GLOSTFM: A global spatiotemporal fusion model integrating multi-source satellite observations to enhance land surface temperature resolution

Abstract

Land surface temperature (LST) data are crucial for global climate change research. While remote sensing data serve as a key source for LST, single-source sensor data often lack spatiotemporal continuity due to long satellite revisit intervals and cloud cover. Spatiotemporal fusion, which combines the strengths of multiple sources, can increase the available information. However, most current spatiotemporal fusion methods are designed for local-scale applications. This research proposes the Global Spatiotemporal Fusion Model (GLOSTFM) to generate global LST products. GLOSTFM, built on image pyramid principles, addresses the computational and complexity challenges of global-scale spatiotemporal fusion. Moreover, the model utilizes data from the novel Fengyun-3D satellite, which has a daily revisit capability and provides LST products separately derived from its thermal infrared (MERSI, 1 km) and microwave (MWRI, 25 km) sensors. By leveraging the cloud-penetrating capabilities of the microwave data to compensate for missing information, GLOSTFM increases the available information and reduces observational uncertainties. The results showcase high processing efficiency and enhanced spatiotemporal continuity, with an average RMSE of 2.874 K and an excellent R² of 0.980. The utility of the GLOSTFM model for monitoring urban heat island effects in Beijing was explored to illustrate one application among a broad range of potential applications of the proposed GLOSTFM that require global data on LST across the Earth's surface.