issue_comments: 407614809

That is interesting, I am definitely not an expert with non-uniform datasets. From the satellite datasets I have used, the 2D latitude and longitude coordinates are stored in the datasets and are not super useful. I usually have to use other ways to recreate the grid coordinates in the original projection (ex. SMAP uses the EASE Grid 2.0 but it stores the latitude/longitude of the points in the file) or reproject & flatten the coordinates. I have had to do this with weather data and made an xarray extension pangaea to handle it. So, that is what I was referring to when I misunderstood your question.

For your example of adding a crs attribute, ...

The files I have created have the crs coordinate variable inside the netCDF file already and it is always there when I load it in with xarray.open_dataset(). The method set_crs() could be used to add the crs coordinate variable and grid_mapping attributes to the dataset in the proper way so that it would be there on xarray.open_dataset() after dumping it to the file with to_netcdf().

The CF stuff is supported by rasterio, GDAL, QGIS and that is why I like it. If there is another way that is as well supported, I am not opposed to it.

In your example of methods is to_projection a remapping/resampling operation? If not, how does it differ from set_crs?

The to_projection() method would be a reproject/resampling operation.