遥感地表温度产品是城市热环境演变研究的重要数据源。然而，受遥感器回访周期长及云雨天气情况下数据缺失等因素影响，高分辨率地表温度产品的代表性不足，导致精细尺度下城市长时序热环境的研究受限。本研究利用Landsat和MODIS遥感数据，采用时空融合方法重建了2013–2022年武汉市核心城区夏季长时序高分辨率地表温度均值，并在精细尺度对武汉市热环境演变进行了分析。结果表明：（1）重建的高分辨率地表温度均值产品与地面站点观测数据具有较强的一致性，同时可以反映精细尺度下城市热环境时空变化的高度异质性；（2）2013年–2022年，武汉市主城区高地表温度区占比呈现降低趋势并沿新城组群向周边区域扩张，原本独立的高温区域逐渐连接成片；（3）2013–2022年，武汉市各新城组群夏季高地表温度区除东南新城组群外皆呈现出扩张趋势，其中北部、西部、西南部扩张明显。本研究可为精细尺度下城市热环境的时空格局研究提供支撑，对城市生态文明建设和可持续发展具有重要意义。

Objective: Remote sensing-derived land surface temperature (LST) products are essential for studying urban thermal environment dynamics. However, limitations such as long revisit intervals of remote sensors and data gaps caused by cloudy or rainy weather hinder the representativeness of high-resolution LST products. As a result, long-term studies of urban thermal environments at fine spatial scales remain constrained. This study aims to reconstruct high-resolution summer mean LST data for Wuhan"s core urban area from 2013 to 2022 using Landsat and MODIS remote sensing data through spatiotemporal fusion methods, and to analyze the evolution of Wuhan"s thermal environment at a fine scale. Method: The research employed spatiotemporal fusion techniques to integrate Landsat and MODIS data, reconstructing long-term high-resolution summer mean LST for Wuhan"s core urban area. The study area covered Wuhan"s central city and urban development zones. Validation was conducted using ground meteorological station data, with accuracy assessed through MAE, RMSE and R2 metrics. LST classification and trend analysis were performed to examine spatial-temporal patterns of thermal environment changes. Result: Key findings include: (1) The reconstructed high-resolution mean LST product demonstrated strong consistency with ground observations (MAE=0.478℃, RMSE=0.5965℃, R2=0.8538), effectively capturing the high spatiotemporal heterogeneity of urban thermal environments at fine scales; (2) From 2013 to 2022, the proportion of high-temperature zones in Wuhan"s main urban area showed a decreasing trend while expanding towards surrounding new town clusters along development axes, with previously isolated high-temperature areas gradually merging; (3) During 2013-2022, all new town clusters except the southeastern cluster exhibited expansion of high-temperature zones, with particularly significant growth in northern, western and southwestern areas. Conclusion: This study provides an effective approach for reconstructing high-resolution LST data and analyzing fine-scale urban thermal environment patterns. The findings offer valuable insights for urban ecological civilization construction and sustainable development, supporting evidence-based urban planning and heat island mitigation strategies. The methodology and results contribute to advancing research on spatiotemporal patterns of urban thermal environments at fine scales.