4.2.1.8. Access to WCS data

The CEDA data server at https://data.cci.ceda.ac.uk/ provides access via the OGC Web Coverage Service (WCS) to CCI datasets. This notebook demonstrates how to retrieve CCI data via WCS from the CEDA server, using OWSLib as the client.

If owslib is not already installed in the Python environment, uncomment the following cell to install it with mamba.

# ! mamba install --yes owslib

Import some required libraries, including OWSLib as the WCS client.

from owslib.wcs import WebCoverageService
import xarray as xr
from esa_climate_toolbox.core import list_ecvs, list_ecv_datasets
%matplotlib inline
import matplotlib.pyplot as plt

<frozen abc>:106: FutureWarning: xarray subclass VectorDataCube should explicitly define __slots__

Define a simple convenience class CciWcs to manage the web service.

class CciWcs:

    def __init__(self, dataset_id: str):
        self.service = WebCoverageService(
            f"https://data.cci.ceda.ac.uk/thredds/wcs/{dataset_id}?service=WCS&version=1.0.0",
            version="1.0.0"
        )

    def coverage(self, data_id: str, bbox: tuple[float]) -> xr.Dataset:
        coverage = self.service.getCoverage(
          identifier=data_id,
          bbox=bbox,
          format="NetCDF3",
          timeout=600
        )
        with open("coverage.nc", "wb") as fh:
            fh.write(coverage.read())
        return xr.open_dataset("coverage.nc")

List the available ECVs.

list_ecvs()

['SEASTATE',
 'AEROSOL',
 'SEAICE',
 'ICESHEETS',
 'BIOMASS',
 'RD',
 'PERMAFROST',
 'OC',
 'SOILMOISTURE',
 'SST',
 'VEGETATION',
 'FIRE',
 'GHG',
 'WATERVAPOUR',
 'SNOW',
 'LC',
 'SEASURFACESALINITY',
 'LAKES',
 'LST',
 'OZONE',
 'CLOUD',
 'SEALEVEL']

List the available datasets for the Land Surface Temperature (LST) ECV.

print("\n".join(map(lambda x: x[0], list_ecv_datasets("LST"))))

esacci.LST.3-hours.L3S.LST.multi-sensor.multi-platform.IRMGP.1-00.r1
esacci.LST.day.L3C.LST.AATSR.Envisat.ATSR_3.3-00.DAY
esacci.LST.day.L3C.LST.AATSR.Envisat.ATSR_3.3-00.NIGHT
esacci.LST.day.L3C.LST.ATSR-2.ERS-2.ATSR_2.3-00.DAY
esacci.LST.day.L3C.LST.ATSR-2.ERS-2.ATSR_2.3-00.NIGHT
esacci.LST.day.L3C.LST.MODIS.Aqua.MODISA.3-00.DAY
esacci.LST.day.L3C.LST.MODIS.Aqua.MODISA.3-00.NIGHT
esacci.LST.day.L3C.LST.MODIS.Terra.MODIST.3-00.DAY
esacci.LST.day.L3C.LST.MODIS.Terra.MODIST.3-00.NIGHT
esacci.LST.day.L3C.LST.SLSTR.Sentinel-3A.SLSTRA.3-00.DAY
esacci.LST.day.L3C.LST.SLSTR.Sentinel-3A.SLSTRA.3-00.NIGHT
esacci.LST.day.L3C.LST.SLSTR.Sentinel-3B.SLSTRB.3-00.DAY
esacci.LST.day.L3C.LST.SLSTR.Sentinel-3B.SLSTRB.3-00.NIGHT
esacci.LST.day.L3C.LST.multi-sensor.multi-platform.SSMI_SSMIS.v2-33.ASC
esacci.LST.day.L3C.LST.multi-sensor.multi-platform.SSMI_SSMIS.v2-33.DES
esacci.LST.day.L3S.LST.multi-sensor.multi-platform.IRCDR.2-00.DAY
esacci.LST.day.L3S.LST.multi-sensor.multi-platform.IRCDR.2-00.NIGHT
esacci.LST.mon.L3C.LST.AATSR.Envisat.ATSR_3.3-00.DAY
esacci.LST.mon.L3C.LST.AATSR.Envisat.ATSR_3.3-00.NIGHT
esacci.LST.mon.L3C.LST.ATSR-2.ERS-2.ATSR_2.3-00.DAY
esacci.LST.mon.L3C.LST.ATSR-2.ERS-2.ATSR_2.3-00.NIGHT
esacci.LST.mon.L3C.LST.MODIS.Aqua.MODISA.3-00.DAY
esacci.LST.mon.L3C.LST.MODIS.Aqua.MODISA.3-00.NIGHT
esacci.LST.mon.L3C.LST.MODIS.Terra.MODIST.3-00.DAY
esacci.LST.mon.L3C.LST.MODIS.Terra.MODIST.3-00.NIGHT
esacci.LST.mon.L3C.LST.SLSTR.Sentinel-3A.SLSTRA.3-00.DAY
esacci.LST.mon.L3C.LST.SLSTR.Sentinel-3A.SLSTRA.3-00.NIGHT
esacci.LST.mon.L3C.LST.SLSTR.Sentinel-3B.SLSTRB.3-00.DAY
esacci.LST.mon.L3C.LST.SLSTR.Sentinel-3B.SLSTRB.3-00.NIGHT
esacci.LST.mon.L3C.LST.multi-sensor.multi-platform.SSMI_SSMIS.v2-33.ASC
esacci.LST.mon.L3C.LST.multi-sensor.multi-platform.SSMI_SSMIS.v2-33.DES
esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRCDR.2-00.DAY
esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRCDR.2-00.NIGHT
esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRMGP.1-00.00:00UTC
esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRMGP.1-00.03:00UTC
esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRMGP.1-00.06:00UTC
esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRMGP.1-00.09:00UTC
esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRMGP.1-00.12:00UTC
esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRMGP.1-00.15:00UTC
esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRMGP.1-00.18:00UTC
esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRMGP.1-00.21:00UTC
esacci.LST.yr.L3C.LST.multi-sensor.multi-platform.SSMI_SSMIS.v2-33.ASC
esacci.LST.yr.L3C.LST.multi-sensor.multi-platform.SSMI_SSMIS.v2-33.DES

Select a dataset from the list and store its name in a variable for use in the following steps.

ds_id = "esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRCDR.2-00.DAY"

Create a local representation of the WCS service for the selected dataset.

cciwcs = CciWcs(ds_id)

List the data variables available from this service.

list(cciwcs.service.contents.keys())

['solaz',
 'lst_unc_ran',
 'lst_unc_loc_cor',
 'dtime',
 'lst_unc_loc_sfc',
 'lst',
 'lcc',
 'n',
 'satze',
 'solze',
 'lst_unc_loc_atm',
 'lst_uncertainty',
 'sataz']

Select the lst variable from the list and store its name in a variable.

data_id = "lst"

Check the bounding box for the selected dataset and variable; we can see that it is of global extent.

cciwcs.service.contents[data_id].boundingBoxWGS84

(-179.9949951171875, -89.99500274658203, 179.9949951171875, 89.99498748779297)

For a quick demonstration with a small amount of data, request a coverage for a small sub-region and return it as a xarray dataset.

ds = cciwcs.coverage(
    data_id="lst",
    bbox=(9.7, 53.3, 10.3, 53.5)
)

Show some of the metadata for the returned coverage.

ds

<xarray.Dataset> Size: 2MB
Dimensions:  (time: 294, lat: 21, lon: 61)
Coordinates:
  * time     (time) datetime64[ns] 2kB 1995-08-01 1995-09-01 ... 2020-12-01
  * lat      (lat) float32 84B 53.31 53.31 53.32 53.34 ... 53.48 53.49 53.51
  * lon      (lon) float32 244B 9.705 9.715 9.725 9.735 ... 10.28 10.29 10.31
Data variables:
    lst      (time, lat, lon) float32 2MB ...
Attributes: (12/43)
    source:                     ESA LST CCI IRCDR L3S V2.00
    title:                      ESA LST CCI land surface temperature time ser...
    institution:                University of Leicester
    history:                    2023-05-24 17:10:44: The CCI Open Data Portal...
    references:                 https://climate.esa.int/en/projects/land-surf...
    Conventions:                CF-1.8
    ...                         ...
    key_variables:              land_surface_temperature
    format_version:             CCI Data Standards v2.2
    spatial_resolution:         0.01 degree
    doi:                        10.5285/785ef9d3965442669bff899540747e28
    tracking_id:                66f44ae9-1873-48be-a2df-c349022a9a97
    History:                    Translated to CF-1.0 Conventions by Netcdf-Ja...

xarray.Dataset

• Dimensions:
  • time: 294
  • lat: 21
  • lon: 61
• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    1995-08-01 ... 2020-12-01

    long_name :

    reference time of file

    standard_name :

    time

    _CoordinateAxisType :

    Time

    array(['1995-08-01T00:00:00.000000000', '1995-09-01T00:00:00.000000000',
           '1995-10-01T00:00:00.000000000', ..., '2020-10-01T00:00:00.000000000',
           '2020-11-01T00:00:00.000000000', '2020-12-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • lat
    (lat)
    float32
    53.31 53.31 53.32 ... 53.49 53.51

    long_name :

    latitude_coordinates

    standard_name :

    latitude

    units :

    degrees_north

    valid_min :

    -90.0

    valid_max :

    90.0

    reference_datum :

    geographical coordinates, WGS84 projection

    _ChunkSizes :

    1000

    _CoordinateAxisType :

    Lat

    array([53.305004, 53.315   , 53.324993, 53.335003, 53.344997, 53.354992,
           53.365   , 53.374996, 53.38499 , 53.395   , 53.404995, 53.415005,
           53.425   , 53.434994, 53.445004, 53.454998, 53.464993, 53.475002,
           53.484997, 53.49499 , 53.505   ], dtype=float32)

  • lon
    (lon)
    float32
    9.705 9.715 9.725 ... 10.29 10.31

    long_name :

    longitude_coordinates

    standard_name :

    longitude

    units :

    degrees_east

    valid_min :

    -180.0

    valid_max :

    180.0

    reference_datum :

    geographical coordinates, WGS84 projection

    _ChunkSizes :

    1000

    _CoordinateAxisType :

    Lon

    array([ 9.704997,  9.714992,  9.725001,  9.734996,  9.74499 ,  9.755   ,
            9.764995,  9.774989,  9.784999,  9.794993,  9.805003,  9.814998,
            9.824992,  9.835002,  9.844996,  9.854991,  9.865001,  9.874995,
            9.88499 ,  9.895   ,  9.904994,  9.914989,  9.924998,  9.934993,
            9.945003,  9.954997,  9.964992,  9.975001,  9.984996,  9.99499 ,
           10.005   , 10.014995, 10.024989, 10.034999, 10.044993, 10.055003,
           10.064998, 10.074992, 10.085002, 10.094996, 10.104991, 10.115001,
           10.124995, 10.13499 , 10.145   , 10.154994, 10.164989, 10.174998,
           10.184993, 10.195003, 10.204997, 10.214992, 10.225001, 10.234996,
           10.24499 , 10.255   , 10.264995, 10.274989, 10.284999, 10.294993,
           10.305003], dtype=float32)

• Data variables: (1)
  • lst
    (time, lat, lon)
    float32
    ...

    long_name :

    land surface temperature

    units :

    kelvin

    _ChunkSizes :

    [ 1 1000 1000]

    [376614 values with dtype=float32]

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1995-08-01', '1995-09-01', '1995-10-01', '1995-11-01',
                   '1995-12-01', '1996-07-01', '1996-08-01', '1996-09-01',
                   '1996-10-01', '1996-11-01',
                   ...
                   '2020-03-01', '2020-04-01', '2020-05-01', '2020-06-01',
                   '2020-07-01', '2020-08-01', '2020-09-01', '2020-10-01',
                   '2020-11-01', '2020-12-01'],
                  dtype='datetime64[ns]', name='time', length=294, freq=None))

  • lat
    PandasIndex

    PandasIndex(Index([ 53.30500411987305, 53.314998626708984,  53.32499313354492,
            53.33500289916992,  53.34499740600586,   53.3549919128418,
             53.3650016784668, 53.374996185302734,  53.38499069213867,
            53.39500045776367,  53.40499496459961,  53.41500473022461,
            53.42499923706055, 53.434993743896484, 53.445003509521484,
            53.45499801635742,  53.46499252319336,  53.47500228881836,
             53.4849967956543, 53.494991302490234, 53.505001068115234],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([ 9.704997062683105,  9.714991569519043,  9.725001335144043,
             9.73499584197998,  9.744990348815918,  9.755000114440918,
            9.764994621276855,  9.774989128112793,  9.784998893737793,
             9.79499340057373,   9.80500316619873,  9.814997673034668,
            9.824992179870605,  9.835001945495605,  9.844996452331543,
             9.85499095916748,   9.86500072479248,  9.874995231628418,
            9.884989738464355,  9.894999504089355,  9.904994010925293,
             9.91498851776123,   9.92499828338623,  9.934992790222168,
            9.945002555847168,  9.954997062683105,  9.964991569519043,
            9.975001335144043,   9.98499584197998,  9.994990348815918,
           10.005000114440918, 10.014994621276855, 10.024989128112793,
           10.034998893737793,  10.04499340057373,  10.05500316619873,
           10.064997673034668, 10.074992179870605, 10.085001945495605,
           10.094996452331543,  10.10499095916748,  10.11500072479248,
           10.124995231628418, 10.134989738464355, 10.144999504089355,
           10.154994010925293,  10.16498851776123,  10.17499828338623,
           10.184992790222168, 10.195002555847168, 10.204997062683105,
           10.214991569519043, 10.225001335144043,  10.23499584197998,
           10.244990348815918, 10.255000114440918, 10.264994621276855,
           10.274989128112793, 10.284998893737793,  10.29499340057373,
            10.30500316619873],
          dtype='float32', name='lon'))

• Attributes: (43)

  source :

  ESA LST CCI IRCDR L3S V2.00

  title :

  ESA LST CCI land surface temperature time series from multiple IR sensors on LEO platforms.

  institution :

  University of Leicester

  history :

  2023-05-24 17:10:44: The CCI Open Data Portal aggregated all files in the dataset over the time variable for OPeNDAP access. For earlier history information see the original individual files at data.cci.ceda.ac.uk

  references :

  https://climate.esa.int/en/projects/land-surface-temperature

  Conventions :

  CF-1.8

  product_version :

  2.00

  summary :

  This file contains level L3S global land surface temperatures from SLSTRA. L3S data are derived from more than one sensor combined on a space and/or time grid.

  keywords :

  Earth Science; Land Surface; Land Temperature; Land Surface Temperature

  id :

  esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRCDR.2-00.DAY

  naming_authority :

  le.ac.uk

  keywords_vocabulary :

  NASA Global change Master Directory (GCMD) Science Keywords

  cdm_data_type :

  grid

  comment :

  These data were produced as part of the ESA LST CCI project.

  date_created :

  20230524T161044Z

  creator_name :

  University of Leicester Surface Temperature Group

  creator_url :

  https://climate.esa.int/en/projects/land-surface-temperature

  creator_email :

  djg20@le.ac.uk

  project :

  Climate Change Initiative - European Space Agency

  geospatial_lat_min :

  53.30500411987305

  geospatial_lat_max :

  53.505001068115234

  geospatial_lon_min :

  9.704997062683105

  geospatial_lon_max :

  10.30500316619873

  geospatial_vertical_min :

  0.0

  geospatial_vertical_max :

  0.0

  time_coverage_start :

  19950801T000000

  time_coverage_end :

  20201231T235959

  time_coverage_duration :

  P9284DT23H59M59S

  time_coverage_resolution :

  P1M

  standard_name_vocabulary :

  CF Standard Name Table v71

  license :

  ESA CCI Data Policy: free and open access

  platform :

  ERS-2,Envisat,Sentinel-3A,Terra

  sensor :

  AATSR,ATSR-2,MODIS,SLSTRA

  geospatial_lat_units :

  degrees_north

  geospatial_lon_units :

  degrees_east

  geospatial_lon_resolution :

  0.01

  geospatial_lat_resolution :

  0.01

  key_variables :

  land_surface_temperature

  format_version :

  CCI Data Standards v2.2

  spatial_resolution :

  0.01 degree

  doi :

  10.5285/785ef9d3965442669bff899540747e28

  tracking_id :

  66f44ae9-1873-48be-a2df-c349022a9a97

  History :

  Translated to CF-1.0 Conventions by Netcdf-Java CDM (CFGridWriter2) Original Dataset = esacci.LST.mon.L3S.LST.multi-sensor.multi-platform.IRCDR.2-00.DAY; Translation Date = 2025-03-21T11:09:46.355Z

Use the coverage data to plot a map of land surface temperature for a specific point in time.

ds.lst.isel(time=100).plot.imshow()

<matplotlib.image.AxesImage at 0x7fa880314140>

Use the coverage data to plot a graph of land surface temperature against time for a specific location.

ds.lst.sel(lat=53.4, lon=10.0, method="nearest").plot()

[<matplotlib.lines.Line2D at 0x7fa87d47d7c0>]