EO-LINCS project: Cube generation for Scientific Case Study (SCS) 1¶

Explanatory power of novel EO data streams for predicting net carbon fluxes¶

Objective: The SCS1 trains an artificial neural network (ANN) to predict carbon fluxes, where meteorological and reflectance data from satellites are taken as input. The training will be based on the in-situ observations from eddy covariance flux tower provided by the FLUXNET2015 dataset.

Outcomes: A working data processing chain to incorporate Sentinel-2 data into the FLUXCOM-X framework that is updatable and expandable to all sites and other Sentinel data products. An analysis of the contributions of Sentinel-2 data for predicting NEE and analysis into the added value with regards to interannual variability, drought responses, and disturbance.

Required datasets:

• Sentinel-2 L2A from CDSE
• ERA-5 land
• ESA CCI Biomass

---

The following notebook shows how the users can load data from various sources defined in scs1_config.yml using the MultiSourceDataStore tool.

What You Can Do with This Notebook¶

• Generate a configuration file based on fluxtower locations
• Load datasets from various sources as defined in the generated scs1_config.yml
• View the progress of each data request to the MultiSourceDataStore
• Quickly preview the datasets by plotting them.

Requirements¶

Before you begin, follow these steps:

• Install xcube-multistore via conda-forge by running: conda install --channel conda-forge xcube-multistore
• To access EO data via S3 from CDSE, generate your S3 credentials and add them to the data_stores section in the scs1_config.yml file.
• To access ERA5-Land data from the Copernicus Climate Data Store, obtain a CDS Personal Access Token by creating an account on the CDS Website. After logging in, navigate to your user page to find your Personal Access Token. Add this token to the data_stores section in the scs1_config.yml file.

Once you have it installed, you are ready to proceed.

This Multistore mainly works with a file called scs1_config.yml which is at the same file level as this notebook. To understand what goes into the schema, you can read more here.

---

Let's import the MultiSourceDataStore

In [1]:

import yaml

import pandas as pd
from xcube_multistore import MultiSourceDataStore

import yaml import pandas as pd from xcube_multistore import MultiSourceDataStore

You can find out how to fill out the config file by also using this super helpful function get_config_schema(). Run it and try expand the fields to learn more about the possible properties that the configuration file accepts along with the Configuration Guide.

In [2]:

MultiSourceDataStore.get_config_schema()

MultiSourceDataStore.get_config_schema()

Out[2]:

<xcube.util.jsonschema.JsonObjectSchema at 0x7314671ee270>

This science case requires data from several flux sites. So we define them in a scs1_sites.csv file for easier management and access. For the purpose of this example, we will focus on the first 2 sites.

In [3]:

sites = pd.read_csv("scs1_sites.csv")
sites = sites.iloc[:2]
sites

sites = pd.read_csv("scs1_sites.csv") sites = sites.iloc[:2] sites

Out[3]:

	Site ID	latitude	longitude	IGBP
0	AU-Dry	-15.2588	132.3706	SAV
1	AU-How	-12.4943	131.1523	WSA

In the following cell, we will create the config object which will then be saved as scs1_config.yml for persistance and ready to be read by MultiSourceDataStore.

To read more about how this config file is structured, you can find the Configuration Guide here, or refer to the notebook setup_config.ipynb.

Specifically, we are using the single dataset object and data stores schemas here.

In [4]:

time_range = ["2020-01-01", "2020-03-30"]

time_range = ["2020-01-01", "2020-03-30"]

In [5]:

config = dict(datasets=[])
for index, site in sites.iterrows():
    # append config for Sentinel-2
    config_ds = dict(
        identifier=f"{site['Site ID']}_sen2",
        store="stac-cdse",
        data_id="sentinel-2-l2a",
        open_params=dict(
            time_range=time_range,
            point=[site["longitude"], site["latitude"]],
            bbox_width=4000,
            spatial_res=10,
            asset_names=[
                "B01",
                "B02",
                "B03",
                "B04",
                "B05",
                "B06",
                "B07",
                "B08",
                "B8A",
                "B09",
                "B11",
                "B12",
                "SCL",
            ],
        ),
    )
    config["datasets"].append(config_ds)

    # append config for ERA5
    config_ds = dict(
        identifier=f"{site['Site ID']}_era5land",
        store="cds",
        data_id="reanalysis-era5-land",
        open_params=dict(
            variable_names=["2m_temperature", "total_precipitation"],
            time_range=time_range,
            point=[site["longitude"], site["latitude"]],
            spatial_res=0.1,
        ),
    )
    config["datasets"].append(config_ds)

    # append config for ESA CCI
    config_ds = dict(
        identifier=f"{site['Site ID']}_ccibiomass",
        store="esa_cci",
        grid_mapping=f"{site['Site ID']}_sen2",
        data_id="esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.5-0.100m",
        open_params=dict(
            time_range=time_range,
        ),
    )
    config["datasets"].append(config_ds)

# define stores
config["data_stores"] = []
# add storage data store
config_store = dict(
    identifier="storage",
    store_id="file",
    store_params=dict(root="data"),
)
config["data_stores"].append(config_store)
# add ESA CCI data store
config_store = dict(
    identifier="esa_cci",
    store_id="cciodp",
)
config["data_stores"].append(config_store)
# add STAC data store
config_store = dict(
    identifier="stac-cdse",
    store_id="stac-cdse-ardc",
    store_params=dict(
        key="<CDSE_S3_key>",
        secret="<CDSE_S3_secret>",
    ),
)
config["data_stores"].append(config_store)
# add CDS data store
config_store = dict(
    identifier="cds",
    store_id="cds",
    store_params=dict(
        endpoint_url="https://cds.climate.copernicus.eu/api",
        cds_api_key="CDS_API_key",
        normalize_names=True,
    ),
)
config["data_stores"].append(config_store)

config = dict(datasets=[]) for index, site in sites.iterrows(): # append config for Sentinel-2 config_ds = dict( identifier=f"{site['Site ID']}_sen2", store="stac-cdse", data_id="sentinel-2-l2a", open_params=dict( time_range=time_range, point=[site["longitude"], site["latitude"]], bbox_width=4000, spatial_res=10, asset_names=[ "B01", "B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B09", "B11", "B12", "SCL", ], ), ) config["datasets"].append(config_ds) # append config for ERA5 config_ds = dict( identifier=f"{site['Site ID']}_era5land", store="cds", data_id="reanalysis-era5-land", open_params=dict( variable_names=["2m_temperature", "total_precipitation"], time_range=time_range, point=[site["longitude"], site["latitude"]], spatial_res=0.1, ), ) config["datasets"].append(config_ds) # append config for ESA CCI config_ds = dict( identifier=f"{site['Site ID']}_ccibiomass", store="esa_cci", grid_mapping=f"{site['Site ID']}_sen2", data_id="esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.5-0.100m", open_params=dict( time_range=time_range, ), ) config["datasets"].append(config_ds) # define stores config["data_stores"] = [] # add storage data store config_store = dict( identifier="storage", store_id="file", store_params=dict(root="data"), ) config["data_stores"].append(config_store) # add ESA CCI data store config_store = dict( identifier="esa_cci", store_id="cciodp", ) config["data_stores"].append(config_store) # add STAC data store config_store = dict( identifier="stac-cdse", store_id="stac-cdse-ardc", store_params=dict( key="", secret="", ), ) config["data_stores"].append(config_store) # add CDS data store config_store = dict( identifier="cds", store_id="cds", store_params=dict( endpoint_url="https://cds.climate.copernicus.eu/api", cds_api_key="CDS_API_key", normalize_names=True, ), ) config["data_stores"].append(config_store)

In [6]:

with open("scs1_config.yml", "w") as file:
    yaml.dump(config, file, sort_keys=False)

with open("scs1_config.yml", "w") as file: yaml.dump(config, file, sort_keys=False)

Now, we can initialize the MultiSourceDataStore by passing the path to the scs1_config.yml which currently is on the same level as this notebook.

In [7]:

msds = MultiSourceDataStore("scs1_config.yml")

msds = MultiSourceDataStore("scs1_config.yml")

And we can display the overview of the configuration file for each dataset.

In [8]:

msds.display_config()

msds.display_config()

Configuration

User-defined ID	Data Store ID	Data Store Params	Data ID	Open Data Params	Grid-Mapping	Format
AU-Dry_sen2	stac-cdse-ardc	key: <CDSE_S3_key>; secret: <CDSE_S3_secret>	sentinel-2-l2a	time_range: ['2020-01-01', '2020-03-30']; point: [132.3706, -15.2588]; bbox_width: 4000; spatial_res: 10; asset_names: ['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09', 'B11', 'B12', 'SCL']	-	Zarr
AU-Dry_era5land	cds	endpoint_url: https://cds.climate.copernicus.eu/api; cds_api_key: CDS_API_key; normalize_names: True	reanalysis-era5-land	variable_names: ['2m_temperature', 'total_precipitation']; time_range: ['2020-01-01', '2020-03-30']; point: [132.3706, -15.2588]; spatial_res: 0.1	-	Zarr
AU-Dry_ccibiomass	cciodp	-	esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.5-0.100m	time_range: ['2020-01-01', '2020-03-30']	Like 'AU-Dry_sen2'	Zarr
AU-How_sen2	stac-cdse-ardc	key: <CDSE_S3_key>; secret: <CDSE_S3_secret>	sentinel-2-l2a	time_range: ['2020-01-01', '2020-03-30']; point: [131.1523, -12.4943]; bbox_width: 4000; spatial_res: 10; asset_names: ['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09', 'B11', 'B12', 'SCL']	-	Zarr
AU-How_era5land	cds	endpoint_url: https://cds.climate.copernicus.eu/api; cds_api_key: CDS_API_key; normalize_names: True	reanalysis-era5-land	variable_names: ['2m_temperature', 'total_precipitation']; time_range: ['2020-01-01', '2020-03-30']; point: [131.1523, -12.4943]; spatial_res: 0.1	-	Zarr
AU-How_ccibiomass	cciodp	-	esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.5-0.100m	time_range: ['2020-01-01', '2020-03-30']	Like 'AU-How_sen2'	Zarr

We can display the selected bounding box as shown in the following cell.

In [9]:

msds.display_geolocations()

msds.display_geolocations()

Out[9]:

Make this Notebook Trusted to load map: File -> Trust Notebook

We can now generate the datacubes:

In [10]:

msds.generate()

msds.generate()

Cube Generation

Dataset identifier	Status	Message	Exception
AU-Dry_sen2	COMPLETED	Dataset 'AU-Dry_sen2' finished: 0:31:22	-
AU-Dry_era5land	COMPLETED	Dataset 'AU-Dry_era5land' finished: 0:22:45	-
AU-Dry_ccibiomass	COMPLETED	Dataset 'AU-Dry_ccibiomass' finished: 0:00:35	-
AU-How_sen2	COMPLETED	Dataset 'AU-How_sen2' finished: 0:29:30	-
AU-How_era5land	COMPLETED	Dataset 'AU-How_era5land' finished: 0:16:44	-
AU-How_ccibiomass	COMPLETED	Dataset 'AU-How_ccibiomass' finished: 0:00:36	-

xcube-cds version 1.1.0.dev0
2025-12-16 14:22:28,942 INFO [2025-12-03T00:00:00Z] To improve our C3S service, we need to hear from you! Please complete this very short [survey](https://confluence.ecmwf.int/x/E7uBEQ/). Thank you.
xcube-cds version 1.1.0.dev0
2025-12-16 14:22:29,180 INFO [2025-12-03T00:00:00Z] To improve our C3S service, we need to hear from you! Please complete this very short [survey](https://confluence.ecmwf.int/x/E7uBEQ/). Thank you.
2025-12-16 14:22:29,373 INFO [2025-12-11T00:00:00] Please note that a dedicated catalogue entry for this dataset, post-processed and stored in Analysis Ready Cloud Optimized (ARCO) format (Zarr), is available for optimised time-series retrievals (i.e. for retrieving data from selected variables for a single point over an extended period of time in an efficient way). You can discover it [here](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-timeseries?tab=overview)
2025-12-16 14:22:29,374 INFO Request ID is e8fea833-88e9-45a0-9702-c202115a72a8
2025-12-16 14:22:29,440 INFO status has been updated to accepted
2025-12-16 14:22:50,751 INFO status has been updated to running
2025-12-16 14:25:21,440 INFO status has been updated to accepted
2025-12-16 14:28:48,411 INFO status has been updated to running
Recovering from connection error [('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer'))], attempt 1 of 500
Retrying in 120 seconds
2025-12-16 14:36:51,631 INFO status has been updated to successful

51313d4fc8684df65e1bfd4feb96618.zip:   0%|          | 0.00/226k [00:00<?, ?B/s]

xcube-cds version 1.1.0.dev0
2025-12-16 14:36:53,151 INFO [2025-12-03T00:00:00Z] To improve our C3S service, we need to hear from you! Please complete this very short [survey](https://confluence.ecmwf.int/x/E7uBEQ/). Thank you.
2025-12-16 14:36:53,336 INFO [2025-12-11T00:00:00] Please note that a dedicated catalogue entry for this dataset, post-processed and stored in Analysis Ready Cloud Optimized (ARCO) format (Zarr), is available for optimised time-series retrievals (i.e. for retrieving data from selected variables for a single point over an extended period of time in an efficient way). You can discover it [here](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-timeseries?tab=overview)
2025-12-16 14:36:53,337 INFO Request ID is 4a87fa3c-0d21-4371-9429-9ea44b5cfcbf
2025-12-16 14:36:53,406 INFO status has been updated to accepted
2025-12-16 14:37:26,699 INFO status has been updated to running
2025-12-16 14:45:13,179 INFO status has been updated to successful

c851b1f6d1d80a20d6730cf4359d1942.zip:   0%|          | 0.00/211k [00:00<?, ?B/s]

xcube-cds version 1.1.0.dev0
2025-12-16 15:15:22,289 INFO [2025-12-03T00:00:00Z] To improve our C3S service, we need to hear from you! Please complete this very short [survey](https://confluence.ecmwf.int/x/E7uBEQ/). Thank you.
xcube-cds version 1.1.0.dev0
2025-12-16 15:15:22,553 INFO [2025-12-03T00:00:00Z] To improve our C3S service, we need to hear from you! Please complete this very short [survey](https://confluence.ecmwf.int/x/E7uBEQ/). Thank you.
2025-12-16 15:15:22,760 INFO [2025-12-11T00:00:00] Please note that a dedicated catalogue entry for this dataset, post-processed and stored in Analysis Ready Cloud Optimized (ARCO) format (Zarr), is available for optimised time-series retrievals (i.e. for retrieving data from selected variables for a single point over an extended period of time in an efficient way). You can discover it [here](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-timeseries?tab=overview)
2025-12-16 15:15:22,761 INFO Request ID is 1facf12d-28cc-4bff-834b-4da6fc2395f2
2025-12-16 15:15:22,830 INFO status has been updated to accepted
2025-12-16 15:16:12,693 INFO status has been updated to running
2025-12-16 15:23:42,021 INFO status has been updated to successful

e8c4f05e569f96ddc795e77ef5c61fc8.zip:   0%|          | 0.00/235k [00:00<?, ?B/s]

xcube-cds version 1.1.0.dev0
2025-12-16 15:23:42,926 INFO [2025-12-03T00:00:00Z] To improve our C3S service, we need to hear from you! Please complete this very short [survey](https://confluence.ecmwf.int/x/E7uBEQ/). Thank you.
2025-12-16 15:23:43,103 INFO [2025-12-11T00:00:00] Please note that a dedicated catalogue entry for this dataset, post-processed and stored in Analysis Ready Cloud Optimized (ARCO) format (Zarr), is available for optimised time-series retrievals (i.e. for retrieving data from selected variables for a single point over an extended period of time in an efficient way). You can discover it [here](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-timeseries?tab=overview)
2025-12-16 15:23:43,104 INFO Request ID is cb378b7a-84ff-422c-bf62-c10313dcfbd3
2025-12-16 15:23:43,159 INFO status has been updated to accepted
2025-12-16 15:23:51,599 INFO status has been updated to running
2025-12-16 15:32:04,834 INFO status has been updated to successful

14d54f70d512f1331ca8fdefc49ae26a.zip:   0%|          | 0.00/231k [00:00<?, ?B/s]

We can now open the data using the xcube datastore framework API as usual. Note that the multi-source data store requires a data store called storage, which is configured in our scs1_config.yml under the data_stores section.

In [11]:

ds = msds.stores.storage.open_data("AU-Dry_sen2.zarr")
ds

ds = msds.stores.storage.open_data("AU-Dry_sen2.zarr") ds

Out[11]:

<xarray.Dataset> Size: 150MB
Dimensions:      (time: 18, y: 400, x: 400)
Coordinates:
  * time         (time) datetime64[ns] 144B 2020-01-02T01:27:09.024000 ... 20...
  * y            (y) float64 3kB 8.312e+06 8.312e+06 ... 8.308e+06 8.308e+06
  * x            (x) float64 3kB 8.601e+05 8.601e+05 ... 8.641e+05 8.641e+05
    spatial_ref  int64 8B ...
Data variables: (12/13)
    B01          (time, y, x) float32 12MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B02          (time, y, x) float32 12MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B03          (time, y, x) float32 12MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B04          (time, y, x) float32 12MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B05          (time, y, x) float32 12MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B06          (time, y, x) float32 12MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    ...           ...
    B08          (time, y, x) float32 12MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B09          (time, y, x) float32 12MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B11          (time, y, x) float32 12MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B12          (time, y, x) float32 12MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B8A          (time, y, x) float32 12MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    SCL          (time, y, x) float32 12MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
Attributes:
    stac_catalog_url:    https://stac.dataspace.copernicus.eu/v1
    stac_item_id:        S2B_MSIL2A_20200102T012709_N0500_R131_T52LHJ_2023042...
    stac_item_ids:       {'2020-01-02T01:27:09.024000': ['S2B_MSIL2A_20200102...
    xcube_stac_version:  1.1.2

xarray.Dataset

• Dimensions:
  • time: 18
  • y: 400
  • x: 400
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2020-01-02T01:27:09.024000 ... 2...

    array(['2020-01-02T01:27:09.024000000', '2020-01-07T01:27:11.024000000',
           '2020-01-12T01:27:09.024000000', '2020-01-17T01:27:11.024000000',
           '2020-01-22T01:27:09.024000000', '2020-01-27T01:27:11.024000000',
           '2020-02-01T01:27:09.024000000', '2020-02-06T01:27:11.024000000',
           '2020-02-11T01:27:09.024000000', '2020-02-16T01:27:11.024000000',
           '2020-02-21T01:27:09.024000000', '2020-02-26T01:27:11.024000000',
           '2020-03-02T01:27:19.024000000', '2020-03-07T01:27:11.024000000',
           '2020-03-12T01:27:19.024000000', '2020-03-17T01:27:11.024000000',
           '2020-03-22T01:27:19.024000000', '2020-03-27T01:27:11.024000000'],
          dtype='datetime64[ns]')

  • y
    (y)
    float64
    8.312e+06 8.312e+06 ... 8.308e+06

    long_name :

    y coordinate of projection

    standard_name :

    projection_y_coordinate

    array([8312235., 8312225., 8312215., ..., 8308265., 8308255., 8308245.],
          shape=(400,))

  • x
    (x)
    float64
    8.601e+05 8.601e+05 ... 8.641e+05

    long_name :

    x coordinate of projection

    standard_name :

    projection_x_coordinate

    array([860115., 860125., 860135., ..., 864085., 864095., 864105.], shape=(400,))

  • spatial_ref
    ()
    int64
    ...

    crs_wkt :

    PROJCRS["WGS 84 / UTM zone 52S",BASEGEOGCRS["WGS 84",DATUM["World Geodetic System 1984",ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1]]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],ID["EPSG",4326]],CONVERSION["UTM zone 52S",METHOD["Transverse Mercator",ID["EPSG",9807]],PARAMETER["Latitude of natural origin",0,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8801]],PARAMETER["Longitude of natural origin",129,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8802]],PARAMETER["Scale factor at natural origin",0.9996,SCALEUNIT["unity",1],ID["EPSG",8805]],PARAMETER["False easting",500000,LENGTHUNIT["metre",1],ID["EPSG",8806]],PARAMETER["False northing",10000000,LENGTHUNIT["metre",1],ID["EPSG",8807]]],CS[Cartesian,2],AXIS["easting",east,ORDER[1],LENGTHUNIT["metre",1]],AXIS["northing",north,ORDER[2],LENGTHUNIT["metre",1]],ID["EPSG",32752]]

    false_easting :

    500000.0

    false_northing :

    10000000.0

    geographic_crs_name :

    WGS 84

    grid_mapping_name :

    transverse_mercator

    horizontal_datum_name :

    World Geodetic System 1984

    inverse_flattening :

    298.257223563

    latitude_of_projection_origin :

    0.0

    longitude_of_central_meridian :

    129.0

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    projected_crs_name :

    WGS 84 / UTM zone 52S

    reference_ellipsoid_name :

    WGS 84

    scale_factor_at_central_meridian :

    0.9996

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    [1 values with dtype=int64]

• Data variables: (13)
  • B01
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 10.99 MiB 625.00 kiB Shape (18, 400, 400) (1, 400, 400) Dask graph 18 chunks in 2 graph layers Data type float32 numpy.ndarray

  • B02
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 10.99 MiB 625.00 kiB Shape (18, 400, 400) (1, 400, 400) Dask graph 18 chunks in 2 graph layers Data type float32 numpy.ndarray

  • B03
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 10.99 MiB 625.00 kiB Shape (18, 400, 400) (1, 400, 400) Dask graph 18 chunks in 2 graph layers Data type float32 numpy.ndarray

  • B04
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 10.99 MiB 625.00 kiB Shape (18, 400, 400) (1, 400, 400) Dask graph 18 chunks in 2 graph layers Data type float32 numpy.ndarray

  • B05
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 10.99 MiB 625.00 kiB Shape (18, 400, 400) (1, 400, 400) Dask graph 18 chunks in 2 graph layers Data type float32 numpy.ndarray

  • B06
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 10.99 MiB 625.00 kiB Shape (18, 400, 400) (1, 400, 400) Dask graph 18 chunks in 2 graph layers Data type float32 numpy.ndarray

  • B07
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 10.99 MiB 625.00 kiB Shape (18, 400, 400) (1, 400, 400) Dask graph 18 chunks in 2 graph layers Data type float32 numpy.ndarray

  • B08
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 10.99 MiB 625.00 kiB Shape (18, 400, 400) (1, 400, 400) Dask graph 18 chunks in 2 graph layers Data type float32 numpy.ndarray

  • B09
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 10.99 MiB 625.00 kiB Shape (18, 400, 400) (1, 400, 400) Dask graph 18 chunks in 2 graph layers Data type float32 numpy.ndarray

  • B11
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 10.99 MiB 625.00 kiB Shape (18, 400, 400) (1, 400, 400) Dask graph 18 chunks in 2 graph layers Data type float32 numpy.ndarray

  • B12
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 10.99 MiB 625.00 kiB Shape (18, 400, 400) (1, 400, 400) Dask graph 18 chunks in 2 graph layers Data type float32 numpy.ndarray

  • B8A
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 10.99 MiB 625.00 kiB Shape (18, 400, 400) (1, 400, 400) Dask graph 18 chunks in 2 graph layers Data type float32 numpy.ndarray

  • SCL
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 10.99 MiB 625.00 kiB Shape (18, 400, 400) (1, 400, 400) Dask graph 18 chunks in 2 graph layers Data type float32 numpy.ndarray

• Attributes: (4)

  stac_catalog_url :

  https://stac.dataspace.copernicus.eu/v1

  stac_item_id :

  S2B_MSIL2A_20200102T012709_N0500_R131_T52LHJ_20230425T213030

  stac_item_ids :

  {'2020-01-02T01:27:09.024000': ['S2B_MSIL2A_20200102T012709_N0500_R131_T52LHJ_20230425T213030', 'S2B_MSIL2A_20200102T012709_N0500_R131_T53LKD_20230425T213030'], '2020-01-07T01:27:11.024000': ['S2A_MSIL2A_20200107T012711_N0500_R131_T52LHJ_20230429T073828', 'S2A_MSIL2A_20200107T012711_N0500_R131_T53LKD_20230429T073828'], '2020-01-12T01:27:09.024000': ['S2B_MSIL2A_20200112T012709_N0500_R131_T52LHJ_20230428T203339', 'S2B_MSIL2A_20200112T012709_N0500_R131_T53LKD_20230428T203339'], '2020-01-17T01:27:11.024000': ['S2A_MSIL2A_20200117T012711_N0500_R131_T52LHJ_20230604T051504', 'S2A_MSIL2A_20200117T012711_N0500_R131_T53LKD_20230604T051504'], '2020-01-22T01:27:09.024000': ['S2B_MSIL2A_20200122T012709_N0500_R131_T52LHJ_20230427T025618', 'S2B_MSIL2A_20200122T012709_N0500_R131_T53LKD_20230427T025618'], '2020-01-27T01:27:11.024000': ['S2A_MSIL2A_20200127T012711_N0500_R131_T52LHJ_20230428T051621', 'S2A_MSIL2A_20200127T012711_N0500_R131_T53LKD_20230428T051621'], '2020-02-01T01:27:09.024000': ['S2B_MSIL2A_20200201T012709_N0500_R131_T52LHJ_20230411T210401', 'S2B_MSIL2A_20200201T012709_N0500_R131_T53LKD_20230411T210401'], '2020-02-06T01:27:11.024000': ['S2A_MSIL2A_20200206T012711_N0500_R131_T52LHJ_20230426T172019', 'S2A_MSIL2A_20200206T012711_N0500_R131_T53LKD_20230426T172019'], '2020-02-11T01:27:09.024000': ['S2B_MSIL2A_20200211T012709_N0500_R131_T52LHJ_20230516T010645', 'S2B_MSIL2A_20200211T012709_N0500_R131_T53LKD_20230516T010645'], '2020-02-16T01:27:11.024000': ['S2A_MSIL2A_20200216T012711_N0500_R131_T52LHJ_20230429T201815', 'S2A_MSIL2A_20200216T012711_N0500_R131_T53LKD_20230429T201815'], '2020-02-21T01:27:09.024000': ['S2B_MSIL2A_20200221T012709_N0500_R131_T52LHJ_20230426T201925', 'S2B_MSIL2A_20200221T012709_N0500_R131_T53LKD_20230426T201925'], '2020-02-26T01:27:11.024000': ['S2A_MSIL2A_20200226T012711_N0500_R131_T52LHJ_20230423T102619', 'S2A_MSIL2A_20200226T012711_N0500_R131_T53LKD_20230423T102619'], '2020-03-02T01:27:19.024000': ['S2B_MSIL2A_20200302T012719_N0500_R131_T52LHJ_20230412T164327', 'S2B_MSIL2A_20200302T012719_N0500_R131_T53LKD_20230412T164327'], '2020-03-07T01:27:11.024000': ['S2A_MSIL2A_20200307T012711_N0500_R131_T52LHJ_20230410T155005', 'S2A_MSIL2A_20200307T012711_N0500_R131_T53LKD_20230410T155005'], '2020-03-12T01:27:19.024000': ['S2B_MSIL2A_20200312T012719_N0500_R131_T52LHJ_20230411T065030', 'S2B_MSIL2A_20200312T012719_N0500_R131_T53LKD_20230411T065030'], '2020-03-17T01:27:11.024000': ['S2A_MSIL2A_20200317T012711_N0500_R131_T52LHJ_20230423T175936', 'S2A_MSIL2A_20200317T012711_N0500_R131_T53LKD_20230423T175936'], '2020-03-22T01:27:19.024000': ['S2B_MSIL2A_20200322T012719_N0500_R131_T52LHJ_20230408T185345', 'S2B_MSIL2A_20200322T012719_N0500_R131_T53LKD_20230408T185345'], '2020-03-27T01:27:11.024000': ['S2A_MSIL2A_20200327T012711_N0500_R131_T52LHJ_20230409T175619', 'S2A_MSIL2A_20200327T012711_N0500_R131_T53LKD_20230409T175619']}

  xcube_stac_version :

  1.1.2

We can now select a variable for one timestep and plot it for a quick preview of the data

In [12]:

ds.B04.isel(time=0).plot(vmin=0., vmax=0.2)

ds.B04.isel(time=0).plot(vmin=0., vmax=0.2)

Out[12]:

<matplotlib.collections.QuadMesh at 0x7066963fd2b0>

In [13]:

ds = msds.stores.storage.open_data("AU-Dry_era5land.zarr")
ds

ds = msds.stores.storage.open_data("AU-Dry_era5land.zarr") ds

Out[13]:

<xarray.Dataset> Size: 69kB
Dimensions:  (time: 2160)
Coordinates:
  * time     (time) datetime64[ns] 17kB 2020-01-01 ... 2020-03-30T23:00:00
    expver   (time) <U4 35kB dask.array<chunksize=(1080,), meta=np.ndarray>
    lat      float64 8B ...
    lon      float64 8B ...
    number   int64 8B ...
Data variables:
    t2m      (time) float32 9kB dask.array<chunksize=(1080,), meta=np.ndarray>
    tp       (time) float32 9kB dask.array<chunksize=(1080,), meta=np.ndarray>
Attributes:
    Conventions:             CF-1.7
    GRIB_centre:             ecmf
    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
    GRIB_subCentre:          0
    history:                 2025-12-16T13:33 GRIB to CDM+CF via cfgrib-0.9.1...
    institution:             European Centre for Medium-Range Weather Forecasts

xarray.Dataset

• Dimensions:
  • time: 2160
• Coordinates: (5)
  • time
    (time)
    datetime64[ns]
    2020-01-01 ... 2020-03-30T23:00:00

    long_name :

    time

    standard_name :

    time

    array(['2020-01-01T00:00:00.000000000', '2020-01-01T01:00:00.000000000',
           '2020-01-01T02:00:00.000000000', ..., '2020-03-30T21:00:00.000000000',
           '2020-03-30T22:00:00.000000000', '2020-03-30T23:00:00.000000000'],
          shape=(2160,), dtype='datetime64[ns]')

  • expver
    (time)
    <U4
    dask.array<chunksize=(1080,), meta=np.ndarray>

    Array Chunk Bytes 33.75 kiB 16.88 kiB Shape (2160,) (1080,) Dask graph 2 chunks in 2 graph layers Data type

  • lat
    ()
    float64
    ...

    long_name :

    latitude

    standard_name :

    latitude

    stored_direction :

    decreasing

    units :

    degrees_north

    [1 values with dtype=float64]

  • lon
    ()
    float64
    ...

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    [1 values with dtype=float64]

  • number
    ()
    int64
    ...

    long_name :

    ensemble member numerical id

    standard_name :

    realization

    units :

    1

    [1 values with dtype=int64]

• Data variables: (2)
  • t2m
    (time)
    float32
    dask.array<chunksize=(1080,), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    4

    GRIB_Ny :

    4

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    t2m

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.1

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.1

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    -15.108

    GRIB_latitudeOfLastGridPointInDegrees :

    -15.409

    GRIB_longitudeOfFirstGridPointInDegrees :

    132.22

    GRIB_longitudeOfLastGridPointInDegrees :

    132.521

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    2 metre temperature

    GRIB_numberOfPoints :

    16

    GRIB_paramId :

    167

    GRIB_shortName :

    2t

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_surface :

    0.0

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    K

    GRIB_uvRelativeToGrid :

    0

    long_name :

    2 metre temperature

    standard_name :

    unknown

    units :

    K

    Array Chunk Bytes 8.44 kiB 4.22 kiB Shape (2160,) (1080,) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

  • tp
    (time)
    float32
    dask.array<chunksize=(1080,), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    4

    GRIB_Ny :

    4

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    tp

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.1

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.1

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    -15.108

    GRIB_latitudeOfLastGridPointInDegrees :

    -15.409

    GRIB_longitudeOfFirstGridPointInDegrees :

    132.22

    GRIB_longitudeOfLastGridPointInDegrees :

    132.521

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Total precipitation

    GRIB_numberOfPoints :

    16

    GRIB_paramId :

    228

    GRIB_shortName :

    tp

    GRIB_stepType :

    accum

    GRIB_stepUnits :

    1

    GRIB_surface :

    0.0

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    m

    GRIB_uvRelativeToGrid :

    0

    long_name :

    Total precipitation

    standard_name :

    unknown

    units :

    m

    Array Chunk Bytes 8.44 kiB 4.22 kiB Shape (2160,) (1080,) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

• Attributes: (6)

  Conventions :

  CF-1.7

  GRIB_centre :

  ecmf

  GRIB_centreDescription :

  European Centre for Medium-Range Weather Forecasts

  GRIB_subCentre :

  0

  history :

  2025-12-16T13:33 GRIB to CDM+CF via cfgrib-0.9.15.1/ecCodes-2.42.0 with {"source": "data.grib", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}

  institution :

  European Centre for Medium-Range Weather Forecasts

In [14]:

ds.t2m.plot()

ds.t2m.plot()

Out[14]:

[<matplotlib.lines.Line2D at 0x70669d62a5d0>]

In [15]:

ds = msds.stores.storage.open_data("AU-Dry_ccibiomass.zarr")
ds

ds = msds.stores.storage.open_data("AU-Dry_ccibiomass.zarr") ds

Out[15]:

<xarray.Dataset> Size: 1MB
Dimensions:      (time: 1, y: 400, x: 400)
Coordinates:
  * time         (time) datetime64[ns] 8B 2020-07-01T23:59:59
  * y            (y) float64 3kB 8.312e+06 8.312e+06 ... 8.308e+06 8.308e+06
  * x            (x) float64 3kB 8.601e+05 8.601e+05 ... 8.641e+05 8.641e+05
    spatial_ref  int64 8B ...
Data variables:
    agb          (time, y, x) float32 640kB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    agb_sd       (time, y, x) float32 640kB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
Attributes:
    Conventions:             CF-1.7
    date_created:            2025-12-16T14:45:28.713382
    history:                 [{'cube_params': {'time_range': ['2020-01-01T00:...
    processing_level:        L4
    time_coverage_duration:  P365DT23H59M59S
    time_coverage_end:       2020-12-31T23:59:59
    time_coverage_start:     2020-01-01T00:00:00
    title:                   esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-plat...

xarray.Dataset

• Dimensions:
  • time: 1
  • y: 400
  • x: 400
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2020-07-01T23:59:59

    bounds :

    time_bnds

    standard_name :

    time

    array(['2020-07-01T23:59:59.000000000'], dtype='datetime64[ns]')

  • y
    (y)
    float64
    8.312e+06 8.312e+06 ... 8.308e+06

    long_name :

    y coordinate of projection

    standard_name :

    projection_y_coordinate

    array([8312235., 8312225., 8312215., ..., 8308265., 8308255., 8308245.],
          shape=(400,))

  • x
    (x)
    float64
    8.601e+05 8.601e+05 ... 8.641e+05

    long_name :

    x coordinate of projection

    standard_name :

    projection_x_coordinate

    array([860115., 860125., 860135., ..., 864085., 864095., 864105.], shape=(400,))

  • spatial_ref
    ()
    int64
    ...

    crs_wkt :

    PROJCRS["WGS 84 / UTM zone 52S",BASEGEOGCRS["WGS 84",DATUM["World Geodetic System 1984",ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1]]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],ID["EPSG",4326]],CONVERSION["UTM zone 52S",METHOD["Transverse Mercator",ID["EPSG",9807]],PARAMETER["Latitude of natural origin",0,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8801]],PARAMETER["Longitude of natural origin",129,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8802]],PARAMETER["Scale factor at natural origin",0.9996,SCALEUNIT["unity",1],ID["EPSG",8805]],PARAMETER["False easting",500000,LENGTHUNIT["metre",1],ID["EPSG",8806]],PARAMETER["False northing",10000000,LENGTHUNIT["metre",1],ID["EPSG",8807]]],CS[Cartesian,2],AXIS["easting",east,ORDER[1],LENGTHUNIT["metre",1]],AXIS["northing",north,ORDER[2],LENGTHUNIT["metre",1]],ID["EPSG",32752]]

    false_easting :

    500000.0

    false_northing :

    10000000.0

    geographic_crs_name :

    WGS 84

    grid_mapping_name :

    transverse_mercator

    horizontal_datum_name :

    World Geodetic System 1984

    inverse_flattening :

    298.257223563

    latitude_of_projection_origin :

    0.0

    longitude_of_central_meridian :

    129.0

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    projected_crs_name :

    WGS 84 / UTM zone 52S

    reference_ellipsoid_name :

    WGS 84

    scale_factor_at_central_meridian :

    0.9996

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    [1 values with dtype=int64]

• Data variables: (2)
  • agb
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    ancillary_variables :

    agb_se

    chunk_sizes :

    [1, 1875, 1875]

    data_type :

    int16

    dimensions :

    ['time', 'lat', 'lon']

    file_chunk_sizes :

    [1, 1875, 1875]

    file_dimensions :

    ['time', 'lat', 'lon']

    fill_value :

    -32768

    grid_mapping :

    spatial_ref

    long_name :

    Above-ground biomass

    orig_data_type :

    int16

    shape :

    [1, 157500, 405000]

    size :

    63787500000

    units :

    Mg/ha

    valid_max :

    10000.0

    valid_min :

    0.0

    Array Chunk Bytes 625.00 kiB 625.00 kiB Shape (1, 400, 400) (1, 400, 400) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • agb_sd
    (time, y, x)
    float32
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    chunk_sizes :

    [1, 1875, 1875]

    data_type :

    int16

    dimensions :

    ['time', 'lat', 'lon']

    file_chunk_sizes :

    [1, 1875, 1875]

    file_dimensions :

    ['time', 'lat', 'lon']

    fill_value :

    -32768

    grid_mapping :

    spatial_ref

    long_name :

    Above-ground biomass standard_deviation

    orig_data_type :

    int16

    shape :

    [1, 157500, 405000]

    size :

    63787500000

    units :

    Mg/ha

    valid_max :

    10000.0

    valid_min :

    0.0

    Array Chunk Bytes 625.00 kiB 625.00 kiB Shape (1, 400, 400) (1, 400, 400) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Attributes: (8)

  Conventions :

  CF-1.7

  date_created :

  2025-12-16T14:45:28.713382

  history :

  [{'cube_params': {'time_range': ['2020-01-01T00:00:00', '2020-03-30T00:00:00'], 'variable_names': ['agb', 'agb_sd', 'crs']}, 'program': 'xcube_cci.chunkstore.CciChunkStore'}]

  processing_level :

  L4

  time_coverage_duration :

  P365DT23H59M59S

  time_coverage_end :

  2020-12-31T23:59:59

  time_coverage_start :

  2020-01-01T00:00:00

  title :

  esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.5-0.100m

In [16]:

ds["agb"].plot()

ds["agb"].plot()

Out[16]:

<matplotlib.collections.QuadMesh at 0x70669d59d450>