**7. Conclusions**

The capability of the radiometric intersensor comparison of multispectral sensors using four major sensors has been examined to attain robust 1% precision and better in multiyear time series. The "nadir-only" restriction of SNO-based comparison analysis provides a framework within which the operational on-orbit RSB calibration performance can be evaluated in isolation from other issues arising from larger area size or viewing angles, such as the RVS effect or scene-BRDF. With the use of pixel-based homogeneity and sample size constraint, the procedure successfully stabilizes ratio, tightens error bars, and makes fuller time series. The procedure makes error bar a meaningful discriminator of SNO events of varying level of statistical quality. A well-behaved time series can attain even better precision making it possible to detect a persistent multiyear drift as small as 0.3%. This study also clarifies that the application of targeted removal algorithm, such as cloud removal, not to be effective in overcoming variability at least not on the level of reaching 1% result. Various issues are also discussed and presented, such as the SZA impact not being important under the "nadir-only" framework, the impact of RSR mismatch to be radiometric ratio offset and seasonal modulation, and that the 2% scene-based effect, loosely called the "scaling phenomenon", is pervasively present in both the northern and southern polar scenes to affect all polar-orbiting RSBs. However, arguably the most important aspect is the multisensor cross-comparisons made even more useful by the 1% precision capability. Limitations in intercomparison certainly exist, and the lack of spectrally matching bands between sensors is arguably the most basic one making full intercomparison impossible, thus requiring a more comprehensive strategy. Nevertheless, this study strengthens intersensor comparison as a powerful tool of monitoring and discovery for multispectral sensors in the coming era.

**Author Contributions:** M.C. is responsible for initiating and leading this effort. J.D. has provided significant contribution to operational information, data, processing, and plotting using python.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors thank Junqiang Sun for continual support.

**Conflicts of Interest:** The authors declare no conflicts of interest.
