Global intraday land surface temperature estimation of enhanced coverage by fusion of passive microwave data between different polar orbits

Land surface temperature (LST) is a critical parameter for understanding land–atmosphere interactions, hydrology, and ecological dynamics. While thermal infrared (TIR) remote sensing has traditionally been used for LST retrieval, its effectiveness is limited by cloud cover and atmospheric interference. Passive microwave (PMW) remote sensing offers a significant advantage by enabling all-weather LST retrieval, as microwave signals can penetrate clouds and precipitation. However, PMW-based LST observations from a single satellite platform suffer from significant orbital gaps, particularly in middle and low latitudes, due to the limited swath width of current sensors. Additionally, the intraday revisit frequency of PMW LST is constrained to a maximum of two times per day (ascending and descending modes), which is insufficient for capturing rapid diurnal temperature variations or supporting high-temporal-resolution applications.In this study, therefore, we address these limitations by proposing an innovative framework for intraday LST estimation with enhanced spatial coverage. This is achieved by fusing PMW data from two polar-orbiting satellites, Fengyun-3D (FY-3D) and Fengyun-3F (FY-3F), which operate at different equatorial crossing times. A sophisticated gap-filling algorithm is introduced, leveraging temporally adjacent LST estimates from intraday brightness temperature (TB) observations. Results demonstrate that the gap-filled LST from the optimal data fusion scheme exhibits a minimal positive bias of approximately 0.1–0.2 K compared to the original LST retrievals, while achieving an intraday revisit frequency of up to four times per day in middle latitudes. This represents a significant improvement over the pre-gap-filling frequency of 2.5 times per day. The framework not only enhances the spatial coverage of PMW-based LST but also provides a foundation for future satellite missions to further improve global LST monitoring. By enabling all-weather, high-frequency LST observations, this framework advances our understanding of land–atmosphere interactions, supports climate modeling, and enhances environmental monitoring capabilities.