AUTHOR=Xin Jiaxing , Yang Jun , Wang Ling-en , Jin Cui , Xiao Xiangming , Xia Jianhong (Cecilia) TITLE=Seasonal differences in the dominant factors of surface urban heat islands along the urban-rural gradient JOURNAL=Frontiers in Environmental Science VOLUME=10 YEAR=2022 URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2022.974811 DOI=10.3389/fenvs.2022.974811 ISSN=2296-665X ABSTRACT=

Urbanization has been accelerating; hence the effect of urban heat island (UHI) has increased. There has been extensive research on spatiotemporal UHI changes and drivers, however, data on the dominant seasonal factors of UHIs and the differences along urban-rural gradients remain limited. Based on Luojia-1A, Landsat 8, and moderate resolution imaging spectroradiometer (MODIS) data, we assessed the seasonal differences in surface UHI (SUHI), normalized differences in vegetation index (NDVI), built-up index (NDBI), and water index (NDWI) and their relationships in the Dalian City, Northeast China. We found that in the urban built-up area, the mean SUHI intensity (SUHII) decreased from that in summer (2.74°C) > autumn (1.65°C) > winter (0.28°C) > spring (−0.79°C). SUHII was more strongly affected by NDWI and NDBI than NDVI, and NDBI and NDWI showed positive and negative correlations with SUHII in different seasons, while NDVI and SUHII were positively correlated in spring and negatively correlated in the other seasons. When analyzing the dominant factors of SUHII, the importance results showed that, in spring, NDBI > NDVI > NDWI, in autumn, NDVI > NDWI > NDBI, in summer and winter, NDWI > NDVI > NDBI. In addition, SUHII changed the most in summer along the urban-rural gradient, decreasing from 2.74°C to −2.74°C. Among these indicators, except for spring NDVI which increased from 0.09 to 0.59 with distance from built-up areas, there was minimal change in NDVI, NDBI, and NDWI along the urban-rural gradient in other seasons (i.e., all were within 0.2). In this study, the difference analysis of SUHI and remote sensing indices along the urban-rural gradient can help to facilitate the rational layout of cities.