rasterio

Currently, we use a multiprocess pool to retrieve RGB bands in parallel, and fall back to sequential retrieval if that is not available. I would prefer having this as a setting, potentially also with the possibility to use a thread pool. We moved away from threading after experiencing race conditions within GDAL, but that might not always be a problem (for example, the issue might be fixed in rece

https://carpentries-incubator.github.io/geospatial-python/ is a good course that uses rioxarray. Could add a link to it in https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html

Follow on from corteva/geocube#108

Worth adding an example in the docs to make the solution more discoverable

Without thinking I put resampling="bilinear" and got an error when I called .compute()

Traceback (most recent call last):
  File "carajas.py", line 92, in <module>
    band_medianNP = band_median.compute()
  File "/home/ubuntu/anaconda3/envs/richard/lib/python3.8/site-packages/xarray/core/dataarray.py", line 899, in compute
    return new.load(**kwargs)
  File "/home/ubuntu/anaco