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Article

Spatiotemporal Evolution and Influencing Factors of Heat Island Intensity in the Yangtze River Delta Urban Agglomeration Based on GEE

by
Fei Meng
1,
Lifan Qi
1,
Hongda Li
2,
Xinyue Yang
3 and
Jiantao Liu
1,*
1
School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
2
Shandong Survey and Design Institute of Water Conservancy Co., Ltd., Jinan 250013, China
3
School of Design and the Built Environment, Curtin University, Kent Street, Perth, WA 6102, Australia
*
Author to whom correspondence should be addressed.
Atmosphere 2024, 15(9), 1080; https://doi.org/10.3390/atmos15091080
Submission received: 22 July 2024 / Revised: 2 September 2024 / Accepted: 3 September 2024 / Published: 6 September 2024
(This article belongs to the Section Biometeorology and Bioclimatology)
Browse Figures
Versions Notes

Abstract

:
Urban agglomerations significantly alter the regional thermal environment. It is urgent to investigate the evolution and influence mechanisms of urban agglomeration heat island intensity from a regional perspective. This study is supported by Google Earth Engine long-term MODIS data series. On the basis of estimating surface urban heat island intensity (SUHI) in the Yangtze River Delta urban agglomeration from 2001 to 2020 based on the suburban temperature difference method, the causes of heat islands in the urban agglomeration were analyzed by using geographical detector analysis. Additionally, the heat island proportion (PHI) and SUHI indicators were used to compare and analyze the changing characteristics of the urban heat island effect of ten representative cities. The research reveals the following: (1) The average SUHI of the study area increased from 0.11 °C in 2001 to 0.29 °C in 2020, with an average annual increase rate of 0.009 °C. (2) According to the results of the geographical detector analysis, SUHI was influenced by several driving factors exhibiting obvious seasonal variations. (3) SUHI difference between cities is significant in the summer (1.52 °C), but smallest in the winter; the PHI difference between cities is larger in the autumn (46.7%), while it is smaller in the summer. The research findings aim to effectively serve the formulation of collaborative development plans for the Yangtze River Delta urban agglomeration.

1. Introduction

In recent decades, with rapid economic development and a significant influx of migrants, the Yangtze River Delta region has experienced rapid urbanization and has evolved into the sixth-largest urban agglomeration in the world. The expansion of cities in the region has led to the substantial growth of impervious surfaces [1], triggering a series of environmental problems such as the urban heat island effect [2,3]. The urban heat island effect refers to the significant temperature difference between urban areas and their surrounding rural areas. It has long been recognized as a critical urban climate issue [4] which has had a profound impact on urban public health, air quality, energy consumption, social production, etc. [5,6], and has also attracted continuous attention from urban planners [7,8,9].
At present, the methods for studying the urban heat island effect mainly encompass the following: meteorological observation [10], site observation [11], numerical simulation [12], and remote sensing technology analysis [13,14,15], etc. Due to the characteristics of remote sensing technology such as wide simultaneous detection range, short data period, high spatial resolution, and spatial continuity, it is progressively becoming the mainstream approach for quantitative calculation and analysis of urban heat island effect. Scholars globally have produced a lot of research on the quantitative assessment [16,17], distribution characteristics [18,19], and analysis of influencing factors of urban heat islands [20,21,22], leveraging remote sensing methods. Surface urban heat island intensity (SUHI) is defined as the surface temperature difference between a city and surrounding areas, and stands as a widely used metric for comprehending the spatiotemporal patterns of the urban heat island effect. Levermore et al. (2018) [23] observed an increasing SUHI over time in Manchester, UK, presenting relationships between weather parameters, cloud cover, wind speed, and urban morphology and SUHI. According to Huang et al. (2018) [24], SUHI increases significantly when the temperature is at the average and lowest conditions, with a stable trend when temperatures are at their highest. Hu et al. (2022) [25] used the heat island proportion (PHI) and surface urban heat island intensity (SUHI) indicators to describe the regional heat island effect, and then explored the spatial pattern of thermal environment in China’s urban agglomerations.
Urban agglomeration refers to a specific region, with one or more large cities and megacities as the core, relying on modern transportation and information networks, closely connected with the intrinsic functions of surrounding large, medium, and small cities and small towns, and with a high degree of network connectivity [26]. Since SUHI research became a hot topic in 2006, there are still few studies focusing on the urban agglomeration-scale heat island effect [25], which mainly focus on representative individual independent urban agglomerations such as the Beijing–Tianjin–Hebei, Yangtze River Delta, and Pearl River Delta urban agglomerations, and the Guangdong–Hong Kong–Macao Greater Bay Area [27,28,29]. For example, Yan et al. (2021) [30] compared the thermal effects of urban and agricultural lands and their drivers in three major urban agglomerations in China, namely, Beijing–Tianjin–Hebei (BTHUA), Yangtze River Delta (YRDUA), and Pearl River Delta (PRDUA). The study indicated that the surface thermal environmental effects of urban and agricultural lands varied greatly with space and time. They were mainly controlled by vegetation activities, surface albedo, background climate, and population density. Lin et al. (2018) [31] studied the relationship between the urban heat island effect and regional development of the three major urban agglomerations in the eastern coastal areas of China, namely, Beijing–Tianjin–Tangshan, Yangtze River Delta, and Pearl River Delta, from 2001 to 2013, and found that expansions of urban agglomerations have induced a strong regional heat island effect. Du et al. (2016) [32] assessed the surface UHI and its relationship with types of land, meteorological conditions, anthropogenic heat sources, and urban areas in the Yangtze River Delta urban agglomeration. Wang et al. (2021) [33] studied SUHI and its multiple influencing factors in 16 large cities within the Yangtze River Delta urban agglomeration in eastern China and ranked the influencing factors according to their relative impact on SUHI.
Given this, the present study aims to deepen the understanding of the evolution law of the surface heat island effect in the Yangtze River Delta urban agglomeration, and provide important theoretical support for future urban construction and renewal and ecological environment governance and improvement. Additionally, it can offer a scientific basis for decision making to alleviate the heat island effect. More specifically, this study takes the Yangtze River Delta urban agglomeration as the research object, and aims to achieve the following: (i) to describe the surface urban heat island intensity (SUHI) and percentage of heat island (PHI) based on MODIS ground temperature products, (ii) to analyze the spatiotemporal pattern and evolution characteristics of the Yangtze River Delta urban agglomeration from 2001 to 2020, and (iii) to combine population, precipitation data, etc., using factor detection and interactive detection analysis to further reveal the influence mechanism of the urban heat island effect in four seasons.

2. Material and Methods

2.1. Overview of the Study Area

In according with the 2016 “Yangtze River Delta Urban Agglomeration Development Plan”, the study focuses on 26 cities in the Yangtze River Delta region (Figure 1), hereafter collectively referred to as the Yangtze River Delta urban agglomeration (YRDUA). The Yangtze River Delta urban agglomeration is located along the eastern coast of China, and consists of Shanghai, southern Jiangsu, northern Zhejiang, and parts of Anhui. It includes Shanghai and nine cities in Jiangsu Province—Nanjing, Wuxi, Changzhou, Suzhou, Nantong, Yancheng, Yangzhou, Zhenjiang, and Taizhou. Additionally, it encompasses eight cities in Zhejiang Province—Hangzhou, Ningbo, Jiaxing, Huzhou, Shaoxing, Jinhua, Zhoushan, and Taizhou. Finally, there are eight cities in Anhui Province—Hefei, Wuhu, Ma’anshan, Tongling, Anqing, Chuzhou, Chizhou, and Xuancheng [34]. The Yangtze River Delta urban agglomeration is dominated by a variety of landforms including plains, hills, and mountains, with an area of 211,700 km2, accounting for about 2.2% of China. Its GDP reached CNY 25.42 trillion in 2023 and its permanent population exceeded 236 million in 2021. The region is influenced by the subtropical monsoon climate, with hot and humid summers and cool and dry winters. Recognized as one of the most economically dynamic and innovation-driven areas in China [35], the Yangtze River Delta urban agglomeration holds a pivotal strategic position in the national modernization agenda and comprehensive openness. As the sixth-largest urban agglomeration globally, it stands out as one of the most pronounced urbanized regions in China. However, rapid urbanization has led to a shortage of land resources, accelerated surface warming, and continued deterioration of environmental quality. Therefore, for the abovementioned reasons, the Yangtze River Delta urban agglomeration was selected as our research area.

2.2. Data Sources

This study utilizes the MODIS product from the GEE cloud platform (http://earthengine.google.com/, accessed on 13 February 2024) as the fundamental data, and uses the GEE planetary cloud computing platform to complete the preprocessing of MODIS data, including processes such as screening, splicing, reprojection, clipping, temperature conversion, outlier removal, and calculation. Referring to the land use classification standard of the Chinese Academy of Sciences, the land use data are reclassified into six categories: cultivated land, forest land, grassland, water body, urban land, and bare land. The data encompass the following (Table 1):
(1) Surface Temperature Data. The MOD11A2 dataset obtained by MODIS/Terra from 2001 to 2020, which underwent radiometric calibration and atmospheric correction, was selected. This dataset is an image data product with a resolution of 1 km after processing the average of the optimal pixel values within 8 days. The data were preprocessed by mosaic, clip, temperature conversion, and outlier removal, and the annual and seasonal (four seasons) surface temperature data of the Yangtze River Delta urban agglomeration from 2001 to 2020 were calculated. Spring, summer, autumn, and winter were defined as March–May, June–August, September–November, and December to February of the subsequent year. This study uses Equation (1) to convert the pixel brightness value (Digital Number, DN) into the annual scale land surface temperature (Land Surface Temperature, LST) [36].
T s = D N × 0.02 − 273.15
where Ts represents the surface temperature (LST/°C) and DN is the brightness value of the pixel.
(2) Vegetation Index Data. The Normalized Difference Vegetation Index (NDVI), as a remote sensing indicator of land cover, is one of the important parameters for detecting vegetation growth status and vegetation coverage [37,38]. The vegetation index dataset used in this article is the 1 km scale MODIS MOD13A2, a 16-day composite product.
(3) Land Use Data. This study utilizes the 500 m scale data product MCD12Q1 from 2001 to 2020 as land use data. This product adopts IGBP’s global vegetation classification scheme, with the “LC_Type1” band representing land use types.
The DEM data employed in this study were from the spaceborne thermal emission and reflection radiometer global digital elevation model (ASTER GDEM). With a spatial resolution of 30 m, the data were obtained through the Geospatial Data Cloud (http://www.gscloud.cn/, accessed on 16 February 2024), and resampled to 1 km to match the MODIS data. Population density data with a spatial resolution of 1 km came from Worldpop (https://hub.worldpop.org, accessed on 18 February 2024). GDP data, evapotranspiration data, temperature data, precipitation data, and wind speed data were obtained from the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences (https://www.resdc.cn/, accessed on 22 February 2024), and the spatial resolution was 1 km. This paper uses the annual average values of various meteorological elements in 2020 to analyze the influencing factors of SUHI.

2.3. Methods

A detailed workflow was established for this study (Figure 2). First, we leveraged the processing power and analysis-ready data available on Google Earth Engine (GEE) to process the MODIS land surface temperature product, and used DEM, land cover type, and NDVI data to define the urban and rural areas. We selected all farmland pixels with an altitude difference of less than 50 m from the urban area and an NDVI greater than 0.7, and considered these pixels to be the pixels with the least interference from the surrounding cities. The LST average of these pixels was used as the rural background temperature value. The urban and rural temperature threshold method was used to subtract the urban temperature from the rural temperature to obtain SUHI, so as to analyze the spatiotemporal variation characteristics of the urban heat island effect in the Yangtze River Delta urban agglomeration from 2001 to 2020. Secondly, after obtaining the patch-scale SUHI, we screened the urban patches with SUHI greater than 0 in each urban agglomeration, calculated their mean, and further calculated the proportion of heat island patches to the total number of patches in the urban agglomeration, i.e., PHI. This helps understanding of the overall distribution of heat islands within urban agglomerations. Finally, geographic detector analysis was used to analyze the impact of 10 driving factors on SUHI from factor detection and interaction detection.

2.3.1. Calculation of Surface Urban Heat Island Intensity (SUHI) and Heat Island Proportion (PHI)

The interannual and intra-annual surface temperature data of different seasons in the Yangtze River Delta urban agglomeration from 2001 to 2020 were calculated using Equation (1).
(1)
PHI Calculation
In order to elucidate and illustrate the spatial and temporal distribution patterns of thermal environment in the Yangtze River Delta urban agglomeration more intensively, PHI and SUHI indicators were used to describe the urban agglomeration heat island effect. PHI (Equation (2)) was utilized to denote the proportion of heat island patches in the thermal landscape concerning the total number of patches in the urban agglomeration [39,40]. This paper was based on the Python computer programming language and used Python editor to realize the calculation of the PHI index. Compared with other indicators, PHI can effectively capture the distribution characteristics of heat islands within the urban agglomeration.
P H I i = m i M × 100 %
where PHIi is the heat island proportion of city i, mi is the number of heat island pixels in city i, and M is the total number of pixels in the study area.
(2)
SUHI Calculation
The spatial and temporal variation patterns of an urban heat island can be obtained through quantitative analysis of the SUHI index. The disparity between urban surface temperature and suburban surface temperature can be converted into SUHI [41]. The SUHI index used in this paper to measure the intensity of urban heat islands was calculated based on the GEE cloud platform. For MODIS data with raster attributes, the pixel-level SUHI calculation equation was as follows [42], with the suburb average land surface temperature subtracted from the land surface temperature for each pixel:
S U H I i = T i − 1 N ∑ i N T r
where SUHIi is the surface urban heat island intensity (°C) of the i-th pixel, Ti represents the surface temperature (°C), N is the number of effective pixels in the rural background, and Tr represents the temperature of the rural background (°C).
Following the classification method proposed by Ye et al. (2011) [43], urban heat islands were categorized into 7 levels: strong cold island, relatively strong cold island, weak cold island, no-heat island, weak heat island, moderate heat island, strong heat island, with a value of 1~7 (Table 2).

2.3.2. Driving Analysis

The heat island effect is closely related to the natural surface, human interference, and meteorological factors [24,29]. Surface features mainly refer to the environmental background formed by geographical elements such as topography and location, human interference mainly refers to the large amount of heat emitted by residents’ lives and industrial production, and meteorological elements mainly refer to the various factors that indicate the physical state and physical phenomena of the atmosphere, including precipitation, evapotranspiration, and wind speed. The three play an important role in the regional heat island effect.
In order to explore the driving factors that may affect spatiotemporal variations in SUHI, based on the existing research on the relationship between urban-scale thermal environment and surface characteristic factors [28,44], this study used land cover type, DEM, and NDVI as surface characteristic factors, impervious surface, gross domestic product (GDP), and population density (POP) as human interference factors, and wind speed, evapotranspiration, precipitation, and temperature as meteorological factors. Geographical detector analysis was used to analyze the driving effect of each factor on SUHI.

2.3.3. Geographical Detector Analysis

Spatial differences in geographical phenomena can be elucidated through geographical detector analysis [45,46]. In order to delve deeper into the driving factors of the heat island effect in the Yangtze River Delta urban agglomeration, this study uses factor detection and interaction detection from the Geo-explore model to analyze the impact of different factors on SUHI. The independent variable factors of the geographic detector should be discrete values rather than continuous values. Before calculation, factors such as NDVI, DEM, POP, GDP, and precipitation must be reclassified (land use data are already type values). First, the independent variables are divided into 6 categories using the natural break point classification method. This method has been used by relevant scholars [47] to discretize driving factors in urban thermal environment research. Then, the sampling tool is used to divide the study area into grid points with an interval of 2.5 km. SUHI and the reclassified influencing factors are extracted to each grid point. Finally, the data are imported into the geographic detector software for calculation.
The influence of the independent variable on the dependent variable is measured by the q value. The equation of the geographical detector is as follows:
q = 1 − ∑ h = 1 L N h σ h 2 N σ 2 = 1 − S S W S S T
S S W = ∑ h = 1 L N h σ h 2
S S T = N σ 2
where h (h = 1, 2 … L) is the number of the layer Y or factor X, Nh and N are the layer number and number of units in the whole study area, and σ h 2 and σ 2 are the variance in the variables h and Y. SSW and SST are the sum of the squares and the total squares, respectively. q ranges from 0 to 1, where a higher number indicates that spatial differentiation of the study area becomes more serious. When q = 1, the variable Y is completely controlled by the factor X; when q = 0, there is no relationship between the variable Y and the factor X. In this study, the calculation and statistical analysis were conducted using PyCharm Community 2023.3 and geographical detector analysis.

3. Results and Discussion

3.1. Temporal and Spatial Patterns and Changing Characteristics

3.1.1. Spatial Variation Characteristics of SUHI

Figure 3 illustrates the spatial distribution of the annual mean value of SUHI in the Yangtze River Delta urban agglomeration from 2001 to 2020 calculated according to Equation (3). As can be seen from the figure, the 26 cities in the study area all exhibit significant heat island effects and high spatial heterogeneity. The heat island effect of urban agglomerations in the Yangtze River Delta is most prominent among the regions of Shanghai, Suzhou–Wuxi–Changzhou, Shanghai–Hangzhou–Ningbo, and Nanjing–Zhenjiang–Yangzhou. It presents a Z-shaped distribution pattern in space, with Shanghai and Hangzhou as turning points, running through Suzhou, Wuchang, Jiaxing, Shaoxing, and Ningbo, with an overall slight tilt to the right.
Delving into specifics, Shanghai’s heat islands are distributed in concentrated clusters, with most partly strong heat islands in the outer ring. The regions have the most obvious heat island effect, and strong heat island pixels account for the largest proportion. Nanjing’s heat islands are dispersed in a subtle strip shape along the Yangtze River, with primary heat islands forming the shape of branches in the southern region of the Yangtze River. Hefei’s heat island follows a radial distribution. Both Hangzhou and Ningbo display heat islands distributed in vertical blocks, tilting to the right. There are significant differences in SUHI between coastal cities and inland cities. Different distances from the ocean and vegetation coverage lead to different industrial development and transportation forms, which in turn have different impacts on the intensity of urban heat islands.

3.1.2. Analysis of Seasonal Changes in SUHI

Figure 4 shows that SUHI of the Yangtze River Delta urban agglomeration is on an overall upward trend. The ascending rates in the spring, summer, autumn, and winter were 0.012, 0.014, 0.007 and 0.004 °C·a−1, respectively. SUHI reached its peak in the spring and summer in 2020 (0.35 °C, 0.45 °C) and its nadir in 2001 (0.12 °C, 0.19 °C). Autumn SUHI peaked in 2015 (0.27 °C) and the lowest point in 2001 (0.12 °C), while the winter SUHI peaked in 2016 (0.15 °C) and reached its minimum point in 2008 (0.03 °C).
There is obvious seasonal variation in SUHI of the Yangtze River Delta urban agglomeration (Figure 5). It can be seen that the heat island effect is more crucial in the spring and summer, reaching its weakest point in the winter. From the perspective of heat island distribution, weak heat islands are widespread in the spring, while strong heat islands are more concentrated in the summer. The heat island effect is the weakest in the winter, and the areas of weak heat islands, moderate heat islands, and strong heat islands decrease significantly.
The cold island phenomenon mainly occurs in the winter, while their frequency is lowest in the summer, in line with findings in some studies [48,49]. The reason for this phenomenon may be that the suburban background in the winter is dominated by bare soil. Under the influence of intense solar radiation, the surface temperature of the bare soil in the suburbs rises. However, blocked by various structures, urban areas exhibit lower temperatures than the suburbs. Consequently, SUHI demonstrates a negative value. Cold islands in the summer predominantly appear in areas with urban vegetation (forests and lawns) or water bodies (rivers or lakes), and could be primarily attributed to lower surface temperature in these areas compared to the surrounding impervious surface [50].

3.1.3. Analysis of Interannual Changes in SUHI

Judging from the spatial distribution of SUHI (Figure 6), the heat island effect has progressively intensified from 2001 to 2020. The growth rate of SUHI in the suburbs surpasses that in urban areas, and heat islands are expanding beyond urban areas. Although SUHI in most urban central areas shows a significant decline, SUHI in the suburbs demonstrates a significant upward trajectory. Extreme heat island conditions have alleviated, resulting in a small but wide distribution. This is consistent with the research results of Wang et al. (2020) [51], Yin et al. (2023) [52], and Shi et al. (2013) [53]. The underlying cause may be attributed to core urban areas undergoing renewal in the urbanization process [54], the continuous expansion of urban land to the surrounding non-urban land, the increase in traffic density, the dispersion and fragmentation of urban suburbs, and the increasing impact of heat stored in urban areas on the surface temperature of suburbs. The vigorous development of urban greening and ecological environmental protection, urban planning, green space and water coverage, and industrial relocation out of urban areas have led to a series of measures such as a downward trend in SUHI in the core area, while SUHI in the suburbs has shown an upward trend as the urban area has increased significantly.
Figure 6 illustrates that the heat island center of the Yangtze River Delta urban agglomeration extends in space from west to east in an M shape and from north to south in a Z shape. In certain Yangtze River Delta cities, the development of an urban heat island manifests itself as the expansion of urban central areas to surrounding areas, such as Shanghai, Hefei, Suzhou, etc. Shanghai, Hefei, Suzhou, Wuxi, and Changzhou expanded in a “pie” shape around the city center, while Nanjing, Ningbo, Jinhua, and Nantong expanded in a “belt” shape, and other cities mainly expanded in a “star” and “point” pattern. The planning and construction of urban agglomerations should avoid the concentration of heat islands within urban agglomerations, moderately disperse the direction of urban expansion, control the proportion of impervious surfaces in urban agglomerations to reduce the risk of deterioration of the thermal environment in urban agglomerations, and then consider the construction of internal green landscapes and increase the proportion of urban green space in urban planning to enhance the regulatory service functions of urban ecosystems.

3.2. Analysis of Driving Factors

In order to deeply understand the influencing factors of SUHI in the Yangtze River Delta urban agglomeration in different geographical environments, this study employs the geographical detector to analyze the impact of 10 driving factors such as population density, GDP, and impervious surface on SUHI. The analysis is conducted from two aspects: factor detection and interaction detection. In the geographical detector results, the smaller the q value, the weaker the factor’s explanatory power for SUHI, and vice versa, as illustrated in Table 3 and Figure 7. According to Table 2, it can be found that there are significant seasonal disparities in the impact of each driving factor on SUHI.
In the summer, human interference factors play a dominant role in SUHI, while in the spring, autumn, and winter, surface characteristic factors play a dominant role in SUHI. The difference in the contribution of human interference factors and meteorological elements to SUHI is not obvious in the winter. This result is consistent with the findings of Hu et al. (2022) [25].
In addition, the influence of human interference factors (population density, GDP, and impervious surface) on SUHI gradually decreases as the temperature decreases. Seasonal variations are also apparent in surface characteristic factors. The influence of DEM on SUHI is minimal in the spring and maximal in the autumn. The impact of NDVI on SUHI is largest in the summer (0.174) and lowest in the winter (0.103). This is consistent with the results of Li et al. (2022) [55]. Vegetation can accelerate the heat exchange between the ground and the atmosphere through transpiration and reduce the ground surface temperature through the shade of plant branches and leaves. In the summer, vegetation is lush and the transpiration is strong, while in the winter, most plant leaves fall off or turn yellow, which weakens the degree of plant transpiration. Therefore, the relief of high temperature by vegetation in the winter is not obvious [56].
The impact of land cover type on SUHI among the surface characteristic factors demonstrates a notable descending trend from spring to winter. Land cover type has a relatively large influence on SUHI in the spring and winter (0.174, 0.135), which is consistent with the research conclusion of Xiang et al. (2020) [47] that land cover type explains the largest effect on SUHI in the spring and the second-largest effect in the winter.
There are significant seasonal disparities in the influence of various meteorological elements on SUHI. The influence of wind speed progressively increases from spring to winter to reach the highest value. Evapotranspiration experiences an ascent from spring to autumn, followed by a gradual descent; temperature increases from spring to autumn, and subsequently undergoes a gradual decline.
The results of the interaction detection of driving factors (Figure 7) reveal that the interaction between any two factors has a greater impact on SUHI than that of a single factor. The evolution of SUHI of the Yangtze River Delta urban agglomeration is jointly influenced by multiple factors. The lower the q value of the interaction between factors, the smaller the influence of the interaction between the two corresponding factors on SUHI.
The two driving factors with the most substantial influence on the SUHI interaction in the spring are as follows: land cover type ∩ temperature (0.31), which is related to the higher factor influence of land cover type on SUHI (0.174) in the spring. The two most influential driving factors affecting SUHI interaction in the summer are as follows: population density ∩ temperature (0.38). This is connected to the higher factor influence of population density on SUHI (0.256) in the summer. The impact of human interference on SUHI is most obvious in the summer. The main reason may be that the solar altitude angle is large and the sunshine time is long during the day in the summer. A surface with high heat capacity, such as an impervious surface and bare land, warms rapidly, which also confirms that the change inurban underlying surface and human heat will enhance SUHI [57]. The two driving factors with a significant impact on the SUHI interaction in the autumn and winter are as follows: land cover type ∩ air temperature (0.4, 0.35). This is associated with the higher factor influence of temperature on SUHI (0.262, 0.2) in the autumn and winter, and its interaction effect weakens with decreasing temperatures.
Combined with previous research results, it can be seen that the intensification of the urban heat island effect is due to changes in the properties of the urban underlying surface, the high reflectivity and ability to absorb solar radiation of buildings and roads [57], as well as large amounts of anthropogenic heat release (such as industry, transportation, etc.) and air pollution (such as greenhouse gases, dust, and automobile exhaust fumes, etc.) [58,59]. The intensification of the urban heat island effect causes the temperature in urban areas to rise, forming a warm “island”. This in turn will cause changes in the air flow pattern within the city, causing problems such as high temperatures and air pollution in the city. At the same time, the urban heat island effect will also affect the precipitation pattern of the city, causing changes in the amount and intensity of precipitation in the urban area, further affecting the ecological environment of the city and the quality of life of residents [60].

3.3. Comparative Analysis of Representative Cities

Considering the population, GDP, and geographical location of the study area in 2020, five megacities, Shanghai, Nanjing, Hangzhou, Hefei, and Ningbo, and five relatively economically developed cities, Suzhou, Wuxi, Shaoxing, Taizhou, and Wuhu, were selected to comparatively analyze the spatiotemporal variability characteristics of urban heat islands. The PHI index effectively illustrates the distribution of the urban heat island effect, while the SUHI index reflects the intensity of heat islands in urban areas [25].
The line chart of SUHI changes from 2001 to 2020 (Figure 8a) shows that the seasonal differences in SUHI in Shanghai, Hangzhou, Ningbo, Suzhou, and Wuxi are the most palpable. Notably, the differences in the summer and winter SUHI persistently surpass those in other cities, with Shanghai, Suzhou, and Wuxi experiencing prolonged variations and more substantial cooling intensities.
The line chart of PHI changes from 2001 to 2020 (Figure 8b) indicates that the seasonal differences in PHI in Suzhou, Hangzhou, and Wuxi are the most conspicuous, while this phenomenon is less apparent in other cities. Simultaneously, the PHI of the 10 representative cities has an increasing trend to varying degrees, with the PHI of Suzhou and Wuxi exhibiting the most rapid rises.
From 2001 to 2020, the average SUHI of the 10 representative cities in the Yangtze River Delta urban agglomeration was 1.52 °C, which was higher in the summer (1.97 ± 0.15 °C) compared to spring (1.73 ± 0.09 °C), while autumn (1.39 ± 0.11 °C) was higher than winter (0.99 ± 0.09 °C) (Figure 9a). SUHI difference between cities in the summer was large (1.53 °C), such as Shanghai (2.66 °C) and Nanjing (1.65 °C), and Ningbo (2.67 °C) and Hefei (1.35 °C), while SUHI difference between cities in the winter was small (0.95 °C) (Figure 9a). The summer SUHI of eight cities was >1.53 °C, and the lowest was only 1.14 °C (Wuhu). The average PHI was 35.6%. The difference in PHI between cities was substantial in the autumn (46.7%), such as Shanghai (61.95) and Hangzhou (15.23), Ningbo (40.94%) and Wuhu (22.95%), while the differences were less pronounced in the summer (34.3%) (Figure 9b).
In summary, the summer SUHI differs greatly among the selected representative cities. The reason for this phenomenon may be that the population density in more developed cities such as Suzhou, Ningbo, Shanghai, and Hangzhou are relatively high. Additionally, these cities have a relatively large proportion of impervious surface area, so the vegetation coverage rate is lower than that of relatively underdeveloped cities such as Shaoxing, Hefei, and Wuhu. As urban heat islands continue to expand, thermal environment problems are further exacerbated. Urban planning should consider the construction of internal green landscapes, increasing the proportion of urban green space such as wetland parks and roof gardens, and controlling anthropogenic heat emissions such as automobile exhausts, which will help to reduce the heat island effect [61]. Furthermore, influenced by the differences in thermal properties between sea and land [62], cities such as Nanjing, Hefei, and Wuhu located closer to the inland display significantly lower average SUHI. Conversely, cities near the sea such as Suzhou, Ningbo, and Hangzhou exhibit higher average SUHI.

4. Conclusions

Supported by the MODIS surface temperature data product of the 2001–2020 time series on GEE, this study utilizes methods such as SUHI, PHI, and geographical detector analysis. It expounds the spatiotemporal variation characteristics of surface heat island intensity (SUHI) in the Yangtze River Delta urban agglomeration over a 20-year period and reveals the driving factors affecting SUHI. The main conclusions are as follows:
(1)
The heat island effect in the Yangtze River Delta urban agglomeration presents a Z-shaped distribution pattern in space. The overall performance is an M-shaped distribution expansion from west to east and a Z-shaped distribution expansion from north to south. The annual average SUHI in the study area from 2001 to 2020 was 0.21 °C, with an upward trend. The urban heat island effect is strongest in the spring and summer, followed by autumn, and weakest in the winter. The areas with a significant heat island effect in the Yangtze River Delta urban agglomeration are consistent with the urban core areas. Most cities show a trend of declining SUHI in the core area, while SUHI in the suburbs significantly enhances. This is associated with urban renewal in core areas and vigorous development of urban greening and ecological environmental protection during the process of urbanization.
(2)
The geographical detector results reveal that population density, DEM, and temperature are pivotal driving factors of the heat island effect in the Yangtze River Delta urban agglomeration. The two driving factors with the strongest influence on the SUHI interaction are land cover type and temperature in the spring, autumn, and the winter, and the two driving factors of population density and temperature have the most substantial influence on the SUHI interaction in the summer. The interaction between human interference factors and meteorological elements is particularly strong with significant seasonal changes. The two have the most significant correlation in the summer, and the least impact in the spring. SUHI is closely related to human activities, and rapid urbanization has exacerbated the conversion of a large number of natural surfaces into artificial impervious surfaces. Human interference factors play a dominant role in the summer SUHI.
(3)
The PHI and SUHI of the 10 representative cities in the study area exhibit increasing trends to varying degrees. There are substantial variations in the summer SUHI among these cities. The distribution of heat islands is relatively wide in the spring and summer, with minimal differences between cities in the summer and more significant variations in the autumn.
The research area selected in this paper is typical and the research method is innovative, but there are still some key issues to be resolved. When describing the impact of the urban heat island effect, more statistical analysis methods need to be considered for research, and geographically weighted regression models and spatial correlation analysis need to be used to obtain clear quantitative analysis results of spatial relationships. In addition, the article lacks an explanation of the mechanism of action, and the next step will be to consider using numerical modeling methods for in-depth research. This paper explores the impact of 10 influencing factors on SUHI in the Yangtze River Delta urban agglomeration, but the impact mechanism of the urban heat island effect is relatively complex, and the actual factors affecting the urban heat island effect are far more than the 10 factors discussed in this paper. In view of the research results, the impact of local policies, urban planning, natural disasters, meteorology, hydrology, and other aspects can be considered in the future. With the accelerated evolution of urbanization in the 21st century, how to strike a balance between urban agglomeration development and environmental protection is a major challenge facing government policy planning. This study will deepen the understanding of the changing laws of the urban heat island effect in the Yangtze River Delta urban agglomeration and provide a reference for the formulation of urban land use and development plans to alleviate high temperature heat waves.

Author Contributions

Conceptualization, F.M.; Data curation, X.Y.; Formal analysis, L.Q.; Funding acquisition, F.M. and J.L.; Investigation, L.Q. and H.L.; Methodology, F.M., L.Q., and X.Y.; Project administration, H.L. and J.L.; Resources, H.L. and X.Y.; Software, L.Q. and H.L.; Supervision, J.L.; Validation, F.M. and X.Y.; Visualization, F.M. and L.Q.; Writing—original draft, L.Q.; Writing—review and editing, J.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Shandong Natural Science Foundation grant number [ZR2022MD070]; Jinan City and University Integration Development Project grant number [JNSX2023065l]; the Open Fund of Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University grant number [KLGIS2021A02]; the National Natural Science Foundation of China grant number [42171113]. And the APC was funded by [JNSX2023065l].

Data Availability Statement

All the MODIS datasets and data processing used for the current research had been based on Google Earth Engine platform https://code.earthengine.google.com/. The detailed code of algorithm can be accessed through https://code.earthengine.google.com/9f72a2597d8bc00482c000f4f9625557 (accessed on 13 February 2024).

Acknowledgments

We would like to thank all anonymous reviewers for their insightful comments.

Conflicts of Interest

Hongda Li was employed by Shandong Survey and Design Institute of Water Conservancy Co. Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. Survey of the Yangtze River Delta urban agglomeration. The map is based on a standard map with no modifications to the base map. No: GS (2019)1823; PAC: Provincial Administration Center; PMAC: Prefectural Municipal Administrative Center; CSJ: Yangtze River Delta urban agglomeration; DEM: digital elevation model.
Figure 1. Survey of the Yangtze River Delta urban agglomeration. The map is based on a standard map with no modifications to the base map. No: GS (2019)1823; PAC: Provincial Administration Center; PMAC: Prefectural Municipal Administrative Center; CSJ: Yangtze River Delta urban agglomeration; DEM: digital elevation model.
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Figure 2. Flowchart of the spatiotemporal evolution and influencing factors analysis of SUHI in the Yangtze River Delta urban agglomeration.
Figure 2. Flowchart of the spatiotemporal evolution and influencing factors analysis of SUHI in the Yangtze River Delta urban agglomeration.
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Figure 3. Spatial distribution of 20-year average SUHI of the Yangtze River Delta urban agglomeration. SCI: strong cold island; MCI: moderate cold island; WCI: weak cold island; NHI: no-heat island; WHI: weak heat island; MHI: moderate heat island; SHI: strong heat island.
Figure 3. Spatial distribution of 20-year average SUHI of the Yangtze River Delta urban agglomeration. SCI: strong cold island; MCI: moderate cold island; WCI: weak cold island; NHI: no-heat island; WHI: weak heat island; MHI: moderate heat island; SHI: strong heat island.
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Figure 4. Interannual variation in SUHI in four seasons in the Yangtze River Delta urban agglomeration from 2001 to 2020.
Figure 4. Interannual variation in SUHI in four seasons in the Yangtze River Delta urban agglomeration from 2001 to 2020.
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Figure 5. Spatial distribution of seasonal mean SUHI in the Yangtze River Delta urban agglomeration from 2001 to 2020.
Figure 5. Spatial distribution of seasonal mean SUHI in the Yangtze River Delta urban agglomeration from 2001 to 2020.
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Figure 6. Changing characteristics of spatial distribution of SUHI in the Yangtze River Delta urban agglomeration from 2001 to 2020.
Figure 6. Changing characteristics of spatial distribution of SUHI in the Yangtze River Delta urban agglomeration from 2001 to 2020.
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Figure 7. Interactive detector results. LUCC: land cover type; DEM: digital elevation model; NDVI: normalized difference vegetation index; GDP: gross domestic product; USI: impervious surface; POP: population density; EVP: evapotranspiration; TEM: temperature; PRE: precipitation; WIN: wind speed.
Figure 7. Interactive detector results. LUCC: land cover type; DEM: digital elevation model; NDVI: normalized difference vegetation index; GDP: gross domestic product; USI: impervious surface; POP: population density; EVP: evapotranspiration; TEM: temperature; PRE: precipitation; WIN: wind speed.
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Figure 8. Changes across four seasons in SUHI (a) and PHI (b) of representative cities in the Yangtze River Delta urban agglomeration from 2001 to 2020.
Figure 8. Changes across four seasons in SUHI (a) and PHI (b) of representative cities in the Yangtze River Delta urban agglomeration from 2001 to 2020.
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Figure 9. Mean SUHI (a) and PHI (b) across four seasons in representative cities in the Yangtze River Delta urban agglomeration from 2001 to 2020.
Figure 9. Mean SUHI (a) and PHI (b) across four seasons in representative cities in the Yangtze River Delta urban agglomeration from 2001 to 2020.
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Table 1. Data sources.
Table 1. Data sources.
Data NameData SourceResolutionTime
Surface temperature data http://earthengine.google.com/
accessed on 13 February 2024		1 km2001–2020
Vegetation index datahttp://earthengine.google.com/
accessed on 15 February 2024		1 km2001–2020
Land use datahttp://earthengine.google.com/
accessed on 15 February 2024		500 m2001–2020
DEM datahttp://www.gscloud.cn/
accessed on 16 February 2024		30 m2000
Population density datahttps://hub.worldpop.org
accessed on 18 February 2024		1 km2020
GDP datahttps://www.resdc.cn/
accessed on 22February 2024		1 km2020
Other meteorological datahttps://www.resdc.cn/
accessed on 22 February 2024		1 km2020
Table 2. Surface urban heat island intensity (SUHI) grade division and meaning.
Table 2. Surface urban heat island intensity (SUHI) grade division and meaning.
Grade ValueSUHI (°C)Meaning
1SUHI ≤ −5Strong cold island
2−5 ≤ SUHI ≤ −3Moderate cold island
3−3 ≤ SUHI ≤ −1Weak cold island
4−1 ≤ SUHI ≤ 1No-heat island
51 ≤ SUHI ≤ 3Weak heat island
63 ≤ SUHI ≤ 5Moderate heat island
7SUHI > 5Strong heat island
Table 3. SUHI driving factor geographical detector analysis table.
Table 3. SUHI driving factor geographical detector analysis table.
Factor CategoryIndexSpring (q Value)Summer (q Value)Autumn (q Value)Winter (q Value)
Human InterferencePopulation Density (POP)0.141 **0.256 **0.152 **0.068 **
Gross Domestic Product (GDP)0.135 **0.249 **0.134 **0.045 **
Impervious Surface (USI)0.086 **0.157 **0.092 **0.044 **
Surface FeatureDigital Elevation Model (DEM)0.104 **0.145 **0.222 **0.142 **
Normalized Difference Vegetation Index (NDVI)0.125 **0.174 **0.149 **0.103 **
Land Cover Type (LUCC)0.174 **0.170 **0.154 **0.135 **
Meteorological ElementWind Speed (WIN)0.042 **0.044 **0.051 **0.064 **
Precipitation (PRE)0.063 **0.069 **0.089 **0.044 **
Evapotranspiration (EVP)0.052 **0.070 **0.108 **0.090 **
Temperature (TEM)0.108 **0.167 **0.262 **0.200 **
Note: ** means that the q value is significant at the level of 0.001 (p < 0.001).
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Meng, F.; Qi, L.; Li, H.; Yang, X.; Liu, J. Spatiotemporal Evolution and Influencing Factors of Heat Island Intensity in the Yangtze River Delta Urban Agglomeration Based on GEE. Atmosphere 2024, 15, 1080. https://doi.org/10.3390/atmos15091080

AMA Style

Meng F, Qi L, Li H, Yang X, Liu J. Spatiotemporal Evolution and Influencing Factors of Heat Island Intensity in the Yangtze River Delta Urban Agglomeration Based on GEE. Atmosphere. 2024; 15(9):1080. https://doi.org/10.3390/atmos15091080

Chicago/Turabian Style

Meng, Fei, Lifan Qi, Hongda Li, Xinyue Yang, and Jiantao Liu. 2024. "Spatiotemporal Evolution and Influencing Factors of Heat Island Intensity in the Yangtze River Delta Urban Agglomeration Based on GEE" Atmosphere 15, no. 9: 1080. https://doi.org/10.3390/atmos15091080

APA Style

Meng, F., Qi, L., Li, H., Yang, X., & Liu, J. (2024). Spatiotemporal Evolution and Influencing Factors of Heat Island Intensity in the Yangtze River Delta Urban Agglomeration Based on GEE. Atmosphere, 15(9), 1080. https://doi.org/10.3390/atmos15091080

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