Su Qin, Yao Yuan, Chen Cheng, Chen Bo
School of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China.
Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Province, Chengdu University, Chengdu 610106, China.
Sensors (Basel). 2024 Nov 21;24(23):7424. doi: 10.3390/s24237424.
Land surface temperature (LST) is a critical parameter for understanding climate change and maintaining hydrological balance across local and global scales. However, existing satellite LST products face trade-offs between spatial and temporal resolutions, making it challenging to provide all-weather LST with high spatiotemporal resolution. In this study, focusing on Chengdu city, a framework combining a spatiotemporal fusion model and machine learning algorithm was proposed and applied to retrieve hourly high spatial resolution LST data from Chinese geostationary weather satellite data and multi-scale polar-orbiting satellite observations. The predicted 30 m hourly LST values were evaluated against in situ LST measurements and Sentinel-3 SLSTR data on 11 August 2019 and 21 April 2022, respectively. The results demonstrate that validation based on the in situ LST, the root mean squared error (RMSE) of the predicted LST using the proposed framework are around 0.89 °C to 1.23 °C. The predicted LST is highly consistent with the Sentinel-3 SLSTR data, and the RMSE varies from 0.95 °C to 1.25 °C. In addition, the proposed framework was applied to Xi'an City, and the final validation results indicate that the method is accurate to within about 1.33 °C. The generated 30 m hourly LST can provide important data with fine spatial resolution for urban thermal environment monitoring.
地表温度（LST）是理解气候变化以及维持局部和全球尺度水文平衡的关键参数。然而，现有的卫星LST产品在空间和时间分辨率之间面临权衡，这使得提供具有高时空分辨率的全天候LST具有挑战性。在本研究中，以成都市为重点，提出了一种结合时空融合模型和机器学习算法的框架，并将其应用于从中国静止气象卫星数据和多尺度极轨卫星观测中检索每小时的高空间分辨率LST数据。分别于2019年8月11日和2022年4月21日，根据现场LST测量值和哨兵-3 SLSTR数据对预测的每小时30米LST值进行了评估。结果表明，基于现场LST进行验证时，使用所提出框架预测的LST的均方根误差（RMSE）约为0.89℃至1.23℃。预测的LST与哨兵-3 SLSTR数据高度一致，RMSE在0.95℃至1.25℃之间变化。此外，将所提出的框架应用于西安市，最终验证结果表明该方法的精度在约1.33℃以内。生成的每小时30米LST可为城市热环境监测提供具有精细空间分辨率的重要数据。
