Abstract
Urban heat island networks characterize the spatial organization and potential connectivity of relatively high-temperature patches, but multi-temporal analyses are constrained by the limited availability of fine-resolution and temporally dense land surface temperature observations. This study develops a dynamic analytical framework integrating LST reconstruction, morphological spatial pattern analysis, circuit theory, and multidimensional network indicators. Landsat 8/9 LST and daily 1 km TRIMS LST were integrated using STARFM to reconstruct daily 30 m LST for May–October during 2019–2023, which was subsequently aggregated to monthly mean LST for repeated UHIN identification. The reconstructed LST showed close agreement with Landsat observations, with an overall RMSE of 2.4675 °C and an SSIM of 0.9883. At the interannual scale, warm-season mean PLAND ranged from 16.38% to 18.16%, while PD, ACL, and the β index showed different temporal responses. At the intra-annual scale, increases in relative source-area extent toward August were generally accompanied by decreasing patch density, followed by renewed fragmentation later in the warm season. These asynchronous variations indicate that relative source-area extent alone cannot fully characterize UHIN changes. The proposed framework provides a consistent basis for multi-temporal UHIN analysis and network-informed urban heat-mitigation planning.