As the other response also noted correctly, SST data is derived from radiances in the IR/"near" microwave range, and this radiation can be detected through light cloud cover when visible radiation is blocked. So for a cloudy region, there may be more SST observations than visible wavelength observations.
For water, binning over time is affected by water motion, which has a smoothing effect. Using a high resolution for monthly data costs more in
storage, processing, and download times without adding information.
With a simple binning algorithm, binning at high resolutions introduces Moire patterns.
We have updated the binner to allow an area weighting of the L2 pixel over the L3 bins it covers, so in the not-too-distant future, this ungainly side-effect of the current binning behavior will be eliminated.
If you have the OCSSW Processing System working, I recommend installing and running the SeaDAS benchmark (bottom of this page: https://seadas.gsfc.nasa.gov/build_ocssw/). This will give an end-to-end example from Level-0 to Level-3 mapped chlor_a data. It serves as a check on a recent installation or new version of the processing software and provides snippets of code you can adapt for your own batch processing. This script uses the new "area_weighting" to reduce Moire effects. To understand why they occur, the wikipedia article may be useful together with the fact that the ground footprint of level-2 pixels becomes much larger at pass edges. If you search for "supersampling" in the SeaDAS GUI you should get an explanation (wth pictures):
You can modify "seadas_benchmark.bash" to increase the resolution and disable "area_weighting" starting with the l2bin step and see Moire patterns.Supersampling
As long as the area of an input pixel is small compared to the area of a bin, a simple binning is sufficient. In this case, the geodetic center coordinate of the Level 2 pixel is used to find the bin in the Level 3 grid whose area is intersected by this point. If the area of the contributing pixel is equal or even larger than the bin area, this simple binning will produce composites with insufficient accuracy and visual artifacts such as Moiré effects will dominate the resulting datasets.