Integrating geographic knowledge into deep learning for spatiotemporal local climate zone mapping derived thermal environment exploration across Chinese climate zones

The Local Climate Zone (LCZ) scheme representing urban structure and land use pattern is essential for urban heat island (UHI) research. Fine-grained LCZ mapping considering spatial and temporal heterogeneity can provide a more precise characterization of surface thermal properties, thereby enabling a comprehensive analysis and understanding of spatiotemporal trends in climate change research. However, data-driven deep learning-based methods have limitations in coping with the complex urban landscapes of the real-world scenarios, including the spectral similarity of different LCZ and the geospatial heterogeneity of urban LCZ categories. In this study, we constructed a geographic knowledge base for enhanced LCZ characterization with the consideration of prior surface spatial information, including a set of spectral indices and urban morphological parameters (UMPs). Then, we integrated the explicable geographic knowledge base into a learnable deep learning framework in an end-to-end manner for accurate LCZ mapping by fusing multi-source heterogeneous data with a multi-level fusion strategy. The constructed Chinese Climate Zone Time Series LCZ (CClimate-TLCZ) dataset derived from Landsat-8 data with a 30 m spatial resolution, covering 18 representative cities in China, were used to evaluate the proposed framework. The experimental results demonstrate that the proposed framework achieved optimal outcomes across 18 cities, with an average overall accuracy exceeding 94 %, which is more than 20 % higher than that obtained by the standard WUDAPT method. Furthermore, the analysis of LCZ mapping-driven land surface temperature (LST) and surface UHI (SUHI) applications shows that cities within the same climate zone have similar LST distribution patterns, while significant heterogeneity exist between different zones. The annual consistency of LST patterns within each climate zone supports the validity of the LCZ classification scheme and accurate LCZ mapping for urban thermal environment studies. From 2016 to 2022, SUHI intensity initially increases and then decreases, indicating improvements in the urban thermal environment. These findings underscore the critical role of precise LCZ mapping in urban climate resilience and sustainable urban planning.