###### Abstract  
Combining data from multiple sensors into a single seamless time series, also known as data interoperability, has the potential for unlocking new understanding of how the Earth functions as a system. However, our ability to produce these advanced data sets is hampered by the differences in design and function of the various optical remote-sensing satellite systems. A key factor is the impact that calibration of these instruments has on data interoperability. To address this issue, a workshop with a panel of experts was convened in conjunction with the Pecora 20 conference to focus on data interoperability between Landsat and the Sentinel 2 sensors. Four major areas of recommendation were the outcome of the workshop. The first was to improve communications between satellite agencies and the remote-sensing community. The second was to adopt a collections-based approach to processing the data. As expected, a third recommendation was to improve calibration methodologies in several specific areas. Lastly, and the most ambitious of the four, was to develop a comprehensive process for validating surface reflectance products produced from the data sets. Collectively, these recommendations have significant potential for improving satellite sensor calibration in a focused manner that can directly catalyze efforts to develop data that are closer to being seamlessly interoperable. calibration; geometric; radiometric; Landsat; Sentinel; interoperability 10.3390/rs100913401  
calibrating the data both before and after launch. The traditional goal is to provide an accurate measure of the upwelling energy from the Earth at a known location on the Earth. If the calibration effort is done well, the at-sensor imagery (or Level 1 imagery) is an accurate measure of the energy received at the sensor. The current state of the art for science grade systems, such as Landsat and Sentinel 2, is in the order of 3% absolute radiometric uncertainty and less than one half pixel for geometric uncertainty [1; 2]. A variety of improvements in calibration systems, both onboard and vicarious, have made this level of accuracy possible. Onboard calibrators, consisting of SpectralonTM reflectance panels and lamps, have been able to achieve a degree of precision of less than 1%. Vicarious calibration methods, such as the deployment of teams at calibration sites and the use of pseudo invariant calibration sites (PICS), have improved over the years from 10% uncertainty to approximately 3% uncertainty today. Improved ground control points and geometric reference images have also allowed sub-pixel geometric accuracy. This improvement in calibration, as well as improvements in signal-to-noise ratio (SNR), spectral bandpasses, radiometric resolution, star trackers, onboard GPS, and many other technologies, has resulted in at-sensor imagery that is better than anything heretofore available. All of these improvements, obtained steadily over the last three decades, have now been made available to users in an unprecedented fashion through free and open data policies for many science-grade sensors. While many such systems are in orbit, two that are quite representative are the Landsat and Sentinel 2 sensors. Both of these systems cover the optical, near infrared (NIR) and shortwave infrared (SWIR) portions of the electromagnetic spectrum, and have been shown to be quite accurate with respect to geometric and radiometric calibration. Thus, users are able to develop time series from these data that are unprecedented with respect to calibration accuracy and temporal resolution. However, despite all of those improvements in calibration, and the presence of multiple similar sensors in space, the ability to blend these data together nearly seamlessly remains elusive.