Variations in Surface Urban Heat Island and Urban Cool Island Intensity:A Review Across Major Climate Zones

The climate has an impact on the urban thermal environment,and the magnitude of the surface urban heat island(SUHI)and urban cool island(UCI)vary across the world’s climatic zones.This literature review investigated:1)the variations in the SUHI and UCI intensity under different climatic backgrounds,and 2)the effect of vegetation types,landscape composition,urban configuration,and water bodies on the SUHI.The SUHI had a higher intensity in tropical(Af(tropical rainy climate,Köppen climate classification),Am(tropical monsoon climate),subtropical(Cfa,subtropical humid climate),and humid continental(Dwa,semi-humid and semi-arid monsoon climate)climate zones.The magnitude of the UCI was low compared to the SUHI across the climate zones.The cool and dry Mediterranean(Cfb,temperate marine climate;Csb,temperate mediterranean climate;Cfa)and tropical climate(Af)areas had a higher cooling intensity.For cities with a desert climate(BWh,tropical desert climate),a reverse pattern was found.The difference in the SUHI in the night-time was greater than in the daytime for most cities across the climate zones.The extent of green space cooling was related to city size,the adjacent impervious surface,and the local climate.Additionally,the composition of urban landscape elements was more significant than their configuration for sustaining the urban thermal environment.Finally,we identified future research gaps for possible solutions in the context of sustainable urbanization in different climate zones.

Exploring diurnal and seasonal variabilities in surface urban heat island intensity in the Guangdong-Hong Kong-Macao Greater Bay Area

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.

Impacts of the Urban Spatial Landscape in Beijing on Surface and Canopy Urban Heat Islands

How does the urban spatial landscape(USL)pattern affect the land surface urban heat islands(SUHIs)and canopy urban heat islands(CUHIs)?Based on satellite and meteorological observations,this case study compares the impacts of the USL pattern on SUHI and CUHI in the central urban area(CUA)of Beijing using the satellite land-surface-temperature product and hourly temperature data from automatic meteorological stations from 2009 to 2018.Eleven USL metrics—building height(BH),building density(BD),standard deviation of building height(BSD),floor area ratio(FAR),frontal area index(FAI),roughness length(RL),sky view factor(SVF),urban fractal dimension(FD),vegetation coverage(VC),impervious coverage(IC),and albedo(AB)—with a 500-m spatial resolution in the CUA are extracted for comparative analysis.The results show that SUHI is higher than CUHI at night,and SUHI is only consistent with CUHI at spatial-temporal scales at night,particularly in winter.Spatially,all 11 metrics are strongly correlated with both the SUHI and CUHI at night,with stronger correlation between most metrics and SUHI.VC,AB,and SVF have the greatest impact on both the SUHI and CUHI.High SUHI and CUHI values tend to appear in areas with BD≥0.26,VC≤0.09,AB≤0.09,and SVF≤0.67.In summer,most metrics have a greater impact on the SUHI than CUHI;the opposite is observed in winter.SUHI variation is affected primarily by VC in summer and by VC and AB in winter,which is different for the CUHI variation.The collective contribution of all 11metrics to SUHI spatial variation in summer(61.8%)is higher than that to CUHI;however,the opposite holds in winter and for the entire year,where the cumulative contribution of the factors accounts for 66.6%and 49.6%,respectively,of the SUHI variation.

An Analysis of Spatio-Temporal Trends of Land Surface Temperature in the Dhaka Metropolitan Area by Applying Landsat Images

Land surface temperature (LST) is a basic determinant of the global thermal behavior of the Earth surface. LST is a vital consideration for the appraisal of gradual thermal change for urban areas to examine the strength of the thermal intensity of the surface of urban heat island (SUHI) and to see how hot the surface of the Earth would be in a particular location. In this respect, the most developed urban city like Dhaka Metropolitan Area (DMA), Bangladesh is considered for estimation of LST, and Normalized Difference Vegetation Index (NDVI) changes trend in more developed and growing developing areas. The focus of this study is to find out the critical hotspot zones for further instantaneous analysis between these two types of areas. The trends of long-term spatial and temporal LST and NDVI are estimated applying Landsat images-Landsat 5-TM and Landsat OLI_TIRS-8 for the period of 1988 to 2018 for DMA and for developed and growing developing areas during the summer season like for the month of March. The supervised classification was used to estimate the land cover categories and to generate the LST trends maps of the different percentiles of LSTs over time using the emissivity and effective at sensor brightness temperature. The study found the change in land cover patterns by different LST groups based on 50th, 75th, and 90th percentile where the maximum LST for the whole DMA went up by 2.48°C, 1.01°C, and 3.76°C for the months of March, April, and May, respectively for the period of 1988 to 2018. The highest difference in LST was found for the most recently developed area. The moderate change of LST increased in the built-up areas where LST was found more sensitive to climate change than the growing developed areas. The vegetation coverage area decreased by 6.74% in the growing, developing areas compared to the developed areas from 1988 to 2018. The findings of the study might be helpful for urban planners and researchers to take up appropriate measures to mitigate the thermal effect on urban environment.

地表城市热岛时空演变——对夏季热浪时期的关注

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℃ in most years. The highest SUHI occurred in 2019, reaching 3.99℃ and covering the largest area(10,887 km^(2)). 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.

A detailed comparison of MYD11 and MYD21 land surface temperature products in China's Mainland

Land surface temperature(LST)is a key parameter in land surface system.The National Aeronautics and Space Administration(NASA)recently released new Moderate Resolution Imaging Spectroradiometer(MODIS)LST products(MOD21 and MYD21).Here,we conducted a detailed comparison between the MYD11 and MYD21 LST data in China's Mainland.The LSTs of MYD21 were approximately 1℃ higher than those of MYD11 averaged for China's Mainland,as MYD21 corrected the cold bias of MYD11.The proportions of the valid value of MYD21 were generally lower than those of MYD11 because the cloud removal method of MYD21 was stricter than that of MYD11.Furthermore,the outliers were less significant in MYD11 than in MYD21 because the outliers in MYD11 were removed using temporal constraints on LST.The outliers in MYD21A2 resulted in a difference of greater than 3℃ in average seasonal surface urban heat island intensity(SUHII)between MYD11A2 and MYD21A2.Finally,using MYD11 may underestimate the slope of long-term trends of SUHII.MYD21 LST data may have some uncertainties in urban areas.This study provided a reference for users for selecting LST products and for data producers to further improve MODIS LST products.

Air temperature field distribution estimations over a Chinese mega-city using MODIS land surface temperature data： the case of Shanghai

The capability of obtaining spatially distrib- uted air temperature data from remote sensing measure- ments is an improvement for many environmental applications focused on urban heat island, carbon emis-sions, climate change, etc. This paper is based on the MODIS/Terra and Aqua data utilized to study the effect of the urban atmospheric heat island in Shanghai, China. The correlation between retrieved MODIS land surface tem- perature （LST） and air temperature measured at local weather stations was initially studied at different temporal and spatial scales. Secondly, the air temperature data with spatial resolutions of 250 m and 1 km were estimated from MODIS LST data and in-situ measured air temperature. The results showed that there is a slightly higher correlation between air temperature and MODIS LST at a 250 m resolution in spring and autumn on an annual scale than observed at a I km resolution. Although the distribution pattern of the air temperature thermal field varies in different seasons, the urban heat island （UHI） in Shanghai is characterized by a distribution pattern of multiple centers, with the central urban area as the primary center and the built-up regions in each district as the sub- centers. This study demonstrates the potential not only for estimating the distribution of the air temperature thermal field from MODIS LST with 250 m resolution in spring and autumn in Shanghai, but also for providing scientific and effective methods for monitoring and studying UHI effect in a Chinese mega-city such as Shanghai.