The thermal environment is closely related to human well-being. Diurnal and seasonal variations in surface urban heat islands (SUHIs) have been extensively studied. Nevertheless, interannual changes in SUHIs as well as in land surface temperatures (LSTs) in cities and their corresponding villages remain poorly understood, particularly using data from several continuous years to analyse change rates and corresponding significance levels. Using Aqua/Terra moderate resolution imaging spectroradiometer (MODIS) data for 2003–2013, we explored not only the interannual changes in annual and seasonal mean LSTs in rural and urban regions which were identified based on modified criteria, but also the SUHI intensities (SUHIIs) for these cities. The results showed that most of LSTs and SUHIIs did not change significantly (
The thermal environment can directly and indirectly affect human health, comfort and quality of life, energy use, air quality, occurrence and activity level of creatures, hydrology, soil physicochemical properties, etc. Changes in the thermal environment and effects of human activities on these changes are research hotspots, throughout China, which has experienced an extremely rapid and intense urbanization over the past few decades [
Locations of the 1449 cities selected for this study in the five environmental subareas.
The ecological function recognition data were provided by the Data Sharing Infrastructure of Urban and Regional Ecological Science. China was divided into three first-level, 50 second-level, 206 third-level, and 1434 fourth-level ecological function regions based on their landforms, water and heat combinations, and vegetation characteristics.
Digital elevation data with a 1-km resolution were taken from the Cold and Arid Regions Sciences Data Center at Lanzhou. The monthly mean daytime and night-time LST MODIS/Aqua data with a 1 km resolution from 2003 to 2013 were downloaded from the Geospatial Cloud of Computer Network Information Centre, Chinese Academy of Sciences website. The Aqua satellite passed over the study area twice a day, at approximately 1 : 30 am and 1 : 30 pm, local time, during the daytime, and night-time, respectively. Land use data from 2000 to 2010, which had a 1-km resolution, were provided by the Data Centre for Resources and Environmental Sciences, Chinese Academy of Sciences. Data were primarily produced from the interpretation of Landsat TM/ETM + images [
China was divided into five regions based on ecological function recognition results at the first, second, and third levels (Figure
Urban land polygons were first aggregated at a distance of 1 km, which was sufficient to include most adjacent and scattered urban land polygons in the urban class and was able to distinguish main city zones and satellite cities or two closely adjacent cities that should be considered separately according to standard perceptions. Cities with areas larger than 6 km2 in 2010 were considered, which included the vast majority of cities in the eastern regions in China; the latter were cities with the highest population densities and most well-developed economies. The urban regions only considered pixels that belonged to cities in both 2000 and 2010. The corresponding rural zones were defined as the buffer zones of cities in 2010, with buffer distances between 5 and 10 km [
First, mean daytime and night-time LSTs in the 1449 urban and corresponding rural regions and SUHIIs for these cities were calculated in each of the four seasons and the whole year for each year. The seasons were defined based on definitions of the meteorological seasons [
The LSTs in the vast majority of rural regions did not change significantly from 2003 to 2013 for the five environmental regions of China (
Spatial distribution of the annual and seasonal change rates of LSTs in rural regions during the daytime and night-time from 2003 to 2013 in the five environmental regions of China: (a–e) the results during the whole year, winter, spring, summer, and autumn daytime, respectively; (f–j) corresponding values during night-time. (a) Annual day. (b) Winter day. (c) Spring day. (d) Summer day. (e) Autumn day. (f) Annual night. (g) Winter night. (h) Spring night. (i) Summer night. (j) Autumn night.
From a regional comparison aspect, the lowest spatial heterogeneity in the LST change rates in rural regions with significant variations was observed in the autumn during both the daytime and night-time, as shown in Figure
Statistics of the variation rates of mean LSTs in rural regions that significantly changed in the whole year and four seasons during the daytime and night-time from 2003 to 2013 in the five environmental regions, China (A–E represent the mean comparison results among regions, using nonparametric tests of
Limited by the number of samples, comparisons of the mean rates in different periods for the same region were difficult to perform. However, distinct seasonal variation laws could be observed for changes in the LSTs in rural regions of most of the regions in China during both the daytime and night-time. The rates were usually highest in the summer, lower in the spring and autumn, and lowest in the winter. In China, the annual mean rates of the daytime and night-time LSTs with significant changes (
The LSTs can increase notably in newly developed urban regions. Nevertheless, this study focused only on land parcels that had always belonged to urban lands during the study period. The spatiotemporal change laws of LSTs in urban regions were always closely related to those in corresponding rural regions (Figure
Spatial distribution of the annual and seasonal change rates of LSTs in urban regions during the daytime and night-time from 2003 to 2013 in the five environmental regions of China: (a–e) the results during the whole year, winter, spring, summer, and autumn daytime, respectively; (f–j) corresponding values during night-time. (a) Annual day. (b) Winter day. (c) Spring day. (d) Summer day. (e) Autumn day. (f) Annual night. (g) Winter night. (h) Spring night. (i) Summer night. (j) Autumn night.
The regional comparison findings for the change rates of LSTs with significant variations in the urban regions were similar to those in rural regions (Figure
Statistics of the variation rates of mean LSTs in urban regions that significantly changed in the whole year and four seasons during the daytime and night-time from 2003 to 2013 in the five environmental regions of China (A–E represent the mean comparison results among regions, using nonparametric tests of
During the daytime, the largest differences occurred in the summer between the spatiotemporal variations in LSTs in urban regions and corresponding rural regions. Not only was there a clear increase in the number of cities whose LSTs significantly changed with positive rates, but the change rates also notably increased. The largest change was observed in Simao City in the southern Yunnan–Guizhou Plateau during the daytime in the summer, with a rate of 0.73°C (yr−1), which was 0.20°C (yr−1) higher than the highest change rate in rural regions. In the spring, the change rates in urban regions were also higher than those in rural regions, particularly for certain cities in the middle and lower reaches of the Yangtze River. The lowest change rate was observed in Baicheng City in the western Songnen Plain during the daytime in the winter, which was −1.18°C (yr−1). The abovementioned changes were harmful to humans in most cases in China. During the night-time, a certain degree of differences existed for the change laws of LSTs between urban and corresponding rural regions. The main change types were as follows. First, LSTs did not change significantly in rural regions but changed significantly in urban regions with positive rates, such as the change rates in the northern central Yunnan Province at the scale of the whole year, winter and spring. Second, LSTs changed significantly with negative rates in the rural regions but did not change significantly in urban regions, such as in northeastern and northern China in the autumn, or in Jiaodong Peninsula in the spring. Third, the magnitudes of these negative changes decreased, such as that occurred in northeastern and northern China in the whole year. Finally, the rates increased for positive significant changes, such as in certain regions in the northern China in the summer. Both the change rates and spatial scopes of LSTs could be affected by human activities in the four seasons during the night-time, although the effects were less pronounced than those during daytime. The LSTs could decrease in urban regions if effective measures were taken [
The SUHIIs in the vast majority or most of rural regions did not change significantly from 2003 to 2013 in the five environmental regions of China in the whole year and four seasons (Figure
Spatial distribution of the annual and seasonal change rates of SUHIIs during the daytime and night-time from 2003 to 2013 in the five environmental regions of China: (a–e) the results during the whole year, winter, spring, summer, and autumn daytime, respectively; (f–j) corresponding values during night-time. (a) Annual day. (b) Winter day. (c) Spring day. (d) Summer day. (e) Autumn day. (f) Annual night. (g) Winter night. (h) Spring night. (i) Summer night. (j) Autumn night.
In the winter, the change rates of daytime SUHIIs with significant variations showed a distinct spatial heterogeneity (Figure
The statistical of variation rates of mean city SUHIIs that changed significantly throughout the year and during four seasons during the daytime and night-time from 2003 to 2013 in the five environmental regions of China (A–E represent the mean comparison results among regions, using nonparametric tests of
The change rates of the annual mean daytime and night-time SUHIIs in China that had significantly changed were 0.03 ± 0.11 and 0.05 ± 0.03°C (yr−1), respectively. The cities with significant positive changes were mainly concentrated in certain regions of Anhui and Jiangsu Provinces in the middle and lower reaches of the Yangtze River during the daytime and in central northern China during the night-time. Most of rates were most 0.0–0.2°C (yr−1). During the daytime, the interannual variations in the SUHIIs exhibited clear seasonal laws. In the summer, not only the change rates but, in general, also the spatial scopes with significant changes were generally notably the highest. The highest rate was 0.78°C (yr−1), which occurred in Simao City in the southern Yunnan–Guizhou Plateau during the daytime in the summer. In the spring, the change rates were generally lower and primarily positive. In the autumn, the rates were generally further lower and even negative in the vast majority of cities with significant changes in SUHIIs occurring in central northern China. The change rates were generally lowest in the winter. In addition, the lowest rate was −0.77°C (yr−1), which occurred in the Wulatezhong Banner in central northern Inner Mongolia during the daytime in the winter. These results were consistent with those from previous studies, in which it had been observed that the intensities or areas of SUHIs were largest in the summer, smaller in the spring and autumn, and smallest in the winter with cold island effects in the temperate region [
Although MODIS LST data have been validated and have been found to be highly accurate in many cases, and as a result, have been widely accepted and used, certain uncertainties and issues remain, particularly related to urban environments and rural areas with complex terrains or vegetation types caused by the highly complicated spatial heterogeneity in landscape components, higher air pollution levels in cities, and well-known anisotropy issues [
We studied interannual spatiotemporal changes in the LSTs in 1449 urban and corresponding rural regions and changes in SUHIIs for these cities from 2003 to 2013 in five environmental regions with different ecological contexts in China. Certain important conclusions can be summarized as follows: Most of LSTs in urban and rural regions and SUHIIs for cities did not change significantly. Their changes had clear spatiotemporal agglomeration and variation laws. In general, the changes in LSTs in rural regions exhibited distinct seasonal variations in most regions of China during both the daytime and night-time. The rates with significant changes were usually highest in the summer, and most of them were 0.1–0.5°C (yr−1) in China except for Xinjiang Province, which had negative rates during the daytime, and most of the rates with significant changes were 0.1–0.2°C (yr−1) during night-time. The rates with significant changes were lowest in the winter, and most of them were −0.4 to −0.1°C (yr−1). The spatiotemporal change laws of LSTs in urban regions were closely related to those in corresponding rural regions. During the daytime, the differences were the largest in the summer. Not only was there a notable increase in the number of cities whose mean LST values significantly changed with positive rates, but the change rates also clearly increased. During the night-time, human activities resulted in increased LSTs in the four seasons. During the daytime, the interannual variations in SUHIIs exhibited clear seasonal change laws. In the summer, not only the change rates but also the spatial scopes with significant variations were clearly the largest. The next highest rates occurred in the spring, in which the change rates were mainly positive, followed by the rates in the autumn. The variations were smallest in the winter, when the change rates in northcentral China were even negative. During the night-time, the variations in SUHIIs exhibited fewer seasonal fluctuations. The change rates remained almost unchanged, and most of them were 0.0–0.1°C (yr−1).
The digital elevation data were taken from
The authors declare that there are no conflicts of interest regarding the publication of this paper.
This research was funded by the Natural Science Foundation of China (grant nos. 41701501 and 41771141) and Social Science Fund of China (General Projects) (grant no. 17BJL065).
The following supporting information is available as part of the online article. Figure S1: the percentages of reference rural regions whose annual and seasonal variation rates of mean LSTs significantly changed during both the daytime and night-time from 2003 to 2013 in the five environmental regions of China. Figure S2: the percentages of reference rural regions whose annual and seasonal variation rates of mean LSTs significantly positively changed during both the daytime and night-time from 2003 to 2013 in the five environmental regions of China. Figure S3: the percentages of urban regions whose annual and seasonal variation rates of mean LSTs significantly changed during both the daytime and night-time from 2003 to 2013 in the five environmental regions of China. Figure S4: the percentages of urban regions whose annual and seasonal variation rates of mean LSTs significantly positively changed during both the daytime and night-time from 2003 to 2013 in the five environmental regions of China. Figure S5: the percentages of cities whose annual and seasonal variation rates of mean SUHIIs significantly changed during both the daytime and night-time from 2003 to 2013 in the five environmental regions of China. Figure S6: the percentages of cities whose annual and seasonal variation rates of mean SUHIIs significantly positively changed during the daytime and night-time, and differences between the daytime and night-time rates from 2003 to 2013 in the five environmental regions of China. Table S1: typical monitoring indicators of SUHIIs.