Impacts of Urban Grey–Green Spaces on Diurnal and Nocturnal LST in Summer: A Comparison of Two Local Spatial Identification Approaches

Urban heat islands pose increasing risks to human settlements, yet the differential mechanisms by which grey–green spaces regulate diurnal and nocturnal land surface temperature across local climate zones remain insufficiently understood. This study addresses this gap through a Hangzhou case study, integrating a ten-indicator grey–green space system with two local spatial identification approaches—K-means clustering and an LCZ-inspired simplified scheme—and a Random Forest-SHAP framework. The LCZ-inspired scheme outperformed K-means clustering, with a mean diurnal–nocturnal Test R2 of 0.4344 across twelve models, compared to 0.2977 for K-means. Diurnal and nocturnal LST were driven by systematically different factors: building density dominated daytime LST in most LCZ types (22.0% to 27.8%), while canopy height dominated nighttime LST (22.7% to 30.2%), revealing a systematic shift from building-dominated daytime to vegetation-dominated nighttime. This shift did not occur in compact built-up areas, suggesting that built-up density may be a threshold condition for the shift. Key variables exhibited nonlinear threshold effects with saturation points varying by LCZ type: canopy height cooling saturated at approximately 4 m in LCZ2 but required 17–21 m in LCZ3 and LCZA. These SHAP-based patterns and turning points should be regarded as exploratory, sample-dependent associations evaluated within the training data; their spatial stability across held-out regions was not assessed. Factor interactions were interval-dependent rather than globally fixed. Spatial cross-validation confirmed that random splitting substantially overestimated model performance, highlighting the necessity of spatially explicit validation. The methodological framework provides a replicable approach for urban thermal environment research and offers LCZ-specific threshold hypotheses for thermal regulation planning in subtropical megacities, subject to further spatial and cross-city validation.