Nighttime Surface Urban Heat Island Research Articles

Assessment of daytime and nighttime surface urban heat islands across local climate zones – A case study in Florianópolis, Brazil

Affected air movement, artificial heat production, increased solar absorption and suppressed vegetation cause significant temperature differences between urban and suburban/rural regions, forming Surface Urban Heat Islands (SUHI). Here, we propose a methodological framework combining daytime and nighttime Landsat 8 data with comprehensive statistical evaluation based on the retrieved information. To quantify SUHI, we selected a pair of daytime and nighttime Landsat 8 scenes, calculated the Urban Thermal Field Variance Index and applied a comprehensive statistical approach to assess differences in SUHI behaviour across Local Climate Zones and between day and night, examining its relationships with albedo, elevation, land surface emissivity and vegetation cover. Open urban typologies were characterised by milder day temperatures, especially over open areas with higher buildings, while compact low-rise areas presented the highest SUHI intensity. Higher buildings presented more intense SUHI at night, with milder temperatures over open and low-rise regions. These results confirm the SUHI dynamics and its strong association with the urban structure and the presence of vegetation. The proposed complex methodological framework can be, with minor adjustments, applied to other regions as well, which can improve the comparability among studies on SUHI and promote our understanding of its causes and possible mitigation measures.

Climatic comparison of surface urban heat island using satellite remote sensing in Tehran and suburbs

In this study, we aim to compare the climatic conditions of Surface Urban Heat Island (SUHI) in Tehran and its suburbs using day/night time data from three satellites. A high-resolution Land Surface Temperature (LST) data from MODIS Aqua, Sentinel-3, and Landsat 8 were selected to facilitate this study. The highest values of LST/UHI are observed in downtown Tehran and suburban areas at night. The temperature difference also shows an increase at night in Tehran and the western suburbs, while it decreases during the day. When comparing LST/UHI with altitude in different directions, it is found that urban areas and the south, southeast, southwest, and west suburban areas experience higher temperatures at night. MODIS LST products are more appropriate for checking nighttime SUHI in Tehran's Great area in comparison to other products. Moran's I indicates that the highest positive values occur during seasonal and annual periods at night. The Getis index demonstrates a consistent pattern across all seasons, and this trend persists throughout the year. The seasonal and annual UHI difference between Tehran and its suburbs is 5 °C. The LST diagram reveals that higher temperatures occur during warm months. The temporal NDVI distribution indicates lower NDVI values from June to February and summer to winter. The spatial distribution shows that due to the lack of NDVI index in urban areas, LST/UHI values are higher at night in Tehran compared to the suburbs. UHI is not limited to urban areas but has also spread beyond the city borders. As a result, the highest UHI values are found in downtown Tehran and its southeast, south, southwest, and west suburbs.

Spatial Characteristics and Temporal Trend of Urban Heat Island Effect over Major Cities in India Using Long-Term Space-Based MODIS Land Surface Temperature Observations (2000–2023)

The alteration of the Earth’s surface due to urbanization and the formation of urban heat islands is one of the most evident and widely discussed anthropogenic impacts on Earth’s microclimate. The elevated land surface temperature in the urban perimeter compared with the surrounding non-urban area is known as the surface urban heat island (SUHI) effect. India has experienced swift urban growth over the past few decades, and this trend is expected to persist in years to come. The literature published on SUHI in India focuses only on a few specific cities, and there is limited understanding of its geospatial variation across a broader region and its long-term trend. Here, we present one of the first studies exploring the long-term diurnal (daytime, and nighttime), seasonal, and annual characteristics of SUHI in the 20 largest urban centers of India and its neighboring countries. The study highlights a statistically significant (95% confidence interval) rise in nighttime surface temperatures across major cities based on a linear fit over 23 years (2000–2023) of MODIS land surface temperature satellite observations. The nighttime SUHI was found to be more conspicuous, positive, and consistent when compared with daytime satellite observations. The nighttime SUHI for April–May–June representing the pre-monsoon and onset of monsoon months for the top 10 cities, ranged from 0.92 to 2.33 °C; for December–January–February, representing the winter season, it ranged from 1.38 to 2.63 °C. In general, the total change in the nighttime SUHI based on linear fit (2000–2023) for the top ten cities showed warming over the urban region ranging from 2.04 to 3.7 °C. The highest warming trend was observed during the months of May–June–July (3.7 and 3.01 °C) in Ahmedabad and Delhi, cities that have undergone rapid urbanization in the last two to three decades. The study identified strongly positive annual SUHI intensity during nighttime, and weakly negative to moderately positive annual SUHI intensity during daytime, for major cities. Jaipur (India), Lahore (Pakistan), Dhaka (Bangladesh), and Colombo (Sri Lanka) showed a nighttime SUHI intensity of 2.17, 2.33, 0.32, and 0.21 °C, respectively, during the months of April–May–June, and a nighttime SUHI intensity of 2.63, 1.68, 0.94, 0.33 °C, respectively, for the months of December–January–February (2000–2023). It is apparent that the geographical location (inland/coastal) of the city has a high influence on the daytime and nighttime SUHI patterns. The current research is intended to help city planners and policymakers better understand SUHI intensity (day and night/seasonal basis) for developing strategies to mitigate urban heat island effects.

Urbanization influenced SUHI Of 41 megacities of the world using big geospatial data assisted with google earth engine

The rapid pace of urbanization with climate change pushed megacities into the forefront of human habitation, emphasizing the urgent need to unravel the relationship between urban expansion and environmental dynamics. This study examines the Surface Urban Heat Island (SUHI) within the 41 megacities, to interpret the complex relationship that emerges from the nexus of urbanization and climatic influence. Through a comprehensive analysis, it has been observed that daytime and nighttime temperature is increasing particularly in Asian regions. An interesting SUHI intensity emerges in all megacities of the world. The SUHI does vary greatly across the megacities of the world. Similarly, the average daytime ocean/coastal cities SUHI (1.19 °C) was higher than inland cities (0.77 °C) but at nighttime inland cities had a higher average SUHI (0.95 °C) than ocean/coastal cities (0.93 °C). Notably, the findings illuminate the complex relationship between city size, elevation, and location with inland and ocean/coastal cities. An inverse yet substantively significant relationship emerges between, city size, elevation, and SUHI magnitude. Our study delivers a positive but moderate correlation between daytime and nighttime SUHI and population density (r2 = 0.3/r2 = 0.5, p<0.05). In summary, this study elevates our understanding of SUHI phenomena. These insights are for well-informed urban policies and sustainable development.

Characterizing the indicator-based, day-and-night, and climate-based variations in response of surface urban heat island during heat wave across global 561 cities

Increasing surface urban heat island (SUHI) has been reported to be closely associated with a large number of environmental issues. The response of SUHI during heat wave was important for further mitigation proposal. However, it remained unknown whether the response of SUHI during heat wave varies with different SUHI indicators (intensity, frequency and maximum duration), day-and-night contrast, and climate zones, and how to characterize and explain these response variations. Therefore, this paper quantified and explained for these variations in response of SUHI during heat wave across global 561 cities. There were three major findings. (1) The average percentage of response in intensity, frequency and maximum duration at daytime was -20%, -12% and -47%, while it was 10%, 11% and -10% at nighttime. During heat wave, daytime SUHI decreased in the equatorial and arid climates, while nighttime SUHI increased. (2) The attenuated daytime SUHI during heat wave in the equatorial and arid climates resulted from a decrease in latent heat flux and vegetation cooling effect. (3) Increased solar radiation during heat wave contributed to intensified nighttime SUHI, of which energy sources were determined on surface solar absorption. Additionally, differences in evaporation, vegetation, and albedo during heat wave were discussed.

Spatial heterogeneity of driving factors-induced impacts for global long-term surface urban heat island

ABSTRACT A series of empirical analytical tools have been adopted to investigate the driving mechanisms of surface urban heat islands (SUHI) on a global scale, among which spatial heterogeneity is yet to be fully elucidated. In this study, we investigated the spatial non-stationarity of the driving factors concerning surface properties, climate conditions, and urbanization processes for global long-term SUHI. First, the potential impact on SUHI was explored using global ordinary least squares regression. Geographically weighted regression (GWR) and multi-scale GWR (MGWR) from local perspectives were employed for comparison. The results show that the MGWR has the highest goodness of fit at 0.87, 0.73, 0.90, 0.74, 0.85, and 0.76 for annual day/night (AD/AN), summer day/night (SD/SN), and winter day/night (WD/WN) scales, respectively. Although both global and local schemes exhibit similar influencing magnitudes and signs on the SUHI, the MGWR is better at capturing spatial non-stationarity. Globally, for AD, AN, SD, SN, WD, and WN, the coefficients of the urban-rural vegetation index difference (ΔEVI) and surface albedo difference (ΔWSA), urban mean precipitation (MAP), wind speed (WS), population density (PD), and urban area (UA) are −0.50, +0.30, +0.16, +1.31, −0.03, and +0.03, respectively, at daytime, and −0.38, −0.33, −0.39, −0.10, +0.18, and +0.08, respectively, at night-time. Given the spatial heterogeneity of multiple factors, ΔEVI exhibits a strong mitigation effect on the SD SUHI especially in arid zones. The negative influence of ΔWSA on night-time SUHI demonstrates a strong latitudinal disparity and greater sensitivity in the equatorial zone. The positive correlations between MAP and AD/SD SUHIs have evident latitudinal and longitudinal variations. The mitigation effect of WS displayed distinct coastal amplification, especially in WD. In contrast, the PD and UA presented prominent positive impacts on night-time SUHI with less seasonal contrast.

Two decades of nighttime surface urban heat island intensity analysis over nine major populated cities of India and implications for heat stress

Warmer global climate and urban heat islands (UHIs) interact, by exacerbating heatwaves and increasing the extreme heat days in cities. The implications of added heat stress in urban environments due to intensifying surface UHIs (SUHIs) is of utmost concern. Seasonal, annual and decadal nighttime SUHI intensities (SUHIIs), from 2001 to 2020, for nine major populated cities of India are analyzed. This includes five megacities- Delhi, Mumbai, Kolkata, Bangalore, and Chennai, and four incipient megacities- Hyderabad, Ahmedabad, Surat, and Pune. The key role of increasing urbanization (pre- and post-2010) in expansion and intensification of nighttime SUHIs in India is highlighted. For all cities either pre-monsoon (MAM) or winter (December-February; DJF) seasons show the strongest SUHII development. During the 2001–2010, and the 2011–2020 decade, a nighttime SUHII maxima of respectively (i) 2.1°C and 2.5°C for Delhi, (ii) 1.3°C and 1.5°C for Mumbai, (iii) 1.3°C and 1.5°C for Kolkata, (iv) 0.6°C and 1.0°C Bangalore, (v) 1.7°C and 1.9°C for Chennai, (vi) 1.8°C and 2.3°C for Hyderabad, (vii) 2.8°C and 3.1°C for Ahmedabad, (viii) 1.9°C and 2.4°C for Surat, and (ix) 0.8°C and 1.3°C for Pune is noted. Further, all incipient megacities showed a mean annual growth rate of nighttime SUHII of over 0.007°C/year, substantially greater than in the megacities. High SUHII magnitudes, greater growth rates of SUHII, and huge populations, severely compounds the vulnerability of Indian cities to excessive heat exposure risk, especially during MAM heatwaves. Lastly, the implications of nighttime SUHII findings from the present study, on the increase in heat stress, the loss of labor productivity and the rise in heat-related mortality rate is emphasized. The study recommends implementation of city-specific action plans to mitigate the heat stressed urban environment. Targeted use of cooling strategies in localized hotspots within the urban areas where high intensity SUHIs are likely to form is also suggested.

Nonlinear effects of urban and industrial forms on surface urban heat island: Evidence from 162 Chinese prefecture-level cities

Urban and industrial forms were widely concerned as influential factors of surface urban heat island (SUHI) at the macroscale. However, there is still a lack of systematic understanding of urban and industrial forms’ nonlinear and threshold effects on SUHI. This study aimed to disclose their nonlinear effects on daytime and nighttime SUHI based on the random forest (RF) model, using the case of 162 prefecture-level cities in China. The RF results showed that industrial factors collectively contributed 43.38% to daytime SUHI variance, whereas urban factors contributed 20.44% to nighttime SUHI variance. Industrial diversity and urban contiguity were the most critical non-natural factors influencing daytime and nighttime SUHI, respectively. Most factors positively affected daytime and nighttime SUHI, except for the urban elongation. Urban boundary complexity had a distinct effect on daytime and nighttime SUHI. Urban compactness/contiguity and industrial agglomeration/diversity had nonlinear and threshold effects on SUHI. Based on the findings, we have a further understanding of the effects of urban and industrial forms on SUHI in the effective ranges.

Modeling of Daytime and Nighttime Surface Urban Heat Island Distribution Combined with LCZ in Beijing, China

Along with urbanization, surface urban heat island (SUHI) has attracted more attention. Due to the lack of perspective of spatial heterogeneity in relevant studies, it is difficult to propose specific strategies to alleviate the SUHI. This study discusses the impact of spatial heterogeneity on the day and night SUHI by taking one day and night in Beijing as an example, and uses it to improve the efficiency of SUHI simulation for related planning. This study, based on the local climate zone (LCZ), deeply discusses the relationship between urban morphology and the SUHI. Then, an artificial neural network (ANN) model with the LCZ is developed to predict the distribution of the SUHI. The results show that: (1) In summer, the general SUHI intensity distribution patterns are compact zone &gt; large low-rise zone &gt; open zone and medium floor zone &gt; low floor zone &gt; high floor zone. (2) Building density and albedo in dense areas are higher correlated with the SUHI than open areas. The building height has a significant negative correlation with the SUHI in high-rise zone, but has a positive correlation in middle and low floors. (3) The LCZ improves the overall accuracy of the ANN model, especially the simulation accuracy in the daytime. In terms of regions, LCZ2, LCZ8, and LCZ10 are improved to a higher degree. This study is helpful to formulate the SUHI mitigation strategies of “adapting to the conditions of the LCZ” and provide reference for improving the sustainable development of the urban thermal environment.

Investigating the impacts of COVID-19 lockdown on air quality, surface Urban Heat Island, air temperature and lighting energy consumption in City of Melbourne

The COVID-19 pandemic has threatened city economies and residents' public health and quality of life. Similar to most cities, Melbourne imposed extreme preventive lockdown measures to address this situation. It would be reasonable to assume that during the two phases of lockdowns, in autumn (March) and winter (June to August) 2020, air quality parameters, air temperature, Surface Urban Heat Island (SUHI), and lighting energy consumption most likely increased. As such, to test this assumption, Sentinel 5, ERA-5 LAND, Sentinel 1 and 2, NASA SRTM, MODIS Aqua and Terra, and VIIRS satellite imageries are utilized to investigate the alterations of NO₂, SO₂, CO, UV Aerosol Index (UAI), air temperature, SUHI, and lighting energy consumption factors in the City of Melbourne. Furthermore, satellite imageries of SentiThe results indicate that the change rates of NO₂ (1.17 mol/m2) and CO (1.64 mol/m2) factors were positive. Further, the nighttime SUHI values increased by approximately 0.417 °C during the winter phase of the lockdown, while during the summer phase of the lockdown, the largest negative change rate was in NO₂ (−100.40 mol/m2). By contrast, the largest positive change rate was in SO₂ and SUHI at night. The SO₂ values increased from very low to 330 μm mol/m2, and the SUHI nighttime values increased by approximately 4.8 °C. From the spatial point of view, this study also shows how the effects on such parameters shifted based on the urban form and land types across the City of Melbourne by using satellite data as a significant resource to analyze the spatial coverage of these factors. The findings of this study demonstrate how air quality factors, SUHI, air temperature, and lighting energy consumption changed from pre-lockdown (2019) to lockdown (2020), offering valuable insights regarding practices for managing SUHI, lighting energy consumption, and air pollution.

Urban Warming of the Two Most Populated Cities in the Canadian Province of Alberta, and Its Influencing Factors.

Continuous urban expansion transforms the natural land cover into impervious surfaces across the world. It increases the city’s thermal intensity that impacts the local climate, thus, warming the urban environment. Surface urban heat island (SUHI) is an indicator of quantifying such local urban warming. In this study, we quantified SUHI for the two most populated cities in Alberta, Canada, i.e., the city of Calgary and the city of Edmonton. We used the moderate resolution imaging spectroradiometer (MODIS) acquired land surface temperature (LST) to estimate the day and nighttime SUHI and its trends during 2001–2020. We also performed a correlation analysis between SUHI and selected seven influencing factors, such as urban expansion, population, precipitation, and four large-scale atmospheric oscillations, i.e., Sea Surface Temperature (SST), Pacific North America (PNA), Pacific Decadal Oscillation (PDO), and Arctic Oscillation (AO). Our results indicated a continuous increase in the annual day and nighttime SUHI values from 2001 to 2020 in both cities, with a higher magnitude found for Calgary. Moreover, the highest value of daytime SUHI was observed in July for both cities. While significant warming trends of SUHI were noticed in the annual daytime for the cities, only Calgary showed it in the annual nighttime. The monthly significant warming trends of SUHI showed an increasing pattern during daytime in June, July, August, and September in Calgary, and March and September in Edmonton. Here, only Calgary showed the nighttime significant warming trends in March, May, and August. Further, our correlation analysis indicated that population and built-up expansion were the main factors that influenced the SUHI in the cities during the study period. Moreover, SST indicated an acceptable relationship with SUHI in Edmonton only, while PDO, PNA, and AO did not show any relation in either of the two cities. We conclude that population, built-up size, and landscape pattern could better explain the variations of the SUHI intensity and trends. These findings may help to develop the adaptation and mitigating strategies in fighting the impact of SUHI and ensure a sustainable city environment.

Strong regulation of daily variations in nighttime surface urban heat islands by meteorological variables across global cities

Knowledge of the day-to-day dynamics of surface urban heat island (SUHI) as well as their underlying determinants is crucial to a better design of effective heat mitigation. However, there remains a lack of a globally comprehensive investigation of the responsiveness of SUHI variations to meteorological variables. Based on the MODIS land surface temperature and auxiliary data in 2017, here we investigated 10 000+ cities worldwide to reveal day-to-day SUHI intensity (SUHII) variations (termed as SUHIIdv) in response to meteorological variables using Google Earth Engine. We found that: (a) meteorological variables related to the thermal admittance, e.g. precipitation, specific humidity (SH) and soil moisture (SM) (represented by daily temperature range in rural area, DTRr), reveal a larger regulation on SUHIIdv than those related to the air conditions (e.g. wind speed and near-surface air temperature) over a global scale. (b) Meteorological regulations on SUHIIdv can differ greatly by background climates. The control of SH on SUHIIdv is significantly strengthened in arid zones, while that of wind speed is weakened prominently in equatorial zones. SUHIIdv is more sensitive to SM in cities with higher background temperatures. (c) All meteorological variables, except that related to SM (DTRr), show larger impact on SUHIIdv with antecedent precipitation over the global scale. Precipitation is observed to mitigate the SUHIIdv globally, and such effects are even more pronounced in equatorial and arid zones. We consider that our findings should be helpful in enriching the knowledge of SUHI dynamics on multiple timescales.

Identifying Surface Urban Heat Island Drivers and Their Spatial Heterogeneity in China’s 281 Cities: An Empirical Study Based on Multiscale Geographically Weighted Regression

The spatially heterogeneous nature and geographical scale of surface urban heat island (SUHI) driving mechanisms remain largely unknown, as most previous studies have focused solely on their global performance and impact strength. This paper analyzes diurnal and nocturnal SUHIs in China based on the multiscale geographically weighted regression (MGWR) model for 2005, 2010, 2015, and 2018. Compared to results obtained using the ordinary least square (OLS) model, the MGWR model has a lower corrected Akaike information criterion value and significantly improves the model’s coefficient of determination (OLS: 0.087–0.666, MGWR: 0.616–0.894). The normalized difference vegetation index (NDVI) and nighttime light (NTL) are the most critical drivers of daytime and nighttime SUHIs, respectively. In terms of model bandwidth, population and Δfine particulate matter are typically global variables, while ΔNDVI, intercept (i.e., spatial context), and NTL are local variables. The nighttime coefficient of ΔNDVI is significantly negative in the more economically developed southern coastal region, while it is significantly positive in northwestern China. Our study not only improves the understanding of the complex drivers of SUHIs from a multiscale perspective but also provides a basis for urban heat island mitigation by more precisely identifying the heterogeneity of drivers.

The impact of COVID-19 lockdowns on surface urban heat island changes and air-quality improvements across 21 major cities in the Middle East

This study investigates changes in air quality conditions during the restricted COVID-19 lockdown period in 2020 across 21 metropolitan areas in the Middle East and how these relate to surface urban heat island (SUHI) characteristics. Based on satellite observations of atmospheric gases from Sentinel-5, results indicate significant reductions in the levels of atmospheric pollutants, particularly nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). Air quality improved significantly during the middle phases of the lockdown (April and May), especially in small metropolitan cities like Amman, Beirut, and Jeddah, while it was less significant in “mega” cities like Cairo, Tehran, and Istanbul. For example, the concentrations of NO2 in Amman, Beirut, and Jeddah decreased by −56.6%, −43.4%, and −32.3%, respectively, during April 2020, compared to April 2019. Rather, there was a small decrease in NO2 levels in megacities like Tehran (−0.9%) and Cairo (−3.1%). Notably, during the lockdown period, there was a decrease in the mean intensity of nighttime SUHI, while the mean intensity of daytime SUHI experienced either an increase or a slight decrease across these locations. Together with the Gulf metropolitans (e.g. Kuwait, Dubai, and Muscat), the megacities (e.g. Tehran, Ankara, and Istanbul) exhibited anomalous increases in the intensity of daytime SUHI, which may exceed 2 °C. Statistical relationships were established to explore the association between changes in the mean intensity and the hotspot area in each metropolitan location during the lockdown. The findings indicate that the mean intensity of SUHI and the spatial extension of hotspot areas within each metropolitan had a statistically significant negative relationship, with Pearson's r values generally exceeding - 0.55, especially for daytime SUHI. This negative dependency was evident for both daytime and nighttime SUHI during all months of the lockdown. Our findings demonstrate that the decrease in primary pollutant levels during the lockdown contributed to the decrease in the intensity of nighttime SUHIs in the Middle East, especially in April and May. Changes in the characteristics of SUHIs during the lockdown period should be interpreted in the context of long-term climate change, rather than just the consequence of restrictive measures. This is simply because short-term air quality improvements were insufficient to generate meaningful changes in the region's urban climate.

Urban Forests as Main Regulator of the Evaporative Cooling Effect in Cities

AbstractHigher temperatures in urban areas expose a large fraction of the human population to potentially dangerous heat stress. Green spaces are promoted worldwide as local and city‐scale cooling strategies but the amount, type, and functioning of vegetation in cities lack quantification and their interaction with urban climate in different settings remains a matter of debate. Here we use state‐of‐the‐art remote sensing data from 145 city clusters to disentangle the drivers of surface urban heat islands (SUHI) intensity and quantify urban‐rural differences in vegetation cover, species composition, and evaporative cooling. We show that nighttime SUHIs are affected mostly by abiotic factors, while daytime SUHIs are highly correlated with vegetation characteristics and the wetness of the background climate. Magnitude and seasonality of daytime SUHIs are controlled by urban‐rural differences in plant transpiration and leaf area, which explain the dependence of SUHIs on wetness conditions. Leaf area differences are caused primarily by changes in vegetation type and a loss of in‐city forested areas, highlighting the importance of maintaining “natural reserves” as a sustainable heat mitigation policy.

Statistical estimation of next-day nighttime surface urban heat islands

Estimating future temporal patterns of Surface Urban Heat Islands (SUHIs) on multiple time scales is an ongoing research endeavor. Among these time scales, estimation of next-day SUHIs is of special significance to urban residents, yet we currently lack a simple but efficient approach for making such estimations. In the present study, we propose a statistical strategy for estimating next-day nighttime SUHIs, based on incorporating various SUHI controls into a support vector machine regression (SVR) model. The majority of both the surface controls (including factors related to land cover and solar radiation) and meteorological controls (including temperature fluctuations, relative humidity, accumulated precipitation, wind speed, aerosol optical depth, and soil moisture) that have previously been found to account for daily SUHI variations were used as estimators, and we provide estimations for both the overall SUHI intensity (SUHII) and pixel-by-pixel Gaussian-based LSTs over 59 Chinese megacities. For the overall SUHII, the mean absolute error (MAE) is 0.67 K on average, and the mean absolute percentage error (MAPE) is no more than 25% for more than 90% of the cities. For the pixel-by-pixel LSTs, the associated MAE is less than 2.0 K in most scenarios. In addition, the contribution from each selected estimator to SUHII estimation is assessed comprehensively. Among all the estimators, the contribution from relative humidity is the greatest, followed by rural surface temperature and surface air temperature. Moreover, for nearly 78% of the cities, the estimators related to day-to-day SUHI variations make a larger contribution than those related to intra-annual SUHI variations. We conclude that our simple yet effective statistical approach for estimating next-day SUHIs can potentially help urban residents to better adapt to urban heat stress.

Surface Urban Heat Island (SUHI) Over Riverside Cities Along the Gangetic Plain of India

Rapid urbanisation in India has led to increased land use and energy demand, and the consequent uncontrolled development and increased human-induced activities are changing the micrometeorology and the local environment. Of late, the surface urban heat island (SUHI) effect is quite evident in India. It is known that river banks are where the most densely populated cities can be found around the globe, and the River Ganga (Ganges) in India is no exception. Town/cities located over the Gangetic Plain are witnessing a fast urbanisation, and associated perturbation is taking place in the micro/meso-scale meteorology/environment. Discussion about the influence of the river on the SUHI and dynamics over different cities along a single major river is needed, and therefore this study is undertaken to investigate the SUHI effect on riverside towns/cities over the Gangetic Plain, India. For this purpose, the observation data for the period of 2001–2014 from the Terra-MODIS satellite are used. The study quantifies the land surface temperature (LST) under different zones namely urban, suburban, and rural, delineated on the basis of International Geosphere Biosphere Programme-Land Cover (IGBP-LC) products. The intensity and trend of SUHI are measured as an indicator of environmental and micro-climate change. The study found a prevailing regime of nighttime SUHI intensity (SUHII) as well as the daytime formation of SUHI. The interaction of the wind blowing from the river and other land cover to towns/cities was examined with respect to the SUHII of Kanpur and Patna and was found to be quite important.

The Spatial and Temporal Characteristics of Urban Heat Island Intensity: Implications for East Africa’s Urban Development

Due to the combination of climate change and the rapid growth in urban populations in Africa, many urban areas are encountering exacerbated urban heat island (UHI) effects. It is important to understand UHI effects in order to develop suitable adaptation and mitigation strategies. However, little work has been done in this regard in Africa. In this study, we compared surface UHI (SUHI) effects between cities located in different climate zones in East Africa, investigating how they change, both spatially and temporally. We quantified the annual daytime and night-time SUHI intensities in the five most populated cities in East Africa in 2003 and 2017, and investigated the links to urban area size. We consider the possible drivers of SUHI change and consider the implication for future development, highlighting the role of factors such as topography and building/construction materials. We suggest that UHI mitigation strategies targeting East African cities may benefit from more comprehensive analyses of blue and green infrastructure as this offers potential opportunities to enhance human comfort in areas where UHI effects are highest. However, this needs careful planning to avoid increasing associated issues such as disease risks linked to a changing climate.

Impact of Urban Agglomeration and Physical and Socioeconomic Factors on Surface Urban Heat Islands in the Pearl River Delta Region, China

An understanding of the driving factors of urban heat islands could improve the urban thermal environment and provide planning strategies for sustainable urban/regional development. We selected Guangdong province in China as a case area, which covers 21 cities and more than 110 million people. We used multitemporal remote sensing data from multiple sources and socioeconomic statistical data to analyze the effects of various drivers of surface urban heat islands (SUHIs). The tested drivers include urban agglomeration, physical indicators (e.g., vegetation and built-up indices), and socioeconomic indicators (e.g., population, gross domestic production, and nightlight intensity). The results show that physical indicators are determinants of daytime SUHIs whereas socioeconomic indicators are determinants of nighttime SUHIs, which indicates that daytime and nighttime SUHIs have different causal mechanisms in this region. Moreover, the results reveal that the influence of urban agglomeration on urban surface temperature is more significant at nighttime than in daytime, which implies that the warming effect of urban agglomeration is stronger at night. Together, the results indicate that joint control of urban size, urbanization level, and socioeconomic activities is crucial to alleviate SUHIs and safeguard sustainable development in this region.

Exploring diurnal cycles of surface urban heat island intensity in Boston with land surface temperature data derived from GOES-R geostationary satellites

The surface urban heat island (SUHI) is one of the most significant human-induced alterations to the Earth's surface climate and can aggravate health risks for city dwellers during heat waves. Although the SUHI effect has received growing attention, its diurnal cycles (i.e., the variations over the full 24 h within the diel cycle) are poorly understood because polar-orbiting satellites (e.g., Landsat Series, Sentinel, Terra, Aqua) only provide one or two observations over each repeat cycle (e.g., 16 days) with constant overpass time for the same area. Geostationary satellites provide high-frequency land surface temperature (LST) observations throughout the day and the night, and thereby offer unprecedented opportunities for exploring the diurnal cycles of SUHI. Here we examined how the SUHI intensity varied over the course of the diurnal cycle in the Boston Metropolitan Area using LST observations from the NOAA's latest generation of Geostationary Operational Environmental Satellites (GOES-R). GOES-R LST was strongly correlated with MODIS LST (R2 = 0.98, p < 0.0001) across urban core, suburban, and rural areas. We calculated the SUHI intensity at an hourly time step for both the urban core and suburban areas using GOES-R LST data. The maximum SUHI intensity for the urban core occurred near noon, and was +3.0 °C (12:00), +5.4 °C (12:00), +4.9 °C (11:00), and +3.7 °C (12:00) in winter, spring, summer, and autumn, respectively. The maximum intensity for the suburban area was about 3.0 °C lower in spring and summer and 2.0 °C lower in autumn and winter than that of the urban-core area. The minimum SUHI intensity occurred at nighttime, and ranged from −1.0 °C to +1.0 °C. The difference in the nighttime SUHI intensity between urban core and suburban area was insignificant for all seasons except the summer. The SUHI intensity showed similar diurnal variations across the seasons. Throughout the year, the maximum SUHI intensity (+2.7–+5.8 °C) at the urban core occurred at 11:00–14:00 (local time), while the minimum SUHI intensity (−0.6–+0.9 °C) was commonly observed at 00:00–07:00 and 17:00–23:00. We also found different relationships between SUHI intensity and potential drivers within a diurnal cycle, characterized by the strongest correlation with impervious surface area and population size during the middle of the day, and with tree canopy cover at night. Our research highlights the great potential of the new-generation geostationary satellites in revealing the detailed diurnal variations of SUHI. Our findings have implications for informing urban planning and public health risk management.