xpystac

xpystac provides the glue that allows xarray.open_dataset to accept pystac objects.

The goal is that as long as this library is in your env, you should never need to think about it.

• Open one asset: Reads data for an asset pointing to a COG, a zarr store, or a kerchunk reference file.
• Open one item: Reads data for all the assets in a particular item (commonly each COG represents a band).
• Open many items: Reads all the assets in all the items for a particular item collection iterable of items, or output of pystac_client.Client.search.

What works

file format	one asset (item or collection-level)	one item	many items
COG	x	x	x
zarr	x
kerchunk	x	x*	x*
* if stored in item alongside the datacube extension properties

Install

pip install xpystac

Examples

Open a single asset

Read from a COG

import pystac
import xarray as xr

item = pystac.Item.from_file(
    "https://raw.githubusercontent.com/stac-utils/pystac/v1.12.2/tests/data-files/examples/1.0.0/simple-item.json"
)
asset = item.assets["visual"]

xr.open_dataset(asset)

Here are a few examples from the Planetary Computer Docs which has some good examples of collection-level assets used to catalog zarr stores and kerchunk reference files.

import planetary_computer
import pystac_client
import xarray as xr


catalog = pystac_client.Client.open(
    "https://planetarycomputer.microsoft.com/api/stac/v1",
    modifier=planetary_computer.sign_inplace,
)

Read from a kerchunk reference file (ref):

collection = catalog.get_collection("nasa-nex-gddp-cmip6")
asset = collection.assets["ACCESS-CM2.historical"]

xr.open_dataset(asset, patch_url=planetary_computer.sign)

Read from a zarr file (ref)

collection = catalog.get_collection("daymet-daily-hi")
asset = collection.assets["zarr-abfs"]

xr.open_dataset(asset, patch_url=planetary_computer.sign)

Note that this zarr asset uses the xarray-assets extension to store open_kwargs and storage_options which xpystac can then pass along to xr.open_dataset.

Open a single item

A single item containing many COGs:

import pystac
import xarray as xr


item = pystac.Item.from_file(
    "https://earth-search.aws.element84.com/v1/collections/landsat-c2-l2/items/LC09_L2SR_081108_20250311_02_T2"
)

xr.open_dataset(item)

This takes advantage of a stacking library (either odc-stac or stackstac - configurable via the stacking_library option)

Open many items

Read all the data from the search results for a collection of COGs:

import pystac_client
import xarray as xr


catalog = pystac_client.Client.open(
    "https://earth-search.aws.element84.com/v1",
)

search = catalog.search(
    intersects=dict(type="Point", coordinates=[-105.78, 35.79]),
    collections=['sentinel-2-l2a'],
    datetime="2022-04-01/2022-05-01",
)

xr.open_dataset(search, engine="stac")

Read data from an item collection that uses the exploratory approach of storing kerchunked metadata within the datacube extension metadata:

import pystac
import xarray as xr

item_collection = pystac.ItemCollection.from_file(
    "https://raw.githubusercontent.com/stac-utils/xpystac/main/tests/data/data-cube-kerchunk-item-collection.json"
)

xr.open_dataset(item_collection)

How it works

When you call xarray.open_dataset(object, engine="stac") this library maps that open call to the correct library. Depending on the type of object that might be a stacking library (either odc-stac or stackstac) or back to xarray.open_dataset itself but with the engine and other options pulled from the pystac object.

Prior Art

This work is inspired by https://github.com/TomAugspurger/staccontainers and the discussion in stac-utils/pystac#846