S1

# 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

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

SH = SentinelHub()
SH.dataset_names

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

SH.band_names('S1GRD')

['VV', 'HH', 'VH', 'localIncidenceAngle', 'scatteringArea', 'shadowMask', 'HV']

bbox = catchment_outline.bounds.iloc[0]

start_date = date(2017, 12, 1)
end_date = date(2018, 1, 31)

cube_config = CubeConfig(
    dataset_name='S1GRD',
    band_names=['VV', 'VH', 'localIncidenceAngle'],
    bbox=bbox.tolist(),
    spatial_res=0.0018,   # = 100 meters in degree>
    time_range=[start_date.strftime("%Y-%m-%d"), end_date.strftime("%Y-%m-%d")],
    time_tolerance='6D'
)

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

<xarray.Dataset>
Dimensions:              (time: 21, lat: 167, lon: 192, bnds: 2)
Coordinates:
  * lat                  (lat) float64 46.95 46.95 46.95 ... 46.66 46.66 46.65
  * lon                  (lon) float64 11.02 11.02 11.03 ... 11.36 11.36 11.37
  * time                 (time) datetime64[ns] 2017-12-01T05:17:50 ... 2018-0...
    time_bnds            (time, bnds) datetime64[ns] dask.array<chunksize=(21, 2), meta=np.ndarray>
Dimensions without coordinates: bnds
Data variables:
    VH                   (time, lat, lon) float32 dask.array<chunksize=(1, 167, 192), meta=np.ndarray>
    VV                   (time, lat, lon) float32 dask.array<chunksize=(1, 167, 192), meta=np.ndarray>
    localIncidenceAngle  (time, lat, lon) float32 dask.array<chunksize=(1, 167, 192), meta=np.ndarray>
Attributes:
    Conventions:             CF-1.7
    title:                   S1GRD Data Cube Subset
    history:                 [{'program': 'xcube_sh.chunkstore.SentinelHubChu...
    date_created:            2023-02-21T09:40:18.783677
    time_coverage_start:     2017-12-01T05:17:50+00:00
    time_coverage_end:       2018-01-30T05:17:47+00:00
    time_coverage_duration:  P59DT23H59M57S
    geospatial_lon_min:      11.020833333333357
    geospatial_lat_min:      46.653599378797765
    geospatial_lon_max:      11.366433333333356
    geospatial_lat_max:      46.95419937879777
    processing_level:        L1B

xarray.Dataset

• Dimensions:
  • time: 21
  • lat: 167
  • lon: 192
  • bnds: 2
• Coordinates: (4)
  • lat
    (lat)
    float64
    46.95 46.95 46.95 ... 46.66 46.65

    units :

    decimal_degrees

    long_name :

    latitude

    standard_name :

    latitude

    array([46.953299, 46.951499, 46.949699, 46.947899, 46.946099, 46.944299,
           46.942499, 46.940699, 46.938899, 46.937099, 46.935299, 46.933499,
           46.931699, 46.929899, 46.928099, 46.926299, 46.924499, 46.922699,
           46.920899, 46.919099, 46.917299, 46.915499, 46.913699, 46.911899,
           46.910099, 46.908299, 46.906499, 46.904699, 46.902899, 46.901099,
           46.899299, 46.897499, 46.895699, 46.893899, 46.892099, 46.890299,
           46.888499, 46.886699, 46.884899, 46.883099, 46.881299, 46.879499,
           46.877699, 46.875899, 46.874099, 46.872299, 46.870499, 46.868699,
           46.866899, 46.865099, 46.863299, 46.861499, 46.859699, 46.857899,
           46.856099, 46.854299, 46.852499, 46.850699, 46.848899, 46.847099,
           46.845299, 46.843499, 46.841699, 46.839899, 46.838099, 46.836299,
           46.834499, 46.832699, 46.830899, 46.829099, 46.827299, 46.825499,
           46.823699, 46.821899, 46.820099, 46.818299, 46.816499, 46.814699,
           46.812899, 46.811099, 46.809299, 46.807499, 46.805699, 46.803899,
           46.802099, 46.800299, 46.798499, 46.796699, 46.794899, 46.793099,
           46.791299, 46.789499, 46.787699, 46.785899, 46.784099, 46.782299,
           46.780499, 46.778699, 46.776899, 46.775099, 46.773299, 46.771499,
           46.769699, 46.767899, 46.766099, 46.764299, 46.762499, 46.760699,
           46.758899, 46.757099, 46.755299, 46.753499, 46.751699, 46.749899,
           46.748099, 46.746299, 46.744499, 46.742699, 46.740899, 46.739099,
           46.737299, 46.735499, 46.733699, 46.731899, 46.730099, 46.728299,
           46.726499, 46.724699, 46.722899, 46.721099, 46.719299, 46.717499,
           46.715699, 46.713899, 46.712099, 46.710299, 46.708499, 46.706699,
           46.704899, 46.703099, 46.701299, 46.699499, 46.697699, 46.695899,
           46.694099, 46.692299, 46.690499, 46.688699, 46.686899, 46.685099,
           46.683299, 46.681499, 46.679699, 46.677899, 46.676099, 46.674299,
           46.672499, 46.670699, 46.668899, 46.667099, 46.665299, 46.663499,
           46.661699, 46.659899, 46.658099, 46.656299, 46.654499])

  • lon
    (lon)
    float64
    11.02 11.02 11.03 ... 11.36 11.37

    units :

    decimal_degrees

    long_name :

    longitude

    standard_name :

    longitude

    array([11.021733, 11.023533, 11.025333, 11.027133, 11.028933, 11.030733,
           11.032533, 11.034333, 11.036133, 11.037933, 11.039733, 11.041533,
           11.043333, 11.045133, 11.046933, 11.048733, 11.050533, 11.052333,
           11.054133, 11.055933, 11.057733, 11.059533, 11.061333, 11.063133,
           11.064933, 11.066733, 11.068533, 11.070333, 11.072133, 11.073933,
           11.075733, 11.077533, 11.079333, 11.081133, 11.082933, 11.084733,
           11.086533, 11.088333, 11.090133, 11.091933, 11.093733, 11.095533,
           11.097333, 11.099133, 11.100933, 11.102733, 11.104533, 11.106333,
           11.108133, 11.109933, 11.111733, 11.113533, 11.115333, 11.117133,
           11.118933, 11.120733, 11.122533, 11.124333, 11.126133, 11.127933,
           11.129733, 11.131533, 11.133333, 11.135133, 11.136933, 11.138733,
           11.140533, 11.142333, 11.144133, 11.145933, 11.147733, 11.149533,
           11.151333, 11.153133, 11.154933, 11.156733, 11.158533, 11.160333,
           11.162133, 11.163933, 11.165733, 11.167533, 11.169333, 11.171133,
           11.172933, 11.174733, 11.176533, 11.178333, 11.180133, 11.181933,
           11.183733, 11.185533, 11.187333, 11.189133, 11.190933, 11.192733,
           11.194533, 11.196333, 11.198133, 11.199933, 11.201733, 11.203533,
           11.205333, 11.207133, 11.208933, 11.210733, 11.212533, 11.214333,
           11.216133, 11.217933, 11.219733, 11.221533, 11.223333, 11.225133,
           11.226933, 11.228733, 11.230533, 11.232333, 11.234133, 11.235933,
           11.237733, 11.239533, 11.241333, 11.243133, 11.244933, 11.246733,
           11.248533, 11.250333, 11.252133, 11.253933, 11.255733, 11.257533,
           11.259333, 11.261133, 11.262933, 11.264733, 11.266533, 11.268333,
           11.270133, 11.271933, 11.273733, 11.275533, 11.277333, 11.279133,
           11.280933, 11.282733, 11.284533, 11.286333, 11.288133, 11.289933,
           11.291733, 11.293533, 11.295333, 11.297133, 11.298933, 11.300733,
           11.302533, 11.304333, 11.306133, 11.307933, 11.309733, 11.311533,
           11.313333, 11.315133, 11.316933, 11.318733, 11.320533, 11.322333,
           11.324133, 11.325933, 11.327733, 11.329533, 11.331333, 11.333133,
           11.334933, 11.336733, 11.338533, 11.340333, 11.342133, 11.343933,
           11.345733, 11.347533, 11.349333, 11.351133, 11.352933, 11.354733,
           11.356533, 11.358333, 11.360133, 11.361933, 11.363733, 11.365533])

  • time
    (time)
    datetime64[ns]
    2017-12-01T05:17:50 ... 2018-01-...

    standard_name :

    time

    bounds :

    time_bnds

    array(['2017-12-01T05:17:50.000000000', '2017-12-06T05:26:04.000000000',
           '2017-12-07T05:18:25.000000000', '2017-12-12T05:26:34.000000000',
           '2017-12-13T05:17:49.000000000', '2017-12-18T05:26:03.000000000',
           '2017-12-19T05:18:25.000000000', '2017-12-24T05:26:33.000000000',
           '2017-12-25T05:17:49.000000000', '2017-12-30T05:26:03.000000000',
           '2017-12-31T05:18:24.000000000', '2018-01-05T05:26:33.000000000',
           '2018-01-06T05:17:48.000000000', '2018-01-11T05:26:02.000000000',
           '2018-01-12T05:18:24.000000000', '2018-01-17T05:26:33.000000000',
           '2018-01-18T05:17:48.000000000', '2018-01-23T05:26:02.000000000',
           '2018-01-24T05:18:23.000000000', '2018-01-29T05:26:32.000000000',
           '2018-01-30T05:17:47.000000000'], dtype='datetime64[ns]')

  • time_bnds
    (time, bnds)
    datetime64[ns]
    dask.array<chunksize=(21, 2), meta=np.ndarray>

    standard_name :

    time

    Array Chunk Bytes 336 B 336 B Shape (21, 2) (21, 2) Dask graph 1 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

• Data variables: (3)
  • VH
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 167, 192), meta=np.ndarray>

    sample_type :

    FLOAT32

    units :

    Linear power in the chosen backscattering coefficient

    Array Chunk Bytes 2.57 MiB 125.25 kiB Shape (21, 167, 192) (1, 167, 192) Dask graph 21 chunks in 2 graph layers Data type float32 numpy.ndarray

  • VV
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 167, 192), meta=np.ndarray>

    sample_type :

    FLOAT32

    units :

    Linear power in the chosen backscattering coefficient

    Array Chunk Bytes 2.57 MiB 125.25 kiB Shape (21, 167, 192) (1, 167, 192) Dask graph 21 chunks in 2 graph layers Data type float32 numpy.ndarray

  • localIncidenceAngle
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 167, 192), meta=np.ndarray>

    Array Chunk Bytes 2.57 MiB 125.25 kiB Shape (21, 167, 192) (1, 167, 192) Dask graph 21 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Float64Index([46.953299378797766,  46.95149937879776,  46.94969937879777,
                  46.947899378797764,  46.94609937879777, 46.944299378797766,
                   46.94249937879776,  46.94069937879777, 46.938899378797764,
                   46.93709937879777,
                  ...
                  46.670699378797764,  46.66889937879777, 46.667099378797765,
                   46.66529937879777, 46.663499378797766,  46.66169937879776,
                   46.65989937879777, 46.658099378797765,  46.65629937879777,
                  46.654499378797766],
                 dtype='float64', name='lat', length=167))

  • lon
    PandasIndex

    PandasIndex(Float64Index([11.021733333333357, 11.023533333333356, 11.025333333333357,
                  11.027133333333357, 11.028933333333356, 11.030733333333357,
                  11.032533333333356, 11.034333333333358, 11.036133333333357,
                  11.037933333333356,
                  ...
                  11.349333333333357, 11.351133333333356, 11.352933333333356,
                  11.354733333333357, 11.356533333333356, 11.358333333333357,
                  11.360133333333357, 11.361933333333356, 11.363733333333357,
                  11.365533333333357],
                 dtype='float64', name='lon', length=192))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2017-12-01 05:17:50', '2017-12-06 05:26:04',
                   '2017-12-07 05:18:25', '2017-12-12 05:26:34',
                   '2017-12-13 05:17:49', '2017-12-18 05:26:03',
                   '2017-12-19 05:18:25', '2017-12-24 05:26:33',
                   '2017-12-25 05:17:49', '2017-12-30 05:26:03',
                   '2017-12-31 05:18:24', '2018-01-05 05:26:33',
                   '2018-01-06 05:17:48', '2018-01-11 05:26:02',
                   '2018-01-12 05:18:24', '2018-01-17 05:26:33',
                   '2018-01-18 05:17:48', '2018-01-23 05:26:02',
                   '2018-01-24 05:18:23', '2018-01-29 05:26:32',
                   '2018-01-30 05:17:47'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (12)

  Conventions :

  CF-1.7

  title :

  S1GRD Data Cube Subset

  history :

  [{'program': 'xcube_sh.chunkstore.SentinelHubChunkStore', 'cube_config': {'dataset_name': 'S1GRD', 'band_names': ['VV', 'VH', 'localIncidenceAngle'], 'band_fill_values': None, 'band_sample_types': None, 'band_units': None, 'tile_size': [192, 167], 'bbox': [11.020833333333357, 46.653599378797765, 11.366433333333356, 46.95419937879777], 'spatial_res': 0.0018, 'crs': 'WGS84', 'upsampling': 'NEAREST', 'downsampling': 'NEAREST', 'mosaicking_order': 'mostRecent', 'time_range': ['2017-12-01T00:00:00+00:00', '2018-01-31T00:00:00+00:00'], 'time_period': None, 'time_tolerance': '6 days 00:00:00', 'collection_id': None, 'four_d': False}}]

  date_created :

  2023-02-21T09:40:18.783677

  time_coverage_start :

  2017-12-01T05:17:50+00:00

  time_coverage_end :

  2018-01-30T05:17:47+00:00

  time_coverage_duration :

  P59DT23H59M57S

  geospatial_lon_min :

  11.020833333333357

  geospatial_lat_min :

  46.653599378797765

  geospatial_lon_max :

  11.366433333333356

  geospatial_lat_max :

  46.95419937879777

  processing_level :

  L1B

cube.time

<xarray.DataArray 'time' (time: 21)>
array(['2017-12-01T05:17:50.000000000', '2017-12-06T05:26:04.000000000',
       '2017-12-07T05:18:25.000000000', '2017-12-12T05:26:34.000000000',
       '2017-12-13T05:17:49.000000000', '2017-12-18T05:26:03.000000000',
       '2017-12-19T05:18:25.000000000', '2017-12-24T05:26:33.000000000',
       '2017-12-25T05:17:49.000000000', '2017-12-30T05:26:03.000000000',
       '2017-12-31T05:18:24.000000000', '2018-01-05T05:26:33.000000000',
       '2018-01-06T05:17:48.000000000', '2018-01-11T05:26:02.000000000',
       '2018-01-12T05:18:24.000000000', '2018-01-17T05:26:33.000000000',
       '2018-01-18T05:17:48.000000000', '2018-01-23T05:26:02.000000000',
       '2018-01-24T05:18:23.000000000', '2018-01-29T05:26:32.000000000',
       '2018-01-30T05:17:47.000000000'], dtype='datetime64[ns]')
Coordinates:
  * time     (time) datetime64[ns] 2017-12-01T05:17:50 ... 2018-01-30T05:17:47
Attributes:
    standard_name:  time
    bounds:         time_bnds

xarray.DataArray

'time'

• time: 21

• 2017-12-01T05:17:50 2017-12-06T05:26:04 ... 2018-01-30T05:17:47

  array(['2017-12-01T05:17:50.000000000', '2017-12-06T05:26:04.000000000',
         '2017-12-07T05:18:25.000000000', '2017-12-12T05:26:34.000000000',
         '2017-12-13T05:17:49.000000000', '2017-12-18T05:26:03.000000000',
         '2017-12-19T05:18:25.000000000', '2017-12-24T05:26:33.000000000',
         '2017-12-25T05:17:49.000000000', '2017-12-30T05:26:03.000000000',
         '2017-12-31T05:18:24.000000000', '2018-01-05T05:26:33.000000000',
         '2018-01-06T05:17:48.000000000', '2018-01-11T05:26:02.000000000',
         '2018-01-12T05:18:24.000000000', '2018-01-17T05:26:33.000000000',
         '2018-01-18T05:17:48.000000000', '2018-01-23T05:26:02.000000000',
         '2018-01-24T05:18:23.000000000', '2018-01-29T05:26:32.000000000',
         '2018-01-30T05:17:47.000000000'], dtype='datetime64[ns]')

• Coordinates: (1)
  • time
    (time)
    datetime64[ns]
    2017-12-01T05:17:50 ... 2018-01-...

    standard_name :

    time

    bounds :

    time_bnds

    array(['2017-12-01T05:17:50.000000000', '2017-12-06T05:26:04.000000000',
           '2017-12-07T05:18:25.000000000', '2017-12-12T05:26:34.000000000',
           '2017-12-13T05:17:49.000000000', '2017-12-18T05:26:03.000000000',
           '2017-12-19T05:18:25.000000000', '2017-12-24T05:26:33.000000000',
           '2017-12-25T05:17:49.000000000', '2017-12-30T05:26:03.000000000',
           '2017-12-31T05:18:24.000000000', '2018-01-05T05:26:33.000000000',
           '2018-01-06T05:17:48.000000000', '2018-01-11T05:26:02.000000000',
           '2018-01-12T05:18:24.000000000', '2018-01-17T05:26:33.000000000',
           '2018-01-18T05:17:48.000000000', '2018-01-23T05:26:02.000000000',
           '2018-01-24T05:18:23.000000000', '2018-01-29T05:26:32.000000000',
           '2018-01-30T05:17:47.000000000'], dtype='datetime64[ns]')

• Indexes: (1)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2017-12-01 05:17:50', '2017-12-06 05:26:04',
                   '2017-12-07 05:18:25', '2017-12-12 05:26:34',
                   '2017-12-13 05:17:49', '2017-12-18 05:26:03',
                   '2017-12-19 05:18:25', '2017-12-24 05:26:33',
                   '2017-12-25 05:17:49', '2017-12-30 05:26:03',
                   '2017-12-31 05:18:24', '2018-01-05 05:26:33',
                   '2018-01-06 05:17:48', '2018-01-11 05:26:02',
                   '2018-01-12 05:18:24', '2018-01-17 05:26:33',
                   '2018-01-18 05:17:48', '2018-01-23 05:26:02',
                   '2018-01-24 05:18:23', '2018-01-29 05:26:32',
                   '2018-01-30 05:17:47'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (2)

  standard_name :

  time

  bounds :

  time_bnds

Only acquisition in morning are available (descending) no afternoon acquisition are available in the afternoon (ascending)

We need to filter for ASCENDING only. Better for track number.