Hi,

Thanks for providing a detailed description of the problem. However, somehow I’m not able to reproduce it. Here is a minimal working example:

from sentinelhub import DataCollection, BBox, CRS
from eolearn.core import FeatureType
from eolearn.io import SentinelHubInputTask

sh_task = SentinelHubInputTask(
    bands_feature=(FeatureType.DATA, 'GRD_data'),
    additional_data=a
        (FeatureType.DATA, 'localIncidenceAngle'),
        (FeatureType.DATA, 'scatteringArea'),
        (FeatureType.MASK, 'dataMask')
    ],
    size=(50, 40),
    data_collection=DataCollection.SENTINEL1_IW,
    aux_request_args={
        'processing': {
            'orthorectify': True,
            'backCoeff': 'GAMMA0_TERRAIN'
        }
    }
)
eopatch = sh_task.execute(
    bbox=BBox((-5.05, 48.0, -5.00, 48.05), CRS.WGS84),
    time_interval=('2020-06-01', '2020-06-05')
)
print(eopatch)

which produces an output:

EOPatch(
  data: {
    GRD_data: numpy.ndarray(shape=(4, 40, 50, 2), dtype=float32)
    localIncidenceAngle: numpy.ndarray(shape=(4, 40, 50, 1), dtype=float32)
    scatteringArea: numpy.ndarray(shape=(4, 40, 50, 1), dtype=float32)
  }
  mask: {
    dataMask: numpy.ndarray(shape=(4, 40, 50, 1), dtype=bool)
  }
  scalar: {}
  label: {}
  vector: {}
  data_timeless: {}
  mask_timeless: {}
  scalar_timeless: {}
  label_timeless: {}
  vector_timeless: {}
  meta_info: {
    size_x: 50
    size_y: 40
    time_difference: datetime.timedelta(seconds=1)
    time_interval: (datetime.datetime(2020, 6, 1, 0, 0), datetime.datetime(2020, 6, 5, 23, 59, 59))
  }
  bbox: BBox(((-5.05, 48.0), (-5.0, 48.05)), crs=CRS('4326'))
  timestamp: :datetime.datetime(2020, 6, 3, 6, 32, 29), ..., datetime.datetime(2020, 6, 4, 6, 23, 33)], length=4
)

So in my case the output feature GRD_data still has 2 bands (VV and VH). Could you check if this code also works for your AOI parameters? Is it possible that a subsequent EOTask in your workflow modifies and removes 1 band from GRD_data feature?

I’m using eo-learn 0.10.1 and sentinelhub 3.4.2.

Regarding older versions of eo-learn package, you are correct that they don’t work anymore for this task. It is because of a change on Sentinel Hub service which introduced a stricter validation of units in request evalscripts.

Hi,
Thanks for the quick answer! So I tweaked my code slightly and it seems that the shape problem on patch.data['GRD_data'] was linked to how I moved the shadowMask from patch.data['GRD_data'] to the mask feature. Now I indeed have both VV and VH in the patch -sorry, for taking your time, fully my mistake! Indeed it was the workflow.

However, this does not explain why suddenly localIncidenceAngle, scatteringArea and shadowMask are now out of patch.data['GRD_data']. Do you have any information on that? We’ll be able to adapt our codes but honestly it’s pretty confusing to us and we would like to understand where this comes from.
Originally, all additionnal data was automatically put in patch.datad'GRD_data'] for S1 (on the contrary, S2 additionnal data all had they own sub-features). It was strange but it was working and we adapted our code to it (thus why we were moving the shadowMask to the mask feature, for instance). We did not see any mention of it changing, thus the confusion.

Have a good day,
 

Good to hear you were able to resolve the issue.

The change regarding localIncidenceAngle, scatteringArea, etc. is a design choice we had to make in eo-learn 0.10.1 so that SentinelHubInputTask would still make sense for all data collections. If you check any DataCollection enum you can see the following:

> from sentinelhub import DataCollection

> 

> DataCollection.SENTINEL1_IW



<DataCollection.SENTINEL1_IW: DataCollectionDefinition(

  api_id: sentinel-1-grd

  catalog_id: sentinel-1-grd

  wfs_id: DSS3

  service_url: https://services.sentinel-hub.com

  collection_type: Sentinel-1

  sensor_type: C-SAR

  processing_level: GRD

  swath_mode: IW

  polarization: DV

  resolution: HIGH

  orbit_direction: BOTH

  bands: (Band(name='VV', units=(<Unit.LINEAR_POWER: 'LINEAR_POWER'>,), output_types=(<class 'numpy.float32'>,)), Band(name='VH', units=(<Unit.LINEAR_POWER: 'LINEAR_POWER'>,), output_types=(<class 'numpy.float32'>,)))

  metabands: (Band(name='localIncidenceAngle', units=(<Unit.DN: 'DN'>,), output_types=(<class 'numpy.float32'>,)), Band(name='scatteringArea', units=(<Unit.DN: 'DN'>,), output_types=(<class 'numpy.float32'>,)), Band(name='shadowMask', units=(<Unit.DN: 'DN'>,), output_types=(<class 'bool'>,)), Band(name='dataMask', units=(<Unit.DN: 'DN'>,), output_types=(<class 'bool'>,)))

  is_timeless: False

  has_cloud_coverage: False

)>

Basically the data is split into bands and metabands. Bands are the main raster satelite images while metabands are extra channels containing meta information. Typically bands are all cast into the same numpy dtype, while each metaband can have a completely different dtype. Because of that SentinelHubInputTask now stacks together only bands into a single feature, while each metaband is put into a separate feature.

I see our change log was not very detailed about changes SentinelHubInputTask. I hope this explanation clears it up.

Hi again,
Thanks for clearing this up. Indeed it makes more sense to put metadata elsewhere than with the bands data! We understand why we got the issue in the first place.
Have a good day!
 

Hi at all,

i tried your example and got an Error.

What ist is the fault?

Thanks in advance

Hi, what are your sentinelhub and eolearn.io versions? To me it seems like you’re using a very old eolearn.io version, are you perhaps using conda to install eolearn?

You are right.I had also problems to update on windows with conda. I tried the Docker-pull-request and it works.

Thank you for your helpfull comment.

Is eo-learn for Windows up to date. I have Problems with installing and updating eo-learn on windows.

Does it fail if you follow the installation steps for the pypi distribution?