ESA Antartica Data Cubes

Science Clusters & Working Groups Polar Science Cluster
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As part of an EarthCODE initiative to facilitate easier analysis of and access to EO Earth Sciences data, several datasets are combined into analysis-ready data cubes using GeoZarr as the standard. The data are collected and combined by theme for:

  • Antarctic Land Ice

    For land ice data, the datasets were combined and reprojected to a common 100x100m grid in EPSG:3031.

  • Antarctic Sea Ice

    For sea ice data, the datasets were combined and reprojected to a common 12.5km grid in EPSG:6932.

Together, these thematic cubes form the ESA Antarctica Datacube. The data and accompanying examples are explored in the Antarctica Datacube Hackathon. Visit the hackathon website to learn more, or browse the notebooks on GitHub.

ESA Antarctica Datacube composite
ESA Antarctica Datacube composite1920×1650 345 KB

The Data

Antarctic Land Ice

The land-ice datasets we have processed are:

Antarctic Sea Ice and Southern Ocean

The sea-ice cube includes:

Visualization

You can explore a visualization of the ESA Antarctica Datacube in eodash:

ESA Antarctica Datacube overview

Access

Antarctic Land Ice

The land-ice datasets are grouped into several multiscale GeoZarr stores:

Antarctic Sea Ice

The sea-ice and Southern Ocean datasets are available from a multiscale GeoZarr store:

Reading the Data

You can read an individual land-ice store using Xarray:

import xarray as xr

url = (
    "https://s3.waw4-1.cloudferro.com/EarthCODE/OSCAssets/"
    "antarctica_cube/land-ice/antarctica-combined.zarr"
)

ds = xr.open_zarr(url, group="0", chunks={})
ds

The sea-ice GeoZarr store can be opened using its highest-resolution group:

import xarray as xr

sea_ice_url = (
    "https://s3.waw4-1.cloudferro.com/EarthCODE/OSCAssets/"
    "antarctica_cube/sea-ice/sea-ice-cube.zarr"
)

sea_ice_ds = xr.open_zarr(
    sea_ice_url,
    group="0",
    chunks={},
)

sea_ice_ds

The land-ice stores share a common grid and can be opened together at full-resolution group 0, or groups 1–4 for coarser levels:

import xarray as xr

CUBE_LEVEL = "0"
cube_paths = [
    "https://s3.waw4-1.cloudferro.com/EarthCODE/OSCAssets/antarctica_cube/land-ice/ice-temp-cube.zarr",
    "https://s3.waw4-1.cloudferro.com/EarthCODE/OSCAssets/antarctica_cube/land-ice/sec.zarr",
    "https://s3.waw4-1.cloudferro.com/EarthCODE/OSCAssets/antarctica_cube/land-ice/antarctica-combined.zarr",
    "https://s3.waw4-1.cloudferro.com/EarthCODE/OSCAssets/antarctica_cube/land-ice/icemask_composite.zarr",
    "https://s3.waw4-1.cloudferro.com/EarthCODE/OSCAssets/antarctica_cube/land-ice/ice_velocity.zarr",
]

land_ice_ds = xr.open_mfdataset(
    cube_paths,
    engine="zarr",
    group=CUBE_LEVEL,
    chunks={},
    compat="no_conflicts",
    join="outer",
)

land_ice_ds

For tutorials and worked examples, visit the Antarctica Datacube Hackathon website or GitHub repository.

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