Enhancing SUHI monitoring in mountainous cities: A comparative study of Chongqing and Chengdu, China

Accurately defining rural reference areas is a key challenge in monitoring the surface urban heat island (SUHI). This challenge is especially pronounced in mountainous cities with complex terrains, where factors such as urban-rural elevation differences, scattered rural settlements, and pixel scale significantly affect SUHI evaluation. Based on the traditional buffer method (TBM), this study proposes a multi-factor decision method (MFD) for defining rural reference areas in SUHI monitoring for mountainous cities. The MFD integrates digital elevation model (DEM), nighttime light (NTL), ‌land use and land cover change (LUCC), and Landsat normalized difference vegetation index (NDVI) data. The applicability of the MFD algorithm was evaluated by comparing its performance with that of the TBM in a mountainous city (Chongqing metropolitan circle) and a plain city (Chengdu). The results show that, as the buffer scale increases from 5 km to 25 km, the mean land surface temperature (LST) of rural reference pixels extracted using the TBM algorithm ( LST t ) decreases by 1.6 K in Chengdu and by 1.9 K in Chongqing metropolitan circle, whereas the LST extracted using the MFD algorithm ( LST m ) decreases by only 0.4 K in Chengdu and 0.6 K in Chongqing metropolitan circle. The changes in LST m are consistently smaller than those of LST t in both study area. These findings indicate that the MFD algorithm provides more stable rural reference areas for SUHI estimation than the TBM algorithm and is more suitable for SUHI monitoring in mountainous cities. SUHI monitoring results reveal that SUHI t (SUHI estimated using TBM) includes some “false heat island” pixels, and its range and intensity increase significantly with buffer scale, whereas the range and intensity of SUHI m (SUHI estimated using MFD) show minimal variation. Additionally, the R-values between SUHI m and socioeconomic factors are consistently higher than those of SUHI t . These findings demonstrate that the MFD algorithm, incorporating multiple factors, provides higher accuracy in SUHI monitoring for mountainous cities compared to the TBM algorithm.