The paper presents two new high-resolution sea surface temperature (SST) analysis products developed using optimum interpolation (OI). These products have a spatial resolution of 0.25° and a temporal resolution of 1 day. One product uses AVHRR infrared satellite data, while the other combines AVHRR and AMSR data. Both products incorporate in situ data from ships and buoys and include a large-scale adjustment of satellite biases relative to in situ data. AMSR's near-all-weather coverage increases OI signal variance when combined with AVHRR, necessitating two separate products to avoid analysis variance jumps when AMSR became available in June 2002. The AVHRR-only product uses Pathfinder AVHRR data from 1985 to 2005 and operational data from 2006 onward. The AMSR–AVHRR product begins in June 2002, with AVHRR contributing in regions near land where AMSR is not available. In cloud-free regions, both instruments reduce systematic biases due to independent error characteristics. The OI.v2 analysis, previously used for weekly 1° grid SST analyses, is refined to produce a higher-resolution reanalysis product dating back to 1985. This analysis improves spatial and temporal resolution compared to previous weekly OI analyses. The new analysis uses AVHRR and AMSR data, with AVHRR data used for regions where AMSR is not available. The analysis includes a new satellite bias correction method and a more complete error estimate that includes sampling, random, and bias errors. The results show improved resolution and accuracy compared to previous analyses. The OI analysis is performed on a regular grid using irregularly spaced data. The analysis is formed by a weighted sum of the data using OI linear weights. The weights are determined by regression and depend on the distance between data points and the noise-to-signal ratio. The analysis includes a preliminary correction of AVHRR satellite data relative to in situ data. The bias correction method uses empirical orthogonal teleconnection (EOT) functions to correct satellite data relative to in situ data. The EOT method allows for the determination of bias error and is used to correct satellite data in the daily OI. The results show that the new daily OI analysis has improved spatial and temporal resolution compared to previous analyses. The analysis includes a bias correction method that reduces large-scale biases and improves accuracy. The analysis also includes a more complete error estimate that includes sampling, random, and bias errors. The results show that the new daily OI analysis has improved resolution and accuracy compared to previous analyses. The analysis is used for climate monitoring, hurricane forecasting, fisheries, and as a boundary condition for atmospheric models. The new analysis is based on OI and includes AVHRR and AMSR data, with AVHRR data used for regions where AMSR is not available. The analysis is designed to better resolveThe paper presents two new high-resolution sea surface temperature (SST) analysis products developed using optimum interpolation (OI). These products have a spatial resolution of 0.25° and a temporal resolution of 1 day. One product uses AVHRR infrared satellite data, while the other combines AVHRR and AMSR data. Both products incorporate in situ data from ships and buoys and include a large-scale adjustment of satellite biases relative to in situ data. AMSR's near-all-weather coverage increases OI signal variance when combined with AVHRR, necessitating two separate products to avoid analysis variance jumps when AMSR became available in June 2002. The AVHRR-only product uses Pathfinder AVHRR data from 1985 to 2005 and operational data from 2006 onward. The AMSR–AVHRR product begins in June 2002, with AVHRR contributing in regions near land where AMSR is not available. In cloud-free regions, both instruments reduce systematic biases due to independent error characteristics. The OI.v2 analysis, previously used for weekly 1° grid SST analyses, is refined to produce a higher-resolution reanalysis product dating back to 1985. This analysis improves spatial and temporal resolution compared to previous weekly OI analyses. The new analysis uses AVHRR and AMSR data, with AVHRR data used for regions where AMSR is not available. The analysis includes a new satellite bias correction method and a more complete error estimate that includes sampling, random, and bias errors. The results show improved resolution and accuracy compared to previous analyses. The OI analysis is performed on a regular grid using irregularly spaced data. The analysis is formed by a weighted sum of the data using OI linear weights. The weights are determined by regression and depend on the distance between data points and the noise-to-signal ratio. The analysis includes a preliminary correction of AVHRR satellite data relative to in situ data. The bias correction method uses empirical orthogonal teleconnection (EOT) functions to correct satellite data relative to in situ data. The EOT method allows for the determination of bias error and is used to correct satellite data in the daily OI. The results show that the new daily OI analysis has improved spatial and temporal resolution compared to previous analyses. The analysis includes a bias correction method that reduces large-scale biases and improves accuracy. The analysis also includes a more complete error estimate that includes sampling, random, and bias errors. The results show that the new daily OI analysis has improved resolution and accuracy compared to previous analyses. The analysis is used for climate monitoring, hurricane forecasting, fisheries, and as a boundary condition for atmospheric models. The new analysis is based on OI and includes AVHRR and AMSR data, with AVHRR data used for regions where AMSR is not available. The analysis is designed to better resolve