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The Olympic Games are a sporting event and a catalyst for urban development in their host city. In this study, we utilized remote sensing and GIS techniques to examine the impact of the Olympic infrastructure on the surface temperature of urban areas. Using Landsat Series Collection 2 Tier 1 Level 2 data and cloud computing provided by Google Earth Engine (GEE), this study examines the effects of various forms of Olympic Games facility urban planning in different historical moments and location typologies, as follows: monocentric, polycentric, peripheric and clustered Olympic ring. The GEE code applies to the Olympic Games that occurred from Paris 2024 to Montreal 1976. However, this paper focuses specifically on the representative cases of Paris 2024, Tokyo 2020, Rio 2016, Beijing 2008, Sydney 2000, Barcelona 1992, Seoul 1988, and Montreal 1976. The study is not only concerned with obtaining absolute land surface temperatures (LST), but rather the relative influence of mega-event infrastructures on mitigating or increasing the urban heat. As such, the locally normalized land surface temperature (NLST) was utilized for this purpose. In some cities (Paris, Tokyo, Beijing, and Barcelona), it has been determined that Olympic planning has resulted in the development of green spaces, creating "green spots" that contribute to lower-than-average temperatures. However, it should be noted that there is a significant variation in temperature within intensely built-up areas, such as Olympic villages and the surrounding areas of the Olympic stadium, which can become "hotspots." Therefore, it is important to acknowledge that different planning typologies of Olympic infrastructure can have varying impacts on city heat islands, with the polycentric and clustered Olympic ring typologies displaying a mitigating effect. This research contributes to a cloud computing method that can be updated for future Olympic Games or adapted for other mega-events and utilizes a widely available remote sensing data source to study a specific urban planning context. [ABSTRACT FROM AUTHOR]

The surface urban heat island (SUHI) phenomenon has become increasingly severe due to the combined effects of global warming and rapid urban expansion, and the difference between urban and rural thermal environments has increased significantly. This trend has profound impacts on social, economic, and ecological environments. Research related to SUHI has achieved fruitful results; however, quantitative research methods for SUHI have not been unified with standards and systems, which will certainly affect the comparability of the results of SUHI research. Few studies have combined and compared multiple SUHI methods. Therefore, we designed a study of the Yangtze River Delta (YRD) urban agglomeration as a test case to quantitatively analyze the differences between SUHI results in different urban and rural contexts based on five different SUHI research methods. It was found that (1) there were significant differences in the SUHI intensity results among the different methods. The maximum difference in the SUHI intensity obtained by different methods can be up to 6 °C. The lowest SUHI intensity was observed during the day in the urban–buffer method, and the lowest SUHI intensity was observed at night in the urban–water method. (2) Different methods affected the distribution of SUHI areas and their evolutionary characteristics. The NHI (no heat island), WCI (weak cold island), and WHI (weak heat island) zones were larger, with proportions exceeding 70%. The expansion range of the heat island zone during the daytime was mainly in the west and north of the YRD urban agglomeration, whereas the expansion of the heat island range at night was mainly concentrated in the center and south. (3) The trend changes observed using different methods were significantly different. When we applied the urban–buffer and municipal–nonmunicipal methods, most cities showed an upward trend. However, when the other methods were applied, most cities exhibited a downward trend. The differences in trend results owing to the choice of different methods were greater with respect to values in the summer months and smaller in the winter months. [ABSTRACT FROM AUTHOR]

Global warming and climate change are significantly impacting local climates, causing more intense heat during the summer season, which poses risks to individuals with pre-existing health conditions and negatively affects overall human health. While various studies have examined the Surface Urban Heat Island (SUHI) phenomenon, these studies often focus on small to large geographic regions using low-to-moderate-resolution data, highlighting general thermal trends across large administrative areas. However, there is a growing need for methods that can detect microscale thermal patterns in environments familiar to urban residents, such as streets and alleys. The temperature-humidity index (THI), which incorporates both temperature and humidity data, serves as a critical measure of human-perceived heat. However, few studies have explored microscale THI variations within urban settings and identified potential THI hotspots at a local level where SUHI effects are pronounced. This research aims to address this gap by estimating THI at a finer resolution scale using data from multiple sensor platforms. We developed a model with the random forest algorithm to assess THI trends at a resolution of 0.5 m, utilizing various variables from different sources, including Landsat 8 land surface temperature (LST), unmanned aerial system (UAS)-derived LST, Sentinel-2 NDVI and NDMI, a wind exposure index, solar radiation modeled from aircraft and UAS-derived Digital Surface Models, and vehicle density and building floor area from social big data. Two models were constructed with different variables: Modelnatural, which includes variables related to only natural factors, and Modelmix, which includes all variables, including anthropogenic factors. The two models were compared to reveal how each source contributes to the model development and SUHI effects. The results show significant improvements, as Modelnatural had a fitting R2 = 0.5846, a root mean square error (RMSE) = 0.5936 and a mean absolute error (MAE) = 0.4294. Moreover, when anthropogenic factors were introduced, Modelmix performed even better, with R2 = 0.9638, RMSE = 0.1751, and MAE = 0.1065 (n = 923). This study contributes to the future of microscale SUHI analysis and offers important insights into urban planning and smart city development. [ABSTRACT FROM AUTHOR]

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The urban heat island (UHI) is an environmental problem of wide concern because it poses a threat to both the human living environment and the sustainable development of cities. Knowledge of the spatiotemporal characteristics and the driving factors of UHI is essential for mitigating their impact. However, current understanding of the UHI in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is inadequate. Combined with data (e.g., land surface temperature and land use.) acquired from the Google Earth Engine and other sources for the period 2001–2020, this study examined the diurnal and seasonal variabilities, spatial heterogeneities, temporal trends, and drivers of surface UHI intensity (SUHII) in the GBA. The SUHII was calculated based on the urban-rural dichotomy, which has been proven an effective method. The average SUHII was generally 0–2°C, and the SUHII in daytime was generally greater than that at night. The maximum (minimum) SUHII was found in summer (winter); similarly, the largest (smallest) diurnal difference in SUHII was during summer (winter). Generally, the Mann-Kendall trend test and the Sen's slope estimator revealed a statistically insignificant upward trend in SUHII on all time scales. The influence of driving factors on SUHII was examined using the Geo-Detector model. It was found that the number of continuous impervious pixels had the greatest impact, and that the urban-rural difference in the enhanced vegetation index had the smallest impact, suggesting that anthropogenic heat emissions and urban size are the main influencing factors. Thus, controlling urban expansion and reducing anthropogenic heat generation are effective approaches for alleviating surface UHI. [ABSTRACT FROM AUTHOR]

The Local Climate Zone (LCZ) scheme representing urban structure and land use pattern is essential for urban heat island (UHI) research. Fine-grained LCZ mapping considering spatial and temporal heterogeneity can provide a more precise characterization of surface thermal properties, thereby enabling a comprehensive analysis and understanding of spatiotemporal trends in climate change research. However, data-driven deep learning-based methods have limitations in coping with the complex urban landscapes of the real-world scenarios, including the spectral similarity of different LCZ and the geospatial heterogeneity of urban LCZ categories. In this study, we constructed a geographic knowledge base for enhanced LCZ characterization with the consideration of prior surface spatial information, including a set of spectral indices and urban morphological parameters (UMPs). Then, we integrated the explicable geographic knowledge base into a learnable deep learning framework in an end-to-end manner for accurate LCZ mapping by fusing multi-source heterogeneous data with a multi-level fusion strategy. The constructed Chinese Climate Zone Time Series LCZ (CClimate-TLCZ) dataset derived from Landsat-8 data with a 30 m spatial resolution, covering 18 representative cities in China, were used to evaluate the proposed framework. The experimental results demonstrate that the proposed framework achieved optimal outcomes across 18 cities, with an average overall accuracy exceeding 94 %, which is more than 20 % higher than that obtained by the standard WUDAPT method. Furthermore, the analysis of LCZ mapping-driven land surface temperature (LST) and surface UHI (SUHI) applications shows that cities within the same climate zone have similar LST distribution patterns, while significant heterogeneity exist between different zones. The annual consistency of LST patterns within each climate zone supports the validity of the LCZ classification scheme and accurate LCZ mapping for urban thermal environment studies. From 2016 to 2022, SUHI intensity initially increases and then decreases, indicating improvements in the urban thermal environment. These findings underscore the critical role of precise LCZ mapping in urban climate resilience and sustainable urban planning. [ABSTRACT FROM AUTHOR]

AbstractPast decades have seen substantial efforts devoted to observing, assessing, and documenting the urban heat island (UHI) phenomenon. However, the discrepant criteria of non-urban references and ambiguous distinctions between urban and rural landscapes pose great challenges in measuring UHI magnitudes and spatial extents. This study goes beyond the conventional urban-rural dichotomy and introduces a new two-step approach based on the continuous transition of thermal environments along urban-rural gradients. The approach is applied to quantify Surface UHI (SUHI) intensities and footprints across 283 Chinese cities from 2005 to 2018 using multiple satellite-derived data sources. The results include: 1) The two-step approach avoids the limitations in subjective rural reference selections and provides reliable quantification of SUHI characteristics in various cities over time. 2) The SUHI footprints extracted by our approach are more reasonable than those obtained by two existing methods, with footprint ratios generally ranging within 0 − 6 times the urban area. 3) The two-step approach provides more concentrated estimates of SUHI intensity. Typically, ignoring heat sources in non-built-up areas can cause an overestimation of SUHI effect and misidentification of remote rural areas with high temperatures. Overall, the two-step approach enables more accurate estimates of SUHI effect, thereby facilitating policy-making for SUHI mitigation. [ABSTRACT FROM AUTHOR]

Numerous studies have examined land surface temperature (LST) changes in Thailand using remote sensing, but there has been little research on LST variations within urban land use zones. This study addressed this gap by analyzing summer LST changes in land use zoning (LUZ) blocks in the 2012 Chiang Mai Comprehensive Plan and their relationship with surface biophysical parameters (NDVI, NDBI, MNDWI). The approach integrated detailed zoning data with remote sensing for granular LST analysis. Correlation and stepwise regression analyses (SRA) revealed that NDBI significantly impacted LST in most block types, while NDVI and MNDWI also influenced LST, particularly in 2023. The findings demonstrated the complexity of LST dynamics across various LUZs in Chiang Mai, with SRA results explaining 45.7% to 53.2% of summer LST variations over three years. To enhance the urban environment, adaptive planning strategies for different block categories were developed and will be considered in the upcoming revision of the Chiang Mai Comprehensive Plan. This research offers a new method to monitor the urban heat island phenomenon at the block level, providing valuable insights for adaptive urban planning. [ABSTRACT FROM AUTHOR]

Research has demonstrated that urban green spaces play a crucial role in mitigating the severe urban heat island (UHI) effect. However, existing studies often suffer from limitations such as the neglect of the cooling effect of water bodies within the green spaces and incomplete considerations of the overall cooling effect. These limitations may lead to inaccuracies in the research findings. Therefore, the present study takes the city of Xi'an as a case study to explore the optimal green space size for achieving efficient cooling. The results indicate that (i) urban green spaces exhibit robust cooling effects, with variations observed among the various types; (ii) for community parks without water, and for street gardens, the optimal areas of these green spaces are 3.44 and 0.83 hectares, respectively; (iii) for community parks with water, the area of internal water bodies should ideally be maintained at around 29.43% of the total green space area in order to achieve an optimal cooling efficiency. In conclusion, this study introduces a new perspective and new optimization methods for urban green space planning, thereby offering scientific guidance to urban planners in formulating effective development and management policies and urban planning schemes. [ABSTRACT FROM AUTHOR]

Studying urban heat islands holds significance for the sustainable development of cities. This comprehensive study analyzed the temporal characteristics of a Surface Urban Heat Island and Canopy Layer Heat Island by employing Moderate-Resolution Imaging Spectroradiometer image data spanning from 2003 to 2020 over Beijing, China. Leveraging the Gaussian capacity model, the geometrical characteristics of the Surface Urban Heat Island and Canopy Layer Heat Island, such as intensity, center, direction, and range, were examined among three different timescales of day, month, and year. Results indicate that the intensities of the Surface Urban Heat Island and Canopy Layer Heat Island tend to have bigger seasonal variations during winter nights and summer daytime. In addition, at night the centers of Surface Urban Heat Island and Canopy Layer Heat Island are mainly concentrated in the range of 116.3°~116.4° E in longitude and 39.90°~39.95° N in latitude, while during the daytime they are more scattered, mainly in the range of 116.2°~116.5° E in longitude and 39.7°~40.0° N in latitude. In the hot season, the center of the heat island moves east to north, while in the cold season it moves west to south. Monthly average ellipse areas of Surface Urban Heat Island and Canopy Layer Heat Island vary more during the day than that at night, the maximum daytime differences were 2662 km2 and 2293 km2, while the maximum nighttime differences were 484 km2 and 265 km2. Overall, the average area is increasing, with the heat island center moving eastward and deflecting towards the northeast-southwest direction. The expansion of urban areas will continue to influence the movement and extent of heat islands. The study offers insights to inform strategies for mitigating urban heat islands. [ABSTRACT FROM AUTHOR]

The lack of a solid methodology defining urban and non-urban areas has hindered accurately estimating the Surface Urban Heat Island (SUHI). This study addresses this issue by using the official national urban areas limit together with a surrounding areas classification to define three different reference classes: the urban adjacent (Ua), the future urban adjacent (FUa), and the peri-urban (PUa), consequently providing a more accurate SUHI estimation on the nine northeastern Brazilian capitals. The land surface temperature was obtained in this study using the Sentinel-3 satellite data for 2019 and 2020. Subsequently, the maximum and average SUHI and the complementary indexes, specifically the Urban Thermal Field Variation Index (UTFVI) and the Thermal Discomfort Index (TDI), were calculated. The UTFVI expresses how harmful the eco-environmental spaces are, with a very strong SUHI for three capitals. In addition, the TDI, with values between 24.6–28.8 °C, expresses the population's thermal comfort, with six capitals showing a very hot TDI. These findings highlight the need for strategies to mitigate the effects of the SUHI and ensure the population's thermal comfort. Therefore, this study provides a better SUHI understanding and comparison for the Brazilian northeastern region, which has diverse areas, populations, and demographic variations. [ABSTRACT FROM AUTHOR]

Many cities face heat wave (HW) events, combined with the existent surface urban heat island (SUHI) effects. This places pressure on human settlements and sustainable development. However, few studies have investigated the SUHI effects from the perspective of HWs. In this study, the summer HWs in Beijing from 2001 to 2021 were calculated, and the evolution of HWs and SUHIs was quantitatively analyzed based on the dynamic nature of the urban-rural boundary. Beijing experienced 27 HWs in the 21 years, including 10 instances in June, four in July, and 13 in August. The SUHI varied during HWs, between 2–3°C in most years. The highest SUHI occurred in 2019, reaching 3.99°C and covering the largest area (10,887 km2). The fluctuation in HWs and SUHIs generally displayed the same spatiotemporal pattern, and HWs amplified the SUHIs to a certain extent, with the highest correlation coefficient being 0.44. Additionally, impervious surfaces and cropland contributed most to SUHIs, and night light enhanced SUHIs. Observing the co-evolution of HWs and SUHIs will be helpful for ecological maintenance and urban infrastructure optimization and provide theoretical support for reducing heat risk and improving the human settlement environment. [ABSTRACT FROM AUTHOR]

Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat island (SUHI) phenomena. This research focuses on the nexus between LULC alterations and variations in LST and air temperature (Tair), with a specific emphasis on the intensified SUHI effect in Kharkiv, Ukraine. Employing an integrated approach, this study analyzes time-series data from Landsat and MODIS satellites, alongside Tair climate records, utilizing machine learning techniques and linear regression analysis. Key findings indicate a statistically significant upward trend in Tair and LST during the summer months from 1984 to 2023, with a notable positive correlation between Tair and LST across both datasets. MODIS data exhibit a stronger correlation (R2 = 0.879) compared to Landsat (R2 = 0.663). The application of a supervised classification through Random Forest algorithms and vegetation indices on LULC data reveals significant alterations: a 70.3% increase in urban land and a decrement in vegetative cover comprising a 15.5% reduction in dense vegetation and a 62.9% decrease in sparse vegetation. Change detection analysis elucidates a 24.6% conversion of sparse vegetation into urban land, underscoring a pronounced trajectory towards urbanization. Temporal and seasonal LST variations across different LULC classes were analyzed using kernel density estimation (KDE) and boxplot analysis. Urban areas and sparse vegetation had the smallest average LST fluctuations, at 2.09 °C and 2.16 °C, respectively, but recorded the most extreme LST values. Water and dense vegetation classes exhibited slightly larger fluctuations of 2.30 °C and 2.24 °C, with the bare land class showing the highest fluctuation 2.46 °C, but fewer extremes. Quantitative analysis with the application of Kolmogorov-Smirnov tests across various LULC classes substantiated the normality of LST distributions p > 0.05 for both monthly and annual datasets. Conversely, the Shapiro-Wilk test validated the normal distribution hypothesis exclusively for monthly data, indicating deviations from normality in the annual data. Thresholded LST classifies urban and bare lands as the warmest classes at 39.51 °C and 38.20 °C, respectively, and classifies water at 35.96 °C, dense vegetation at 35.52 °C, and sparse vegetation 37.71 °C as the coldest, which is a trend that is consistent annually and monthly. The analysis of SUHI effects demonstrates an increasing trend in UHI intensity, with statistical trends indicating a growth in average SUHI values over time. This comprehensive study underscores the critical role of remote sensing in understanding and addressing the impacts of climate change and urbanization on local and global climates, emphasizing the need for sustainable urban planning and green infrastructure to mitigate UHI effects. [ABSTRACT FROM AUTHOR]

Rapid urbanisation over the years has led to the loss of natural land cover, thereby affecting Land Surface Temperature (LST) distribution in urban areas. This study aims to analyse LST anomalies (calculated as the deviation from the normal) over selected Indian cities and check if critical land cover changes can be identified. LST from Landsat Thermal Infrared (TIR) images acquired in March, April and May from 1988 to 2020 were used to estimate LST anomalies. Positive LST anomalies were observed mainly over barren and impervious areas; however, some areas showed a negative anomaly where the barren lands were converted to vegetated areas. The study has demonstrated that while some developed areas exhibit a positive anomaly indicative of significant changes or development, there are instances where the conversion of barren land to developed (i.e. built up) areas has resulted in a negative anomaly. Developed areas that are closer to the water creek or mangroves were associated with lower anomaly values indicating the cooling effect of the water body and vegetation. Conversely, the core urban areas generally exhibited higher LST values with positive anomalies indicating a warming effect. These findings can be used by city planners to identify hotspot areas and develop more effective strategies and policies to address the challenges of urban heat. They also highlight the regions that require infrastructural resources and policy changes to reduce the temperature.

The urban–rural temperature difference is widely used in measuring surface urban heat island intensity (SUHII), where the accurate determination of rural background is crucial. However, traditionally, the entire permeable rural surface has been selected to represent the background temperature, leaving uncertainty about the impact of non-uniform rural surfaces with multiple land covers on the accuracy of SUHII quantification. In this study, we proposed two quantifications of SUHII derived from the primary (SUHII1) and secondary (SUHII2) land types, respectively, which successively occupy over 40–50% of whole rural regions. The spatial integration and temporal variation of SUHII1 and SUHII2 were compared with the result from whole rural regions (SUHII) within 34 urban agglomerations (UAs) in China. The results showed that the SUHII1 and SUHII2 differed slightly with SUHII, and the correlation coefficients of SUHII and SUHII1/SUHII2 are generally above 0.9 in most (32) UAs. Regarding the long-term SUHII between 2003 and 2019, the three methods demonstrated similar seasonal patterns, although SUHII1 (or SUHII2) tended to overestimate or underestimate compared to SUHII. As for the multi-year integration at the regional scale, the day–night cycle and monthly variations of SUHII1 and SUHII were found to be identical for each geographical division separately, indicating that the spatiotemporal pattern revealed by SUHII is minimally affected by the diversity of rural landcover types. The findings confirmed the viability of the urban–rural LST difference method for measuring long-term regional SUHII patterns under non-uniform rural land cover types. [ABSTRACT FROM AUTHOR]

Urban heat islands (UHI) are a well-known phenomenon adversely affecting human health and urban environments. The worldwide COVID-19 lockdown in 2020 provided a unique opportunity to investigate the effects of decreased emission of air pollution and anthropogenic heat flux (AHF) on UHI. Although studies have suggested that reduced AHF during lockdown decreased atmospheric UHI (AUHI) and surface UHI (SUHI), these results contain inherent uncertainties due to unaccounted weather variability and urban-rural dynamics. Our study comprehensively analyzes the impact of the COVID-19 lockdown on AUHI and SUHI in Prague, Czechia. By selecting days with similar weather conditions, we examined changes in mean SUHI using MODIS satellite images and in AUHI based on air temperature from Prague weather stations for the Lockdown period during March–April 2020 versus a Reference period from March–April 2017–2019. Our results show that, in comparison to the Reference period, the Lockdown period was associated with a 15% (0.1 °C) reduction of SUHI in urbanized areas of Prague and a 0.7 °C decline in AUHI in the city center. Additionally, the observed decreases in satellite-based aerosol optical depth and nitrogen dioxide by 12% and 29%, respectively, support our hypothesis that the weakened UHI effects were linked to reduction in anthropogenic activities during the lockdown. Revealing the largest decrease of mean SUHI magnitude around the periphery of Prague, which has predominantly rural land cover, our study emphasizes the need to consider the effects of urban-rural dynamics when attributing changes in SUHI to AHF. Our findings provide additional insights into the role of reduced anthropogenic activities in UHI dynamics during the COVID-19 lockdown and offer policymakers a comprehensive understanding of how the complex interaction between urban and rural microclimate dynamics influences the SUHI phenomenon. [ABSTRACT FROM AUTHOR]

The present work evaluates the impact of land use changes in one of the important capital cities of Central India to understand the relation between land use practices and urbanization on the increasing trend of land surface temperature. Therefore, quantify the spatial changes in land use land cover (LULC) and Urban Heat Island (UHI) of the Bhopal city over 12 years (2008 to 2020) carried out considering multi-temporal satellite and field data. The Landsat-5 TM and Landsat-8 OLI/TIRS sensors were used to estimate the physical changes such as land use, normalized differential vegetation index (NDVI), normalized differential built-up index (NDBI), Land Surface Temperature (LST) and co-relate their interrelationship. It was found that the built-up area increased by 12.57% from 2008 to 2020, and vegetation increased by 15.95% from 2008 to 2020. It was also found that the bare rocky region has converted to vegetation by 22.95%. The Pearson coefficient between LST with NDBI was found to be 0.55 to 0.79. Pearson coefficient for NDVI association with LST was found to be negative, which varies between − 0.4 and − 0.7. The build-up area increase has been strongly influenced by the conversion of fallow land (5.84%) and bare rocky regions (22.92). Surface Urban Heat Island (SUHI) has also been calculated and found to be 4 °C higher in 2020 than in 2008. Overall, based on these findings, it is recommended to prioritize effective urban planning and land use policies that promote sustainable development and mitigate the adverse effects of increasing LST on the urban environment. [ABSTRACT FROM AUTHOR]

• The results indicate a decrease in concentration of polluting gases in atmosphere during Covid-19. • Remote sensing indices exhibit high accuracy in estimating climatic and vegetation. • A significant correlation exists between temperature and the concentration of gases. • Neural networks demonstrate high accuracy in predicting thermal pollution. The COVID-19 pandemic, spanning nearly two years, instigated a worldwide lockdown, resulting in a notable decline in pollution levels across numerous urban centers. This investigation leveraged satellite data to scrutinize alterations in air quality pre and post the onset of the COVID-19 pandemic, focusing on the Isfahan region in central Iran and Fars province in the south. We assessed vegetation conditions employing remote sensing indices, including the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-Up Index (NDBI). Additionally, we evaluated thermal pollution in urban environments using indices such as Land Surface Temperature (LST), Surface Urban Heat Island (SUHI), and Urban Thermal Features Vegetation Index (UTFVI). Linear regression analysis revealed significant correlations between LST and pollutants, with SO 2 (R2 = 0.983), NO 2 (R2 = 0.824), CO 2 (0.808), and CO (R2 = 0.710) exhibiting strong associations. Pollution maps for CO 2 , CH 4 , CO, NO 2 , O 3 , and SO 2 were generated for the pre-COVID-19 (2019) and COVID-19 (2020) periods, highlighting reduced pollution levels during the pandemic due to decreased industrial activities and vehicle traffic. We employed Markov and Cellular Automata (CA)-Markov methods to predict future LST trends, using probability matrices as input data. The CA-Markov chain results forecasted an increase in LST in 2040. Additionally, we compared Radial Basis Function (RBF) and Multilayer Perceptron (MLP) neural network methods. Our investigation found that MLP outperforms RBF, with optimal neuron counts in the hidden layers varying by index. MLP demonstrated higher accuracy, with R2 values of 0.98 for SUHI, 0.93 for UTFVI, 0.98 for CO, and 0.99 for O 3 , and lower RMSE. In conclusion, our study, focused on Isfahan and Fars province in Iran, reveals that the reduction in urban air pollution during the COVID-19 pandemic primarily resulted from diminished industrial activities and reduced vehicular traffic. MLP neural networks displayed superior performance in predicting index values, highlighting the role of reduced human activities on urban environmental quality. [ABSTRACT FROM AUTHOR]

The Olympic Games are a sporting event and a catalyst for urban development in their host city. In this study, we utilized remote sensing and GIS techniques to examine the impact of the Olympic infrastructure on the surface temperature of urban areas. Using Landsat Series Collection 2 Tier 1 Level 2 data and cloud computing provided by Google Earth Engine (GEE), this study examines the effects of various forms of Olympic Games facility urban planning in different historical moments and location typologies, as follows: monocentric, polycentric, peripheric and clustered Olympic ring. The GEE code applies to the Olympic Games that occurred from Paris 2024 to Montreal 1976. However, this paper focuses specifically on the representative cases of Paris 2024, Tokyo 2020, Rio 2016, Beijing 2008, Sydney 2000, Barcelona 1992, Seoul 1988, and Montreal 1976. The study is not only concerned with obtaining absolute land surface temperatures (LST), but rather the relative influence of mega-event infrastructures on mitigating or increasing the urban heat. As such, the locally normalized land surface temperature (NLST) was utilized for this purpose. In some cities (Paris, Tokyo, Beijing, and Barcelona), it has been determined that Olympic planning has resulted in the development of green spaces, creating “green spots” that contribute to lower-than-average temperatures. However, it should be noted that there is a significant variation in temperature within intensely built-up areas, such as Olympic villages and the surrounding areas of the Olympic stadium, which can become “hotspots.” Therefore, it is important to acknowledge that different planning typologies of Olympic infrastructure can have varying impacts on city heat islands, with the polycentric and clustered Olympic ring typologies displaying a mitigating effect. This research contributes to a cloud computing method that can be updated for future Olympic Games or adapted for other mega-events and utilizes a widely available remote sensing data source to study a specific urban planning context.

Global warming and climate change are significantly impacting local climates, causing more intense heat during the summer season, which poses risks to individuals with pre-existing health conditions and negatively affects overall human health. While various studies have examined the Surface Urban Heat Island (SUHI) phenomenon, these studies often focus on small to large geographic regions using low-to-moderate-resolution data, highlighting general thermal trends across large administrative areas. However, there is a growing need for methods that can detect microscale thermal patterns in environments familiar to urban residents, such as streets and alleys. The temperature-humidity index (THI), which incorporates both temperature and humidity data, serves as a critical measure of human-perceived heat. However, few studies have explored microscale THI variations within urban settings and identified potential THI hotspots at a local level where SUHI effects are pronounced. This research aims to address this gap by estimating THI at a finer resolution scale using data from multiple sensor platforms. We developed a model with the random forest algorithm to assess THI trends at a resolution of 0.5 m, utilizing various variables from different sources, including Landsat 8 land surface temperature (LST), unmanned aerial system (UAS)-derived LST, Sentinel-2 NDVI and NDMI, a wind exposure index, solar radiation modeled from aircraft and UAS-derived Digital Surface Models, and vehicle density and building floor area from social big data. Two models were constructed with different variables: Modelnatural, which includes variables related to only natural factors, and Modelmix, which includes all variables, including anthropogenic factors. The two models were compared to reveal how each source contributes to the model development and SUHI effects. The results show significant improvements, as Modelnatural had a fitting R2 = 0.5846, a root mean square error (RMSE) = 0.5936 and a mean absolute error (MAE) = 0.4294. Moreover, when anthropogenic factors were introduced, Modelmix performed even better, with R2 = 0.9638, RMSE = 0.1751, and MAE = 0.1065 (n = 923). This study contributes to the future of microscale SUHI analysis and offers important insights into urban planning and smart city development.

The surface urban heat island (SUHI) phenomenon has become increasingly severe due to the combined effects of global warming and rapid urban expansion, and the difference between urban and rural thermal environments has increased significantly. This trend has profound impacts on social, economic, and ecological environments. Research related to SUHI has achieved fruitful results; however, quantitative research methods for SUHI have not been unified with standards and systems, which will certainly affect the comparability of the results of SUHI research. Few studies have combined and compared multiple SUHI methods. Therefore, we designed a study of the Yangtze River Delta (YRD) urban agglomeration as a test case to quantitatively analyze the differences between SUHI results in different urban and rural contexts based on five different SUHI research methods. It was found that (1) there were significant differences in the SUHI intensity results among the different methods. The maximum difference in the SUHI intensity obtained by different methods can be up to 6 °C. The lowest SUHI intensity was observed during the day in the urban–buffer method, and the lowest SUHI intensity was observed at night in the urban–water method. (2) Different methods affected the distribution of SUHI areas and their evolutionary characteristics. The NHI (no heat island), WCI (weak cold island), and WHI (weak heat island) zones were larger, with proportions exceeding 70%. The expansion range of the heat island zone during the daytime was mainly in the west and north of the YRD urban agglomeration, whereas the expansion of the heat island range at night was mainly concentrated in the center and south. (3) The trend changes observed using different methods were significantly different. When we applied the urban–buffer and municipal–nonmunicipal methods, most cities showed an upward trend. However, when the other methods were applied, most cities exhibited a downward trend. The differences in trend results owing to the choice of different methods were greater with respect to values in the summer months and smaller in the winter months.

Surface urban heat islands (SUHIs) are mostly an urban ecological issue. There is a growing demand for the quantification of the SUHI effect, and for its optimization to mitigate the increasing possible hazards caused by SUHI. Satellite-derived land surface temperature (LST) is an important indicator for quantifying SUHIs with frequent coverage. Current LST data with high spatiotemporal resolution is still lacking due to no single satellite sensor that can resolve the trade-off between spatial and temporal resolutions and this greatly limits its applications. To address this issue, we propose a multiscale geographically weighted regression (MGWR) coupling the comprehensive, flexible, spatiotemporal data fusion (CFSDAF) method to generate a high-spatiotemporal-resolution LST dataset. We then analyzed the SUHI intensity (SUHII) in Chengdu City, a typical cloudy and rainy city in China, from 2002 to 2022. Finally, we selected thirteen potential driving factors of SUHIs and analyzed the relation between these thirteen influential drivers and SUHIIs. Results show that: (1) an MGWR outperforms classic methods for downscaling LST, namely geographically weighted regression (GWR) and thermal image sharpening (TsHARP); (2) compared to classic spatiotemporal fusion methods, our method produces more accurate predicted LST images (R2, RMSE, AAD values were in the range of 0.8103 to 0.9476, 1.0601 to 1.4974, 0.8455 to 1.3380); (3) the average summer daytime SUHII increased form 2.08 °C (suburban area as 50% of the urban area) and 2.32 °C (suburban area as 100% of the urban area) in 2002 to 4.93 °C and 5.07 °C, respectively, in 2022 over Chengdu City; and (4) the anthropogenic activity drivers have a higher relative influence on SUHII than other drivers. Therefore, anthropogenic activity driving factors should be considered with CO2 emissions and land use changes for urban planning to mitigate the SUHI effect. [ABSTRACT FROM AUTHOR]

A fundamental aspect of ensuring urban sustainability is a comprehensive understanding of the driving mechanisms behind the urban heat island (UHI) phenomenon. The primary objective of this study is to investigate the spatiotemporal variations and underlying mechanisms of the surface urban heat island (SUHI) in Hefei. The study employed the local climate zone (LCZ) method to analyze land surface morphology and spatial structure for 2014 and 2021. Subsequently, calculations were conducted to derive surface urban heat island intensity (SUHII), normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), gravity water index (GWI), building surface fraction (BSF), road density (RD), poi density (PD), and population density (PPD). The exploration of the mechanisms by which factors influence SUHI was conducted by utilizing both Pearson correlation analysis and geographic detector models. The results revealed that sparsely built (LCZ 9) and low plants (LCZ D) predominantly characterized built-up and natural coverage areas, respectively. The summer season was distinguished by the most extensive SUHI distribution and the highest SUHII levels. Significantly, SUHII consistently exceeded those of built-up LCZs when contrasted to natural LCZs. Large lowrise (LCZ 8) consistently displayed the highest SUHII levels, whereas water (LCZ G) consistently exhibited the lowest SUHII values. NDBI took precedence and showed a positive correlation with SUHI. Among the socio-economic factors, building height (BH) demonstrated a superior explanatory capability for SUHI compared to other variables. The interaction between NDBI and NDVI maximized the explanation of SUHI under different seasons. The findings of this study will serve as critical insights for urban planners and policymakers, enabling the development of scientifically-based and efficacious strategies to mitigate SUHI.

Research has demonstrated that urban green spaces play a crucial role in mitigating the severe urban heat island (UHI) effect. However, existing studies often suffer from limitations such as the neglect of the cooling effect of water bodies within the green spaces and incomplete considerations of the overall cooling effect. These limitations may lead to inaccuracies in the research findings. Therefore, the present study takes the city of Xi’an as a case study to explore the optimal green space size for achieving efficient cooling. The results indicate that (i) urban green spaces exhibit robust cooling effects, with variations observed among the various types; (ii) for community parks without water, and for street gardens, the optimal areas of these green spaces are 3.44 and 0.83 hectares, respectively; (iii) for community parks with water, the area of internal water bodies should ideally be maintained at around 29.43% of the total green space area in order to achieve an optimal cooling efficiency. In conclusion, this study introduces a new perspective and new optimization methods for urban green space planning, thereby offering scientific guidance to urban planners in formulating effective development and management policies and urban planning schemes.

Numerous studies have examined land surface temperature (LST) changes in Thailand using remote sensing, but there has been little research on LST variations within urban land use zones. This study addressed this gap by analyzing summer LST changes in land use zoning (LUZ) blocks in the 2012 Chiang Mai Comprehensive Plan and their relationship with surface biophysical parameters (NDVI, NDBI, MNDWI). The approach integrated detailed zoning data with remote sensing for granular LST analysis. Correlation and stepwise regression analyses (SRA) revealed that NDBI significantly impacted LST in most block types, while NDVI and MNDWI also influenced LST, particularly in 2023. The findings demonstrated the complexity of LST dynamics across various LUZs in Chiang Mai, with SRA results explaining 45.7% to 53.2% of summer LST variations over three years. To enhance the urban environment, adaptive planning strategies for different block categories were developed and will be considered in the upcoming revision of the Chiang Mai Comprehensive Plan. This research offers a new method to monitor the urban heat island phenomenon at the block level, providing valuable insights for adaptive urban planning.

Studying urban heat islands holds significance for the sustainable development of cities. This comprehensive study analyzed the temporal characteristics of a Surface Urban Heat Island and Canopy Layer Heat Island by employing Moderate-Resolution Imaging Spectroradiometer image data spanning from 2003 to 2020 over Beijing, China. Leveraging the Gaussian capacity model, the geometrical characteristics of the Surface Urban Heat Island and Canopy Layer Heat Island, such as intensity, center, direction, and range, were examined among three different timescales of day, month, and year. Results indicate that the intensities of the Surface Urban Heat Island and Canopy Layer Heat Island tend to have bigger seasonal variations during winter nights and summer daytime. In addition, at night the centers of Surface Urban Heat Island and Canopy Layer Heat Island are mainly concentrated in the range of 116.3°~116.4° E in longitude and 39.90°~39.95° N in latitude, while during the daytime they are more scattered, mainly in the range of 116.2°~116.5° E in longitude and 39.7°~40.0° N in latitude. In the hot season, the center of the heat island moves east to north, while in the cold season it moves west to south. Monthly average ellipse areas of Surface Urban Heat Island and Canopy Layer Heat Island vary more during the day than that at night, the maximum daytime differences were 2662 km2 and 2293 km2, while the maximum nighttime differences were 484 km2 and 265 km2. Overall, the average area is increasing, with the heat island center moving eastward and deflecting towards the northeast-southwest direction. The expansion of urban areas will continue to influence the movement and extent of heat islands. The study offers insights to inform strategies for mitigating urban heat islands.

AbstractThe Urban Heat Island (UHI) effect is a phenomenon where urban areas experience higher temperatures than surrounding rural areas. In these issues, enhanced air or surface temperature is one of the major issues that led to the UHI phenomenon. In this article, we come up with a study on the diurnal UHI effect caused in Jaipur city, India, and surrounding areas of Jaipur. In-situ temperature monitoring has been carried out at seven dispersed locations to properly understand and evaluate the effects of surface UHI (SUHI) and atmospheric UHI (AUHI), as well as to assess the thermal profile of diverse land surfaces in Jaipur. With the use of satellite data, the intensity of AUHI and SUHI has been determined between 10.30 a.m. and 10.30 p.m. The observations point out that positive AUHI intensity (AUHII) exists at many locations, irrespective of time periods. During the day period, negative SUHI intensity (SUHII) was noticed at many locations, whereas during the night period, positive SUHII was noticed. According to our observations, AUHI and SUHI have a direct correlation at night but a negative or inverse correlation during the day. That is, AUHI was active both during the day and at night. Various land surfaces play a significant role in contrasting the diurnal UHI effect. This study evaluates the potential of remotely sensed data in monitoring the UHI effect and provides recommendations for urban planners and policymakers to mitigate the UHI effect in the city of Jaipur. [Figure: see text]

AbstractThe urbanization process has significantly intensified surface urban heat island (SUHI) effects in the Bangkok Metropolitan Region (BMR). However, understanding the evolution of the urban thermal environment is challenging due to the difficulty in obtaining consistent remote sensing data of the cloud-prone landscape in the BMR. In this study, the data fusion algorithm was utilized to fill cloud-induced data gap and create high spatiotemporal-resolution data by blending Landsat and MODIS remote sensing images. The fused data was used to retrieve land surface temperature (LST) for winter months from 2000 to 2020. The spatiotemporal variations in SUHI were then captured using spatial cluster analysis. Finally, gradient analysis and geographically weighted regression (GWR) were employed to analyse the effects of land cover composition on LST. The SUHI intensity in winter increased from 4.40 °C in 2000 to 5.76 °C in 2020. The areal percentage of SUHI hot spots increased from 24.86% to 29.13%. The gradient analysis results indicated that vegetation with a density higher than 0.3 had a significant effect on LST compared to low-density areas. The woody lands were more effective in lowering LST than cultivated lands. These results provide useful information for developing heat mitigation strategies in the metropolitan regions.

Deepening the understanding of diurnal characteristics and driving mechanisms of surface urban heat islands (SUHIs) across different local climate zones (LCZs) and time scales is of great significance for guiding urban surface heat mitigation. However, a comprehensive investigation of SUHIs from the diurnal, local, multi-seasonal, and interactive perspectives remains a large gap. Here, we generalized major diurnal patterns of LCZ-based SUHI intensities (SUHIIs) throughout 2020 over the urban area of Beijing, China, based on diurnal temperature cycle modeling, block-level LCZ mapping, and hierarchical clustering. A geographical detector was then employed to explore the individual and interactive impacts of 10 morphological, socioeconomic, and meteorological factors on the multi-temporal spatial differentiations of SUHIIs. Results indicate six prevalent diurnal SUHII patterns with distinct features among built LCZ types. LCZs 4 and 5 (open high- and mid-rise buildings) predominantly display patterns one, two, and five, characterized by an afternoon increase and persistently higher values during the night. Conversely, LCZs 6, 8, and 9 (open, large, and sparsely built low-rise buildings) mainly exhibit patterns three, four, and six, with a decrease in SUHII during the afternoon and lower intensities at night. The maximum/minimum SUHIIs occur in the afternoon–evening/morning for patterns 1–3 but in the morning/afternoon for patterns 5–6. In all four seasons, the enhanced vegetation index (EVI) and gross domestic product (GDP) have the top two individual effects for daytime spatial differentiations of SUHIIs, while the air temperature (TEM) has the largest explanatory power for nighttime differentiations of SUHIIs. All factor interactions are categorized as two-factor or nonlinear enhancements, where nighttime interactions exhibit notably greater explanatory powers than daytime ones. The strongest interactions are EVI ∩ GDP (q = 0.80) during the day and TEM ∩ EVI (q = 0.86) at night. The findings of this study contribute to an improved interpretation of the diurnal continuous dynamics of local SUHIIs in response to various environmental conditions. [ABSTRACT FROM AUTHOR]

Using the China National Knowledge Infrastructure (CNKI) and Web of Science (WoS) databases, 487 articles that used remote sensing methods to study the intensity of surface urban heat islands (SUHIs) over the past 20 years were obtained using keyword searches. A multidimensional analysis was conducted on these articles from the perspectives of the research methods used, spatiotemporal distribution characteristics of the research area, research development trends, and main challenges. The research found that (1) the growth trend of the various SUHI research methods over the years was similar to the overall trend in the number of publications, which has rapidly increased since 2009. (2) Among the SUHI research methods, temperature dichotomy is the most widely used worldwide; however, defining urban and rural areas is a main challenge. The Gaussian surface and local climate zoning methods have gradually emerged in recent years; however, owing to the limitations of the different urban development levels and scales, these methods require further improvement. (3) There are certain differences in the application of SUHI research methods between China and other countries. [ABSTRACT FROM AUTHOR]

One of the basic factors that should be investigated and monitored in the field of urban heat islands is the exploration and detection of their spatiotemporal changes, which have been well addressed in spatial statistics. The current study aimed to detect the spatiotemporal changes of surface urban heat islands (SUHI) in Tehran metropolis during the daytime/nighttime at monthly and seasonal scales and over the warm and cold periods of the year. The consequences of many elements like as daytime/nighttime land surface temperature (LST) extracted by the MODIS/006/MOD11A1 and the NDVI extracted by MODIS/006/MOD13A2 over a 20-year period (2001–2020) were first investigated. Then, the SUHI index was computed for the study area. The correlations between the heat islands and urban land use (traffic, population density, airport, etc.), air pollutants (CO, NO2, SO2, etc.), and NDVI were investigated in the next stage. Finally, Moran's algorithm was used to measure the spatial autocorrelation, and Gi statistic was used to analyze the cold and warm spots. The results indicated that the LST trend was constant during the daytime/nighttime, and the NDVI also had a slight rising trend. The results of the SUHI maps indicated that the zones with heat islands during the daytime over the seasons' warm and cold times are located in the south, southeast, and west of the city. During the nighttime, the central zones of the city as well as some parts in the east and southeast have had higher heat islands. The results of the correlation between the heat islands and land use, vegetation, and air pollutants indicated a direct correlation between the heat islands and the airport and industrial land use over time, while it was inversely correlated with other land uses. During the nighttime, all land uses had a direct correlation with the heat islands. Regarding the air pollutants, PM2.5 and PM10 were most correlated with the heat islands during both daytime/nighttime while other pollutants have been inversely correlated. The heat islands and the NDVI were also inversely correlated during both daytime/nighttime. The OLS (ordinary least-squares) model results also indicated that the R2 values during the daytime/nighttime were 0.70 and 0.59, respectively, over the cold period of the year, compared to values of 0.69 and 0.68 over the warm period of the year. The results of global Moran's I and G*i statistics also indicated that the heat islands of the Tehran metropolis had a spatial structure distributed in a cluster in which the southern, western, southwestern, and northern parts had warm spots during the daytime and cold spots during the nighttime. Moreover, the northern and northeastern parts had cold spots during the daytime, and the central and eastern parts had warm spots during the nighttime. [ABSTRACT FROM AUTHOR]

A comprehensive comparison of the trends and drivers of global surface and canopy urban heat islands (termed Is and Ic trends, respectively) is critical for better designing urban heat mitigation strategies. However, such a global comparison remains largely absent. Using spatially continuous land surface temperatures and surface air temperatures (2003–2020), here we find that the magnitude of the global mean Is trend (0.19 ± 0.006°C/decade, mean ± SE) for 5,643 cities worldwide is nearly six‐times the corresponding Ic trend (0.03 ± 0.002°C/decade) during the day, while the former (0.06 ± 0.004°C/decade) is double the latter (0.03 ± 0.002°C/decade) at night. Variable importance scores indicate that global daytime Is trend is slightly more controlled by surface property, while background climate plays a more dominant role in regulating global daytime Ic trend. At night, both global Is and Ic trends are mainly controlled by background climate. Plain Language Summary: Surface and canopy urban heat islands (surface and canopy UHIs, termed Is and Ic) are two major UHI types. These two counterparts are both related to urban population heat exposure and have long been a focus of urban climate research. However, the differences in the trends and major determinants of Is and Ic over global cities remain largely unclear. Based on spatially continuous land surface temperature and surface air temperature observations from 2003 to 2020, we find that the global mean Is trends are about 6.3 times and 2 times the Ic trends during the day and at night, respectively. During the day, the global Is trend is more regulated by surface property than by background climate, and vice versa for global Ic trend. At night, both the global Is and Ic trends are mainly regulated by background climate. These findings are important for better understanding global urban climate change and informing heat mitigation strategies. Key Points: The global Is trend is six‐fold and twofold larger than the Ic trend during the day and at night, respectivelyDuring the day, global Is trend is slightly more controlled by surface property, yet background climate plays a dominant role in Ic trendAt night, both global Is and Ic trends are more regulated by background climate [ABSTRACT FROM AUTHOR]

The Surface Urban Heat Island (SUHI) effect refers to the difference in Land Surface Temperature (LST) between an urban area and its surrounding non-urban area. LST can provide detailed information on the variations in different types of land cover. This study, therefore, analyzes the behavior of LST and SUHIs in fourteen cities in the El Bajío Industrial Corridor, Mexico, using Landsat satellite images from 2020, with QGIS software. It utilizes thermal profiles to identify the land uses that intensify LST, which are essentially those that are anthropologically altered. The results show that the increases in LST and SUHI are more pronounced in cities with greater urban conglomeration, as well as those where there are few green areas and a sizeable industrial or mixed area, with few or no bodies of water. In addition, the increase in temperature in the SUHI is due to certain crops such as vegetables, red fruits, and basic grains such as corn, wheat, and sorghum that use fallow as part of agricultural practices, located around urban areas, which minimizes natural areas with arboreal vegetation.

Urban heat islands (UHI) are a well-known phenomenon adversely affecting human health and urban environments. The worldwide COVID-19 lockdown in 2020 provided a unique opportunity to investigate the effects of decreased emission of air pollution and anthropogenic heat flux (AHF) on UHI. Although studies have suggested that reduced AHF during lockdown decreased atmospheric UHI (AUHI) and surface UHI (SUHI), these results contain inherent uncertainties due to unaccounted weather variability and urban-rural dynamics. Our study comprehensively analyzes the impact of the COVID-19 lockdown on AUHI and SUHI in Prague, Czechia. By selecting days with similar weather conditions, we examined changes in mean SUHI using MODIS satellite images and in AUHI based on air temperature from Prague weather stations for the Lockdown period during March–April 2020 versus a Reference period from March–April 2017–2019. Our results show that, in comparison to the Reference period, the Lockdown period was associated with a 15% (0.1 °C) reduction of SUHI in urbanized areas of Prague and a 0.7 °C decline in AUHI in the city center. Additionally, the observed decreases in satellite-based aerosol optical depth and nitrogen dioxide by 12% and 29%, respectively, support our hypothesis that the weakened UHI effects were linked to reduction in anthropogenic activities during the lockdown. Revealing the largest decrease of mean SUHI magnitude around the periphery of Prague, which has predominantly rural land cover, our study emphasizes the need to consider the effects of urban-rural dynamics when attributing changes in SUHI to AHF. Our findings provide additional insights into the role of reduced anthropogenic activities in UHI dynamics during the COVID-19 lockdown and offer policymakers a comprehensive understanding of how the complex interaction between urban and rural microclimate dynamics influences the SUHI phenomenon.

The urban–rural temperature difference is widely used in measuring surface urban heat island intensity (SUHII), where the accurate determination of rural background is crucial. However, traditionally, the entire permeable rural surface has been selected to represent the background temperature, leaving uncertainty about the impact of non-uniform rural surfaces with multiple land covers on the accuracy of SUHII quantification. In this study, we proposed two quantifications of SUHII derived from the primary (SUHII1) and secondary (SUHII2) land types, respectively, which successively occupy over 40–50% of whole rural regions. The spatial integration and temporal variation of SUHII1 and SUHII2 were compared with the result from whole rural regions (SUHII) within 34 urban agglomerations (UAs) in China. The results showed that the SUHII1 and SUHII2 differed slightly with SUHII, and the correlation coefficients of SUHII and SUHII1/SUHII2 are generally above 0.9 in most (32) UAs. Regarding the long-term SUHII between 2003 and 2019, the three methods demonstrated similar seasonal patterns, although SUHII1 (or SUHII2) tended to overestimate or underestimate compared to SUHII. As for the multi-year integration at the regional scale, the day–night cycle and monthly variations of SUHII1 and SUHII were found to be identical for each geographical division separately, indicating that the spatiotemporal pattern revealed by SUHII is minimally affected by the diversity of rural landcover types. The findings confirmed the viability of the urban–rural LST difference method for measuring long-term regional SUHII patterns under non-uniform rural land cover types.

A surface urban heat island (SUHI) is a phenomenon whereby temperatures in urban areas are significantly higher than that of surrounding rural and natural areas due to replacing natural and semi-natural areas with impervious surfaces. The phenomenon is evaluated through the SUHI intensity, which is the difference in temperatures between urban and non-urban areas. In this study, we assessed the spatial and temporal dynamics of SUHI in two urban areas of the French Guiana, namely Ile de Cayenne and Saint-Laurent du Maroni, for the year 2020 using MODIS-based gap-filled LST data. Our results show that the north and southwest of Ile de Cayenne, where there is a high concentration of build-up areas, were experiencing SUHI compared to the rest of the region. Furthermore, the northeast and west of Saint-Laurent du Maroni were also hotspots of the SUHI phenomenon. We further observed that the peak of high SUHI intensity could reach 5 °C for both Ile de Cayenne and Saint-Laurent du Maroni during the dry season when the temperature is high with limited rainfall. This study sets the stage for future SUHI studies in French Guiana and aims to contribute to the knowledge needed by decision-makers to achieve sustainable urbanization.

Significant urbanization resulted in increasing surface urban heat island (SUHI) that caused negative impacts on urban ecological environment, and residential comfort. Accurately monitoring the spatiotemporal variations and understanding controls of SUHI were essential to propose effective mitigation measurements. However, SUHI grades across global cities remained unknown, which cloud greatly support for global mitigations. Additionally, quantitative evaluating factor weights for different SUHI indicators and grades worldwide remained further investigations. Therefore, this paper proposed SUHI grading based on agglomerative hierarchical clustering, and further quantified factor weights for different indicators and grades based on an interoperable machine learning named TabNet. There were three major findings. (1) Global cities were grouped into five grades, including SUCI (surface urban cool island), insignificant, low-value, medium-value, and high-value SUHI grades, indicating significant differences among different grades. SUHI grades showed significant climate-based variations, wherein the arid climate was dominated by the SUCI grade at daytime but the high-value grade at nighttime. (2) Vegetation difference was an important factor for daytime SUHII accounting for 27%. Daytime frequency of SUHI was controlled by vegetation difference, temperature, evaporation and nighttime light, accounting for 78%. The major factors for nighttime frequency were albedo differences and nighttime light, accounting for 45%. (3) Related factors contributed differently to various SUHI grades. The weight of △EVI for daytime SUHII gradually increased with grades, while it for daytime frequency and maximum duration of SUHI decreased with grades. The nighttime SUHII of the low-value grade was greatly affected by the background climate, while that of the medium-value and high-value grades were strongly impacted by anthropogenic heat flux. The diurnal contrast of grades and coupling effects with heat wave were further discussed. This paper aimed to provide information on grades and controls of SUHI for further mitigation proposal.

Abstract A comprehensive comparison of the trends and drivers of global surface and canopy urban heat islands (termed Is and Ic trends, respectively) is critical for better designing urban heat mitigation strategies. However, such a global comparison remains largely absent. Using spatially continuous land surface temperatures and surface air temperatures (2003–2020), here we find that the magnitude of the global mean Is trend (0.19 ± 0.006°C/decade, mean ± SE) for 5,643 cities worldwide is nearly six‐times the corresponding Ic trend (0.03 ± 0.002°C/decade) during the day, while the former (0.06 ± 0.004°C/decade) is double the latter (0.03 ± 0.002°C/decade) at night. Variable importance scores indicate that global daytime Is trend is slightly more controlled by surface property, while background climate plays a more dominant role in regulating global daytime Ic trend. At night, both global Is and Ic trends are mainly controlled by background climate.

The Urban Heat Island (UHI) effect is a phenomenon where urban areas experience higher temperatures than surrounding rural areas. In these issues, enhanced air or surface temperature is one of the major issues that led to the UHI phenomenon. In this article, we come up with a study on the diurnal UHI effect caused in Jaipur city, India, and surrounding areas of Jaipur. In-situ temperature monitoring has been carried out at seven dispersed locations to properly understand and evaluate the effects of surface UHI (SUHI) and atmospheric UHI (AUHI), as well as to assess the thermal profile of diverse land surfaces in Jaipur. With the use of satellite data, the intensity of AUHI and SUHI has been determined between 10.30 a.m. and 10.30 p.m. The observations point out that positive AUHI intensity (AUHII) exists at many locations, irrespective of time periods. During the day period, negative SUHI intensity (SUHII) was noticed at many locations, whereas during the night period, positive SUHII was noticed. According to our observations, AUHI and SUHI have a direct correlation at night but a negative or inverse correlation during the day. That is, AUHI was active both during the day and at night. Various land surfaces play a significant role in contrasting the diurnal UHI effect. This study evaluates the potential of remotely sensed data in monitoring the UHI effect and provides recommendations for urban planners and policymakers to mitigate the UHI effect in the city of Jaipur. [ABSTRACT FROM AUTHOR]

Smart cities must deal with climate change and find solutions to mitigate phenomena such as urban heat islands (UHI). The land surface temperature (LST) extracted from thermal images is a primary source of information to study UHI, characterizing the surface urban heat islands (SUHI). In addition to LST, environmental and socioeconomic variables have been adopted to explain the SUHI phenomenon. Although machine learning algorithms have potential in several areas, their application in the study of the contribution of these variables in the prediction of LST to characterize SUHI is still unknown. Therefore, the work proposes a machine learning approach to fill this gap. The LST was extracted from 15 Landsat 8 images from 2019 to 2021. Data on socioeconomic variables were obtained from the official demographic census, and environmental variables were extracted from Sentinel-2 and Planet images. Six algorithms were tested to assess the ability to estimate the LST based on the above-mentioned variables. The results showed that the Decision Tree algorithm had the best performance (r = 0.96, MAE = 1.49 °C and RMSE = 1.88 °C), followed by Random Forest. In addition, the inclusion of all seasons of the year and socioeconomic variables was shown to be relevant to the results. The main contribution of this work is to verify if the algorithms can optimize the SUHI characterization process, analyzing the influence exerted by the studied variables. In the social sphere, the information produced can help urban planning in the construction of smart cities. [ABSTRACT FROM AUTHOR]

The significant transformation of land use as a consequence of current population growth, together with global warming (atmospheric emissions and extreme weather events), is generating increases in ambient temperatures. This circumstance is affecting people's quality of life, especially those considered more vulnerable or with fewer economic resources. Currently, 30% of the world's population suffers climatic conditions of extreme heat, and forecasts indicate that in the next 20 years, this number will reach 74%. The present study analyzes the effectiveness of the main mitigation strategies for the surface urban heat island (SUHI) effect between the years 2002 and 2022 in the different local climate zones of the city of Granada (Spain). Using Landsat 5 and 8 images, the evolution experienced by the land surface temperature and the surface urban heat island was determined and connected to the following variables: normalized difference vegetation index, vegetal proportion, normalized difference building index, and albedo. Our results indicate that compact and industrial areas have higher temperatures and lower vegetation and albedo in contrast to open areas, which have lower temperatures and higher vegetation and albedo. The mitigation measures analyzed presented similar efficiencies, but a greater minimization of the SUHI was reported when vegetation was increased in open areas as opposed to in closed areas, where the increase in albedo was more effective. Our study will allow the implementation of more efficient measures based on the types of LCZs in cities. [ABSTRACT FROM AUTHOR]

Vitoria-Gasteiz has taken several urban greening actions such as the introduction of a ring of parks that connect the city's surroundings, a sustainable mobility plan, and urban green structure strategies. Previous studies establish a connection to the importance of greening to mitigate the surface urban heat island (SUHI) and evaluate the effectiveness of these measures on urban climate. In this study, land surface temperature (LST), a remote sensing (RS) parameter, recorded by Landsat satellites (5, 7, and 8) was used to evaluate the effect of SUHI in Vitoria-Gasteiz between 1985–2021. The aim was to evaluate whether the urban greening actions influenced the local thermal conditions and, consequently, helped minimize the SUHI. Thirty sampling locations were identified, corresponding to different local climate zones (LCZ), at which LST data were extracted. A total of 218 images were processed and separated into summer and winter. Four of the 30 locations had, since 2003, on-site meteorological stations with regular air temperature (Tair) measurements which were used to validate the LST data. The results showed that Spearman's correlation between Tair and LST was higher than 0.88 in all locations. An amount of 21 points maintained the same LCZ classification throughout the analysed period and nine underwent a LCZ transformation. The highest average temperature was identified in the city centre (urbanized area), and the lowest average was in a forest on the outskirts of the city. SUHI was more intense during the summer. A significant increase in SUHI intensity was identified in areas transformed from natural to urban LCZs. However, SUHI during satellite data acquisition periods has shown a minimal change in areas where sustainable practices have been implemented. RS was valuable for analysing the thermal behaviour of the LCZs, despite the limitation inherent in the satellite's time of passage, in which the SUHI effect is not as evident. [ABSTRACT FROM AUTHOR]

The present study has been carried out to analyze the seasonal variation of the Urban Heat and Cool Island over the nine developing cities of India. The magnitude of urban heat/cool island and vegetation gradient (∆NDVI) were measured from the daytime satellite datasets. Results of this study show that during the pre-monsoon (March to May) season, the maximum magnitude of the Surface Urban Heat Island (SUHI) was experienced over Kolhapur city, whereas, in the winter, the highest intensity of SUHI was noticed over Pune city. Subsequently, outcomes also depict that the changes in ∆NDVI restrain the pre-monsoon means and the seasonal alterations in SUHI magnitude. However, during the winter (November to February) season, it is controlled by the temperature–vegetation conditions of the rural areas. For pre-monsoon and seasonal changes in SUHI, with the aid of ∆NDVI and the surface temperature of the urban area, regression equations were fitted for pre-monsoon and seasonal changes in SUHI, which explains nearly 90% of SUHI variation. Similarly, the variation of SUHI has been modeled for winter, which elucidates up to 84% of SUHI discrepancy. The study reveals that, on a seasonal scale, a decrement of 0.1 in seasonal ∆NDVI leads to an increase in the seasonal intensity of SUHI by 1.74 °C, which is quite a significant augmentation. [ABSTRACT FROM AUTHOR]

The influence of the COronaVIrus Disease 2019 (COVID-19) pandemic lockdown (the period of strict quarantine measures) in the spring of 2020 on the ‘Surface Urban Heat Island’ (SUHI) geographical phenomenon in Moscow has been studied. For this purpose, we used the measurements of the surface temperature TS made by Moderate Resolution Imaging Spectroradiometer (MODIS) radiometer installed on Terra and Aqua satellites. As a result, TS during the 2020 lockdown, both in the city and surrounding rural zone, was found lower than at the same calendar time in the previous 20 years due to the relatively cold spring. The SUHI intensity as the difference between TS inside Moscow and the surrounding rural zone around it during the lockdown was also lower than usual (on average in the previous 20 years), but this decrease is relatively small and nonsignificant. The Normalized Difference Vegetation Index (NDVI) in Moscow and Moscow region during the lockdown was close to its usual values, but the leaf area index (LAI) was significantly lower than its average values in the previous 20 years. Thus, the weakening of the SUHI during the lockdown in 2020 was caused mostly by lower heat loss due to transpiration in the rural zone. This was associated with the slowdown in vegetation development as a result of the cold spring. Besides, an additional possible reason was the reduction of human activity due to the collapse of many anthropogenic heat sources in the city. According to long-term MODIS data, the SUHI intensity in Moscow and the surface temperature in Moscow region, as well as the NDVI and LAI values, do not demonstrate statistically significant long-term trends in the spring season over the past 21 years, despite climate changes. In spring, during faster snow melting in cities, when it still persists in the rural zone, the SUHI intensity can be record high (up to 8 ºC).

Surface urban heat island (SUHI) can considerably influence the urban environment and the quality of life. It is vital to examine how underlying surface properties impact seasonal and diurnal SUHIs. However, the influence of three-dimensional (3D) urban morphological parameters (UMPs) on SUHIs has not been thoroughly studied under varying climatic settings. To fill this knowledge gap, the present study investigated seasonal and diurnal changes in SUHI intensities (ΔT) in 208 cities in China from 2014 to 2016 using moderate-resolution imaging spectroradiometer (MODIS) land surface temperature products. In addition, the influence of potential factors in urban surface energy balance, including two-dimensional (2D) and 3D UMPs, socio-economic indices, urban greening, and surface albedo, on seasonal and diurnal ΔT were assessed under different climatic settings and with different city sizes using the method of Geographically Weighted Regression (GWR). Results show that negative summer daytime ΔT was observed in some cities under dry climates. Generally, in summer, the ΔT during daytime was higher than at nighttime. The 3D UMPs (i.e. building height and volume) yielded more decisive influences on ΔT than 2D UMP (i.e. building coverage). This is particularly true for the summer diurnal cycle and under dry climatic settings. Building height was found to be negatively correlated with surface temperatures, while building volume was positively correlated. Additionally, the 3D UMPs yielded more influences on winter ΔT than summer ΔT. The capability of vegetation to regulate ΔT was more potent in dry climates than in wet climates and in small cities than in large cities. Varying climates and city sizes can modify the significance of the 2D and 3D UMPs on the urban surface energy balance, suggesting that urban thermal mitigation should consider climate background and population size.

Surface urban heat islands (SUHIs) have received worldwide attention owing to their significant adverse impacts on socio-economic and environmental conditions. However, how the SUHIs vary across cities of different sizes and whether there is a threshold for city size that affect SUHIs were not explicit due to the limited selection of large cities in previous studies. Our study attempts to support comprehensive decision-making for national sustainable spatial management of SUHI in urbanized areas of different sizes. We used impervious surface area data to define the urbanized boundary for obtaining the SUHI in China. Then we analyzed the spatio-temporal patterns of SUHIs under China’s rapid urbanization. We furtherly constructed spatial autoregressive and structural equation models to verify the drivers’ direct and indirect contributions to SUHI. The SUHI effects existed extensively in most urbanized areas, not only in large or mega urbanized areas (average value: 2.08 ± 0.68°C) but also in petty urbanized areas (average value: 0.64 ± 0.19°C). We also found that the size of urbanized areas contributed the most to the increase in SUHI intensity (SUHII). Natural drivers are mediators indirectly contributing to variations in SUHII, influenced by anthropogenic drivers. In addition, we also found that the size of urbanized areas contributed the most to the increase in SUHI intensity (SUHII). The size of the urbanized areas had a positive non-linear relationship with SUHIIs. When the size was larger than 400 km2, the growth of SUHIIs maintained an equilibrium state. This study highlights the importance of impervious surface expansion for increasing SUHII. To confront SUHIs, it is necessary to perform proper urban planning.

Local landscape patterns in cities can substantially alter surrounding land surface temperature (LST) and affect the surface urban heat island effect. Previous studies have confirmed that some urban functional areas produce pronounced local heat/cold island effects in cities; however, the mitigating of this effect from the perspective of surrounding landscapes has not been investigated in depth. Additionally, different types of industrial plants have not been individually studied across multiple cities as major contributors to the local heat island effect. Therefore, based on 17 mega plants in various cities in the Northern Hemisphere, this study explores the impact of surrounding landscapes on the surface industrial heat island (SIHI) effect, proposes cooling metrics from a new landscape patch perspective, and quantifies the impact of different patch configurations on the SIHI using Extreme Gradient Boosting regression models and Shapley Additive exPlanations. The primary results are as follows: 1) Regarding to the footprint of the SIHI effect, the coverage of impervious surfaces dominates the ambient LST pattern. Industrial plant types and latitudes are moderately influencing factors. 2) In terms of the cooling effect, landscape patch size and width relative to industrial plant size has a pronounced impact on the LST cooling speed. Besides, various land cover types have distinct relative cooling thresholds for patch area and distance from the plants. The influence of patch attributes on LST cooling speed is determined by the distinct land cover type. 3) What is prominent is that the patches of water bodies with larger relative footprints often exhibit higher cooling speeds. This study aims to provide guidelines for urban planners in assessing the local thermal environment and mitigating further urban warming.