Xarray + rio-tiler

In [1]:

import matplotlib.pyplot as plt
import xarray

from rio_tiler.io.xarray import XarrayReader

import matplotlib.pyplot as plt import xarray from rio_tiler.io.xarray import XarrayReader

MUR SST¶

In [2]:

ds = xarray.open_dataset(
    "https://mur-sst.s3.us-west-2.amazonaws.com/zarr-v1",
    engine="zarr",
    decode_coords="all",
    consolidated=True,
)
ds

ds = xarray.open_dataset( "https://mur-sst.s3.us-west-2.amazonaws.com/zarr-v1", engine="zarr", decode_coords="all", consolidated=True, ) ds

Out[2]:

<xarray.Dataset> Size: 117TB
Dimensions:           (time: 6443, lat: 17999, lon: 36000)
Coordinates:
  * lat               (lat) float32 72kB -89.99 -89.98 -89.97 ... 89.98 89.99
  * lon               (lon) float32 144kB -180.0 -180.0 -180.0 ... 180.0 180.0
  * time              (time) datetime64[ns] 52kB 2002-06-01T09:00:00 ... 2020...
Data variables:
    analysed_sst      (time, lat, lon) float64 33TB ...
    analysis_error    (time, lat, lon) float64 33TB ...
    mask              (time, lat, lon) float32 17TB ...
    sea_ice_fraction  (time, lat, lon) float64 33TB ...
Attributes: (12/47)
    Conventions:                CF-1.7
    Metadata_Conventions:       Unidata Observation Dataset v1.0
    acknowledgment:             Please acknowledge the use of these data with...
    cdm_data_type:              grid
    comment:                    MUR = "Multi-scale Ultra-high Resolution"
    creator_email:              ghrsst@podaac.jpl.nasa.gov
    ...                         ...
    summary:                    A merged, multi-sensor L4 Foundation SST anal...
    time_coverage_end:          20200116T210000Z
    time_coverage_start:        20200115T210000Z
    title:                      Daily MUR SST, Final product
    uuid:                       27665bc0-d5fc-11e1-9b23-0800200c9a66
    westernmost_longitude:      -180.0

xarray.Dataset

• Dimensions:
  • time: 6443
  • lat: 17999
  • lon: 36000
• Coordinates: (3)
  • lat
    (lat)
    float32
    -89.99 -89.98 ... 89.98 89.99

    axis :

    Y

    comment :

    none

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    valid_max :

    90.0

    valid_min :

    -90.0

    array([-89.99, -89.98, -89.97, ...,  89.97,  89.98,  89.99], dtype=float32)

  • lon
    (lon)
    float32
    -180.0 -180.0 ... 180.0 180.0

    axis :

    X

    comment :

    none

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    valid_max :

    180.0

    valid_min :

    -180.0

    array([-179.99, -179.98, -179.97, ...,  179.98,  179.99,  180.  ],
          dtype=float32)

  • time
    (time)
    datetime64[ns]
    2002-06-01T09:00:00 ... 2020-01-...

    axis :

    T

    comment :

    Nominal time of analyzed fields

    long_name :

    reference time of sst field

    standard_name :

    time

    array(['2002-06-01T09:00:00.000000000', '2002-06-02T09:00:00.000000000',
           '2002-06-03T09:00:00.000000000', ..., '2020-01-18T09:00:00.000000000',
           '2020-01-19T09:00:00.000000000', '2020-01-20T09:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (4)
  • analysed_sst
    (time, lat, lon)
    float64
    ...

    comment :

    "Final" version using Multi-Resolution Variational Analysis (MRVA) method for interpolation

    long_name :

    analysed sea surface temperature

    standard_name :

    sea_surface_foundation_temperature

    units :

    kelvin

    valid_max :

    32767

    valid_min :

    -32767

    [4174832052000 values with dtype=float64]

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

    comment :

    none

    long_name :

    estimated error standard deviation of analysed_sst

    units :

    kelvin

    valid_max :

    32767

    valid_min :

    0

    [4174832052000 values with dtype=float64]

  • mask
    (time, lat, lon)
    float32
    ...

    comment :

    mask can be used to further filter the data.

    flag_masks :

    [1, 2, 4, 8, 16]

    flag_meanings :

    1=open-sea, 2=land, 5=open-lake, 9=open-sea with ice in the grid, 13=open-lake with ice in the grid

    flag_values :

    [1, 2, 5, 9, 13]

    long_name :

    sea/land field composite mask

    source :

    GMT "grdlandmask", ice flag from sea_ice_fraction data

    valid_max :

    31

    valid_min :

    1

    [4174832052000 values with dtype=float32]

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

    comment :

    ice data interpolated by a nearest neighbor approach.

    long_name :

    sea ice area fraction

    source :

    EUMETSAT OSI-SAF, copyright EUMETSAT

    standard_name :

    sea ice area fraction

    units :

    fraction (between 0 and 1)

    valid_max :

    100

    valid_min :

    0

    [4174832052000 values with dtype=float64]

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([-89.98999786376953,  -89.9800033569336, -89.97000122070312,
           -89.95999908447266, -89.94999694824219, -89.94000244140625,
           -89.93000030517578, -89.91999816894531, -89.91000366210938,
            -89.9000015258789,
           ...
             89.9000015258789,  89.91000366210938,  89.91999816894531,
            89.93000030517578,  89.94000244140625,  89.94999694824219,
            89.95999908447266,  89.97000122070312,   89.9800033569336,
            89.98999786376953],
          dtype='float32', name='lat', length=17999))

  • lon
    PandasIndex

    PandasIndex(Index([-179.99000549316406, -179.97999572753906, -179.97000122070312,
            -179.9600067138672,  -179.9499969482422, -179.94000244140625,
           -179.92999267578125,  -179.9199981689453, -179.91000366210938,
           -179.89999389648438,
           ...
            179.91000366210938,   179.9199981689453,  179.92999267578125,
            179.94000244140625,   179.9499969482422,   179.9600067138672,
            179.97000122070312,  179.97999572753906,  179.99000549316406,
                         180.0],
          dtype='float32', name='lon', length=36000))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2002-06-01 09:00:00', '2002-06-02 09:00:00',
                   '2002-06-03 09:00:00', '2002-06-04 09:00:00',
                   '2002-06-05 09:00:00', '2002-06-06 09:00:00',
                   '2002-06-07 09:00:00', '2002-06-08 09:00:00',
                   '2002-06-09 09:00:00', '2002-06-10 09:00:00',
                   ...
                   '2020-01-11 09:00:00', '2020-01-12 09:00:00',
                   '2020-01-13 09:00:00', '2020-01-14 09:00:00',
                   '2020-01-15 09:00:00', '2020-01-16 09:00:00',
                   '2020-01-17 09:00:00', '2020-01-18 09:00:00',
                   '2020-01-19 09:00:00', '2020-01-20 09:00:00'],
                  dtype='datetime64[ns]', name='time', length=6443, freq=None))

• Attributes: (47)

  Conventions :

  CF-1.7

  Metadata_Conventions :

  Unidata Observation Dataset v1.0

  acknowledgment :

  Please acknowledge the use of these data with the following statement: These data were provided by JPL under support by NASA MEaSUREs program.

  cdm_data_type :

  grid

  comment :

  MUR = "Multi-scale Ultra-high Resolution"

  creator_email :

  ghrsst@podaac.jpl.nasa.gov

  creator_name :

  JPL MUR SST project

  creator_url :

  http://mur.jpl.nasa.gov

  date_created :

  20200124T010755Z

  easternmost_longitude :

  180.0

  file_quality_level :

  3

  gds_version_id :

  2.0

  geospatial_lat_resolution :

  0.009999999776482582

  geospatial_lat_units :

  degrees north

  geospatial_lon_resolution :

  0.009999999776482582

  geospatial_lon_units :

  degrees east

  history :

  created at nominal 4-day latency; replaced nrt (1-day latency) version.

  id :

  MUR-JPL-L4-GLOB-v04.1

  institution :

  Jet Propulsion Laboratory

  keywords :

  Oceans > Ocean Temperature > Sea Surface Temperature

  keywords_vocabulary :

  NASA Global Change Master Directory (GCMD) Science Keywords

  license :

  These data are available free of charge under data policy of JPL PO.DAAC.

  metadata_link :

  http://podaac.jpl.nasa.gov/ws/metadata/dataset/?format=iso&shortName=MUR-JPL-L4-GLOB-v04.1

  naming_authority :

  org.ghrsst

  netcdf_version_id :

  4.1

  northernmost_latitude :

  90.0

  platform :

  Terra, Aqua, GCOM-W, MetOp-A, MetOp-B, Buoys/Ships

  processing_level :

  L4

  product_version :

  04.1

  project :

  NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Program

  publisher_email :

  ghrsst-po@nceo.ac.uk

  publisher_name :

  GHRSST Project Office

  publisher_url :

  http://www.ghrsst.org

  references :

  http://podaac.jpl.nasa.gov/Multi-scale_Ultra-high_Resolution_MUR-SST

  sensor :

  MODIS, AMSR2, AVHRR, in-situ

  source :

  MODIS_T-JPL, MODIS_A-JPL, AMSR2-REMSS, AVHRRMTA_G-NAVO, AVHRRMTB_G-NAVO, iQUAM-NOAA/NESDIS, Ice_Conc-OSISAF

  southernmost_latitude :

  -90.0

  spatial_resolution :

  0.01 degrees

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Metadata Convention

  start_time :

  20200116T090000Z

  stop_time :

  20200116T090000Z

  summary :

  A merged, multi-sensor L4 Foundation SST analysis product from JPL.

  time_coverage_end :

  20200116T210000Z

  time_coverage_start :

  20200115T210000Z

  title :

  Daily MUR SST, Final product

  uuid :

  27665bc0-d5fc-11e1-9b23-0800200c9a66

  westernmost_longitude :

  -180.0

In [3]:

da = ds["analysed_sst"]
da

da = ds["analysed_sst"] da

Out[3]:

<xarray.DataArray 'analysed_sst' (time: 6443, lat: 17999, lon: 36000)> Size: 33TB
[4174832052000 values with dtype=float64]
Coordinates:
  * lat      (lat) float32 72kB -89.99 -89.98 -89.97 ... 89.97 89.98 89.99
  * lon      (lon) float32 144kB -180.0 -180.0 -180.0 ... 180.0 180.0 180.0
  * time     (time) datetime64[ns] 52kB 2002-06-01T09:00:00 ... 2020-01-20T09...
Attributes:
    comment:        "Final" version using Multi-Resolution Variational Analys...
    long_name:      analysed sea surface temperature
    standard_name:  sea_surface_foundation_temperature
    units:          kelvin
    valid_max:      32767
    valid_min:      -32767

xarray.DataArray

'analysed_sst'

• time: 6443
• lat: 17999
• lon: 36000

• ...

  [4174832052000 values with dtype=float64]

• Coordinates: (3)
  • lat
    (lat)
    float32
    -89.99 -89.98 ... 89.98 89.99

    axis :

    Y

    comment :

    none

    long_name :

    latitude

    standard_name :

    latitude

    units :

    degrees_north

    valid_max :

    90.0

    valid_min :

    -90.0

    array([-89.99, -89.98, -89.97, ...,  89.97,  89.98,  89.99], dtype=float32)

  • lon
    (lon)
    float32
    -180.0 -180.0 ... 180.0 180.0

    axis :

    X

    comment :

    none

    long_name :

    longitude

    standard_name :

    longitude

    units :

    degrees_east

    valid_max :

    180.0

    valid_min :

    -180.0

    array([-179.99, -179.98, -179.97, ...,  179.98,  179.99,  180.  ],
          dtype=float32)

  • time
    (time)
    datetime64[ns]
    2002-06-01T09:00:00 ... 2020-01-...

    axis :

    T

    comment :

    Nominal time of analyzed fields

    long_name :

    reference time of sst field

    standard_name :

    time

    array(['2002-06-01T09:00:00.000000000', '2002-06-02T09:00:00.000000000',
           '2002-06-03T09:00:00.000000000', ..., '2020-01-18T09:00:00.000000000',
           '2020-01-19T09:00:00.000000000', '2020-01-20T09:00:00.000000000'],
          dtype='datetime64[ns]')

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([-89.98999786376953,  -89.9800033569336, -89.97000122070312,
           -89.95999908447266, -89.94999694824219, -89.94000244140625,
           -89.93000030517578, -89.91999816894531, -89.91000366210938,
            -89.9000015258789,
           ...
             89.9000015258789,  89.91000366210938,  89.91999816894531,
            89.93000030517578,  89.94000244140625,  89.94999694824219,
            89.95999908447266,  89.97000122070312,   89.9800033569336,
            89.98999786376953],
          dtype='float32', name='lat', length=17999))

  • lon
    PandasIndex

    PandasIndex(Index([-179.99000549316406, -179.97999572753906, -179.97000122070312,
            -179.9600067138672,  -179.9499969482422, -179.94000244140625,
           -179.92999267578125,  -179.9199981689453, -179.91000366210938,
           -179.89999389648438,
           ...
            179.91000366210938,   179.9199981689453,  179.92999267578125,
            179.94000244140625,   179.9499969482422,   179.9600067138672,
            179.97000122070312,  179.97999572753906,  179.99000549316406,
                         180.0],
          dtype='float32', name='lon', length=36000))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2002-06-01 09:00:00', '2002-06-02 09:00:00',
                   '2002-06-03 09:00:00', '2002-06-04 09:00:00',
                   '2002-06-05 09:00:00', '2002-06-06 09:00:00',
                   '2002-06-07 09:00:00', '2002-06-08 09:00:00',
                   '2002-06-09 09:00:00', '2002-06-10 09:00:00',
                   ...
                   '2020-01-11 09:00:00', '2020-01-12 09:00:00',
                   '2020-01-13 09:00:00', '2020-01-14 09:00:00',
                   '2020-01-15 09:00:00', '2020-01-16 09:00:00',
                   '2020-01-17 09:00:00', '2020-01-18 09:00:00',
                   '2020-01-19 09:00:00', '2020-01-20 09:00:00'],
                  dtype='datetime64[ns]', name='time', length=6443, freq=None))

• Attributes: (6)

  comment :

  "Final" version using Multi-Resolution Variational Analysis (MRVA) method for interpolation

  long_name :

  analysed sea surface temperature

  standard_name :

  sea_surface_foundation_temperature

  units :

  kelvin

  valid_max :

  32767

  valid_min :

  -32767

In [4]:

da = ds["analysed_sst"]

# Make sure we have a valid CRS
crs = da.rio.crs or "epsg:4326"
da.rio.write_crs(crs, inplace=True)

# Select the first time stamp
da = da.isel(time=0)

with XarrayReader(da) as dst:
    print(dst.info())
    print(dst.minzoom, dst.maxzoom)

da = ds["analysed_sst"] # Make sure we have a valid CRS crs = da.rio.crs or "epsg:4326" da.rio.write_crs(crs, inplace=True) # Select the first time stamp da = da.isel(time=0) with XarrayReader(da) as dst: print(dst.info()) print(dst.minzoom, dst.maxzoom)

bounds=(-179.99500549324037, -89.99499786365084, 180.0050000000763, 89.99499786365084) crs='http://www.opengis.net/def/crs/EPSG/0/4326' band_metadata=[('b1', {})] band_descriptions=[('b1', 'value')] dtype='float64' nodata_type='Nodata' colorinterp=None scales=None offsets=None colormap=None name='analysed_sst' count=1 width=36000 height=17999 attrs={'comment': '"Final" version using Multi-Resolution Variational Analysis (MRVA) method for interpolation', 'long_name': 'analysed sea surface temperature', 'standard_name': 'sea_surface_foundation_temperature', 'units': 'kelvin', 'valid_max': 32767, 'valid_min': -32767}
0 6

In [5]:

with XarrayReader(da) as dst:
    img = dst.tile(31, 22, 6)

plt.imshow(img.data_as_image())

with XarrayReader(da) as dst: img = dst.tile(31, 22, 6) plt.imshow(img.data_as_image())

Out[5]:

<matplotlib.image.AxesImage at 0x11aeca1f0>

In [6]:

with XarrayReader(da) as dst:
    img = dst.part(
        [-5, 45, 0.0, 49],
        max_size=1024,
    )

plt.imshow(img.data_as_image())

with XarrayReader(da) as dst: img = dst.part( [-5, 45, 0.0, 49], max_size=1024, ) plt.imshow(img.data_as_image())

Out[6]:

<matplotlib.image.AxesImage at 0x11b6b6c40>

In [7]:

geojson = {
    "type": "Feature",
    "properties": {},
    "geometry": {
        "coordinates": [
            [
                [-4.262517145703015, 49.58852771128011],
                [-6.121754821536115, 48.07366436953052],
                [-5.679508966604999, 47.26534294147169],
                [-2.890652452854056, 46.8101748147248],
                [-1.8527423498805433, 47.28371740880311],
                [-1.6541805026549241, 49.26565692911578],
                [-3.0440846882388257, 49.565118360747164],
                [-4.262517145703015, 49.58852771128011],
            ]
        ],
        "type": "Polygon",
    },
}

with XarrayReader(da) as dst:
    img = dst.feature(
        geojson,
        max_size=1024,
    )

plt.imshow(img.data_as_image())

geojson = { "type": "Feature", "properties": {}, "geometry": { "coordinates": [ [ [-4.262517145703015, 49.58852771128011], [-6.121754821536115, 48.07366436953052], [-5.679508966604999, 47.26534294147169], [-2.890652452854056, 46.8101748147248], [-1.8527423498805433, 47.28371740880311], [-1.6541805026549241, 49.26565692911578], [-3.0440846882388257, 49.565118360747164], [-4.262517145703015, 49.58852771128011], ] ], "type": "Polygon", }, } with XarrayReader(da) as dst: img = dst.feature( geojson, max_size=1024, ) plt.imshow(img.data_as_image())

Out[7]:

<matplotlib.image.AxesImage at 0x11aeb80d0>

NetCDF¶

In [8]:

import fsspec

filesystem = fsspec.filesystem("https")

fp = filesystem.open(
    "https://dap.ceda.ac.uk/neodc/esacci/land_surface_temperature/data/MULTISENSOR_IRCDR/L3S/0.01/v2.00/monthly/2020/11/ESACCI-LST-L3S-LST-IRCDR_-0.01deg_1MONTHLY_DAY-20201101000000-fv2.00.nc"
)
ds = xarray.open_dataset(
    fp,
    engine="h5netcdf",
    decode_coords="all",
)
ds

import fsspec filesystem = fsspec.filesystem("https") fp = filesystem.open( "https://dap.ceda.ac.uk/neodc/esacci/land_surface_temperature/data/MULTISENSOR_IRCDR/L3S/0.01/v2.00/monthly/2020/11/ESACCI-LST-L3S-LST-IRCDR_-0.01deg_1MONTHLY_DAY-20201101000000-fv2.00.nc" ) ds = xarray.open_dataset( fp, engine="h5netcdf", decode_coords="all", ) ds

Out[8]:

<xarray.Dataset> Size: 36GB
Dimensions:          (time: 1, lat: 18000, lon: 36000, length_scale: 1,
                      channel: 2)
Coordinates:
  * time             (time) datetime64[ns] 8B 2020-11-01
  * lat              (lat) float32 72kB -90.0 -89.99 -89.98 ... 89.98 89.99
  * lon              (lon) float32 144kB -180.0 -180.0 -180.0 ... 180.0 180.0
  * channel          (channel) float32 8B 11.0 12.0
Dimensions without coordinates: length_scale
Data variables: (12/14)
    dtime            (time, lat, lon) timedelta64[ns] 5GB ...
    satze            (time, lat, lon) float32 3GB ...
    sataz            (time, lat, lon) float32 3GB ...
    solze            (time, lat, lon) float32 3GB ...
    solaz            (time, lat, lon) float32 3GB ...
    lst              (time, lat, lon) float32 3GB ...
    ...               ...
    lst_unc_loc_atm  (time, lat, lon) float32 3GB ...
    lst_unc_loc_sfc  (time, lat, lon) float32 3GB ...
    lst_unc_sys      (length_scale) float32 4B ...
    lcc              (time, lat, lon) float32 3GB ...
    n                (time, lat, lon) float32 3GB ...
    lst_unc_loc_cor  (time, lat, lon) float32 3GB ...
Attributes: (12/41)
    source:                     ESA LST CCI IRCDR L3S V2.00
    title:                      ESA LST CCI land surface temperature time ser...
    institution:                University of Leicester
    history:                    Created using software developed at Universit...
    references:                 https://climate.esa.int/en/projects/land-surf...
    Conventions:                CF-1.8
    ...                         ...
    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

xarray.Dataset

• Dimensions:
  • time: 1
  • lat: 18000
  • lon: 36000
  • length_scale: 1
  • channel: 2
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2020-11-01

    long_name :

    reference time of file

    standard_name :

    time

    array(['2020-11-01T00:00:00.000000000'], dtype='datetime64[ns]')

  • lat
    (lat)
    float32
    -90.0 -89.99 -89.98 ... 89.98 89.99

    long_name :

    latitude_coordinates

    standard_name :

    latitude

    units :

    degrees_north

    valid_min :

    -90.0

    valid_max :

    90.0

    reference_datum :

    geographical coordinates, WGS84 projection

    array([-89.995   , -89.985   , -89.975006, ...,  89.975   ,  89.98499 ,
            89.99499 ], dtype=float32)

  • lon
    (lon)
    float32
    -180.0 -180.0 ... 180.0 180.0

    long_name :

    longitude_coordinates

    standard_name :

    longitude

    units :

    degrees_east

    valid_min :

    -180.0

    valid_max :

    180.0

    reference_datum :

    geographical coordinates, WGS84 projection

    array([-179.995  , -179.985  , -179.97499, ...,  179.975  ,  179.98499,
            179.995  ], dtype=float32)

  • channel
    (channel)
    float32
    11.0 12.0

    long_name :

    channel wavelength in microns

    units :

    microns

    valid_min :

    0

    valid_max :

    15000

    array([10.999001, 11.999001], dtype=float32)

• Data variables: (14)
  • dtime
    (time, lat, lon)
    timedelta64[ns]
    ...

    long_name :

    time difference from reference time

    valid_min :

    0.0

    valid_max :

    2592000.0

    [648000000 values with dtype=timedelta64[ns]]

  • satze
    (time, lat, lon)
    float32
    ...

    long_name :

    satellite zenith angle

    units :

    degrees

    valid_min :

    0

    valid_max :

    18000

    [648000000 values with dtype=float32]

  • sataz
    (time, lat, lon)
    float32
    ...

    long_name :

    satellite azimuth angle

    units :

    degrees

    valid_min :

    -18000

    valid_max :

    18000

    [648000000 values with dtype=float32]

  • solze
    (time, lat, lon)
    float32
    ...

    long_name :

    solar zenith angle

    units :

    degrees

    valid_min :

    0

    valid_max :

    18000

    [648000000 values with dtype=float32]

  • solaz
    (time, lat, lon)
    float32
    ...

    long_name :

    solar azimuth angle

    units :

    degrees

    valid_min :

    -18000

    valid_max :

    18000

    [648000000 values with dtype=float32]

  • lst
    (time, lat, lon)
    float32
    ...

    long_name :

    land surface temperature

    units :

    kelvin

    valid_min :

    -8315

    valid_max :

    7685

    [648000000 values with dtype=float32]

  • lst_uncertainty
    (time, lat, lon)
    float32
    ...

    long_name :

    land surface temperature total uncertainty

    units :

    kelvin

    valid_min :

    0

    valid_max :

    10000

    [648000000 values with dtype=float32]

  • lst_unc_ran
    (time, lat, lon)
    float32
    ...

    long_name :

    uncertainty from uncorrelated errors

    units :

    kelvin

    valid_min :

    0

    valid_max :

    10000

    [648000000 values with dtype=float32]

  • lst_unc_loc_atm
    (time, lat, lon)
    float32
    ...

    long_name :

    uncertainty from locally correlated errors on atmospheric scales

    units :

    kelvin

    valid_min :

    0

    valid_max :

    10000

    [648000000 values with dtype=float32]

  • lst_unc_loc_sfc
    (time, lat, lon)
    float32
    ...

    long_name :

    uncertainty from locally correlated errors on surface scales

    units :

    kelvin

    valid_min :

    0

    valid_max :

    10000

    [648000000 values with dtype=float32]

  • lst_unc_sys
    (length_scale)
    float32
    ...

    long_name :

    uncertainty from large-scale systematic errors

    units :

    kelvin

    valid_min :

    0

    valid_max :

    10000

    [1 values with dtype=float32]

  • lcc
    (time, lat, lon)
    float32
    ...

    long_name :

    land cover class

    units :

    1

    flag_meanings :

    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_of_soil_types_not_contained_in_biomes_203_to_207 Unconsolidated_bare_areas_of_soil_types_not_contained_in_biomes_203_to_207 Consolidated_bare_areas_of_soil_types_not_contained_in_biomes_203_to_207 Bare_areas_of_soil_type_Entisols_Orthents Bare_areas_of_soil_type_Shifting_sand Bare_areas_of_soil_type_Aridisols_Calcids Bare_areas_of_soil_type_Aridisols_Cambids Bare_areas_of_soil_type_Gelisols_Orthels water snow_and_ice Sea_ice

    flag_values :

    [ 10 11 12 20 30 40 50 60 61 62 70 71 72 80 81 82 90 100 110 120 121 122 130 140 150 151 152 153 160 170 180 190 200 201 202 203 204 205 206 207 210 220 230]

    valid_min :

    10

    valid_max :

    230

    [648000000 values with dtype=float32]

  • n
    (time, lat, lon)
    float32
    ...

    long_name :

    number of clear-sky pixels

    valid_min :

    0

    valid_max :

    18750

    [648000000 values with dtype=float32]

  • lst_unc_loc_cor
    (time, lat, lon)
    float32
    ...

    long_name :

    uncertainty from locally correlated errors on LST corrections

    units :

    kelvin

    valid_min :

    0

    valid_max :

    10000

    [648000000 values with dtype=float32]

• Indexes: (4)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-11-01'], dtype='datetime64[ns]', name='time', freq=None))

  • lat
    PandasIndex

    PandasIndex(Index([-89.99500274658203, -89.98500061035156, -89.97500610351562,
           -89.96500396728516, -89.95500183105469, -89.94499969482422,
           -89.93500518798828, -89.92500305175781, -89.91500091552734,
            -89.9050064086914,
           ...
            89.90499114990234,  89.91498565673828,  89.92499542236328,
            89.93498992919922,  89.94499969482422,  89.95499420166016,
             89.9649887084961,   89.9749984741211,  89.98499298095703,
            89.99498748779297],
          dtype='float32', name='lat', length=18000))

  • lon
    PandasIndex

    PandasIndex(Index([ -179.9949951171875, -179.98500061035156, -179.97499084472656,
           -179.96499633789062,  -179.9550018310547,  -179.9449920654297,
           -179.93499755859375, -179.92498779296875,  -179.9149932861328,
           -179.90499877929688,
           ...
            179.90499877929688,  179.91500854492188,  179.92498779296875,
            179.93499755859375,  179.94500732421875,  179.95498657226562,
            179.96499633789062,  179.97500610351562,   179.9849853515625,
             179.9949951171875],
          dtype='float32', name='lon', length=36000))

  • channel
    PandasIndex

    PandasIndex(Index([10.999001, 11.999001], dtype='float32', name='channel'))

• Attributes: (41)

  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 :

  Created using software developed at University of Leicester

  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-L3S-LST-IRCDR_-0.01deg_1MONTHLY_DAY-20201101000000-fv2.00.nc

  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 :

  20220616T135237Z

  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 :

  -89.995

  geospatial_lat_max :

  89.99499

  geospatial_lon_min :

  -179.995

  geospatial_lon_max :

  179.995

  geospatial_vertical_min :

  0.0

  geospatial_vertical_max :

  0.0

  time_coverage_start :

  20201101T000000

  time_coverage_end :

  20201130T235959

  time_coverage_duration :

  P1M

  time_coverage_resolution :

  P1M

  standard_name_vocabulary :

  CF Standard Name Table v71

  license :

  ESA CCI Data Policy: free and open access

  platform :

  Sentinel-3A

  sensor :

  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

In [9]:

da = ds["lst"]

# # Make sure we have a valid CRS
crs = da.rio.crs or "epsg:4326"
da.rio.write_crs(crs, inplace=True)

# Select the first time stamp
# da = da.isel(time=0)
da

da = ds["lst"] # # Make sure we have a valid CRS crs = da.rio.crs or "epsg:4326" da.rio.write_crs(crs, inplace=True) # Select the first time stamp # da = da.isel(time=0) da

Out[9]:

<xarray.DataArray 'lst' (time: 1, lat: 18000, lon: 36000)> Size: 3GB
[648000000 values with dtype=float32]
Coordinates:
  * time         (time) datetime64[ns] 8B 2020-11-01
  * lat          (lat) float32 72kB -90.0 -89.99 -89.98 ... 89.97 89.98 89.99
  * lon          (lon) float32 144kB -180.0 -180.0 -180.0 ... 180.0 180.0 180.0
    spatial_ref  int64 8B 0
Attributes:
    long_name:  land surface temperature
    units:      kelvin
    valid_min:  -8315
    valid_max:  7685

xarray.DataArray

'lst'

• time: 1
• lat: 18000
• lon: 36000

• ...

  [648000000 values with dtype=float32]

• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2020-11-01

    long_name :

    reference time of file

    standard_name :

    time

    array(['2020-11-01T00:00:00.000000000'], dtype='datetime64[ns]')

  • lat
    (lat)
    float32
    -90.0 -89.99 -89.98 ... 89.98 89.99

    long_name :

    latitude_coordinates

    standard_name :

    latitude

    units :

    degrees_north

    valid_min :

    -90.0

    valid_max :

    90.0

    reference_datum :

    geographical coordinates, WGS84 projection

    array([-89.995   , -89.985   , -89.975006, ...,  89.975   ,  89.98499 ,
            89.99499 ], dtype=float32)

  • lon
    (lon)
    float32
    -180.0 -180.0 ... 180.0 180.0

    long_name :

    longitude_coordinates

    standard_name :

    longitude

    units :

    degrees_east

    valid_min :

    -180.0

    valid_max :

    180.0

    reference_datum :

    geographical coordinates, WGS84 projection

    array([-179.995  , -179.985  , -179.97499, ...,  179.975  ,  179.98499,
            179.995  ], dtype=float32)

  • spatial_ref
    ()
    int64
    0

    crs_wkt :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    inverse_flattening :

    298.257223563

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    horizontal_datum_name :

    World Geodetic System 1984

    grid_mapping_name :

    latitude_longitude

    spatial_ref :

    GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]

    array(0)

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2020-11-01'], dtype='datetime64[ns]', name='time', freq=None))

  • lat
    PandasIndex

    PandasIndex(Index([-89.99500274658203, -89.98500061035156, -89.97500610351562,
           -89.96500396728516, -89.95500183105469, -89.94499969482422,
           -89.93500518798828, -89.92500305175781, -89.91500091552734,
            -89.9050064086914,
           ...
            89.90499114990234,  89.91498565673828,  89.92499542236328,
            89.93498992919922,  89.94499969482422,  89.95499420166016,
             89.9649887084961,   89.9749984741211,  89.98499298095703,
            89.99498748779297],
          dtype='float32', name='lat', length=18000))

  • lon
    PandasIndex

    PandasIndex(Index([ -179.9949951171875, -179.98500061035156, -179.97499084472656,
           -179.96499633789062,  -179.9550018310547,  -179.9449920654297,
           -179.93499755859375, -179.92498779296875,  -179.9149932861328,
           -179.90499877929688,
           ...
            179.90499877929688,  179.91500854492188,  179.92498779296875,
            179.93499755859375,  179.94500732421875,  179.95498657226562,
            179.96499633789062,  179.97500610351562,   179.9849853515625,
             179.9949951171875],
          dtype='float32', name='lon', length=36000))

• Attributes: (4)

  long_name :

  land surface temperature

  units :

  kelvin

  valid_min :

  -8315

  valid_max :

  7685

In [10]:

with XarrayReader(da) as dst:
    print(dst.info())
    print(dst.minzoom, dst.maxzoom)

with XarrayReader(da) as dst: print(dst.info()) print(dst.minzoom, dst.maxzoom)

bounds=(-179.99999511705187, -90.00000274631076, 179.99999511705187, 89.9999874875217) crs='http://www.opengis.net/def/crs/EPSG/0/4326' band_metadata=[('b1', {'long_name': 'reference time of file', 'standard_name': 'time'})] band_descriptions=[('b1', '2020-11-01T00:00:00.000000000')] dtype='float32' nodata_type='Nodata' colorinterp=None scales=None offsets=None colormap=None name='lst' count=1 width=36000 height=18000 attrs={'long_name': 'land surface temperature', 'units': 'kelvin', 'valid_min': -8315, 'valid_max': 7685}
0 6

In [11]:

with XarrayReader(da) as dst:
    img = dst.tile(31, 22, 6)

plt.imshow(img.data_as_image())

with XarrayReader(da) as dst: img = dst.tile(31, 22, 6) plt.imshow(img.data_as_image())

Out[11]:

<matplotlib.image.AxesImage at 0x11b0d3e20>

In [ ]: