Annual cyclical variations of urban green space cold island(GSCI) effect by combining remote sensing inversion and...

Combining multiple methods offers a practical approach to studying long-term variations in the urban green space cold island(GSCI) effect. This research integrates remote sensing inversion and numerical simulation to investigate the annual cycle of the GSCI in Huachong Park, Hefei City. Initially, 59 remote sensing images from various seasons between 2010 and 2020 were retrieved using Landsat series image data and atmospheric correction methods to invert Land Surface Temperatures(LST), which preliminary identified the GSCI's annual cycle variations. Subsequently, meteorological data for Hefei from 2010 to 2020 were extracted using the Solar Terms Typical Meteorological Day(STTMD) method to obtain representative annual meteorological data. These data were then input into the ENVI-met software for numerical simulations of the study area, capturing diurnal variations of the cold island effect at 24-time points and predicting annual changes in cold island intensity. The results indicate that: (1) The GSCI exhibits an annual cycle and seasonal variations characterized by "strong in summer and weak in winter, cooler in summer and warmer in winter"; (2) A progressive relationship exists between remote sensing inversion and ENVI-met numerical simulation in studying the temporal variation of the GSCI, with the integration of these methods yielding a more comprehensive spatiotemporal analysis of the GSCI over long-term scales;(3) The STTMD method effectively simplifies representative meteorological data, progressively combining remote sensing retrievals and numerical simulations to facilitate the acquisition of comprehensive spatiotemporal variations of the green space heat effect over extended periods. These findings advance understanding of the long-term dynamics of cold island effects within urban green spaces, providing valuable insights for urban planners and environmental researchers.