Public spaces in residential areas are the main places for the daily activities of residents, and thermally comfortable activity space is particularly important for promoting the activities and public health of residents. Previous studies have mostly focused on tropical regions, with less research on outdoor thermal comfort in subtropical regions. In this study, we aimed to investigate outdoor thermal benchmarks and outdoor activities in summer subtropical residential areas. Through physical measurements, questionnaires, and field observations, four residential areas in Changsha city were studied. The outdoor thermal comfort and activity of residents were studied with the physiological equivalent temperature (PET) as a model. The results showed that: (1) the neutral PET and neutral PET ranges for residents were 26.2 °C and 24.7–27.8 °C, respectively, and the range of 90% acceptable PET was lower than 28.5 °C; (2) globe temperature and air temperature were the primary factors influencing the thermal sensation of the residents; and (3) residents adapted to the thermal environment by adjusting their activity time and space in summer. These results help us to better understand the outdoor thermal comfort and activities of people living in subtropical regions and can provide guidance for the design of future residential open spaces.

The Landsat land surface temperature (LST) product is widely used to understand the impact of urbanization on surface temperature changes. However, directly comparing multi-temporal Landsat LST is challenging, as the observed LST might be strongly affected by climatic factors. This study validated the utility of the pseudo-invariant feature-based linear regression model (PIF-LRM) in normalizing multi-temporal Landsat LST to highlight the urbanization impact on temperature changes, based on five Landsat LST images during 2000–2018 in Changsha, China. Results showed that LST of PIFs between the reference and the target images was highly correlated, indicating high applicability of the PIF-LRM to relatively normalize LST. The PIF-LRM effectively removed the temporal variation of LST caused by climate factors and highlighted the impacts of urbanization caused land use and land cover changes. The PIF-LRM normalized LST showed stronger correlations with the time series of normalized difference of vegetation index (NDVI) than the observed LST and the LST normalized by the commonly used mean method (subtracting LST by the average, respectively for each image). The PIF-LRM uncovered the spatially heterogeneous responses of LST to urban expansion. For example, LST decreased in the urban center (the already developed regions) and increased in the urbanizing regions. PIF-LRM is highly recommended to normalize multi-temporal Landsat LST to understand the impact of urbanization on surface temperature changes from a temporal point of view.