Satellites have been taking measurements of sea surface salinity since 2010 and scientists have applied this data to broad research topics like ocean dynamics, climate variability, Earth’s water and carbon cycles, and marine biogeochemistry. Since these observations are so widely used, it is important to fully evaluate and understand their associated error. There are errors in the data due to spatial and temporal resolution of sampling, and the results may not capture fine details of patterns over time or space. There is limited research that disentangles this error from errors inherent to the instrument measuring process, and at a high resolution. A new study, partially funded by a partnership between CPO’s Climate Observations and Monitoring (COM) Program, Climate Variability and Predictability (CVP) Program, and NOAA’s Global Ocean Monitoring and Observing (GOMO) program, uses a 12-month high-resolution ocean model to isolate and quantify these sampling errors. An international group of researchers, including NOAA-supported Mikael Kuusela, developed a full characterization of the sampling error from three satellite products, demonstrating high error in regions where sea surface salinity is more variable. These results, published in Remote Sensing, improves the understanding of satellite data validation and the extent to which it can be used in Earth system applications. This work was funded by a collaboration between COM, CVP, and GOMO to increase the use and value of ocean observations, advance our understanding of climate variability and change, and enhance NOAA’s ability to model and predict the Earth System.