EO-LINCS project: Cube generation for Scientific Case Study (SCS) 4¶

EO enhanced benchmarking of GCB DGVMs¶

Objective: SCS4 aims to deepen the understanding of the processes that drive the European land carbon sink, with a focus on productivity, turnover, and the impacts of disturbances and land management. Leveraging new EO data and the International Land Model Benchmarking (ILAMB) system, it will assess Dynamic Global Vegetation Models (DGVMs) that contribute to the Global Carbon Budget (GCB) reports. The project will result in an enhanced ILAMB tool, offering insights into carbon dynamics and DGVM performance, and providing a roadmap for future model improvements.

Outcomes: An enhanced ILAMB evaluation tool with a focus on internal carbon dynamics and temporal change able to provide novel insights into DGVM capabilities to simulate the European land carbon sink and identify its main drivers. The spatiotemporal analysis will enable us to produce a roadmap for model improvements, in particular regarding forest management.

Requried datasets:

• ESA-CCI Biomass
• Changes in Global Terrestrial Live Biomass over the 21st Century
• GLOBMAP global Leaf Area Index since 1981
• FLUXCOM-X-BASE gross primary productivity
• FLUXCOM-X-BASE evapotranspiration

---

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. The recommended approach is to create a Conda/Mamba environment using the provided environment specification:

conda env create -f environment.yml

The corresponding file can be found here: environment.yml

After creation, activate the environment:

conda activate eo-lincs-scs4

Next, to access the FLUXCOM-X-BASE data from the ICOS Data Portal, users must create an account and provide their registered email address and password to the configuration YAML config.yml#L71.

---

Data Preparation¶

Before running the cube generation workflow, the dataset GLOBMAP Global Leaf Area Index since 1981 must be prepared.

The individual time slices are accessed via the preload method of xcube’s Zenodo data store. However, constructing a consistent and correct time axis requires additional custom processing. For this reason, the dataset is prepared separately in a dedicated notebook.

In this step, the individual temporal slices are stacked into a single 3D data cube (time, latitude, longitude) with a properly defined time coordinate. This preprocessing is implemented in the notebook prepare_lai.ipynb. For further details, please refer to the notebook.

---

Running the Pipeline¶

Once all dependencies are installed and the LAI dataset has been prepared, the final data cubes can be generated by executing this notebook.

The results will be stored locally in the top-level data directory, as defined in the configuration YAML file. Initial visualizations of the generated cubes are displayed at the end of the notebook.

---

Imports¶

Let's import everything we need:

In [1]:

import dask
import numpy as np 
from xcube_multistore import MultiSourceDataStore

import dask import numpy as np from xcube_multistore import MultiSourceDataStore

We suppress the warning "RuntimeWarning: invalid value encountered in divide", which originates from NumPy, by configuring NumPy to ignore floating-point error handling during execution.

In [2]:

np.seterr(divide='ignore', invalid='ignore')
print(np.geterr())

np.seterr(divide='ignore', invalid='ignore') print(np.geterr())

{'divide': 'ignore', 'over': 'warn', 'under': 'ignore', 'invalid': 'ignore'}

We configure Dask to run in a single-threaded mode for two main reasons.

1. It avoid overwhelming the Zenodo API, which introduced stricter rate limits in November 2025 (see https://blog.zenodo.org/2025/11/25/2025-11-14-search-api-updates/). Restricting the cluster to a single worker prevents concurrent requests that could exceed these limits.
2. It ensure safe and deterministic I/O when writing NetCDF files. Without this restriction, we observed warnings and unstable behavior caused by concurrent access to partially written outputs, where multiple tasks may attempt to read from or write to the same file simultaneously. This reflects a known limitation of NetCDF/HDF5-based workflows, where parallel writes are not inherently safe. For this reason, we execute the pipeline sequentially to ensure robustness. Since computational time is not a critical constraint for this SCS, we prioritize stability over performance.

In [3]:

dask.config.set(scheduler="single-threaded")

dask.config.set(scheduler="single-threaded")

Out[3]:

<dask.config.set at 0x79a155d560d0>

---

Configuration 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.ipynb or consult the Configuration Guide in the documentation.

In [4]:

MultiSourceDataStore.get_config_schema()

MultiSourceDataStore.get_config_schema()

Out[4]:

<xcube.util.jsonschema.JsonObjectSchema at 0x79a155bf1c50>

We can now initialize the MultiSourceDataStore by passing the path to the config.yml, which is located in the same directory as this notebook.

Below are a few notes explaining what happens during cube generation:

• The datasets FLUXCOM-X-BASE_GPP and FLUXCOM-X-BASE_ET are preloaded using the xcube-icosdp package, as these aggregated products are only available via the ICOS Data Portal API. During preloading, the individual time slices are downloaded and combined into a consistent data cube. This step prepares the datasets as described in the xcube-icosdp documentation (see the aggregated products section in the README).

• The config.yml also makes use of the custom_processing feature of xcube-multistore. This allows us to apply a custom processing function to each dataset individually. In this example, we define a module called modify_dataset, which contains a function that takes an xarray.Dataset as input and returns a modified xarray.Dataset. More details about the custom_processing option can be found in the xcube-multistore documentation.

In [5]:

msds = MultiSourceDataStore("config.yml")

msds = MultiSourceDataStore("config.yml")

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

In [6]:

msds.display_config()

msds.display_config()

Configuration

User-defined ID	Data Store ID	Data Store Params	Data ID	Open Data Params	Grid-Mapping	Format
biomass_xu	zenodo	root: 4161694	test10a_cd_ab_pred_corr_2000_2019_v2.tif	band_as_variable: False	bbox: [-180, -90, 180, 90]; spatial_res: 0.1; crs: EPSG:4326; tile_size: [1800, 1800]	netcdf
biomass_esa_cci	cciodp	-	esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.6-0.10000m	-	bbox: [-180, -90, 180, 90]; spatial_res: 0.1; crs: EPSG:4326; tile_size: [1800, 1800]	netcdf
fluxcom_x_gpp	icosdp	email: eo.lincs@gmail.com; password: 3`YA(U)Zh[sÄq	FLUXCOM-X-BASE_GPP_monthly_2000_2021.zarr	-	bbox: [-180, -90, 180, 90]; spatial_res: 0.1; crs: EPSG:4326; tile_size: [1800, 1800]	netcdf
fluxcom_x_et	icosdp	email: eo.lincs@gmail.com; password: 3`YA(U)Zh[sÄq	FLUXCOM-X-BASE_ET_monthly_2000_2021.zarr	-	bbox: [-180, -90, 180, 90]; spatial_res: 0.1; crs: EPSG:4326; tile_size: [1800, 1800]	netcdf
laiv3	file	root: zenodo_cache/12698637	GlobMapLAIV3.zarr	-	bbox: [-180, -90, 180, 90]; spatial_res: 0.1; crs: EPSG:4326; tile_size: [1800, 1800]	netcdf

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

In [7]:

msds.display_geolocations()

msds.display_geolocations()

Out[7]:

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

---

Cube Generation¶

Cube generation is triggered with a single command and consists of two stages:

1. Preloading¶

The required datasets from the icosdp store are preloaded.
In this case, the FLUXCOM-X-BASE datasets were already available in the local cache, so no additional download was necessary.

2. Sequential Processing¶

After preloading, each dataset is processed sequentially. The following steps are applied as required:

• Spatial resampling to the target grid
• Temporal aggregation or resampling (e.g., for LAI)
• Writing the final datacube to the data/ directory on the top level of the repository

Dataset Notes¶

• biomass_xu Stored as GeoTIFF and accessed lazily. Resampling is performed on the fly before writing the final cube.

• biomass_esa_cci Accessed lazily via the xcube ESA CCI datastore, with on-the-fly resampling.

• fluxcom_x_gpp and fluxcom_x_et Loaded from the local icosdp cache (output of the preloading step). User-defined processing specifications are applied during cube generation.

• laiv3 Prepared in prepare_laiv3.ipynb. It requires both spatial resampling and temporal aggregation. As the largest dataset, it has the longest processing time.

Note: Harmless internal HDF5 diagnostic messages are printed to standard output.

In [8]:

%%time
msds.generate()

%%time msds.generate()

Preload Datasets from store 'icosdp'

Data ID	Status	Progress	Message	Exception
FLUXCOM-X-BASE_GPP	COMPLETED	100%	Datacube written to 'FLUXCOM-X-BASE_GPP_monthly_2000_2021.zarr'.	-
FLUXCOM-X-BASE_ET	COMPLETED	100%	Datacube written to 'FLUXCOM-X-BASE_ET_monthly_2000_2021.zarr'.	-

Cube Generation

Dataset identifier	Status	Message	Exception
biomass_xu	COMPLETED	Dataset 'biomass_xu' finished: 0:00:33	-
biomass_esa_cci	COMPLETED	Dataset 'biomass_esa_cci' finished: 0:03:23	-
fluxcom_x_gpp	COMPLETED	Dataset 'fluxcom_x_gpp' finished: 0:12:36	-
fluxcom_x_et	COMPLETED	Dataset 'fluxcom_x_et' finished: 0:11:46	-
laiv3	COMPLETED	Dataset 'laiv3' finished: 0:18:51	-

HDF5-DIAG: Error detected in HDF5 (2.1.0) thread 3:
  #000: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5F.c line 490 in H5Fis_accessible(): unable to determine if file is accessible as HDF5
    major: File accessibility
    minor: Not an HDF5 file
  #001: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5VLcallback.c line 4109 in H5VL_file_specific(): file specific failed
    major: Virtual Object Layer
    minor: Can't operate on object
  #002: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5VLcallback.c line 4045 in H5VL__file_specific(): file specific failed
    major: Virtual Object Layer
    minor: Can't operate on object
  #003: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5VLnative_file.c line 344 in H5VL__native_file_specific(): error in HDF5 file check
    major: File accessibility
    minor: Can't get value
  #004: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5Fint.c line 1083 in H5F__is_hdf5(): unable to open file
    major: File accessibility
    minor: Unable to initialize object
  #005: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5FD.c line 966 in H5FD_open(): can't open file
    major: Virtual File Layer
    minor: Unable to open file
  #006: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5FDsec2.c line 285 in H5FD__sec2_open(): unable to open file: name = '/home/yogesh/Projects/BC/EO-LINCS/eo-lincs-scs4/data/biomass_xu.nc', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0
    major: File accessibility
    minor: Unable to open file
HDF5-DIAG: Error detected in HDF5 (2.1.0) thread 3:
  #000: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5F.c line 490 in H5Fis_accessible(): unable to determine if file is accessible as HDF5
    major: File accessibility
    minor: Not an HDF5 file
  #001: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5VLcallback.c line 4109 in H5VL_file_specific(): file specific failed
    major: Virtual Object Layer
    minor: Can't operate on object
  #002: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5VLcallback.c line 4045 in H5VL__file_specific(): file specific failed
    major: Virtual Object Layer
    minor: Can't operate on object
  #003: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5VLnative_file.c line 344 in H5VL__native_file_specific(): error in HDF5 file check
    major: File accessibility
    minor: Can't get value
  #004: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5Fint.c line 1083 in H5F__is_hdf5(): unable to open file
    major: File accessibility
    minor: Unable to initialize object
  #005: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5FD.c line 966 in H5FD_open(): can't open file
    major: Virtual File Layer
    minor: Unable to open file
  #006: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5FDsec2.c line 285 in H5FD__sec2_open(): unable to open file: name = '/home/yogesh/Projects/BC/EO-LINCS/eo-lincs-scs4/data/biomass_esa_cci.nc', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0
    major: File accessibility
    minor: Unable to open file
HDF5-DIAG: Error detected in HDF5 (2.1.0) thread 3:
  #000: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5F.c line 490 in H5Fis_accessible(): unable to determine if file is accessible as HDF5
    major: File accessibility
    minor: Not an HDF5 file
  #001: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5VLcallback.c line 4109 in H5VL_file_specific(): file specific failed
    major: Virtual Object Layer
    minor: Can't operate on object
  #002: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5VLcallback.c line 4045 in H5VL__file_specific(): file specific failed
    major: Virtual Object Layer
    minor: Can't operate on object
  #003: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5VLnative_file.c line 344 in H5VL__native_file_specific(): error in HDF5 file check
    major: File accessibility
    minor: Can't get value
  #004: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5Fint.c line 1083 in H5F__is_hdf5(): unable to open file
    major: File accessibility
    minor: Unable to initialize object
  #005: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5FD.c line 966 in H5FD_open(): can't open file
    major: Virtual File Layer
    minor: Unable to open file
  #006: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5FDsec2.c line 285 in H5FD__sec2_open(): unable to open file: name = '/home/yogesh/Projects/BC/EO-LINCS/eo-lincs-scs4/data/fluxcom_x_gpp.nc', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0
    major: File accessibility
    minor: Unable to open file
HDF5-DIAG: Error detected in HDF5 (2.1.0) thread 3:
  #000: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5F.c line 490 in H5Fis_accessible(): unable to determine if file is accessible as HDF5
    major: File accessibility
    minor: Not an HDF5 file
  #001: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5VLcallback.c line 4109 in H5VL_file_specific(): file specific failed
    major: Virtual Object Layer
    minor: Can't operate on object
  #002: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5VLcallback.c line 4045 in H5VL__file_specific(): file specific failed
    major: Virtual Object Layer
    minor: Can't operate on object
  #003: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5VLnative_file.c line 344 in H5VL__native_file_specific(): error in HDF5 file check
    major: File accessibility
    minor: Can't get value
  #004: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5Fint.c line 1083 in H5F__is_hdf5(): unable to open file
    major: File accessibility
    minor: Unable to initialize object
  #005: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5FD.c line 966 in H5FD_open(): can't open file
    major: Virtual File Layer
    minor: Unable to open file
  #006: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5FDsec2.c line 285 in H5FD__sec2_open(): unable to open file: name = '/home/yogesh/Projects/BC/EO-LINCS/eo-lincs-scs4/data/fluxcom_x_et.nc', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0
    major: File accessibility
    minor: Unable to open file
HDF5-DIAG: Error detected in HDF5 (2.1.0) thread 3:
  #000: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5F.c line 490 in H5Fis_accessible(): unable to determine if file is accessible as HDF5
    major: File accessibility
    minor: Not an HDF5 file
  #001: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5VLcallback.c line 4109 in H5VL_file_specific(): file specific failed
    major: Virtual Object Layer
    minor: Can't operate on object
  #002: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5VLcallback.c line 4045 in H5VL__file_specific(): file specific failed
    major: Virtual Object Layer
    minor: Can't operate on object
  #003: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5VLnative_file.c line 344 in H5VL__native_file_specific(): error in HDF5 file check
    major: File accessibility
    minor: Can't get value
  #004: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5Fint.c line 1083 in H5F__is_hdf5(): unable to open file
    major: File accessibility
    minor: Unable to initialize object
  #005: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5FD.c line 966 in H5FD_open(): can't open file
    major: Virtual File Layer
    minor: Unable to open file
  #006: /home/conda/feedstock_root/build_artifacts/hdf5_1780922703635/work/src/H5FDsec2.c line 285 in H5FD__sec2_open(): unable to open file: name = '/home/yogesh/Projects/BC/EO-LINCS/eo-lincs-scs4/data/laiv3.nc', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0
    major: File accessibility
    minor: Unable to open file

CPU times: user 42min 2s, sys: 6min 3s, total: 48min 6s
Wall time: 1h 4min 23s

---

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 [9]:

ds = msds.stores.storage.open_data("biomass_xu.nc")
ds

ds = msds.stores.storage.open_data("biomass_xu.nc") ds

Out[9]:

<xarray.Dataset> Size: 1GB
Dimensions:         (time: 20, lat: 1800, lon: 3600)
Coordinates:
  * time            (time) datetime64[ns] 160B 2000-01-01 ... 2019-01-01
  * lat             (lat) float64 14kB 89.95 89.85 89.75 ... -89.85 -89.95
  * lon             (lon) float64 29kB -179.9 -179.8 -179.8 ... 179.8 179.9
    spatial_ref     int64 8B ...
Data variables:
    carbon_density  (time, lat, lon) float64 1GB ...

xarray.Dataset

• Dimensions:
  • time: 20
  • lat: 1800
  • lon: 3600
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2000-01-01 ... 2019-01-01

    array(['2000-01-01T00:00:00.000000000', '2001-01-01T00:00:00.000000000',
           '2002-01-01T00:00:00.000000000', '2003-01-01T00:00:00.000000000',
           '2004-01-01T00:00:00.000000000', '2005-01-01T00:00:00.000000000',
           '2006-01-01T00:00:00.000000000', '2007-01-01T00:00:00.000000000',
           '2008-01-01T00:00:00.000000000', '2009-01-01T00:00:00.000000000',
           '2010-01-01T00:00:00.000000000', '2011-01-01T00:00:00.000000000',
           '2012-01-01T00:00:00.000000000', '2013-01-01T00:00:00.000000000',
           '2014-01-01T00:00:00.000000000', '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'],
          dtype='datetime64[ns]')

  • lat
    (lat)
    float64
    89.95 89.85 89.75 ... -89.85 -89.95

    array([ 89.95,  89.85,  89.75, ..., -89.75, -89.85, -89.95], shape=(1800,))

  • lon
    (lon)
    float64
    -179.9 -179.8 ... 179.8 179.9

    array([-179.95, -179.85, -179.75, ...,  179.75,  179.85,  179.95],
          shape=(3600,))

  • spatial_ref
    ()
    int64
    ...

    crs_wkt :

    GEOGCRS["WGS 84",DATUM["World Geodetic System 1984",ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1]]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],CS[ellipsoidal,2],AXIS["geodetic latitude (Lat)",north,ORDER[1],ANGLEUNIT["degree",0.0174532925199433]],AXIS["geodetic longitude (Lon)",east,ORDER[2],ANGLEUNIT["degree",0.0174532925199433]],ID["EPSG",4326]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    inverse_flattening :

    298.257223563

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    horizontal_datum_name :

    World Geodetic System 1984

    grid_mapping_name :

    latitude_longitude

    [1 values with dtype=int64]

• Data variables: (1)
  • carbon_density
    (time, lat, lon)
    float64
    ...

    NETCDF_DIM_EXTRA :

    {time}

    NETCDF_DIM_time_DEF :

    [20. 10.]

    NETCDF_DIM_time_VALUES :

    [ 0. 366. 731. 1096. 1461. 1827. 2192. 2557. 2922. 3288. 3653. 4018. 4383. 4749. 5114. 5479. 5844. 6210. 6575. 6940.]

    AREA_OR_POINT :

    Area

    grid_mapping :

    spatial_ref

    [129600000 values with dtype=float64]

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

In [10]:

ds.carbon_density.isel(time=1).plot()

ds.carbon_density.isel(time=1).plot()

Out[10]:

<matplotlib.collections.QuadMesh at 0x79a1477e6ba0>

The same quicklook can be created for any generated dataset.

In [11]:

ds = msds.stores.storage.open_data("biomass_esa_cci.nc")
ds

ds = msds.stores.storage.open_data("biomass_esa_cci.nc") ds

Out[11]:

<xarray.Dataset> Size: 1GB
Dimensions:      (time: 10, lat: 1800, lon: 3600)
Coordinates:
  * time         (time) datetime64[ns] 80B 2007-07-02 2010-07-02 ... 2022-07-02
  * lat          (lat) float64 14kB 89.95 89.85 89.75 ... -89.75 -89.85 -89.95
  * lon          (lon) float64 29kB -179.9 -179.8 -179.8 ... 179.7 179.8 179.9
    spatial_ref  int64 8B ...
Data variables:
    agb          (time, lat, lon) float64 518MB ...
    agb_sd       (time, lat, lon) float64 518MB ...
Attributes:
    Conventions:             CF-1.7
    title:                   esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-plat...
    date_created:            2026-06-16T14:31:44.825369
    processing_level:        L4
    time_coverage_start:     2007-01-01T00:00:00
    time_coverage_end:       2022-12-31T00:00:00
    time_coverage_duration:  P5843DT0H0M0S
    history:                 [{'program': 'xcube_cci.chunkstore.CciChunkStore...

xarray.Dataset

• Dimensions:
  • time: 10
  • lat: 1800
  • lon: 3600
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2007-07-02 ... 2022-07-02

    standard_name :

    time

    bounds :

    time_bnds

    array(['2007-07-02T00:00:00.000000000', '2010-07-02T00:00:00.000000000',
           '2015-07-02T00:00:00.000000000', '2016-07-01T12:00:00.000000000',
           '2017-07-02T00:00:00.000000000', '2018-07-02T00:00:00.000000000',
           '2019-07-02T00:00:00.000000000', '2020-07-01T12:00:00.000000000',
           '2021-07-02T00:00:00.000000000', '2022-07-02T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • lat
    (lat)
    float64
    89.95 89.85 89.75 ... -89.85 -89.95

    array([ 89.95,  89.85,  89.75, ..., -89.75, -89.85, -89.95], shape=(1800,))

  • lon
    (lon)
    float64
    -179.9 -179.8 ... 179.8 179.9

    array([-179.95, -179.85, -179.75, ...,  179.75,  179.85,  179.95],
          shape=(3600,))

  • spatial_ref
    ()
    int64
    ...

    crs_wkt :

    GEOGCRS["\""WGS 84\""",DATUM["\""WGS_1984\""",ELLIPSOID["\""WGS 84\""",6378137,298.257223563,LENGTHUNIT["metre",1]]],PRIMEM["\""Greenwich\""",0,ANGLEUNIT["\""degree\""",0.0174532925199433]],CS[ellipsoidal,2],AXIS["longitude",east,ORDER[1],ANGLEUNIT["\""degree\""",0.0174532925199433]],AXIS["latitude",north,ORDER[2],ANGLEUNIT["\""degree\""",0.0174532925199433]],ID["\""EPSG\""","\""4326\"""]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    inverse_flattening :

    298.257223563

    reference_ellipsoid_name :

    \"WGS 84\"

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    \"Greenwich\"

    geographic_crs_name :

    \"WGS 84\"

    horizontal_datum_name :

    \"WGS_1984\"

    grid_mapping_name :

    latitude_longitude

    [1 values with dtype=int64]

• Data variables: (2)
  • agb
    (time, lat, lon)
    float64
    ...

    long_name :

    Above-ground biomass

    grid_mapping :

    spatial_ref

    units :

    Mg/ha

    valid_min :

    0.0

    valid_max :

    10000.0

    orig_data_type :

    int16

    fill_value :

    -32768

    size :

    64800000

    shape :

    [ 10 1800 3600]

    chunk_sizes :

    [ 10 600 600]

    file_chunk_sizes :

    [ 10 600 600]

    data_type :

    int16

    dimensions :

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

    file_dimensions :

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

    [64800000 values with dtype=float64]

  • agb_sd
    (time, lat, lon)
    float64
    ...

    long_name :

    Above-ground biomass standard_deviation

    grid_mapping :

    spatial_ref

    units :

    Mg/ha

    valid_min :

    0.0

    valid_max :

    10000.0

    orig_data_type :

    int16

    fill_value :

    -32768

    size :

    64800000

    shape :

    [ 10 1800 3600]

    chunk_sizes :

    [ 10 600 600]

    file_chunk_sizes :

    [ 10 600 600]

    data_type :

    int16

    dimensions :

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

    file_dimensions :

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

    [64800000 values with dtype=float64]

• Attributes: (8)

  Conventions :

  CF-1.7

  title :

  esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.6-0.10000m

  date_created :

  2026-06-16T14:31:44.825369

  processing_level :

  L4

  time_coverage_start :

  2007-01-01T00:00:00

  time_coverage_end :

  2022-12-31T00:00:00

  time_coverage_duration :

  P5843DT0H0M0S

  history :

  [{'program': 'xcube_cci.chunkstore.CciChunkStore', 'cube_params': {'time_range': ['2007-01-01T00:00:00', '2022-12-31T23:59:59'], 'variable_names': ['agb', 'agb_sd', 'crs']}}]

In [12]:

ds.agb.isel(time=0).plot()

ds.agb.isel(time=0).plot()

Out[12]:

<matplotlib.collections.QuadMesh at 0x79a14491fd90>

In [13]:

ds = msds.stores.storage.open_data("fluxcom_x_gpp.nc")
ds

ds = msds.stores.storage.open_data("fluxcom_x_gpp.nc") ds

Out[13]:

<xarray.Dataset> Size: 13GB
Dimensions:        (time: 252, lat: 1800, lon: 3600)
Coordinates:
  * time           (time) datetime64[ns] 2kB 2015-01-01 ... 2004-12-01
  * lat            (lat) float64 14kB 89.95 89.85 89.75 ... -89.75 -89.85 -89.95
  * lon            (lon) float64 29kB -179.9 -179.8 -179.8 ... 179.7 179.8 179.9
    spatial_ref    int64 8B ...
Data variables:
    GPP            (time, lat, lon) float64 13GB ...
    land_fraction  (lat, lon) float64 52MB ...
Attributes: (12/29)
    Full_resolution_data:       Visit the dataset documentation at https://gi...
    contact:                    The FLUXCOM-X team, fluxcomx@bgc-jena.mpg.de
    contributor:                ['Jacob Nelson https://orcid.org/0000-0002-46...
    conventions:                CF-1.8
    creation_date:              2023-11-10
    creator:                    The FLUXCOM-X team, fluxcomx@bgc-jena.mpg.de
    ...                         ...
    references:                 
    source:                     ['MODIS Reflectance 10.5067/MODIS/MCD43C2.006...
    summary:                    X-BASE GPP is based on the FLUXCOM-X framewor...
    time_coverage_end:          2021-12-01T00:00:00.000000000
    time_coverage_start:        2001-01-01T00:00:00.000000000
    training_features:          ['TA', 'VPD', 'SW_IN', 'SW_IN_POT', 'dSW_IN_P...

xarray.Dataset

• Dimensions:
  • time: 252
  • lat: 1800
  • lon: 3600
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2015-01-01 ... 2004-12-01

    bounds :

    time_bnds

    long_name :

    Time

    standard_name :

    time

    array(['2015-01-01T00:00:00.000000000', '2015-02-01T00:00:00.000000000',
           '2015-03-01T00:00:00.000000000', ..., '2004-10-01T00:00:00.000000000',
           '2004-11-01T00:00:00.000000000', '2004-12-01T00:00:00.000000000'],
          shape=(252,), dtype='datetime64[ns]')

  • lat
    (lat)
    float64
    89.95 89.85 89.75 ... -89.85 -89.95

    array([ 89.95,  89.85,  89.75, ..., -89.75, -89.85, -89.95], shape=(1800,))

  • lon
    (lon)
    float64
    -179.9 -179.8 ... 179.8 179.9

    array([-179.95, -179.85, -179.75, ...,  179.75,  179.85,  179.95],
          shape=(3600,))

  • spatial_ref
    ()
    int64
    ...

    crs_wkt :

    GEOGCRS["WGS 84",ENSEMBLE["World Geodetic System 1984 ensemble",MEMBER["World Geodetic System 1984 (Transit)"],MEMBER["World Geodetic System 1984 (G730)"],MEMBER["World Geodetic System 1984 (G873)"],MEMBER["World Geodetic System 1984 (G1150)"],MEMBER["World Geodetic System 1984 (G1674)"],MEMBER["World Geodetic System 1984 (G1762)"],MEMBER["World Geodetic System 1984 (G2139)"],MEMBER["World Geodetic System 1984 (G2296)"],ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1]],ENSEMBLEACCURACY[2.0]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],CS[ellipsoidal,2],AXIS["geodetic latitude (Lat)",north,ORDER[1],ANGLEUNIT["degree",0.0174532925199433]],AXIS["geodetic longitude (Lon)",east,ORDER[2],ANGLEUNIT["degree",0.0174532925199433]],USAGE[SCOPE["Horizontal component of 3D system."],AREA["World."],BBOX[-90,-180,90,180]],ID["EPSG",4326]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    inverse_flattening :

    298.257223563

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    horizontal_datum_name :

    World Geodetic System 1984 ensemble

    grid_mapping_name :

    latitude_longitude

    [1 values with dtype=int64]

• Data variables: (2)
  • GPP
    (time, lat, lon)
    float64
    ...

    long_name :

    Gross Primary Productivity

    partitioning :

    NT

    standard_name :

    gross_primary_productivity_of_carbon

    units :

    gC m-2 d-1

    version :

    CCBY

    grid_mapping :

    spatial_ref

    [1632960000 values with dtype=float64]

  • land_fraction
    (lat, lon)
    float64
    ...

    description :

    fraction of land for which GPP is defined

    long_name :

    land_area_fraction

    standard_name :

    land_area_fraction

    units :

    1

    grid_mapping :

    spatial_ref

    [6480000 values with dtype=float64]

• Attributes: (29)

  Full_resolution_data :

  Visit the dataset documentation at https://gitlab.gwdg.de/fluxcom/fluxcomxdata for examples how to access the full resolution datasets

  contact :

  The FLUXCOM-X team, fluxcomx@bgc-jena.mpg.de

  contributor :

  ['Jacob Nelson https://orcid.org/0000-0002-4663-2420', 'Sophia Walther https://orcid.org/0000-0003-1681-9304', 'Ulrich Weber https://orcid.org/0000-0001-7116-035X', 'Fabian Gans https://orcid.org/0000-0001-9614-0435', 'Zayd Hamdi https://orcid.org/0000-0003-1658-8243', 'Basil Kraft https://orcid.org/0000-0002-8491-2730', 'Weijie Zhang', 'Gregory Duveiller https://orcid.org/0000-0002-6471-8404', 'Martin Jung']

  conventions :

  CF-1.8

  creation_date :

  2023-11-10

  creator :

  The FLUXCOM-X team, fluxcomx@bgc-jena.mpg.de

  crs :

  WGS 84 / Plate Carree

  data_access_url :

  https://gitlab.gwdg.de/fluxcom/fluxcomxdata

  frequency :

  1mo

  geospatial_lat_max :

  89.975

  geospatial_lat_min :

  -89.975

  geospatial_lat_resolution :

  0.05

  geospatial_lon_max :

  179.975

  geospatial_lon_min :

  -179.975

  geospatial_lon_resolution :

  0.05

  included_sites :

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https://doi.org/10.17190/AMF/1832157', 'RU-Che https://doi.org/10.18140/FLX/1440181', 'RU-Cok https://doi.org/10.18140/FLX/1440182', 'RU-Fy2 https://doi.org/10.18160/2G60-ZHAK', 'RU-Fyo https://doi.org/10.18160/2G60-ZHAK', 'RU-Ha1 https://doi.org/10.18140/FLX/1440184', 'SD-Dem https://doi.org/10.18140/FLX/1440186', 'SE-Deg https://doi.org/10.18160/2G60-ZHAK', 'SE-Htm https://doi.org/10.18160/2G60-ZHAK', 'SE-Lnn https://doi.org/10.18160/YVR0-4898', 'SE-Nor https://doi.org/10.18160/2G60-ZHAK', 'SE-Ros https://doi.org/10.18160/2G60-ZHAK', 'SE-Svb https://doi.org/10.18160/2G60-ZHAK', 'SJ-Adv https://doi.org/10.18140/FLX/1440241', 'SJ-Blv https://doi.org/10.18140/FLX/1440242', 'SN-Dhr https://doi.org/10.18140/FLX/1440246', 'US-A32 https://doi.org/10.17190/AMF/1881568', 'US-AR1 https://doi.org/10.18140/FLX/1440103', 'US-AR2 https://doi.org/10.18140/FLX/1440104', 'US-ARM https://doi.org/10.17190/AMF/1854366', 'US-ARb https://doi.org/10.18140/FLX/1440064', 'US-ARc https://doi.org/10.18140/FLX/1440065', 'US-Atq https://doi.org/10.18140/FLX/1440067', 'US-BZB https://doi.org/10.17190/AMF/1881569', 'US-BZF https://doi.org/10.17190/AMF/1881570', 'US-BZS https://doi.org/10.17190/AMF/1881572', 'US-BZo https://doi.org/10.17190/AMF/1881571', 'US-Bi1 https://doi.org/10.17190/AMF/1871134', 'US-Bi2 https://doi.org/10.17190/AMF/1871135', 'US-Blo https://doi.org/10.18140/FLX/1440068', 'US-CF1 https://doi.org/10.17190/AMF/1832158', 'US-CF2 https://doi.org/10.17190/AMF/1881573', 'US-CF3 https://doi.org/10.17190/AMF/1881574', 'US-CF4 https://doi.org/10.17190/AMF/1881575', 'US-CRT https://doi.org/10.18140/FLX/1440117', 'US-CS1 https://doi.org/10.17190/AMF/1881576', 'US-CS2 https://doi.org/10.17190/AMF/1881577', 'US-CS3 https://doi.org/10.17190/AMF/1881578', 'US-CS4 https://doi.org/10.17190/AMF/1881579', 'US-Cop https://doi.org/10.18140/FLX/1440100', 'US-EDN https://doi.org/10.17190/AMF/1832159', 'US-GBT https://doi.org/10.18140/FLX/1440118', 'US-GLE https://doi.org/10.17190/AMF/1871136', 'US-Goo https://doi.org/10.18140/FLX/1440070', 'US-HB1 https://doi.org/10.17190/AMF/1832160', 'US-HWB https://doi.org/10.17190/AMF/1881582', 'US-Ha1 https://doi.org/10.18140/FLX/1440071', 'US-Hn3 https://doi.org/10.17190/AMF/1881580', 'US-Ho2 https://doi.org/10.17190/AMF/1881581', 'US-IB2 https://doi.org/10.18140/FLX/1440072', 'US-ICs https://doi.org/10.17190/AMF/1871138', 'US-ICt https://doi.org/10.17190/AMF/1881583', 'US-Ivo https://doi.org/10.18140/FLX/1440073', 'US-Jo2 https://doi.org/10.17190/AMF/1881584', 'US-KFS https://doi.org/10.17190/AMF/1881585', 'US-KLS https://doi.org/10.17190/AMF/1854367', 'US-KS1 https://doi.org/10.18140/FLX/1440074', 'US-KS2 https://doi.org/10.18140/FLX/1440075', 'US-KS3 https://doi.org/10.17190/AMF/1881586', 'US-LWW https://doi.org/10.18140/FLX/1440077', 'US-Lin https://doi.org/10.18140/FLX/1440107', 'US-Los https://doi.org/10.18140/FLX/1440076', 'US-MMS https://doi.org/10.17190/AMF/1854369', 'US-MOz https://doi.org/10.17190/AMF/1854370', 'US-Me1 https://doi.org/10.18140/FLX/1440078', 'US-Me2 https://doi.org/10.17190/AMF/1854368', 'US-Me3 https://doi.org/10.18140/FLX/1440080', 'US-Me4 https://doi.org/10.18140/FLX/1440081', 'US-Me5 https://doi.org/10.18140/FLX/1440082', 'US-Me6 https://doi.org/10.18140/FLX/1440099', 'US-Mpj https://doi.org/10.17190/AMF/1832161', 'US-Myb https://doi.org/10.18140/FLX/1440105', 'US-NGB https://doi.org/10.17190/AMF/1832162', 'US-NR1 https://doi.org/10.17190/AMF/1871141', 'US-Ne1 https://doi.org/10.17190/AMF/1871140', 'US-Ne2 https://doi.org/10.18140/FLX/1440085', 'US-Ne3 https://doi.org/10.18140/FLX/1440086', 'US-ONA https://doi.org/10.17190/AMF/1832163', 'US-ORv https://doi.org/10.17190/AMF/1832164', 'US-OWC https://doi.org/10.17190/AMF/1871142', 'US-Oho https://doi.org/10.18140/FLX/1440088', 'US-PFa https://doi.org/10.18140/FLX/1440089', 'US-Prr https://doi.org/10.18140/FLX/1440113', 'US-Rms https://doi.org/10.17190/AMF/1881587', 'US-Ro1 https://doi.org/10.17190/AMF/1881588', 'US-Ro4 https://doi.org/10.17190/AMF/1881589', 'US-Ro5 https://doi.org/10.17190/AMF/1818371', 'US-Ro6 https://doi.org/10.17190/AMF/1881590', 'US-Rwe https://doi.org/10.17190/AMF/1871143', 'US-Rwf https://doi.org/10.17190/AMF/1881591', 'US-Rws https://doi.org/10.17190/AMF/1881592', 'US-SRC https://doi.org/10.17190/AMF/1871145', 'US-SRG https://doi.org/10.18140/FLX/1440114', 'US-SRM https://doi.org/10.18140/FLX/1440090', 'US-Sne https://doi.org/10.17190/AMF/1871144', 'US-Snf https://doi.org/10.17190/AMF/1854371', 'US-Sta https://doi.org/10.18140/FLX/1440115', 'US-Syv https://doi.org/10.18140/FLX/1440091', 'US-Ton https://doi.org/10.18140/FLX/1440092', 'US-Tw1 https://doi.org/10.17190/AMF/1832165', 'US-Tw2 https://doi.org/10.17190/AMF/1881593', 'US-Tw3 https://doi.org/10.17190/AMF/1881594', 'US-Tw4 https://doi.org/10.18140/FLX/1440111', 'US-Tw5 https://doi.org/10.17190/AMF/1881595', 'US-Twt https://doi.org/10.18140/FLX/1440106', 'US-UM3 https://doi.org/10.17190/AMF/1881596', 'US-UMB https://doi.org/10.18140/FLX/1440093', 'US-UMd https://doi.org/10.17190/AMF/1881597', 'US-Var https://doi.org/10.18140/FLX/1440094', 'US-WCr https://doi.org/10.18140/FLX/1440095', 'US-WPT https://doi.org/10.18140/FLX/1440116', 'US-Whs https://doi.org/10.18140/FLX/1440097', 'US-Wi0 https://doi.org/10.18140/FLX/1440055', 'US-Wi1 https://doi.org/10.18140/FLX/1440054', 'US-Wi2 https://doi.org/10.18140/FLX/1440056', 'US-Wi3 https://doi.org/10.18140/FLX/1440057', 'US-Wi4 https://doi.org/10.18140/FLX/1440058', 'US-Wi5 https://doi.org/10.18140/FLX/1440059', 'US-Wi6 https://doi.org/10.18140/FLX/1440060', 'US-Wi7 https://doi.org/10.18140/FLX/1440061', 'US-Wi8 https://doi.org/10.18140/FLX/1440062', 'US-Wi9 https://doi.org/10.18140/FLX/1440063', 'US-Wjs https://doi.org/10.17190/AMF/1871146', 'US-Wkg https://doi.org/10.18140/FLX/1440096', 'US-xBR https://doi.org/10.17190/AMF/1881598', 'ZM-Mon https://doi.org/10.18140/FLX/1440189']

  institution :

  Max-Planck-Institute for Biogeochemistry

  keywords :

  ['BIOGEOCHEMICAL CYCLES', 'ECOSYSTEM FUNCTIONS', 'TERRESTRIAL ECOSYSTEMS', 'VEGETATION', 'CARBON', 'LAND SURFACE']

  license :

  CC-BY 4.0

  product_version :

  X-BASE 1.0

  project :

  FLUXCOM-X

  publisher_name :

  The FLUXCOM-X Team

  publisher_url :

  http://fluxcom.org/

  references :

  source :

  ['MODIS Reflectance 10.5067/MODIS/MCD43C2.006 10.5067/MODIS/MCD43C4.006', 'MODIS LST 10.5067/MODIS/MOD11C1.006 10.5067/MODIS/MYD11C1.006', 'ERA5 https://doi.org/10.1002/qj.3803']

  summary :

  X-BASE GPP is based on the FLUXCOM-X framework which trains machine learning models on in-situ eddy covariance data and uses them to produce this global product. The X-BASE experiment is a basic configuration to serve as a baseline for the FLUXCOM-X framework and includes as predictors the core meteorlogical data, plant functional type classification as well as MODIS based vegitation indicies and land surface temperature. XGBoost was used as the machine learning algorithm. The GPP estimates from the eddy covariance data was based on the Nighttime Partitioning method.

  time_coverage_end :

  2021-12-01T00:00:00.000000000

  time_coverage_start :

  2001-01-01T00:00:00.000000000

  training_features :

  ['TA', 'VPD', 'SW_IN', 'SW_IN_POT', 'dSW_IN_POT_DAY', 'dSW_IN_POT', 'LST', 'LST', 'EVI', 'NIRv', 'NDWI_band7', 'Trees', 'Shrubs', 'Grasses', 'Crops', 'Wetland', 'C4_photosynthesis', 'Needleleaf', 'Broadleaf', 'Deciduous', 'Evergreen']

In [14]:

ds.GPP.isel(time=0).plot(vmin=0, vmax=20)

ds.GPP.isel(time=0).plot(vmin=0, vmax=20)

Out[14]:

<matplotlib.collections.QuadMesh at 0x79a13a8fed50>

In [15]:

ds = msds.stores.storage.open_data("fluxcom_x_et.nc")
ds

ds = msds.stores.storage.open_data("fluxcom_x_et.nc") ds

Out[15]:

<xarray.Dataset> Size: 13GB
Dimensions:        (time: 252, lat: 1800, lon: 3600)
Coordinates:
  * time           (time) datetime64[ns] 2kB 2004-01-01 ... 2011-12-01
  * lat            (lat) float64 14kB 89.95 89.85 89.75 ... -89.75 -89.85 -89.95
  * lon            (lon) float64 29kB -179.9 -179.8 -179.8 ... 179.7 179.8 179.9
    spatial_ref    int64 8B ...
Data variables:
    ET             (time, lat, lon) float64 13GB ...
    land_fraction  (lat, lon) float64 52MB ...
Attributes: (12/29)
    Full_resolution_data:       Visit the dataset documentation at https://gi...
    contact:                    The FLUXCOM-X team, fluxcomx@bgc-jena.mpg.de
    contributor:                ['Jacob Nelson https://orcid.org/0000-0002-46...
    conventions:                CF-1.8
    creation_date:              2023-11-10
    creator:                    The FLUXCOM-X team, fluxcomx@bgc-jena.mpg.de
    ...                         ...
    references:                 
    source:                     ['MODIS Reflectance 10.5067/MODIS/MCD43C2.006...
    summary:                    X-BASE ET is based on the FLUXCOM-X framework...
    time_coverage_end:          2021-12-01T00:00:00.000000000
    time_coverage_start:        2001-01-01T00:00:00.000000000
    training_features:          ['TA', 'VPD', 'SW_IN', 'SW_IN_POT', 'dSW_IN_P...

xarray.Dataset

• Dimensions:
  • time: 252
  • lat: 1800
  • lon: 3600
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2004-01-01 ... 2011-12-01

    bounds :

    time_bnds

    long_name :

    Time

    standard_name :

    time

    array(['2004-01-01T00:00:00.000000000', '2004-02-01T00:00:00.000000000',
           '2004-03-01T00:00:00.000000000', ..., '2011-10-01T00:00:00.000000000',
           '2011-11-01T00:00:00.000000000', '2011-12-01T00:00:00.000000000'],
          shape=(252,), dtype='datetime64[ns]')

  • lat
    (lat)
    float64
    89.95 89.85 89.75 ... -89.85 -89.95

    array([ 89.95,  89.85,  89.75, ..., -89.75, -89.85, -89.95], shape=(1800,))

  • lon
    (lon)
    float64
    -179.9 -179.8 ... 179.8 179.9

    array([-179.95, -179.85, -179.75, ...,  179.75,  179.85,  179.95],
          shape=(3600,))

  • spatial_ref
    ()
    int64
    ...

    crs_wkt :

    GEOGCRS["WGS 84",ENSEMBLE["World Geodetic System 1984 ensemble",MEMBER["World Geodetic System 1984 (Transit)"],MEMBER["World Geodetic System 1984 (G730)"],MEMBER["World Geodetic System 1984 (G873)"],MEMBER["World Geodetic System 1984 (G1150)"],MEMBER["World Geodetic System 1984 (G1674)"],MEMBER["World Geodetic System 1984 (G1762)"],MEMBER["World Geodetic System 1984 (G2139)"],MEMBER["World Geodetic System 1984 (G2296)"],ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1]],ENSEMBLEACCURACY[2.0]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],CS[ellipsoidal,2],AXIS["geodetic latitude (Lat)",north,ORDER[1],ANGLEUNIT["degree",0.0174532925199433]],AXIS["geodetic longitude (Lon)",east,ORDER[2],ANGLEUNIT["degree",0.0174532925199433]],USAGE[SCOPE["Horizontal component of 3D system."],AREA["World."],BBOX[-90,-180,90,180]],ID["EPSG",4326]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    inverse_flattening :

    298.257223563

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    horizontal_datum_name :

    World Geodetic System 1984 ensemble

    grid_mapping_name :

    latitude_longitude

    [1 values with dtype=int64]

• Data variables: (2)
  • ET
    (time, lat, lon)
    float64
    ...

    long_name :

    Evapotranspiration

    standard_name :

    evapotranspiration

    units :

    mm hr-1

    version :

    CCBY

    grid_mapping :

    spatial_ref

    [1632960000 values with dtype=float64]

  • land_fraction
    (lat, lon)
    float64
    ...

    description :

    fraction of land for which ET is defined

    long_name :

    land_area_fraction

    standard_name :

    land_area_fraction

    units :

    1

    grid_mapping :

    spatial_ref

    [6480000 values with dtype=float64]

• Attributes: (29)

  Full_resolution_data :

  Visit the dataset documentation at https://gitlab.gwdg.de/fluxcom/fluxcomxdata for examples how to access the full resolution datasets

  contact :

  The FLUXCOM-X team, fluxcomx@bgc-jena.mpg.de

  contributor :

  ['Jacob Nelson https://orcid.org/0000-0002-4663-2420', 'Sophia Walther https://orcid.org/0000-0003-1681-9304', 'Ulrich Weber https://orcid.org/0000-0001-7116-035X', 'Fabian Gans https://orcid.org/0000-0001-9614-0435', 'Zayd Hamdi https://orcid.org/0000-0003-1658-8243', 'Basil Kraft https://orcid.org/0000-0002-8491-2730', 'Weijie Zhang', 'Gregory Duveiller https://orcid.org/0000-0002-6471-8404', 'Martin Jung']

  conventions :

  CF-1.8

  creation_date :

  2023-11-10

  creator :

  The FLUXCOM-X team, fluxcomx@bgc-jena.mpg.de

  crs :

  WGS 84 / Plate Carree

  data_access_url :

  https://gitlab.gwdg.de/fluxcom/fluxcomxdata

  frequency :

  1mo

  geospatial_lat_max :

  89.975

  geospatial_lat_min :

  -89.975

  geospatial_lat_resolution :

  0.05

  geospatial_lon_max :

  179.975

  geospatial_lon_min :

  -179.975

  geospatial_lon_resolution :

  0.05

  included_sites :

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https://doi.org/10.18140/FLX/1440041', 'CA-NS7 https://doi.org/10.18140/FLX/1440042', 'CA-Oas https://doi.org/10.18140/FLX/1440043', 'CA-Obs https://doi.org/10.18140/FLX/1440044', 'CA-Qfo https://doi.org/10.18140/FLX/1440045', 'CA-SF1 https://doi.org/10.18140/FLX/1440046', 'CA-SF2 https://doi.org/10.18140/FLX/1440047', 'CA-SF3 https://doi.org/10.18140/FLX/1440048', 'CA-TP1 https://doi.org/10.18140/FLX/1440050', 'CA-TP2 https://doi.org/10.18140/FLX/1440051', 'CA-TP3 https://doi.org/10.17190/AMF/1881566', 'CA-TP4 https://doi.org/10.18140/FLX/1440053', 'CA-TPD https://doi.org/10.17190/AMF/1881567', 'CG-Tch https://doi.org/10.18140/FLX/1440142', 'CH-Aws https://doi.org/10.18160/2G60-ZHAK', 'CH-Cha https://doi.org/10.18160/2G60-ZHAK', 'CH-Dav https://doi.org/10.18160/2G60-ZHAK', 'CH-Fru https://doi.org/10.18160/2G60-ZHAK', 'CH-Lae https://doi.org/10.18160/2G60-ZHAK', 'CH-Oe1 https://doi.org/10.18140/FLX/1440135', 'CH-Oe2 https://doi.org/10.18160/2G60-ZHAK', 'CN-Cha 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https://doi.org/10.18160/YVR0-4898', 'DE-Hzd https://doi.org/10.18160/2G60-ZHAK', 'DE-Kli https://doi.org/10.18160/2G60-ZHAK', 'DE-Lkb https://doi.org/10.18140/FLX/1440214', 'DE-Lnf https://doi.org/10.18140/FLX/1440150', 'DE-Obe https://doi.org/10.18160/2G60-ZHAK', 'DE-RuR https://doi.org/10.18160/PAD9-HQHU', 'DE-RuS https://doi.org/10.18160/2G60-ZHAK', 'DE-RuW https://doi.org/10.18160/2G60-ZHAK', 'DE-Seh https://doi.org/10.18140/FLX/1440217', 'DE-SfN https://doi.org/10.18140/FLX/1440219', 'DE-Spw https://doi.org/10.18140/FLX/1440220', 'DE-Tha https://doi.org/10.18160/2G60-ZHAK', 'DE-Zrk https://doi.org/10.18140/FLX/1440221', 'DK-Eng https://doi.org/10.18140/FLX/1440153', 'DK-Fou https://doi.org/10.18140/FLX/1440154', 'DK-Gds https://doi.org/10.18160/PAD9-HQHU', 'DK-Sor https://doi.org/10.18160/2G60-ZHAK', 'ES-Abr https://doi.org/10.18160/2G60-ZHAK', 'ES-Agu https://doi.org/10.18160/2G60-ZHAK', 'ES-Amo https://doi.org/10.18140/FLX/1440156', 'ES-Cnd https://doi.org/10.18160/2G60-ZHAK', 'ES-LJu https://doi.org/10.18160/2G60-ZHAK', 'ES-LM1 https://doi.org/10.18160/2G60-ZHAK', 'ES-LM2 https://doi.org/10.18160/2G60-ZHAK', 'ES-LgS https://doi.org/10.18140/FLX/1440225', 'ES-Ln2 https://doi.org/10.18140/FLX/1440226', 'FI-Hyy https://doi.org/10.18160/2G60-ZHAK', 'FI-Jok https://doi.org/10.18140/FLX/1440159', 'FI-Ken https://doi.org/10.18160/2G60-ZHAK', 'FI-Let https://doi.org/10.18160/2G60-ZHAK', 'FI-Lom https://doi.org/10.18140/FLX/1440228', 'FI-Qvd https://doi.org/10.18160/2G60-ZHAK', 'FI-Sii https://doi.org/10.18160/2G60-ZHAK', 'FI-Sod https://doi.org/10.18140/FLX/1440160', 'FI-Var https://doi.org/10.18160/PAD9-HQHU', 'FR-Aur https://doi.org/10.18160/2G60-ZHAK', 'FR-Bil https://doi.org/10.18160/2G60-ZHAK', 'FR-EM2 https://doi.org/10.18160/PAD9-HQHU', 'FR-FBn https://doi.org/10.18160/2G60-ZHAK', 'FR-Fon https://doi.org/10.18160/2G60-ZHAK', 'FR-Gri https://doi.org/10.18160/2G60-ZHAK', 'FR-Hes https://doi.org/10.18160/2G60-ZHAK', 'FR-LBr https://doi.org/10.18140/FLX/1440163', 'FR-LGt https://doi.org/10.18160/PAD9-HQHU', 'FR-Lam https://doi.org/10.18160/2G60-ZHAK', 'FR-Pue https://doi.org/10.18140/FLX/1440164', 'FR-Tou https://doi.org/10.18160/PAD9-HQHU', 'GF-Guy https://doi.org/10.18160/2G60-ZHAK', 'GH-Ank https://doi.org/10.18140/FLX/1440229', 'GL-Dsk https://doi.org/10.18160/PAD9-HQHU', 'GL-NuF https://doi.org/10.18140/FLX/1440222', 'GL-ZaF https://doi.org/10.18140/FLX/1440223', 'GL-ZaH https://doi.org/10.18140/FLX/1440224', 'IE-Cra https://doi.org/10.18160/2G60-ZHAK', 'IL-Yat https://doi.org/10.18160/2G60-ZHAK', 'IT-BCi https://doi.org/10.18160/2G60-ZHAK', 'IT-BFt https://doi.org/10.18160/PAD9-HQHU', 'IT-CA1 https://doi.org/10.18140/FLX/1440230', 'IT-CA2 https://doi.org/10.18140/FLX/1440231', 'IT-CA3 https://doi.org/10.18140/FLX/1440232', 'IT-Col https://doi.org/10.18140/FLX/1440167', 'IT-Cp2 https://doi.org/10.18160/2G60-ZHAK', 'IT-Cpz https://doi.org/10.18140/FLX/1440168', 'IT-Isp https://doi.org/10.18140/FLX/1440234', 'IT-La2 https://doi.org/10.18140/FLX/1440235', 'IT-Lav https://doi.org/10.18160/2G60-ZHAK', 'IT-Lsn https://doi.org/10.18160/PAD9-HQHU', 'IT-MBo https://doi.org/10.18160/2G60-ZHAK', 'IT-Noe https://doi.org/10.18140/FLX/1440171', 'IT-PT1 https://doi.org/10.18140/FLX/1440172', 'IT-Ren https://doi.org/10.18160/2G60-ZHAK', 'IT-Ro1 https://doi.org/10.18140/FLX/1440174', 'IT-Ro2 https://doi.org/10.18140/FLX/1440175', 'IT-SR2 https://doi.org/10.18160/2G60-ZHAK', 'IT-SRo https://doi.org/10.18140/FLX/1440176', 'IT-Tor https://doi.org/10.18160/2G60-ZHAK', 'JP-MBF https://doi.org/10.18140/FLX/1440238', 'JP-SMF https://doi.org/10.18140/FLX/1440239', 'MX-Tes https://doi.org/10.17190/AMF/1832156', 'MY-PSO https://doi.org/10.18140/FLX/1440240', 'NL-Hor https://doi.org/10.18140/FLX/1440177', 'NL-Loo https://doi.org/10.18160/YVR0-4898', 'PA-SPn https://doi.org/10.18140/FLX/1440180', 'PA-SPs https://doi.org/10.18140/FLX/1440179', 'PE-QFR https://doi.org/10.17190/AMF/1832157', 'RU-Che https://doi.org/10.18140/FLX/1440181', 'RU-Cok https://doi.org/10.18140/FLX/1440182', 'RU-Fy2 https://doi.org/10.18160/2G60-ZHAK', 'RU-Fyo https://doi.org/10.18160/2G60-ZHAK', 'RU-Ha1 https://doi.org/10.18140/FLX/1440184', 'SD-Dem https://doi.org/10.18140/FLX/1440186', 'SE-Deg https://doi.org/10.18160/2G60-ZHAK', 'SE-Htm https://doi.org/10.18160/2G60-ZHAK', 'SE-Lnn https://doi.org/10.18160/YVR0-4898', 'SE-Nor https://doi.org/10.18160/2G60-ZHAK', 'SE-Ros https://doi.org/10.18160/2G60-ZHAK', 'SE-Svb https://doi.org/10.18160/2G60-ZHAK', 'SJ-Adv https://doi.org/10.18140/FLX/1440241', 'SJ-Blv https://doi.org/10.18140/FLX/1440242', 'SN-Dhr https://doi.org/10.18140/FLX/1440246', 'US-A32 https://doi.org/10.17190/AMF/1881568', 'US-AR1 https://doi.org/10.18140/FLX/1440103', 'US-AR2 https://doi.org/10.18140/FLX/1440104', 'US-ARM https://doi.org/10.17190/AMF/1854366', 'US-ARb https://doi.org/10.18140/FLX/1440064', 'US-ARc https://doi.org/10.18140/FLX/1440065', 'US-Atq https://doi.org/10.18140/FLX/1440067', 'US-BZB https://doi.org/10.17190/AMF/1881569', 'US-BZF https://doi.org/10.17190/AMF/1881570', 'US-BZS https://doi.org/10.17190/AMF/1881572', 'US-BZo https://doi.org/10.17190/AMF/1881571', 'US-Bi1 https://doi.org/10.17190/AMF/1871134', 'US-Bi2 https://doi.org/10.17190/AMF/1871135', 'US-Blo https://doi.org/10.18140/FLX/1440068', 'US-CF1 https://doi.org/10.17190/AMF/1832158', 'US-CF2 https://doi.org/10.17190/AMF/1881573', 'US-CF3 https://doi.org/10.17190/AMF/1881574', 'US-CF4 https://doi.org/10.17190/AMF/1881575', 'US-CRT https://doi.org/10.18140/FLX/1440117', 'US-CS1 https://doi.org/10.17190/AMF/1881576', 'US-CS2 https://doi.org/10.17190/AMF/1881577', 'US-CS3 https://doi.org/10.17190/AMF/1881578', 'US-CS4 https://doi.org/10.17190/AMF/1881579', 'US-Cop https://doi.org/10.18140/FLX/1440100', 'US-EDN https://doi.org/10.17190/AMF/1832159', 'US-GBT https://doi.org/10.18140/FLX/1440118', 'US-GLE https://doi.org/10.17190/AMF/1871136', 'US-Goo https://doi.org/10.18140/FLX/1440070', 'US-HB1 https://doi.org/10.17190/AMF/1832160', 'US-HWB https://doi.org/10.17190/AMF/1881582', 'US-Ha1 https://doi.org/10.18140/FLX/1440071', 'US-Hn3 https://doi.org/10.17190/AMF/1881580', 'US-Ho2 https://doi.org/10.17190/AMF/1881581', 'US-IB2 https://doi.org/10.18140/FLX/1440072', 'US-ICs https://doi.org/10.17190/AMF/1871138', 'US-ICt https://doi.org/10.17190/AMF/1881583', 'US-Ivo https://doi.org/10.18140/FLX/1440073', 'US-Jo2 https://doi.org/10.17190/AMF/1881584', 'US-KFS https://doi.org/10.17190/AMF/1881585', 'US-KLS https://doi.org/10.17190/AMF/1854367', 'US-KS1 https://doi.org/10.18140/FLX/1440074', 'US-KS2 https://doi.org/10.18140/FLX/1440075', 'US-KS3 https://doi.org/10.17190/AMF/1881586', 'US-LWW https://doi.org/10.18140/FLX/1440077', 'US-Lin https://doi.org/10.18140/FLX/1440107', 'US-Los https://doi.org/10.18140/FLX/1440076', 'US-MMS https://doi.org/10.17190/AMF/1854369', 'US-MOz https://doi.org/10.17190/AMF/1854370', 'US-Me1 https://doi.org/10.18140/FLX/1440078', 'US-Me2 https://doi.org/10.17190/AMF/1854368', 'US-Me3 https://doi.org/10.18140/FLX/1440080', 'US-Me4 https://doi.org/10.18140/FLX/1440081', 'US-Me5 https://doi.org/10.18140/FLX/1440082', 'US-Me6 https://doi.org/10.18140/FLX/1440099', 'US-Mpj https://doi.org/10.17190/AMF/1832161', 'US-Myb https://doi.org/10.18140/FLX/1440105', 'US-NGB https://doi.org/10.17190/AMF/1832162', 'US-NR1 https://doi.org/10.17190/AMF/1871141', 'US-Ne1 https://doi.org/10.17190/AMF/1871140', 'US-Ne2 https://doi.org/10.18140/FLX/1440085', 'US-Ne3 https://doi.org/10.18140/FLX/1440086', 'US-ONA https://doi.org/10.17190/AMF/1832163', 'US-ORv https://doi.org/10.17190/AMF/1832164', 'US-OWC https://doi.org/10.17190/AMF/1871142', 'US-Oho https://doi.org/10.18140/FLX/1440088', 'US-PFa https://doi.org/10.18140/FLX/1440089', 'US-Prr https://doi.org/10.18140/FLX/1440113', 'US-Rms https://doi.org/10.17190/AMF/1881587', 'US-Ro1 https://doi.org/10.17190/AMF/1881588', 'US-Ro4 https://doi.org/10.17190/AMF/1881589', 'US-Ro5 https://doi.org/10.17190/AMF/1818371', 'US-Ro6 https://doi.org/10.17190/AMF/1881590', 'US-Rwe https://doi.org/10.17190/AMF/1871143', 'US-Rwf https://doi.org/10.17190/AMF/1881591', 'US-Rws https://doi.org/10.17190/AMF/1881592', 'US-SRC https://doi.org/10.17190/AMF/1871145', 'US-SRG https://doi.org/10.18140/FLX/1440114', 'US-SRM https://doi.org/10.18140/FLX/1440090', 'US-Sne https://doi.org/10.17190/AMF/1871144', 'US-Snf https://doi.org/10.17190/AMF/1854371', 'US-Sta https://doi.org/10.18140/FLX/1440115', 'US-Syv https://doi.org/10.18140/FLX/1440091', 'US-Ton https://doi.org/10.18140/FLX/1440092', 'US-Tw1 https://doi.org/10.17190/AMF/1832165', 'US-Tw2 https://doi.org/10.17190/AMF/1881593', 'US-Tw3 https://doi.org/10.17190/AMF/1881594', 'US-Tw4 https://doi.org/10.18140/FLX/1440111', 'US-Tw5 https://doi.org/10.17190/AMF/1881595', 'US-Twt https://doi.org/10.18140/FLX/1440106', 'US-UM3 https://doi.org/10.17190/AMF/1881596', 'US-UMB https://doi.org/10.18140/FLX/1440093', 'US-UMd https://doi.org/10.17190/AMF/1881597', 'US-Var https://doi.org/10.18140/FLX/1440094', 'US-WCr https://doi.org/10.18140/FLX/1440095', 'US-WPT https://doi.org/10.18140/FLX/1440116', 'US-Whs https://doi.org/10.18140/FLX/1440097', 'US-Wi0 https://doi.org/10.18140/FLX/1440055', 'US-Wi1 https://doi.org/10.18140/FLX/1440054', 'US-Wi2 https://doi.org/10.18140/FLX/1440056', 'US-Wi3 https://doi.org/10.18140/FLX/1440057', 'US-Wi4 https://doi.org/10.18140/FLX/1440058', 'US-Wi5 https://doi.org/10.18140/FLX/1440059', 'US-Wi6 https://doi.org/10.18140/FLX/1440060', 'US-Wi7 https://doi.org/10.18140/FLX/1440061', 'US-Wi8 https://doi.org/10.18140/FLX/1440062', 'US-Wi9 https://doi.org/10.18140/FLX/1440063', 'US-Wjs https://doi.org/10.17190/AMF/1871146', 'US-Wkg https://doi.org/10.18140/FLX/1440096', 'US-xBR https://doi.org/10.17190/AMF/1881598', 'ZM-Mon https://doi.org/10.18140/FLX/1440189']

  institution :

  Max-Planck-Institute for Biogeochemistry

  keywords :

  ['BIOGEOCHEMICAL CYCLES', 'ECOSYSTEM FUNCTIONS', 'TERRESTRIAL ECOSYSTEMS', 'VEGETATION', 'CARBON', 'LAND SURFACE']

  license :

  CC-BY 4.0

  product_version :

  X-BASE 1.0

  project :

  FLUXCOM-X

  publisher_name :

  The FLUXCOM-X Team

  publisher_url :

  http://fluxcom.org/

  references :

  source :

  ['MODIS Reflectance 10.5067/MODIS/MCD43C2.006 10.5067/MODIS/MCD43C4.006', 'MODIS LST 10.5067/MODIS/MOD11C1.006 10.5067/MODIS/MYD11C1.006', 'ERA5 https://doi.org/10.1002/qj.3803']

  summary :

  X-BASE ET is based on the FLUXCOM-X framework which trains machine learning models on in-situ eddy covariance data and uses them to produce this global product. The X-BASE experiment is a basic configuration to serve as a baseline for the FLUXCOM-X framework and includes as predictors the core meteorlogical data, plant functional type classification as well as MODIS based vegitation indicies and land surface temperature. XGBoost was used as the machine learning algorithm. The GPP estimates from the eddy covariance data was based on the Nighttime Partitioning method.

  time_coverage_end :

  2021-12-01T00:00:00.000000000

  time_coverage_start :

  2001-01-01T00:00:00.000000000

  training_features :

  ['TA', 'VPD', 'SW_IN', 'SW_IN_POT', 'dSW_IN_POT_DAY', 'dSW_IN_POT', 'LST', 'LST', 'EVI', 'NIRv', 'NDWI_band7', 'Trees', 'Shrubs', 'Grasses', 'Crops', 'Wetland', 'C4_photosynthesis', 'Needleleaf', 'Broadleaf', 'Deciduous', 'Evergreen']

In [16]:

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

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

Out[16]:

<matplotlib.collections.QuadMesh at 0x79a130bd5e50>

In [17]:

ds = msds.stores.storage.open_data("laiv3.nc")
ds

ds = msds.stores.storage.open_data("laiv3.nc") ds

Out[17]:

<xarray.Dataset> Size: 21GB
Dimensions:      (time: 396, lat: 1800, lon: 3600)
Coordinates:
  * time         (time) datetime64[ns] 3kB 1991-01-01 1991-02-01 ... 2023-12-01
  * lat          (lat) float64 14kB 89.95 89.85 89.75 ... -89.75 -89.85 -89.95
  * lon          (lon) float64 29kB -179.9 -179.8 -179.8 ... 179.7 179.8 179.9
    spatial_ref  int64 8B ...
Data variables:
    lai          (time, lat, lon) float64 21GB ...
Attributes:
    AREA_OR_POINT:        Area
    TIFFTAG_SOFTWARE:     MODISSoft
    time_coverage_start:  1991-01-01T00:00:00
    time_coverage_end:    2023-12-27T00:00:00

xarray.Dataset

• Dimensions:
  • time: 396
  • lat: 1800
  • lon: 3600
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    1991-01-01 ... 2023-12-01

    array(['1991-01-01T00:00:00.000000000', '1991-02-01T00:00:00.000000000',
           '1991-03-01T00:00:00.000000000', ..., '2023-10-01T00:00:00.000000000',
           '2023-11-01T00:00:00.000000000', '2023-12-01T00:00:00.000000000'],
          shape=(396,), dtype='datetime64[ns]')

  • lat
    (lat)
    float64
    89.95 89.85 89.75 ... -89.85 -89.95

    array([ 89.95,  89.85,  89.75, ..., -89.75, -89.85, -89.95], shape=(1800,))

  • lon
    (lon)
    float64
    -179.9 -179.8 ... 179.8 179.9

    array([-179.95, -179.85, -179.75, ...,  179.75,  179.85,  179.95],
          shape=(3600,))

  • spatial_ref
    ()
    int64
    ...

    crs_wkt :

    GEOGCRS["WGS 84",DATUM["World Geodetic System 1984",ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1]]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],CS[ellipsoidal,2],AXIS["geodetic latitude (Lat)",north,ORDER[1],ANGLEUNIT["degree",0.0174532925199433]],AXIS["geodetic longitude (Lon)",east,ORDER[2],ANGLEUNIT["degree",0.0174532925199433]],ID["EPSG",4326]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    inverse_flattening :

    298.257223563

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    horizontal_datum_name :

    World Geodetic System 1984

    grid_mapping_name :

    latitude_longitude

    [1 values with dtype=int64]

• Data variables: (1)
  • lai
    (time, lat, lon)
    float64
    ...

    grid_mapping :

    spatial_ref

    units :

    m2 m-2

    [2566080000 values with dtype=float64]

• Attributes: (4)

  AREA_OR_POINT :

  Area

  TIFFTAG_SOFTWARE :

  MODISSoft

  time_coverage_start :

  1991-01-01T00:00:00

  time_coverage_end :

  2023-12-27T00:00:00

In [18]:

ds.lai.isel(time=0).plot(vmin=0, vmax=10)

ds.lai.isel(time=0).plot(vmin=0, vmax=10)

Out[18]:

<matplotlib.collections.QuadMesh at 0x79a138534f50>