pycontrails.datalib.sentinel

Support for Sentinel-2 imagery retrieval through Google Cloud Platform.

Module Attributes

BQ_TABLE	BigQuery table with imagery metadata
BQ_DEFAULT_COLUMNS	Default columns to include in queries
BQ_DEFAULT_EXTENT	Default spatial extent for queries
DEFAULT_BANDS	Default Sentinel channels to use if none are specified.
GCP_STRIP_PREFIX	Strip this prefix from GCP URLs when caching Sentinel files locally

Functions

extract_sentinel_visualization(ds[, ...])	Extract artifacts for visualizing Sentinel data with the given color scheme.
intersect(flight[, columns])	Find Sentinel-2 imagery intersecting with flight track.
query(start_time, end_time[, extent, columns])	Find Sentinel-2 imagery within spatiotemporal region of interest.
to_true_color(ds)	Compute 3d RGB array for the true color scheme.

Classes

Sentinel(base_url, granule_id[, bands, ...])

Support for Sentinel-2 data handling.

pycontrails.datalib.sentinel.BQ_DEFAULT_COLUMNS = ['base_url', 'granule_id', 'sensing_time']

Default columns to include in queries

pycontrails.datalib.sentinel.BQ_DEFAULT_EXTENT = '{"type": "Polygon", "coordinates": [[[-180, -90], [180, -90], [180, 90], [-180, 90], [-180, -90]]]}'

Default spatial extent for queries

pycontrails.datalib.sentinel.BQ_TABLE = 'bigquery-public-data.cloud_storage_geo_index.sentinel_2_index'

BigQuery table with imagery metadata

pycontrails.datalib.sentinel.DEFAULT_BANDS = ['B02', 'B03', 'B04']

Default Sentinel channels to use if none are specified. These are visible bands for producing a true color composite.

pycontrails.datalib.sentinel.GCP_STRIP_PREFIX = 'gs://gcp-public-data-sentinel-2/'

Strip this prefix from GCP URLs when caching Sentinel files locally

class pycontrails.datalib.sentinel.Sentinel(base_url, granule_id, bands=None, cachestore=None)

Bases: object

Support for Sentinel-2 data handling.

This class uses the PROJ coordinate transformation software through the pyproj python interface. pyproj is installed as part of the sat set of optional dependencies (pip install pycontrails[sat]), but PROJ must be installed manually.

Parameters:

• base_url (str) – Base URL of Sentinel-2 scene. To find URLs for Sentinel-2 scenes at specific locations and times, see query() and intersect().

• granule_id (str) – Granule ID of Sentinel-2 scene. To find URLs for Sentinel-2 scenes at specific locations and times, see query() and intersect().

• bands (str | set[str] | None) – Set of bands to retrieve. The 13 possible bands are represented by the string “B01” to “B12” plus “B8A”. For the true color scheme, set bands=("B02", "B03", "B04"). By default, bands for the true color scheme are used. Bands must share a common resolution. The resolutions of each band are:

  • B02-B04, B08: 10 m

  • B05-B07, B8A, B11, B12: 20 m

  • B01, B09, B10: 60 m

• cachestore (cache.CacheStore, optional) – Cache store for Landsat data. If None, a DiskCacheStore is used.

See also

query, intersect

get(reflective='reflectance')

Retrieve Sentinel-2 imagery.

Parameters:

reflective (str = {"raw", "reflectance"}, optional) – Whether to return raw values or rescaled reflectances for reflective bands. By default, return reflectances.

Returns:

xarray.DataArray – DataArray of Sentinel-2 data.

pycontrails.datalib.sentinel.extract_sentinel_visualization(ds, color_scheme='true')

Extract artifacts for visualizing Sentinel data with the given color scheme.

Parameters:

• ds (xarray.Dataset) – Dataset of Sentinel data as returned by Sentinel.get().

• color_scheme (str = {"true"}) – Color scheme to use for visualization. The true color scheme (the only option currently implemented) requires bands B02, B03, and B04.

Returns:

• rgb (npt.NDArray[np.float32]) – 3D RGB array of shape (height, width, 3).

• src_crs (pyproj.CRS) – Imagery projection

• src_extent (tuple[float,float,float,float]) – Imagery extent in projected coordinates

pycontrails.datalib.sentinel.intersect(flight, columns=None)

Find Sentinel-2 imagery intersecting with flight track.

This function will return all scenes with a bounding box that includes flight waypoints both before and after the sensing time.

This function requires access to the Google BigQuery API and uses the BigQuery python library.

Parameters:

• flight (Flight) – Flight for intersection

• columns (list[str], optional.) – Columns to return from Google BigQuery table. By default, returns imagery base URL, granule ID, and sensing time.

Returns:

pandas.DataFrame – Query results in pandas DataFrame

See also

search.intersect()

pycontrails.datalib.sentinel.query(start_time, end_time, extent=None, columns=None)

Find Sentinel-2 imagery within spatiotemporal region of interest.

This function requires access to the Google BigQuery API and uses the BigQuery python library.

Parameters:

• start_time (numpy.datetime64) – Start of time period for search

• end_time (numpy.datetime64) – End of time period for search

• extent (str, optional) – Spatial region of interest as a GeoJSON string. If not provided, defaults to a global extent.

• columns (list[str], optional) – Columns to return from Google BigQuery table. By default, returns imagery base URL, granule ID, and sensing time.

Returns:

pandas.DataFrame – Query results in pandas DataFrame

See also

search.query()

pycontrails.datalib.sentinel.to_true_color(ds)

Compute 3d RGB array for the true color scheme.

Parameters:

ds (xarray.Dataset) – DataArray of Sentinel data with bands B02, B03, and B04.

Returns:

• numpy.ndarray – 3d RGB array with true color scheme.

• src_crs (pyproj.CRS) – Imagery projection