thom, E.C. The Discomfort Index. Weatherwise 1959, 12, 57–61. [Google Scholar] [CrossRef]
- Atmospheric Infrared Sounder (AIRS L3 Product) on Board NASA’s AQUA Satellite. Available online: https://worldview.earthdata.nasa.gov/?p=geographic&l=MODIS_Terra_CorrectedReflectance_TrueColor,AIRS_L3_Surface_Relative_Humidity_Daily_Night (accessed on 10 January 2020).
- UN; United Nations Department of Economic Social Affairs Population Division. World Urbanization Prospects: The 2018 Revision; United Nations: New York, NY, USA, 2018. [Google Scholar]
- Seto, K.C.; Güneralp, B.; Hutyra, L.R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl. Acad. Sci. USA 2012, 109, 16083–16088. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Field, C.B.; Barros, V.R.; Dokken, D.J.; Mach, K.J.; Mastrandrea, M.D. Climate Change 2014—Impacts, Adaptation and Vulnerability: Part A: Global and Sectoral Aspects by Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK, 2014. [Google Scholar]
- European Space Agency—ESA. Nitrogen dioxide worldwide. 2019. Available online: https://www.esa.int/spaceinimages/Images/2019/03/Nitrogen_dioxide_worldwide (accessed on 16 May 2020).
- ESA Sentinel-3 Data Product Quality Reports. Available online: https://earth.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/data-quality-reports (accessed on 25 February 2020).
- Sobrino, J.A.; Julien, Y.; García-Monteiro, S. Surface Temperature of the Planet Earth from Satellite Data. Remote Sens. 2020, 12, 218. [Google Scholar] [CrossRef] [Green Version]
- Kottek, M.; Grieser, J.; Beck, C.; Rudolf, B.; Rubel, F. World Map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift 2006, 15, 259–263. [Google Scholar] [CrossRef]
- United Nations, Department of Economic and Social Affairs, Population Division, United Nations. The World’s Cities in 2018—Data Booklet 2018 (ST/ESA/ SER.A/417). Available online: https://www.un.org/en/development/desa/population/publications/pdf/urbanization/the_worlds_cities_in_2018_data_booklet.pdf (accessed on 10 January 2020).
Urban Thermal Field Variation Index | Urban Heat Island Phenomenon | Ecological Evaluation Index |
---|---|---|
<0 | None | Excellent |
0–0.005 | Weak | Good |
0.005–0.010 | Middle | Normal |
0.010–0.015 | Strong | Bad |
0.015–0.020 | Stronger | Worse |
>0.020 | Strongest | Worst |
DI Categories | DI 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 |
Criteria | SUHIMEAN (Su) (°C) | SUHIMEAN (Sf) (°C) | SUHIMEAN (Sp) (°C) |
---|---|---|---|
Africa | 1.1 ± 0.5 | 2.0 ± 0.5 | 2.4 ± 1.0 |
America | 1.9 ± 0.8 | 2.8 ± 1.6 | 3.3 ± 2.4 |
Asia | 1.4 ± 1.0 | 2.3 ± 1.6 | 2.7 ± 1.9 |
Europe | 2.4 ± 0.6 | 3.1 ± 0.6 | 3.6 ± 0.7 |
Oceania | 1.7 ± 0.6 | 2.5 ± 0.3 | 2.7 ± 0.7 |
(1)Population > 20 millions | 2.4 ± 0.8 | 3.3 ± 1.3 | 3.5 ± 1.8 |
(2)Urban surface > 1000 km2 | 2.1 ± 0.8 | 3.0 ± 1.3 | 3.3 ± 2.0 |
(3)Coast distance > 100 km | 1.9 ± 0.9 | 2.7 ± 1.3 | 3.2 ± 1.5 |
(4)Elevation > 1 km | 1.5 ± 1.1 | 2.9 ± 1.7 | 3.3 ± 1.9 |
(5)Equatorial | 1.4 ± 0.9 | 2.4 ± 1.6 | 2.8 ± 1.5 |
Arid | 1.2 ± 0.9 | 2.1 ± 1.5 | 2.8 ± 1.9 |
Warm Temperate | 2.0 ± 0.7 | 2.7 ± 1.1 | 3.1 ± 1.7 |
Snow | 2.1 ± 1.1 | ||
All (71 agglomerations) | 1.8 ± 0.9 | 2.6 ± 1.3 | 3.1 ± 1.7 |
RANKING | SUHIMEAN (Su) (°C) | UTFVIMAX | DIMEAN (°C) | |||
---|---|---|---|---|---|---|
1 | Saint Petersburg | 3.9 | San José | 0.020 | Wuhan | 30.1 |
2 | Ciudad de México | 3.5 | Ürümqi | 0.020 | Shanghai | 29.3 |
3 | Athens | 3.1 | Nueva York | 0.019 | Karachi | 28.9 |
4 | Moscow | 3.1 | Ciudad México | 0.019 | Monterrey | 28.6 |
5 | Kuala Lumpur | 3.0 | Toronto | 0.018 | Hyderabad | 28.5 |
6 | Berlin | 2.9 | Los Ángeles | 0.018 | Manila | 28.5 |
7 | Beijing | 2.8 | Kuala Lumpur | 0.016 | Kolkata | 28.3 |
8 | Tokyo | 2.8 | Yakarta | 0.016 | Asunción | 27.8 |
9 | Sao Paulo | 2.8 | Buenos Aires | 0.015 | Las Vegas | 27.4 |
10 | Chicago | 2.8 | San Diego | 0.014 | Jeddah | 27.3 |
11 | Madrid | 2.7 | Perth | 0.014 | Tokyo | 27.3 |
12 | Sevilla | 2.6 | Manaus | 0.013 | Beijing | 27.1 |
13 | Paris | 2.6 | Berlín | 0.013 | Riyad | 27.0 |
14 | Taskent | 2.6 | Vancouver | 0.013 | El Cairo | 26.2 |
15 | Riyad | 2.6 | Estambul | 0.013 | Rio de Janeiro | 26.1 |
16 | Milan | 2.5 | Athens | 0.012 | Bangkok | 26.0 |
17 | London | 2.5 | Melbourne | 0-012 | Paris | 25.9 |
18 | Rio de Janeiro | 2.4 | Taskent | 0.012 | Madrid | 25.7 |
19 | Vancouver | 2.4 | Yakuts | 0.012 | Valencia | 25.7 |
20 | Las Vegas | 2.4 | Moscow | 0.011 | Oklahoma City | 25.7 |
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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://doi.org/10.3390/rs12122052
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://doi.org/10.3390/rs12122052
Chicago/Turabian StyleSobrino, 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://doi.org/10.3390/rs12122052