Reproject the ESA-CCI land cover map for Europe¶

In [1]:

from xcube.core.store import new_data_store
from xcube_resampling.spatial import resample_in_space
from xcube_resampling.gridmapping import GridMapping
from datetime import datetime
import matplotlib.pyplot as plt
import pyproj
import numpy as np

from xcube.core.store import new_data_store from xcube_resampling.spatial import resample_in_space from xcube_resampling.gridmapping import GridMapping from datetime import datetime import matplotlib.pyplot as plt import pyproj import numpy as np

We use the ESA CCI Land Cover map available from the DeepESDL public S3 bucket. In the following cell, we access the multi-resolution dataset "LC-1x2025x2025-2.0.0.levels" and select the base level, which corresponds to the original spatial resolution of approximately 300 meters.

In [2]:

%%time
store = new_data_store("s3", root="deep-esdl-public")
mlds_lc = store.open_data("LC-1x2025x2025-2.0.0.levels")
ds = mlds_lc.base_dataset
ds_time_slice = ds.sel(time=datetime(2022, 1, 1))
ds

%%time store = new_data_store("s3", root="deep-esdl-public") mlds_lc = store.open_data("LC-1x2025x2025-2.0.0.levels") ds = mlds_lc.base_dataset ds_time_slice = ds.sel(time=datetime(2022, 1, 1)) ds

CPU times: user 226 ms, sys: 33.7 ms, total: 260 ms
Wall time: 1.01 s

Out[2]:

<xarray.Dataset> Size: 3TB
Dimensions:              (time: 31, lat: 64800, lon: 129600, bounds: 2)
Coordinates:
  * lat                  (lat) float64 518kB 90.0 90.0 89.99 ... -90.0 -90.0
  * lon                  (lon) float64 1MB -180.0 -180.0 -180.0 ... 180.0 180.0
  * time                 (time) datetime64[ns] 248B 1992-01-01 ... 2022-01-01
Dimensions without coordinates: bounds
Data variables:
    change_count         (time, lat, lon) uint8 260GB dask.array<chunksize=(1, 2025, 2025), meta=np.ndarray>
    crs                  int32 4B ...
    current_pixel_state  (time, lat, lon) float32 1TB dask.array<chunksize=(1, 2025, 2025), meta=np.ndarray>
    lat_bounds           (lat, bounds) float64 1MB dask.array<chunksize=(2025, 2), meta=np.ndarray>
    lccs_class           (time, lat, lon) uint8 260GB dask.array<chunksize=(1, 2025, 2025), meta=np.ndarray>
    lon_bounds           (lon, bounds) float64 2MB dask.array<chunksize=(2025, 2), meta=np.ndarray>
    observation_count    (time, lat, lon) uint16 521GB dask.array<chunksize=(1, 2025, 2025), meta=np.ndarray>
    processed_flag       (time, lat, lon) float32 1TB dask.array<chunksize=(1, 2025, 2025), meta=np.ndarray>
    time_bounds          (time, bounds) datetime64[ns] 496B dask.array<chunksize=(1, 2), meta=np.ndarray>
Attributes: (12/38)
    Conventions:                CF-1.6
    TileSize:                   2025:2025
    cdm_data_type:              grid
    comment:                    
    contact:                    https://www.ecmwf.int/en/about/contact-us/get...
    creation_date:              20181130T095451Z
    ...                         ...
    time_coverage_end:          19921231
    time_coverage_resolution:   P1Y
    time_coverage_start:        19920101
    title:                      Land Cover Map of ESA CCI brokered by CDS
    tracking_id:                61b96fd7-42c3-4374-9de1-0dc3b0bcae2a
    type:                       ESACCI-LC-L4-LCCS-Map-300m-P1Y

xarray.Dataset

• Dimensions:
  • time: 31
  • lat: 64800
  • lon: 129600
  • bounds: 2
• Coordinates: (3)
  • lat
    (lat)
    float64
    90.0 90.0 89.99 ... -90.0 -90.0

    axis :

    Y

    bounds :

    lat_bounds

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    valid_max :

    90.0

    valid_min :

    -90.0

    array([ 89.998611,  89.995833,  89.993056, ..., -89.993056, -89.995833,
           -89.998611], shape=(64800,))

  • lon
    (lon)
    float64
    -180.0 -180.0 ... 180.0 180.0

    axis :

    X

    bounds :

    lon_bounds

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    valid_max :

    180.0

    valid_min :

    -180.0

    array([-179.998611, -179.995833, -179.993056, ...,  179.993056,  179.995833,
            179.998611], shape=(129600,))

  • time
    (time)
    datetime64[ns]
    1992-01-01 ... 2022-01-01

    axis :

    T

    bounds :

    time_bounds

    long_name :

    time

    standard_name :

    time

    array(['1992-01-01T00:00:00.000000000', '1993-01-01T00:00:00.000000000',
           '1994-01-01T00:00:00.000000000', '1995-01-01T00:00:00.000000000',
           '1996-01-01T00:00:00.000000000', '1997-01-01T00:00:00.000000000',
           '1998-01-01T00:00:00.000000000', '1999-01-01T00:00:00.000000000',
           '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',
           '2020-01-01T00:00:00.000000000', '2021-01-01T00:00:00.000000000',
           '2022-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

• Data variables: (9)
  • change_count
    (time, lat, lon)
    uint8
    dask.array<chunksize=(1, 2025, 2025), meta=np.ndarray>

    long_name :

    number of class changes

    valid_max :

    100

    valid_min :

    0

    Array Chunk Bytes 242.46 GiB 3.91 MiB Shape (31, 64800, 129600) (1, 2025, 2025) Dask graph 63488 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • crs
    ()
    int32
    ...

    i2m :

    0.002777777777778,0.0,0.0,-0.002777777777778,-180.0,90.0

    wkt :

    GEOGCS["WGS 84", DATUM["World Geodetic System 1984", SPHEROID["WGS 84", 6378137.0, 298.257223563, AUTHORITY["EPSG","7030"]], AUTHORITY["EPSG","6326"]], PRIMEM["Greenwich", 0.0, AUTHORITY["EPSG","8901"]], UNIT["degree", 0.017453292519943295], AXIS["Geodetic longitude", EAST], AXIS["Geodetic latitude", NORTH], AUTHORITY["EPSG","4326"]]

    [1 values with dtype=int32]

  • current_pixel_state
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 2025, 2025), meta=np.ndarray>

    flag_meanings :

    invalid clear_land clear_water clear_snow_ice cloud cloud_shadow

    flag_values :

    [0, 1, 2, 3, 4, 5]

    long_name :

    LC pixel type mask

    standard_name :

    land_cover_lccs status_flag

    valid_max :

    5

    valid_min :

    0

    Array Chunk Bytes 0.95 TiB 15.64 MiB Shape (31, 64800, 129600) (1, 2025, 2025) Dask graph 63488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • lat_bounds
    (lat, bounds)
    float64
    dask.array<chunksize=(2025, 2), meta=np.ndarray>

    Array Chunk Bytes 0.99 MiB 31.64 kiB Shape (64800, 2) (2025, 2) Dask graph 32 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lccs_class
    (time, lat, lon)
    uint8
    dask.array<chunksize=(1, 2025, 2025), meta=np.ndarray>

    ancillary_variables :

    processed_flag current_pixel_state observation_count change_count

    flag_colors :

    #ffff64 #ffff64 #ffff00 #aaf0f0 #dcf064 #c8c864 #006400 #00a000 #00a000 #aac800 #003c00 #003c00 #005000 #285000 #285000 #286400 #788200 #8ca000 #be9600 #966400 #966400 #966400 #ffb432 #ffdcd2 #ffebaf #ffc864 #ffd278 #ffebaf #00785a #009678 #00dc82 #c31400 #fff5d7 #dcdcdc #fff5d7 #0046c8 #ffffff

    flag_meanings :

    no_data cropland_rainfed cropland_rainfed_herbaceous_cover cropland_rainfed_tree_or_shrub_cover cropland_irrigated mosaic_cropland mosaic_natural_vegetation tree_broadleaved_evergreen_closed_to_open tree_broadleaved_deciduous_closed_to_open tree_broadleaved_deciduous_closed tree_broadleaved_deciduous_open tree_needleleaved_evergreen_closed_to_open tree_needleleaved_evergreen_closed tree_needleleaved_evergreen_open tree_needleleaved_deciduous_closed_to_open tree_needleleaved_deciduous_closed tree_needleleaved_deciduous_open tree_mixed mosaic_tree_and_shrub mosaic_herbaceous shrubland shrubland_evergreen shrubland_deciduous grassland lichens_and_mosses sparse_vegetation sparse_tree sparse_shrub sparse_herbaceous tree_cover_flooded_fresh_or_brakish_water tree_cover_flooded_saline_water shrub_or_herbaceous_cover_flooded urban bare_areas bare_areas_consolidated bare_areas_unconsolidated water snow_and_ice

    flag_values :

    [0, 10, 11, 12, 20, 30, 40, 50, 60, 61, 62, 70, 71, 72, 80, 81, 82, 90, 100, 110, 120, 121, 122, 130, 140, 150, 151, 152, 153, 160, 170, 180, 190, 200, 201, 202, 210, 220]

    long_name :

    Land cover class defined in LCCS

    standard_name :

    land_cover_lccs

    valid_max :

    220

    valid_min :

    1

    Array Chunk Bytes 242.46 GiB 3.91 MiB Shape (31, 64800, 129600) (1, 2025, 2025) Dask graph 63488 chunks in 2 graph layers Data type uint8 numpy.ndarray

  • lon_bounds
    (lon, bounds)
    float64
    dask.array<chunksize=(2025, 2), meta=np.ndarray>

    Array Chunk Bytes 1.98 MiB 31.64 kiB Shape (129600, 2) (2025, 2) Dask graph 64 chunks in 2 graph layers Data type float64 numpy.ndarray

  • observation_count
    (time, lat, lon)
    uint16
    dask.array<chunksize=(1, 2025, 2025), meta=np.ndarray>

    long_name :

    number of valid observations

    standard_name :

    land_cover_lccs number_of_observations

    valid_max :

    32767

    valid_min :

    0

    Array Chunk Bytes 484.92 GiB 7.82 MiB Shape (31, 64800, 129600) (1, 2025, 2025) Dask graph 63488 chunks in 2 graph layers Data type uint16 numpy.ndarray

  • processed_flag
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 2025, 2025), meta=np.ndarray>

    flag_meanings :

    not_processed processed

    flag_values :

    [0, 1]

    long_name :

    LC map processed area flag

    standard_name :

    land_cover_lccs status_flag

    valid_max :

    1

    valid_min :

    0

    Array Chunk Bytes 0.95 TiB 15.64 MiB Shape (31, 64800, 129600) (1, 2025, 2025) Dask graph 63488 chunks in 2 graph layers Data type float32 numpy.ndarray

  • time_bounds
    (time, bounds)
    datetime64[ns]
    dask.array<chunksize=(1, 2), meta=np.ndarray>

    Array Chunk Bytes 496 B 16 B Shape (31, 2) (1, 2) Dask graph 31 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([ 89.99861111111113,  89.99583333333334,  89.99305555555557,
            89.99027777777778,  89.98750000000001,  89.98472222222222,
            89.98194444444445,  89.97916666666669,  89.97638888888889,
            89.97361111111113,
           ...
           -89.97361111111111, -89.97638888888889, -89.97916666666667,
           -89.98194444444445, -89.98472222222222,           -89.9875,
           -89.99027777777778, -89.99305555555556, -89.99583333333334,
           -89.99861111111112],
          dtype='float64', name='lat', length=64800))

  • lon
    PandasIndex

    PandasIndex(Index([ -179.9986111111111, -179.99583333333334, -179.99305555555554,
           -179.99027777777778,           -179.9875, -179.98472222222222,
           -179.98194444444445, -179.97916666666666,  -179.9763888888889,
            -179.9736111111111,
           ...
             179.9736111111111,   179.9763888888889,  179.97916666666669,
            179.98194444444448,  179.98472222222222,            179.9875,
             179.9902777777778,  179.99305555555554,  179.99583333333334,
            179.99861111111113],
          dtype='float64', name='lon', length=129600))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1992-01-01', '1993-01-01', '1994-01-01', '1995-01-01',
                   '1996-01-01', '1997-01-01', '1998-01-01', '1999-01-01',
                   '2000-01-01', '2001-01-01', '2002-01-01', '2003-01-01',
                   '2004-01-01', '2005-01-01', '2006-01-01', '2007-01-01',
                   '2008-01-01', '2009-01-01', '2010-01-01', '2011-01-01',
                   '2012-01-01', '2013-01-01', '2014-01-01', '2015-01-01',
                   '2016-01-01', '2017-01-01', '2018-01-01', '2019-01-01',
                   '2020-01-01', '2021-01-01', '2022-01-01'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (38)

  Conventions :

  CF-1.6

  TileSize :

  2025:2025

  cdm_data_type :

  grid

  comment :

  contact :

  https://www.ecmwf.int/en/about/contact-us/get-support

  creation_date :

  20181130T095451Z

  creator_email :

  landcover-cci@uclouvain.be

  creator_name :

  UCLouvain

  creator_url :

  http://www.uclouvain.be/

  geospatial_lat_max :

  90.0

  geospatial_lat_min :

  -90.0

  geospatial_lat_resolution :

  0.002778

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  180

  geospatial_lon_min :

  -180

  geospatial_lon_resolution :

  0.002778

  geospatial_lon_units :

  degrees_east

  history :

  amorgos-4,0, lc-sdr-1.0, lc-sr-1.0, lc-classification-1.0,lc-user-tools-3.13,lc-user-tools-4.3

  id :

  ESACCI-LC-L4-LCCS-Map-300m-P1Y-1992-v2.0.7cds

  institution :

  UCLouvain

  keywords :

  land cover classification,satellite,observation

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  license :

  ESA CCI Data Policy: free and open access

  naming_authority :

  org.esa-cci

  product_version :

  2.0.7cds

  project :

  Climate Change Initiative - European Space Agency

  references :

  http://www.esa-landcover-cci.org/

  source :

  MERIS FR L1B version 5.05, MERIS RR L1B version 8.0, SPOT VGT P

  spatial_resolution :

  300m

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Standard Names version 21

  summary :

  This dataset characterizes the land cover of a particular year (see time_coverage). The land cover was derived from the analysis of satellite data time series of the full period.

  time_coverage_duration :

  P1Y

  time_coverage_end :

  19921231

  time_coverage_resolution :

  P1Y

  time_coverage_start :

  19920101

  title :

  Land Cover Map of ESA CCI brokered by CDS

  tracking_id :

  61b96fd7-42c3-4374-9de1-0dc3b0bcae2a

  type :

  ESACCI-LC-L4-LCCS-Map-300m-P1Y

We generate a regular grid mapping covering Germany and demonstrate that the new reproject_dataset method produces results very similar to those of the resample_in_space method.

As a first step, we create a regular grid mapping using the EPSG:3035 coordinate reference system.

In [3]:

%%time
bbox = [5, 46, 15, 57]
target_crs = "EPSG:3035"
t = pyproj.Transformer.from_crs("EPSG:4326", target_crs, always_xy=True)
target_bbox = t.transform_bounds(*bbox)
spatial_res = 150
x_size = int((target_bbox[2] - target_bbox[0]) / spatial_res) + 1
y_size = int(abs(target_bbox[3] - target_bbox[1]) / spatial_res) + 1
target_gm = GridMapping.regular(
    size=(x_size, y_size),
    xy_min=(target_bbox[0] - spatial_res / 2, target_bbox[1] - spatial_res / 2),
    xy_res=spatial_res,
    crs=target_crs,
    tile_size=4000,
)

%%time bbox = [5, 46, 15, 57] target_crs = "EPSG:3035" t = pyproj.Transformer.from_crs("EPSG:4326", target_crs, always_xy=True) target_bbox = t.transform_bounds(*bbox) spatial_res = 150 x_size = int((target_bbox[2] - target_bbox[0]) / spatial_res) + 1 y_size = int(abs(target_bbox[3] - target_bbox[1]) / spatial_res) + 1 target_gm = GridMapping.regular( size=(x_size, y_size), xy_min=(target_bbox[0] - spatial_res / 2, target_bbox[1] - spatial_res / 2), xy_res=spatial_res, crs=target_crs, tile_size=4000, )

CPU times: user 33.8 ms, sys: 1.07 ms, total: 34.9 ms
Wall time: 33.8 ms

Firstly, we apply the exsisting resample_in_space method, where we clip the source dataset. Otherwise, resampling_in_space would take for ever.

In [4]:

%%time
ds_resampling = resample_in_space(ds_time_slice, target_gm=target_gm)
ds_resampling

%%time ds_resampling = resample_in_space(ds_time_slice, target_gm=target_gm) ds_resampling

CPU times: user 9.24 s, sys: 205 ms, total: 9.45 s
Wall time: 9.44 s

Out[4]:

<xarray.Dataset> Size: 510MB
Dimensions:              (x: 5167, y: 8227, bounds: 2)
Coordinates:
    time                 datetime64[ns] 8B 2022-01-01
    spatial_ref          int64 8B 0
  * x                    (x) float64 41kB 3.934e+06 3.934e+06 ... 4.708e+06
  * y                    (y) float64 66kB 3.777e+06 3.777e+06 ... 2.543e+06
Dimensions without coordinates: bounds
Data variables:
    change_count         (y, x) uint8 43MB dask.array<chunksize=(4000, 4000), meta=np.ndarray>
    current_pixel_state  (y, x) float32 170MB dask.array<chunksize=(4000, 4000), meta=np.ndarray>
    lccs_class           (y, x) uint8 43MB dask.array<chunksize=(4000, 4000), meta=np.ndarray>
    observation_count    (y, x) uint16 85MB dask.array<chunksize=(4000, 4000), meta=np.ndarray>
    processed_flag       (y, x) float32 170MB dask.array<chunksize=(4000, 4000), meta=np.ndarray>
    time_bounds          (bounds) datetime64[ns] 16B dask.array<chunksize=(2,), meta=np.ndarray>
Attributes: (12/38)
    Conventions:                CF-1.6
    TileSize:                   2025:2025
    cdm_data_type:              grid
    comment:                    
    contact:                    https://www.ecmwf.int/en/about/contact-us/get...
    creation_date:              20181130T095451Z
    ...                         ...
    time_coverage_end:          19921231
    time_coverage_resolution:   P1Y
    time_coverage_start:        19920101
    title:                      Land Cover Map of ESA CCI brokered by CDS
    tracking_id:                61b96fd7-42c3-4374-9de1-0dc3b0bcae2a
    type:                       ESACCI-LC-L4-LCCS-Map-300m-P1Y

xarray.Dataset

• Dimensions:
  • x: 5167
  • y: 8227
  • bounds: 2
• Coordinates: (4)
  • time
    ()
    datetime64[ns]
    2022-01-01

    axis :

    T

    bounds :

    time_bounds

    long_name :

    time

    standard_name :

    time

    array('2022-01-01T00:00:00.000000000', dtype='datetime64[ns]')

  • spatial_ref
    ()
    int64
    0

    crs_wkt :

    PROJCRS["ETRS89-extended / LAEA Europe",BASEGEOGCRS["ETRS89",ENSEMBLE["European Terrestrial Reference System 1989 ensemble",MEMBER["European Terrestrial Reference Frame 1989"],MEMBER["European Terrestrial Reference Frame 1990"],MEMBER["European Terrestrial Reference Frame 1991"],MEMBER["European Terrestrial Reference Frame 1992"],MEMBER["European Terrestrial Reference Frame 1993"],MEMBER["European Terrestrial Reference Frame 1994"],MEMBER["European Terrestrial Reference Frame 1996"],MEMBER["European Terrestrial Reference Frame 1997"],MEMBER["European Terrestrial Reference Frame 2000"],MEMBER["European Terrestrial Reference Frame 2005"],MEMBER["European Terrestrial Reference Frame 2014"],MEMBER["European Terrestrial Reference Frame 2020"],ELLIPSOID["GRS 1980",6378137,298.257222101,LENGTHUNIT["metre",1]],ENSEMBLEACCURACY[0.1]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],ID["EPSG",4258]],CONVERSION["Europe Equal Area 2001",METHOD["Lambert Azimuthal Equal Area",ID["EPSG",9820]],PARAMETER["Latitude of natural origin",52,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8801]],PARAMETER["Longitude of natural origin",10,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8802]],PARAMETER["False easting",4321000,LENGTHUNIT["metre",1],ID["EPSG",8806]],PARAMETER["False northing",3210000,LENGTHUNIT["metre",1],ID["EPSG",8807]]],CS[Cartesian,2],AXIS["northing (Y)",north,ORDER[1],LENGTHUNIT["metre",1]],AXIS["easting (X)",east,ORDER[2],LENGTHUNIT["metre",1]],USAGE[SCOPE["Statistical analysis."],AREA["Europe - European Union (EU) countries and candidates. Europe - onshore and offshore: Albania; Andorra; Austria; Belgium; Bosnia and Herzegovina; Bulgaria; Croatia; Cyprus; Czechia; Denmark; Estonia; Faroe Islands; Finland; France; Germany; Gibraltar; Greece; Hungary; Iceland; Ireland; Italy; Kosovo; Latvia; Liechtenstein; Lithuania; Luxembourg; Malta; Monaco; Montenegro; Netherlands; North Macedonia; Norway including Svalbard and Jan Mayen; Poland; Portugal including Madeira and Azores; Romania; San Marino; Serbia; Slovakia; Slovenia; Spain including Canary Islands; Sweden; Switzerland; Türkiye (Turkey); United Kingdom (UK) including Channel Islands and Isle of Man; Vatican City State."],BBOX[24.6,-35.58,84.73,44.83]],ID["EPSG",3035]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314140356

    inverse_flattening :

    298.257222101

    reference_ellipsoid_name :

    GRS 1980

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    ETRS89

    horizontal_datum_name :

    European Terrestrial Reference System 1989 ensemble

    projected_crs_name :

    ETRS89-extended / LAEA Europe

    grid_mapping_name :

    lambert_azimuthal_equal_area

    latitude_of_projection_origin :

    52.0

    longitude_of_projection_origin :

    10.0

    false_easting :

    4321000.0

    false_northing :

    3210000.0

    array(0)

  • x
    (x)
    float64
    3.934e+06 3.934e+06 ... 4.708e+06

    array([3933529., 3933679., 3933829., ..., 4708129., 4708279., 4708429.],
          shape=(5167,))

  • y
    (y)
    float64
    3.777e+06 3.777e+06 ... 2.543e+06

    array([3776909., 3776759., 3776609., ..., 2543309., 2543159., 2543009.],
          shape=(8227,))

• Data variables: (6)
  • change_count
    (y, x)
    uint8
    dask.array<chunksize=(4000, 4000), meta=np.ndarray>

    long_name :

    number of class changes

    valid_max :

    100

    valid_min :

    0

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 40.54 MiB 15.26 MiB Shape (8227, 5167) (4000, 4000) Dask graph 6 chunks in 21 graph layers Data type uint8 numpy.ndarray

  • current_pixel_state
    (y, x)
    float32
    dask.array<chunksize=(4000, 4000), meta=np.ndarray>

    flag_meanings :

    invalid clear_land clear_water clear_snow_ice cloud cloud_shadow

    flag_values :

    [0, 1, 2, 3, 4, 5]

    long_name :

    LC pixel type mask

    standard_name :

    land_cover_lccs status_flag

    valid_max :

    5

    valid_min :

    0

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 162.16 MiB 61.04 MiB Shape (8227, 5167) (4000, 4000) Dask graph 6 chunks in 21 graph layers Data type float32 numpy.ndarray

  • lccs_class
    (y, x)
    uint8
    dask.array<chunksize=(4000, 4000), meta=np.ndarray>

    ancillary_variables :

    processed_flag current_pixel_state observation_count change_count

    flag_colors :

    #ffff64 #ffff64 #ffff00 #aaf0f0 #dcf064 #c8c864 #006400 #00a000 #00a000 #aac800 #003c00 #003c00 #005000 #285000 #285000 #286400 #788200 #8ca000 #be9600 #966400 #966400 #966400 #ffb432 #ffdcd2 #ffebaf #ffc864 #ffd278 #ffebaf #00785a #009678 #00dc82 #c31400 #fff5d7 #dcdcdc #fff5d7 #0046c8 #ffffff

    flag_meanings :

    no_data cropland_rainfed cropland_rainfed_herbaceous_cover cropland_rainfed_tree_or_shrub_cover cropland_irrigated mosaic_cropland mosaic_natural_vegetation tree_broadleaved_evergreen_closed_to_open tree_broadleaved_deciduous_closed_to_open tree_broadleaved_deciduous_closed tree_broadleaved_deciduous_open tree_needleleaved_evergreen_closed_to_open tree_needleleaved_evergreen_closed tree_needleleaved_evergreen_open tree_needleleaved_deciduous_closed_to_open tree_needleleaved_deciduous_closed tree_needleleaved_deciduous_open tree_mixed mosaic_tree_and_shrub mosaic_herbaceous shrubland shrubland_evergreen shrubland_deciduous grassland lichens_and_mosses sparse_vegetation sparse_tree sparse_shrub sparse_herbaceous tree_cover_flooded_fresh_or_brakish_water tree_cover_flooded_saline_water shrub_or_herbaceous_cover_flooded urban bare_areas bare_areas_consolidated bare_areas_unconsolidated water snow_and_ice

    flag_values :

    [0, 10, 11, 12, 20, 30, 40, 50, 60, 61, 62, 70, 71, 72, 80, 81, 82, 90, 100, 110, 120, 121, 122, 130, 140, 150, 151, 152, 153, 160, 170, 180, 190, 200, 201, 202, 210, 220]

    long_name :

    Land cover class defined in LCCS

    standard_name :

    land_cover_lccs

    valid_max :

    220

    valid_min :

    1

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 40.54 MiB 15.26 MiB Shape (8227, 5167) (4000, 4000) Dask graph 6 chunks in 21 graph layers Data type uint8 numpy.ndarray

  • observation_count
    (y, x)
    uint16
    dask.array<chunksize=(4000, 4000), meta=np.ndarray>

    long_name :

    number of valid observations

    standard_name :

    land_cover_lccs number_of_observations

    valid_max :

    32767

    valid_min :

    0

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 81.08 MiB 30.52 MiB Shape (8227, 5167) (4000, 4000) Dask graph 6 chunks in 21 graph layers Data type uint16 numpy.ndarray

  • processed_flag
    (y, x)
    float32
    dask.array<chunksize=(4000, 4000), meta=np.ndarray>

    flag_meanings :

    not_processed processed

    flag_values :

    [0, 1]

    long_name :

    LC map processed area flag

    standard_name :

    land_cover_lccs status_flag

    valid_max :

    1

    valid_min :

    0

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 162.16 MiB 61.04 MiB Shape (8227, 5167) (4000, 4000) Dask graph 6 chunks in 21 graph layers Data type float32 numpy.ndarray

  • time_bounds
    (bounds)
    datetime64[ns]
    dask.array<chunksize=(2,), meta=np.ndarray>

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 16 B 16 B Shape (2,) (2,) Dask graph 1 chunks in 3 graph layers Data type datetime64[ns] numpy.ndarray

• Indexes: (2)
  • x
    PandasIndex

    PandasIndex(Index([3933529.0, 3933679.0, 3933829.0, 3933979.0, 3934129.0, 3934279.0,
           3934429.0, 3934579.0, 3934729.0, 3934879.0,
           ...
           4707079.0, 4707229.0, 4707379.0, 4707529.0, 4707679.0, 4707829.0,
           4707979.0, 4708129.0, 4708279.0, 4708429.0],
          dtype='float64', name='x', length=5167))

  • y
    PandasIndex

    PandasIndex(Index([3776909.0, 3776759.0, 3776609.0, 3776459.0, 3776309.0, 3776159.0,
           3776009.0, 3775859.0, 3775709.0, 3775559.0,
           ...
           2544359.0, 2544209.0, 2544059.0, 2543909.0, 2543759.0, 2543609.0,
           2543459.0, 2543309.0, 2543159.0, 2543009.0],
          dtype='float64', name='y', length=8227))

• Attributes: (38)

  Conventions :

  CF-1.6

  TileSize :

  2025:2025

  cdm_data_type :

  grid

  comment :

  contact :

  https://www.ecmwf.int/en/about/contact-us/get-support

  creation_date :

  20181130T095451Z

  creator_email :

  landcover-cci@uclouvain.be

  creator_name :

  UCLouvain

  creator_url :

  http://www.uclouvain.be/

  geospatial_lat_max :

  90.0

  geospatial_lat_min :

  -90.0

  geospatial_lat_resolution :

  0.002778

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  180

  geospatial_lon_min :

  -180

  geospatial_lon_resolution :

  0.002778

  geospatial_lon_units :

  degrees_east

  history :

  amorgos-4,0, lc-sdr-1.0, lc-sr-1.0, lc-classification-1.0,lc-user-tools-3.13,lc-user-tools-4.3

  id :

  ESACCI-LC-L4-LCCS-Map-300m-P1Y-1992-v2.0.7cds

  institution :

  UCLouvain

  keywords :

  land cover classification,satellite,observation

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  license :

  ESA CCI Data Policy: free and open access

  naming_authority :

  org.esa-cci

  product_version :

  2.0.7cds

  project :

  Climate Change Initiative - European Space Agency

  references :

  http://www.esa-landcover-cci.org/

  source :

  MERIS FR L1B version 5.05, MERIS RR L1B version 8.0, SPOT VGT P

  spatial_resolution :

  300m

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Standard Names version 21

  summary :

  This dataset characterizes the land cover of a particular year (see time_coverage). The land cover was derived from the analysis of satellite data time series of the full period.

  time_coverage_duration :

  P1Y

  time_coverage_end :

  19921231

  time_coverage_resolution :

  P1Y

  time_coverage_start :

  19920101

  title :

  Land Cover Map of ESA CCI brokered by CDS

  tracking_id :

  61b96fd7-42c3-4374-9de1-0dc3b0bcae2a

  type :

  ESACCI-LC-L4-LCCS-Map-300m-P1Y

In [5]:

%%time
ds_resampling.lccs_class[::5, ::5].plot()

%%time ds_resampling.lccs_class[::5, ::5].plot()

CPU times: user 6.74 s, sys: 805 ms, total: 7.55 s
Wall time: 3.48 s

Out[5]:

<matplotlib.collections.QuadMesh at 0x758de04f27b0>

The reproject_dataset method can handle source datasets of any size. It automatically selects the overlapping portions of the source dataset that intersect with the target grid mapping.

If the extent of the source dataset is too small and the target grid mapping extends beyond its boundaries, padding with a fill value is required. This fill value can either be provided by the user or is determined by the data type of the data array, as described in the docstring of the reproject_dataset method.

In [6]:

%%time
ds = ds.sel(time=slice(datetime(2018, 1, 1), datetime(2022, 1, 1)))
ds = ds[["lccs_class"]]
ds

%%time ds = ds.sel(time=slice(datetime(2018, 1, 1), datetime(2022, 1, 1))) ds = ds[["lccs_class"]] ds

CPU times: user 36.2 ms, sys: 8.95 ms, total: 45.1 ms
Wall time: 44.9 ms

Out[6]:

<xarray.Dataset> Size: 42GB
Dimensions:     (time: 5, lat: 64800, lon: 129600)
Coordinates:
  * lat         (lat) float64 518kB 90.0 90.0 89.99 89.99 ... -89.99 -90.0 -90.0
  * lon         (lon) float64 1MB -180.0 -180.0 -180.0 ... 180.0 180.0 180.0
  * time        (time) datetime64[ns] 40B 2018-01-01 2019-01-01 ... 2022-01-01
Data variables:
    lccs_class  (time, lat, lon) uint8 42GB dask.array<chunksize=(1, 2025, 2025), meta=np.ndarray>
Attributes: (12/38)
    Conventions:                CF-1.6
    TileSize:                   2025:2025
    cdm_data_type:              grid
    comment:                    
    contact:                    https://www.ecmwf.int/en/about/contact-us/get...
    creation_date:              20181130T095451Z
    ...                         ...
    time_coverage_end:          19921231
    time_coverage_resolution:   P1Y
    time_coverage_start:        19920101
    title:                      Land Cover Map of ESA CCI brokered by CDS
    tracking_id:                61b96fd7-42c3-4374-9de1-0dc3b0bcae2a
    type:                       ESACCI-LC-L4-LCCS-Map-300m-P1Y

xarray.Dataset

• Dimensions:
  • time: 5
  • lat: 64800
  • lon: 129600
• Coordinates: (3)
  • lat
    (lat)
    float64
    90.0 90.0 89.99 ... -90.0 -90.0

    axis :

    Y

    bounds :

    lat_bounds

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    valid_max :

    90.0

    valid_min :

    -90.0

    array([ 89.998611,  89.995833,  89.993056, ..., -89.993056, -89.995833,
           -89.998611], shape=(64800,))

  • lon
    (lon)
    float64
    -180.0 -180.0 ... 180.0 180.0

    axis :

    X

    bounds :

    lon_bounds

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    valid_max :

    180.0

    valid_min :

    -180.0

    array([-179.998611, -179.995833, -179.993056, ...,  179.993056,  179.995833,
            179.998611], shape=(129600,))

  • time
    (time)
    datetime64[ns]
    2018-01-01 ... 2022-01-01

    axis :

    T

    bounds :

    time_bounds

    long_name :

    time

    standard_name :

    time

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

• Data variables: (1)
  • lccs_class
    (time, lat, lon)
    uint8
    dask.array<chunksize=(1, 2025, 2025), meta=np.ndarray>

    ancillary_variables :

    processed_flag current_pixel_state observation_count change_count

    flag_colors :

    #ffff64 #ffff64 #ffff00 #aaf0f0 #dcf064 #c8c864 #006400 #00a000 #00a000 #aac800 #003c00 #003c00 #005000 #285000 #285000 #286400 #788200 #8ca000 #be9600 #966400 #966400 #966400 #ffb432 #ffdcd2 #ffebaf #ffc864 #ffd278 #ffebaf #00785a #009678 #00dc82 #c31400 #fff5d7 #dcdcdc #fff5d7 #0046c8 #ffffff

    flag_meanings :

    no_data cropland_rainfed cropland_rainfed_herbaceous_cover cropland_rainfed_tree_or_shrub_cover cropland_irrigated mosaic_cropland mosaic_natural_vegetation tree_broadleaved_evergreen_closed_to_open tree_broadleaved_deciduous_closed_to_open tree_broadleaved_deciduous_closed tree_broadleaved_deciduous_open tree_needleleaved_evergreen_closed_to_open tree_needleleaved_evergreen_closed tree_needleleaved_evergreen_open tree_needleleaved_deciduous_closed_to_open tree_needleleaved_deciduous_closed tree_needleleaved_deciduous_open tree_mixed mosaic_tree_and_shrub mosaic_herbaceous shrubland shrubland_evergreen shrubland_deciduous grassland lichens_and_mosses sparse_vegetation sparse_tree sparse_shrub sparse_herbaceous tree_cover_flooded_fresh_or_brakish_water tree_cover_flooded_saline_water shrub_or_herbaceous_cover_flooded urban bare_areas bare_areas_consolidated bare_areas_unconsolidated water snow_and_ice

    flag_values :

    [0, 10, 11, 12, 20, 30, 40, 50, 60, 61, 62, 70, 71, 72, 80, 81, 82, 90, 100, 110, 120, 121, 122, 130, 140, 150, 151, 152, 153, 160, 170, 180, 190, 200, 201, 202, 210, 220]

    long_name :

    Land cover class defined in LCCS

    standard_name :

    land_cover_lccs

    valid_max :

    220

    valid_min :

    1

    Array Chunk Bytes 39.11 GiB 3.91 MiB Shape (5, 64800, 129600) (1, 2025, 2025) Dask graph 10240 chunks in 3 graph layers Data type uint8 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([ 89.99861111111113,  89.99583333333334,  89.99305555555557,
            89.99027777777778,  89.98750000000001,  89.98472222222222,
            89.98194444444445,  89.97916666666669,  89.97638888888889,
            89.97361111111113,
           ...
           -89.97361111111111, -89.97638888888889, -89.97916666666667,
           -89.98194444444445, -89.98472222222222,           -89.9875,
           -89.99027777777778, -89.99305555555556, -89.99583333333334,
           -89.99861111111112],
          dtype='float64', name='lat', length=64800))

  • lon
    PandasIndex

    PandasIndex(Index([ -179.9986111111111, -179.99583333333334, -179.99305555555554,
           -179.99027777777778,           -179.9875, -179.98472222222222,
           -179.98194444444445, -179.97916666666666,  -179.9763888888889,
            -179.9736111111111,
           ...
             179.9736111111111,   179.9763888888889,  179.97916666666669,
            179.98194444444448,  179.98472222222222,            179.9875,
             179.9902777777778,  179.99305555555554,  179.99583333333334,
            179.99861111111113],
          dtype='float64', name='lon', length=129600))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2018-01-01', '2019-01-01', '2020-01-01', '2021-01-01',
                   '2022-01-01'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (38)

  Conventions :

  CF-1.6

  TileSize :

  2025:2025

  cdm_data_type :

  grid

  comment :

  contact :

  https://www.ecmwf.int/en/about/contact-us/get-support

  creation_date :

  20181130T095451Z

  creator_email :

  landcover-cci@uclouvain.be

  creator_name :

  UCLouvain

  creator_url :

  http://www.uclouvain.be/

  geospatial_lat_max :

  90.0

  geospatial_lat_min :

  -90.0

  geospatial_lat_resolution :

  0.002778

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  180

  geospatial_lon_min :

  -180

  geospatial_lon_resolution :

  0.002778

  geospatial_lon_units :

  degrees_east

  history :

  amorgos-4,0, lc-sdr-1.0, lc-sr-1.0, lc-classification-1.0,lc-user-tools-3.13,lc-user-tools-4.3

  id :

  ESACCI-LC-L4-LCCS-Map-300m-P1Y-1992-v2.0.7cds

  institution :

  UCLouvain

  keywords :

  land cover classification,satellite,observation

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  license :

  ESA CCI Data Policy: free and open access

  naming_authority :

  org.esa-cci

  product_version :

  2.0.7cds

  project :

  Climate Change Initiative - European Space Agency

  references :

  http://www.esa-landcover-cci.org/

  source :

  MERIS FR L1B version 5.05, MERIS RR L1B version 8.0, SPOT VGT P

  spatial_resolution :

  300m

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Standard Names version 21

  summary :

  This dataset characterizes the land cover of a particular year (see time_coverage). The land cover was derived from the analysis of satellite data time series of the full period.

  time_coverage_duration :

  P1Y

  time_coverage_end :

  19921231

  time_coverage_resolution :

  P1Y

  time_coverage_start :

  19920101

  title :

  Land Cover Map of ESA CCI brokered by CDS

  tracking_id :

  61b96fd7-42c3-4374-9de1-0dc3b0bcae2a

  type :

  ESACCI-LC-L4-LCCS-Map-300m-P1Y

In [7]:

%%time
bbox = [-20, 25, 40, 85]
target_crs = "EPSG:3035"
t = pyproj.Transformer.from_crs("EPSG:4326", target_crs, always_xy=True)
target_bbox = t.transform_bounds(*bbox)
spatial_res = 300
x_size = int((target_bbox[2] - target_bbox[0]) / spatial_res) + 1
y_size = int(abs(target_bbox[3] - target_bbox[1]) / spatial_res) + 1
target_gm = GridMapping.regular(
    size=(x_size, y_size),
    xy_min=(target_bbox[0] - spatial_res / 2, target_bbox[1] - spatial_res / 2),
    xy_res=spatial_res,
    crs=target_crs,
    tile_size=4000,
)
target_gm

%%time bbox = [-20, 25, 40, 85] target_crs = "EPSG:3035" t = pyproj.Transformer.from_crs("EPSG:4326", target_crs, always_xy=True) target_bbox = t.transform_bounds(*bbox) spatial_res = 300 x_size = int((target_bbox[2] - target_bbox[0]) / spatial_res) + 1 y_size = int(abs(target_bbox[3] - target_bbox[1]) / spatial_res) + 1 target_gm = GridMapping.regular( size=(x_size, y_size), xy_min=(target_bbox[0] - spatial_res / 2, target_bbox[1] - spatial_res / 2), xy_res=spatial_res, crs=target_crs, tile_size=4000, ) target_gm

CPU times: user 18.3 ms, sys: 3.03 ms, total: 21.3 ms
Wall time: 20.5 ms

Out[7]:

class: RegularGridMapping

• is_regular: True
• is_j_axis_up: False
• is_lon_360: False
• crs: EPSG:3035
• xy_res: (300, 300)
• xy_bbox: (1287527, 239402, 7354427, 6906902)
• ij_bbox: (0, 0, 20223, 22225)
• xy_dim_names: ('x', 'y')
• xy_var_names: ('x', 'y')
• size: (20223, 22225)
• tile_size: (4000, 4000)

In [8]:

%%time
ds_reproject = resample_in_space(ds, target_gm=target_gm)
ds_reproject

%%time ds_reproject = resample_in_space(ds, target_gm=target_gm) ds_reproject

CPU times: user 13.9 s, sys: 101 ms, total: 14 s
Wall time: 14 s

Out[8]:

<xarray.Dataset> Size: 2GB
Dimensions:      (time: 5, x: 20223, y: 22225)
Coordinates:
  * time         (time) datetime64[ns] 40B 2018-01-01 2019-01-01 ... 2022-01-01
    spatial_ref  int64 8B 0
  * x            (x) float64 162kB 1.288e+06 1.288e+06 ... 7.354e+06 7.354e+06
  * y            (y) float64 178kB 6.907e+06 6.906e+06 ... 2.399e+05 2.396e+05
Data variables:
    lccs_class   (time, y, x) uint8 2GB dask.array<chunksize=(1, 4000, 4000), meta=np.ndarray>
Attributes: (12/38)
    Conventions:                CF-1.6
    TileSize:                   2025:2025
    cdm_data_type:              grid
    comment:                    
    contact:                    https://www.ecmwf.int/en/about/contact-us/get...
    creation_date:              20181130T095451Z
    ...                         ...
    time_coverage_end:          19921231
    time_coverage_resolution:   P1Y
    time_coverage_start:        19920101
    title:                      Land Cover Map of ESA CCI brokered by CDS
    tracking_id:                61b96fd7-42c3-4374-9de1-0dc3b0bcae2a
    type:                       ESACCI-LC-L4-LCCS-Map-300m-P1Y

xarray.Dataset

• Dimensions:
  • time: 5
  • x: 20223
  • y: 22225
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2018-01-01 ... 2022-01-01

    axis :

    T

    bounds :

    time_bounds

    long_name :

    time

    standard_name :

    time

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

  • spatial_ref
    ()
    int64
    0

    crs_wkt :

    PROJCRS["ETRS89-extended / LAEA Europe",BASEGEOGCRS["ETRS89",ENSEMBLE["European Terrestrial Reference System 1989 ensemble",MEMBER["European Terrestrial Reference Frame 1989"],MEMBER["European Terrestrial Reference Frame 1990"],MEMBER["European Terrestrial Reference Frame 1991"],MEMBER["European Terrestrial Reference Frame 1992"],MEMBER["European Terrestrial Reference Frame 1993"],MEMBER["European Terrestrial Reference Frame 1994"],MEMBER["European Terrestrial Reference Frame 1996"],MEMBER["European Terrestrial Reference Frame 1997"],MEMBER["European Terrestrial Reference Frame 2000"],MEMBER["European Terrestrial Reference Frame 2005"],MEMBER["European Terrestrial Reference Frame 2014"],MEMBER["European Terrestrial Reference Frame 2020"],ELLIPSOID["GRS 1980",6378137,298.257222101,LENGTHUNIT["metre",1]],ENSEMBLEACCURACY[0.1]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],ID["EPSG",4258]],CONVERSION["Europe Equal Area 2001",METHOD["Lambert Azimuthal Equal Area",ID["EPSG",9820]],PARAMETER["Latitude of natural origin",52,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8801]],PARAMETER["Longitude of natural origin",10,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8802]],PARAMETER["False easting",4321000,LENGTHUNIT["metre",1],ID["EPSG",8806]],PARAMETER["False northing",3210000,LENGTHUNIT["metre",1],ID["EPSG",8807]]],CS[Cartesian,2],AXIS["northing (Y)",north,ORDER[1],LENGTHUNIT["metre",1]],AXIS["easting (X)",east,ORDER[2],LENGTHUNIT["metre",1]],USAGE[SCOPE["Statistical analysis."],AREA["Europe - European Union (EU) countries and candidates. Europe - onshore and offshore: Albania; Andorra; Austria; Belgium; Bosnia and Herzegovina; Bulgaria; Croatia; Cyprus; Czechia; Denmark; Estonia; Faroe Islands; Finland; France; Germany; Gibraltar; Greece; Hungary; Iceland; Ireland; Italy; Kosovo; Latvia; Liechtenstein; Lithuania; Luxembourg; Malta; Monaco; Montenegro; Netherlands; North Macedonia; Norway including Svalbard and Jan Mayen; Poland; Portugal including Madeira and Azores; Romania; San Marino; Serbia; Slovakia; Slovenia; Spain including Canary Islands; Sweden; Switzerland; Türkiye (Turkey); United Kingdom (UK) including Channel Islands and Isle of Man; Vatican City State."],BBOX[24.6,-35.58,84.73,44.83]],ID["EPSG",3035]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314140356

    inverse_flattening :

    298.257222101

    reference_ellipsoid_name :

    GRS 1980

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    ETRS89

    horizontal_datum_name :

    European Terrestrial Reference System 1989 ensemble

    projected_crs_name :

    ETRS89-extended / LAEA Europe

    grid_mapping_name :

    lambert_azimuthal_equal_area

    latitude_of_projection_origin :

    52.0

    longitude_of_projection_origin :

    10.0

    false_easting :

    4321000.0

    false_northing :

    3210000.0

    array(0)

  • x
    (x)
    float64
    1.288e+06 1.288e+06 ... 7.354e+06

    array([1287677., 1287977., 1288277., ..., 7353677., 7353977., 7354277.],
          shape=(20223,))

  • y
    (y)
    float64
    6.907e+06 6.906e+06 ... 2.396e+05

    array([6906752., 6906452., 6906152., ...,  240152.,  239852.,  239552.],
          shape=(22225,))

• Data variables: (1)
  • lccs_class
    (time, y, x)
    uint8
    dask.array<chunksize=(1, 4000, 4000), meta=np.ndarray>

    ancillary_variables :

    processed_flag current_pixel_state observation_count change_count

    flag_colors :

    #ffff64 #ffff64 #ffff00 #aaf0f0 #dcf064 #c8c864 #006400 #00a000 #00a000 #aac800 #003c00 #003c00 #005000 #285000 #285000 #286400 #788200 #8ca000 #be9600 #966400 #966400 #966400 #ffb432 #ffdcd2 #ffebaf #ffc864 #ffd278 #ffebaf #00785a #009678 #00dc82 #c31400 #fff5d7 #dcdcdc #fff5d7 #0046c8 #ffffff

    flag_meanings :

    no_data cropland_rainfed cropland_rainfed_herbaceous_cover cropland_rainfed_tree_or_shrub_cover cropland_irrigated mosaic_cropland mosaic_natural_vegetation tree_broadleaved_evergreen_closed_to_open tree_broadleaved_deciduous_closed_to_open tree_broadleaved_deciduous_closed tree_broadleaved_deciduous_open tree_needleleaved_evergreen_closed_to_open tree_needleleaved_evergreen_closed tree_needleleaved_evergreen_open tree_needleleaved_deciduous_closed_to_open tree_needleleaved_deciduous_closed tree_needleleaved_deciduous_open tree_mixed mosaic_tree_and_shrub mosaic_herbaceous shrubland shrubland_evergreen shrubland_deciduous grassland lichens_and_mosses sparse_vegetation sparse_tree sparse_shrub sparse_herbaceous tree_cover_flooded_fresh_or_brakish_water tree_cover_flooded_saline_water shrub_or_herbaceous_cover_flooded urban bare_areas bare_areas_consolidated bare_areas_unconsolidated water snow_and_ice

    flag_values :

    [0, 10, 11, 12, 20, 30, 40, 50, 60, 61, 62, 70, 71, 72, 80, 81, 82, 90, 100, 110, 120, 121, 122, 130, 140, 150, 151, 152, 153, 160, 170, 180, 190, 200, 201, 202, 210, 220]

    long_name :

    Land cover class defined in LCCS

    standard_name :

    land_cover_lccs

    valid_max :

    220

    valid_min :

    1

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 2.09 GiB 15.26 MiB Shape (5, 22225, 20223) (1, 4000, 4000) Dask graph 180 chunks in 64 graph layers Data type uint8 numpy.ndarray

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2018-01-01', '2019-01-01', '2020-01-01', '2021-01-01',
                   '2022-01-01'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • x
    PandasIndex

    PandasIndex(Index([1287677.0, 1287977.0, 1288277.0, 1288577.0, 1288877.0, 1289177.0,
           1289477.0, 1289777.0, 1290077.0, 1290377.0,
           ...
           7351577.0, 7351877.0, 7352177.0, 7352477.0, 7352777.0, 7353077.0,
           7353377.0, 7353677.0, 7353977.0, 7354277.0],
          dtype='float64', name='x', length=20223))

  • y
    PandasIndex

    PandasIndex(Index([6906752.0, 6906452.0, 6906152.0, 6905852.0, 6905552.0, 6905252.0,
           6904952.0, 6904652.0, 6904352.0, 6904052.0,
           ...
            242252.0,  241952.0,  241652.0,  241352.0,  241052.0,  240752.0,
            240452.0,  240152.0,  239852.0,  239552.0],
          dtype='float64', name='y', length=22225))

• Attributes: (38)

  Conventions :

  CF-1.6

  TileSize :

  2025:2025

  cdm_data_type :

  grid

  comment :

  contact :

  https://www.ecmwf.int/en/about/contact-us/get-support

  creation_date :

  20181130T095451Z

  creator_email :

  landcover-cci@uclouvain.be

  creator_name :

  UCLouvain

  creator_url :

  http://www.uclouvain.be/

  geospatial_lat_max :

  90.0

  geospatial_lat_min :

  -90.0

  geospatial_lat_resolution :

  0.002778

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  180

  geospatial_lon_min :

  -180

  geospatial_lon_resolution :

  0.002778

  geospatial_lon_units :

  degrees_east

  history :

  amorgos-4,0, lc-sdr-1.0, lc-sr-1.0, lc-classification-1.0,lc-user-tools-3.13,lc-user-tools-4.3

  id :

  ESACCI-LC-L4-LCCS-Map-300m-P1Y-1992-v2.0.7cds

  institution :

  UCLouvain

  keywords :

  land cover classification,satellite,observation

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  license :

  ESA CCI Data Policy: free and open access

  naming_authority :

  org.esa-cci

  product_version :

  2.0.7cds

  project :

  Climate Change Initiative - European Space Agency

  references :

  http://www.esa-landcover-cci.org/

  source :

  MERIS FR L1B version 5.05, MERIS RR L1B version 8.0, SPOT VGT P

  spatial_resolution :

  300m

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Standard Names version 21

  summary :

  This dataset characterizes the land cover of a particular year (see time_coverage). The land cover was derived from the analysis of satellite data time series of the full period.

  time_coverage_duration :

  P1Y

  time_coverage_end :

  19921231

  time_coverage_resolution :

  P1Y

  time_coverage_start :

  19920101

  title :

  Land Cover Map of ESA CCI brokered by CDS

  tracking_id :

  61b96fd7-42c3-4374-9de1-0dc3b0bcae2a

  type :

  ESACCI-LC-L4-LCCS-Map-300m-P1Y

In [9]:

%%time
ds_reproject.lccs_class.isel(time=-1)[::20, ::20].plot()

%%time ds_reproject.lccs_class.isel(time=-1)[::20, ::20].plot()

CPU times: user 2min 53s, sys: 46.9 s, total: 3min 40s
Wall time: 50.4 s

Out[9]:

<matplotlib.collections.QuadMesh at 0x758df9d80050>

In [10]:

%%time
ds_clip = ds.sel(lon=slice(-20, 40), lat=slice(85, 25))
ds_clip_reproject = resample_in_space(ds_clip, target_gm=target_gm)
ds_clip_reproject

%%time ds_clip = ds.sel(lon=slice(-20, 40), lat=slice(85, 25)) ds_clip_reproject = resample_in_space(ds_clip, target_gm=target_gm) ds_clip_reproject

CPU times: user 11.9 s, sys: 55.2 ms, total: 12 s
Wall time: 12 s

Out[10]:

<xarray.Dataset> Size: 2GB
Dimensions:      (time: 5, x: 20223, y: 22225)
Coordinates:
  * time         (time) datetime64[ns] 40B 2018-01-01 2019-01-01 ... 2022-01-01
    spatial_ref  int64 8B 0
  * x            (x) float64 162kB 1.288e+06 1.288e+06 ... 7.354e+06 7.354e+06
  * y            (y) float64 178kB 6.907e+06 6.906e+06 ... 2.399e+05 2.396e+05
Data variables:
    lccs_class   (time, y, x) uint8 2GB dask.array<chunksize=(1, 4000, 4000), meta=np.ndarray>
Attributes: (12/38)
    Conventions:                CF-1.6
    TileSize:                   2025:2025
    cdm_data_type:              grid
    comment:                    
    contact:                    https://www.ecmwf.int/en/about/contact-us/get...
    creation_date:              20181130T095451Z
    ...                         ...
    time_coverage_end:          19921231
    time_coverage_resolution:   P1Y
    time_coverage_start:        19920101
    title:                      Land Cover Map of ESA CCI brokered by CDS
    tracking_id:                61b96fd7-42c3-4374-9de1-0dc3b0bcae2a
    type:                       ESACCI-LC-L4-LCCS-Map-300m-P1Y

xarray.Dataset

• Dimensions:
  • time: 5
  • x: 20223
  • y: 22225
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2018-01-01 ... 2022-01-01

    axis :

    T

    bounds :

    time_bounds

    long_name :

    time

    standard_name :

    time

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

  • spatial_ref
    ()
    int64
    0

    crs_wkt :

    PROJCRS["ETRS89-extended / LAEA Europe",BASEGEOGCRS["ETRS89",ENSEMBLE["European Terrestrial Reference System 1989 ensemble",MEMBER["European Terrestrial Reference Frame 1989"],MEMBER["European Terrestrial Reference Frame 1990"],MEMBER["European Terrestrial Reference Frame 1991"],MEMBER["European Terrestrial Reference Frame 1992"],MEMBER["European Terrestrial Reference Frame 1993"],MEMBER["European Terrestrial Reference Frame 1994"],MEMBER["European Terrestrial Reference Frame 1996"],MEMBER["European Terrestrial Reference Frame 1997"],MEMBER["European Terrestrial Reference Frame 2000"],MEMBER["European Terrestrial Reference Frame 2005"],MEMBER["European Terrestrial Reference Frame 2014"],MEMBER["European Terrestrial Reference Frame 2020"],ELLIPSOID["GRS 1980",6378137,298.257222101,LENGTHUNIT["metre",1]],ENSEMBLEACCURACY[0.1]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],ID["EPSG",4258]],CONVERSION["Europe Equal Area 2001",METHOD["Lambert Azimuthal Equal Area",ID["EPSG",9820]],PARAMETER["Latitude of natural origin",52,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8801]],PARAMETER["Longitude of natural origin",10,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8802]],PARAMETER["False easting",4321000,LENGTHUNIT["metre",1],ID["EPSG",8806]],PARAMETER["False northing",3210000,LENGTHUNIT["metre",1],ID["EPSG",8807]]],CS[Cartesian,2],AXIS["northing (Y)",north,ORDER[1],LENGTHUNIT["metre",1]],AXIS["easting (X)",east,ORDER[2],LENGTHUNIT["metre",1]],USAGE[SCOPE["Statistical analysis."],AREA["Europe - European Union (EU) countries and candidates. Europe - onshore and offshore: Albania; Andorra; Austria; Belgium; Bosnia and Herzegovina; Bulgaria; Croatia; Cyprus; Czechia; Denmark; Estonia; Faroe Islands; Finland; France; Germany; Gibraltar; Greece; Hungary; Iceland; Ireland; Italy; Kosovo; Latvia; Liechtenstein; Lithuania; Luxembourg; Malta; Monaco; Montenegro; Netherlands; North Macedonia; Norway including Svalbard and Jan Mayen; Poland; Portugal including Madeira and Azores; Romania; San Marino; Serbia; Slovakia; Slovenia; Spain including Canary Islands; Sweden; Switzerland; Türkiye (Turkey); United Kingdom (UK) including Channel Islands and Isle of Man; Vatican City State."],BBOX[24.6,-35.58,84.73,44.83]],ID["EPSG",3035]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314140356

    inverse_flattening :

    298.257222101

    reference_ellipsoid_name :

    GRS 1980

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    ETRS89

    horizontal_datum_name :

    European Terrestrial Reference System 1989 ensemble

    projected_crs_name :

    ETRS89-extended / LAEA Europe

    grid_mapping_name :

    lambert_azimuthal_equal_area

    latitude_of_projection_origin :

    52.0

    longitude_of_projection_origin :

    10.0

    false_easting :

    4321000.0

    false_northing :

    3210000.0

    array(0)

  • x
    (x)
    float64
    1.288e+06 1.288e+06 ... 7.354e+06

    array([1287677., 1287977., 1288277., ..., 7353677., 7353977., 7354277.],
          shape=(20223,))

  • y
    (y)
    float64
    6.907e+06 6.906e+06 ... 2.396e+05

    array([6906752., 6906452., 6906152., ...,  240152.,  239852.,  239552.],
          shape=(22225,))

• Data variables: (1)
  • lccs_class
    (time, y, x)
    uint8
    dask.array<chunksize=(1, 4000, 4000), meta=np.ndarray>

    ancillary_variables :

    processed_flag current_pixel_state observation_count change_count

    flag_colors :

    #ffff64 #ffff64 #ffff00 #aaf0f0 #dcf064 #c8c864 #006400 #00a000 #00a000 #aac800 #003c00 #003c00 #005000 #285000 #285000 #286400 #788200 #8ca000 #be9600 #966400 #966400 #966400 #ffb432 #ffdcd2 #ffebaf #ffc864 #ffd278 #ffebaf #00785a #009678 #00dc82 #c31400 #fff5d7 #dcdcdc #fff5d7 #0046c8 #ffffff

    flag_meanings :

    no_data cropland_rainfed cropland_rainfed_herbaceous_cover cropland_rainfed_tree_or_shrub_cover cropland_irrigated mosaic_cropland mosaic_natural_vegetation tree_broadleaved_evergreen_closed_to_open tree_broadleaved_deciduous_closed_to_open tree_broadleaved_deciduous_closed tree_broadleaved_deciduous_open tree_needleleaved_evergreen_closed_to_open tree_needleleaved_evergreen_closed tree_needleleaved_evergreen_open tree_needleleaved_deciduous_closed_to_open tree_needleleaved_deciduous_closed tree_needleleaved_deciduous_open tree_mixed mosaic_tree_and_shrub mosaic_herbaceous shrubland shrubland_evergreen shrubland_deciduous grassland lichens_and_mosses sparse_vegetation sparse_tree sparse_shrub sparse_herbaceous tree_cover_flooded_fresh_or_brakish_water tree_cover_flooded_saline_water shrub_or_herbaceous_cover_flooded urban bare_areas bare_areas_consolidated bare_areas_unconsolidated water snow_and_ice

    flag_values :

    [0, 10, 11, 12, 20, 30, 40, 50, 60, 61, 62, 70, 71, 72, 80, 81, 82, 90, 100, 110, 120, 121, 122, 130, 140, 150, 151, 152, 153, 160, 170, 180, 190, 200, 201, 202, 210, 220]

    long_name :

    Land cover class defined in LCCS

    standard_name :

    land_cover_lccs

    valid_max :

    220

    valid_min :

    1

    grid_mapping :

    spatial_ref

    Array Chunk Bytes 2.09 GiB 15.26 MiB Shape (5, 22225, 20223) (1, 4000, 4000) Dask graph 180 chunks in 64 graph layers Data type uint8 numpy.ndarray

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2018-01-01', '2019-01-01', '2020-01-01', '2021-01-01',
                   '2022-01-01'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • x
    PandasIndex

    PandasIndex(Index([1287677.0, 1287977.0, 1288277.0, 1288577.0, 1288877.0, 1289177.0,
           1289477.0, 1289777.0, 1290077.0, 1290377.0,
           ...
           7351577.0, 7351877.0, 7352177.0, 7352477.0, 7352777.0, 7353077.0,
           7353377.0, 7353677.0, 7353977.0, 7354277.0],
          dtype='float64', name='x', length=20223))

  • y
    PandasIndex

    PandasIndex(Index([6906752.0, 6906452.0, 6906152.0, 6905852.0, 6905552.0, 6905252.0,
           6904952.0, 6904652.0, 6904352.0, 6904052.0,
           ...
            242252.0,  241952.0,  241652.0,  241352.0,  241052.0,  240752.0,
            240452.0,  240152.0,  239852.0,  239552.0],
          dtype='float64', name='y', length=22225))

• Attributes: (38)

  Conventions :

  CF-1.6

  TileSize :

  2025:2025

  cdm_data_type :

  grid

  comment :

  contact :

  https://www.ecmwf.int/en/about/contact-us/get-support

  creation_date :

  20181130T095451Z

  creator_email :

  landcover-cci@uclouvain.be

  creator_name :

  UCLouvain

  creator_url :

  http://www.uclouvain.be/

  geospatial_lat_max :

  90.0

  geospatial_lat_min :

  -90.0

  geospatial_lat_resolution :

  0.002778

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  180

  geospatial_lon_min :

  -180

  geospatial_lon_resolution :

  0.002778

  geospatial_lon_units :

  degrees_east

  history :

  amorgos-4,0, lc-sdr-1.0, lc-sr-1.0, lc-classification-1.0,lc-user-tools-3.13,lc-user-tools-4.3

  id :

  ESACCI-LC-L4-LCCS-Map-300m-P1Y-1992-v2.0.7cds

  institution :

  UCLouvain

  keywords :

  land cover classification,satellite,observation

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  license :

  ESA CCI Data Policy: free and open access

  naming_authority :

  org.esa-cci

  product_version :

  2.0.7cds

  project :

  Climate Change Initiative - European Space Agency

  references :

  http://www.esa-landcover-cci.org/

  source :

  MERIS FR L1B version 5.05, MERIS RR L1B version 8.0, SPOT VGT P

  spatial_resolution :

  300m

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Standard Names version 21

  summary :

  This dataset characterizes the land cover of a particular year (see time_coverage). The land cover was derived from the analysis of satellite data time series of the full period.

  time_coverage_duration :

  P1Y

  time_coverage_end :

  19921231

  time_coverage_resolution :

  P1Y

  time_coverage_start :

  19920101

  title :

  Land Cover Map of ESA CCI brokered by CDS

  tracking_id :

  61b96fd7-42c3-4374-9de1-0dc3b0bcae2a

  type :

  ESACCI-LC-L4-LCCS-Map-300m-P1Y

In [11]:

%%time
ds_clip_reproject.lccs_class.isel(time=-1)[::20, ::20].plot()

%%time ds_clip_reproject.lccs_class.isel(time=-1)[::20, ::20].plot()

CPU times: user 3min 1s, sys: 56.1 s, total: 3min 57s
Wall time: 27.1 s

Out[11]:

<matplotlib.collections.QuadMesh at 0x758d913c6ad0>