Petrenko et al (2014)

"Evaluation and selection of SST regression algorithms for JPSS VIIRS" by Petrenko et al has been published in the Journal of Geophysical Research: DOI: 10.1002/2013JD020637. It compares validation statistics for a number of coefficient-based algorithms for sea surface temperature (SST) on a common basis, which is a good thing to see. The formulation from the Ocean and Sea Ice Satellite Application Facility turns out to be the best performing.

A point of interest is that the OSI-SAF algorithm was not the one that gave the minimum standard deviation against drifting buoys. An algorithm from the Navy Research Laboratory gave considerably lower spread (0.36 K compared to 0.42 K). However, the NRL algorithm was rejected on the basis that its sensitivity to SST* was greatly suppressed -- only 40%. This would mean, for example, that diurnal variability would be under-estimated by 60% using the NRL algorithm. This trade-off (apparent "accuracy" vs. sensitivity) has been discussed in the literature before, but as far as I recall, this is the first paper not involving SST CCI team members that has seriously used sensitivity as a criterion in algorithm selection.

*Sensitivity is the amount the satellite SST changes per unit of real SST change. Ideally, we would want 100% -- i.e., 1 K change in the satellite SST for a 1 K change in the real SST. However, this does not happen where a retrieval relies heavily on information brought to the observation ("prior information").