Update sentinel 2 to sentinel 2 collection

[nanaboamah89]

Proposed changes

change s2_l2a to s2_l2a_c1.

Checklist (replace [ ] with [x] to check off)

• Remove any unused Python packages from Load packages
• Remove any unused/empty code cells
• Remove any guidance cells (e.g. General advice)
• Ensure that all code cells follow the PEP8 standard for code. The jupyterlab_code_formatter tool can be used to format code cells to a consistent style: select each code cell, then click Edit and then one of the Apply X Formatter options (YAPF or Black are recommended)
• Include relevant tags in the first notebook cell and re-use tags if possible
• Ensure appropriate colour schemes have been used to maximise accessibility for vision impairment. Test your images or learn more with Coblis or TPGI
• Clear all outputs, run notebook from start to finish, and save the notebook in the state where all cells have been sequentially evaluated

Closes issues (optional)

• Closes Issue #000

[mickwelli]

Thanks @nanaboamah89,

There are some changes required before we can merge the Datasets notebook.

• s2_l2a_c1 includes an offset of -1000. We will need this to be included in the odc metadata so it shows up in the add_offset field.
• We will need to show users how to apply this offset when using dc.load
• PR load_ard changes #512 deals with the offset in the proposed changes to load_ard. We will need this PR to be merged before we merge the Datasets notebook.

[mickwelli]

Setting dtype=native in load_ard returns strange values of 64536 for masked pixels. I suspect this is because the native nodata value is the max value for unit16 65536 Element84/earth-search#23. 1000 is subtracted in the re-scaling process. Therefore we should not set dtype=native in load_ard()

[mickwelli]

Hi @nanaboamah89, it looks like there are two notebooks that don't perform as well with the change:

1. Mangrove analysis - might need to revert this to s2_l2a until data gaps are resolved. Looks like data gaps in 2017 are affecting visualisation.
2. Water_extent_sentinel_2 - we could look at removing mask_filters=[("opening", 3),("dilation", 2)] from load_ard(), otherwise there is too much cloud masking on the dates of interest.

[nanaboamah89]

• Mangrove analysis - might need to revert this to s2_l2a until data gaps are resolved. Looks like data gaps in 2017 are affecting visualisation.
• Water_extent_sentinel_2 - we could look at removing mask_filters=[("opening", 3),("dilation", 2)] from load_ard(), otherwise there is too much cloud masking on the dates of interest.

I am reverting the two notebooks back to s2_l2a for now, till the gap is work on.

[mickwelli]

Thanks @nanaboamah89