Analysis of Driving Factors of Surface Thermal Environment in Zhejiang Province Based on Remote Sensing Day and Night Temperature Data

Land surface temperature (LST) is a key metric of surface thermal state, governing heat fluxes while supporting climate and environmental applications. Using multi-temporal Landsat 8 Level-2 daytime LST and downscaled MODIS 1 km products (2017) over Zhejiang Province, this study examined the relationship between LST and natural terrain factors across scales, seasons, and day–night cycles. The main findings are as follows: (1) The thermal environment is shaped by multiple driving factors in concert. Their influences on LST show clear spatial and temporal heterogeneity across seasons and times of day. Quantitatively, the models explained 53.6–75.8% of LST variance (R2 = 0.536–0.758). NDBI was positive in spring and summer (β = 0.338 and 0.221) but negative in autumn and winter (β = −0.214 and −0.551), while NDVI and MNDWI were consistently negative (e.g., β = −1.394 and −1.517 in spring). Stable factors were significant at p < 0.05 or p < 0.01. (2) Seasonally, the elevation-induced temperature reduction is most consistent in daytime datasets during autumn, when radiation is moderate and vegetation withers. Vegetation cooling peaks in this season. In summer, strong solar radiation intensifies heat absorption by impervious surfaces, producing the most pronounced urban heat island. In winter, cold air accumulation causes a slope temperature inversion. (3) Diurnally, daytime LST is governed mainly by shortwave radiation, with slope, shaded relief, and evaporative cooling all playing significant roles. Data of different resolution consistently capture a stable negative correlation pattern. At night, LST is controlled primarily by longwave cooling and anthropogenic heat storage; cooling accelerates on mountaintops, and the thermal signals of water and vegetation reverse. These results highlight the utility of multi-scale, day–night LST data for understanding thermal environments in subtropical monsoon regions.