Urban Heat Research Articles

Urban Agriculture: Exploring Its Potential, Challenges, and Socio-Economic Impacts

Urban agriculture, the practice of growing and cultivating food within urban and peri-urban areas, has garnered increasing attention in recent years due to its potential to address various challenges related to food security, environmental sustainability, and socio-economic development. This review article provides a comprehensive exploration of urban agriculture, examining its multifaceted potential, the challenges it faces, and its socio-economic impacts in greater detail. The article delves into the diverse forms of urban agriculture, ranging from rooftop gardens and community allotments to vertical farming and aquaponics, highlighting their contributions to local food production, green spaces, and community cohesion. Additionally, the review discusses the challenges and constraints associated with urban agriculture in more depth, including issues related to land availability, soil contamination, water management, and regulatory hurdles. Furthermore, it examines the socio-economic impacts of urban agriculture with a nuanced perspective, exploring how it can lead to improved access to fresh produce, job creation, poverty alleviation, and social empowerment at both individual and community levels. By shedding light on the complexities of urban agriculture and providing a more comprehensive analysis of its potential and challenges, this review aims to inform policymakers, urban planners, researchers, and practitioners about the transformative potential of urban agriculture and the need for supportive policies and interventions to foster its sustainable growth and maximize its benefits for urban communities, the review dives deeper into the various forms of urban agriculture, providing detailed insights into their unique characteristics, benefits, and challenges. Rooftop gardens, for example, are explored not only for their contribution to local food production but also for their role in mitigating urban heat island effects, improving air quality, and enhancing building energy efficiency. Community allotments are examined in the context of social cohesion, community engagement, and opportunities for education and skill-building. Vertical farming and aquaponics are discussed in terms of their innovative technologies, resource efficiency, and potential scalability to meet the growing demand for fresh produce in urban areas, the review delves into the challenges and constraints associated with urban agriculture, offering nuanced analyses of their underlying causes and potential solutions. Land availability issues are examined in relation to urban sprawl, land-use planning policies, and competing demands for space in densely populated cities. Soil contamination concerns are explored in light of industrial legacies, brownfield redevelopment, and strategies for soil remediation and regeneration. Water management challenges are discussed in the context of water scarcity, pollution, and the need for sustainable irrigation practices in urban agriculture. Regulatory hurdles are analyzed in terms of their impact on the viability and scalability of urban agriculture initiatives, highlighting the importance of supportive policy frameworks, zoning regulations, and land-use planning strategies. the review provides a comprehensive examination of the socio-economic impacts of urban agriculture, drawing on empirical evidence from case studies and research findings. It explores how urban agriculture can contribute to improved food access and nutrition outcomes, particularly for vulnerable populations living in food deserts and low-income neighborhoods.

The effect of urban form parameters on annual and diurnal cycles of land surface temperature with 30-meter hourly resolution

Understanding dynamic changes in urban land surface temperature (U-LST) is vital for improving urban thermal environment. Previous studies noted a close association between two/three-dimensional (2D/3D) urban form parameters (UFPs) and U-LST, but divergent conclusions emerged due to study scale and data spatiotemporal resolution. This study investigated the effect of 2D/3D UFPs on multi-temporal (hourly, diurnal, and annual) U-LSTs with 30-m spatial resolution, which is the first exploration of such dynamics at this fine scale. In addition, the influence of local climate zones (LCZs) was considered. Results showed that 2D/3D vegetation UFPs have the greatest summer cooling effect at 21:00, while 3D building UFP sky view factor (SVF) impacts minimum nighttime temperature more (5:00). The independently total explained variances (R2) of 2D UFPs (0.2–0.7) on seasonal average U-LSTs were higher than that of 3D UFPs (0.1–0.4) in most LCZs. LCZ 3 and LCZ 2 had the highest seasonal and daytime U-LST, respectively. Interestingly, SVF replaced normalized difference vegetation index as the dominant factor in LCZs 4–6 and LCZs A-C after summer. Findings of this study are useful for guiding urban planning, formulating urban heat island mitigation policies, and promoting sustainable development goals (SDGs 11, 13, 15) of cities and communities.

Delineating the climate change impacts on urban environment along with heat stress in the Indian tropical city

India experiences significant levels of temperature variance as a result of urbanization, deforestation, and industrial growth, all of which contribute to a progressive increase in heat island effects. Significant landform change has occurred in Bhubaneswar City, Odisha, in recent decades as a result of major plant loss and temperature unpredictability. Thus, during the summer and winter months, an examination of the long-term heat island impacts is essential for Bhubaneswar. The evolution of landforms and heat island impacts in Bhubaneswar are evaluated using Landsat images from 1991 to 2021 datasets and correlation analysis of heat islands and geospatial variables. Between 1991 and 2021 with a 10-year gap, the landforms studied showed an increase in built-up land (38.009 km2), a decrease in vegetation (13.875 km2), bare ground (16.306 km2), and water bodies (7.828 km2). The annual temperature variance in Bhubaneswar was 0.124 °C (summer), and 0.293 °C (winter). Surface temperatures varied by about 3.71 °C (summer) and 8.80 °C (winter). During the study years, heat islands increased from 2.89 to 4.27 (summer) and 2.30 to 3.87 (winter). In Bhubaneswar, there was an increase in ecological variation of 0.092 (summer), and 0.071 (winter). There is a statistically significant negative correlation between the R2 values and the temperature and the built-up indices. Using this dataset, administrative and development authorities can choose which measures to take to counteract the effects of variations in thermal variation. Finding out how man-made land use structures, industrial zones, and other areas contribute to the built-up area a direct consequence of heat islands was the aim of this study.

Development of predictive indices for evaluating the UHI adaptation potential of green roof- and wall-based scenarios in the Mediterranean climate

Urban heat islands can jeopardize urban inhabitants, but the installation of green roofs (GRs) and walls (GWs) can contribute to mitigating urban overheating. The present study provides novel indices to easily predict the spatial median variation in air temperature at pedestrian heights related to the application of GR- and GW-based scenarios during the hottest hours of a typical summer day by varying the building height (BH), coverage percentage, and leaf area index. The indices are meant to be applied to built areas with 0.3–0.4 urban density in the Mediterranean climate and are derived from regression models fed with the outputs of 281 simulations of three urban areas developed and run in ENVI-met software. The developed models are all highly significant. The GR model shows that mitigation is influenced by all three parameters, and it can estimate mitigation with a root mean square error of 0.05 °C. Compared with the other parameters, the GW models revealed that the BH did not influence the decrease in air temperature. The green façade and living wall (LW) indices predict mitigation with errors of 0.04 °C and 0.05 °C, respectively. However, for the LW model, further parameters should be considered to improve its reliability.

Site suitability analysis of urban green parks in Ranchi city using GIS–AHP based multi-criteria decision analysis

Purpose The presence of green spaces plays a vital role in promoting urban sustainability. Urban green parks (UGPs) help create sustainable cities while providing fundamental ecological functions. However, rapid urbanization has destroyed crucial green areas in Ranchi City, endangering inhabitants’ health. This study aims to locate current UGPs and predict future UGP sites in Ranchi City, Jharkhand. Design/methodology/approach It uses geographic information system (GIS) and analytical hierarchical process (AHP) to evaluate potential UGP sites. It involves the active participation of urban communities to ensure that the UGPs are designed to meet dweller’s needs. The site suitability assessment is based on several parameters, including the normalized difference vegetation index (NDVI), land use and land cover (LULC), population distribution, PM 2.5 levels and the Urban Heat Island (UHI) effect. The integration of these factors enables an evaluation of potential UGP’s sites. Findings The findings of this research reveal that 54.39% of the evaluated areas are unsuitable, 15.55% are less suitable, 12.76% are moderately suitable, 11.52% are highly suitable and 5.78% are very highly suitable for UGPs site selection. These results emphasize that the middle and outer regions of Ranchi City are the most favorable locations for establishing UGPs. The NDVI is the most important element in UGP site appropriateness, followed by LULC, population distribution, PM 2.5 levels and the UHI effect. Originality/value This study improves the process of integrating AHP and GIS, and UGPs site selection maps help urban planners and decision-makers make better choices for Ranchi City’s sustainability and greenness.

Spatiotemporal Patterns of Land Surface Temperature, Urban Heat Island, and Potential Heat Stress Risk Areas Assessment for Tropical Inland City and Coastal City in Thailand

Global warming and rapid urban growth, compounded by the urban heat island (UHI) effect, are posing substantial challenges for urban populations and ecological well-being due to increased heat stress risks. This study aimed to analyze the spatiotemporal patterns of land surface temperature (LST), UHI, and potential heat stress risk areas (PHSRA) in two distinct cities, an inland city, Nakhon Ratchasima, and a coastal city, Si Racha, during the summer. Geoinformatics techniques and Crichton’s Risk Triangle method were employed, using updated demographic data and Sentinel-3 data from 2017 to 2022. The findings indicate that the inland city daytime average LST values were higher than in the coastal city, while the opposite was true at nighttime. Additionally, high daytime UHI patterns in the inland city were observed on the outskirts, and nighttime UHI concentrated in the urban center. The coastal city exhibited a strong daytime and nighttime UHI compared to the inland city, with daytime UHI peaking in the inner area and nighttime UHI near the coastline. The PHSRA was categorized into five risk levels (very high: > 0.8, high: 0.8–0.6, moderate: 0.6–0.4, low: 0.4–0.2 and very low: ≤ 0.2). During the daytime the highest risk areas, the high and very high levels of PHSRA in the inland city (23.84%) were greater than those in the coastal city (16.46%). Conversely, at nighttime, the coastal city (16.46%) exhibited higher levels than the inland city (5.26%). In nighttime conditions, all levels of PHSRA occurred more significantly than during the daytime in these cities. Understanding the spatiotemporal variations in PHSRA is vital for pinpointing places at risk of heat stress accidents in summer. This data is a goldmine for city planners and healthcare authorities, helping them plan interventions and effective measures for the future.

Influence of PV panels on convective heat flux in different roofs in the Mediterranean: Effects on the urban heat island

As the European Union transitions towards cleaner energy production, significant emphasis is placed on the use of photovoltaic (PV) panels on roofs. Although PV panels offer the advantage of renewable energy generation, they alter the energy balance of roofs and increase heat emissions into the atmosphere. This raises concerns about their potential to intensify the urban heat island (UHI). Hence, this study evaluates the effects of eight different roof models, including four roof types, analyzed both with and without PV panels, on the UHI in the Mediterranean, a region experiencing increasingly urban overheating. Convective heat fluxes are used as a clear indicator. The models were conducted using EnergyPlus simulations throughout the year. Roof types include a typical Mediterranean roof, a cool roof, a soil roof and an extensive green roof. The main results indicate an intensification of the UHI for all roof types when PV panels are installed, both in summer and winter. However, among the PV-panelled roof types, the cool roof is effective in reducing the UHI impact compared to the other roofs during both seasons. Contrary, in winter, the extensive green roof with PV panels increases the impact on the UHI among all roof types the strongest.

Effect of the reference rural landscape on annual variations in surface urban heat island intensity

The urban heat island effect poses a significant climate risk associated with urbanization. Existing research primarily focuses on the spatiotemporal variations in land surface temperature (LST) within urban areas and their effects on surface urban heat island intensity (SUHII). However, investigations into how annual dynamic changes in rural landscapes impact reference LST and SUHII remain limited. This study examines 28 representative cities worldwide that experience seasonal snow cover and are predominantly surrounded by croplands or forests. The objective is to explore the patterns of continuous annual variations in SUHII and to elucidate the underlying physical mechanisms through which changes in rural landscapes affect these variations. The results reveal that SUHII consistently exhibits a double-peak pattern, with peaks occurring during the transition from winter to spring and in mid-summer. The first, moderate-intensity peak is attributed to differential snowmelt between urban and rural areas, while the more pronounced second peak is associated with the enhanced cooling effect of vegetation evapotranspiration (ET) during the growing season. Furthermore, a strong correlation between leaf area index, ET, and SUHII underscores the significance of rural vegetation in shaping SUHII variations. These findings highlight the necessity for detailed descriptions of rural reference landscapes to improve the interpretability and comparability of SUHII results. Additionally, the study suggests the need for differentiated SUHII evaluation thresholds tailored to cities with diverse rural landscapes.

Synergistic interactions between urban heat islands and heat waves in the Greater Kuala Lumpur and surrounding areas

AbstractThe synergistic interactions between urban heat islands (UHI) and heat waves (HW) continue to be debated. Despite the expectations of UHI intensification during HW, several studies have demonstrated variations. Notably, there is a dearth of investigations concerning the UHI–HW synergy in tropical climate cities amidst the escalating trend of more frequent and severe HW in Southeast Asia. To address this gap, our study aimed to investigate the synergies between the UHI and HW phenomena in Greater Kuala Lumpur (GKL) and its surrounding areas. We employed the advanced research version 4.2.2 of the Weather Research and Forecasting (WRF) model, coupled with a single‐layer Urban Canopy Model (UCM), to examine the impact of UHI during two heat wave events in 2016 (Case 1) and 2020 (Case 2), against the periods immediately before and after these events, which we refer to as Pre‐Post HW (PPHW), in GKL. An elevated UHI intensity (UHII) was evident during the HW in both observations and simulations, with a noticeable distinction particularly observed in Case 1. During HW, observed data indicates average UHII peaks at 1.8°C (0100 LST (UTC+8)) and 1.7°C (1500 LST) in Cases 1 and 2, respectively. In contrast, those for PPHW days for Cases 1 and 2 are 1.5°C (0000 LST) and 1.2°C (0100 LST), respectively. The maximum observed heat loads are likely to occur at noon, reaching 2.3°C at 1600 LST in Case 1 and 3.7°C at 1500 LST in Case 2. LST stands for local standard time. Heat flux component analysis from the surface energy balance model confirmed the UHI–HW synergy. A notable difference in the Bowen Ratio between urban and rural areas highlights the effect of urbanisation on heat fluxes, potentially exacerbating urban discomfort during HW. Consistent across all measurement methods, the evidence indicates a clear and positive synergy between the UHI and HW in the GKL. This study can potentially deliver valuable insights, especially in urban planning, where the implications of weather events are substantial.

ASSESSING URBAN HEAT ISLAND IMPACT AND IDENTIFYING VULNERABILITY ZONES THROUGH GEOSPATIAL AND GEO-STATISTICAL TECHNIQUES

An urban heat island emerged due to micro urban temperature variations are also referred to as urban heat islands or urban hot spots. The high-rise buildings along the roads form "Urban Canyons" that inhibit reflected radiation from the built-up surface. Urban heat island develops over the cities due to man-made activity and the landscape. An understanding of the urban heat island and its formation is not only helpful in understanding urban thermal characteristics but also helps in understanding human comfort. A geospatial technique has the ability to acquire updated and cost-effective data over large regions. For urban climatology studies, remote sensing and geographic information systems are an important source of information and an effective methodology. Since 1971, the city of Krishnanagar and its vicinity have been witnessing rapid urban growth. Due to its dense population, urban climate and rapid urban expansion, they cause environmental degradation. Appraisal and Impact of urbanization on micro-climate in the Krishnanagar city complex based on satellite derived parameters. For the years 1995, 2007 and 2018, several satellite image analysis approaches such as NDVI, NDWI and NDVI were computed. Significant differences in land surface temperature were observed between 1995 and 2007, as compared to 2007 and 2018.

Combining visual intelligence and social-physical urban features facilitates fine-scale seasonality characterization of urban thermal environments

Traditional methods for assessing urban thermal environments (UTEs) often rely on GIS and remote sensing data, suffering from data limitations in coverage, accuracy, and availability. To address these challenges, we established a novel framework developing visual intelligence simulated indices (VISIs) for improved UTE characterization. This framework combines GIS spatial data and pre-trained large-scale and local-trained GeoAI models to generate visual intelligence, extracting GIS-like visual elements from remote sensing imagery. These GIS-like VISIs comprehensively represent land surface temperature (LST) and urban fabric for cost-effective UTE analysis. We applied our framework to Zhengzhou, China, a city with diverse landscapes. Comparative experiments among Random Forests (RF), Neural Networks (NN), and Convolutional Neural Networks (CNN) demonstrated that RF models exhibited superior performance in LST representation (0.7 test R2 and 0.9 training R2). Interestingly, CNNs performed better than RFs and NNs when less cloud cover (<6 %) was present, suggesting that data diversity is essential for CNN to learn generalizable features for LST representation. For urban fabric representation, feature importance analysis indicated that VISIs (37 %) derived from large-scale GeoAI models outperform spectral bands (20 %). Furthermore, both Pearson correlation and Shapley values showed that VISIs (e.g., building features) better characterize urban heat islands than traditional remote sensing ecological indices (RSEIs) such as NDBI, underscoring the cost-effectiveness of our visual intelligence approach. In summary, our visual intelligence method effectively consolidates UTE seasonality characterization into a predictable scenario even given the unpredictable availability of remote sensing and GIS data, revealing novel perspectives on sensing unseen details for UTEs.

Impact of urban spatial dynamics and blue-green infrastructure on urban heat islands: A case study of Guangzhou using Local Climate Zones and predictive modeling

Amidst mounting concerns regarding health risks associated with excessive carbon emissions, attention has turned to the heat mitigation ability of blue-green infrastructure (BGI). BGI offers heat mitigation through transpiration cooling, effectively countering the urban heat island (UHI) effect while facilitating energy conservation and carbon reduction. This study adopted the InVEST model to assess the spatial pattern of UHI and heat mitigation service based on the Local Climate Zone (LCZ) classification, along with quantifying the energy savings in Guangzhou for 2019. The results revealed that the UHI effect exhibited greater intensity in the southwest built-up area. Compact Lowrise (LCZ-3) and Heavy Industry (LCZ-10) areas demonstrated higher UHI intensity, while Water (LCZ-G) and Dense Trees (LCZ-A) areas showcased the highest heat mitigation index (HMI) and optimal cooling capacities. Three urban development scenarios were devised to simulate the UHI distribution, heat mitigation capacity, and energy savings by 2025, using the CA-Markov model. In comparison to the Baseline scenario, the Environmental Protection scenario showed a modest 3.67 % decrease in the built-up area, but a noteworthy increase in HMI and energy saving, by 21.2 % and 6.6 % respectively. This scenario can serve as a reference pathway for urban energy conservation and sustainable development.

Dynamics of urban latent heat in response to climate change and urbanization: What would be a global threshold?

Urban latent heat is influenced by climate change-induced greening, CO2 fertilization, and the urban heat island effect (Effect 1), which offsets reductions in latent heat caused by urbanization’s expansion of impermeable surfaces (Effect 2). The interaction between these effects remains unclear. The Hong Kong-Shenzhen region, characterized by a stable Leaf Area Index, intense anthropogenic activities, and abundant water resources, amplifies Effect 1. This region serves as an experimental site to explore global thresholds where these impacts reach equilibrium. Using Landsat and ERA5-Land data, monthly latent heat fluxes were computed from 1990 to 2019 using a stomatal process-based urban energy balance model. Shenzhen experienced a decline in latent heat (slope: −0.02), whereas Hong Kong showed an increase (slope: 0.08), confirming the presence of such thresholds. The region was segmented into 129 units, establishing a global benchmark indicating that Effect 1 significantly influences regional latent heat when the vegetation-to-impermeable surface ratio exceeds 2.71. Climate change impacts on Shenzhen’s latent heat increased by 94 %, compared to Hong Kong’s 55 %. Hong Kong demonstrates greater resilience to climate change than Shenzhen. This study underscores the importance of assessing urban dynamics for developing climate mitigation and sustainability strategies.

Evaluation of the Urban Canopy Scheme TERRA-URB in the ICON Model at Hectometric Scale over the Naples Metropolitan Area

The present work is focused on the validation of the urban canopy scheme TERRA-URB, implemented in ICON weather forecast model. TERRA-URB is used to capture the behavior of urbanized areas as sources of heat fluxes, mainly due to anthropogenic activities that can influence temperature, humidity, and other atmospheric variables of the surrounding areas. Heat fluxes occur especially during the nighttime in large urbanized areas, characterized by poor vegetation, and are responsible for the formation of Urban Heat and Dry Island, i.e., higher temperatures and lower humidity compared to rural areas. They can be exacerbated under severe conditions, with dangerous consequences for people living in these urban areas. For these reasons, the need of accurately forecasting these phenomena is particularly felt. The present work represents one of the first attempts of using a very high resolution (about 600 m) in a Numerical Weather Prediction model. Performances of this advanced version of ICON have been investigated over a domain located in southern Italy, including the urban metropolitan area of Naples, considering a week characterized by extremely high temperatures. Results highlight that the activation of TERRA-URB scheme entails a better representation of temperature, relative humidity, and wind speed in urban areas, especially during nighttime, also allowing a proper reproduction of Urban Heat and Dry Island effects. Over rural areas, instead, no significant differences are found in model results when the urban canopy scheme is used.

Seasonal environmental cooling benefits of urban green and blue spaces in arid regions

Green and blue spaces are vital for mitigating urban heat island impacts but are poorly studied in arid regions. In this study, we quantify monthly and seasonal cooling for five contrasting types of green and blue infrastructure (GBI): rivers, lakes, "captured" agricultural areas, urban parks, and golf courses in the Cairo and Giza provinces of Egypt. Using Landsat-8 images of Land Surface Temperature (LST) we assessed change in LST along bisecting transects and in circle plots for three replicates of each GBI type, in each of four seasons. Cooling was greatest in summer for all GBI types. Cooling differentials of LST were greater for water bodies than for green spaces. Ordered by increasing cooling potential (May LST cooling) they were: Agricultural areas (3.3 °C), Golf courses (4.3 °C), Parks (4.4 °C), Lakes (8.2 °C) and Rivers (12.2 °C). The cooling effects extending into adjacent buffer areas were greatest for blue spaces like rivers and lakes. This paper provides the first data for cooling by less-studied GBI types in arid regions, such as golf courses and urban agriculture. It provides information to support city planners to embrace green and blue spaces within metropolitan areas and to protect them from urban sprawl.

Evaluating Urban Heat Island Mitigation Strategies in Rajshahi, Using ENVI-Met: A Remote Sensing Approach

The Urban Heat Island (UHI) effect is a critical environmental challenge in the 21st century, intensified by rapid urbanization and industrialization. This study focuses on Rajshahi, a rapidly urbanizing city in Bangladesh, where the UHI effect has already begun to manifest significantly. Utilizing ENVI-met software, a comprehensive analysis was conducted to evaluate the effectiveness of urban vegetation strategies, such as green roofs and street planting, in mitigating local temperatures and improving outdoor thermal comfort in Rajshahi's Central Business District. The findings reveal that these mitigation strategies can reduce air temperatures by up to 10 Kelvin, providing substantial cooling benefits. This research highlights the importance of integrating green infrastructure into urban planning to combat the UHI effect, enhance sustainability, and improve the overall livability of urban environments. The study offers valuable insights and practical recommendations for urban planners and policymakers, aiming to foster resilient and sustainable urban development in rapidly growing cities like Rajshahi.

Mitigating Urban Heat Islands in the Global South: Data-driven Approach for Effective Cooling Strategies

Mitigating Urban Heat Islands in the Global South: Data-driven Approach for Effective Cooling Strategies

Towards more equitable cooling services of urban parks: Linking cooling effect, accessibility and attractiveness

Global warming and rapid urbanization have caused frequent occurrences of heat waves and urban heat island effect, presenting a significant threat to health of urban residents. Researches have indicated that cooling services provided by parks are essential in alleviating impact of heat wave events and urban heat island effect. However, previous researches on park cooling services center around cooling effect, with a lack of exploration regarding the fairness of such services. To fill this gap, this study quantifies the level of equity in cooling services in 18 parks in the core area of Hangzhou. Through this study, we hope to clarify the current situation of fairness of cooling services in urban parks and provide fairer park cooling services through scientific and reasonable park layouts. This will alleviate the threat of rapid urbanization and climate change to urban residents, and make the urban environment develop in a more livable direction. We assessed the cooling effect using remote sensing and the ArcGIS platform to screen parks with cooling effect and to quantify their cooling service efficiency. We utilized spatial network analysis to quantify the accessibility and origin-destination matrix data to quantify the attractiveness to reflect the level of park cooling services. The results reveal that 18 parks exhibit a noticeable cooling effect, albeit with variations observed among parks. The percentage of urban parks with low accessibility is 77.80%, indicating that the distribution of accessible space presents an uneven status quo. In addition, 72.20% of parks have low attractiveness of cooling services, indicating that some parks have insufficient attractiveness of cooling services. Based on each indicator of cooling services, we categorize urban parks into four types based on supply and demand, and propose adaptive planning measures and intervention strategies to provide a reference for equitable distribution of cooling services in urban parks.

Sampling Error of Mean and Trend of Nighttime Air Temperature at Weather Stations Over China Using Satellite Data as Proxy

AbstractMeteorological observations of surface air temperature have provided fundamental data for climate change detection and attribution. However, the weather stations are unevenly distributed, and are still very sparse in remote regions. The possible sampling error is well known, but not well quantified because we are lack of the adequate and regularly distributed measurements. The high resolution of satellite land surface temperature retrieval during night time provide a nice proxy for near surface temperature as both temperatures controlled by surface longwave radiative cooling and the nocturnal temperature inversion depress land‐atmosphere turbulent exchange. The sampling error of mean value and trend were assessed by comparing station point measurements (pixel of ∼0.01°) with grid (1°) mean and national mean from 2001 to 2021. This method permits us to make the first assessment of under‐sampling error and spatial representative error on both national mean and trend of air temperature during nighttime collected at ∼2,400 weather stations over China. The sampling error in national mean temperature is more than 3°C. The under‐sampling error due to lack of observation explains two thirds and the spatial representative error due to the difference between station and grid/regional mean elevation contribute the other one third. The sampling error in trend account for one third of the national mean trend. The urban heat island effect associated with urbanization around the weather stations (spatial representative error) can explain four fifths of the sampling error in trend, which is consistent with existing studies based on air temperature collected at paired weather station.

Radiation cooling textiles countering urban heat islands

Radiation cooling textiles countering urban heat islands