A Study of Global Hourly Sea Surface Temperature Fusion Based on the Triple-Collocation Fusion Algorithm

Sea surface temperature (SST) is vital for climate monitoring and extreme weather forecasting. Existing global SST datasets are typically provided at daily to seasonal resolutions, while hourly data remain limited to regional scales. Polar-orbiting satellites offer global coverage but low temporal resolution, providing only 1–2 observations per day. Geostationary satellites provide high temporal resolution but cover only part of the region. These limitations create a gap in the availability of global, hourly SST data. To address this, we propose a Triple-Collocation (TC)-based fusion algorithm for generating accurate global hourly SST data through multi-source integration. The method includes data preprocessing (quality control and linear interpolation), merging five geostationary SST datasets into two global sets by priority, applying TC fusion to three polar-orbiting datasets, and finally combining all sources via multi-source TC fusion. Results show improved temporal resolution and increased spatial coverage to 32%. The fused dataset achieves high accuracy, with a daily mean Bias below 0.0427 °C, RMSE about 0.5938 °C to 0.6965 °C, and R2 exceeding 0.9879. These outcomes demonstrate the method’s reliability and its potential for supporting climate and environmental research.