4.2.1.6. Access to Data in NetCDF format viewed as Zarr

Some datasets offered by the Open Data Portal are provided via references format as specified by the kerchunk package. This allows accessing to the original NetCDF data files with an I/O performance comparable to Zarr.

The data is available via store identifier ‘cci-kerchunk-store’.

from xcube.core.store import new_data_store

kerchunk_store = new_data_store('esa-cci-kc')

Again, let’s see what data sets are available.

datasets = kerchunk_store.list_data_ids()
datasets

['ESACCI-BIOMASS-L4-AGB-CHANGE-100m-2018-2017-fv4.0-kr1.1',
 'ESACCI-BIOMASS-L4-AGB-CHANGE-100m-2019-2018-fv4.0-kr1.1',
 'ESACCI-BIOMASS-L4-AGB-CHANGE-100m-2020-2010-fv4.0-kr1.1',
 'ESACCI-BIOMASS-L4-AGB-CHANGE-100m-2020-2019-fv4.0-kr1.1',
 'ESACCI-BIOMASS-L4-AGB-MERGED-100m-2010-2020-fv4.0-kr1.1',
 'ESACCI-L3C_CLOUD-CLD_PRODUCTS-ATSR2_AATSR-199506-201204-fv3.0-kr1.1',
 'ESACCI-L3C_CLOUD-CLD_PRODUCTS-AVHRR_AM-199109-201612-fv3.0-kr1.1',
 'ESACCI-L3C_CLOUD-CLD_PRODUCTS-AVHRR_PM-198201-201612-fv3.0-kr1.1',
 'ESACCI-L3C_SNOW-SWE-MERGED-19790102-20200524-fv2.0-kr1.1',
 'ESACCI-L4_FIRE-BA-MODIS-20010101-20200120-fv5.1-kr1.2',
 'ESACCI-LC-L4-LCCS-Map-300m-P1Y-1992-2015-v2.0.7b-kr1.1',
 'ESACCI-LC-L4-PFT-Map-300m-P1Y-1992-2020-v2.0.8-kr1.1',
 'ESACCI-LST-L3C-LST-MODISA-0.01deg_1MONTHLY_DAY-200207-201812-fv3.00-kr1.1',
 'ESACCI-LST-L3C-LST-MODISA-0.01deg_1MONTHLY_NIGHT-200207-201812-fv3.00-kr1.1',
 'ESACCI-LST-L3C-LST-MODIST-0.01deg_1MONTHLY_DAY-200003-201812-fv3.00-kr1.1',
 'ESACCI-LST-L3C-LST-MODIST-0.01deg_1MONTHLY_NIGHT-200003-201812-fv3.00-kr1.1',
 'ESACCI-LST-L3S-LST-IRCDR_-0.01deg_1MONTHLY_DAY-199508-202012-fv2.00-kr1.1',
 'ESACCI-LST-L3S-LST-IRCDR_-0.01deg_1MONTHLY_NIGHT-199508-202012-fv2.00-kr1.1',
 'ESACCI-PERMAFROST-L4-ALT-ERA5_MODISLST_BIASCORRECTED-AREA4_PP-1997-2002-fv03.0-kr1.1',
 'ESACCI-PERMAFROST-L4-ALT-MODISLST_CRYOGRID-AREA4_PP-2003-2019-fv03.0-kr1.1',
 'ESACCI-SOILMOISTURE-L3S-SSMS-ACTIVE-19910805-20211231-fv07.1-kr1.1',
 'ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-19781101-20211231-fv07.1-kr1.1',
 'ESACCI-SOILMOISTURE-L3S-SSMV-PASSIVE-19781101-20211231-fv07.1-kr1.1',
 'ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED_ADJUSTED-19781101-20211231-fv07.1-kr1.1']

The names are similar but different to the ones from the Climate Toolbox store. We can have a look at a dataset’s metadata to find out more about it.

kerchunk_store.describe_data('ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-19781101-20211231-fv07.1-kr1.1')

<xcube.core.store.descriptor.DatasetDescriptor at 0x7f84834486e0>

Cubes can easily be opened from the store like this:

cube = kerchunk_store.open_data('ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-19781101-20211231-fv07.1-kr1.1')
cube

<xarray.Dataset> Size: 654GB
Dimensions:         (time: 15767, lat: 720, lon: 1440)
Coordinates:
  * lat             (lat) float64 6kB 89.88 89.62 89.38 ... -89.38 -89.62 -89.88
  * lon             (lon) float64 12kB -179.9 -179.6 -179.4 ... 179.6 179.9
  * time            (time) datetime64[ns] 126kB 1978-11-01 ... 2021-12-31
Data variables:
    dnflag          (time, lat, lon) float32 65GB dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
    flag            (time, lat, lon) float32 65GB dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
    freqbandID      (time, lat, lon) float32 65GB dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
    mode            (time, lat, lon) float32 65GB dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
    sensor          (time, lat, lon) float64 131GB dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
    sm              (time, lat, lon) float32 65GB dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
    sm_uncertainty  (time, lat, lon) float32 65GB dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
    t0              (time, lat, lon) datetime64[ns] 131GB dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>
Attributes: (12/46)
    Conventions:                  CF-1.9
    cdm_data_type:                Grid
    comment:                      This dataset was produced with funding of t...
    contact:                      cci_sm_contact@eodc.eu
    creator_email:                cci_sm_developer@eodc.eu
    creator_name:                 Department of Geodesy and Geoinformation, V...
    ...                           ...
    time_coverage_start:          19781101T000000Z
    time_coverage_start_product:  19781101T000000Z
    title:                        ESA CCI Surface Soil Moisture COMBINED acti...
    tracking_id:                  dc6ade2c-e51b-4a94-81fa-751df95a85a6
    kerchunk_revision:            kr1.1
    kerchunk_creation_date:       031023T093359

xarray.Dataset

• Dimensions:
  • time: 15767
  • lat: 720
  • lon: 1440
• Coordinates: (3)
  • lat
    (lat)
    float64
    89.88 89.62 89.38 ... -89.62 -89.88

    _CoordinateAxisType :

    Lat

    standard_name :

    latitude

    units :

    degrees_north

    valid_range :

    [-90.0, 90.0]

    array([ 89.875,  89.625,  89.375, ..., -89.375, -89.625, -89.875])

  • lon
    (lon)
    float64
    -179.9 -179.6 ... 179.6 179.9

    _CoordinateAxisType :

    Lon

    standard_name :

    longitude

    units :

    degrees_east

    valid_range :

    [-180.0, 180.0]

    array([-179.875, -179.625, -179.375, ...,  179.375,  179.625,  179.875])

  • time
    (time)
    datetime64[ns]
    1978-11-01 ... 2021-12-31

    array(['1978-11-01T00:00:00.000000000', '1978-11-02T00:00:00.000000000',
           '1978-11-03T00:00:00.000000000', ..., '2021-12-29T00:00:00.000000000',
           '2021-12-30T00:00:00.000000000', '2021-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (8)
  • dnflag
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>

    _CoordinateAxes :

    time lat lon

    bit_meanings :

    ['NaN', 'day', 'night']

    bits :

    ['0b0', '0b1', '0b10']

    dtype :

    int8

    flag_meanings :

    ['NaN', 'day', 'night', 'day_night_combination']

    flag_values :

    [0, 1, 2, 3]

    long_name :

    Day / Night Flag

    valid_range :

    [0, 3]

    Array Chunk Bytes 60.90 GiB 3.96 MiB Shape (15767, 720, 1440) (1, 720, 1440) Dask graph 15767 chunks in 2 graph layers Data type float32 numpy.ndarray

  • flag
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>

    _CoordinateAxes :

    time lat lon

    bit_meanings :

    ['no_data_inconsistency_detected', 'snow_coverage_or_temperature_below_zero', 'dense_vegetation', 'others_no_convergence_in_the_model_thus_no_valid_sm_estimates', 'soil_moisture_value_exceeds_physical_boundary', 'weight_of_measurement_below_threshold', 'all_datasets_deemed_unreliable', 'barren_ground_advisory_flag', 'NaN']

    bits :

    ['0b0', '0b1', '0b10', '0b100', '0b1000', '0b10000', '0b100000', '0b1000000', '0b10000000']

    dtype :

    int16

    flag_meanings :

    ['no_data_inconsistency_detected', 'snow_coverage_or_temperature_below_zero', 'dense_vegetation', 'combination_of_flag_values_1_and_2', 'soil_moisture_value_exceeds_physical_boundary', 'combination_of_flag_values_1_and_8', 'combination_of_flag_values_2_and_8', 'barren_ground_advisory_flag', 'combination_of_flag_values_1_and_64', 'combination_of_flag_values_2_and_64', 'combination_of_flag_values_1_and_2_and_64', 'combination_of_flag_values_8_and_64', 'combination_of_flag_values_1_and_8_and_64', 'combination_of_flag_values_2_and_8_and_64', 'combination_of_flag_values_1_and_2_and_4_and_8_and_16_and_32_and_64']

    flag_values :

    [0, 1, 2, 3, 8, 9, 10, 64, 65, 66, 67, 72, 73, 74, 127]

    long_name :

    Flag

    standard_name :

    soil_moisture_content status_flag

    valid_range :

    [0, 255]

    Array Chunk Bytes 60.90 GiB 3.96 MiB Shape (15767, 720, 1440) (1, 720, 1440) Dask graph 15767 chunks in 2 graph layers Data type float32 numpy.ndarray

  • freqbandID
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>

    _CoordinateAxes :

    time lat lon

    bit_meanings :

    ['NaN', 'L14', 'C53', 'C66', 'C68', 'C69', 'C73', 'X107', 'K194', 'MODEL']

    bits :

    ['0b0', '0b1', '0b10', '0b100', '0b1000', '0b10000', '0b100000', '0b1000000', '0b10000000', '0b100000000']

    dtype :

    int16

    flag_meanings :

    ['NaN', 'C66', 'X107', 'C66+X107']

    flag_values :

    [0, 4, 64, 68]

    long_name :

    Frequency Band Identification

    valid_range :

    [0, 511]

    Array Chunk Bytes 60.90 GiB 3.96 MiB Shape (15767, 720, 1440) (1, 720, 1440) Dask graph 15767 chunks in 2 graph layers Data type float32 numpy.ndarray

  • mode
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>

    _CoordinateAxes :

    time lat lon

    bit_meanings :

    ['NaN', 'ascending', 'descending']

    bits :

    ['0b0', '0b1', '0b10']

    dtype :

    int8

    flag_meanings :

    ['NaN', 'ascending', 'descending', 'ascending_descending_combination']

    flag_values :

    [0, 1, 2, 3]

    long_name :

    Satellite Mode

    valid_range :

    [0, 3]

    Array Chunk Bytes 60.90 GiB 3.96 MiB Shape (15767, 720, 1440) (1, 720, 1440) Dask graph 15767 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sensor
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>

    _CoordinateAxes :

    time lat lon

    bit_meanings :

    ['NaN', 'SMMR', 'SSMI', 'TMI', 'AMSRE', 'WindSat', 'AMSR2', 'SMOS', 'AMIWS', 'ASCATA', 'ASCATB', 'SMAP', 'MODEL', 'GPM', 'FY3B', 'FY3D', 'ASCATC', 'FY3C']

    bits :

    ['0b0', '0b1', '0b10', '0b100', '0b1000', '0b10000', '0b100000', '0b1000000', '0b10000000', '0b100000000', '0b1000000000', '0b10000000000', '0b100000000000', '0b1000000000000', '0b10000000000000', '0b100000000000000', '0b1000000000000000', '0b10000000000000000']

    dtype :

    int32

    flag_meanings :

    ['NaN', 'SMMR']

    flag_values :

    [0, 1]

    long_name :

    Sensor

    valid_range :

    [0, 131071]

    Array Chunk Bytes 121.80 GiB 7.91 MiB Shape (15767, 720, 1440) (1, 720, 1440) Dask graph 15767 chunks in 2 graph layers Data type float64 numpy.ndarray

  • sm
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>

    _CoordinateAxes :

    time lat lon

    ancillary_variables :

    sm_uncertainty flag t0

    dtype :

    float32

    long_name :

    Volumetric Soil Moisture

    standard_name :

    soil_moisture_content

    units :

    m3 m-3

    valid_range :

    [0.0, 1.0]

    Array Chunk Bytes 60.90 GiB 3.96 MiB Shape (15767, 720, 1440) (1, 720, 1440) Dask graph 15767 chunks in 2 graph layers Data type float32 numpy.ndarray

  • sm_uncertainty
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>

    _CoordinateAxes :

    time lat lon

    dtype :

    float32

    long_name :

    Volumetric Soil Moisture Uncertainty

    standard_name :

    soil_moisture_content standard_error

    units :

    m3 m-3

    valid_range :

    [0.0, 1.0]

    Array Chunk Bytes 60.90 GiB 3.96 MiB Shape (15767, 720, 1440) (1, 720, 1440) Dask graph 15767 chunks in 2 graph layers Data type float32 numpy.ndarray

  • t0
    (time, lat, lon)
    datetime64[ns]
    dask.array<chunksize=(1, 720, 1440), meta=np.ndarray>

    _CoordinateAxes :

    time lat lon

    dtype :

    float64

    long_name :

    Observation Timestamp

    valid_range :

    [3225.0, 3227.0]

    Array Chunk Bytes 121.80 GiB 7.91 MiB Shape (15767, 720, 1440) (1, 720, 1440) Dask graph 15767 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([ 89.875,  89.625,  89.375,  89.125,  88.875,  88.625,  88.375,  88.125,
            87.875,  87.625,
           ...
           -87.625, -87.875, -88.125, -88.375, -88.625, -88.875, -89.125, -89.375,
           -89.625, -89.875],
          dtype='float64', name='lat', length=720))

  • lon
    PandasIndex

    PandasIndex(Index([-179.875, -179.625, -179.375, -179.125, -178.875, -178.625, -178.375,
           -178.125, -177.875, -177.625,
           ...
            177.625,  177.875,  178.125,  178.375,  178.625,  178.875,  179.125,
            179.375,  179.625,  179.875],
          dtype='float64', name='lon', length=1440))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1978-11-01', '1978-11-02', '1978-11-03', '1978-11-04',
                   '1978-11-05', '1978-11-06', '1978-11-07', '1978-11-08',
                   '1978-11-09', '1978-11-10',
                   ...
                   '2021-12-22', '2021-12-23', '2021-12-24', '2021-12-25',
                   '2021-12-26', '2021-12-27', '2021-12-28', '2021-12-29',
                   '2021-12-30', '2021-12-31'],
                  dtype='datetime64[ns]', name='time', length=15767, freq=None))

• Attributes: (46)

  Conventions :

  CF-1.9

  cdm_data_type :

  Grid

  comment :

  This dataset was produced with funding of the ESA CCI+ Soil Moisture project; ESRIN Contract No: 4000126684/19/I-NB

  contact :

  cci_sm_contact@eodc.eu

  creator_email :

  cci_sm_developer@eodc.eu

  creator_name :

  Department of Geodesy and Geoinformation, Vienna University of Technology

  creator_url :

  https://climers.geo.tuwien.ac.at/

  date_created :

  File created: 2022-04-06 09:13:45.810237

  geospatial_lat_max :

  90.0

  geospatial_lat_min :

  -90.0

  geospatial_lat_resolution :

  0.25 degree

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  180.0

  geospatial_lon_min :

  -180.0

  geospatial_lon_resolution :

  0.25 degree

  geospatial_lon_units :

  degrees_east

  geospatial_vertical_max :

  0.0

  geospatial_vertical_min :

  0.0

  history :

  2022-04-06 09:12:42 - product produced Kerchunk file created on 26/09/23 Kerchunk file last updated by CEDA on 03/10/23 in the context of the CCI Knowledge Exchange Project

  id :

  ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-19781101-20211231-fv07.1

  institution :

  TU Wien (AUT); VanderSat B.V. (NL)

  key_variables :

  sm

  keywords :

  soil moisture, soil water content, microwave remote sensing, active, passive, combined, climate data record

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  license :

  Data use is free and open for all registered users.

  naming_authority :

  TU Wien

  platform :

  Nimbus 7, DMSP, TRMM, AQUA, Coriolis, GCOM-W1, MIRAS, SMAP, GPM, FengYun-3B, FengYun-3C, FengYun-3D; ERS-1, ERS-2, METOP-A, METOP-B

  processing_level :

  Quality-controlled, super-collocated (L3S) Satellite Soil Moisture (SM) data from multiple sensors

  product_version :

  v07.1

  project :

  Climate Change Initiative - European Space Agency

  references :

  http://www.esa-soilmoisture-cci.org; Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017) ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001; Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., Dorigo, W. (2019) Evolution of the ESA CCI Soil Moisture Climate Data Records and their underlying merging methodology. Earth System Science Data 11, 717-739, https://doi.org/10.5194/essd-11-717-2019; Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. https://doi.org/10.1109/TGRS.2017.2734070

  sensor :

  SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP_radiometer, GMI, VIRR-3B, VIRR-3C, VIRR-3D; AMI-WS, ASCAT-A, ASCAT-B, ASCAT-C

  source :

  WARP 5.5R1.1/AMI-WS/ERS12 Level 2 Soil Moisture; WARP 5.4R1.0/AMI-WS/ERS2 Level 2 Soil Moisture; H115: Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling, DOI: 10.15770/EUM_SAF_H_0006; H116: Metop ASCAT Surface Soil Moisture Climate Data Record v5 Extension 12.5 km sampling; H115: Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling, DOI: 10.15770/EUM_SAF_H_0006; H116: Metop ASCAT Surface Soil Moisture Climate Data Record v5 Extension 12.5 km sampling; H115: Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling, DOI: 10.15770/EUM_SAF_H_0006; H116: Metop ASCAT Surface Soil Moisture Climate Data Record v5 Extension 12.5 km sampling;; LPRMv7/SMMR/Nimbus 7 L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/SSMI/F08, F11, F13 DMSP L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/TMI/TRMM L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/AMSR-E/Aqua L2B Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/WINDSAT/CORIOLIS L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/AMSR2/GCOM-W1 L3 Surface Soil Moisture, Ancillary Params; LPRMv7/SMOS/MIRAS L3 Surface Soil Moisture, CATDS Level 3 Brightness Temperatures (L3TB) version 300 RE03 & RE04; LPRMv7/SMAP_radiometer/SMAP L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/GMI/GPM L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/VIRR/FengYun-3B L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/VIRR/FengYun-3C L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/VIRR/FengYun-3D L3 Surface Soil Moisture, Ancillary Params, and quality flags;;

  spatial_resolution :

  25km

  standard_name_vocabulary :

  CF Standard Name Table v77

  summary :

  This dataset was produced with funding of the ESA CCI+ Soil Moisture project; ESRIN Contract No: 4000126684/19/I-NB

  time_coverage_duration :

  P43Y

  time_coverage_end :

  20211231T235959Z

  time_coverage_end_product :

  20211231T235959Z

  time_coverage_resolution :

  P1D

  time_coverage_start :

  19781101T000000Z

  time_coverage_start_product :

  19781101T000000Z

  title :

  ESA CCI Surface Soil Moisture COMBINED active+passive Product

  tracking_id :

  dc6ade2c-e51b-4a94-81fa-751df95a85a6

  kerchunk_revision :

  kr1.1

  kerchunk_creation_date :

  031023T093359

Subsets of the data may easily be created like this:

sub_cube = cube.sel({
    'lat': slice(40.40, -40.40),
    'lon': slice(-23.40, 57.40),
    'time': slice('2000-01-01', '2000-12-31')
    }
)
sub_cube

<xarray.Dataset> Size: 2GB
Dimensions:         (time: 366, lat: 324, lon: 324)
Coordinates:
  * lat             (lat) float64 3kB 40.38 40.12 39.88 ... -39.88 -40.12 -40.38
  * lon             (lon) float64 3kB -23.38 -23.12 -22.88 ... 56.88 57.12 57.38
  * time            (time) datetime64[ns] 3kB 2000-01-01 ... 2000-12-31
Data variables:
    dnflag          (time, lat, lon) float32 154MB dask.array<chunksize=(1, 324, 324), meta=np.ndarray>
    flag            (time, lat, lon) float32 154MB dask.array<chunksize=(1, 324, 324), meta=np.ndarray>
    freqbandID      (time, lat, lon) float32 154MB dask.array<chunksize=(1, 324, 324), meta=np.ndarray>
    mode            (time, lat, lon) float32 154MB dask.array<chunksize=(1, 324, 324), meta=np.ndarray>
    sensor          (time, lat, lon) float64 307MB dask.array<chunksize=(1, 324, 324), meta=np.ndarray>
    sm              (time, lat, lon) float32 154MB dask.array<chunksize=(1, 324, 324), meta=np.ndarray>
    sm_uncertainty  (time, lat, lon) float32 154MB dask.array<chunksize=(1, 324, 324), meta=np.ndarray>
    t0              (time, lat, lon) datetime64[ns] 307MB dask.array<chunksize=(1, 324, 324), meta=np.ndarray>
Attributes: (12/46)
    Conventions:                  CF-1.9
    cdm_data_type:                Grid
    comment:                      This dataset was produced with funding of t...
    contact:                      cci_sm_contact@eodc.eu
    creator_email:                cci_sm_developer@eodc.eu
    creator_name:                 Department of Geodesy and Geoinformation, V...
    ...                           ...
    time_coverage_start:          19781101T000000Z
    time_coverage_start_product:  19781101T000000Z
    title:                        ESA CCI Surface Soil Moisture COMBINED acti...
    tracking_id:                  dc6ade2c-e51b-4a94-81fa-751df95a85a6
    kerchunk_revision:            kr1.1
    kerchunk_creation_date:       031023T093359

xarray.Dataset

• Dimensions:
  • time: 366
  • lat: 324
  • lon: 324
• Coordinates: (3)
  • lat
    (lat)
    float64
    40.38 40.12 39.88 ... -40.12 -40.38

    _CoordinateAxisType :

    Lat

    standard_name :

    latitude

    units :

    degrees_north

    valid_range :

    [-90.0, 90.0]

    array([ 40.375,  40.125,  39.875, ..., -39.875, -40.125, -40.375])

  • lon
    (lon)
    float64
    -23.38 -23.12 ... 57.12 57.38

    _CoordinateAxisType :

    Lon

    standard_name :

    longitude

    units :

    degrees_east

    valid_range :

    [-180.0, 180.0]

    array([-23.375, -23.125, -22.875, ...,  56.875,  57.125,  57.375])

  • time
    (time)
    datetime64[ns]
    2000-01-01 ... 2000-12-31

    array(['2000-01-01T00:00:00.000000000', '2000-01-02T00:00:00.000000000',
           '2000-01-03T00:00:00.000000000', ..., '2000-12-29T00:00:00.000000000',
           '2000-12-30T00:00:00.000000000', '2000-12-31T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (8)
  • dnflag
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 324, 324), meta=np.ndarray>

    _CoordinateAxes :

    time lat lon

    bit_meanings :

    ['NaN', 'day', 'night']

    bits :

    ['0b0', '0b1', '0b10']

    dtype :

    int8

    flag_meanings :

    ['NaN', 'day', 'night', 'day_night_combination']

    flag_values :

    [0, 1, 2, 3]

    long_name :

    Day / Night Flag

    valid_range :

    [0, 3]

    Array Chunk Bytes 146.57 MiB 410.06 kiB Shape (366, 324, 324) (1, 324, 324) Dask graph 366 chunks in 3 graph layers Data type float32 numpy.ndarray

  • flag
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 324, 324), meta=np.ndarray>

    _CoordinateAxes :

    time lat lon

    bit_meanings :

    ['no_data_inconsistency_detected', 'snow_coverage_or_temperature_below_zero', 'dense_vegetation', 'others_no_convergence_in_the_model_thus_no_valid_sm_estimates', 'soil_moisture_value_exceeds_physical_boundary', 'weight_of_measurement_below_threshold', 'all_datasets_deemed_unreliable', 'barren_ground_advisory_flag', 'NaN']

    bits :

    ['0b0', '0b1', '0b10', '0b100', '0b1000', '0b10000', '0b100000', '0b1000000', '0b10000000']

    dtype :

    int16

    flag_meanings :

    ['no_data_inconsistency_detected', 'snow_coverage_or_temperature_below_zero', 'dense_vegetation', 'combination_of_flag_values_1_and_2', 'soil_moisture_value_exceeds_physical_boundary', 'combination_of_flag_values_1_and_8', 'combination_of_flag_values_2_and_8', 'barren_ground_advisory_flag', 'combination_of_flag_values_1_and_64', 'combination_of_flag_values_2_and_64', 'combination_of_flag_values_1_and_2_and_64', 'combination_of_flag_values_8_and_64', 'combination_of_flag_values_1_and_8_and_64', 'combination_of_flag_values_2_and_8_and_64', 'combination_of_flag_values_1_and_2_and_4_and_8_and_16_and_32_and_64']

    flag_values :

    [0, 1, 2, 3, 8, 9, 10, 64, 65, 66, 67, 72, 73, 74, 127]

    long_name :

    Flag

    standard_name :

    soil_moisture_content status_flag

    valid_range :

    [0, 255]

    Array Chunk Bytes 146.57 MiB 410.06 kiB Shape (366, 324, 324) (1, 324, 324) Dask graph 366 chunks in 3 graph layers Data type float32 numpy.ndarray

  • freqbandID
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 324, 324), meta=np.ndarray>

    _CoordinateAxes :

    time lat lon

    bit_meanings :

    ['NaN', 'L14', 'C53', 'C66', 'C68', 'C69', 'C73', 'X107', 'K194', 'MODEL']

    bits :

    ['0b0', '0b1', '0b10', '0b100', '0b1000', '0b10000', '0b100000', '0b1000000', '0b10000000', '0b100000000']

    dtype :

    int16

    flag_meanings :

    ['NaN', 'C66', 'X107', 'C66+X107']

    flag_values :

    [0, 4, 64, 68]

    long_name :

    Frequency Band Identification

    valid_range :

    [0, 511]

    Array Chunk Bytes 146.57 MiB 410.06 kiB Shape (366, 324, 324) (1, 324, 324) Dask graph 366 chunks in 3 graph layers Data type float32 numpy.ndarray

  • mode
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 324, 324), meta=np.ndarray>

    _CoordinateAxes :

    time lat lon

    bit_meanings :

    ['NaN', 'ascending', 'descending']

    bits :

    ['0b0', '0b1', '0b10']

    dtype :

    int8

    flag_meanings :

    ['NaN', 'ascending', 'descending', 'ascending_descending_combination']

    flag_values :

    [0, 1, 2, 3]

    long_name :

    Satellite Mode

    valid_range :

    [0, 3]

    Array Chunk Bytes 146.57 MiB 410.06 kiB Shape (366, 324, 324) (1, 324, 324) Dask graph 366 chunks in 3 graph layers Data type float32 numpy.ndarray

  • sensor
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 324, 324), meta=np.ndarray>

    _CoordinateAxes :

    time lat lon

    bit_meanings :

    ['NaN', 'SMMR', 'SSMI', 'TMI', 'AMSRE', 'WindSat', 'AMSR2', 'SMOS', 'AMIWS', 'ASCATA', 'ASCATB', 'SMAP', 'MODEL', 'GPM', 'FY3B', 'FY3D', 'ASCATC', 'FY3C']

    bits :

    ['0b0', '0b1', '0b10', '0b100', '0b1000', '0b10000', '0b100000', '0b1000000', '0b10000000', '0b100000000', '0b1000000000', '0b10000000000', '0b100000000000', '0b1000000000000', '0b10000000000000', '0b100000000000000', '0b1000000000000000', '0b10000000000000000']

    dtype :

    int32

    flag_meanings :

    ['NaN', 'SMMR']

    flag_values :

    [0, 1]

    long_name :

    Sensor

    valid_range :

    [0, 131071]

    Array Chunk Bytes 293.13 MiB 820.12 kiB Shape (366, 324, 324) (1, 324, 324) Dask graph 366 chunks in 3 graph layers Data type float64 numpy.ndarray

  • sm
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 324, 324), meta=np.ndarray>

    _CoordinateAxes :

    time lat lon

    ancillary_variables :

    sm_uncertainty flag t0

    dtype :

    float32

    long_name :

    Volumetric Soil Moisture

    standard_name :

    soil_moisture_content

    units :

    m3 m-3

    valid_range :

    [0.0, 1.0]

    Array Chunk Bytes 146.57 MiB 410.06 kiB Shape (366, 324, 324) (1, 324, 324) Dask graph 366 chunks in 3 graph layers Data type float32 numpy.ndarray

  • sm_uncertainty
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 324, 324), meta=np.ndarray>

    _CoordinateAxes :

    time lat lon

    dtype :

    float32

    long_name :

    Volumetric Soil Moisture Uncertainty

    standard_name :

    soil_moisture_content standard_error

    units :

    m3 m-3

    valid_range :

    [0.0, 1.0]

    Array Chunk Bytes 146.57 MiB 410.06 kiB Shape (366, 324, 324) (1, 324, 324) Dask graph 366 chunks in 3 graph layers Data type float32 numpy.ndarray

  • t0
    (time, lat, lon)
    datetime64[ns]
    dask.array<chunksize=(1, 324, 324), meta=np.ndarray>

    _CoordinateAxes :

    time lat lon

    dtype :

    float64

    long_name :

    Observation Timestamp

    valid_range :

    [3225.0, 3227.0]

    Array Chunk Bytes 293.13 MiB 820.12 kiB Shape (366, 324, 324) (1, 324, 324) Dask graph 366 chunks in 3 graph layers Data type datetime64[ns] numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([ 40.375,  40.125,  39.875,  39.625,  39.375,  39.125,  38.875,  38.625,
            38.375,  38.125,
           ...
           -38.125, -38.375, -38.625, -38.875, -39.125, -39.375, -39.625, -39.875,
           -40.125, -40.375],
          dtype='float64', name='lat', length=324))

  • lon
    PandasIndex

    PandasIndex(Index([-23.375, -23.125, -22.875, -22.625, -22.375, -22.125, -21.875, -21.625,
           -21.375, -21.125,
           ...
            55.125,  55.375,  55.625,  55.875,  56.125,  56.375,  56.625,  56.875,
            57.125,  57.375],
          dtype='float64', name='lon', length=324))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-03', '2000-01-04',
                   '2000-01-05', '2000-01-06', '2000-01-07', '2000-01-08',
                   '2000-01-09', '2000-01-10',
                   ...
                   '2000-12-22', '2000-12-23', '2000-12-24', '2000-12-25',
                   '2000-12-26', '2000-12-27', '2000-12-28', '2000-12-29',
                   '2000-12-30', '2000-12-31'],
                  dtype='datetime64[ns]', name='time', length=366, freq=None))

• Attributes: (46)

  Conventions :

  CF-1.9

  cdm_data_type :

  Grid

  comment :

  This dataset was produced with funding of the ESA CCI+ Soil Moisture project; ESRIN Contract No: 4000126684/19/I-NB

  contact :

  cci_sm_contact@eodc.eu

  creator_email :

  cci_sm_developer@eodc.eu

  creator_name :

  Department of Geodesy and Geoinformation, Vienna University of Technology

  creator_url :

  https://climers.geo.tuwien.ac.at/

  date_created :

  File created: 2022-04-06 09:13:45.810237

  geospatial_lat_max :

  90.0

  geospatial_lat_min :

  -90.0

  geospatial_lat_resolution :

  0.25 degree

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  180.0

  geospatial_lon_min :

  -180.0

  geospatial_lon_resolution :

  0.25 degree

  geospatial_lon_units :

  degrees_east

  geospatial_vertical_max :

  0.0

  geospatial_vertical_min :

  0.0

  history :

  2022-04-06 09:12:42 - product produced Kerchunk file created on 26/09/23 Kerchunk file last updated by CEDA on 03/10/23 in the context of the CCI Knowledge Exchange Project

  id :

  ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-19781101-20211231-fv07.1

  institution :

  TU Wien (AUT); VanderSat B.V. (NL)

  key_variables :

  sm

  keywords :

  soil moisture, soil water content, microwave remote sensing, active, passive, combined, climate data record

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  license :

  Data use is free and open for all registered users.

  naming_authority :

  TU Wien

  platform :

  Nimbus 7, DMSP, TRMM, AQUA, Coriolis, GCOM-W1, MIRAS, SMAP, GPM, FengYun-3B, FengYun-3C, FengYun-3D; ERS-1, ERS-2, METOP-A, METOP-B

  processing_level :

  Quality-controlled, super-collocated (L3S) Satellite Soil Moisture (SM) data from multiple sensors

  product_version :

  v07.1

  project :

  Climate Change Initiative - European Space Agency

  references :

  http://www.esa-soilmoisture-cci.org; Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017) ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001; Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., Dorigo, W. (2019) Evolution of the ESA CCI Soil Moisture Climate Data Records and their underlying merging methodology. Earth System Science Data 11, 717-739, https://doi.org/10.5194/essd-11-717-2019; Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. https://doi.org/10.1109/TGRS.2017.2734070

  sensor :

  SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP_radiometer, GMI, VIRR-3B, VIRR-3C, VIRR-3D; AMI-WS, ASCAT-A, ASCAT-B, ASCAT-C

  source :

  WARP 5.5R1.1/AMI-WS/ERS12 Level 2 Soil Moisture; WARP 5.4R1.0/AMI-WS/ERS2 Level 2 Soil Moisture; H115: Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling, DOI: 10.15770/EUM_SAF_H_0006; H116: Metop ASCAT Surface Soil Moisture Climate Data Record v5 Extension 12.5 km sampling; H115: Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling, DOI: 10.15770/EUM_SAF_H_0006; H116: Metop ASCAT Surface Soil Moisture Climate Data Record v5 Extension 12.5 km sampling; H115: Metop ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling, DOI: 10.15770/EUM_SAF_H_0006; H116: Metop ASCAT Surface Soil Moisture Climate Data Record v5 Extension 12.5 km sampling;; LPRMv7/SMMR/Nimbus 7 L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/SSMI/F08, F11, F13 DMSP L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/TMI/TRMM L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/AMSR-E/Aqua L2B Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/WINDSAT/CORIOLIS L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/AMSR2/GCOM-W1 L3 Surface Soil Moisture, Ancillary Params; LPRMv7/SMOS/MIRAS L3 Surface Soil Moisture, CATDS Level 3 Brightness Temperatures (L3TB) version 300 RE03 & RE04; LPRMv7/SMAP_radiometer/SMAP L2 Surface Soil Moisture, Ancillary Params, and QC; LPRMv7/GMI/GPM L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/VIRR/FengYun-3B L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/VIRR/FengYun-3C L3 Surface Soil Moisture, Ancillary Params, and quality flags; LPRMv7/VIRR/FengYun-3D L3 Surface Soil Moisture, Ancillary Params, and quality flags;;

  spatial_resolution :

  25km

  standard_name_vocabulary :

  CF Standard Name Table v77

  summary :

  This dataset was produced with funding of the ESA CCI+ Soil Moisture project; ESRIN Contract No: 4000126684/19/I-NB

  time_coverage_duration :

  P43Y

  time_coverage_end :

  20211231T235959Z

  time_coverage_end_product :

  20211231T235959Z

  time_coverage_resolution :

  P1D

  time_coverage_start :

  19781101T000000Z

  time_coverage_start_product :

  19781101T000000Z

  title :

  ESA CCI Surface Soil Moisture COMBINED active+passive Product

  tracking_id :

  dc6ade2c-e51b-4a94-81fa-751df95a85a6

  kerchunk_revision :

  kr1.1

  kerchunk_creation_date :

  031023T093359

… and we can plot the data.

sub_cube.sm.sel(time='2000-07-01 12:00:00', method='nearest').plot.imshow(cmap='Greys_r', figsize=(8, 8))

<matplotlib.image.AxesImage at 0x7f8472243080>