Urban heat islands are representative problems in urban environments. The impact of spectral indexes on land-surface temperature (LST) under different urban forms, climates, and functions is not fully understood. Local climate zones (LCZs) are used to characterize heterogeneous cities. In this study, we quantified the contribution of three cities to high-temperature zones and surface urban heat island intensity (SUHII) across LCZs and seasons, used Welch and Games–Howell tests to analyze the difference in LST, then described the spatial pattern characteristics of LST, and used a geographically weighted regression model to analyze the relationship between spectral indexes and LST. The results showed that compact midrise, compact low-rise (LCZ 3), large low-rise (LCZ 8), heavy industry (LCZ 10), and bare rock or paved (LCZ E) contributed greatly to high-temperature zones and had strong SUHII. There were 92–98% significant differences between different LCZs. The spatial aggregation of LST gradually weakened with a decrease in temperature. The modified normalized difference water index (MNDWI) in most LCZs of all seasons for Wuhan could reduce LST well, while MNDWI only had cooling effects in winter for Nanjing and Shanghai. Normalized difference vegetation index (NDVI) in most LCZs performed a cooling role during summer and transition seasons (spring and autumn), while it showed a warming effect in winter. The cooling effect of NDVI in open building types was stronger than that of compact building types, while the cooling effect of MNDWI was better in compact building types than in open building types. With the increase of normalized difference built-up index (NDBI), all LCZs showed warming effects, and the magnitude of LST increase varied in different cities and seasons. These results contribute further insight into thermal environment in heterogeneous urban areas.

Urban blue spaces (UBS) have been shown to provide a multitude of cultural ecosystem services to urban residents, while also having a considerable impact on the surrounding community’s house prices. However, the impact of different types of UBS and the effect of their abundance on house prices have been largely understudied. This study aims to address this gap by examining the impact of different types of UBS on house prices using eight megacities in China as a case study. Spatial hedonic price models are developed to assess the impact of different types of UBS on house prices, and differences in their impact across cities are identified. Variance partitioning analysis is also used to decompose the relative contributions of UBS variables and explore the relationship between UBS-attributable premiums and the abundance of UBS. The results indicate that lakes and the main river have a significant positive impact on house prices in most cities, while the impact of small rivers on house prices varies across cities. The influence of UBS variables differs significantly across cities, but these differences are not solely driven by the abundance of UBS. This study provides valuable information for UBS planning and management and contributes to the equitable distribution of urban public services.