# xcube_sh imports
from xcube_sh.cube import open_cube
from xcube_sh.config import CubeConfig
from xcube_sh.sentinelhub import SentinelHub

# xcube imports
from xcube.core.geom import mask_dataset_by_geometry

# Various utilities
from datetime import date
import numpy as np
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt

Load the catchment outline

catchment_outline = gpd.read_file('catchment_outline.geojson')

List all the available dataset

SH = SentinelHub()
SH.dataset_names

['LETML1',
 'LOTL1',
 'LETML2',
 'CUSTOM',
 'LMSSL1',
 'LTML2',
 'LOTL2',
 'S3OLCI',
 'S3SLSTR',
 'DEM',
 'MODIS',
 'S5PL2',
 'HLS',
 'LTML1',
 'S1GRD',
 'S2L2A',
 'S2L1C']

SH.band_names('S3SLSTR')

['S1',
 'S2',
 'S3',
 'S4',
 'S4_A',
 'S4_B',
 'S5',
 'S5_A',
 'S5_B',
 'S6',
 'S6_A',
 'S6_B',
 'S7',
 'S8',
 'S9',
 'F1',
 'F2',
 'CLOUD_FRACTION',
 'SEA_ICE_FRACTION',
 'SEA_SURFACE_TEMPERATURE',
 'DEW_POINT',
 'SKIN_TEMPERATURE',
 'SNOW_ALBEDO',
 'SNOW_DEPTH',
 'SOIL_WETNESS',
 'TEMPERATURE',
 'TOTAL_COLUMN_OZONE',
 'TOTAL_COLUMN_WATER_VAPOR']

Configuring the data content of the cube

bbox = catchment_outline.bounds.iloc[0]

start_date = date(2021, 2, 1)
end_date = date(2021, 7, 1)

cube_config = CubeConfig(
    dataset_name='S3SLSTR',
    band_names=['S1', 'S2', 'S3', 'S5', 'S6', 'F1', 'SNOW_DEPTH', 'SNOW_ALBEDO'],
    bbox=bbox.tolist(),
    spatial_res=0.009,   # = 500 meters in degree>
    time_range=[start_date.strftime("%Y-%m-%d"), end_date.strftime("%Y-%m-%d")]
)

/home/conda/master-asi-conae/315434deb1b1e05eacacc41e1b0529b1837100f46402a32041d77ba54ae6c5f3-20230213-125825-013239-74-edc-2022.10-14/lib/python3.9/site-packages/xcube_sh/config.py:248: FutureWarning: Units 'M', 'Y' and 'y' do not represent unambiguous timedelta values and will be removed in a future version.
  time_tolerance = pd.to_timedelta(time_tolerance)

cube = open_cube(cube_config, api_url="https://creodias.sentinel-hub.com")
cube

<xarray.Dataset>
Dimensions:      (time: 353, lat: 33, lon: 38, bnds: 2)
Coordinates:
  * lat          (lat) float64 46.95 46.94 46.93 46.92 ... 46.68 46.67 46.66
  * lon          (lon) float64 11.03 11.03 11.04 11.05 ... 11.34 11.35 11.36
  * time         (time) datetime64[ns] 2021-02-01T09:57:08 ... 2021-06-30T20:...
    time_bnds    (time, bnds) datetime64[ns] dask.array<chunksize=(353, 2), meta=np.ndarray>
Dimensions without coordinates: bnds
Data variables:
    F1           (time, lat, lon) float32 dask.array<chunksize=(1, 33, 38), meta=np.ndarray>
    S1           (time, lat, lon) float32 dask.array<chunksize=(1, 33, 38), meta=np.ndarray>
    S2           (time, lat, lon) float32 dask.array<chunksize=(1, 33, 38), meta=np.ndarray>
    S3           (time, lat, lon) float32 dask.array<chunksize=(1, 33, 38), meta=np.ndarray>
    S5           (time, lat, lon) float32 dask.array<chunksize=(1, 33, 38), meta=np.ndarray>
    S6           (time, lat, lon) float32 dask.array<chunksize=(1, 33, 38), meta=np.ndarray>
    SNOW_ALBEDO  (time, lat, lon) float32 dask.array<chunksize=(1, 33, 38), meta=np.ndarray>
    SNOW_DEPTH   (time, lat, lon) float32 dask.array<chunksize=(1, 33, 38), meta=np.ndarray>
Attributes:
    Conventions:             CF-1.7
    title:                   S3SLSTR Data Cube Subset
    history:                 [{'program': 'xcube_sh.chunkstore.SentinelHubChu...
    date_created:            2023-02-21T09:41:27.870387
    time_coverage_start:     2021-02-01T09:37:22.665000+00:00
    time_coverage_end:       2021-06-30T20:36:19.311000+00:00
    time_coverage_duration:  P149DT10H58M56.646S
    geospatial_lon_min:      11.020833333333357
    geospatial_lat_min:      46.653599378797765
    geospatial_lon_max:      11.362833333333358
    geospatial_lat_max:      46.95059937879776
    processing_level:        L1B

xarray.Dataset

• Dimensions:
  • time: 353
  • lat: 33
  • lon: 38
  • bnds: 2
• Coordinates: (4)
  • lat
    (lat)
    float64
    46.95 46.94 46.93 ... 46.67 46.66

    units :

    decimal_degrees

    long_name :

    latitude

    standard_name :

    latitude

    array([46.946099, 46.937099, 46.928099, 46.919099, 46.910099, 46.901099,
           46.892099, 46.883099, 46.874099, 46.865099, 46.856099, 46.847099,
           46.838099, 46.829099, 46.820099, 46.811099, 46.802099, 46.793099,
           46.784099, 46.775099, 46.766099, 46.757099, 46.748099, 46.739099,
           46.730099, 46.721099, 46.712099, 46.703099, 46.694099, 46.685099,
           46.676099, 46.667099, 46.658099])

  • lon
    (lon)
    float64
    11.03 11.03 11.04 ... 11.35 11.36

    units :

    decimal_degrees

    long_name :

    longitude

    standard_name :

    longitude

    array([11.025333, 11.034333, 11.043333, 11.052333, 11.061333, 11.070333,
           11.079333, 11.088333, 11.097333, 11.106333, 11.115333, 11.124333,
           11.133333, 11.142333, 11.151333, 11.160333, 11.169333, 11.178333,
           11.187333, 11.196333, 11.205333, 11.214333, 11.223333, 11.232333,
           11.241333, 11.250333, 11.259333, 11.268333, 11.277333, 11.286333,
           11.295333, 11.304333, 11.313333, 11.322333, 11.331333, 11.340333,
           11.349333, 11.358333])

  • time
    (time)
    datetime64[ns]
    2021-02-01T09:57:08 ... 2021-06-...

    standard_name :

    time

    bounds :

    time_bnds

    array(['2021-02-01T09:57:08.000000000', '2021-02-01T21:00:17.000000000',
           '2021-02-02T09:30:57.000000000', ..., '2021-06-30T09:13:24.000000000',
           '2021-06-30T10:14:59.000000000', '2021-06-30T20:36:19.000000000'],
          dtype='datetime64[ns]')

  • time_bnds
    (time, bnds)
    datetime64[ns]
    dask.array<chunksize=(353, 2), meta=np.ndarray>

    standard_name :

    time

    Array Chunk Bytes 5.52 kiB 5.52 kiB Shape (353, 2) (353, 2) Dask graph 1 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

• Data variables: (8)
  • F1
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 33, 38), meta=np.ndarray>

    sample_type :

    UINT16

    units :

    kelvin

    wavelength :

    3742

    bandwidth :

    398.0

    resolution :

    1000

    Array Chunk Bytes 1.69 MiB 4.90 kiB Shape (353, 33, 38) (1, 33, 38) Dask graph 353 chunks in 2 graph layers Data type float32 numpy.ndarray

  • S1
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 33, 38), meta=np.ndarray>

    sample_type :

    UINT16

    units :

    reflectance

    wavelength :

    554.27

    bandwidth :

    19.26

    resolution :

    500

    Array Chunk Bytes 1.69 MiB 4.90 kiB Shape (353, 33, 38) (1, 33, 38) Dask graph 353 chunks in 2 graph layers Data type float32 numpy.ndarray

  • S2
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 33, 38), meta=np.ndarray>

    sample_type :

    UINT16

    units :

    reflectance

    wavelength :

    659.47

    bandwidth :

    19.25

    resolution :

    500

    Array Chunk Bytes 1.69 MiB 4.90 kiB Shape (353, 33, 38) (1, 33, 38) Dask graph 353 chunks in 2 graph layers Data type float32 numpy.ndarray

  • S3
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 33, 38), meta=np.ndarray>

    sample_type :

    UINT16

    units :

    reflectance

    wavelength :

    868

    bandwidth :

    20.6

    resolution :

    500

    Array Chunk Bytes 1.69 MiB 4.90 kiB Shape (353, 33, 38) (1, 33, 38) Dask graph 353 chunks in 2 graph layers Data type float32 numpy.ndarray

  • S5
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 33, 38), meta=np.ndarray>

    sample_type :

    UINT16

    units :

    reflectance

    wavelength :

    1613.4

    bandwidth :

    60.68

    resolution :

    500

    Array Chunk Bytes 1.69 MiB 4.90 kiB Shape (353, 33, 38) (1, 33, 38) Dask graph 353 chunks in 2 graph layers Data type float32 numpy.ndarray

  • S6
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 33, 38), meta=np.ndarray>

    sample_type :

    UINT16

    units :

    reflectance

    wavelength :

    2255.7

    bandwidth :

    50.15

    resolution :

    500

    Array Chunk Bytes 1.69 MiB 4.90 kiB Shape (353, 33, 38) (1, 33, 38) Dask graph 353 chunks in 2 graph layers Data type float32 numpy.ndarray

  • SNOW_ALBEDO
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 33, 38), meta=np.ndarray>

    Array Chunk Bytes 1.69 MiB 4.90 kiB Shape (353, 33, 38) (1, 33, 38) Dask graph 353 chunks in 2 graph layers Data type float32 numpy.ndarray

  • SNOW_DEPTH
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 33, 38), meta=np.ndarray>

    Array Chunk Bytes 1.69 MiB 4.90 kiB Shape (353, 33, 38) (1, 33, 38) Dask graph 353 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Float64Index([ 46.94609937879776,  46.93709937879776,  46.92809937879776,
                   46.91909937879776,  46.91009937879776,  46.90109937879776,
                   46.89209937879776,  46.88309937879776, 46.874099378797766,
                  46.865099378797765, 46.856099378797765, 46.847099378797765,
                  46.838099378797764, 46.829099378797764, 46.820099378797764,
                   46.81109937879776,  46.80209937879776,  46.79309937879776,
                   46.78409937879776,  46.77509937879776,  46.76609937879776,
                   46.75709937879776,  46.74809937879776,  46.73909937879776,
                   46.73009937879776,  46.72109937879777,  46.71209937879777,
                  46.703099378797766, 46.694099378797766, 46.685099378797766,
                  46.676099378797765, 46.667099378797765, 46.658099378797765],
                 dtype='float64', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Float64Index([11.025333333333357, 11.034333333333358, 11.043333333333358,
                  11.052333333333356, 11.061333333333357, 11.070333333333357,
                  11.079333333333357, 11.088333333333358, 11.097333333333356,
                  11.106333333333357, 11.115333333333357, 11.124333333333357,
                  11.133333333333358, 11.142333333333358, 11.151333333333357,
                  11.160333333333357, 11.169333333333357, 11.178333333333358,
                  11.187333333333358, 11.196333333333357, 11.205333333333357,
                  11.214333333333357, 11.223333333333358, 11.232333333333358,
                  11.241333333333356, 11.250333333333357, 11.259333333333357,
                  11.268333333333358, 11.277333333333358, 11.286333333333356,
                  11.295333333333357, 11.304333333333357, 11.313333333333357,
                  11.322333333333358, 11.331333333333358, 11.340333333333357,
                  11.349333333333357, 11.358333333333357],
                 dtype='float64', name='lon'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2021-02-01 09:57:08', '2021-02-01 21:00:17',
                   '2021-02-02 09:30:57', '2021-02-02 20:53:51',
                   '2021-02-03 09:24:32', '2021-02-03 20:47:26',
                   '2021-02-04 09:59:48', '2021-02-04 20:21:15',
                   '2021-02-04 21:22:43', '2021-02-05 09:53:23',
                   ...
                   '2021-06-27 09:52:33', '2021-06-27 20:13:52',
                   '2021-06-27 21:15:27', '2021-06-28 09:46:04',
                   '2021-06-28 21:08:59', '2021-06-29 09:19:53',
                   '2021-06-29 20:42:48', '2021-06-30 09:13:24',
                   '2021-06-30 10:14:59', '2021-06-30 20:36:19'],
                  dtype='datetime64[ns]', name='time', length=353, freq=None))

• Attributes: (12)

  Conventions :

  CF-1.7

  title :

  S3SLSTR Data Cube Subset

  history :

  [{'program': 'xcube_sh.chunkstore.SentinelHubChunkStore', 'cube_config': {'dataset_name': 'S3SLSTR', 'band_names': ['S1', 'S2', 'S3', 'S5', 'S6', 'F1', 'SNOW_DEPTH', 'SNOW_ALBEDO'], 'band_fill_values': None, 'band_sample_types': None, 'band_units': None, 'tile_size': [38, 33], 'bbox': [11.020833333333357, 46.653599378797765, 11.362833333333358, 46.95059937879776], 'spatial_res': 0.009, 'crs': 'WGS84', 'upsampling': 'NEAREST', 'downsampling': 'NEAREST', 'mosaicking_order': 'mostRecent', 'time_range': ['2021-02-01T00:00:00+00:00', '2021-07-01T00:00:00+00:00'], 'time_period': None, 'time_tolerance': '0 days 00:10:00', 'collection_id': None, 'four_d': False}}]

  date_created :

  2023-02-21T09:41:27.870387

  time_coverage_start :

  2021-02-01T09:37:22.665000+00:00

  time_coverage_end :

  2021-06-30T20:36:19.311000+00:00

  time_coverage_duration :

  P149DT10H58M56.646S

  geospatial_lon_min :

  11.020833333333357

  geospatial_lat_min :

  46.653599378797765

  geospatial_lon_max :

  11.362833333333358

  geospatial_lat_max :

  46.95059937879776

  processing_level :

  L1B

cube.SNOW_DEPTH.sel(time='2018-04-02T10:24:35.000000000', method='nearest').plot.imshow()

<matplotlib.image.AxesImage at 0x7f3532680820>

cube.SNOW_ALBEDO.sel(time='2018-04-02T10:24:35.000000000', method='nearest').plot.imshow()

<matplotlib.image.AxesImage at 0x7f35324f3bb0>

cube.S2.sel(time='2018-05-09', method='nearest').plot.imshow()

<matplotlib.image.AxesImage at 0x7f35323e7430>

cube.F1.sel(time='2018-04-02T10:24:35.000000000', method='nearest').plot.imshow()

<matplotlib.image.AxesImage at 0x7ff4f9bf4fa0>