Annual 1-kilometer spatial dataset of summer urban heat island and spatial expansion analysis in China from 2005 to 2020
The Earth's climate system is undergoing global climate change characterized by warming,which is influenced by both natural climate and human activities.In the context of global warming and accelerated urbanization,extreme climate risks such as heat waves are intensifying,leading to serious consequences such as deaths.As an important manifestation of the disturbance of the Earth's climate system,Urban Heat Island(UHI)is a typical phenomenon of the combined effects of global climate change and human activities.Therefore,establishing a long time series and high spatiotemporal resolution dataset of UHI effects is of great scientific significance and practical value for establishing a systematic high-temperature response framework,such as high-temperature response plans,mitigation and adaptation guidelines,decision support systems,policy incentive guidelines,etc.This study coupled data of land surface temperature,land use and cover,urban built-up boundary,and digital elevation model,and based on the Google Earth Engine(GEE)platform,performed a temporal linear interpolation on the 8-day MODIS LST(Land Surface Temperature)product,filled the missing data of MODIS from the temporal dimension,and used the values obtained by the temporal linear interpolation to fill the missing values,which are the average temperatures of the adjacent times under the missing values,producing a seamless LST data for the whole country.Furthermore,using a dynamic simplified urban boundary algorithm,within the urban built-up boundary,according to the different types of land use and cover,the urban built-up areas and water bodies such as rivers were excluded,and the cases with large differences in digital elevation were also excluded,obtaining the rural areas,and then calculating the average rural temperature,and then using the average rural temperature as the background,calculating the UHI intensity according to the temperature within the urban built-up boundary,obtaining a spatial dataset of summer land surface UHI in China with an annual 1 km spatial resolution from 2005 to 2020.And according to the morphological relationship of UHI changes,the UHI morphological changes were expressed by the UHI spatial expansion index,and according to the size of the index,they were divided into edge type,filled type,and enclave type,and finally the spatial expansion characteristics of the summer day and night UHI in China from 2005 to 2020 were revealed by the UHI spatial expansion index.The research results show that the summer daytime and nighttime land surface UHI area in China increased by 1.95 and 2.49 times,respectively,from 2005 to 2020.The summer daytime and nighttime land surface UHI intensity in China in 2020 were 1.36°C and 1.33°C,respectively,an increase of 0.08°C and 0.38°C compared to 2005.The UHI intensity is relatively stable in the eastern region,but high and fluctuates greatly in the western region.In summer 2005,the surface UHI intensity was higher during daytime than at night.In summer 2020,the nighttime surface UHI intensity increased significantly,especially in the central and eastern regions,which was higher than the daytime surface UHI intensity.The spatial expansion of the summer daytime and nighttime land surface UHI in China from 2005 to 2020 was dominated by the edge type,and the degree of UHI spatial expansion was the highest in 2015-2020.The filled type UHI spatial expansion had the highest UHI intensity.This study used the land surface temperature temporal linear interpolation algorithm,which improved the temporal accuracy of the original MODIS LST temperature data,ensuring that the land surface temperature data had no missing data,and used the GUB data of multiple years to identify the UHI effects of the corresponding years,and dynamically updating the GUB data was the most important guarantee for improving the spatial identification accuracy of the UHI effects.And based on the research,it proposed to use the spatiotemporal interpolation algorithm and annual GUB data to further improve the accuracy of the spatial dataset of UHI in China.The temporal linear interpolation algorithm and the dynamic simplified urban boundary algorithm used in this study provide a technical paradigm for the quantitative identification of UHI effects in long time series,and the spatial dataset of land surface UHI in China provides data support for actively adapting and mitigating urban thermal environmental risks and promoting urban sustainable development.
国家科技期刊平台
NSTL
万方数据
