This paper presents two new high-resolution sea surface temperature (SST) analysis products developed using optimum interpolation (OI). The analyses have a spatial grid resolution of 0.25° and a temporal resolution of 1 day. One product uses Advanced Very High Resolution Radiometer (AVHRR) infrared satellite SST data, while the other combines AVHRR with Advanced Microwave Scanning Radiometer (AMSR) data from the NASA Earth Observing System satellite. Both products incorporate in situ data from ships and buoys and include a large-scale adjustment of satellite biases relative to in situ data. The addition of AMSR, which has near-all-weather coverage, increases the OI signal variance, necessitating two products to avoid an analysis variance jump when AMSR became available in June 2002. The AVHRR-only product uses Pathfinder AVHRR data (January 1985 to December 2005) and operational AVHRR data (2006 onwards). The AMSR–AVHRR product begins with the start of AMSR data in June 2002. The primary contribution of AVHRR in the AMSR–AVHRR product is in regions near land where AMSR is not available. However, in cloud-free regions, combining infrared and microwave instruments can reduce systematic biases due to their independent error characteristics. The paper discusses the data sources, including satellite SST retrievals, in situ SST data, and sea ice to SST conversion algorithms. It also details the OI analysis procedure, including the satellite bias correction method and error estimates. The results show improved spatial and temporal resolution compared to previous weekly 1° OI analyses, with better resolution in western boundary current regions and stronger gradients in areas like the Gulf Stream and tropical eastern Pacific. The AMSR–AVHRR product provides the highest resolution, similar to AMSR data in offshore regions, while the AVHRR-only product shows more detail in cloud-free regions. The paper concludes with a discussion of the impact of different satellite instruments and the importance of bias correction and error estimation in high-resolution SST analyses.This paper presents two new high-resolution sea surface temperature (SST) analysis products developed using optimum interpolation (OI). The analyses have a spatial grid resolution of 0.25° and a temporal resolution of 1 day. One product uses Advanced Very High Resolution Radiometer (AVHRR) infrared satellite SST data, while the other combines AVHRR with Advanced Microwave Scanning Radiometer (AMSR) data from the NASA Earth Observing System satellite. Both products incorporate in situ data from ships and buoys and include a large-scale adjustment of satellite biases relative to in situ data. The addition of AMSR, which has near-all-weather coverage, increases the OI signal variance, necessitating two products to avoid an analysis variance jump when AMSR became available in June 2002. The AVHRR-only product uses Pathfinder AVHRR data (January 1985 to December 2005) and operational AVHRR data (2006 onwards). The AMSR–AVHRR product begins with the start of AMSR data in June 2002. The primary contribution of AVHRR in the AMSR–AVHRR product is in regions near land where AMSR is not available. However, in cloud-free regions, combining infrared and microwave instruments can reduce systematic biases due to their independent error characteristics. The paper discusses the data sources, including satellite SST retrievals, in situ SST data, and sea ice to SST conversion algorithms. It also details the OI analysis procedure, including the satellite bias correction method and error estimates. The results show improved spatial and temporal resolution compared to previous weekly 1° OI analyses, with better resolution in western boundary current regions and stronger gradients in areas like the Gulf Stream and tropical eastern Pacific. The AMSR–AVHRR product provides the highest resolution, similar to AMSR data in offshore regions, while the AVHRR-only product shows more detail in cloud-free regions. The paper concludes with a discussion of the impact of different satellite instruments and the importance of bias correction and error estimation in high-resolution SST analyses.