Cities are well known to be hotter than the rural areas that surround them; this phenomenon is called the urban heat island. Heat waves are excessively hot periods during which the air temperatures of both urban and rural areas increase significantly. However, whether urban and rural temperatures respond in the same way to heat waves remains a critical unanswered question. In this study, a combination of observational and modeling analyses indicates synergies between urban heat islands and heat waves. That is, not only do heat waves increase the ambient temperatures, but they also intensify the difference between urban and rural temperatures. As a result, the added heat stress in cities will be even higher than the sum of the background urban heat island effect and the heat wave effect. Results presented here also attribute this added impact of heat waves on urban areas to the lack of surface moisture in urban areas and the low wind speed associated with heat waves. Given that heat waves are projected to become more frequent and that urban populations are substantially increasing, these findings underline the serious heat-related health risks facing urban residents in the twenty-first century. Adaptation and mitigation strategies will require joint efforts to reinvent the city, allowing for more green spaces and lesser disruption of the natural water cycle.

Urban heat island (UHI) is a hotspot in the study about urban ecological and environmental effects. UHI effects are caused by multiple factors, and the synthesized mechanism study can supply a foundation to release the negative effects of UHI. This study proposes a theory framework for causative factors of surface urban heat island (SUHI) by analyzing the process of surface energy on the basis of landscape ecology. Since surface temperature represents the process of surface energy, we examine the causative factors of this process, which includes energy absorption/emission, energy translation, and energy transmission. The internal and external progresses on each kind of causative factors are reviewed in this study. We also compare the internal studies on synthesized mechanism of SUHI with external studies. By the comparison of progresses of causative factor studies and the mechanism of SUHI, we deduce the prospect on this field.</br>The energy absorption and emission of surface represent the ability to absorb solar short-wave radiation, and the capacity to emit earth&rsquo;s surface long-wave radiation, which are controlled by the physical properties of land surface. The studies on this theme focus on land use and biophysical properties. It has reached a consensus on the functions of land use/land cover. The biophysical properties can better describe the relationship between surface characteristics and temperature with higher accuracy. The physical properties of the surface include vegetation, impervious surface, and surface moisture, which are represented by land cover indices derived from remote sensed data including NDVI, ISA, NDBI, NDMI, etc. Energy translation is the process of translating one form of energy into another, which, in this case, is surface heat determined by the intensity of human activities. Population density, energy consumption intensity, and automobile flux are normal indices to depict the intensity of human activities. However, the studies in this field are limited by the spatial resolution of social-economic data. Energy transmission represents the energy flow between different patches referring to the temperature gradient, which primarily depends on the spatial relationship between landscape patches. Two themes in this field have been developed. One is the relationship between temporal heterogeneity of landscape patterns and surface temperature changes. The other is the influence of spatial characteristics of landscape patches on surface temperature. In the field of synthesized causative factors, the external study focuses on the triangle model of temperature, vegetation, and soil moisture. Most of the internal analyses are about the statistical model consisted of land cover and social-economic components.</br>There are two tendencies of the relating studies in the future. Firstly, high resolution data and field survey data will promote the study on the analysis of energy translation and transmission. Secondly, following a description of energy process, we can involve social-economic indicators and landscape patterns in the triangle mechanism model to establish the systemized mechanism of SUHI.

明晰城市热岛（UHI）效应相关概念内涵、厘清其定量刻画方法是有效开展UHI效应研究的重要基础。全球城市化进程使UHI效应越发普遍,相关研究数量迅速增长并出现了UHI效应的多样认知,尤其对具有空间异质性表征优势的地表城市热岛（SUHI）效应开发了多样的定量刻画方法,但目前尚缺乏对其定量刻画方法的系统梳理。因此,本文对城市热岛、城市热岛效应、地表温度和城市热环境等易混淆概念进行了辨析,总结了各类UHI典型空间位置和尺度范围。在SUHI效应定量刻画中,将SUHI范围识别方法归结为城乡温度阈值、温度等级阈值、高斯拟合参数、温度衰减突变四大类,指出当前SUHI范围识别研究侧重于对SUHI影响规模的认知。研究同时对各类范围识别方法所对应的SUHI强度表征指标进行了梳理,认为理解指标本质内涵是掌握指标间潜在差异的前提。未来研究应整合多维度数据以突破单一SUHI监测途径,发展大尺度SUHI定量刻画方法以拓展定量研究的广度,认知连通化SUHI空间形态以挖掘范围识别研究的深度。

Understanding the conceptual connotation related to urban heat island (UHI) effect and clarifying its quantitative characterization methods are important foundations for effective UHI effect research. The global urbanization has made the UHI effect more and more common, which leads to a rapid increase in the number of related studies. In this context, different concepts related to UHI effect have emerged, and particularly, various quantitative characterization methods have been developed for surface urban heat island (SUHI) which has the advantage of spatial heterogeneity characterization. However, there is still a lack of systematic review of quantitative characterization methods of SUHI. Therefore, this study distinguished and analyzed the confusing concepts such as urban heat island, urban heat island effect, surface temperature and urban thermal environment. Then, the typical spatial locations and scale ranges of various UHIs were summarized. In the quantitative characterization of SUHI effect, this study categorized the SUHI range identification methods into four categories: methods based on urban and rural temperature threshold, temperature grade threshold, Gaussian fitting parameter, and temperature attenuation mutation. The current SUHI range identification research focuses on the cognition of the SUHI impact scale. This study also sorted out the SUHI intensity indicators corresponding to various range identification methods. Understanding the essential connotation of each indicator is the prerequisite for grasping the potential differences between the indicators. Future research should integrate multi-source SUHI monitoring methods, develop large-scale SUHI quantitative characterization methods, and cognize connected SUHI spatial morphology.

基于长期不透水面和MODIS地表温度数据，分析2000—2015年中国城市群扩张及热岛效应时空演变，进而综合采用冗余分析(RDA)、线性回归分析和变异分配分析(VPA)等方法，揭示城市群城市热岛效应的驱动机制。结果表明：城市群内建成区面积快速扩张，不透水面比例从2000年2.08%增长到2015年5.33%，且主要集中于珠江三角洲等沿海城市群；2000—2015年，夏季热岛分布较广，且白天强度高于夜晚。东部以及大部分北部城市群如哈长城市群等，降温强度较大，但其夜晚热岛效应在不同程度增强。冬季夜晚比白天热岛分布广、强度高，北方、西北、东部等地区夜晚热岛效应也在增强；自然环境因素显著影响城市群热岛强度，降水对夏季夜晚热岛强度起显著负贡献(22%)，纬度越高，冬季夜晚热岛强度也越高。人为因素显著影响夜晚热岛分布和城市群内热岛强度的平衡，城市植被覆盖显著减少夜晚城市群内热岛分布，灯光强度对夏季夜晚热岛强度起显著负贡献(24%)，对热岛比例起显著正贡献(27%)，人口密度对夏季夜晚热岛强度起显著负贡献(31%)；自然环境因素对热岛强度的贡献占主导，而人为干扰因素对热岛比例的贡献占主导。

Urban agglomeration characterized by compact regional cooperation and frequent human mobility has been an obvious direction of urban and socio-economic development in China. However, in the formation of urban agglomeration, rapid impervious expansion and tremendous anthropogenic heat sources making urban areas hotter, which generates the urban heat island (UHI) effect and associated extreme heat events, threatening public health and sustainable development. Although studies have documented the impact of UHI on the urban environment by using a single urban agglomeration as the research object, there is still a lack of knowledge of the driving mechanism of the spatiotemporal pattern of UHI on the national scale. In this study, we observed the urban expansion and the spatiotemporal pattern of UHI in 19 urban agglomerations of China from 2000 to 2015 by using long-term impervious data and MODIS surface temperature data. Furthermore, by using Redundancy analysis (RDA) and linear regression model with the datasets that represent the nature and socio-economic driving factors, we quantified the driving mechanism of spatiotemporal pattern of UHI for all urban agglomerations. Results show that the impervious surface expanded rapidly with its proportion increased from 2.08% to 5.33% during 2000-2015 in China’s urban agglomerations, concentrating in coastal urban agglomerations such as the Pearl River Delta. The proportion of heat island (PHI) was high and the intensity of heat island (SUHIIagg) was higher in summer nighttime than in daytime. The eastern and numerous of northern urban agglomerations such as Harbin-Changchun had strong cooling capacity in summer, however the SUHIIagg increased in nighttime to varying degrees. Besides, the PHI and the SUHIIagg in winter nighttime was higher than that in daytime. And the SUHIIagg in the north, northwest and east urban agglomerations increased in winter nighttime. We found vegetation was significantly reducing the nighttime PHI, while the precipitation was negatively affecting the SUHIIagg in summer nighttime (22%) and latitude was positively affecting the SUHIIagg in winter nighttime (56%). Meanwhile, the nighttime lights was negatively affecting the SUHIIagg(24%) and positively affecting the PHI (27%) while the population negatively affecting the SUHIIagg in summer nighttime (31%).These show that natural environmental factors dominantly contribute to the SUHIIagg, while the human disturbance factors dominantly contribute to the PHI. These show that the interaction between radiation changes and human activities has an important impact on the nighttime UHI effect in China. Since the urban expansion and immigration keep ongoing, the UHI effect is predicted to be more intense and of longer duration in China. Thus, the pathway to balance the development of urban agglomeration and the mitigation of the urban heat environment is a major challenge for government policymaking in China. This study expands the knowledge of the spatiotemporal UHI change at the national scale, which provides a scientific basis for urban planning, alleviating urban heat challenges, and achieving sustainable development.

A suitable thermal environment is important for the economy, society and public health in urban areas. However, the understanding of the relationship between the urban heat island (UHI) effect and background temperature (T-UHI) is very limited. In this study, the UHI effect induced by the urbanization of the megacity Beijing was investigated using the weather research and forecasting model. Urban expansion and heatwaves both considerably enhanced the UHI effect over urban areas in summer. The strengthened UHI effect during the heatwave period can be clearly explained by the positive sensitivity of T-UHI. The urban expansion increased the sensitivity of T-UHI from 0.0207 °C °C−1 in 2000 to 0.0569 °C °C−1 in 2010 in the daytime and from 0.0715 °C °C−1 in 2000 to 0.0995 °C °C−1 in 2010 at nighttime, thus resulting in a much stronger UHI effect mainly by increasing the difference between the latent heat flux and sensible heat flux. This enhanced sensitivity may exacerbate the urban heat stress in the situation of further urban expansion and background climate warming. Our results suggest that the sensitivity of T-UHI is a meaningful indicator to assess the urban thermal environment change and support the designing of heat mitigation strategies in urban planning.

The surface urban heat island (SUHI), which represents the difference of land surface temperature (LST) in urban relativity to neighboring non-urban surfaces, is usually measured using satellite LST data. Over the last few decades, advancements of remote sensing along with spatial science have considerably increased the number and quality of SUHI studies that form the major body of the urban heat island (UHI) literature. This paper provides a systematic review of satellite-based SUHI studies, from their origin in 1972 to the present. We find an exponentially increasing trend of SUHI research since 2005, with clear preferences for geographic areas, time of day, seasons, research foci, and platforms/sensors. The most frequently studied region and time period of research are China and summer daytime, respectively. Nearly two-thirds of the studies focus on the SUHI/LST variability at a local scale. The Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+)/Thermal Infrared Sensor (TIRS) and Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) are the two most commonly-used satellite sensors and account for about 78% of the total publications. We systematically reviewed the main satellite/sensors, methods, key findings, and challenges of the SUHI research. Previous studies confirm that the large spatial (local to global scales) and temporal (diurnal, seasonal, and inter-annual) variations of SUHI are contributed by a variety of factors such as impervious surface area, vegetation cover, landscape structure, albedo, and climate. However, applications of SUHI research are largely impeded by a series of data and methodological limitations. Lastly, we propose key potential directions and opportunities for future efforts. Besides improving the quality and quantity of LST data, more attention should be focused on understudied regions/cities, methods to examine SUHI intensity, inter-annual variability and long-term trends of SUHI, scaling issues of SUHI, the relationship between surface and subsurface UHIs, and the integration of remote sensing with field observations and numeric modeling.

随着全球城市化的快速发展和气候变暖及极端高温事件的加剧，城市热岛效应已成为了21世纪影响人类生存发展的重要环境问题。因此，阐明城市热岛强度时空变化特征，揭示多空间尺度下的形成机制是人文地理学和气候学研究的热点和前沿交叉科学问题。从街道峡谷微观尺度、城市街区局部尺度和城市宏观尺度全面总结和系统梳理了城市空间形态对城市热岛效应的多尺度影响和耦合机制，提出了未来通过定量表征城市热岛强度、城市空间形态的空间格局、时间演化特征及规律，构建空间关系分析模型，进而揭示城市空间形态在不同时空尺度对城市热岛强度的作用机制，为城市规划、宜居城市建设以及气候变化的响应与适应研究提供理论指导。

With the rapid development of global urbanization, the further increased global warming and extreme high temperature events, urban heat island effect has also become an important environmental problem affecting human survival and development in the 21st century. Therefore, it is a hot and frontier cross-scientific issue in the study of human geography and climatology to clarify the spatio-temporal variations of urban heat island intensity and reveal the driving mechanism at multi-spatial scales. Urban geometry is regarded as a key factor affecting urban-rural and intra-urban air temperature variations, and the urban form manipulation provides an opportunity to mitigate the adverse effects on urban climate. Therefore, it is critical to understand the relationship between urban form and the UHI effect to guide future planning practice. This paper systematically reviews and summarizes the multi-scale affecting and coupling mechanism of urban spatial morphology on urban heat island intensity from three different spatial scale perspectives including street canyons microscale, urban block local scale, and urban macroscale. Based on the understanding of the spatial distribution and temporal evolution characteristics of urban spatial morphology on urban heat island intensity, we will characterize the specific effects of urban spatial morphology on urban heat island intensity and construct the spatial relationship analysis model to reveal the affecting mechanism of urban spatial morphology on urban heat island intensity at different temporal and spatial scales. Our knowledge and insights will provide theoretical guidance for urban planning, livable city construction and climate change response and adaptation study.

Urban boundaries, an essential property of cities, are widely used in many urban studies. However, extracting urban boundaries from satellite images is still a great challenge, especially at a global scale and a fine resolution. In this study, we developed an automatic delineation framework to generate a multi-temporal dataset of global urban boundaries (GUB) using 30 m global artificial impervious area (GAIA) data. First, we delineated an initial urban boundary by filling inner non-urban areas of each city. A kernel density estimation approach and cellular-automata based urban growth modeling were jointly used in this step. Second, we improved the initial urban boundaries around urban fringe areas, using a morphological approach by dilating and eroding the derived urban extent. We implemented this delineation on the Google Earth Engine platform and generated a 30 m resolution global urban boundary dataset in seven representative years (i.e. 1990, 1995, 2000, 2005, 2010, 2015, and 2018). Our extracted urban boundaries show a good agreement with results derived from nighttime light data and human interpretation, and they can well delineate the urban extent of cities when compared with high-resolution Google Earth images. The total area of 65 582 GUBs, each of which exceeds 1 km2, is 809 664 km2 in 2018. The impervious surface areas account for approximately 60% of the total. From 1990 to 2018, the proportion of impervious areas in delineated boundaries increased from 53% to 60%, suggesting a compact urban growth over the past decades. We found that the United States has the highest per capita urban area (i.e. more than 900 m2) among the top 10 most urbanized nations in 2018. This dataset provides a physical boundary of urban areas that can be used to study the impact of urbanization on food security, biodiversity, climate change, and urban health. The GUB dataset can be accessed from http://data.ess.tsinghua.edu.cn.

Although there is still a dispute about the performance of the BoyceClark shape index, as a quantitative method for measuring the shape, the BoyceClark shape index is a good one in that it can present the general character of city shapes through the experiment. Shape indices of 31 Chinese cities at 1990 and 2000 were computed by the BoyceClark shape index method based on the dynamic landuse data from the National Resources and Environmental Database establishedby the IGSNRR, CAS. According to Lo (1980), the range of shape indices for Chinese cities varies from 10.410 to 40.620 in 1934, 16.170 to 73.070 in 1974, 13.004 to 69.902 in 1990 and from 12.687 to 116.398 in 2000. Chengdu is a city persisting the square shape with the shape indices of 10.550, 16.170, 13.004 and 14.144 in those four years respectively. The averages of the index in the four years are 23.950, 27.090, 32.507 and 31.507 respectively. It was found that, in general, shapes of Chinese cities tended to be less compact in the period from 1934 to 1990, while more compact in the period from 1990 to 2000, and it is perhaps a result of strong control to urban sprawl at the latter period. Generally, city shapes suggest the persistence of square or rectangle shapes from 1934 to 2000, although some cities also experienced linear development in the period from 1990 to 2000. Out of 20 cities, 14 cities showed a decreased trend in shape indices and six cities showed an increased trend from 1934 to 1974, and out of 22 cities, 11 cities’ shape indices decreased and the rest had increased shape index from 1974 to 1990. Moreover, shape indices of several cities have been drastically changed during the period from 1934 to 2000. Lanzhou is a city with a highest increment in shape index (41.810) during 1934 to 1974, and a highest decrease (16.753) during the period from 1934 to 1974. Wuhan and Kunming are the first two cities with a high increment of 28.512 and 26.845 respectively in shape index from 1974 to 1990, but a high decrease of 15.571 and 17.615 respectively in shape index from 1990 to 2000. It means that city shapes are not considered by urban planners and managers as important issues until some urban problems such as heavy traffic and environment pollution appear to affect the life and work of urban inhabitants, therefore city shape is an important issue in urban spatial planning.

The single‐channel (SC) algorithm has been widely used to retrieve land surface temperature from Landsat series data for its simplicity and requirement of only one thermal infrared channel. The main error sources of the existing SC algorithms are the linearization of the Planck's function and atmospheric correction. This paper proposed a practical SC (PSC) algorithm to retrieve land surface temperature from Landsat series data aiming at avoiding the aforementioned error sources. The sensitivity of the PSC algorithm to the input parameters was analyzed. The performance of the proposed PSC algorithm was compared with the most commonly used SC algorithm (the generalized SC, GSC) using a simulation data set and satellite measurements. Results showed that the PSC algorithm was less sensitive to uncertainties in the input parameters than the GSC algorithm. When validated with the simulation data set, the root‐mean‐square error (RMSE) of the PSC algorithm was 1.23 K, with an improvement by 0.57 K compared with the GSC algorithm. For the validation with 71 clear‐sky Landsat 8 images, the RMSE of the PSC algorithm was 1.77 K when using the measurements from U.S. surface radiation budget network as real values. Compared with the GSC algorithm, the RMSE improvement for the PSC algorithm was 0.47 K. We conclude that the PSC algorithm is more accurate than the GSC algorithm and the sensitivity to input parameters in the PSC algorithm is weaker than in the GSC algorithm.

利用ASTER数据反演地表温度,采用等间距法和均值-标准差法,将研究区温度场分别划分为4级、5级、6级,并根据热岛区的界定进一步将4级、6级细分为4级(a)、4级(b)、6级(a)和6级(b)。在此基础上,对两种方法从城市热岛数量结构差异、热岛空间分布及细节表达等方面进行了系统对比分析。结果表明:两种方法所界定热岛的面积百分比随着分级数不同均出现跳跃现象,趋势基本一致。但就热岛强度而言,均值-标准差法对分级数的敏感性较等间距法小,在热岛的空间分布和温度变异的细节表现力等方面,均值-标准差法也优于等间距分级法。因此,综合来看,均值-标准差法是城市热岛界定的较适合方法。均值-标准差法以地表温度相对于平均温度的变异程度为依据进行热场划分,在多时相城市热岛演变、对比等研究中,一定程度上可以避开时相的差异。

深入研究粤港澳大湾区建设用地扩张与城市热岛扩张的耦合态势,对改善大湾区生态环境、实现城市化的健康发展,具有重要的意义和科学价值。本文以粤港澳大湾区为研究区域,选取2000、2008和2016年的遥感影像数据和地表温度数据,提取大湾区的建设用地、划分城市热岛强度等级、识别城市热岛区域;在此基础上,使用总体耦合态势和空间耦合特征两种模型揭示大湾区在不同时期建设用地扩张和城市热岛扩张的耦合关系。结果表明：① 2000-2016年,粤港澳大湾区的建设用地扩张和城市热岛扩张趋势具有同步性;② 城市热岛区域面积不断增加,在珠江口两岸逐步形成了连接中山-佛山-广州-东莞-深圳-香港,大范围分布的倒“U”形城市热岛条带;③ 大湾区建设用地与城市热岛的总体耦合态势处于不断加强的过程,至2016年,两类指标的重心距离达到研究期内的最小距离,此时的总体耦合态势最强;④ 研究期内,大湾区未出现建设用地扩张和城市热岛扩张严重失调情况,建设用地扩张和城市热岛扩张耦合类型以耦合型、基本耦合型以及轻度不耦合型为主,空间耦合程度高。

<p>Regional gap is an important problem that the regional development of China is facing. The distribution of economy and population, as an embodiment of the spatial equilibrium process of regional development, has a close relation with the state of regional gap. Using gravity models, it works out the center of economic gravity and population gravity (abbr. CEG and CPG), illustrates the coupling process by measuring the overlapping of two centers of gravity and the consistency of their movement, and proves that the coupling process has a high correlation with the evolution of regional gap. Then, a model of the coupling mechanism of CEG and CPG is built. Based on the model, it illustrates the spatial equilibrium process of regional development by the transition of equilibrium location and the conversion of regional potential energy difference, and reveals how the internal and external factors affect the process. Therefore, the authors advance a &ldquo;multistage inverted-U-curve evolution law&rdquo; of regional gap. Finally, it analyses the mechanism of the periodic variation of regional gap in China since 1952, and brings up a discussion of the path to coordinating the regional development of China.</p>