Analysis of surface urban heat islands based on local climate zones via spatiotemporally enhanced land surface temperature

Abstract

Surface Urban heat island (SUHI) is a major adverse environmental consequence of urbanization. Many algorithms measuring SUHI across varying spatial or temporal scales are developed rapidly with the availability of thermal infrared (TIR) remote sensing data from satellites. However, the trade-off between the spatial and temporal resolution of TIR sensors limits the study of SUHI on both spatial and temporal domains. Therefore, this study aims to improve the characterization of SUHI using spatiotemporally enhanced land surface temperature (LST) derived from the synergistic use of coarse- and fine-spatial-resolution TIR data. Combining the spatial downscaling and temporal interpolation techniques, we generated daily 100 m-resolution LST in both daytime and nighttime to analyze the SUHI in different local climate zones (LCZs) in Beijing. LCZ is a manifestation of the urban form on the thermal environment, covering hundreds of meters to several kilometers in horizontal scale. The results indicate the spatiotemporally enhanced LST is reliable in capturing the LCZ-based SUHI magnitude compared with original observations, and providing a more accurate time range when the SUHI reaches to its maximum compared with those time-discontinuous original observations. Compared with temporally interpolated coarse-resolution LST, the spatiotemporally enhanced LST shows a larger annual variation of SUHI (especially in LCZ 2 with a mean absolute SUHI difference of 0.8 K and 1.3 K for daytime and nighttime, respectively) and provides larger SUHI difference between compact building and open building (especially when there is a significant SUHI effect). The superiority of the spatiotemporally enhanced LSTs in analyzing LCZ-based SUHI is more evident in daily and monthly SUHI analysis than in single-day analysis or annual analysis, especially in compact building types (LCZ 1 and 2). These findings are valuable information for better and healthier urban planning for SUHI mitigation and public health care.