Resample Operators with Pangeo

Resample Operators

Sometimes we need to align the spatial or temporal dimension of two datacubes, so that they can be merged together.

Spatial resampling Sentinel-2 to match Landsat

Start importing the necessary libraries and initialize a local connection for Client-Side Processing.

# STAC Catalogue Libraries
import pystac_client
import stackstac
from rasterio.enums import Resampling

Create two datacubes, one for Sentinel-2 and one for Landsat

spatial_extent = [11.4, 45.5, 11.42, 45.52]
temporal_extent = ["2023-06-01", "2023-06-30"]

Datacube for Sentinel-2

URL = "https://earth-search.aws.element84.com/v1"
catalog = pystac_client.Client.open(URL)
s2_items = catalog.search(
    bbox=spatial_extent,
    datetime=temporal_extent,
    collections=["sentinel-2-l2a"]
).item_collection()

s2_cube = stackstac.stack(s2_items,
                     bounds_latlon=spatial_extent,
                     assets=["red","nir"]
)
s2_cube

/srv/conda/envs/notebook/lib/python3.11/site-packages/stackstac/prepare.py:408: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
  times = pd.to_datetime(

<xarray.DataArray 'stackstac-e2f40d96c48e834b1e77d88ce8ce66ff' (time: 6,
                                                                band: 2,
                                                                y: 228, x: 164)>
dask.array<fetch_raster_window, shape=(6, 2, 228, 164), dtype=float64, chunksize=(1, 1, 228, 164), chunktype=numpy.ndarray>
Coordinates: (12/54)
  * time                                     (time) datetime64[ns] 2023-06-02...
    id                                       (time) <U24 'S2A_32TPR_20230602_...
  * band                                     (band) <U3 'red' 'nir'
  * x                                        (x) float64 6.874e+05 ... 6.891e+05
  * y                                        (y) float64 5.044e+06 ... 5.041e+06
    mgrs:latitude_band                       <U1 'T'
    ...                                       ...
    raster:bands                             object {'nodata': 0, 'data_type'...
    proj:shape                               object {10980}
    common_name                              (band) <U3 'red' 'nir'
    center_wavelength                        (band) float64 0.665 0.842
    full_width_half_max                      (band) float64 0.038 0.145
    epsg                                     int64 32632
Attributes:
    spec:        RasterSpec(epsg=32632, bounds=(687430.0, 5041290.0, 689070.0...
    crs:         epsg:32632
    transform:   | 10.00, 0.00, 687430.00|\n| 0.00,-10.00, 5043570.00|\n| 0.0...
    resolution:  10.0

xarray.DataArray

'stackstac-e2f40d96c48e834b1e77d88ce8ce66ff'

• time: 6
• band: 2
• y: 228
• x: 164

• dask.array<chunksize=(1, 1, 228, 164), meta=np.ndarray>

  Array Chunk Bytes 3.42 MiB 292.12 kiB Shape (6, 2, 228, 164) (1, 1, 228, 164) Dask graph 12 chunks in 3 graph layers Data type float64 numpy.ndarray

• Coordinates: (54)
  • time
    (time)
    datetime64[ns]
    2023-06-02T10:18:17.688000 ... 2...

    array(['2023-06-02T10:18:17.688000000', '2023-06-07T10:18:18.556000000',
           '2023-06-12T10:18:17.891000000', '2023-06-17T10:18:18.551000000',
           '2023-06-22T10:18:17.412000000', '2023-06-27T10:18:18.575000000'],
          dtype='datetime64[ns]')

  • id
    (time)
    <U24
    'S2A_32TPR_20230602_0_L2A' ... '...

    array(['S2A_32TPR_20230602_0_L2A', 'S2B_32TPR_20230607_0_L2A',
           'S2A_32TPR_20230612_0_L2A', 'S2B_32TPR_20230617_0_L2A',
           'S2A_32TPR_20230622_0_L2A', 'S2B_32TPR_20230627_0_L2A'],
          dtype='<U24')

  • band
    (band)
    <U3
    'red' 'nir'

    array(['red', 'nir'], dtype='<U3')

  • x
    (x)
    float64
    6.874e+05 6.874e+05 ... 6.891e+05

    array([687430., 687440., 687450., 687460., 687470., 687480., 687490., 687500.,
           687510., 687520., 687530., 687540., 687550., 687560., 687570., 687580.,
           687590., 687600., 687610., 687620., 687630., 687640., 687650., 687660.,
           687670., 687680., 687690., 687700., 687710., 687720., 687730., 687740.,
           687750., 687760., 687770., 687780., 687790., 687800., 687810., 687820.,
           687830., 687840., 687850., 687860., 687870., 687880., 687890., 687900.,
           687910., 687920., 687930., 687940., 687950., 687960., 687970., 687980.,
           687990., 688000., 688010., 688020., 688030., 688040., 688050., 688060.,
           688070., 688080., 688090., 688100., 688110., 688120., 688130., 688140.,
           688150., 688160., 688170., 688180., 688190., 688200., 688210., 688220.,
           688230., 688240., 688250., 688260., 688270., 688280., 688290., 688300.,
           688310., 688320., 688330., 688340., 688350., 688360., 688370., 688380.,
           688390., 688400., 688410., 688420., 688430., 688440., 688450., 688460.,
           688470., 688480., 688490., 688500., 688510., 688520., 688530., 688540.,
           688550., 688560., 688570., 688580., 688590., 688600., 688610., 688620.,
           688630., 688640., 688650., 688660., 688670., 688680., 688690., 688700.,
           688710., 688720., 688730., 688740., 688750., 688760., 688770., 688780.,
           688790., 688800., 688810., 688820., 688830., 688840., 688850., 688860.,
           688870., 688880., 688890., 688900., 688910., 688920., 688930., 688940.,
           688950., 688960., 688970., 688980., 688990., 689000., 689010., 689020.,
           689030., 689040., 689050., 689060.])

  • y
    (y)
    float64
    5.044e+06 5.044e+06 ... 5.041e+06

    array([5043570., 5043560., 5043550., ..., 5041320., 5041310., 5041300.])

  • mgrs:latitude_band
    ()
    <U1
    'T'

    array('T', dtype='<U1')

  • view:sun_elevation
    (time)
    float64
    64.02 64.46 64.74 64.85 64.79 64.59

    array([64.01848838, 64.46298062, 64.73695023, 64.84658693, 64.79190282,
           64.58617238])

  • s2:nodata_pixel_percentage
    ()
    int64
    0

    array(0)

  • earthsearch:boa_offset_applied
    ()
    bool
    True

    array(True)

  • s2:degraded_msi_data_percentage
    (time)
    float64
    1.286 0.1973 ... 0.0011 0.0234

    array([1.2861e+00, 1.9730e-01, 1.1340e-01, 2.5200e-02, 1.1000e-03,
           2.3400e-02])

  • s2:saturated_defective_pixel_percentage
    ()
    int64
    0

    array(0)

  • s2:granule_id
    (time)
    <U62
    'S2A_OPER_MSI_L2A_TL_2APS_202306...

    array(['S2A_OPER_MSI_L2A_TL_2APS_20230602T162001_A041489_T32TPR_N05.09',
           'S2B_OPER_MSI_L2A_TL_2BPS_20230607T131155_A032652_T32TPR_N05.09',
           'S2A_OPER_MSI_L2A_TL_2APS_20230612T161300_A041632_T32TPR_N05.09',
           'S2B_OPER_MSI_L2A_TL_2BPS_20230617T131301_A032795_T32TPR_N05.09',
           'S2A_OPER_MSI_L2A_TL_2APS_20230622T162904_A041775_T32TPR_N05.09',
           'S2B_OPER_MSI_L2A_TL_2BPS_20230627T131250_A032938_T32TPR_N05.09'],
          dtype='<U62')

  • proj:epsg
    ()
    int64
    32632

    array(32632)

  • s2:generation_time
    (time)
    <U27
    '2023-06-02T16:20:01.000000Z' .....

    array(['2023-06-02T16:20:01.000000Z', '2023-06-07T13:11:55.000000Z',
           '2023-06-12T16:13:00.000000Z', '2023-06-17T13:13:01.000000Z',
           '2023-06-22T16:29:04.000000Z', '2023-06-27T13:12:50.000000Z'],
          dtype='<U27')

  • s2:snow_ice_percentage
    (time)
    float64
    0.0607 0.04177 ... 0.07538 0.0036

    array([0.060697, 0.041768, 0.044456, 0.145461, 0.075385, 0.0036  ])

  • s2:cloud_shadow_percentage
    (time)
    float64
    4.366 6.759 10.86 1.166 0.5344 1.96

    array([ 4.366356,  6.759151, 10.85787 ,  1.166293,  0.534411,  1.959519])

  • constellation
    ()
    <U10
    'sentinel-2'

    array('sentinel-2', dtype='<U10')

  • s2:sequence
    ()
    <U1
    '0'

    array('0', dtype='<U1')

  • view:sun_azimuth
    (time)
    float64
    149.4 148.5 147.6 146.9 146.3 145.9

    array([149.40420966, 148.48122768, 147.61656707, 146.87442537,
           146.27113222, 145.87479277])

  • instruments
    ()
    <U3
    'msi'

    array('msi', dtype='<U3')

  • s2:not_vegetated_percentage
    (time)
    float64
    15.78 6.497 10.21 19.92 18.83 8.217

    array([15.775366,  6.497338, 10.205106, 19.92415 , 18.830648,  8.217338])

  • earthsearch:payload_id
    (time)
    <U74
    'roda-sentinel2/workflow-sentine...

    array(['roda-sentinel2/workflow-sentinel2-to-stac/ad9869a7b305d629a191f5b06a1eff23',
           'roda-sentinel2/workflow-sentinel2-to-stac/6d4c39c6835990e229795110dd8cf5e9',
           'roda-sentinel2/workflow-sentinel2-to-stac/16a383889e9acde93facc3022892db54',
           'roda-sentinel2/workflow-sentinel2-to-stac/7a270201bf9a01b59fb881206aa3cba5',
           'roda-sentinel2/workflow-sentinel2-to-stac/4e0e47f7bbbbd149469c4b49748f6667',
           'roda-sentinel2/workflow-sentinel2-to-stac/87400105ca2f5bd78c6cbc5c64a0711f'],
          dtype='<U74')

  • platform
    (time)
    <U11
    'sentinel-2a' ... 'sentinel-2b'

    array(['sentinel-2a', 'sentinel-2b', 'sentinel-2a', 'sentinel-2b',
           'sentinel-2a', 'sentinel-2b'], dtype='<U11')

  • s2:product_uri
    (time)
    <U65
    'S2A_MSIL2A_20230602T100601_N050...

    array(['S2A_MSIL2A_20230602T100601_N0509_R022_T32TPR_20230602T162001.SAFE',
           'S2B_MSIL2A_20230607T100559_N0509_R022_T32TPR_20230607T131155.SAFE',
           'S2A_MSIL2A_20230612T100601_N0509_R022_T32TPR_20230612T161300.SAFE',
           'S2B_MSIL2A_20230617T100559_N0509_R022_T32TPR_20230617T131301.SAFE',
           'S2A_MSIL2A_20230622T100601_N0509_R022_T32TPR_20230622T162904.SAFE',
           'S2B_MSIL2A_20230627T100559_N0509_R022_T32TPR_20230627T131250.SAFE'],
          dtype='<U65')

  • s2:reflectance_conversion_factor
    (time)
    float64
    0.9735 0.972 ... 0.9687 0.968

    array([0.97353759, 0.97200461, 0.97067736, 0.96956438, 0.96867277,
           0.9680082 ])

  • updated
    (time)
    <U24
    '2023-06-02T21:22:58.744Z' ... '...

    array(['2023-06-02T21:22:58.744Z', '2023-06-07T19:51:44.535Z',
           '2023-06-12T19:24:06.172Z', '2023-06-17T17:47:58.849Z',
           '2023-06-22T19:41:08.970Z', '2023-06-27T18:33:10.638Z'],
          dtype='<U24')

  • mgrs:grid_square
    ()
    <U2
    'PR'

    array('PR', dtype='<U2')

  • processing:software
    ()
    object
    {'sentinel2-to-stac': '0.1.0'}

    array({'sentinel2-to-stac': '0.1.0'}, dtype=object)

  • eo:cloud_cover
    (time)
    float64
    33.53 69.85 37.8 2.424 36.81 56.49

    array([33.526164, 69.851595, 37.799138,  2.424053, 36.808422, 56.487781])

  • s2:medium_proba_clouds_percentage
    (time)
    float64
    16.89 28.94 13.1 0.9943 8.044 18.45

    array([16.888879, 28.941682, 13.104455,  0.9943  ,  8.044333, 18.452175])

  • s2:product_type
    ()
    <U7
    'S2MSI2A'

    array('S2MSI2A', dtype='<U7')

  • s2:dark_features_percentage
    (time)
    float64
    0.01803 0.001838 ... 0.007628

    array([0.018029, 0.001838, 0.011486, 0.066357, 0.001374, 0.007628])

  • earthsearch:s3_path
    (time)
    <U79
    's3://sentinel-cogs/sentinel-s2-...

    array(['s3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/PR/2023/6/S2A_32TPR_20230602_0_L2A',
           's3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/PR/2023/6/S2B_32TPR_20230607_0_L2A',
           's3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/PR/2023/6/S2A_32TPR_20230612_0_L2A',
           's3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/PR/2023/6/S2B_32TPR_20230617_0_L2A',
           's3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/PR/2023/6/S2A_32TPR_20230622_0_L2A',
           's3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/PR/2023/6/S2B_32TPR_20230627_0_L2A'],
          dtype='<U79')

  • mgrs:utm_zone
    ()
    int64
    32

    array(32)

  • s2:high_proba_clouds_percentage
    (time)
    float64
    13.25 32.32 24.52 1.026 4.123 20.58

    array([13.247427, 32.320514, 24.524345,  1.026231,  4.123032, 20.580664])

  • s2:water_percentage
    (time)
    float64
    2.291 1.749 3.328 3.511 1.828 2.543

    array([2.291227, 1.74877 , 3.327753, 3.510642, 1.828172, 2.54321 ])

  • created
    (time)
    <U24
    '2023-06-02T21:22:58.744Z' ... '...

    array(['2023-06-02T21:22:58.744Z', '2023-06-07T19:51:44.535Z',
           '2023-06-12T19:24:06.172Z', '2023-06-17T17:47:58.849Z',
           '2023-06-22T19:41:08.970Z', '2023-06-27T18:33:10.638Z'],
          dtype='<U24')

  • s2:datatake_id
    (time)
    <U34
    'GS2A_20230602T100601_041489_N05...

    array(['GS2A_20230602T100601_041489_N05.09',
           'GS2B_20230607T100559_032652_N05.09',
           'GS2A_20230612T100601_041632_N05.09',
           'GS2B_20230617T100559_032795_N05.09',
           'GS2A_20230622T100601_041775_N05.09',
           'GS2B_20230627T100559_032938_N05.09'], dtype='<U34')

  • s2:vegetation_percentage
    (time)
    float64
    43.68 13.67 37.16 72.61 41.24 29.82

    array([43.675384, 13.66789 , 37.159106, 72.614539, 41.235566, 29.816839])

  • s2:processing_baseline
    ()
    <U5
    '05.09'

    array('05.09', dtype='<U5')

  • s2:datatake_type
    ()
    <U8
    'INS-NOBS'

    array('INS-NOBS', dtype='<U8')

  • grid:code
    ()
    <U10
    'MGRS-32TPR'

    array('MGRS-32TPR', dtype='<U10')

  • s2:datastrip_id
    (time)
    <U64
    'S2A_OPER_MSI_L2A_DS_2APS_202306...

    array(['S2A_OPER_MSI_L2A_DS_2APS_20230602T162001_S20230602T100832_N05.09',
           'S2B_OPER_MSI_L2A_DS_2BPS_20230607T131155_S20230607T101430_N05.09',
           'S2A_OPER_MSI_L2A_DS_2APS_20230612T161300_S20230612T100735_N05.09',
           'S2B_OPER_MSI_L2A_DS_2BPS_20230617T131301_S20230617T101430_N05.09',
           'S2A_OPER_MSI_L2A_DS_2APS_20230622T162904_S20230622T100756_N05.09',
           'S2B_OPER_MSI_L2A_DS_2BPS_20230627T131250_S20230627T101430_N05.09'],
          dtype='<U64')

  • s2:thin_cirrus_percentage
    (time)
    float64
    3.39 8.589 0.1703 ... 24.64 17.45

    array([ 3.389856,  8.589394,  0.170338,  0.403522, 24.641056, 17.454942])

  • s2:unclassified_percentage
    (time)
    float64
    0.2868 1.432 ... 0.686 0.9641

    array([0.286781, 1.431654, 0.595084, 0.148506, 0.686023, 0.964084])

  • title
    (band)
    <U20
    'Red (band 4) - 10m' 'NIR 1 (ban...

    array(['Red (band 4) - 10m', 'NIR 1 (band 8) - 10m'], dtype='<U20')

  • gsd
    ()
    int64
    10

    array(10)

  • proj:transform
    ()
    object
    {0, 5100000, 600000, 10, -10}

    array({0, 5100000, 600000, 10, -10}, dtype=object)

  • raster:bands
    ()
    object
    {'nodata': 0, 'data_type': 'uint...

    array({'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1},
          dtype=object)

  • proj:shape
    ()
    object
    {10980}

    array({10980}, dtype=object)

  • common_name
    (band)
    <U3
    'red' 'nir'

    array(['red', 'nir'], dtype='<U3')

  • center_wavelength
    (band)
    float64
    0.665 0.842

    array([0.665, 0.842])

  • full_width_half_max
    (band)
    float64
    0.038 0.145

    array([0.038, 0.145])

  • epsg
    ()
    int64
    32632

    array(32632)

• Indexes: (4)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-06-02 10:18:17.688000', '2023-06-07 10:18:18.556000',
                   '2023-06-12 10:18:17.891000', '2023-06-17 10:18:18.551000',
                   '2023-06-22 10:18:17.412000', '2023-06-27 10:18:18.575000'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • band
    PandasIndex

    PandasIndex(Index(['red', 'nir'], dtype='object', name='band'))

  • x
    PandasIndex

    PandasIndex(Index([687430.0, 687440.0, 687450.0, 687460.0, 687470.0, 687480.0, 687490.0,
           687500.0, 687510.0, 687520.0,
           ...
           688970.0, 688980.0, 688990.0, 689000.0, 689010.0, 689020.0, 689030.0,
           689040.0, 689050.0, 689060.0],
          dtype='float64', name='x', length=164))

  • y
    PandasIndex

    PandasIndex(Index([5043570.0, 5043560.0, 5043550.0, 5043540.0, 5043530.0, 5043520.0,
           5043510.0, 5043500.0, 5043490.0, 5043480.0,
           ...
           5041390.0, 5041380.0, 5041370.0, 5041360.0, 5041350.0, 5041340.0,
           5041330.0, 5041320.0, 5041310.0, 5041300.0],
          dtype='float64', name='y', length=228))

• Attributes: (4)

  spec :

  RasterSpec(epsg=32632, bounds=(687430.0, 5041290.0, 689070.0, 5043570.0), resolutions_xy=(10.0, 10.0))

  crs :

  epsg:32632

  transform :

  | 10.00, 0.00, 687430.00| | 0.00,-10.00, 5043570.00| | 0.00, 0.00, 1.00|

  resolution :

  10.0

Datacube for Landsat

url = "https://planetarycomputer.microsoft.com/api/stac/v1"
catalog = pystac_client.Client.open(URL)
l8_items = catalog.search(
    bbox=spatial_extent,
    datetime=temporal_extent,
    collections=["landsat-c2-l2"]
).item_collection()

l8_cube = stackstac.stack(l8_items,
                     bounds_latlon=spatial_extent,
                     assets=["red","nir08"]
)
l8_cube

/srv/conda/envs/notebook/lib/python3.11/site-packages/stackstac/prepare.py:408: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
  times = pd.to_datetime(

<xarray.DataArray 'stackstac-31be86bd1beff02963ed0d75e074c4f9' (time: 15,
                                                                band: 2, y: 76,
                                                                x: 55)>
dask.array<fetch_raster_window, shape=(15, 2, 76, 55), dtype=float64, chunksize=(1, 1, 76, 55), chunktype=numpy.ndarray>
Coordinates: (12/36)
  * time                         (time) datetime64[ns] 2023-06-03T10:03:52.60...
    id                           (time) <U31 'LC08_L2SP_193028_20230603_02_T1...
  * band                         (band) <U5 'red' 'nir08'
  * x                            (x) float64 6.874e+05 6.874e+05 ... 6.89e+05
  * y                            (y) float64 5.044e+06 5.044e+06 ... 5.041e+06
    view:off_nadir               int64 0
    ...                           ...
    title                        (band) <U22 'Red Band' 'Near Infrared Band 0.8'
    storage:platform             <U3 'AWS'
    storage:requester_pays       bool True
    raster:bands                 object {'nodata': 0, 'data_type': 'uint16', ...
    storage:region               <U9 'us-west-2'
    epsg                         int64 32632
Attributes:
    spec:        RasterSpec(epsg=32632, bounds=(687420.0, 5041290.0, 689070.0...
    crs:         epsg:32632
    transform:   | 30.00, 0.00, 687420.00|\n| 0.00,-30.00, 5043570.00|\n| 0.0...
    resolution:  30.0

xarray.DataArray

'stackstac-31be86bd1beff02963ed0d75e074c4f9'

• time: 15
• band: 2
• y: 76
• x: 55

• dask.array<chunksize=(1, 1, 76, 55), meta=np.ndarray>

  Array Chunk Bytes 0.96 MiB 32.66 kiB Shape (15, 2, 76, 55) (1, 1, 76, 55) Dask graph 30 chunks in 3 graph layers Data type float64 numpy.ndarray

• Coordinates: (36)
  • time
    (time)
    datetime64[ns]
    2023-06-03T10:03:52.607443 ... 2...

    array(['2023-06-03T10:03:52.607443000', '2023-06-04T09:57:49.328531000',
           '2023-06-04T09:58:13.211074000', '2023-06-11T07:50:54.329012000',
           '2023-06-11T07:51:18.162345000', '2023-06-11T10:03:55.647377000',
           '2023-06-12T09:57:48.421422000', '2023-06-12T09:58:12.303990000',
           '2023-06-16T07:54:38.272392000', '2023-06-19T10:04:00.918699000',
           '2023-06-20T09:57:41.542258000', '2023-06-20T09:58:05.424801000',
           '2023-06-27T10:03:54.859164000', '2023-06-28T09:57:56.101163000',
           '2023-06-28T09:58:19.992203000'], dtype='datetime64[ns]')

  • id
    (time)
    <U31
    'LC08_L2SP_193028_20230603_02_T1...

    array(['LC08_L2SP_193028_20230603_02_T1',
           'LC09_L2SP_192028_20230604_02_T1',
           'LC09_L2SP_192029_20230604_02_T1',
           'LE07_L2SP_192028_20230611_02_T1',
           'LE07_L2SP_192029_20230611_02_T1',
           'LC09_L2SP_193028_20230611_02_T1',
           'LC08_L2SP_192028_20230612_02_T1',
           'LC08_L2SP_192029_20230612_02_T1',
           'LE07_L2SP_193028_20230616_02_T1',
           'LC08_L2SP_193028_20230619_02_T1',
           'LC09_L2SP_192028_20230620_02_T1',
           'LC09_L2SP_192029_20230620_02_T1',
           'LC09_L2SP_193028_20230627_02_T1',
           'LC08_L2SP_192028_20230628_02_T1',
           'LC08_L2SP_192029_20230628_02_T1'], dtype='<U31')

  • band
    (band)
    <U5
    'red' 'nir08'

    array(['red', 'nir08'], dtype='<U5')

  • x
    (x)
    float64
    6.874e+05 6.874e+05 ... 6.89e+05

    array([687420., 687450., 687480., 687510., 687540., 687570., 687600., 687630.,
           687660., 687690., 687720., 687750., 687780., 687810., 687840., 687870.,
           687900., 687930., 687960., 687990., 688020., 688050., 688080., 688110.,
           688140., 688170., 688200., 688230., 688260., 688290., 688320., 688350.,
           688380., 688410., 688440., 688470., 688500., 688530., 688560., 688590.,
           688620., 688650., 688680., 688710., 688740., 688770., 688800., 688830.,
           688860., 688890., 688920., 688950., 688980., 689010., 689040.])

  • y
    (y)
    float64
    5.044e+06 5.044e+06 ... 5.041e+06

    array([5043570., 5043540., 5043510., 5043480., 5043450., 5043420., 5043390.,
           5043360., 5043330., 5043300., 5043270., 5043240., 5043210., 5043180.,
           5043150., 5043120., 5043090., 5043060., 5043030., 5043000., 5042970.,
           5042940., 5042910., 5042880., 5042850., 5042820., 5042790., 5042760.,
           5042730., 5042700., 5042670., 5042640., 5042610., 5042580., 5042550.,
           5042520., 5042490., 5042460., 5042430., 5042400., 5042370., 5042340.,
           5042310., 5042280., 5042250., 5042220., 5042190., 5042160., 5042130.,
           5042100., 5042070., 5042040., 5042010., 5041980., 5041950., 5041920.,
           5041890., 5041860., 5041830., 5041800., 5041770., 5041740., 5041710.,
           5041680., 5041650., 5041620., 5041590., 5041560., 5041530., 5041500.,
           5041470., 5041440., 5041410., 5041380., 5041350., 5041320.])

  • view:off_nadir
    ()
    int64
    0

    array(0)

  • landsat:wrs_path
    (time)
    <U3
    '193' '192' '192' ... '192' '192'

    array(['193', '192', '192', '192', '192', '193', '192', '192', '193',
           '193', '192', '192', '193', '192', '192'], dtype='<U3')

  • view:sun_elevation
    (time)
    float64
    62.12 62.19 63.1 ... 62.44 63.29

    array([62.1212979 , 62.18613829, 63.10152637, 43.74166108, 43.74521852,
           62.61286466, 62.68977376, 63.57771664, 43.6454784 , 62.76956979,
           62.72859562, 63.59327024, 62.46925128, 62.43582184, 63.29170048])

  • landsat:wrs_type
    ()
    <U1
    '2'

    array('2', dtype='<U1')

  • landsat:scene_id
    (time)
    <U21
    'LC81930282023154LGN00' ... 'LC8...

    array(['LC81930282023154LGN00', 'LC91920282023155LGN00',
           'LC91920292023155LGN00', 'LE71920282023162ASN01',
           'LE71920292023162ASN00', 'LC91930282023162LGN00',
           'LC81920282023163LGN00', 'LC81920292023163LGN00',
           'LE71930282023167ASN01', 'LC81930282023170LGN00',
           'LC91920282023171LGN00', 'LC91920292023171LGN00',
           'LC91930282023178LGN00', 'LC81920282023179LGN00',
           'LC81920292023179LGN00'], dtype='<U21')

  • landsat:collection_number
    ()
    <U2
    '02'

    array('02', dtype='<U2')

  • gsd
    ()
    int64
    30

    array(30)

  • landsat:collection_category
    ()
    <U2
    'T1'

    array('T1', dtype='<U2')

  • updated
    (time)
    <U24
    '2023-12-02T03:15:31.724Z' ... '...

    array(['2023-12-02T03:15:31.724Z', '2023-12-02T03:03:40.582Z',
           '2023-12-02T02:38:43.817Z', '2023-12-02T02:14:34.559Z',
           '2023-12-02T01:32:22.412Z', '2023-12-02T01:49:05.193Z',
           '2023-12-02T01:51:45.969Z', '2023-12-02T01:51:47.057Z',
           '2023-12-02T01:27:20.398Z', '2023-12-02T02:20:48.372Z',
           '2023-12-02T00:58:07.301Z', '2023-12-02T00:58:44.546Z',
           '2023-12-01T23:28:56.307Z', '2023-12-01T23:30:28.270Z',
           '2023-12-02T00:56:58.629Z'], dtype='<U24')

  • landsat:product_generated
    (time)
    <U20
    '2023-06-07T17:16:56Z' ... '2023...

    array(['2023-06-07T17:16:56Z', '2023-06-06T05:08:41Z',
           '2023-06-06T05:00:46Z', '2023-07-07T11:31:51Z',
           '2023-07-07T11:26:45Z', '2023-06-13T06:46:31Z',
           '2023-06-15T01:13:06Z', '2023-06-15T01:05:25Z',
           '2023-07-12T12:29:31Z', '2023-06-23T11:45:25Z',
           '2023-06-22T05:47:18Z', '2023-06-22T05:38:14Z',
           '2023-06-29T06:29:33Z', '2023-07-11T03:48:40Z',
           '2023-07-11T03:46:25Z'], dtype='<U20')

  • processing:software
    ()
    object
    {'landsat-to-stac': 'v2023.11.16'}

    array({'landsat-to-stac': 'v2023.11.16'}, dtype=object)

  • eo:cloud_cover
    (time)
    object
    74.1 89.6 71.62 ... 73.88 73.96

    array([74.1, 89.6, 71.62, 31, 5, 48.72, 24.41, 25.06, 41, 20.36, 14.86,
           15.71, 59.88, 73.88, 73.96], dtype=object)

  • description
    ()
    <U28
    'Landsat Collection 2 Level-2'

    array('Landsat Collection 2 Level-2', dtype='<U28')

  • proj:epsg
    ()
    int64
    32632

    array(32632)

  • proj:centroid
    (time)
    object
    {'lat': 46.01808, 'lon': 10.5583...

    array([{'lat': 46.01808, 'lon': 10.55837},
           {'lat': 46.01905, 'lon': 12.01606},
           {'lat': 44.59759, 'lon': 11.49935},
           {'lat': 46.06455, 'lon': 12.06734},
           {'lat': 44.64176, 'lon': 11.56275},
           {'lat': 46.01819, 'lon': 10.46999},
           {'lat': 46.01875, 'lon': 12.09321},
           {'lat': 44.59763, 'lon': 11.57405},
           {'lat': 46.06722, 'lon': 10.9915},
           {'lat': 46.01809, 'lon': 10.55621},
           {'lat': 46.01916, 'lon': 12.07697},
           {'lat': 44.598, 'lon': 11.55797},
           {'lat': 46.01825, 'lon': 10.53908},
           {'lat': 46.01919, 'lon': 12.09328},
           {'lat': 44.59762, 'lon': 11.57395}], dtype=object)

  • landsat:wrs_row
    (time)
    <U3
    '028' '028' '029' ... '028' '029'

    array(['028', '028', '029', '028', '029', '028', '028', '029', '028',
           '028', '028', '029', '028', '028', '029'], dtype='<U3')

  • sci:doi
    (time)
    <U16
    '10.5066/P9OGBGM6' ... '10.5066/...

    array(['10.5066/P9OGBGM6', '10.5066/P9OGBGM6', '10.5066/P9OGBGM6',
           '10.5066/P9C7I13B', '10.5066/P9C7I13B', '10.5066/P9OGBGM6',
           '10.5066/P9OGBGM6', '10.5066/P9OGBGM6', '10.5066/P9C7I13B',
           '10.5066/P9OGBGM6', '10.5066/P9OGBGM6', '10.5066/P9OGBGM6',
           '10.5066/P9OGBGM6', '10.5066/P9OGBGM6', '10.5066/P9OGBGM6'],
          dtype='<U16')

  • view:sun_azimuth
    (time)
    float64
    142.2 141.9 139.5 ... 139.1 136.5

    array([142.23267445, 141.94517574, 139.47217497, 102.21101842,
           100.53592374, 140.6697984 , 140.67356904, 138.10964952,
           101.59098662, 139.7475069 , 139.53594689, 136.93463843,
           139.02981901, 139.09098705, 136.50503106])

  • created
    (time)
    <U24
    '2023-06-08T07:32:04.240Z' ... '...

    array(['2023-06-08T07:32:04.240Z', '2023-06-06T07:31:22.056Z',
           '2023-06-06T07:31:17.962Z', '2023-12-02T02:14:34.559Z',
           '2023-12-02T01:32:22.412Z', '2023-06-14T07:31:42.940Z',
           '2023-06-15T07:32:13.862Z', '2023-06-15T07:31:56.791Z',
           '2023-12-02T01:27:20.398Z', '2023-06-24T07:31:33.219Z',
           '2023-06-22T07:31:36.231Z', '2023-06-22T07:31:34.415Z',
           '2023-06-30T07:31:35.653Z', '2023-12-01T23:30:28.270Z',
           '2023-12-02T00:56:58.629Z'], dtype='<U24')

  • instruments
    (time)
    object
    ['oli', 'tirs'] ... ['oli', 'tirs']

    array([list(['oli', 'tirs']), list(['oli', 'tirs']),
           list(['oli', 'tirs']), list(['etm+']), list(['etm+']),
           list(['oli', 'tirs']), list(['oli', 'tirs']),
           list(['oli', 'tirs']), list(['etm+']), list(['oli', 'tirs']),
           list(['oli', 'tirs']), list(['oli', 'tirs']),
           list(['oli', 'tirs']), list(['oli', 'tirs']),
           list(['oli', 'tirs'])], dtype=object)

  • landsat:cloud_cover_land
    (time)
    object
    74.1 91.94 74.7 ... 73.45 73.56

    array([74.1, 91.94, 74.7, 33, 6, 48.72, 27.08, 28.9, 41, 20.36, 15.37,
           14.03, 59.88, 73.45, 73.56], dtype=object)

  • grid:code
    (time)
    <U11
    'WRS2-193028' ... 'WRS2-192029'

    array(['WRS2-193028', 'WRS2-192028', 'WRS2-192029', 'WRS2-192028',
           'WRS2-192029', 'WRS2-193028', 'WRS2-192028', 'WRS2-192029',
           'WRS2-193028', 'WRS2-193028', 'WRS2-192028', 'WRS2-192029',
           'WRS2-193028', 'WRS2-192028', 'WRS2-192029'], dtype='<U11')

  • earthsearch:payload_id
    (time)
    <U86
    'usgs-landsat-c2l2/workflow-land...

    array(['usgs-landsat-c2l2/workflow-landsat-to-stac/LC08_L2SP_193028_20230603_20230607_02_T1_SR',
           'usgs-landsat-c2l2/workflow-landsat-to-stac/LC09_L2SP_192028_20230604_20230606_02_T1_SR',
           'usgs-landsat-c2l2/workflow-landsat-to-stac/LC09_L2SP_192029_20230604_20230606_02_T1_SR',
           'usgs-landsat-c2l2/workflow-landsat-to-stac/LE07_L2SP_192028_20230611_20230707_02_T1_SR',
           'usgs-landsat-c2l2/workflow-landsat-to-stac/LE07_L2SP_192029_20230611_20230707_02_T1_SR',
           'usgs-landsat-c2l2/workflow-landsat-to-stac/LC09_L2SP_193028_20230611_20230613_02_T1_SR',
           'usgs-landsat-c2l2/workflow-landsat-to-stac/LC08_L2SP_192028_20230612_20230615_02_T1_SR',
           'usgs-landsat-c2l2/workflow-landsat-to-stac/LC08_L2SP_192029_20230612_20230615_02_T1_SR',
           'usgs-landsat-c2l2/workflow-landsat-to-stac/LE07_L2SP_193028_20230616_20230712_02_T1_SR',
           'usgs-landsat-c2l2/workflow-landsat-to-stac/LC08_L2SP_193028_20230619_20230623_02_T1_SR',
           'usgs-landsat-c2l2/workflow-landsat-to-stac/LC09_L2SP_192028_20230620_20230622_02_T1_SR',
           'usgs-landsat-c2l2/workflow-landsat-to-stac/LC09_L2SP_192029_20230620_20230622_02_T1_SR',
           'usgs-landsat-c2l2/workflow-landsat-to-stac/LC09_L2SP_193028_20230627_20230629_02_T1_SR',
           'usgs-landsat-c2l2/workflow-landsat-to-stac/LC08_L2SP_192028_20230628_20230711_02_T1_SR',
           'usgs-landsat-c2l2/workflow-landsat-to-stac/LC08_L2SP_192029_20230628_20230711_02_T1_SR'],
          dtype='<U86')

  • landsat:correction
    ()
    <U4
    'L2SP'

    array('L2SP', dtype='<U4')

  • platform
    (time)
    <U9
    'landsat-8' ... 'landsat-8'

    array(['landsat-8', 'landsat-9', 'landsat-9', 'landsat-7', 'landsat-7',
           'landsat-9', 'landsat-8', 'landsat-8', 'landsat-7', 'landsat-8',
           'landsat-9', 'landsat-9', 'landsat-9', 'landsat-8', 'landsat-8'],
          dtype='<U9')

  • title
    (band)
    <U22
    'Red Band' 'Near Infrared Band 0.8'

    array(['Red Band', 'Near Infrared Band 0.8'], dtype='<U22')

  • storage:platform
    ()
    <U3
    'AWS'

    array('AWS', dtype='<U3')

  • storage:requester_pays
    ()
    bool
    True

    array(True)

  • raster:bands
    ()
    object
    {'nodata': 0, 'data_type': 'uint...

    array({'nodata': 0, 'data_type': 'uint16', 'spatial_resolution': 30, 'scale': 2.75e-05, 'offset': -0.2},
          dtype=object)

  • storage:region
    ()
    <U9
    'us-west-2'

    array('us-west-2', dtype='<U9')

  • epsg
    ()
    int64
    32632

    array(32632)

• Indexes: (4)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-06-03 10:03:52.607443', '2023-06-04 09:57:49.328531',
                   '2023-06-04 09:58:13.211074', '2023-06-11 07:50:54.329012',
                   '2023-06-11 07:51:18.162345', '2023-06-11 10:03:55.647377',
                   '2023-06-12 09:57:48.421422', '2023-06-12 09:58:12.303990',
                   '2023-06-16 07:54:38.272392', '2023-06-19 10:04:00.918699',
                   '2023-06-20 09:57:41.542258', '2023-06-20 09:58:05.424801',
                   '2023-06-27 10:03:54.859164', '2023-06-28 09:57:56.101163',
                   '2023-06-28 09:58:19.992203'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • band
    PandasIndex

    PandasIndex(Index(['red', 'nir08'], dtype='object', name='band'))

  • x
    PandasIndex

    PandasIndex(Index([687420.0, 687450.0, 687480.0, 687510.0, 687540.0, 687570.0, 687600.0,
           687630.0, 687660.0, 687690.0, 687720.0, 687750.0, 687780.0, 687810.0,
           687840.0, 687870.0, 687900.0, 687930.0, 687960.0, 687990.0, 688020.0,
           688050.0, 688080.0, 688110.0, 688140.0, 688170.0, 688200.0, 688230.0,
           688260.0, 688290.0, 688320.0, 688350.0, 688380.0, 688410.0, 688440.0,
           688470.0, 688500.0, 688530.0, 688560.0, 688590.0, 688620.0, 688650.0,
           688680.0, 688710.0, 688740.0, 688770.0, 688800.0, 688830.0, 688860.0,
           688890.0, 688920.0, 688950.0, 688980.0, 689010.0, 689040.0],
          dtype='float64', name='x'))

  • y
    PandasIndex

    PandasIndex(Index([5043570.0, 5043540.0, 5043510.0, 5043480.0, 5043450.0, 5043420.0,
           5043390.0, 5043360.0, 5043330.0, 5043300.0, 5043270.0, 5043240.0,
           5043210.0, 5043180.0, 5043150.0, 5043120.0, 5043090.0, 5043060.0,
           5043030.0, 5043000.0, 5042970.0, 5042940.0, 5042910.0, 5042880.0,
           5042850.0, 5042820.0, 5042790.0, 5042760.0, 5042730.0, 5042700.0,
           5042670.0, 5042640.0, 5042610.0, 5042580.0, 5042550.0, 5042520.0,
           5042490.0, 5042460.0, 5042430.0, 5042400.0, 5042370.0, 5042340.0,
           5042310.0, 5042280.0, 5042250.0, 5042220.0, 5042190.0, 5042160.0,
           5042130.0, 5042100.0, 5042070.0, 5042040.0, 5042010.0, 5041980.0,
           5041950.0, 5041920.0, 5041890.0, 5041860.0, 5041830.0, 5041800.0,
           5041770.0, 5041740.0, 5041710.0, 5041680.0, 5041650.0, 5041620.0,
           5041590.0, 5041560.0, 5041530.0, 5041500.0, 5041470.0, 5041440.0,
           5041410.0, 5041380.0, 5041350.0, 5041320.0],
          dtype='float64', name='y'))

• Attributes: (4)

  spec :

  RasterSpec(epsg=32632, bounds=(687420.0, 5041290.0, 689070.0, 5043570.0), resolutions_xy=(30.0, 30.0))

  crs :

  epsg:32632

  transform :

  | 30.00, 0.00, 687420.00| | 0.00,-30.00, 5043570.00| | 0.00, 0.00, 1.00|

  resolution :

  30.0

From the previous outputs, notice the shape difference in the spatial dimensions x and y.

This is due to the different resolution of the two collections: 10m for Sentinel-2, 30m for Landsat.

Now we use the stackstac.stack to resample the Sentinel-2 data to match Landsat.

s2_cube_coarse = stackstac.stack(s2_items,
                                 bounds_latlon=spatial_extent,
                                 assets=["red","nir"],
                                 resolution=30.0, resampling=Resampling.average, epsg=32632)
s2_cube_coarse

/srv/conda/envs/notebook/lib/python3.11/site-packages/stackstac/prepare.py:408: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.
  times = pd.to_datetime(

<xarray.DataArray 'stackstac-1bf939077e08430e59af76748c4bcf5e' (time: 6,
                                                                band: 2, y: 76,
                                                                x: 55)>
dask.array<fetch_raster_window, shape=(6, 2, 76, 55), dtype=float64, chunksize=(1, 1, 76, 55), chunktype=numpy.ndarray>
Coordinates: (12/54)
  * time                                     (time) datetime64[ns] 2023-06-02...
    id                                       (time) <U24 'S2A_32TPR_20230602_...
  * band                                     (band) <U3 'red' 'nir'
  * x                                        (x) float64 6.874e+05 ... 6.89e+05
  * y                                        (y) float64 5.044e+06 ... 5.041e+06
    mgrs:latitude_band                       <U1 'T'
    ...                                       ...
    raster:bands                             object {'nodata': 0, 'data_type'...
    proj:shape                               object {10980}
    common_name                              (band) <U3 'red' 'nir'
    center_wavelength                        (band) float64 0.665 0.842
    full_width_half_max                      (band) float64 0.038 0.145
    epsg                                     int64 32632
Attributes:
    spec:        RasterSpec(epsg=32632, bounds=(687420.0, 5041290.0, 689070.0...
    crs:         epsg:32632
    transform:   | 30.00, 0.00, 687420.00|\n| 0.00,-30.00, 5043570.00|\n| 0.0...
    resolution:  30.0

xarray.DataArray

'stackstac-1bf939077e08430e59af76748c4bcf5e'

• time: 6
• band: 2
• y: 76
• x: 55

• dask.array<chunksize=(1, 1, 76, 55), meta=np.ndarray>

  Array Chunk Bytes 391.88 kiB 32.66 kiB Shape (6, 2, 76, 55) (1, 1, 76, 55) Dask graph 12 chunks in 3 graph layers Data type float64 numpy.ndarray

• Coordinates: (54)
  • time
    (time)
    datetime64[ns]
    2023-06-02T10:18:17.688000 ... 2...

    array(['2023-06-02T10:18:17.688000000', '2023-06-07T10:18:18.556000000',
           '2023-06-12T10:18:17.891000000', '2023-06-17T10:18:18.551000000',
           '2023-06-22T10:18:17.412000000', '2023-06-27T10:18:18.575000000'],
          dtype='datetime64[ns]')

  • id
    (time)
    <U24
    'S2A_32TPR_20230602_0_L2A' ... '...

    array(['S2A_32TPR_20230602_0_L2A', 'S2B_32TPR_20230607_0_L2A',
           'S2A_32TPR_20230612_0_L2A', 'S2B_32TPR_20230617_0_L2A',
           'S2A_32TPR_20230622_0_L2A', 'S2B_32TPR_20230627_0_L2A'],
          dtype='<U24')

  • band
    (band)
    <U3
    'red' 'nir'

    array(['red', 'nir'], dtype='<U3')

  • x
    (x)
    float64
    6.874e+05 6.874e+05 ... 6.89e+05

    array([687420., 687450., 687480., 687510., 687540., 687570., 687600., 687630.,
           687660., 687690., 687720., 687750., 687780., 687810., 687840., 687870.,
           687900., 687930., 687960., 687990., 688020., 688050., 688080., 688110.,
           688140., 688170., 688200., 688230., 688260., 688290., 688320., 688350.,
           688380., 688410., 688440., 688470., 688500., 688530., 688560., 688590.,
           688620., 688650., 688680., 688710., 688740., 688770., 688800., 688830.,
           688860., 688890., 688920., 688950., 688980., 689010., 689040.])

  • y
    (y)
    float64
    5.044e+06 5.044e+06 ... 5.041e+06

    array([5043570., 5043540., 5043510., 5043480., 5043450., 5043420., 5043390.,
           5043360., 5043330., 5043300., 5043270., 5043240., 5043210., 5043180.,
           5043150., 5043120., 5043090., 5043060., 5043030., 5043000., 5042970.,
           5042940., 5042910., 5042880., 5042850., 5042820., 5042790., 5042760.,
           5042730., 5042700., 5042670., 5042640., 5042610., 5042580., 5042550.,
           5042520., 5042490., 5042460., 5042430., 5042400., 5042370., 5042340.,
           5042310., 5042280., 5042250., 5042220., 5042190., 5042160., 5042130.,
           5042100., 5042070., 5042040., 5042010., 5041980., 5041950., 5041920.,
           5041890., 5041860., 5041830., 5041800., 5041770., 5041740., 5041710.,
           5041680., 5041650., 5041620., 5041590., 5041560., 5041530., 5041500.,
           5041470., 5041440., 5041410., 5041380., 5041350., 5041320.])

  • mgrs:latitude_band
    ()
    <U1
    'T'

    array('T', dtype='<U1')

  • view:sun_elevation
    (time)
    float64
    64.02 64.46 64.74 64.85 64.79 64.59

    array([64.01848838, 64.46298062, 64.73695023, 64.84658693, 64.79190282,
           64.58617238])

  • s2:nodata_pixel_percentage
    ()
    int64
    0

    array(0)

  • earthsearch:boa_offset_applied
    ()
    bool
    True

    array(True)

  • s2:degraded_msi_data_percentage
    (time)
    float64
    1.286 0.1973 ... 0.0011 0.0234

    array([1.2861e+00, 1.9730e-01, 1.1340e-01, 2.5200e-02, 1.1000e-03,
           2.3400e-02])

  • s2:saturated_defective_pixel_percentage
    ()
    int64
    0

    array(0)

  • s2:granule_id
    (time)
    <U62
    'S2A_OPER_MSI_L2A_TL_2APS_202306...

    array(['S2A_OPER_MSI_L2A_TL_2APS_20230602T162001_A041489_T32TPR_N05.09',
           'S2B_OPER_MSI_L2A_TL_2BPS_20230607T131155_A032652_T32TPR_N05.09',
           'S2A_OPER_MSI_L2A_TL_2APS_20230612T161300_A041632_T32TPR_N05.09',
           'S2B_OPER_MSI_L2A_TL_2BPS_20230617T131301_A032795_T32TPR_N05.09',
           'S2A_OPER_MSI_L2A_TL_2APS_20230622T162904_A041775_T32TPR_N05.09',
           'S2B_OPER_MSI_L2A_TL_2BPS_20230627T131250_A032938_T32TPR_N05.09'],
          dtype='<U62')

  • proj:epsg
    ()
    int64
    32632

    array(32632)

  • s2:generation_time
    (time)
    <U27
    '2023-06-02T16:20:01.000000Z' .....

    array(['2023-06-02T16:20:01.000000Z', '2023-06-07T13:11:55.000000Z',
           '2023-06-12T16:13:00.000000Z', '2023-06-17T13:13:01.000000Z',
           '2023-06-22T16:29:04.000000Z', '2023-06-27T13:12:50.000000Z'],
          dtype='<U27')

  • s2:snow_ice_percentage
    (time)
    float64
    0.0607 0.04177 ... 0.07538 0.0036

    array([0.060697, 0.041768, 0.044456, 0.145461, 0.075385, 0.0036  ])

  • s2:cloud_shadow_percentage
    (time)
    float64
    4.366 6.759 10.86 1.166 0.5344 1.96

    array([ 4.366356,  6.759151, 10.85787 ,  1.166293,  0.534411,  1.959519])

  • constellation
    ()
    <U10
    'sentinel-2'

    array('sentinel-2', dtype='<U10')

  • s2:sequence
    ()
    <U1
    '0'

    array('0', dtype='<U1')

  • view:sun_azimuth
    (time)
    float64
    149.4 148.5 147.6 146.9 146.3 145.9

    array([149.40420966, 148.48122768, 147.61656707, 146.87442537,
           146.27113222, 145.87479277])

  • instruments
    ()
    <U3
    'msi'

    array('msi', dtype='<U3')

  • s2:not_vegetated_percentage
    (time)
    float64
    15.78 6.497 10.21 19.92 18.83 8.217

    array([15.775366,  6.497338, 10.205106, 19.92415 , 18.830648,  8.217338])

  • earthsearch:payload_id
    (time)
    <U74
    'roda-sentinel2/workflow-sentine...

    array(['roda-sentinel2/workflow-sentinel2-to-stac/ad9869a7b305d629a191f5b06a1eff23',
           'roda-sentinel2/workflow-sentinel2-to-stac/6d4c39c6835990e229795110dd8cf5e9',
           'roda-sentinel2/workflow-sentinel2-to-stac/16a383889e9acde93facc3022892db54',
           'roda-sentinel2/workflow-sentinel2-to-stac/7a270201bf9a01b59fb881206aa3cba5',
           'roda-sentinel2/workflow-sentinel2-to-stac/4e0e47f7bbbbd149469c4b49748f6667',
           'roda-sentinel2/workflow-sentinel2-to-stac/87400105ca2f5bd78c6cbc5c64a0711f'],
          dtype='<U74')

  • platform
    (time)
    <U11
    'sentinel-2a' ... 'sentinel-2b'

    array(['sentinel-2a', 'sentinel-2b', 'sentinel-2a', 'sentinel-2b',
           'sentinel-2a', 'sentinel-2b'], dtype='<U11')

  • s2:product_uri
    (time)
    <U65
    'S2A_MSIL2A_20230602T100601_N050...

    array(['S2A_MSIL2A_20230602T100601_N0509_R022_T32TPR_20230602T162001.SAFE',
           'S2B_MSIL2A_20230607T100559_N0509_R022_T32TPR_20230607T131155.SAFE',
           'S2A_MSIL2A_20230612T100601_N0509_R022_T32TPR_20230612T161300.SAFE',
           'S2B_MSIL2A_20230617T100559_N0509_R022_T32TPR_20230617T131301.SAFE',
           'S2A_MSIL2A_20230622T100601_N0509_R022_T32TPR_20230622T162904.SAFE',
           'S2B_MSIL2A_20230627T100559_N0509_R022_T32TPR_20230627T131250.SAFE'],
          dtype='<U65')

  • s2:reflectance_conversion_factor
    (time)
    float64
    0.9735 0.972 ... 0.9687 0.968

    array([0.97353759, 0.97200461, 0.97067736, 0.96956438, 0.96867277,
           0.9680082 ])

  • updated
    (time)
    <U24
    '2023-06-02T21:22:58.744Z' ... '...

    array(['2023-06-02T21:22:58.744Z', '2023-06-07T19:51:44.535Z',
           '2023-06-12T19:24:06.172Z', '2023-06-17T17:47:58.849Z',
           '2023-06-22T19:41:08.970Z', '2023-06-27T18:33:10.638Z'],
          dtype='<U24')

  • mgrs:grid_square
    ()
    <U2
    'PR'

    array('PR', dtype='<U2')

  • processing:software
    ()
    object
    {'sentinel2-to-stac': '0.1.0'}

    array({'sentinel2-to-stac': '0.1.0'}, dtype=object)

  • eo:cloud_cover
    (time)
    float64
    33.53 69.85 37.8 2.424 36.81 56.49

    array([33.526164, 69.851595, 37.799138,  2.424053, 36.808422, 56.487781])

  • s2:medium_proba_clouds_percentage
    (time)
    float64
    16.89 28.94 13.1 0.9943 8.044 18.45

    array([16.888879, 28.941682, 13.104455,  0.9943  ,  8.044333, 18.452175])

  • s2:product_type
    ()
    <U7
    'S2MSI2A'

    array('S2MSI2A', dtype='<U7')

  • s2:dark_features_percentage
    (time)
    float64
    0.01803 0.001838 ... 0.007628

    array([0.018029, 0.001838, 0.011486, 0.066357, 0.001374, 0.007628])

  • earthsearch:s3_path
    (time)
    <U79
    's3://sentinel-cogs/sentinel-s2-...

    array(['s3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/PR/2023/6/S2A_32TPR_20230602_0_L2A',
           's3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/PR/2023/6/S2B_32TPR_20230607_0_L2A',
           's3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/PR/2023/6/S2A_32TPR_20230612_0_L2A',
           's3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/PR/2023/6/S2B_32TPR_20230617_0_L2A',
           's3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/PR/2023/6/S2A_32TPR_20230622_0_L2A',
           's3://sentinel-cogs/sentinel-s2-l2a-cogs/32/T/PR/2023/6/S2B_32TPR_20230627_0_L2A'],
          dtype='<U79')

  • mgrs:utm_zone
    ()
    int64
    32

    array(32)

  • s2:high_proba_clouds_percentage
    (time)
    float64
    13.25 32.32 24.52 1.026 4.123 20.58

    array([13.247427, 32.320514, 24.524345,  1.026231,  4.123032, 20.580664])

  • s2:water_percentage
    (time)
    float64
    2.291 1.749 3.328 3.511 1.828 2.543

    array([2.291227, 1.74877 , 3.327753, 3.510642, 1.828172, 2.54321 ])

  • created
    (time)
    <U24
    '2023-06-02T21:22:58.744Z' ... '...

    array(['2023-06-02T21:22:58.744Z', '2023-06-07T19:51:44.535Z',
           '2023-06-12T19:24:06.172Z', '2023-06-17T17:47:58.849Z',
           '2023-06-22T19:41:08.970Z', '2023-06-27T18:33:10.638Z'],
          dtype='<U24')

  • s2:datatake_id
    (time)
    <U34
    'GS2A_20230602T100601_041489_N05...

    array(['GS2A_20230602T100601_041489_N05.09',
           'GS2B_20230607T100559_032652_N05.09',
           'GS2A_20230612T100601_041632_N05.09',
           'GS2B_20230617T100559_032795_N05.09',
           'GS2A_20230622T100601_041775_N05.09',
           'GS2B_20230627T100559_032938_N05.09'], dtype='<U34')

  • s2:vegetation_percentage
    (time)
    float64
    43.68 13.67 37.16 72.61 41.24 29.82

    array([43.675384, 13.66789 , 37.159106, 72.614539, 41.235566, 29.816839])

  • s2:processing_baseline
    ()
    <U5
    '05.09'

    array('05.09', dtype='<U5')

  • s2:datatake_type
    ()
    <U8
    'INS-NOBS'

    array('INS-NOBS', dtype='<U8')

  • grid:code
    ()
    <U10
    'MGRS-32TPR'

    array('MGRS-32TPR', dtype='<U10')

  • s2:datastrip_id
    (time)
    <U64
    'S2A_OPER_MSI_L2A_DS_2APS_202306...

    array(['S2A_OPER_MSI_L2A_DS_2APS_20230602T162001_S20230602T100832_N05.09',
           'S2B_OPER_MSI_L2A_DS_2BPS_20230607T131155_S20230607T101430_N05.09',
           'S2A_OPER_MSI_L2A_DS_2APS_20230612T161300_S20230612T100735_N05.09',
           'S2B_OPER_MSI_L2A_DS_2BPS_20230617T131301_S20230617T101430_N05.09',
           'S2A_OPER_MSI_L2A_DS_2APS_20230622T162904_S20230622T100756_N05.09',
           'S2B_OPER_MSI_L2A_DS_2BPS_20230627T131250_S20230627T101430_N05.09'],
          dtype='<U64')

  • s2:thin_cirrus_percentage
    (time)
    float64
    3.39 8.589 0.1703 ... 24.64 17.45

    array([ 3.389856,  8.589394,  0.170338,  0.403522, 24.641056, 17.454942])

  • s2:unclassified_percentage
    (time)
    float64
    0.2868 1.432 ... 0.686 0.9641

    array([0.286781, 1.431654, 0.595084, 0.148506, 0.686023, 0.964084])

  • title
    (band)
    <U20
    'Red (band 4) - 10m' 'NIR 1 (ban...

    array(['Red (band 4) - 10m', 'NIR 1 (band 8) - 10m'], dtype='<U20')

  • gsd
    ()
    int64
    10

    array(10)

  • proj:transform
    ()
    object
    {0, 5100000, 600000, 10, -10}

    array({0, 5100000, 600000, 10, -10}, dtype=object)

  • raster:bands
    ()
    object
    {'nodata': 0, 'data_type': 'uint...

    array({'nodata': 0, 'data_type': 'uint16', 'bits_per_sample': 15, 'spatial_resolution': 10, 'scale': 0.0001, 'offset': -0.1},
          dtype=object)

  • proj:shape
    ()
    object
    {10980}

    array({10980}, dtype=object)

  • common_name
    (band)
    <U3
    'red' 'nir'

    array(['red', 'nir'], dtype='<U3')

  • center_wavelength
    (band)
    float64
    0.665 0.842

    array([0.665, 0.842])

  • full_width_half_max
    (band)
    float64
    0.038 0.145

    array([0.038, 0.145])

  • epsg
    ()
    int64
    32632

    array(32632)

• Indexes: (4)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2023-06-02 10:18:17.688000', '2023-06-07 10:18:18.556000',
                   '2023-06-12 10:18:17.891000', '2023-06-17 10:18:18.551000',
                   '2023-06-22 10:18:17.412000', '2023-06-27 10:18:18.575000'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • band
    PandasIndex

    PandasIndex(Index(['red', 'nir'], dtype='object', name='band'))

  • x
    PandasIndex

    PandasIndex(Index([687420.0, 687450.0, 687480.0, 687510.0, 687540.0, 687570.0, 687600.0,
           687630.0, 687660.0, 687690.0, 687720.0, 687750.0, 687780.0, 687810.0,
           687840.0, 687870.0, 687900.0, 687930.0, 687960.0, 687990.0, 688020.0,
           688050.0, 688080.0, 688110.0, 688140.0, 688170.0, 688200.0, 688230.0,
           688260.0, 688290.0, 688320.0, 688350.0, 688380.0, 688410.0, 688440.0,
           688470.0, 688500.0, 688530.0, 688560.0, 688590.0, 688620.0, 688650.0,
           688680.0, 688710.0, 688740.0, 688770.0, 688800.0, 688830.0, 688860.0,
           688890.0, 688920.0, 688950.0, 688980.0, 689010.0, 689040.0],
          dtype='float64', name='x'))

  • y
    PandasIndex

    PandasIndex(Index([5043570.0, 5043540.0, 5043510.0, 5043480.0, 5043450.0, 5043420.0,
           5043390.0, 5043360.0, 5043330.0, 5043300.0, 5043270.0, 5043240.0,
           5043210.0, 5043180.0, 5043150.0, 5043120.0, 5043090.0, 5043060.0,
           5043030.0, 5043000.0, 5042970.0, 5042940.0, 5042910.0, 5042880.0,
           5042850.0, 5042820.0, 5042790.0, 5042760.0, 5042730.0, 5042700.0,
           5042670.0, 5042640.0, 5042610.0, 5042580.0, 5042550.0, 5042520.0,
           5042490.0, 5042460.0, 5042430.0, 5042400.0, 5042370.0, 5042340.0,
           5042310.0, 5042280.0, 5042250.0, 5042220.0, 5042190.0, 5042160.0,
           5042130.0, 5042100.0, 5042070.0, 5042040.0, 5042010.0, 5041980.0,
           5041950.0, 5041920.0, 5041890.0, 5041860.0, 5041830.0, 5041800.0,
           5041770.0, 5041740.0, 5041710.0, 5041680.0, 5041650.0, 5041620.0,
           5041590.0, 5041560.0, 5041530.0, 5041500.0, 5041470.0, 5041440.0,
           5041410.0, 5041380.0, 5041350.0, 5041320.0],
          dtype='float64', name='y'))

• Attributes: (4)

  spec :

  RasterSpec(epsg=32632, bounds=(687420.0, 5041290.0, 689070.0, 5043570.0), resolutions_xy=(30.0, 30.0))

  crs :

  epsg:32632

  transform :

  | 30.00, 0.00, 687420.00| | 0.00,-30.00, 5043570.00| | 0.00, 0.00, 1.00|

  resolution :

  30.0