Abstract
With the rapid process of urbanization, anthropogenic heat generated by human activities has become an important factor that drives the changes in urban climate and regional environmental quality. The nighttime light (NTL) data can aptly reflect the spatial distribution of social-economic activities and energy consumption, and quantitatively estimate the anthropogenic heat flux (AHF) distribution. However, the commonly used DMSP/OLS and Suomi-NPP/VIIRS NTL data are restricted by their coarse spatial resolution and, therefore, cannot exhibit the spatial details of AHF at city scale. The 130 m high-resolution NTL data obtained by Luojia 1-01 satellite launched in June 2018 shows a promise to solve this problem. In this paper, the gridded AHF spatial estimation is achieved with a resolution of 130 m using Luojia 1-01 NTL data based on three indexes, NTLnor (Normalized Nighttime Light Data), HSI (Human Settlement Index), and VANUI (Vegetation Adjusted NTL Urban Index). We chose Jiangsu, a fast-developing province in China, as an example to determine the best AHF estimation model among the three indexes. The AHF of 96 county-level cities of the province was first calculated using energy-consumption statistics data and then correlated with the corresponding data of three indexes. The results show that based on a 5-fold cross-validation approach, the VANUI power estimation model achieves the highest R2 of 0.8444 along with the smallest RMSE of 4.8277 W·m−2 and therefore has the highest accuracy among the three indexes. According to the VANUI power estimation model, the annual mean AHF of Jiangsu in 2018 was 2.91 W·m−2. Of the 96 cities, Suzhou has the highest annual mean AHF of 7.41 W·m−2, followed by Wuxi, Nanjing, Changzhou and Zhenjiang, with the annual mean of 3.80–5.97 W·m−2, while the figures of Suqian, Yancheng, Lianyungang, and Huaian, the cities in northern Jiangsu, are relatively low, ranging from 1.41 to 1.59 W·m−2. This study has shown that the AHF estimation model developed by Luojia 1-01 NTL data can achieve higher accuracy at city-scale and discriminate the spatial detail of AHF effectively.
Highlights
With the rapid development of urbanization, urban built-up areas have expanded dramatically
The R2 of power function based on NTLnor and Vegetation Adjusted NTL Urban Index (VANUI) are greater than 0.83, which are much higher than 0.26 of Human Settlement Index (HSI)
We found that there are the significant correlations between NTLnor, VANUI. and AHFsta, especially in the power fitting equation, with R2 about 0.8
Summary
With the rapid development of urbanization, urban built-up areas have expanded dramatically. A number of natural landscapes have been replaced with various artificial surface, which causes sensible heat increases and latent heat decreases, resulting in an urban heat island (UHI) [1,2]. The number of urban populations has continued to surge. According to the statistics from the United Nations Population Division, the proportion of world’s urban population has surged from 30% in 1950 to 55.3% in 2018, and which is expected reach 68% by 2050 [3]. A large amount of anthropogenic heat emissions affects the energy exchange between the land surface and atmosphere within the cities and aggravates the temperature rise in urban and surrounding areas. It is very important to accurately quantify the anthropogenic heat for monitoring the UHI effect and improving the urban environments [7]
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