Daytime Surface Urban Heat Island Intensity Research Articles

Assessment of U.S. Urban Surface Temperature using GOES-16 and 17 Data: Urban Heat Island and Temperature Inequality

Abstract This study utilizes hourly Land Surface Temperature (LST) data from the Geostationary Operational Environmental Satellite (GOES) to analyze the seasonal and diurnal characteristics of Surface Urban Heat Island Intensity (SUHII) across 120 of the largest U.S. cities and its surroundings. Distinct patterns emerge in the classification of seasonal daytime and nighttime SUHII. Specifically, vegetation index (EVI) and albedo (ALB) play pivotal roles in influencing these temperature variations. The diurnal cycle of SUHII further reveals different trends, suggesting that climate conditions, urban and non-urban land covers, and anthropogenic activities during nighttime hours affect SUHII peaks. Exploring intracity LST dynamics, the study reveals a significant correlation between urban intensity (UI) and LST, with LST rising as UI increases. Notably, populations identified as more vulnerable by the Social Vulnerability Index (SVI) are found in high UI regions. This results in a discernible LST inequality, where the more vulnerable communities are under higher LST conditions, possibly leading to higher heat exposure. This comprehensive study accentuates the significance of tailoring city-specific climate change mitigation strategies, illuminating LST variations and their intertwined societal implications.

Estimation of the surface urban heat island intensity across 1031 global cities using the regression-modification-estimation (RME) method

The urban heat island (UHI) effect profoundly impacts both humans and the environment. Satellite data have been widely employed to investigate the surface UHI (SUHI) effect. The SUHI intensity (SUHII) is a key indicator of SUHIs, and its estimation necessitates the acquisition of land surface temperature (LST) data for urban areas. However, in many previous studies, urban areas were simply defined as those with the proportion of impervious surfaces exceeding a certain threshold. Therefore, the delineated urban areas often included natural surfaces such as croplands and water bodies, leading to uncertainties in SUHI studies. In this study, the regression-modification-estimation (RME) method was proposed to quantify the SUHII and analyze the relationships between the SUHII and its driving factors across 1031 global cities. The results can be summarized as follows: (1) the RME method mitigates the uncertainties associated with the definitions of urban and rural areas, providing accurate estimation of the impact of urbanization-induced land cover changes on the LST. (2) The annual daytime and nighttime average SUHII values for the 1031 cities derived from the RME method were 2.05 °C and 1.79 °C, respectively. The SUHII values derived from the RME method were 24.8%–53.3% higher than those obtained from previous methods. (3) Vegetation and precipitation emerged as important drivers of the daytime SUHII, while the nighttime SUHII exhibited a significant relationship with the albedo and precipitation. An interesting result was that the correlations between SUHIs and some important drivers derived from the RME method were stronger than those derived from previous methods.

Impacts of climatic zones on urban heat island: Spatiotemporal variations, trends, and drivers in China from 2001–2020

Urban heat island (UHI) adversely impacts the human-earth system. However, the impact of climatic zones on UHI remains unclear. This study, therefore, aimed at investigating how climatic zones affect spatiotemporal variations, trends and potential drivers of surface UHI intensity (SUHII) based upon 253 Chinese cities in five climatic zones: middle temperate zone (MTZ), south temperate zone (STZ), north subtropical zone (NSZ), middle subtropical zone (MSZ), and south subtropical zone (SSZ) during 2001–2020. The results showed that the SUHII ranged from -2.59 to 6.20 °C, average daytime SUHII showed higher seasonal fluctuations and larger variation than nighttime and summer daytime had the highest seasonal and annual SUHII variation (SUHIISAV). Cities in NSZ, MSZ, and SSZ had high average daytime SUHIISAV and a large proportion had significant increasing trends (TrendSI) (P<0.05) from 2001–2020, while the nighttime showed opposite rule in general. Random forest (RF) model explained 68% of day/night SUHIISAV on average. Except for city area, which greatly impacted day/night SUHIISAV in all periods and climatic zones, other drivers more significantly influenced SUHII in certain periods and climatic zones, e.g. aerosol optical depth (AOD) in winter nighttime in MTZ and STZ. Compared with single or serval drivers’ determination of UHI effect, we highlighted the multi-factor driven of day/night SUHIISAV. Specific suggestions, e.g. controlling haze pollution can bring co-benefits on urban air quality and UHI mitigation were proposed. These findings could help to provide valuable reference for future climatic adaptive strategy.

Natural-anthropogenic environment interactively causes the surface urban heat island intensity variations in global climate zones

The inconstant climate change and rapid urbanization substantially disturb the global thermal balance and induce severe urban heat island (UHI) effect, adversely impacting human development and health. Existing literature has revealed the UHI characteristics and driving factors at an urban scale, but interactions between the main factors of a global grid scale assessment on the context of climate zones remain unclear. Therefore, based on the multidimensional climatic and socio-economic statistical datasets, the multi-time scale of surface urban heat island intensity (SUHI) characteristics was investigated in this study to analyze how natural-anthropogenic drivers affect the variance of SUHI and vary in their importance for the changes of other interaction factors. The results show that the mean value of SUHI in summer is higher than in winter, and in daytime is higher than in nighttime on a seasonal and daily scale. SUHIs in different global climate zones have significant differences. When analyzing drivers’ contributions and interactions with LightGBM model and SHAP algorithm, we know that monthly precipitation (PREC), the estimated population (POP) and surface pressure (PRES) are the three major drivers of daytime SUHI. The nighttime SUHI is mainly PREC, POP and anthropogenic heat emission (AHE), the influence rules of the natural driversare mostly opposite to that of daytime. This study highlights the fundamental role of background climate for designing strategies. Irrigation or artificial rainfall will be effective to mitigate SUHI in low rainfall areas, while it is more effective to reduce AHE in high rainfall areas. In where greening can be difficult in the most developed cities, reducing AHE, increasing per capita GDP and controlling the population scale may also contribute to alleviating the SUHI. This study provides ideas for developing responsive urban heat island mitigation policies in a more realistic setting.

The Seasonality of Surface Urban Heat Islands across Climates

In this work, we investigate how the seasonal hysteresis of the Surface Urban Heat Island Intensity (SUHII) differs across climates and provide a detailed typology of the daytime and nighttime SUHII hysteresis loops. Instead of the typical tropical/dry/temperate/continental grouping, we describe Earth’s climate using the Köppen–Geiger system that empirically maps Earth’s biome distribution into 30 climate classes. Our thesis is that aggregating multi-city data without considering the biome of each city results in temporal means that fail to reflect the actual SUHII characteristics. This is because the SUHII is a function of both urban and rural features and the phenology of the rural surroundings can differ considerably between cities, even in the same climate zone. Our investigation covers all the densely populated areas of Earth and uses 18 years (2000–2018) of land surface temperature and land cover data from the European Space Agency’s Climate Change Initiative. Our findings show that, in addition to concave-up and -down shapes, the seasonal hysteresis of the SUHII also exhibits twisted, flat, and triangle-like patterns. They also suggest that, in wet climates, the daytime SUHII hysteresis is almost universally concave-up, but they paint a more complex picture for cities in dry climates.

Surface and canopy urban heat islands: Does urban morphology result in the spatiotemporal differences?

Urban heat island (UHI) is a phenomenon wherein urban areas become warmer than surrounding rural areas, which is one of the most important urban issues arising due to human activities. In this study, UHI was investigated at two different layers: canopy urban heat island (CUHI) and surface urban heat island (SUHI). Kitakyushu City, Japan, which is a highly spatially heterogeneous urban area, was utilized as a case study to explore the spatiotemporal differences of the two types of UHIs, both during the daytime and nighttime from January to December 2010. Subsequently, random forest (RF) regression was applied to reveal the correlation between the differences in the two types of UHI intensity (∆UHII) and urban morphology indices. The results showed that (1) the SUHI and CUHI during daytime had similar spatial patterns, while those during nighttime had different spatial patterns; (2) the daytime SUHI intensity (SUHII) could be greatly overestimated compared to the CUHI intensity (CUHII), and this overestimation significantly varied with months; and (3) highly nonlinear correlations existed between ∆UHII and urban morphology indices. Both the internal and external characteristics of urban morphology in a study grid contributed to the generation of ∆UHII, but internal characteristics were more important.

The Extreme Heat Wave over Western North America in 2021: An Assessment by Means of Land Surface Temperature

In our current global warming climate, the growth of record-breaking heat waves (HWs) is expected to increase in its frequency and intensity. Consequently, the considerably growing and agglomerated world’s urban population becomes more exposed to serious heat-related health risks. In this context, the study of Surface Urban Heat Island (SUHI) intensity during HWs is of substantial importance due to the potential vulnerability urbanized areas might have to HWs in comparison to their surrounding rural areas. This article discusses Land Surface Temperatures (LST) reached during the extreme HW over Western North America during the boreal summer of 2021 using Thermal InfraRed (TIR) imagery acquired from TIR Sensor (TIRS) (30 m spatial resolution) onboard Landsat-8 platform and Moderate Resolution Imaging Spectroradiometer (MODIS) (1 km spatial resolution) onboard Terra/Aqua platforms. We provide an early assessment of maximum LSTs reached over the affected areas, as well as impacts in terms of SUHI over the main cities and towns. MODIS series of LST from 2000 to 2021 over urbanized areas presented the highest recorded LST values in late June 2021, with maximum values around 50 °C for some cities. High spatial resolution LSTs (Landsat-8) were used to map SUHI intensity as well as to assess the impact of SUHI on thermal comfort conditions at intraurban space by means of a thermal environmental quality indicator, the Urban Field Thermal Variance Index (UFTVI). The same high resolution LSTs were used to verify the existence of clusters and employ a Local Indicator of Spatial Association (LISA) to quantify its degree of strength. We identified the spatial distribution of heat patterns within the intraurban space as well as described its behavior across the thermal landscape by fitting a polynomial regression model. We also qualitatively analyze the relationship between both UFTVI and LST clusters with different land cover types. Findings indicate that average daytime SUHI intensity for the studied cities was typically within 1 to 5 °C, with some exceptional values surpassing 7 °C and 9 °C. During night, the SUHI intensity was reduced to variations within 1–3 °C, with a maximum value of +4 °C. The extreme LSTs recorded indicate no significant influence of HW on SUHI intensity. SUHI intensity maps of the intraurban space evidence hotspots of much higher values located at densely built-up areas, while urban green spaces and dense vegetation show lower values. In the same manner, UTFVI has shown “no” SUHI for densely vegetated regions, water bodies, and low-dense built-up areas with intertwined dense vegetation, while the “strongest” SUHI was observed for non-vegetated dense built-up areas with low albedo material such as concrete and pavement. LST was evidenced as a good marker for assessing the influence of HWs on SUHI and recognizing potential thermal environmental consequences of SUHI intensity. This finding highlights that remote-sensing based LST is particularly suitable as an indicator in the analysis of SUHI intensity patterns during HWs at different spatial resolutions. LST used as an indicator for analyzing and detecting extreme temperature events and its consequences seems to be a promising means for rapid and accurate monitoring and mapping.

Reexamining the relationship between surface urban heat island intensity and annual precipitation: Effects of reference rural land cover

Previous studies have shown that surface urban heat island intensity (SUHII) across cities is significantly positively correlated with mean annual precipitation (MAP), including linear and nonlinear correlations. Different explanations for these SUHII–MAP relations have been suggested, yet a systematic examination of the impact of variability in reference rural land cover on geographic variations of daytime SUHII and the relation with MAP is still lacking. In this study, the previously proposed SUHII–MAP relations are reexamined by investigating 60 cities across North America and 346 cities across the globe, respectively. The focus of the study is on the role of variability of reference land cover types in the SUHII–MAP relations. The 10-yr time series of satellite-observed land surface temperature, precipitation and solar radiation data, in conjunction with global land cover data, are used for analysis. Our results confirm the reproducibility of both the linear and nonlinear SUHII–MAP relations; nevertheless, the significant positive correlations between the daytime SUHII and the MAP are found to be dependent mainly on the variability of the reference land cover types associated with MAP levels (i.e., varying from desert to tree cover). In contrast, when a constant land cover type is taken as the reference, no significant correlations between the daytime SUHII and the MAP are observed. The finding highlights that respecting SUHI intensity as a relative measure and accounting for variability of reference land cover is of crucial importance for proper interpretation and understanding of SUHI.

Spatiotemporal pattern and long-term trend of global surface urban heat islands characterized by dynamic urban-extent method and MODIS data

In this study, the global surface urban heat island (SUHI) for 1711 cities during 2003–2019 was quantified by the dynamic urban-extent (DUE) scheme with the land surface temperature datasets from Moderate Resolution Imaging Spectroradiometer Terra and Aqua through the Google Earth Engine platform. The global pattern and regional contrasts of SUHI intensity (SUHII), and the interannual changing rate of SUHII (δSUHII) were revealed at the annual, summer, and winter scales. Further, the associated driving factors for long-term SUHII were explored from a temporal perspective. The main findings are as follows: (1) Globally, the global mean SUHII over 2003–2019 for annual daytime (1.32 °C) and annual nighttime (1.09 °C) by DUE are generally higher than that by previous simplified urban-extent (SUE) scheme. Accordingly, the summer daytime and nighttime SUHIIs are 1.98 °C, 1.05 ℃, while the winter daytime and nighttime SUHIIs are 0.76 ℃, and 1.10 ℃. (2) The annual, summer, and winter δSUHIIs are 0.11 °C/decade, 0.27 °C/decade, and −0.06 °C/decade, respectively, at daytime, and 0.07 °C/decade, 0.09 °C/decade, and 0.10 °C/decade, respectively, at nighttime. (3) The global SUHII and δSUHII demonstrates evident regional contrast. The warm temperate and snow zones show distinct seasonal variations from summer to winter for daytime SUHII. Specifically, the negative daytime SUHII is detected for the arid zone, which exhibits the highest day-night variation and shows decreasing trend. (4) The global SUHII and δSUHII indicate distinct latitudinal variations, and an additional flip-flop (daytime SUHII < nighttime SUHII) region is detected between 10 °S and 20 °S. (5) The long-term daytime SUHII are negatively regulated by the urban–rural difference on evaporative cooling of vegetation; while at nighttime, it is negatively affected by the urban–rural difference on surface thermophysical properties. It implies the urban greening and surface properties should be specifically concerned to increase the evaporation cooling and reduce the heat retention in SUHII mitigation.

Surface Urban Heat Island Assessment of a Cold Desert City: A Case Study over the Isfahan Metropolitan Area of Iran

This study investigates the diurnal, seasonal, monthly and temporal variation of land surface temperature (LST) and surface urban heat island intensity (SUHII) over the Isfahan metropolitan area, Iran, during 2003–2019 using MODIS data. It also examines the driving factors of SUHII like cropland, built-up areas (BI), the urban–rural difference in enhanced vegetation index (ΔEVI), evapotranspiration (ΔET), and white sky albedo (ΔWSA). The results reveal the presence of urban cool islands during the daytime and urban heat islands at night. The maximum SUHII was observed at 22:30 p.m., while the minimum was at 10:30 a.m. The summer months (June to September) show higher SUHII compared to the winter months (February to May). The daytime SUHII demonstrates a robust positive correlation with cropland and ΔWSA, and a negative correlation with ΔET, ΔEVI, and BI. The nighttime SUHII displays a negative correlation with ΔET and ΔEVI.

Quantifying the response of surface urban heat island to urban greening in global north megacities

Urban heat island, a phenomenon that urban temperature is higher than the rural area nearby, affects directly citizens' human health and well-being. However, the cooling effect from urban green space (UGS) and the attribution of the different land processes to surface urban heat island intensity (SUHI) under different background climates remains unclear. The coarse-grained model was used to estimate summer SUHI in three different background climatic zones and for seven agglomerations (BTH, JP, LD, NAAC, NAGL, YZ, UQ). Results indicate that (1) the temperate zone had the highest daytime SUHI (0–10 °C), while the arid zone has the lowest daytime SUHI (−1–2 °C). In both temperate and cold zone, the daytime SUHI was higher than the nighttime SUHI. The SUHI in downtown was higher (more than 2 °C) than in the suburbs. (2) The increasing precipitation can enhance daytime SUHI while can weaken nighttime SUHI in all three climatic zones. The increasing temperature tends to enhance SUHI in both daytime and nighttime (exclude UQ). (3) The cooling effects of UGS in daytime SUHI were highly dependent on the background climate (cold > temperate > arid). (4) The nighttime SUHI could be effectively offset when UGSFs were greater than 0.48, 0.82, 0.97, 0.95 in NAAC, NAGL, YZ, and UQ. This article highlights the different feedback of urban green space to UHII and supports green infrastructure intervention as an effective means of reducing urban heat stress at urban agglomeration scales.

Using Prophet Forecasting Model to Characterize the Temporal Variations of Historical and Future Surface Urban Heat Island in China

AbstractThe changes in surface urban heat island (SUHI) are usually diurnal variations, seasonal variations, and interannual trends, yet previous temporal analysis methods have difficulties in decomposing time‐series SUHI intensity (SUHII) into multiple time scales. This study proposed a prophet forecasting model to decompose time‐series SUHII in 102 Chinese cities for the analysis of temporal variations. The occurrence time of SUHII extremums and annual SUHII amplitude (ASA) were investigated. Results show that the maximum daytime SUHII in 88% of the cities occurred in summer, and the minimum daytime SUHII in 80% of the cities occurred in winter. At night, the maximum SUHII in northern cities occurred in the cold season, while the minimum SUHII occurred in the hot season, which is the opposite of the results of southern cities. Larger ASA was observed in northern cities than southern cities, and the ASA was larger during the day than night. The interannual trend of SUHII was characterized and the SUHII in 2030 was predicted. Results show that the increasing trends of SUHII were found in 78% and 83% of the cities in the day and night, respectively. The interannual trend of daytime SUHII was negative (−0.01 ± 0.28°C/10 yr) in northern cities, while it was positive (0.22 ± 0.20°C/10 yr) in southern cities. At night, northern cities (0.19 ± 0.12°C/10 yr) had a faster increasing trend of SUHII than southern cities (0.06 ± 0.13°C/10 yr). The SUHII in China will be more evident in 2030, and various mitigation measures are needed for SUHII mitigation.

Disproportionate Exposure to Urban Heat Island Across Major U.S. Cities

In 2017, more than three-quarters of the U.S. population lived in urban areas, a proportion growing over time. With the expansion of urban growth and land-use conversion to more densely built environments, urban heat stress poses a major public health issue. Case studies of individual cities suggest that heat exposure, like other environmental stressors, may be unequally distributed across income groups. There is little evidence, however, as to whether such disparities are pervasive at the national level. Here, we combine US-specific surface urban heat island (SUHI) data, a proxy for isolating the urban contribution to additional heat exposure in built environments, with census tract-level demographic data from the American Community Survey to answer these questions for summer days, when heat expo-sure is likely to be at a maximum. We find that the average person of color lives in a census tract with higher summer daytime SUHI intensity than non-Hispanic whites in all but 6 of the 175 largest urbanized areas in the continental United States. A similar pattern emerges for people living in households below the poverty line relative to those at more than two times the poverty line. In nearly half the urbanized areas, the average person of color faces a higher summer daytime SUHI then the average person living below poverty, despite the fact that, on average, only 10 percent of people of color live below the poverty line. This last finding suggests that widespread inequalities in heat exposure by race and ethnicity may not be well explained by differences in income alone.

Surface urban heat islands in Italian metropolitan cities: Tree cover and impervious surface influences

Land surface temperature (LST) predictors, such as impervious and vegetated surfaces, strongly influence the urban landscape mosaic, also changing microclimate conditions and exacerbating the surface urban heat island (SUHI) phenomenon. The aim of this study was to investigate the summer daytime SUHI phenomenon and the role played by impervious and tree cover surfaces in the 10 Italian peninsular metropolitan cities. Summer daytime LST values were assessed by using MODIS data referred to the months of June, July and August from 2016 to 2018. High spatial resolution (10 m) of impervious surface and tree cover layers was calculated based on open-data developed by the Italian National Institute for Environmental Protection and Research. A novel informative urban surface landscape layer was developed combining impervious surfaces and tree cover densities and its mapping for metropolitan cities was performed. Summer daytime SUHI rose significantly, increased especially in inland cities, by increasing the size of areas with low tree cover densities in the metropolitan core (or decreasing areas with low tree cover densities outside the metropolitan core), further increasing its intensity when the impervious density grew. A mitigating effect of the sea on daytime LST and SUHI was observed on coastal cities. The most intense SUHI phenomenon was observed in Turin (the largest Italian metropolitan city): for every 10% increase in areas with highly impervious surfaces and low tree cover densities in the metropolitan core, the SUHI significantly (p < 0.001) increased by 4.0 °C. Increased impervious surfaces combined with low tree cover densities represented the main driving process to increase the summer daytime SUHI intensity in most studied cities. These findings are useful to identify summer daytime LST critical areas and to implement the most efficient urban-heat-island mitigation strategies in order to safeguard the vulnerable urban environment and enhance quality of life for the population.

A spatially explicit surface urban heat island database for the United States: Characterization, uncertainties, and possible applications

The urban heat island (UHI) effect is strongly modulated by urban-scale changes to the aerodynamic, thermal, and radiative properties of the Earth’s land surfaces. Interest in this phenomenon, both from the climatological and public health perspectives, has led to hundreds of UHI studies, mostly conducted on a city-by-city basis. These studies, however, do not provide a complete picture of the UHI for administrative units using a consistent methodology. To address this gap, we characterize clear-sky surface UHI (SUHI) intensities for all urbanized areas in the United States using a modified Simplified Urban-Extent (SUE) approach by combining a fusion of remotely-sensed data products with multiple US census-defined administrative urban delineations. We find the highest daytime SUHI intensities during summer (1.91 ± 0.97 °C) for 418 of the 497 urbanized areas, while the winter daytime SUHI intensity (0.87 ± 0.45 °C) is the lowest in 439 cases. Since urban vegetation has been frequently cited as an effective way to mitigate UHI, we use NDVI, a satellite-derived proxy for live green vegetation, and US census tract delineations to characterize how vegetation density modulates inter-urban, intra-urban, and inter-seasonal variability in SUHI intensity. In addition, we also explore how elevation and distance from the coast confound SUHI estimates. To further quantify the uncertainties in our estimates, we analyze and discuss some limitations of these satellite-derived products across climate zones, particularly issues with using remotely sensed radiometric temperature and vegetation indices as proxies for urban heat and vegetation cover. We demonstrate an application of this spatially explicit dataset, showing that for the majority of the urbanized areas, SUHI intensity is lower in census tracts with higher median income and higher proportion of white people. Our analysis also suggests that poor and non-white urban residents may suffer the possible adverse effects of summer SUHI without reaping the potential benefits (e.g., warmer temperatures) during winter, though establishing this result requires future research using more comprehensive heat stress metrics. This study develops new methodological advancements to characterize SUHI and its intra-urban variability at levels of aggregation consistent with sources of other socioeconomic information, which can be relevant in future inter-disciplinary research and as a possible screening tool for policy-making. The dataset developed in this study is visualized at: https://datadrivenlab.users.earthengine.app/view/usuhiapp.

A New Approach for Surface Urban Heat Island Monitoring Based on Machine Learning Algorithm and Spatiotemporal Fusion Model

Land surface temperature (LST) is an important indicator for assessing the surface urban heat island (SUHI) effect. This paper presents a novel approach to derive LST estimates by integrating machine learning algorithm and spatiotemporal fusion model at high spatial and temporal resolution. The spatial resolutions of Landsat TM and Landsat 8 LST data were first downscaled using random forest (RF) algorithm from 120 m and 100 m, respectively, to 30 m. The resultant LST data were fused with MODerate-resolution Imaging Spectroradiometer (MODIS) LST data, by means of the Flexible Spatiotemporal Data Fusion method (FSDAF), in order to generate high spatiotemporal resolution summer daytime LST data covering the center of Chengdu city in China. The proposed new method was used to estimate the spatiotemporal variations of the summer daytime SUHI from 2009 to 2018 over Chengdu city. Results show that: (1) RF performs way better than the classical downscaling algorithm-thermal sharpening algorithm (TsHARP) for LST, and produces higher accuracy for different land covers; (2) the fused high spatiotemporal resolution summer daytime LST values were evaluated with in situ LST obtained from Chengdu Meteorological Office and the final validation results indicated that the proposed method, in generating LST dataset, can provide more details of urban thermal environment and produce higher accuracy than the traditional FSDAF; (3) significantly increasing trends of summer daytime SUHI intensity (SUHII) in the study area were observed. SUHII increased from 2.78 °C in 2009 to 4.04 °C in 2018. The highest and lowest summer daytime LST estimates were recorded over impervious surface area (ISA) and waters, respectively.

Anthropogenic forcing exacerbating the urban heat islands in India

Urban heat island (UHI) phenomena is among the major consequences of the alteration of earth's surface due to human activities. The relatively warmer temperatures in urban areas compared to suburban areas (i.e. UHI) has potential health hazards, such as mortality due to high temperatures and heat waves. In addition, UHI situation demands more energy (e.g. fans and air-conditioners) that would trigger greenhouse gas emissions. Studies on UHI intensity help to assess its impact on urban population, city planning, and urban health planning. This is particularly important for a country like India, where 32% people (~7% of total world population), live in urban areas. We conducted a detailed study on surface UHI intensity (SUHII), which is the difference between urban and surrounding rural land surface temperatures, across all seasons in 44 major cities of India, which shows that mean daytime SUHII is positive (up to 2 °C) for most cities, as analysed from satellite temperature measurements for the period 2000–2017, in contrast to previous studies. However, although statistically insignificant, most cities show a positive trend in SUHII for monsoon and post-monsoon periods, but negative for winter and summer seasons. The increasing night-time SUHII in all seasons for most cities suggest increasing trend in temperature in cities due to the impact of the rapid urbanisation, and thus, suggesting the influence of anthropogenic forcing on SUHII. This is also supported by the analysis of aerosols, night lights, precipitation and vegetation in the study regions. Therefore, this study shall aid planning and management of urban areas by giving insights about the effects of nature and intensity of development, land cover and land use mix and the structure of cities on SUHII.

Spatial and dynamic perspectives on surface urban heat island and their relationships with vegetation activity in Beijing, China, based on Moderate Resolution Imaging Spectroradiometer data

ABSTRACTSystematically investigating the spatial and dynamic variations in surface urban heat island (SUHI) and their relationships with vegetation is needed in the context of sustainable urban development. In this study, the regional differences in the spatial and temporal variations in SUHI intensity (SUHII) were quantified, and their correlations with vegetation activity were analyzed in Beijing during 2001–2013, based on Moderate Resolution Imaging Spectroradiometer land cover/land use, land surface temperature, normalized difference vegetation index and digital elevation model. The results show that the 13-year averaged daytime SUHII presenting obvious seasonal variations was the highest (2.61°C) in urban core area and the lowest (−2.09°C) in rural area. The night-time SUHII was relatively stable in season, showing the highest SUHII (2.47°C) in urban core area and the lowest SUHII (−1.25°C) in rural area. The annual daytime SUHII decreased apparently in urban core (−0.28°C), urban (−0.10°C) and rural (−0.58°C) areas. The annual night-time SUHII increased weakly in urban core (0.20°C), urban (0.02°C) and suburban (0.08°C) areas. Correlation analyses indicate that the SUHII was negatively and significantly correlated with vegetation activity except in winter days. In addition, the significant negative correlations between daytime SUHII change and vegetation change were observed except in winter. In contrast, the correlation between night-time SUHII change and vegetation change was not observed.

Associated Determinants of Surface Urban Heat Islands across 1449 Cities in China

The thermal environment is closely related to human well-being. Determinants of surface urban heat islands (SUHIs) have been extensively studied. Nevertheless, some research fields remain blank or have conflicting findings, which need to be further addressed. Particularly, few studies focus on drivers of SUHIs in massive cities with different sizes under various contexts at large scales. Using multisource data, we explored 11 determinants of surface urban heat island intensity (SUHII) for 1449 cities in different ecological contexts throughout China in 2010, adopting the Spearman and partial correlation analysis and machine learning method. The main results were as follows: (1) Significant positive partial correlations existed between daytime SUHII and the differences in nighttime light intensity and built-up intensity between cities and their corresponding villages except in arid or semiarid western China. The differences in the enhanced vegetation index were generally partially negatively correlated with daytime and nighttime SUHII. The differences in white sky albedo were usually partially negatively correlated with nighttime SUHII. The mean air temperature was partially positively correlated with nighttime SUHII in 40% of cases. Only a few significant partial relationships existed between SUHII and urban area, total population, and differences in aerosol optical depth. The explanation rates during daytime were larger than during nighttime in 72% of cases. The largest and smallest rates occurred during summer days in humid cold northeastern China (63.84%) and in southern China (10.44%), respectively. (2) Both the daytime and nighttime SUHII could be well determined by drivers using the machine learning method. The RMSE ranged from 0.49°C to 1.54°C at a national scale. The simulation SUHII values were always significantly correlated with the actual SUHII values. The simulation accuracies were always higher during nighttime than daytime. The highest accuracies occurred in central-northern China and were lowest in western China during both daytime and nighttime.

Spatial patterns and driving factors of surface urban heat island intensity: A comparative study for two agriculture-dominated regions in China and the USA

Urban heat island (UHI) effect causes tremendous ecological and environmental consequences. A better understanding of its spatial pattern and driving factors is highly required for designing effective mitigation strategies and adaptation measures. This study compared spatial patterns of surface UHI intensity (SUHII) and their driving factors between two agriculture-dominated regions (i.e., Beijing–Tianjin–Hebei (BTH), China and Illinois–Indiana–Ohio (IIO), the USA) based on the MODIS land surface temperature dataset. The results showed that: (1) IIO had significantly stronger daytime SUHII than BTH, while the nighttime SUHII showed an opposite phenomenon. (2) SUHIIs of different time were positively correlated in IIO, but the winter daytime SUHII was negatively correlated with the nighttime SUHII in BTH. (3) Spatial variations of summer SUHII were better predicted in IIO than that in BTH, but the opposite result was found for winter SUHII. (4) Some regression coefficients of the multiple linear regression models when modeling winter daytime SUHII were opposite between IIO and BTH. Findings of this study indicated different dominant surface energy processes in controlling the spatial pattern of SUHII in these two regions of different countries and recommended varied adaptation measures and mitigation strategies.