A Global Database of Surface Urban Heat Island Intensity

The urban heat island (UHI) effect - the phenomenon of higher temperatures in urban environments - is one of the most well-known consequences of urbanization on local climate. We develop the simplified urban-extent (SUE) algorithm, a new algorithm to estimate the urban heat island (UHI) intensity at a global scale. This algorithm is implemented on the Google Earth Engine platform and uses satellite-derived images to calculate the surface UHI intensity for over 9500 urban clusters covering 15 years, making this the most comprehensive global UHI database. The data are validated against previous multi-city studies and then used to estimate the diurnal, monthly, and long-term variability in the surface UHI in different climate zones. The global mean surface UHI intensity is 0.85 °C during daytime and 0.55 °C at night. Cities in arid climate zone, in particular, show unique diurnal and seasonal patterns, with higher nighttime surface UHI intensity and two peaks throughout the year. The diurnal variability in surface UHI is highest for the equatorial climate zone (0.88 °C) and lowest for the arid climate zone (0.53 °C). The inter-seasonal range is highest in the snow climate zone and lowest in equatorial climate zone. We also investigate the long-term change in the UHI intensity over 15 years and find an increase in the daytime UHI intensity at the rate of 0.03 °C per decade. We make our new database easily accessible by designing an interactive web portal for the data. The application is built on the Google Earth Engine platform and allows users to query the UHI data of urban areas using a simple interface. Users can generate charts showing the seasonal and long-term trend for individual urban clusters and can then download the data from the web portal. The link to the application and further information about it can be found at: https://yceo.yale.edu/research/global-surface-uhi-explore

Temporally Consistent Urban-Rural Delineations for Global Urban Heat Island Monitoring

Urbanization leads to local-scale modification of climate, particularly the urban heat island (UHI) effect - the high temperature in cities compared to their surroundings. The UHI effect is generally quantified by measuring the temperature differential between the city and its surrounding rural reference. Choices of both the city and the rural reference are prone to assumptions, which may affect, among other things, temporal variability in UHI intensity. To reduce these uncertainties, I create a global dataset of urban-rural delineations that can be used to better constrain the temporal trends in UHI intensity throughout the globe using the European Space Agency\u27s land cover data and combining the capabilities of Google Earth Engine and Esri\u27s ArcPy package

Lower Urban Humidity Moderates Outdoor Heat Stress

Surface temperature is often used to examine heat exposure in multi-city studies and for informing urban heat mitigation efforts due to scarcity of urban air temperature measurements. Cities also have lower relative humidity, traditionally not accounted for in large-scale observational urban heat risk assessments. Here, using crowdsourced measurements from over 40,000 weather stations in ≈600 urban clusters in Europe, we show the moderating effect of this urbanization-induced humidity reduction on outdoor heat stress during the 2019 heatwave. We demonstrate that daytime differences in heat index between urban clusters and their surroundings are weak, and associations of this urban-rural difference with background climate, generally examined from the surface temperature perspective, are diminished due to moisture feedbacks. We also examine the spatial variability of surface temperature, air temperature, and heat index within these clusters—relevant for detecting hotspots and potential disparities in heat exposure—and find that surface temperature is a poor proxy for the intra-urban distribution of heat index during daytime. Finally, urban vegetation shows much weaker (∼1/6th as strong) associations with heat index than with surface temperature, which has broad implications for optimizing urban heat stress mitigation strategies. These findings are valid for operational metrics of heat stress for shaded conditions (apparent temperature and humidex), thermodynamic proxies (wet-bulb temperature), and empirical heat indices. Based on this large-scale empirical evidence, surface temperature, used due to the lack of better alternatives, may not be suitable for accurately informing heat mitigation strategies within and across cities, necessitating more urban-scale observations and better urban-resolving modelspublishedVersio

Seasonal hysteresis of surface urban heat islands

Temporal dynamics of urban warming have been extensively studied at the diurnal scale, but the impact of background climate on the observed seasonality of surface urban heat islands (SUHIs) remains largely unexplored. On seasonal time scales, the intensity of urban–rural surface temperature differences (ΔTs) exhibits distinctive hysteretic cycles whose shape and looping direction vary across climatic zones. These observations highlight possible delays underlying the dynamics of the coupled urban–biosphere system. However, a general argument explaining the observed hysteretic patterns remains elusive. A coarse-grained model of SUHI coupled with a stochastic soil water balance is developed to demonstrate that the time lags between radiation forcing, air temperature, and rainfall generate a rate-dependent hysteresis, explaining the observed seasonal variations of ΔTs. If solar radiation is in phase with water availability, summer conditions cause strong SUHI intensities due to high rural evaporative cooling. Conversely, cities in seasonally dry regions where evapotranspiration is out of phase with radiation show a summertime oasis effect controlled by background climate and vegetation properties. These seasonal patterns of warming and cooling have significant implications for heat mitigation strategies as urban green spaces can reduce ΔTs during summertime, while potentially negative effects of albedo management during winter are mitigated by the seasonality of solar radiation

Monitoring the Impact of Rapid Urbanization on Land Surface Temperature and Assessment of Surface Urban Heat Island Using Landsat in Megacity (Lahore) of Pakistan

The present study focused on rapid urbanization due to the change in the existing landforms which has caused substantial adverse impacts on Urban Thermal Environment. In the present study, we have acquired the Landsat data (TM and OLI) for the years 1990, 2000, 2010, and 2020 to observe the land use changes (vegetation cover, built up land, barren land, and water) in Lahore using the supervised image classification method. Later, the impact of urbanization has been examined with Land Surface Temperature (LST) and eventually the Surface Urban Heat Island (SUHI) has been calculated. Accuracy of the classified images revealed an overall accuracy (Kappa co-efficient) of 95.3% (0.929%), 92.05% (0.870%), 89.7% (0.891%), and 85.8% (0.915%) for the years 1990, 2000, 2010, and 2020, respectively. It was found that vegetation cover decreased from 60.5% in 1990 to 47.7% in 2020 at the cost of urbanization. The overall built-up land increased by 23.52% from 1990 to 2020. Urbanization has influenced the LST, and it was examined that maximum LST consistently increased with increase in built-up land. The difference between urban and rural buffer reveals that SUHI has also been increasing over the years. SUHI has been raised from 1.72 C in 1990 to 2.41 C in 2020, and about 0.69 C relative change has been observed. It has also been observed that the Normalized Difference Vegetation Index (NDVI) and LST have an inverse relationship. The research outcomes of this study are useful for urban climatologists, urban planners, architects, and policymakers to devise climate resilient policies, structure, and decisions to balance the urban green spaces for a healthy urban environment

The Spatial and Temporal Characteristics of the Urban Thermal Environment in East Africa: Implications for Sustainable Urban Development

Targeting cities in East Africa, where urbanisation and climate change are posing unprecedented threats to livelihoods and ecosystems, this thesis focuses on the combined effects of rapid urbanisation and climate change on Land Surface Temperature (LST), Surface Urban Heat Island (SUHI) effects and the role of Blue Green infrastructure (BGI) and vegetation dynamics. The aim of this thesis is to advance understanding of the urban thermal environment and the role of factors such as climate, vegetation and urbanisation patterns that add to its complexity. Through the use of satellite and remote sensing data (e.g., Google Earth Engine), spatial and statistical analyses, conducted in ArcGIS, Geoda and R, this thesis provides analyses of temporal trends between 2003 and 2017, and spatial differences in LST and SUHI in five East African cities (Khartoum, Addis Ababa, Kampala, Nairobi, Dar es Salaam). It advances understanding of how the configuration of urban areas affects the urban thermal environment, the amount of vegetation and surface water, and demonstrates the influence of urban density on the changes in SUHI intensity in both space and time. By linking the findings from the three results chapters and placing this in the context of the broader literature, corresponding policy implications and solutions are presented. The urgent need to provide a more detailed understanding of urban thermal environments, including macroclimate differences, seasonal variation and urban morphological characteristics, is highlighted. Recommendations emphasise the use of cloud-based analysis methods to overcome data scarcity, while the results point towards the utility of nature-based solutions for urban sustainable development. The methods and lessons emerging from this study can also be applied in other rapidly urbanising cities, where climate change is posing an unprecedented threat to livelihoods and ecosystems, and where resources are limited

Land Surface Monitoring Based on Satellite Imagery

This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought

Too hot to handle? On the cooling capacity of urban green spaces in a Neotropical Mexican city

Urban areas are particularly vulnerable to climate change due to the Urban Heat Island (UHI) effect, which can be mitigated by urban vegetation through shading and evapotranspiration. Nevertheless, there is still a lack of spatially explicit information on the cooling capacity of green infrastructure for most Latin American cities. In this study, we employed Land Surface Temperature (LST) of the Neotropical Mexican city of Xalapa to (1) analyze its Surface UHI (SUHI) compared to its peri and extra-urban areas, (2) to assess the cooling capacity of urban green spaces larger than 1ha, and (3) to evaluate the role of green spaces’ size, shape and their surrounding tree cover percentage (Tc) on green spaces cooling range. We evaluated the cooling range of green spaces and their relationships with green spaces metrics and Tc via a linear mixed-effect model and identify threshold values for the variables at 25, 50, 100, and 200m from the borders of green spaces through Classification and Regression Trees. Xalapa exhibits a SUHI of 1.70 °C compared to its peri-urban area and 4.95 °C to the extra-urban area. Green spaces > 2ha mitigated heat at ~2 °C and the cooling range was influenced by the size of green spaces ≥ 2.8ha and Tc > 21% at 50m and only by Tc surrounding the green spaces at 100m and 200m. This shows that the size threshold of urban green spaces should be complemented with the presence of Tc starting at least 50m to maximize the cooling capacity provided by the green infrastructure. Planning agendas should account for the interaction between the size of green spaces and the cumulative cooling effect of scattered vegetation inside urban areas towards compact green cities to cope with urban warming.Peer reviewe

Gender disparities in summer outdoor heat risk across China: Findings from a national county-level assessment during 1991-2020

Increasing anthropogenic global warming has emerged as a significant challenge to human health in China, as extreme heat hazards increasingly threaten outdoor-exposed populations. Differences in thermal comfort, outdoor activity duration, and social vulnerability between females and males may exacerbate gender inequalities in heat-related health risks, which have been overlooked by previous studies. Here, we combine three heat hazards and outdoor activity duration to identify the spatiotemporal variation in gender-specific heat risk in China during 1991–2020. We found that females' heat risk tends to be higher than that of males. Gender disparities in heat risk decrease in southern regions, while those in northern regions remain severe. Males are prone to overheating in highly urbanized areas, while females in low urbanized areas. Males' overheating risk is mainly attributed to population clustering associated with prolonged outdoor activity time and skewed social resource allocation. In contrast, females' overheating risk is primarily affected by social inequalities. Our findings suggest that China needs to further diminish gender disparities and accelerate climate adaptation planning