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 aims to link EO data streams to in situ data to predict carbon, water, and energy fluxes. The approach is based on the FLUXCOM-X methodologies, where meteorological and reflectance data from satellites are taken as input to train a machine learning model on the target flux. While the data extraction uses the FLUXCOM-X methodology as a test case, the pipeline is applicable to any use case which matches EO data to eddy covariance measurements. The provided test case is based on eddy covariance data as provided by FLUXNET and associated networks such as the Integrated Carbon Observation System (ICOS) and AmeriFLUX.

Outcomes: A working data processing chain able to incorporate new EO data, including Sentinel-2 and Sentinel-3 data, into the FLUXCOM-X framework that is updatable and expandable to all sites and other data products. The example case demonstrates the utility of these new data streams for predicting NEE and analysis into the added value with regards to interannual variability, drought responses, and disturbance.

Required datasets:

For this science case, we use small spatial cutouts around flux towers from the following datasets:

• Sentinel-2 L2A
• Sentinel-3 SYNERGY
• Sentinel-3 SLSTR Land Surfacce Temperature
• ERA5 hourly timeseries
• ESA CCI Biomass
• ESA CCI Biomass by EOForestSTAC
• Robinson et al. – Chapman-Richards growth-curve parameters for secondary-forest aboveground carbon dynamics
• Saatchi & Yu - 2020 Global aboveground biomass
• Hansen Global Forest Change (GFC) – Tree cover and annual loss/gain
• Global Age Mapping Integration (GAMI) – Global forest age ensemble
• Global Canopy Height (Potapov et al.)
• Global Ecosystem Dynamics Investigation (GEDI)

---

Overview of the Cube Generation Pipeline¶

This notebook demonstrates how users can access the required datasets via the xcube Multi-Source Data Store. The full example is available in the GitHub repository. All parameters controlling the cube generation workflow are defined in the configuration file config.yml.

Comprehensive documentation of the configuration schema is available on the documentation webpage. A step-by-step example showing how to set up a configuration file—while extracting information from multiple data stores—is provided here:
Setup Config YAML File.

In this notebook, we provide:

• A concise preview of the configuration file (summarized in a table)
• The requested geospatial domain
• A walkthrough of the cube generation process

---

Requirements¶

Before proceeding, ensure that all required dependencies are installed.

Create the conda environment using the following command:

conda env create -f environment.yml

and activate that environment to run this notebook using that kernel

conda activate eo-lincs-scs1

Next, accessing ERA5 reanalysis data via the Copernicus Data Store (CDS) requires a valid CDS API key. This can be obtained by following the instructions in the xcube-cds documentation.

Once obtained, the credentials must be added to the configuration file. Because the configuration file is generated programmatically while iterating over the flux tower sites, the credentials should be inserted into the Python code shown below, at the end of Cell 7 in the section labeled # add CDS data store.

---

Running the Pipeline¶

Once all dependencies are installed, the final data cubes can be generated by executing this notebook.

The results will be stored locally in the data directory, as defined in the configuration YAML file. Note that this is a user-defined choice and can be changed to any file-system-based data store (e.g. "file", "s3"). The only requirement is that a writable data store named storage is provided (see the documentation).

Initial visualizations of the generated cubes are displayed at the end of the notebook.

---

Imports¶

Let's import everything we need:

In [1]:

import yaml

import pandas as pd
import utm
import pyproj
import matplotlib.pyplot as plt
from xcube_multistore import MultiSourceDataStore

import yaml import pandas as pd import utm import pyproj import matplotlib.pyplot as plt from xcube_multistore import MultiSourceDataStore

---

Configuration of the Cube Generation Process¶

The full data cube generation is controlled via a configuration YAML file, which must be prepared in advance. This configuration is highly flexible, allowing users to customize all aspects of the processing workflow within the Multi-Source Data Store, while also serving as a reproducible recipe of the cube generation process.

You can explore the available options for the configuration file using the function get_config_schema(). Run it and expand the fields to see all the properties that can be set, in combination with the Configuration Guide.

For guidance on creating your configuration file, you can also refer to the notebook Setup Config YAML File or consult the Configuration Guide in the documentation.

In [2]:

MultiSourceDataStore.get_config_schema()

MultiSourceDataStore.get_config_schema()

Out[2]:

<xcube.util.jsonschema.JsonObjectSchema at 0x73b3f6874050>

Since this science case focuses on small spatial cutouts and long time series around flux towers, the configuration for the MultiSourceDataStore is generated programmatically. This enables efficient iteration over individual flux tower sites.

For Sentinel-2 access, a time-series mode has been implemented in xcube-stac. It identifies the tiles corresponding to the closest WGS Sentinel-2 grid and performs spatial sub-setting while preserving the native 10 m resolution. Bands at lower resolutions (20 m and 60 m) are resampled to the 10 m grid. This approach preserves the data in its most native form and minimises processing during cube generation.

This mode is available for both Planetary Computer and CDSE. On Planetary Computer, data is stored as GeoTIFF, allowing efficient chunked access, while on CDSE it is stored as JPEG2000, which requires full tile downloads. As a result, Planetary Computer is roughly 10× faster than CDSE when retrieving small cutouts. Switching between the two data stores only requires updating the store configuration. Replace the respective data store in the configuration YAML:

- identifier: stac-pc
  store_id: stac-pc-ardc

with:

- identifier: stac-cdse
  store_id: stac-cdse-ardc
  store_params:
    key: <CDSE S3 key>
    secret: <CDSE S3 secret>

The required CDSE S3 credentials can be generated by following the CDSE S3 documentation.

For Sentinel-3 SYNERGY and LST access, the main bottleneck is that the data is stored on an irregular satellite observation grid, so rectification is required to get an anaylsis-ready data cube. This is performed using xcube-resampling. Before rectification, subsetting is necessary, but currently the full latitude-longitude grid must be loaded to determine the region of interest. Efforts are underway to estimate the region without loading the full grid and to subset the dataset before any processing.

However, for LST, significant improvements are unlikely because tiles (1200×1500) are loaded as a single chunk, meaning that any subsetting requires downloading the full observation. Additionally, LST retrieval is slow because terrain correction is not applied in the product; viewing angles and elevation data must be loaded and upsampled, and terrain-induced orthorectification is applied on the fly. All of this makes retrieving Sentinel-3 for long time series and small cutouts infeasibly slow, though improvements are being worked on. For larger regions of interest, performance is reasonable due to the large swath coverage of Sentinel-3, as shown in the xcube-stac examples. For this example here we will include Sentinel-3 SYNERGY to demonstrate the integration into xcube Mutli-Source datastore. Once we improve we the Sentinel-3 access, we plan to include LST data as well.

For ERA5, the newly published ERA5 hourly time series dataset (reanalysis-era5-single-levels-timeseries) is used. It provides point-level access, although only a limited set of variables is available, which is sufficient for this science case. This allows very fast access to ERA5 time series data.

GEDI data is retrieved using xcube-gedidb, which connects to gediDB. The result is a vector dataset* where each LiDAR pulse is stored as a latitude-longitude point, yielding a scattered point dataset.

The ESA CCI dataset is accessed using xcube-cci. Data is loaded lazily and resampled on the fly to match the Sentinel-2 grid. This alignment can be specified in the configuration by setting the grid_mapping field to the Sentinel-2 data ID.

The remaining datasets are accessed through the EO Forest STAC Catalog. All datasets in this catalog are openly available without authentication and are stored in the Zarr format, enabling efficient lazy loading and chunked access. They can also be resampled on the fly to match the spatial resolution and grid of the Sentinel-2 data.

We will now create a configuration file for a flux tower site as an example. The coordinates of the flux tower sites are given in sites.csv. A short time range is selected because Sentinel-3 retrieval is currently slow, although improvements are in progress. Sentinel-2 L2A, in contrast, works well for long time series, as shown in SCS3.

In [3]:

sites = pd.read_csv("sites.csv")
sites = sites.iloc[1:2]
sites

sites = pd.read_csv("sites.csv") sites = sites.iloc[1:2] sites

Out[3]:

	Site ID	latitude	longitude	IGBP
1	AU-How	-12.4943	131.1523	WSA

In [4]:

time_range = ["2020-04-15", "2020-04-30"]

time_range = ["2020-04-15", "2020-04-30"]

Here we define a helper function that retrieves the UTM CRS and calculates a bounding box from a latitude-longitude point. This is used to generate inputs for Sentinel-3 based on the Sentinel-2 time series mode.

In [5]:

def get_utm_bbox_crs(
    lon: float | int, lat: float | int, bbox_width: int
) -> tuple[str, list[float | int]]:
    x, y, zone_number, hemisphere = utm.from_latlon(lat, lon)
    epsg_code = 32600 + zone_number if hemisphere == "N" else 32700 + zone_number
    crs = f"EPSG:{epsg_code}"
    x = int(x)
    y = int(y)
    half_width = bbox_width / 2
    bbox = [x - half_width, y - half_width, x + half_width, y + half_width]

    return crs, bbox

def get_utm_bbox_crs( lon: float | int, lat: float | int, bbox_width: int ) -> tuple[str, list[float | int]]: x, y, zone_number, hemisphere = utm.from_latlon(lat, lon) epsg_code = 32600 + zone_number if hemisphere == "N" else 32700 + zone_number crs = f"EPSG:{epsg_code}" x = int(x) y = int(y) half_width = bbox_width / 2 bbox = [x - half_width, y - half_width, x + half_width, y + half_width] return crs, bbox

Here we define data IDs for the datasets accessed via the EO Forest STAC Catalog. Note that some datasets require custom processing, for which we specify the name of the modifying function stored in utils.py.

In [6]:

foresteo_data_ids = [
    ('biomass-carbon/CCI_BIOMASS/CCI_BIOMASS_v6.0/CCI_BIOMASS_v6.0.json', "modify_cci_biomass"),
    ('biomass-carbon/SAATCHI_BIOMASS/SAATCHI_BIOMASS_v2.0/SAATCHI_BIOMASS_v2.0.json', None),
    ('biomass-carbon/ROBINSON_CR/ROBINSON_CR_v1.0/ROBINSON_CR_v1.0.json', None),
    ('disturbance-change/HANSEN_GFC/HANSEN_GFC_v1.12/HANSEN_GFC_v1.12.json', None),
    ('structure-demography/GAMI/GAMI_v3.1/GAMI_v3.1.json', "modify_gami"),
    ('structure-demography/POTAPOV_HEIGHT/POTAPOV_HEIGHT_v1.0/POTAPOV_HEIGHT_v1.0.json', None),
]

foresteo_data_ids = [ ('biomass-carbon/CCI_BIOMASS/CCI_BIOMASS_v6.0/CCI_BIOMASS_v6.0.json', "modify_cci_biomass"), ('biomass-carbon/SAATCHI_BIOMASS/SAATCHI_BIOMASS_v2.0/SAATCHI_BIOMASS_v2.0.json', None), ('biomass-carbon/ROBINSON_CR/ROBINSON_CR_v1.0/ROBINSON_CR_v1.0.json', None), ('disturbance-change/HANSEN_GFC/HANSEN_GFC_v1.12/HANSEN_GFC_v1.12.json', None), ('structure-demography/GAMI/GAMI_v3.1/GAMI_v3.1.json', "modify_gami"), ('structure-demography/POTAPOV_HEIGHT/POTAPOV_HEIGHT_v1.0/POTAPOV_HEIGHT_v1.0.json', None), ]

In the next cell, we create the full configuration and then write it to a YAML file for persistent storage.

In [7]:

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-pc",
        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)

    # for Sentinel-3 get UTM CRS and bbox in UTM
    # we take bbox_width equals 5000 for some butter for resampling 
    utm_crs, utm_bbox = get_utm_bbox_crs(site["longitude"], site["latitude"], 5000)

    # append config for Sentinel-3 SYNERGY
    config_ds = dict(
        identifier=f"{site['Site ID']}/sen3_syn",
        store="stac-pc",
        data_id="sentinel-3-synergy-syn-l2-netcdf",
        open_params=dict(
            asset_names=[
                "syn-oa04-reflectance",
                "syn-oa08-reflectance",
                "syn-oa10-reflectance",
                "syn-oa11-reflectance",
                "syn-oa12-reflectance",
                "syn-oa17-reflectance",
            ],
            time_range=time_range,
            bbox=utm_bbox,
            spatial_res=200,
            crs=utm_crs,
            add_error_bands=False,
        ),
        grid_mapping=f"{site['Site ID']}/sen2",
    )
    config["datasets"].append(config_ds)

    # append config for ERA5
    config_ds = dict(
        identifier=f"{site['Site ID']}/era5",
        store="cds",
        data_id="reanalysis-era5-single-levels-timeseries",
        open_params=dict(
            variable_names=[
                 '10m_u_component_of_wind',
                 '10m_v_component_of_wind',
                 '2m_dewpoint_temperature',
                 '2m_temperature',
                 'skin_temperature',
                 'surface_pressure',
                 'surface_solar_radiation_downwards',
                 'surface_thermal_radiation_downwards'
            ],
            time_range=["1990-01-01", "2025-12-31"],
            location=[site["longitude"], site["latitude"]],
        ),
    )
    config["datasets"].append(config_ds)

    # append config for ESA CCI
    config_ds = dict(
        identifier=f"{site['Site ID']}/esa_cci_biomass",
        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)

    # append config for forestEO products
    for (data_id, func_name) in foresteo_data_ids:
        config_ds = dict(
            identifier=f"{site['Site ID']}/{data_id.split('/')[1]}",
            store="store_foresteo",
            data_id=data_id,
            grid_mapping=f"{site['Site ID']}/sen2",
        )
    
        if func_name is not None:
            config_ds["custom_processing"] = {
                "module_path": "utils",
                "function_name": func_name,
            }
    
        config["datasets"].append(config_ds)

    # for GEDI get Sentinel-2 bbox in WGS84
    utm_crs, utm_bbox = get_utm_bbox_crs(site["longitude"], site["latitude"], 4000)
    t = pyproj.Transformer.from_crs(utm_crs, "EPSG:4326", always_xy=True)
    wgs8_bbox = t.transform_bounds(*utm_bbox, densify_pts=101)
    
    # append config for GEDI
    config_ds = dict(
        identifier=f"{site['Site ID']}/gedi",
        store="store_gedi",
        data_id="all",
        open_params=dict(
            bbox=list(wgs8_bbox),
            time_range=["2019-04-01", "2025-12-31"],
            variables= [
                'agbd',
                'agbd_pi_lower',
                'agbd_pi_upper',
                'agbd_se',
                'agbd_t',
                'agbd_t_se',
                'algorithmrun_flag',
                'beam_type',
                'degrade_flag',
                'fhd_normal',
                'l2a_quality_flag',
                'l2b_quality_flag',
                'landsat_treecover',
                'leaf_off_flag',
                'num_detectedmodes',
                'omega',
                'quality_flag',
                'rh',
                'sensitivity',
                'solar_elevation',
                'surface_flag',
                'wsci',
                'wsci_pi_lower',
                'wsci_pi_upper',
                'wsci_quality_flag',
                'wsci_xy',
                'wsci_xy_pi_lower',
                'wsci_xy_pi_upper',
                'wsci_z',
                'wsci_z_pi_lower',
                'wsci_z_pi_upper' 
            ]
        ),
    )
    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-pc",
    store_id="stac-pc-ardc",
)
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)
# add ForestEO STAC data store
config_store = dict(
    identifier="store_foresteo",
    store_id="stac",
    store_params={
      "url": "https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded-data/collections/public/catalog.json",
    }
)
config["data_stores"].append(config_store)
# add GEDI data store
config_store = dict(
    identifier="store_gedi",
    store_id="gedidb",
)
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-pc", 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) # for Sentinel-3 get UTM CRS and bbox in UTM # we take bbox_width equals 5000 for some butter for resampling utm_crs, utm_bbox = get_utm_bbox_crs(site["longitude"], site["latitude"], 5000) # append config for Sentinel-3 SYNERGY config_ds = dict( identifier=f"{site['Site ID']}/sen3_syn", store="stac-pc", data_id="sentinel-3-synergy-syn-l2-netcdf", open_params=dict( asset_names=[ "syn-oa04-reflectance", "syn-oa08-reflectance", "syn-oa10-reflectance", "syn-oa11-reflectance", "syn-oa12-reflectance", "syn-oa17-reflectance", ], time_range=time_range, bbox=utm_bbox, spatial_res=200, crs=utm_crs, add_error_bands=False, ), grid_mapping=f"{site['Site ID']}/sen2", ) config["datasets"].append(config_ds) # append config for ERA5 config_ds = dict( identifier=f"{site['Site ID']}/era5", store="cds", data_id="reanalysis-era5-single-levels-timeseries", open_params=dict( variable_names=[ '10m_u_component_of_wind', '10m_v_component_of_wind', '2m_dewpoint_temperature', '2m_temperature', 'skin_temperature', 'surface_pressure', 'surface_solar_radiation_downwards', 'surface_thermal_radiation_downwards' ], time_range=["1990-01-01", "2025-12-31"], location=[site["longitude"], site["latitude"]], ), ) config["datasets"].append(config_ds) # append config for ESA CCI config_ds = dict( identifier=f"{site['Site ID']}/esa_cci_biomass", 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) # append config for forestEO products for (data_id, func_name) in foresteo_data_ids: config_ds = dict( identifier=f"{site['Site ID']}/{data_id.split('/')[1]}", store="store_foresteo", data_id=data_id, grid_mapping=f"{site['Site ID']}/sen2", ) if func_name is not None: config_ds["custom_processing"] = { "module_path": "utils", "function_name": func_name, } config["datasets"].append(config_ds) # for GEDI get Sentinel-2 bbox in WGS84 utm_crs, utm_bbox = get_utm_bbox_crs(site["longitude"], site["latitude"], 4000) t = pyproj.Transformer.from_crs(utm_crs, "EPSG:4326", always_xy=True) wgs8_bbox = t.transform_bounds(*utm_bbox, densify_pts=101) # append config for GEDI config_ds = dict( identifier=f"{site['Site ID']}/gedi", store="store_gedi", data_id="all", open_params=dict( bbox=list(wgs8_bbox), time_range=["2019-04-01", "2025-12-31"], variables= [ 'agbd', 'agbd_pi_lower', 'agbd_pi_upper', 'agbd_se', 'agbd_t', 'agbd_t_se', 'algorithmrun_flag', 'beam_type', 'degrade_flag', 'fhd_normal', 'l2a_quality_flag', 'l2b_quality_flag', 'landsat_treecover', 'leaf_off_flag', 'num_detectedmodes', 'omega', 'quality_flag', 'rh', 'sensitivity', 'solar_elevation', 'surface_flag', 'wsci', 'wsci_pi_lower', 'wsci_pi_upper', 'wsci_quality_flag', 'wsci_xy', 'wsci_xy_pi_lower', 'wsci_xy_pi_upper', 'wsci_z', 'wsci_z_pi_lower', 'wsci_z_pi_upper' ] ), ) 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-pc", store_id="stac-pc-ardc", ) 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) # add ForestEO STAC data store config_store = dict( identifier="store_foresteo", store_id="stac", store_params={ "url": "https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded-data/collections/public/catalog.json", } ) config["data_stores"].append(config_store) # add GEDI data store config_store = dict( identifier="store_gedi", store_id="gedidb", ) config["data_stores"].append(config_store)

In [8]:

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

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

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

Note: The following cell will produce a NoConformsTo warning. This occurs because the EO Forest STAC is a static STAC catalog rather than a full STAC API. It does not expose search capabilities or advertise STAC API conformance classes. This warning is harmless, as xcube-stac can also work with static STAC catalogs.

In [9]:

msds = MultiSourceDataStore("config.yml")

msds = MultiSourceDataStore("config.yml")

/home/konstantin/micromamba/envs/eo-lincs-scs1/lib/python3.13/site-packages/pystac_client/client.py:191: NoConformsTo: Server does not advertise any conformance classes.
  warnings.warn(NoConformsTo())

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

In [10]:

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-How/sen2	stac-pc-ardc	-	sentinel-2-l2a	time_range: ['2020-04-15', '2020-04-30']; 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/sen3_syn	stac-pc-ardc	-	sentinel-3-synergy-syn-l2-netcdf	asset_names: ['syn-oa04-reflectance', 'syn-oa08-reflectance', 'syn-oa10-reflectance', 'syn-oa11-reflectance', 'syn-oa12-reflectance', 'syn-oa17-reflectance']; time_range: ['2020-04-15', '2020-04-30']; bbox: [731412.0, 8615335.0, 736412.0, 8620335.0]; spatial_res: 200; crs: EPSG:32752; add_error_bands: False	Like 'AU-How/sen2'	Zarr
AU-How/era5	cds	endpoint_url: https://cds.climate.copernicus.eu/api; cds_api_key: cds-api-key; normalize_names: True	reanalysis-era5-single-levels-timeseries	variable_names: ['10m_u_component_of_wind', '10m_v_component_of_wind', '2m_dewpoint_temperature', '2m_temperature', 'skin_temperature', 'surface_pressure', 'surface_solar_radiation_downwards', 'surface_thermal_radiation_downwards']; time_range: ['1990-01-01', '2025-12-31']; location: [131.1523, -12.4943]	-	Zarr
AU-How/esa_cci_biomass	cciodp	-	esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.5-0.100m	time_range: ['2020-04-15', '2020-04-30']	Like 'AU-How/sen2'	Zarr
AU-How/CCI_BIOMASS	stac	url: https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded-data/collections/public/catalog.json	biomass-carbon/CCI_BIOMASS/CCI_BIOMASS_v6.0/CCI_BIOMASS_v6.0.json	-	Like 'AU-How/sen2'	Zarr
AU-How/SAATCHI_BIOMASS	stac	url: https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded-data/collections/public/catalog.json	biomass-carbon/SAATCHI_BIOMASS/SAATCHI_BIOMASS_v2.0/SAATCHI_BIOMASS_v2.0.json	-	Like 'AU-How/sen2'	Zarr
AU-How/ROBINSON_CR	stac	url: https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded-data/collections/public/catalog.json	biomass-carbon/ROBINSON_CR/ROBINSON_CR_v1.0/ROBINSON_CR_v1.0.json	-	Like 'AU-How/sen2'	Zarr
AU-How/HANSEN_GFC	stac	url: https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded-data/collections/public/catalog.json	disturbance-change/HANSEN_GFC/HANSEN_GFC_v1.12/HANSEN_GFC_v1.12.json	-	Like 'AU-How/sen2'	Zarr
AU-How/GAMI	stac	url: https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded-data/collections/public/catalog.json	structure-demography/GAMI/GAMI_v3.1/GAMI_v3.1.json	-	Like 'AU-How/sen2'	Zarr
AU-How/POTAPOV_HEIGHT	stac	url: https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded-data/collections/public/catalog.json	structure-demography/POTAPOV_HEIGHT/POTAPOV_HEIGHT_v1.0/POTAPOV_HEIGHT_v1.0.json	-	Like 'AU-How/sen2'	Zarr
AU-How/gedi	gedidb	-	all	bbox: [131.13375765995252, -12.512527494209674, 131.17084382391076, -12.476087024702665]; time_range: ['2019-04-01', '2025-12-31']; variables: ['agbd', 'agbd_pi_lower', 'agbd_pi_upper', 'agbd_se', 'agbd_t', 'agbd_t_se', 'algorithmrun_flag', 'beam_type', 'degrade_flag', 'fhd_normal', 'l2a_quality_flag', 'l2b_quality_flag', 'landsat_treecover', 'leaf_off_flag', 'num_detectedmodes', 'omega', 'quality_flag', 'rh', 'sensitivity', 'solar_elevation', 'surface_flag', 'wsci', 'wsci_pi_lower', 'wsci_pi_upper', 'wsci_quality_flag', 'wsci_xy', 'wsci_xy_pi_lower', 'wsci_xy_pi_upper', 'wsci_z', 'wsci_z_pi_lower', 'wsci_z_pi_upper']	-	Zarr

In the following cell, we visualize the areas of interest for all datasets defined in the configuration YAML. Bounding boxes are displayed as GeoJSON polygons, while point locations are represented as GeoJSON points.

Below, the bounding box and point location for a selected site are shown as an example.

In [11]:

msds.display_geolocations()

msds.display_geolocations()

Out[11]:

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

---

Cube Generation¶

Cube generation is triggered with a single command, and each dataset defined in the configuration YAML is processed sequentially. The workflow applies the following steps as required:

• Request the dataset
• Perform dataset-specific preprocessing (e.g., spatial and/or temporal resampling)
• Write the resulting data cube using the storage datastore, whose root is defined as data in the configuration file. Each dataset is written to its corresponding identifier, such as <site_id>/sen2. For each flux tower site, all related datasets are therefore grouped within a common <site_id>/ directory, forming a single site-specific collection within the output structure.

Dataset Notes¶

• sen2 Generates an analysis-ready datacube by stacking multiple Sentinel-2 Level-2A observations along the time dimension and subsetting them to the defined small spatial cutout. The data remains in the native UTM grid to minimize reprojection errors.

• sen3_syn Generates an analysis-ready datacube by rectifying Sentinel-3 SYNERGY products to a user-defined target grid. Quality flag values are included in the output. The target grid is aligned with the Sentinel-2 grid.

• sen3_lst Generates an analysis-ready datacube by rectifying Sentinel-3 LST products to a user-defined target grid. Quality flag values are included in the output. The grid is aligned with the Sentinel-2 grid.

• era5 Provides time series data at the flux tower location for the selected variables.

• esa_cci_biomass Accessed lazily via the xcube ESA CCI datastore, with on-the-fly resampling to match the Sentinel-2 grid.

• CCI_BIOMASS, SAATCHI_BIOMASS, ROBINSON_CR, HANSEN_GFC, GAMI, POTAPOV_HEIGHT Each dataset is accessed lazily via the xcube STAC datastore, with on-the-fly resampling to match the Sentinel-2 grid.

• gedi Accessed via the xcube gediDB datastore. The bounding box is derived from the Sentinel-2 configuration, retrieving all scattered GEDI data points within that spatial extent.

Note: Most of the logging is related to the Copernicus Data Store (CDS) when retrieving ERA-5 reanalysis data.

In [12]:

msds.generate()

msds.generate()

Cube Generation

Dataset identifier	Status	Message	Exception
AU-How/sen2	COMPLETED	Dataset 'AU-How/sen2' finished: 0:02:26	-
AU-How/sen3_syn	COMPLETED	Dataset 'AU-How/sen3_syn' finished: 0:15:49	-
AU-How/era5	COMPLETED	Dataset 'AU-How/era5' finished: 0:02:21	-
AU-How/esa_cci_biomass	COMPLETED	Dataset 'AU-How/esa_cci_biomass' finished: 0:00:17	-
AU-How/CCI_BIOMASS	COMPLETED	Dataset 'AU-How/CCI_BIOMASS' finished: 0:00:17	-
AU-How/SAATCHI_BIOMASS	COMPLETED	Dataset 'AU-How/SAATCHI_BIOMASS' finished: 0:00:03	-
AU-How/ROBINSON_CR	COMPLETED	Dataset 'AU-How/ROBINSON_CR' finished: 0:00:04	-
AU-How/HANSEN_GFC	COMPLETED	Dataset 'AU-How/HANSEN_GFC' finished: 0:00:24	-
AU-How/GAMI	COMPLETED	Dataset 'AU-How/GAMI' finished: 0:01:11	-
AU-How/POTAPOV_HEIGHT	COMPLETED	Dataset 'AU-How/POTAPOV_HEIGHT' finished: 0:00:16	-
AU-How/gedi	COMPLETED	Dataset 'AU-How/gedi' finished: 0:03:56	-

/home/konstantin/micromamba/envs/eo-lincs-scs1/lib/python3.13/site-packages/xcube/core/store/fs/impl/dataset.py:200: SerializationWarning: saving variable None with floating point data as an integer dtype without any _FillValue to use for NaNs
  data.to_zarr(
/home/konstantin/micromamba/envs/eo-lincs-scs1/lib/python3.13/site-packages/dask/_task_spec.py:768: RuntimeWarning: invalid value encountered in divide
  return self.func(*new_argspec)
xcube-cds version 1.4.1
2026-06-16 14:32:01,321 - WARNING - Recovering from HTTP error [502 Bad Gateway], attempt 1 of 500
2026-06-16 14:32:01,322 - WARNING - Retrying in 120 seconds
2026-06-16 14:34:01,322 - INFO - Retrying now...
2026-06-16 14:34:01,564 INFO [2026-02-16T00:00:00] - To generate this ERA5 hourly time series dataset, **homogenisation conventions have been applied to the ERA5 source GRIB data** to ensure consistency, usability, and alignment across chosen variables and time steps. The processed data were then written to an **ARCO Zarr archive**, enabling efficient cloud-optimised access and scalable data retrieval. Please refer to the [user guide](https://confluence.ecmwf.int/x/R6cfHg) for details.

- The dataset presented here is a subset of selected parameters from the full [CDS ERA5 hourly data on single levels (1940–present)](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview). **Requirements for additional parameters may be considered**. Please raise your request with ECMWF Support [here](https://jira.ecmwf.int/plugins/servlet/desk/portal/1/create/202).
2026-06-16 14:34:01,564 - INFO - [2026-02-16T00:00:00] - To generate this ERA5 hourly time series dataset, **homogenisation conventions have been applied to the ERA5 source GRIB data** to ensure consistency, usability, and alignment across chosen variables and time steps. The processed data were then written to an **ARCO Zarr archive**, enabling efficient cloud-optimised access and scalable data retrieval. Please refer to the [user guide](https://confluence.ecmwf.int/x/R6cfHg) for details.

- The dataset presented here is a subset of selected parameters from the full [CDS ERA5 hourly data on single levels (1940–present)](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview). **Requirements for additional parameters may be considered**. Please raise your request with ECMWF Support [here](https://jira.ecmwf.int/plugins/servlet/desk/portal/1/create/202).
2026-06-16 14:34:01,565 INFO Request ID is d22f75cc-0189-4cf5-ac88-a88c15c7baba
2026-06-16 14:34:01,565 - INFO - Request ID is d22f75cc-0189-4cf5-ac88-a88c15c7baba
2026-06-16 14:34:05,361 INFO status has been updated to accepted
2026-06-16 14:34:05,361 - INFO - status has been updated to accepted
2026-06-16 14:34:18,910 INFO status has been updated to successful
2026-06-16 14:34:18,910 - INFO - status has been updated to successful
2026-06-16 14:34:19,152 - INFO - Downloading https://object-store.os-api.cci2.ecmwf.int:443/cci2-prod-cache-2/2026-06-11/1c47ae9478da59b32b43e9229200e10c.zip

1c47ae9478da59b32b43e9229200e10c.zip:   0%|          | 0.00/12.1M [00:00<?, ?B/s]

---

Initial Inspection of the Generated Datasets¶

After cube generation has completed, the datasets can be opened using the xcube datastore framework API as usual.

Note that the multi-source datastore expects a datastore named storage. This datastore is defined in the data_stores section of the config.yml configuration file.

In [13]:

ds = msds.stores.storage.open_data("AU-How/sen2.zarr")
ds

ds = msds.stores.storage.open_data("AU-How/sen2.zarr") ds

Out[13]:

<xarray.Dataset> Size: 27MB
Dimensions:      (time: 3, y: 400, x: 400)
Coordinates:
  * time         (time) datetime64[ns] 24B 2020-04-19T01:37:11.024000 ... 202...
  * y            (y) float64 3kB 8.62e+06 8.62e+06 ... 8.616e+06 8.616e+06
  * x            (x) float64 3kB 7.319e+05 7.319e+05 ... 7.359e+05 7.359e+05
    spatial_ref  int64 8B ...
Data variables: (12/13)
    B01          (time, y, x) float32 2MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B02          (time, y, x) float32 2MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B03          (time, y, x) float32 2MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B04          (time, y, x) float32 2MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B05          (time, y, x) float32 2MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B06          (time, y, x) float32 2MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    ...           ...
    B08          (time, y, x) float32 2MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B09          (time, y, x) float32 2MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B11          (time, y, x) float32 2MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B12          (time, y, x) float32 2MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    B8A          (time, y, x) float32 2MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    SCL          (time, y, x) float64 4MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
Attributes:
    stac_catalog_url:    https://planetarycomputer.microsoft.com/api/stac/v1
    stac_item_id:        S2A_MSIL2A_20200419T013711_R031_T52LGM_20200924T025946
    stac_item_ids:       {'2020-04-19T01:37:11.024000': ['S2A_MSIL2A_20200419...
    xcube_stac_version:  1.3.1

xarray.Dataset

• Dimensions:
  • time: 3
  • y: 400
  • x: 400
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2020-04-19T01:37:11.024000 ... 2...

    array(['2020-04-19T01:37:11.024000000', '2020-04-24T01:37:09.024000000',
           '2020-04-29T01:37:21.024000000'], dtype='datetime64[ns]')

  • y
    (y)
    float64
    8.62e+06 8.62e+06 ... 8.616e+06

    array([8619835., 8619825., 8619815., ..., 8615865., 8615855., 8615845.])

  • x
    (x)
    float64
    7.319e+05 7.319e+05 ... 7.359e+05

    array([731915., 731925., 731935., ..., 735885., 735895., 735905.])

  • 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>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 1.83 MiB 625.00 kiB Shape (3, 400, 400) (1, 400, 400) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 1.83 MiB 625.00 kiB Shape (3, 400, 400) (1, 400, 400) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 1.83 MiB 625.00 kiB Shape (3, 400, 400) (1, 400, 400) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 1.83 MiB 625.00 kiB Shape (3, 400, 400) (1, 400, 400) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 1.83 MiB 625.00 kiB Shape (3, 400, 400) (1, 400, 400) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 1.83 MiB 625.00 kiB Shape (3, 400, 400) (1, 400, 400) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 1.83 MiB 625.00 kiB Shape (3, 400, 400) (1, 400, 400) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 1.83 MiB 625.00 kiB Shape (3, 400, 400) (1, 400, 400) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 1.83 MiB 625.00 kiB Shape (3, 400, 400) (1, 400, 400) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 1.83 MiB 625.00 kiB Shape (3, 400, 400) (1, 400, 400) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 1.83 MiB 625.00 kiB Shape (3, 400, 400) (1, 400, 400) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

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

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 1.83 MiB 625.00 kiB Shape (3, 400, 400) (1, 400, 400) Dask graph 3 chunks in 2 graph layers Data type float32 numpy.ndarray

  • SCL
    (time, y, x)
    float64
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 3.66 MiB 1.22 MiB Shape (3, 400, 400) (1, 400, 400) Dask graph 3 chunks in 2 graph layers Data type float64 numpy.ndarray

• Attributes: (4)

  stac_catalog_url :

  https://planetarycomputer.microsoft.com/api/stac/v1

  stac_item_id :

  S2A_MSIL2A_20200419T013711_R031_T52LGM_20200924T025946

  stac_item_ids :

  {'2020-04-19T01:37:11.024000': ['S2A_MSIL2A_20200419T013711_R031_T52LGM_20200924T025946'], '2020-04-24T01:37:09.024000': ['S2B_MSIL2A_20200424T013709_R031_T52LGM_20200921T223404'], '2020-04-29T01:37:21.024000': ['S2A_MSIL2A_20200429T013721_R031_T52LGM_20200922T130315']}

  xcube_stac_version :

  1.3.1

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

In [14]:

ds.B04.isel(time=0).plot(vmin=0., vmax=0.1)
plt.tight_layout()

ds.B04.isel(time=0).plot(vmin=0., vmax=0.1) plt.tight_layout()

The same quicklook can be created for any generated dataset.

In [15]:

ds = msds.stores.storage.open_data("AU-How/sen3_syn.zarr")
ds

ds = msds.stores.storage.open_data("AU-How/sen3_syn.zarr") ds

Out[15]:

<xarray.Dataset> Size: 194MB
Dimensions:      (time: 15, y: 400, x: 400)
Coordinates:
  * time         (time) datetime64[ns] 120B 2020-04-15T01:01:56 ... 2020-04-3...
  * y            (y) float64 3kB 8.62e+06 8.62e+06 ... 8.616e+06 8.616e+06
  * x            (x) float64 3kB 7.319e+05 7.319e+05 ... 7.359e+05 7.359e+05
    spatial_ref  int64 8B ...
Data variables:
    CLOUD_flags  (time, y, x) uint8 2MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    OLC_flags    (time, y, x) float64 19MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    SDR_Oa04     (time, y, x) float64 19MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    SDR_Oa08     (time, y, x) float64 19MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    SDR_Oa10     (time, y, x) float64 19MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    SDR_Oa11     (time, y, x) float64 19MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    SDR_Oa12     (time, y, x) float64 19MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    SDR_Oa17     (time, y, x) float64 19MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    SLN_flags    (time, y, x) float64 19MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    SLO_flags    (time, y, x) float64 19MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
    SYN_flags    (time, y, x) float64 19MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
Attributes:
    comment:             
    contact:             eosupport@copernicus.esa.int
    institution:         LN2
    netCDF_version:      4.2 of Mar 13 2018 10:14:33 $
    references:          S3IPF PDS 006 - i1r11 - Product Data Format Specific...
    resolution:          [ 300 300 ]
    source:              IPF-SY-2 06.19
    stac_catalog_url:    https://planetarycomputer.microsoft.com/api/stac/v1
    title:               SYN L2, surface directional reflectance associated w...
    xcube_stac_version:  1.3.1

xarray.Dataset

• Dimensions:
  • time: 15
  • y: 400
  • x: 400
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2020-04-15T01:01:56 ... 2020-04-...

    array(['2020-04-15T01:01:56.000000000', '2020-04-16T00:54:02.000000000',
           '2020-04-17T00:49:09.000000000', '2020-04-19T00:58:12.000000000',
           '2020-04-20T01:08:36.000000000', '2020-04-21T00:45:25.000000000',
           '2020-04-22T01:17:39.000000000', '2020-04-23T00:54:28.000000000',
           '2020-04-24T01:04:52.000000000', '2020-04-25T00:41:41.000000000',
           '2020-04-26T01:13:55.000000000', '2020-04-27T00:50:45.000000000',
           '2020-04-28T01:04:08.000000000', '2020-04-29T00:37:57.000000000',
           '2020-04-30T01:10:11.000000000'], dtype='datetime64[ns]')

  • y
    (y)
    float64
    8.62e+06 8.62e+06 ... 8.616e+06

    array([8619835., 8619825., 8619815., ..., 8615865., 8615855., 8615845.])

  • x
    (x)
    float64
    7.319e+05 7.319e+05 ... 7.359e+05

    array([731915., 731925., 731935., ..., 735885., 735895., 735905.])

  • 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: (11)
  • CLOUD_flags
    (time, y, x)
    uint8
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    flag_masks :

    [1.0, 2.0, 4.0, 8.0]

    flag_meanings :

    CLOUD CLOUD_AMBIGUOUS CLOUD_MARGIN SNOW_ICE

    grid_mapping :

    spatial_ref

    long_name :

    Synergy cloud flags

    standard_name :

    synergy_cloud_flags

    Array Chunk Bytes 2.29 MiB 156.25 kiB Shape (15, 400, 400) (1, 400, 400) Dask graph 15 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • OLC_flags
    (time, y, x)
    float64
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    flag_masks :

    [4096.0, 2048.0, 1024.0, 512.0, 256.0, 16.0, 8.0, 4.0, 2.0, 1.0]

    flag_meanings :

    OLC_land OLC_coastline OLC_fresh_inland_water OLC_tidal_region OLC_bright OLC_invalid OLC_cosmetic OLC_sun_glint_risk OLC_dubious OLC_summary_saturated

    grid_mapping :

    spatial_ref

    long_name :

    Selected quality and classification flags for OLCI SYN channels

    standard_name :

    Selected_quality_and_classification_flags_for_olci_syn_channels

    Array Chunk Bytes 18.31 MiB 1.22 MiB Shape (15, 400, 400) (1, 400, 400) Dask graph 15 chunks in 2 graph layers Data type float64 numpy.ndarray

  • SDR_Oa04
    (time, y, x)
    float64
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    ancillary_variables :

    SDR_Oa04_err

    bandwidth :

    10

    grid_mapping :

    spatial_ref

    long_name :

    Surface directional reflectance associated with OLCI channel 04

    standard_name :

    surface_directional_reflectance

    valid_max :

    10000

    valid_min :

    0

    wavelength :

    490

    Array Chunk Bytes 18.31 MiB 1.22 MiB Shape (15, 400, 400) (1, 400, 400) Dask graph 15 chunks in 2 graph layers Data type float64 numpy.ndarray

  • SDR_Oa08
    (time, y, x)
    float64
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    ancillary_variables :

    SDR_Oa08_err

    bandwidth :

    10

    grid_mapping :

    spatial_ref

    long_name :

    Surface directional reflectance associated with OLCI channel 08

    standard_name :

    surface_directional_reflectance

    valid_max :

    10000

    valid_min :

    0

    wavelength :

    665

    Array Chunk Bytes 18.31 MiB 1.22 MiB Shape (15, 400, 400) (1, 400, 400) Dask graph 15 chunks in 2 graph layers Data type float64 numpy.ndarray

  • SDR_Oa10
    (time, y, x)
    float64
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    ancillary_variables :

    SDR_Oa10_err

    bandwidth :

    7.5

    grid_mapping :

    spatial_ref

    long_name :

    Surface directional reflectance associated with OLCI channel 10

    standard_name :

    surface_directional_reflectance

    valid_max :

    10000

    valid_min :

    0

    wavelength :

    681.25

    Array Chunk Bytes 18.31 MiB 1.22 MiB Shape (15, 400, 400) (1, 400, 400) Dask graph 15 chunks in 2 graph layers Data type float64 numpy.ndarray

  • SDR_Oa11
    (time, y, x)
    float64
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    ancillary_variables :

    SDR_Oa11_err

    bandwidth :

    10

    grid_mapping :

    spatial_ref

    long_name :

    Surface directional reflectance associated with OLCI channel 11

    standard_name :

    surface_directional_reflectance

    valid_max :

    10000

    valid_min :

    0

    wavelength :

    708.75

    Array Chunk Bytes 18.31 MiB 1.22 MiB Shape (15, 400, 400) (1, 400, 400) Dask graph 15 chunks in 2 graph layers Data type float64 numpy.ndarray

  • SDR_Oa12
    (time, y, x)
    float64
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    ancillary_variables :

    SDR_Oa12_err

    bandwidth :

    7.5

    grid_mapping :

    spatial_ref

    long_name :

    Surface directional reflectance associated with OLCI channel 12

    standard_name :

    surface_directional_reflectance

    valid_max :

    10000

    valid_min :

    0

    wavelength :

    753.75

    Array Chunk Bytes 18.31 MiB 1.22 MiB Shape (15, 400, 400) (1, 400, 400) Dask graph 15 chunks in 2 graph layers Data type float64 numpy.ndarray

  • SDR_Oa17
    (time, y, x)
    float64
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    ancillary_variables :

    SDR_Oa17_err

    bandwidth :

    20

    grid_mapping :

    spatial_ref

    long_name :

    Surface directional reflectance associated with OLCI channel 17

    standard_name :

    surface_directional_reflectance

    valid_max :

    10000

    valid_min :

    0

    wavelength :

    865

    Array Chunk Bytes 18.31 MiB 1.22 MiB Shape (15, 400, 400) (1, 400, 400) Dask graph 15 chunks in 2 graph layers Data type float64 numpy.ndarray

  • SLN_flags
    (time, y, x)
    float64
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    flag_masks :

    [8388608.0, 4194304.0, 2097152.0, 1048576.0, 524288.0, 262144.0, 65536.0, 4096.0, 2048.0, 1024.0, 512.0, 256.0, 128.0, 64.0, 32.0, 16.0, 8.0, 4.0, 2.0, 1.0]

    flag_meanings :

    SLN_summary_pointing SLN_summary_cloud SLN_snow SLN_sun_glint SLN_twilight SLN_day SLN_cosmetic SLN_inland_water SLN_land SLN_tidal SLN_ocean SLN_coastline SLN_summary_unfilled_pixel SLN_summary_no_parameters SLN_summary_invalid_radiance SLN_summary_saturation SLN_summary_no_signal SLN_summary_not_decompressed SLN_summary_pixel_absent SLN_summary_ISP_absent

    grid_mapping :

    spatial_ref

    long_name :

    Exception summary and confidence flags for SLSTR nadir-view SYN channels

    standard_name :

    exception_summary_and_confidence_flags_for_slstr_nadir_view_syn_channels

    Array Chunk Bytes 18.31 MiB 1.22 MiB Shape (15, 400, 400) (1, 400, 400) Dask graph 15 chunks in 2 graph layers Data type float64 numpy.ndarray

  • SLO_flags
    (time, y, x)
    float64
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    flag_masks :

    [8388608.0, 4194304.0, 2097152.0, 1048576.0, 524288.0, 262144.0, 65536.0, 4096.0, 2048.0, 1024.0, 512.0, 256.0, 128.0, 64.0, 32.0, 16.0, 8.0, 4.0, 2.0, 1.0]

    flag_meanings :

    SLO_summary_pointing SLO_summary_cloud SLO_snow SLO_sun_glint SLO_twilight SLO_day SLO_cosmetic SLO_inland_water SLO_land SLO_tidal SLO_ocean SLO_coastline SLO_summary_unfilled_pixel SLO_summary_no_parameters SLO_summary_invalid_radiance SLO_summary_saturation SLO_summary_no_signal SLO_summary_not_decompressed SLO_summary_pixel_absent SLO_summary_ISP_absent

    grid_mapping :

    spatial_ref

    long_name :

    Exception summary and confidence flags for SLSTR oblique-view SYN channels

    standard_name :

    exception_summary_and_confidence_flags_for_slstr_oblique_view_syn_channels

    Array Chunk Bytes 18.31 MiB 1.22 MiB Shape (15, 400, 400) (1, 400, 400) Dask graph 15 chunks in 2 graph layers Data type float64 numpy.ndarray

  • SYN_flags
    (time, y, x)
    float64
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    flag_masks :

    [32768.0, 16384.0, 8192.0, 4096.0, 2048.0, 1024.0, 512.0, 256.0, 128.0, 64.0, 32.0, 16.0, 8.0, 4.0, 2.0, 1.0]

    flag_meanings :

    SYN_high_error SYN_too_low SYN_negative_curvature SYN_success SYN_aerosol_filled SYN_border SYN_partly_water SYN_partly_cloudy SYN_no_slo SYN_no_sln SYN_no_olc SYN_land SYN_AOT_climato SYN_SDR_OOR SYN_snow_risk SYN_cloud

    grid_mapping :

    spatial_ref

    long_name :

    Synergy classification and aerosol retrieval flags

    standard_name :

    synergy_classification_and_aerosol_retrieval_flags

    Array Chunk Bytes 18.31 MiB 1.22 MiB Shape (15, 400, 400) (1, 400, 400) Dask graph 15 chunks in 2 graph layers Data type float64 numpy.ndarray

• Attributes: (10)

  comment :

  contact :

  eosupport@copernicus.esa.int

  institution :

  LN2

  netCDF_version :

  4.2 of Mar 13 2018 10:14:33 $

  references :

  S3IPF PDS 006 - i1r11 - Product Data Format Specification - SYNERGY, S3IPF PDS 002 - i1r7 - Product Data Format Specification - Product Structures, S3IPF DPM 004 - i1r8 - Detailed Processing Model - SYN

  resolution :

  [ 300 300 ]

  source :

  IPF-SY-2 06.19

  stac_catalog_url :

  https://planetarycomputer.microsoft.com/api/stac/v1

  title :

  SYN L2, surface directional reflectance associated with OLCI channel 04

  xcube_stac_version :

  1.3.1

In [16]:

fig, ax = plt.subplots(1, 2, figsize=(14, 6))
ds.SDR_Oa04.isel(time=4).plot(ax=ax[0], vmax=0.1, vmin=0.)
ds.SYN_flags.isel(time=4).plot(ax=ax[1])
plt.tight_layout()

fig, ax = plt.subplots(1, 2, figsize=(14, 6)) ds.SDR_Oa04.isel(time=4).plot(ax=ax[0], vmax=0.1, vmin=0.) ds.SYN_flags.isel(time=4).plot(ax=ax[1]) plt.tight_layout()

In [17]:

ds = msds.stores.storage.open_data("AU-How/era5.zarr")
ds

ds = msds.stores.storage.open_data("AU-How/era5.zarr") ds

Out[17]:

<xarray.Dataset> Size: 13MB
Dimensions:    (time: 315576)
Coordinates:
  * time       (time) datetime64[ns] 3MB 1990-01-01 ... 2025-12-31T23:00:00
    latitude   float64 8B ...
    longitude  float64 8B ...
Data variables:
    d2m        (time) float32 1MB dask.array<chunksize=(315576,), meta=np.ndarray>
    skt        (time) float32 1MB dask.array<chunksize=(315576,), meta=np.ndarray>
    sp         (time) float32 1MB dask.array<chunksize=(315576,), meta=np.ndarray>
    ssrd       (time) float32 1MB dask.array<chunksize=(315576,), meta=np.ndarray>
    strd       (time) float32 1MB dask.array<chunksize=(315576,), meta=np.ndarray>
    t2m        (time) float32 1MB dask.array<chunksize=(315576,), meta=np.ndarray>
    u10        (time) float32 1MB dask.array<chunksize=(315576,), meta=np.ndarray>
    v10        (time) float32 1MB dask.array<chunksize=(315576,), meta=np.ndarray>
Attributes:
    Conventions:             CF-1.7
    GRIB_centre:             ecmf
    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
    GRIB_edition:            1
    GRIB_subCentre:          0
    history:                 2024-09-02T04:48 GRIB to CDM+CF via cfgrib-0.9.1...
    institution:             European Centre for Medium-Range Weather Forecasts

xarray.Dataset

• Dimensions:
  • time: 315576
• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    1990-01-01 ... 2025-12-31T23:00:00

    standard_name :

    time

    array(['1990-01-01T00:00:00.000000000', '1990-01-01T01:00:00.000000000',
           '1990-01-01T02:00:00.000000000', ..., '2025-12-31T21:00:00.000000000',
           '2025-12-31T22:00:00.000000000', '2025-12-31T23:00:00.000000000'],
          dtype='datetime64[ns]')

  • latitude
    ()
    float64
    ...

    long_name :

    latitude

    standard_name :

    latitude

    stored_direction :

    decreasing

    units :

    degrees_north

    [1 values with dtype=float64]

  • longitude
    ()
    float64
    ...

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    [1 values with dtype=float64]

• Data variables: (8)
  • d2m
    (time)
    float32
    dask.array<chunksize=(315576,), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    d2m

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    2 metre dewpoint temperature

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    168

    GRIB_shortName :

    2d

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    K

    long_name :

    2 metre dewpoint temperature

    standard_name :

    unknown

    units :

    K

    Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (315576,) (315576,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • skt
    (time)
    float32
    dask.array<chunksize=(315576,), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    skt

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Skin temperature

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    235

    GRIB_shortName :

    skt

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    K

    long_name :

    Skin temperature

    standard_name :

    unknown

    units :

    K

    Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (315576,) (315576,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sp
    (time)
    float32
    dask.array<chunksize=(315576,), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    surface_air_pressure

    GRIB_cfVarName :

    sp

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Surface pressure

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    134

    GRIB_shortName :

    sp

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    Pa

    long_name :

    Surface pressure

    standard_name :

    surface_air_pressure

    units :

    Pa

    Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (315576,) (315576,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • ssrd
    (time)
    float32
    dask.array<chunksize=(315576,), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    surface_downwelling_shortwave_flux_in_air

    GRIB_cfVarName :

    ssrd

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Surface solar radiation downwards

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    169

    GRIB_shortName :

    ssrd

    GRIB_stepType :

    accum

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    J m**-2

    long_name :

    Surface solar radiation downwards

    standard_name :

    surface_downwelling_shortwave_flux_in_air

    units :

    J m**-2

    Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (315576,) (315576,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • strd
    (time)
    float32
    dask.array<chunksize=(315576,), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    strd

    GRIB_dataType :

    fc

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    Surface thermal radiation downwards

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    175

    GRIB_shortName :

    strd

    GRIB_stepType :

    accum

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    J m**-2

    long_name :

    Surface thermal radiation downwards

    standard_name :

    unknown

    units :

    J m**-2

    Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (315576,) (315576,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • t2m
    (time)
    float32
    dask.array<chunksize=(315576,), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    t2m

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    2 metre temperature

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    167

    GRIB_shortName :

    2t

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    K

    long_name :

    2 metre temperature

    standard_name :

    unknown

    units :

    K

    Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (315576,) (315576,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • u10
    (time)
    float32
    dask.array<chunksize=(315576,), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    u10

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    10 metre U wind component

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    165

    GRIB_shortName :

    10u

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    m s**-1

    long_name :

    10 metre U wind component

    standard_name :

    unknown

    units :

    m s**-1

    Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (315576,) (315576,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • v10
    (time)
    float32
    dask.array<chunksize=(315576,), meta=np.ndarray>

    GRIB_NV :

    0

    GRIB_Nx :

    1440

    GRIB_Ny :

    721

    GRIB_cfName :

    unknown

    GRIB_cfVarName :

    v10

    GRIB_dataType :

    an

    GRIB_gridDefinitionDescription :

    Latitude/Longitude Grid

    GRIB_gridType :

    regular_ll

    GRIB_iDirectionIncrementInDegrees :

    0.25

    GRIB_iScansNegatively :

    0

    GRIB_jDirectionIncrementInDegrees :

    0.25

    GRIB_jPointsAreConsecutive :

    0

    GRIB_jScansPositively :

    0

    GRIB_latitudeOfFirstGridPointInDegrees :

    90.0

    GRIB_latitudeOfLastGridPointInDegrees :

    -90.0

    GRIB_longitudeOfFirstGridPointInDegrees :

    0.0

    GRIB_longitudeOfLastGridPointInDegrees :

    359.75

    GRIB_missingValue :

    3.4028234663852886e+38

    GRIB_name :

    10 metre V wind component

    GRIB_numberOfPoints :

    1038240

    GRIB_paramId :

    166

    GRIB_shortName :

    10v

    GRIB_stepType :

    instant

    GRIB_stepUnits :

    1

    GRIB_totalNumber :

    0

    GRIB_typeOfLevel :

    surface

    GRIB_units :

    m s**-1

    long_name :

    10 metre V wind component

    standard_name :

    unknown

    units :

    m s**-1

    Array Chunk Bytes 1.20 MiB 1.20 MiB Shape (315576,) (315576,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Attributes: (7)

  Conventions :

  CF-1.7

  GRIB_centre :

  ecmf

  GRIB_centreDescription :

  European Centre for Medium-Range Weather Forecasts

  GRIB_edition :

  1

  GRIB_subCentre :

  0

  history :

  2024-09-02T04:48 GRIB to CDM+CF via cfgrib-0.9.10.4/ecCodes-2.26.0 with {"source": "../../../../../admp-scratch/data/arco-job-76964210-9c34-4488-a458-d3511cc8a37a-inputs/pre-reanalysis_era5_single_levels-sfc:1-sst-20150131-000000.grib", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]}

  institution :

  European Centre for Medium-Range Weather Forecasts

In [18]:

ds.t2m.plot()

ds.t2m.plot()

Out[18]:

[<matplotlib.lines.Line2D at 0x72ddd43b5f90>]

In [19]:

ds = msds.stores.storage.open_data("AU-How/esa_cci_biomass.zarr")
ds

ds = msds.stores.storage.open_data("AU-How/esa_cci_biomass.zarr") ds

Out[19]:

<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 8.62e+06 8.62e+06 ... 8.616e+06 8.616e+06
  * x            (x) float64 3kB 7.319e+05 7.319e+05 ... 7.359e+05 7.359e+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:            2026-06-16T14:34:24.654447
    history:                 [{'cube_params': {'time_range': ['2020-04-15T00:...
    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
    8.62e+06 8.62e+06 ... 8.616e+06

    array([8619835., 8619825., 8619815., ..., 8615865., 8615855., 8615845.])

  • x
    (x)
    float64
    7.319e+05 7.319e+05 ... 7.359e+05

    array([731915., 731925., 731935., ..., 735885., 735895., 735905.])

  • 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 :

  2026-06-16T14:34:24.654447

  history :

  [{'cube_params': {'time_range': ['2020-04-15T00:00:00', '2020-04-30T00:00:00'], '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 [20]:

ds["agb"].plot()

ds["agb"].plot()

Out[20]:

<matplotlib.collections.QuadMesh at 0x72df54a49090>

In [21]:

ds = msds.stores.storage.open_data("AU-How/CCI_BIOMASS.zarr")
ds

ds = msds.stores.storage.open_data("AU-How/CCI_BIOMASS.zarr") ds

Out[21]:

<xarray.Dataset> Size: 26MB
Dimensions:                  (time: 10, y: 400, x: 400)
Coordinates:
  * time                     (time) datetime64[ns] 80B 2007-01-01 ... 2022-01-01
  * y                        (y) float64 3kB 8.62e+06 8.62e+06 ... 8.616e+06
  * x                        (x) float64 3kB 7.319e+05 7.319e+05 ... 7.359e+05
    spatial_ref              int64 8B ...
Data variables:
    aboveground_biomass      (time, y, x) float64 13MB dask.array<chunksize=(5, 400, 400), meta=np.ndarray>
    aboveground_biomass_std  (time, y, x) float64 13MB dask.array<chunksize=(5, 400, 400), meta=np.ndarray>
Attributes:
    stac_catalog_url:    https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded...
    stac_item_id:        CCI_BIOMASS_v6.0
    xcube_stac_version:  1.3.1

xarray.Dataset

• Dimensions:
  • time: 10
  • y: 400
  • x: 400
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2007-01-01 ... 2022-01-01

    array(['2007-01-01T00:00:00.000000000', '2010-01-01T00:00:00.000000000',
           '2015-01-01T00:00:00.000000000', '2016-01-01T00:00:00.000000000',
           '2017-01-01T00:00:00.000000000', '2018-01-01T00:00:00.000000000',
           '2019-01-01T00:00:00.000000000', '2020-01-01T00:00:00.000000000',
           '2021-01-01T00:00:00.000000000', '2022-01-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • y
    (y)
    float64
    8.62e+06 8.62e+06 ... 8.616e+06

    array([8619835., 8619825., 8619815., ..., 8615865., 8615855., 8615845.])

  • x
    (x)
    float64
    7.319e+05 7.319e+05 ... 7.359e+05

    array([731915., 731925., 731935., ..., 735885., 735895., 735905.])

  • 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)
  • aboveground_biomass
    (time, y, x)
    float64
    dask.array<chunksize=(5, 400, 400), meta=np.ndarray>

    description :

    Mass (oven-dry weight) of the woody parts (stem, bark, branches, twigs) of all living trees per unit area, excluding stumps and roots.

    grid_mapping :

    spatial_ref

    long_name :

    Forest aboveground biomass

    source :

    ESA Climate Change Initiative – Biomass v6, Santoro & Cartus (2025)

    units :

    Mg/ha

    valid_max :

    1000

    valid_min :

    0.0

    Array Chunk Bytes 12.21 MiB 6.10 MiB Shape (10, 400, 400) (5, 400, 400) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

  • aboveground_biomass_std
    (time, y, x)
    float64
    dask.array<chunksize=(5, 400, 400), meta=np.ndarray>

    description :

    Per-pixel estimate of aboveground biomass uncertainty (1-sigma).

    grid_mapping :

    spatial_ref

    long_name :

    Standard deviation of aboveground biomass

    source :

    ESA Climate Change Initiative – Biomass v6

    units :

    Mg/ha

    valid_max :

    1000

    valid_min :

    0.0

    Array Chunk Bytes 12.21 MiB 6.10 MiB Shape (10, 400, 400) (5, 400, 400) Dask graph 2 chunks in 2 graph layers Data type float64 numpy.ndarray

• Attributes: (3)

  stac_catalog_url :

  https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded-data/collections/public/catalog.json

  stac_item_id :

  CCI_BIOMASS_v6.0

  xcube_stac_version :

  1.3.1

In [22]:

ds["aboveground_biomass"].isel(time=-1).plot()

ds["aboveground_biomass"].isel(time=-1).plot()

Out[22]:

<matplotlib.collections.QuadMesh at 0x72df548a3750>

In [23]:

ds = msds.stores.storage.open_data("AU-How/SAATCHI_BIOMASS.zarr")
ds

ds = msds.stores.storage.open_data("AU-How/SAATCHI_BIOMASS.zarr") ds

Out[23]:

<xarray.Dataset> Size: 1MB
Dimensions:      (y: 400, x: 400)
Coordinates:
  * y            (y) float64 3kB 8.62e+06 8.62e+06 ... 8.616e+06 8.616e+06
  * x            (x) float64 3kB 7.319e+05 7.319e+05 ... 7.359e+05 7.359e+05
    spatial_ref  int64 8B ...
    time         datetime64[ns] 8B ...
Data variables:
    agb          (y, x) float64 1MB dask.array<chunksize=(400, 400), meta=np.ndarray>
Attributes:
    stac_catalog_url:    https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded...
    stac_item_id:        SAATCHI_BIOMASS_v2.0
    xcube_stac_version:  1.3.1

xarray.Dataset

• Dimensions:
  • y: 400
  • x: 400
• Coordinates: (4)
  • y
    (y)
    float64
    8.62e+06 8.62e+06 ... 8.616e+06

    array([8619835., 8619825., 8619815., ..., 8615865., 8615855., 8615845.])

  • x
    (x)
    float64
    7.319e+05 7.319e+05 ... 7.359e+05

    array([731915., 731925., 731935., ..., 735885., 735895., 735905.])

  • 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]

  • time
    ()
    datetime64[ns]
    ...

    [1 values with dtype=datetime64[ns]]

• Data variables: (1)
  • agb
    (y, x)
    float64
    dask.array<chunksize=(400, 400), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    long_name :

    Aboveground biomass (AGB), Saatchi et al. (2020)

    source :

    Saatchi et al. 2020 Harmonized Global Biomass dataset

    units :

    Mg/ha

    valid_max :

    1000

    valid_min :

    0

    Array Chunk Bytes 1.22 MiB 1.22 MiB Shape (400, 400) (400, 400) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

• Attributes: (3)

  stac_catalog_url :

  https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded-data/collections/public/catalog.json

  stac_item_id :

  SAATCHI_BIOMASS_v2.0

  xcube_stac_version :

  1.3.1

In [24]:

ds["agb"].plot()

ds["agb"].plot()

Out[24]:

<matplotlib.collections.QuadMesh at 0x72df546465d0>

In [25]:

ds = msds.stores.storage.open_data("AU-How/ROBINSON_CR.zarr")
ds

ds = msds.stores.storage.open_data("AU-How/ROBINSON_CR.zarr") ds

Out[25]:

<xarray.Dataset> Size: 12MB
Dimensions:                           (y: 400, x: 400)
Coordinates:
  * y                                 (y) float64 3kB 8.62e+06 ... 8.616e+06
  * x                                 (x) float64 3kB 7.319e+05 ... 7.359e+05
    spatial_ref                       int64 8B ...
Data variables:
    age_at_max_rate                   (y, x) float64 1MB dask.array<chunksize=(400, 400), meta=np.ndarray>
    cr_a                              (y, x) float64 1MB dask.array<chunksize=(400, 400), meta=np.ndarray>
    cr_a_error                        (y, x) float64 1MB dask.array<chunksize=(400, 400), meta=np.ndarray>
    cr_b                              (y, x) float64 1MB dask.array<chunksize=(400, 400), meta=np.ndarray>
    cr_b_error                        (y, x) float64 1MB dask.array<chunksize=(400, 400), meta=np.ndarray>
    cr_k                              (y, x) float64 1MB dask.array<chunksize=(400, 400), meta=np.ndarray>
    cr_k_error                        (y, x) float64 1MB dask.array<chunksize=(400, 400), meta=np.ndarray>
    max_rate                          (y, x) float64 1MB dask.array<chunksize=(400, 400), meta=np.ndarray>
    max_removal_potential_benefit_25  (y, x) float64 1MB dask.array<chunksize=(400, 400), meta=np.ndarray>
Attributes:
    stac_catalog_url:    https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded...
    stac_item_id:        ROBINSON_CR_v1.0
    xcube_stac_version:  1.3.1

xarray.Dataset

• Dimensions:
  • y: 400
  • x: 400
• Coordinates: (3)
  • y
    (y)
    float64
    8.62e+06 8.62e+06 ... 8.616e+06

    array([8619835., 8619825., 8619815., ..., 8615865., 8615855., 8615845.])

  • x
    (x)
    float64
    7.319e+05 7.319e+05 ... 7.359e+05

    array([731915., 731925., 731935., ..., 735885., 735895., 735905.])

  • 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: (9)
  • age_at_max_rate
    (y, x)
    float64
    dask.array<chunksize=(400, 400), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    long_name :

    Age at maximum carbon accumulation rate

    source :

    Robinson et al., Protect young secondary forests for optimum carbon removal

    units :

    years

    Array Chunk Bytes 1.22 MiB 1.22 MiB Shape (400, 400) (400, 400) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • cr_a
    (y, x)
    float64
    dask.array<chunksize=(400, 400), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    description :

    Asymptotic maximum aboveground carbon stock

    grid_mapping :

    spatial_ref

    long_name :

    Chapman–Richards asymptote (a)

    source :

    Robinson et al., Protect young secondary forests for optimum carbon removal

    units :

    Mg C ha-1

    valid_min :

    0.0

    Array Chunk Bytes 1.22 MiB 1.22 MiB Shape (400, 400) (400, 400) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • cr_a_error
    (y, x)
    float64
    dask.array<chunksize=(400, 400), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    long_name :

    Standard error of CR asymptote (a)

    source :

    Robinson et al., Protect young secondary forests for optimum carbon removal

    units :

    1

    Array Chunk Bytes 1.22 MiB 1.22 MiB Shape (400, 400) (400, 400) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • cr_b
    (y, x)
    float64
    dask.array<chunksize=(400, 400), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    description :

    Intercept parameter controlling curve offset

    grid_mapping :

    spatial_ref

    long_name :

    Chapman–Richards intercept (b)

    source :

    Robinson et al., Protect young secondary forests for optimum carbon removal

    units :

    1

    Array Chunk Bytes 1.22 MiB 1.22 MiB Shape (400, 400) (400, 400) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • cr_b_error
    (y, x)
    float64
    dask.array<chunksize=(400, 400), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    long_name :

    Standard error of CR intercept (b)

    source :

    Robinson et al., Protect young secondary forests for optimum carbon removal

    units :

    1

    Array Chunk Bytes 1.22 MiB 1.22 MiB Shape (400, 400) (400, 400) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • cr_k
    (y, x)
    float64
    dask.array<chunksize=(400, 400), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    description :

    Rate/shape parameter controlling saturation speed

    grid_mapping :

    spatial_ref

    long_name :

    Chapman–Richards rate parameter (k)

    source :

    Robinson et al., Protect young secondary forests for optimum carbon removal

    units :

    1

    Array Chunk Bytes 1.22 MiB 1.22 MiB Shape (400, 400) (400, 400) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • cr_k_error
    (y, x)
    float64
    dask.array<chunksize=(400, 400), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    long_name :

    Standard error of CR rate parameter (k)

    source :

    Robinson et al., Protect young secondary forests for optimum carbon removal

    units :

    1

    Array Chunk Bytes 1.22 MiB 1.22 MiB Shape (400, 400) (400, 400) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • max_rate
    (y, x)
    float64
    dask.array<chunksize=(400, 400), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    long_name :

    Maximum annual carbon accumulation rate

    source :

    Robinson et al., Protect young secondary forests for optimum carbon removal

    units :

    Mg C ha-1 yr-1

    Array Chunk Bytes 1.22 MiB 1.22 MiB Shape (400, 400) (400, 400) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • max_removal_potential_benefit_25
    (y, x)
    float64
    dask.array<chunksize=(400, 400), meta=np.ndarray>

    AREA_OR_POINT :

    Area

    description :

    Difference between accumulation in first 25 years and 25-year window of maximum accumulation

    grid_mapping :

    spatial_ref

    long_name :

    Relative carbon removal benefit of protecting young forests

    source :

    Robinson et al., Protect young secondary forests for optimum carbon removal

    units :

    %

    Array Chunk Bytes 1.22 MiB 1.22 MiB Shape (400, 400) (400, 400) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

• Attributes: (3)

  stac_catalog_url :

  https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded-data/collections/public/catalog.json

  stac_item_id :

  ROBINSON_CR_v1.0

  xcube_stac_version :

  1.3.1

In [26]:

ds["cr_a"].plot()

ds["cr_a"].plot()

Out[26]:

<matplotlib.collections.QuadMesh at 0x72dee4551bd0>

In [27]:

ds = msds.stores.storage.open_data("AU-How/HANSEN_GFC.zarr")
ds

ds = msds.stores.storage.open_data("AU-How/HANSEN_GFC.zarr") ds

Out[27]:

<xarray.Dataset> Size: 5MB
Dimensions:      (y: 400, x: 400)
Coordinates:
  * y            (y) float64 3kB 8.62e+06 8.62e+06 ... 8.616e+06 8.616e+06
  * x            (x) float64 3kB 7.319e+05 7.319e+05 ... 7.359e+05 7.359e+05
    spatial_ref  int64 8B ...
Data variables:
    data_mask    (y, x) float64 1MB dask.array<chunksize=(400, 400), meta=np.ndarray>
    gain         (y, x) float64 1MB dask.array<chunksize=(400, 400), meta=np.ndarray>
    loss_year    (y, x) float64 1MB dask.array<chunksize=(400, 400), meta=np.ndarray>
    tree_cover   (y, x) float64 1MB dask.array<chunksize=(400, 400), meta=np.ndarray>
Attributes:
    stac_catalog_url:    https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded...
    stac_item_id:        HANSEN_GFC_v1.12
    xcube_stac_version:  1.3.1

xarray.Dataset

• Dimensions:
  • y: 400
  • x: 400
• Coordinates: (3)
  • y
    (y)
    float64
    8.62e+06 8.62e+06 ... 8.616e+06

    array([8619835., 8619825., 8619815., ..., 8615865., 8615855., 8615845.])

  • x
    (x)
    float64
    7.319e+05 7.319e+05 ... 7.359e+05

    array([731915., 731925., 731935., ..., 735885., 735895., 735905.])

  • 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: (4)
  • data_mask
    (y, x)
    float64
    dask.array<chunksize=(400, 400), meta=np.ndarray>

    description :

    Mask distinguishing land, water, and areas without valid observations.

    grid_mapping :

    spatial_ref

    long_name :

    Data availability mask

    Array Chunk Bytes 1.22 MiB 1.22 MiB Shape (400, 400) (400, 400) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • gain
    (y, x)
    float64
    dask.array<chunksize=(400, 400), meta=np.ndarray>

    description :

    Forest gain indicator for the period 2000–2012. Value 1 indicates gain; 0 indicates no gain.

    grid_mapping :

    spatial_ref

    long_name :

    Forest cover gain

    valid_max :

    1

    valid_min :

    0

    Array Chunk Bytes 1.22 MiB 1.22 MiB Shape (400, 400) (400, 400) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • loss_year
    (y, x)
    float64
    dask.array<chunksize=(400, 400), meta=np.ndarray>

    description :

    Calendar year in which forest loss occurred. Derived from Hansen GFC loss encoding (1–24 → 2001–2024).

    grid_mapping :

    spatial_ref

    long_name :

    Year of forest cover loss

    units :

    year

    Array Chunk Bytes 1.22 MiB 1.22 MiB Shape (400, 400) (400, 400) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • tree_cover
    (y, x)
    float64
    dask.array<chunksize=(400, 400), meta=np.ndarray>

    description :

    Percent tree canopy cover for vegetation taller than 5 m in the year 2000.

    grid_mapping :

    spatial_ref

    long_name :

    Tree canopy cover in year 2000

    units :

    percent

    valid_max :

    100

    valid_min :

    0

    Array Chunk Bytes 1.22 MiB 1.22 MiB Shape (400, 400) (400, 400) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

• Attributes: (3)

  stac_catalog_url :

  https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded-data/collections/public/catalog.json

  stac_item_id :

  HANSEN_GFC_v1.12

  xcube_stac_version :

  1.3.1

In [28]:

ds["tree_cover"].plot()

ds["tree_cover"].plot()

Out[28]:

<matplotlib.collections.QuadMesh at 0x72df54d70a50>

In [29]:

ds = msds.stores.storage.open_data("AU-How/GAMI.zarr")
ds

ds = msds.stores.storage.open_data("AU-How/GAMI.zarr") ds

Out[29]:

<xarray.Dataset> Size: 1MB
Dimensions:      (time: 2, y: 400, x: 400)
Coordinates:
  * time         (time) datetime64[ns] 16B 2010-01-01 2020-01-01
  * y            (y) float64 3kB 8.62e+06 8.62e+06 ... 8.616e+06 8.616e+06
  * x            (x) float64 3kB 7.319e+05 7.319e+05 ... 7.359e+05 7.359e+05
    spatial_ref  int64 8B ...
Data variables:
    forest_age   (time, y, x) float32 1MB dask.array<chunksize=(2, 400, 400), meta=np.ndarray>
Attributes:
    stac_catalog_url:    https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded...
    stac_item_id:        GAMI_v3.1
    xcube_stac_version:  1.3.1

xarray.Dataset

• Dimensions:
  • time: 2
  • y: 400
  • x: 400
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2010-01-01 2020-01-01

    array(['2010-01-01T00:00:00.000000000', '2020-01-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • y
    (y)
    float64
    8.62e+06 8.62e+06 ... 8.616e+06

    array([8619835., 8619825., 8619815., ..., 8615865., 8615855., 8615845.])

  • x
    (x)
    float64
    7.319e+05 7.319e+05 ... 7.359e+05

    array([731915., 731925., 731935., ..., 735885., 735895., 735905.])

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

    grid_mapping :

    spatial_ref

    long_name :

    Forest age using ML + Landsat-based time since disturbance fusion

    units :

    years

    valid_max :

    300

    valid_min :

    1

    Array Chunk Bytes 1.22 MiB 1.22 MiB Shape (2, 400, 400) (2, 400, 400) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Attributes: (3)

  stac_catalog_url :

  https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded-data/collections/public/catalog.json

  stac_item_id :

  GAMI_v3.1

  xcube_stac_version :

  1.3.1

In [30]:

ds["forest_age"].isel(time=0).plot()

ds["forest_age"].isel(time=0).plot()

Out[30]:

<matplotlib.collections.QuadMesh at 0x72df046e0050>

In [31]:

ds = msds.stores.storage.open_data("AU-How/POTAPOV_HEIGHT.zarr")
ds

ds = msds.stores.storage.open_data("AU-How/POTAPOV_HEIGHT.zarr") ds

Out[31]:

<xarray.Dataset> Size: 6MB
Dimensions:        (time: 5, y: 400, x: 400)
Coordinates:
  * time           (time) datetime64[ns] 40B 2000-01-01 ... 2020-01-01
  * y              (y) float64 3kB 8.62e+06 8.62e+06 ... 8.616e+06 8.616e+06
  * x              (x) float64 3kB 7.319e+05 7.319e+05 ... 7.359e+05 7.359e+05
    spatial_ref    int64 8B ...
Data variables:
    canopy_height  (time, y, x) float64 6MB dask.array<chunksize=(1, 400, 400), meta=np.ndarray>
Attributes:
    stac_catalog_url:    https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded...
    stac_item_id:        POTAPOV_HEIGHT_v1.0
    xcube_stac_version:  1.3.1

xarray.Dataset

• Dimensions:
  • time: 5
  • y: 400
  • x: 400
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2000-01-01 ... 2020-01-01

    array(['2000-01-01T00:00:00.000000000', '2005-01-01T00:00:00.000000000',
           '2010-01-01T00:00:00.000000000', '2015-01-01T00:00:00.000000000',
           '2020-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

  • y
    (y)
    float64
    8.62e+06 8.62e+06 ... 8.616e+06

    array([8619835., 8619825., 8619815., ..., 8615865., 8615855., 8615845.])

  • x
    (x)
    float64
    7.319e+05 7.319e+05 ... 7.359e+05

    array([731915., 731925., 731935., ..., 735885., 735895., 735905.])

  • 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: (1)
  • canopy_height
    (time, y, x)
    float64
    dask.array<chunksize=(1, 400, 400), meta=np.ndarray>

    description :

    Canopy top height for discrete reference years (stacked from annual/epoch VRTs).

    grid_mapping :

    spatial_ref

    long_name :

    Canopy height

    source :

    Potapov et al., University of Maryland / GLAD

    units :

    m

    valid_min :

    0.0

    Array Chunk Bytes 6.10 MiB 1.22 MiB Shape (5, 400, 400) (1, 400, 400) Dask graph 5 chunks in 2 graph layers Data type float64 numpy.ndarray

• Attributes: (3)

  stac_catalog_url :

  https://s3.gfz-potsdam.de/dog.atlaseo-glm.eo-gridded-data/collections/public/catalog.json

  stac_item_id :

  POTAPOV_HEIGHT_v1.0

  xcube_stac_version :

  1.3.1

In [32]:

ds["canopy_height"].isel(time=0).plot()

ds["canopy_height"].isel(time=0).plot()

Out[32]:

<matplotlib.collections.QuadMesh at 0x72ddd421f110>

In [33]:

ds = msds.stores.storage.open_data("AU-How/gedi.zarr")
ds

ds = msds.stores.storage.open_data("AU-How/gedi.zarr") ds

Out[33]:

<xarray.Dataset> Size: 434kB
Dimensions:            (shot_number: 814, profile_points: 101)
Coordinates:
  * shot_number        (shot_number) uint64 7kB 221080800100138599 ... 553708...
    latitude           (shot_number) float64 7kB dask.array<chunksize=(814,), meta=np.ndarray>
    longitude          (shot_number) float64 7kB dask.array<chunksize=(814,), meta=np.ndarray>
    time               (shot_number) datetime64[ns] 7kB dask.array<chunksize=(814,), meta=np.ndarray>
  * profile_points     (profile_points) int16 202B 0 1 2 3 4 ... 96 97 98 99 100
Data variables: (12/31)
    agbd               (shot_number) float32 3kB dask.array<chunksize=(814,), meta=np.ndarray>
    agbd_pi_lower      (shot_number) float32 3kB dask.array<chunksize=(814,), meta=np.ndarray>
    agbd_pi_upper      (shot_number) float32 3kB dask.array<chunksize=(814,), meta=np.ndarray>
    agbd_se            (shot_number) float32 3kB dask.array<chunksize=(814,), meta=np.ndarray>
    agbd_t             (shot_number) float32 3kB dask.array<chunksize=(814,), meta=np.ndarray>
    agbd_t_se          (shot_number) float32 3kB dask.array<chunksize=(814,), meta=np.ndarray>
    ...                 ...
    wsci_xy            (shot_number) float32 3kB dask.array<chunksize=(814,), meta=np.ndarray>
    wsci_xy_pi_lower   (shot_number) float32 3kB dask.array<chunksize=(814,), meta=np.ndarray>
    wsci_xy_pi_upper   (shot_number) float32 3kB dask.array<chunksize=(814,), meta=np.ndarray>
    wsci_z             (shot_number) float32 3kB dask.array<chunksize=(814,), meta=np.ndarray>
    wsci_z_pi_lower    (shot_number) float32 3kB dask.array<chunksize=(814,), meta=np.ndarray>
    wsci_z_pi_upper    (shot_number) float32 3kB dask.array<chunksize=(814,), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • shot_number: 814
  • profile_points: 101
• Coordinates: (5)
  • shot_number
    (shot_number)
    uint64
    221080800100138599 ... 553708004...

    description :

    Unique identifier for each shot

    dtype :

    uint64

    product_level :

    L4C

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    array([221080800100138599, 221080600100138655, 221080600100138654, ...,
            55370800400416162,  55370800400416161,  55370800400416163],
          dtype=uint64)

  • latitude
    (shot_number)
    float64
    dask.array<chunksize=(814,), meta=np.ndarray>

    Array Chunk Bytes 6.36 kiB 6.36 kiB Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • longitude
    (shot_number)
    float64
    dask.array<chunksize=(814,), meta=np.ndarray>

    Array Chunk Bytes 6.36 kiB 6.36 kiB Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • time
    (shot_number)
    datetime64[ns]
    dask.array<chunksize=(814,), meta=np.ndarray>

    Array Chunk Bytes 6.36 kiB 6.36 kiB Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

  • profile_points
    (profile_points)
    int16
    0 1 2 3 4 5 ... 95 96 97 98 99 100

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100], dtype=int16)

• Data variables: (31)
  • agbd
    (shot_number)
    float32
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Aboveground biomass density

    dtype :

    float32

    product_level :

    L4A

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    Mg/ha

    Array Chunk Bytes 3.18 kiB 3.18 kiB Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • agbd_pi_lower
    (shot_number)
    float32
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Lower prediction interval for aboveground biomass density

    dtype :

    float32

    product_level :

    L4A

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    Mg/ha

    Array Chunk Bytes 3.18 kiB 3.18 kiB Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • agbd_pi_upper
    (shot_number)
    float32
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Upper prediction interval for aboveground biomass density

    dtype :

    float32

    product_level :

    L4A

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    Mg/ha

    Array Chunk Bytes 3.18 kiB 3.18 kiB Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • agbd_se
    (shot_number)
    float32
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Standard error of aboveground biomass density

    dtype :

    float32

    product_level :

    L4A

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    Mg/ha

    Array Chunk Bytes 3.18 kiB 3.18 kiB Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • agbd_t
    (shot_number)
    float32
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Model prediction in fit units

    dtype :

    float32

    product_level :

    L4A

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    Array Chunk Bytes 3.18 kiB 3.18 kiB Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • agbd_t_se
    (shot_number)
    float32
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Model prediction standard error in fit units

    dtype :

    float32

    product_level :

    L4A

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    Array Chunk Bytes 3.18 kiB 3.18 kiB Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • algorithmrun_flag
    (shot_number)
    uint8
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    The L2B algorithm run flag

    dtype :

    uint8

    product_level :

    L2B

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    Array Chunk Bytes 814 B 814 B Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • beam_type
    (shot_number)
    object
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Type of beam used

    dtype :

    U10

    product_level :

    L2A

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    Array Chunk Bytes 6.36 kiB 6.36 kiB Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type object numpy.ndarray

  • degrade_flag
    (shot_number)
    uint8
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Flag indicating degraded state of pointing and/or positioning information

    dtype :

    uint8

    product_level :

    L2A

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    Array Chunk Bytes 814 B 814 B Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • fhd_normal
    (shot_number)
    float32
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Foliage Height Diversity

    dtype :

    float32

    product_level :

    L2B

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    Array Chunk Bytes 3.18 kiB 3.18 kiB Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • l2a_quality_flag
    (shot_number)
    uint8
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    L2A quality flag

    dtype :

    uint8

    product_level :

    L2B

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    Array Chunk Bytes 814 B 814 B Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • l2b_quality_flag
    (shot_number)
    uint8
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    L2B quality flag

    dtype :

    uint8

    product_level :

    L2B

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    Array Chunk Bytes 814 B 814 B Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • landsat_treecover
    (shot_number)
    float32
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Tree cover in the year 2010, defined as canopy closure for all vegetation taller than 5 m in height as a percentage per output grid cell

    dtype :

    float32

    product_level :

    L2A

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    Percent

    Array Chunk Bytes 3.18 kiB 3.18 kiB Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • leaf_off_flag
    (shot_number)
    uint8
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    GEDI 1 km EASE 2.0 grid flag

    dtype :

    uint8

    product_level :

    L2A

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    Array Chunk Bytes 814 B 814 B Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • num_detectedmodes
    (shot_number)
    uint8
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Number of detected modes in rxwaveform

    dtype :

    uint8

    product_level :

    L2A

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    Array Chunk Bytes 814 B 814 B Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • omega
    (shot_number)
    float32
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Foliage Clumping Index

    dtype :

    float32

    product_level :

    L2B

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    Array Chunk Bytes 3.18 kiB 3.18 kiB Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • quality_flag
    (shot_number)
    uint8
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Flag simplifying selection of most useful data

    dtype :

    uint8

    product_level :

    L2A

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    Array Chunk Bytes 814 B 814 B Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • rh
    (shot_number, profile_points)
    float32
    dask.array<chunksize=(407, 101), meta=np.ndarray>

    description :

    Relative height metrics at 1% interval

    dtype :

    float32

    product_level :

    L2A

    profile_label_name :

    percentile

    profile_labels :

    0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100

    profile_length :

    101.0

    units :

    Meters

    Array Chunk Bytes 321.15 kiB 160.57 kiB Shape (814, 101) (407, 101) Dask graph 2 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sensitivity
    (shot_number)
    float32
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Maxmimum canopy cover that can be penetrated

    dtype :

    float32

    product_level :

    L2A

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    Array Chunk Bytes 3.18 kiB 3.18 kiB Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • solar_elevation
    (shot_number)
    float32
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Solar elevation angle at the time of the shot

    dtype :

    float32

    product_level :

    L2A

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    Degrees

    Array Chunk Bytes 3.18 kiB 3.18 kiB Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • surface_flag
    (shot_number)
    uint8
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Identifier of algorithm selected as identifying the lowest non-noise mode

    dtype :

    uint8

    product_level :

    L2A

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    Array Chunk Bytes 814 B 814 B Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • wsci
    (shot_number)
    float32
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Waveform Structural Complexity Index

    dtype :

    float32

    product_level :

    L4C

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    Array Chunk Bytes 3.18 kiB 3.18 kiB Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • wsci_pi_lower
    (shot_number)
    float32
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Waveform Structural Complexity Index lower prediction interval

    dtype :

    float32

    product_level :

    L4C

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    Array Chunk Bytes 3.18 kiB 3.18 kiB Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • wsci_pi_upper
    (shot_number)
    float32
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Waveform Structural Complexity Index upper prediction interval

    dtype :

    float32

    product_level :

    L4C

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    Array Chunk Bytes 3.18 kiB 3.18 kiB Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • wsci_quality_flag
    (shot_number)
    uint8
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Waveform Structural Complexity Index quality flag

    dtype :

    uint8

    product_level :

    L4C

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    Array Chunk Bytes 814 B 814 B Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • wsci_xy
    (shot_number)
    float32
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Horizontal Structural Complexity

    dtype :

    float32

    product_level :

    L4C

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    Array Chunk Bytes 3.18 kiB 3.18 kiB Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • wsci_xy_pi_lower
    (shot_number)
    float32
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Horizontal Structural Complexity lower prediction interval

    dtype :

    float32

    product_level :

    L4C

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    Array Chunk Bytes 3.18 kiB 3.18 kiB Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • wsci_xy_pi_upper
    (shot_number)
    float32
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Horizontal Structural Complexity upper prediction interval

    dtype :

    float32

    product_level :

    L4C

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    Array Chunk Bytes 3.18 kiB 3.18 kiB Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • wsci_z
    (shot_number)
    float32
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Vertical Structural Complexity

    dtype :

    float32

    product_level :

    L4C

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    Array Chunk Bytes 3.18 kiB 3.18 kiB Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • wsci_z_pi_lower
    (shot_number)
    float32
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Vertical Structural Complexity lower prediction interval

    dtype :

    float32

    product_level :

    L4C

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    Array Chunk Bytes 3.18 kiB 3.18 kiB Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • wsci_z_pi_upper
    (shot_number)
    float32
    dask.array<chunksize=(814,), meta=np.ndarray>

    description :

    Vertical Structural Complexity upper prediction interval

    dtype :

    float32

    product_level :

    L4C

    profile_label_name :

    nan

    profile_labels :

    nan

    profile_length :

    nan

    units :

    adimensional

    Array Chunk Bytes 3.18 kiB 3.18 kiB Shape (814,) (814,) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

In [34]:

plt.figure(figsize=(8, 5))

sc = plt.scatter(
    ds["longitude"],
    ds["latitude"],
    c=ds["agbd"],
    cmap="viridis",
    s=10,
)

plt.colorbar(sc, label=ds["agbd"].attrs.get("long_name", "agbd"))
plt.xlabel(ds["longitude"].attrs.get("long_name", "Longitude"))
plt.ylabel(ds["latitude"].attrs.get("long_name", "Latitude"))
plt.title("Spatial Plot of agbd")
plt.grid(True)
plt.show()

plt.figure(figsize=(8, 5)) sc = plt.scatter( ds["longitude"], ds["latitude"], c=ds["agbd"], cmap="viridis", s=10, ) plt.colorbar(sc, label=ds["agbd"].attrs.get("long_name", "agbd")) plt.xlabel(ds["longitude"].attrs.get("long_name", "Longitude")) plt.ylabel(ds["latitude"].attrs.get("long_name", "Latitude")) plt.title("Spatial Plot of agbd") plt.grid(True) plt.show()

The sparse GEDI dataset is used in the scientific analysis by aggregating all data points (shots) located within a specified radius around the flux tower. The methodology for translating this sparse satellite data into eddy covariance–comparable estimates is still under development and will be further advanced within the NextGenCarbon project. As a preliminary step, an example analysis is provided in a scientific notebook, illustrating the current approach. An overview of the method’s development status is also presented in Deliverable 5.4.