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Xarray backend to Copernicus Sentinel-1 satellite data products

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xarray-sentinel

Easily explore and access the SAR data products of the Copernicus Sentinel-1 satellite mission in Python.

This Open Source project is sponsored by B-Open - https://www.bopen.eu.

Features

Overall the software is in the alpha phase and the usual caveats apply.

  • supports the following data products as distributed by ESA:
    • Sentinel-1 Ground Range Detected (GRD):
      • Stripmap (SM)
      • Interferometric Wide Swath (IW)
      • Extra Wide Swath (EW)
    • Sentinel-1 Single Look Complex (SLC) SM/IW/EW
  • creates ready-to-use Xarray Datasets that map the data lazily and efficiently in terms of both memory usage and disk / network access
  • reads all SAR imagery data: GRD images, SLC swaths and SLC bursts
  • reads several metadata elements: satellite orbit and attitude, ground control points, radiometric calibration look up tables, Doppler centroid estimation and more
  • reads uncompressed and compressed SAFE data products on the local computer or on a network via fsspec - dependes on rasterio>=1.3a3
  • supports larger-than-memory and distributed data access via Dask and rioxarray / rasterio / GDAL

Install

The easiest way to install xarray-sentinel is in a conda environment. You may create a new environment, activate it, install the package and its dependencies with the following commands:

    conda create -n XARRAY-SENTINEL
    conda activate XARRAY-SENTINEL
    conda install -c conda-forge fsspec rioxarray xarray xmlschema
    pip install xarray-sentinel

Usage

The SAR data products of the Copernicus Sentinel-1 satellite mission are distributed in the SAFE format, composed of a few raster data files in TIFF and several metadata files in XML. The aim of xarray-sentinel is to provide a developer-friendly Python interface to all data and several metadata elements as Xarray Datasets to enable easy processing of SAR data into value-added products.

Due to the inherent complexity and redundancy of the SAFE format xarray-sentinel maps it to a tree of groups where every group may be opened as a Dataset, but it may also contain subgroups, that are listed in the subgroups attribute.

The root dataset

For example let's explore the Sentinel-1 SLC Stripmap product in the local folder ./S1A_S3_SLC__1SDV_20210401T152855_20210401T152914_037258_04638E_6001.SAFE. First, we can open the SAR data product by passing the engine="sentinel-1" option to xr.open_dataset and access the root group of the product, also known as /:

>>> import xarray as xr
>>> slc_sm_path = "tests/data/S1A_S3_SLC__1SDV_20210401T152855_20210401T152914_037258_04638E_6001.SAFE"
>>> xr.open_dataset(slc_sm_path, engine="sentinel-1")
<xarray.Dataset>
Dimensions:  ()
Data variables:
    *empty*
Attributes: ...
    constellation:              sentinel-1
    platform:                   sentinel-1a
    instrument:                 ['c-sar']
    sat:orbit_state:            ascending
    sat:absolute_orbit:         37258
    sat:relative_orbit:         86
    ...                         ...
    sar:product_type:           SLC
    xs:instrument_mode_swaths:  ['S3']
    group:                      /
    subgroups:                  ['S3', 'S3/VH', 'S3/VH/orbit', 'S3/VH/attitud...
    Conventions:                CF-1.8
    history:                    created by xarray_sentinel-...

The root Dataset does not contain any data variable, but only attributes that provide general information on the product and a description of the tree structure of the data. The attribute group contains the name of the current group and the subgroups attribute shows the names of all available groups below this one.

Measurements datasets

To open the other groups we need to add the keyword group to xr.open_dataset. So we can read the measurement by selecting the desired beam mode and the polarization, in this example the data contains the S3 beam mode and we select the VH polarization with group="S3/VH":

>>> slc_s3_vh = xr.open_dataset(slc_sm_path, group="S3/VH", engine="sentinel-1", chunks=2048)
>>> slc_s3_vh
<xarray.Dataset>
Dimensions:           (slant_range_time: 18998, azimuth_time: 36895)
Coordinates:
    pixel             (slant_range_time) int64 ...
    line              (azimuth_time) int64 ...
  * azimuth_time      (azimuth_time) datetime64[ns] ...
  * slant_range_time  (slant_range_time) float64 ...
Data variables:
    measurement       (azimuth_time, slant_range_time) complex64 ...
Attributes: ...
    sar:center_frequency:       5.40500045433435
    sar:pixel_spacing_azimuth:  3.55338
    sar:pixel_spacing_range:    2.246363
    azimuth_time_interval:      0.0005194923129469381
    slant_range_time_interval:  1.498612395219899e-08
    constellation:              sentinel-1
    ...                         ...
    sar:product_type:           SLC
    xs:instrument_mode_swaths:  ['S3']
    group:                      /S3/VH
    subgroups:                  ['orbit', 'attitude', 'azimuth_fm_rate', 'dc_...
    Conventions:                CF-1.8
    history:                    created by xarray_sentinel-...

The measurement variable contains the Single Look Complex measurements as a complex64 and it has dimensions slant_range_time and azimuth_time. The azimuth_time is an np.datetime64 coordinate that contains the UTC zero-Doppler time associated with the image line and slant_range_time is an np.float64 coordinate that contains the two-way range time interval in seconds associated with the image pixel.

Metadata datasets

The measurement group contains several subgroups with metadata associated with the image, at the moment xarray-sentinel supports the following metadata datasets:

  • product XML file
    • orbit from the <orbit> tags
    • attitude from the <attitude> tags
    • azimuth_fm_rate from the <azimuthFmRate> tags
    • dc_estimate from the <dcEstimate> tags
    • gcp from the <geolocationGridPoint> tags
    • coordinate_conversion from the <coordinateConversion> tags
  • calibration XML file
    • calibration from the <calibrationVector> tags
  • noise XML file
    • noise_range from the <noiseRangeVector> tags
    • noise_azimuth from the <noiseAzimuthVector> tags

For example, the image calibration metadata associated with the S3/VH image can be read using group="S3/VH/calibration":

>>> slc_s3_vh_calibration = xr.open_dataset(slc_sm_path, group="S3/VH/calibration", engine="sentinel-1")
>>> slc_s3_vh_calibration
<xarray.Dataset>
Dimensions:       (line: 22, pixel: 476)
Coordinates:
  * line          (line) int64 0 1925 3850 5775 7700 ... 34649 36574 38499 40424
  * pixel         (pixel) int64 0 40 80 120 160 ... 18880 18920 18960 18997
Data variables:
    azimuth_time  (line) datetime64[ns] ...
    sigmaNought   (line, pixel) float32 ...
    betaNought    (line, pixel) float32 ...
    gamma         (line, pixel) float32 ...
    dn            (line, pixel) float32 ...
Attributes: ...
    constellation:              sentinel-1
    platform:                   sentinel-1a
    instrument:                 ['c-sar']
    sat:orbit_state:            ascending
    sat:absolute_orbit:         37258
    sat:relative_orbit:         86
    ...                         ...
    xs:instrument_mode_swaths:  ['S3']
    group:                      /S3/VH/calibration
    Conventions:                CF-1.8
    title:                      Calibration coefficients
    comment:                    The dataset contains calibration information ...
    history:                    created by xarray_sentinel-...

Note that in this case, the dimensions are line and pixel with coordinates corresponding to the sub-grid of the original image where it is defined the calibration Look Up Table.

The groups present in a typical Sentinel-1 Stripmap product are:

/
└─ S3
   ├─ VH
   │  ├─ orbit
   │  ├─ attitude
   │  ├─ azimuth_fm_rate
   │  ├─ dc_estimate
   │  ├─ gcp
   │  ├─ coordinate_conversion
   │  ├─ calibration
   │  ├─ noise_range
   │  └─ noise_azimuth
   └─ VV
      ├─ orbit
      ├─ attitude
      ├─ azimuth_fm_rate
      ├─ dc_estimate
      ├─ gcp
      ├─ coordinate_conversion
      ├─ calibration
      ├─ noise_range
      └─ noise_azimuth

Advanced usage

TOPS burst datasets

The IW and EW products, that use the Terrain Observation with Progressive Scan (TOPS) acquisition mode, are more complex because they contain several beam modes in the same SAFE package, but also because the measurement array is a collage of sub-images called bursts.

xarray-sentinel provides a helper function that crops a burst out of a measurement dataset for you.

You need to first open the desired measurement dataset, for example, the VH polarisation of the first IW swath of the S1B_IW_SLC__1SDV_20210401T052622_20210401T052650_026269_032297_EFA4 product in the current folder:

>>> slc_iw_path = "tests/data/S1B_IW_SLC__1SDV_20210401T052622_20210401T052650_026269_032297_EFA4.SAFE"
>>> slc_iw1_vh = xr.open_dataset(slc_iw_path, group="IW1/VH", engine="sentinel-1")
>>> slc_iw1_vh
<xarray.Dataset>
Dimensions:           (pixel: 21632, line: 13509)
Coordinates:
  * pixel             (pixel) int64 0 1 2 3 4 ... 21627 21628 21629 21630 21631
  * line              (line) int64 0 1 2 3 4 5 ... 13504 13505 13506 13507 13508
    azimuth_time      (line) datetime64[ns] ...
    slant_range_time  (pixel) float64 ...
Data variables:
    measurement       (line, pixel) complex64 ...
Attributes: ...
    sar:center_frequency:       5.40500045433435
    sar:pixel_spacing_azimuth:  13.94053
    sar:pixel_spacing_range:    2.329562
    azimuth_time_interval:      0.002055556299999998
    slant_range_time_interval:  1.554116558005821e-08
    azimuth_steering_rate:      1.590368784
    ...                         ...
    sar:product_type:           SLC
    xs:instrument_mode_swaths:  ['IW1', 'IW2', 'IW3']
    group:                      /IW1/VH
    subgroups:                  ['orbit', 'attitude', 'azimuth_fm_rate', 'dc_...
    Conventions:                CF-1.8
    history:                    created by xarray_sentinel-...

Note that the measurement data for IW and EW acquisition modes can not be indexed by physical coordinates because of the collage nature of the image.

Now the 9th burst out of 9 can be cropped from the swath data using burst_index=8, via:

>>> import xarray_sentinel
>>> xarray_sentinel.crop_burst_dataset(slc_iw1_vh, burst_index=8)
<xarray.Dataset>
Dimensions:           (slant_range_time: 21632, azimuth_time: 1501)
Coordinates:
    pixel             (slant_range_time) int64 0 1 2 3 ... 21629 21630 21631
    line              (azimuth_time) int64 12008 12009 12010 ... 13507 13508
  * azimuth_time      (azimuth_time) datetime64[ns] 2021-04-01T05:26:46.27227...
  * slant_range_time  (slant_range_time) float64 0.005343 0.005343 ... 0.005679
Data variables:
    measurement       (azimuth_time, slant_range_time) complex64 ...
Attributes: ...
    sar:center_frequency:       5.40500045433435
    sar:pixel_spacing_azimuth:  13.94053
    sar:pixel_spacing_range:    2.329562
    azimuth_time_interval:      0.002055556299999998
    slant_range_time_interval:  1.554116558005821e-08
    azimuth_steering_rate:      1.590368784
    ...                         ...
    group:                      /IW1/VH
    subgroups:                  ['orbit', 'attitude', 'azimuth_fm_rate', 'dc_...
    Conventions:                CF-1.8
    history:                    created by xarray_sentinel-...
    azimuth_anx_time:           2210.634453
    burst_index:                8

Note that the helper function also performs additional changes like swapping the dimensions to the physical coordinates and adding burst attributes.

As a quick way to access burst data, you can add the burst_index to the group specification on open, for example, group="IW1/VH/8". The burst groups are not listed in the subgroup attribute because they are not structural.

>>> xr.open_dataset(slc_iw_path, group="IW1/VH/8", engine="sentinel-1")
<xarray.Dataset>
Dimensions:           (slant_range_time: 21632, azimuth_time: 1501)
Coordinates:
    pixel             (slant_range_time) int64 ...
    line              (azimuth_time) int64 ...
  * azimuth_time      (azimuth_time) datetime64[ns] 2021-04-01T05:26:46.27227...
  * slant_range_time  (slant_range_time) float64 0.005343 0.005343 ... 0.005679
Data variables:
    measurement       (azimuth_time, slant_range_time) complex64 ...
Attributes: ...
    sar:center_frequency:       5.40500045433435
    sar:pixel_spacing_azimuth:  13.94053
    sar:pixel_spacing_range:    2.329562
    azimuth_time_interval:      0.002055556299999998
    slant_range_time_interval:  1.554116558005821e-08
    azimuth_steering_rate:      1.590368784
    ...                         ...
    group:                      /IW1/VH
    subgroups:                  ['orbit', 'attitude', 'azimuth_fm_rate', 'dc_...
    azimuth_anx_time:           2210.634453
    burst_index:                8
    Conventions:                CF-1.8
    history:                    created by xarray_sentinel-...

Calibration

xarray-sentinel provides helper functions to calibrate the data using the calibration metadata. You can compute the gamma intensity for part of the Stripmap image above with:

>>> xarray_sentinel.calibrate_intensity(slc_s3_vh.measurement[:2048, :2048], slc_s3_vh_calibration.gamma)
<xarray.DataArray (azimuth_time: 2048, slant_range_time: 2048)>
dask.array<maximum, shape=(2048, 2048), dtype=float32, chunksize=(2048, 2048), chunktype=numpy.ndarray>
Coordinates:
    pixel             (slant_range_time) int64 dask.array<chunksize=(2048,), meta=np.ndarray>
    line              (azimuth_time) int64 dask.array<chunksize=(2048,), meta=np.ndarray>
  * azimuth_time      (azimuth_time) datetime64[ns] 2021-04-01T15:28:55.11150...
  * slant_range_time  (slant_range_time) float64 0.005273 0.005273 ... 0.005303
Attributes:
    units:      dB
    long_name:  gamma

Advanced data access via fsspec

You need the unreleased rasterio>=1.3a3 for fsspec to work on measurement data

You can test the following after: pip install -U --pre --no-deps --no-binary rasterio rasterio>=1.3a3.

xarray-sentinel can read data from a variety of data stores including local file systems, network file systems, cloud object stores and compressed file formats, like Zip. This is done by passing fsspec compatible URLs to xr.open_dataset and optionally the storage_options keyword argument.

For exmaple you can open a product directly from a Zip file with:

>>> slc_iw_zip_path = "tests/data/S1B_IW_SLC__1SDV_20210401T052622_20210401T052650_026269_032297_EFA4.zip"
>>> xr.open_dataset(f"zip://*/manifest.safe::{slc_iw_zip_path}", group="IW1/VH", engine="sentinel-1")  # doctest: +SKIP
<xarray.Dataset>
Dimensions:           (pixel: 21632, line: 13509)
Coordinates:
  * pixel             (pixel) int64 0 1 2 3 4 ... 21627 21628 21629 21630 21631
  * line              (line) int64 0 1 2 3 4 5 ... 13504 13505 13506 13507 13508
    azimuth_time      (line) datetime64[ns] ...
    slant_range_time  (pixel) float64 ...
Data variables:
    measurement       (line, pixel) complex64 ...
Attributes: ...
    sar:center_frequency:       5.40500045433435
    sar:pixel_spacing_azimuth:  13.94053
    sar:pixel_spacing_range:    2.329562
    azimuth_time_interval:      0.002055556299999998
    slant_range_time_interval:  1.554116558005821e-08
    azimuth_steering_rate:      1.590368784
    ...                         ...
    sar:product_type:           SLC
    xs:instrument_mode_swaths:  ['IW1', 'IW2', 'IW3']
    group:                      /IW1/VH
    subgroups:                  ['orbit', 'attitude', 'azimuth_fm_rate', 'dc_...
    Conventions:                CF-1.8
    history:                    created by xarray_sentinel-...

As an exmaple of remote access you can open a product directly from a GitHub repo with:

>>> xr.open_dataset(f"github://bopen:xarray-sentinel@/{slc_iw_path}", group="IW1/VH", engine="sentinel-1")  # doctest: +SKIP
<xarray.Dataset>
Dimensions:           (pixel: 21632, line: 13509)
Coordinates:
  * pixel             (pixel) int64 0 1 2 3 4 ... 21627 21628 21629 21630 21631
  * line              (line) int64 0 1 2 3 4 5 ... 13504 13505 13506 13507 13508
    azimuth_time      (line) datetime64[ns] ...
    slant_range_time  (pixel) float64 ...
Data variables:
    measurement       (line, pixel) complex64 ...
Attributes: ...
    sar:center_frequency:       5.40500045433435
    sar:pixel_spacing_azimuth:  13.94053
    sar:pixel_spacing_range:    2.329562
    azimuth_time_interval:      0.002055556299999998
    slant_range_time_interval:  1.554116558005821e-08
    azimuth_steering_rate:      1.590368784
    ...                         ...
    sar:product_type:           SLC
    xs:instrument_mode_swaths:  ['IW1', 'IW2', 'IW3']
    group:                      /IW1/VH
    subgroups:                  ['orbit', 'attitude', 'azimuth_fm_rate', 'dc_...
    Conventions:                CF-1.8
    history:                    created by xarray_sentinel-...

fsspec is very powerful and supports caching and chaining, for example you can open a zip file off a GitHub repo and cache the file locally with:

>>> xr.open_dataset(
...     f"zip://*/manifest.safe::simplecache::github://bopen:xarray-sentinel@/{slc_iw_zip_path}",
...     engine="sentinel-1",
...     group="IW1/VH",
...     storage_options={
...         "simplecache": {"cache_storage": "/tmp/zipfiles/"},
...     },
... )  # doctest: +SKIP
<xarray.Dataset>
Dimensions:           (pixel: 21632, line: 13509)
Coordinates:
  * pixel             (pixel) int64 0 1 2 3 4 ... 21627 21628 21629 21630 21631
  * line              (line) int64 0 1 2 3 4 5 ... 13504 13505 13506 13507 13508
    azimuth_time      (line) datetime64[ns] ...
    slant_range_time  (pixel) float64 ...
Data variables:
    measurement       (line, pixel) complex64 ...
Attributes: ...
    sar:center_frequency:       5.40500045433435
    sar:pixel_spacing_azimuth:  13.94053
    sar:pixel_spacing_range:    2.329562
    azimuth_time_interval:      0.002055556299999998
    slant_range_time_interval:  1.554116558005821e-08
    azimuth_steering_rate:      1.590368784
    ...                         ...
    sar:product_type:           SLC
    xs:instrument_mode_swaths:  ['IW1', 'IW2', 'IW3']
    group:                      /IW1/VH
    subgroups:                  ['orbit', 'attitude', 'azimuth_fm_rate', 'dc_...
    Conventions:                CF-1.8
    history:                    created by xarray_sentinel-...

Design decisions

  • The main design choice for xarray-sentinel is for it to be as much as possible a pure map of the content of the SAFE data package, with as little interpretation as possible.
    • The tree-like structure follows the structure of the SAFE package even when information, like orbit and attitude, is expected to be identical for different beam modes. We observed at least a case where the number of orbital state vectors reported was different between beam modes.
    • Data and metadata are converted to the closest available data-type in Python / numpy. The most significant conversion is from CInt16 to np.complex64 for the SLC measurements that doubles the space requirements for the data. Also, xarray-sentinel converts UTC times to np.datetime64 and makes no attempt to support leap seconds, acquisitions containing leap seconds may crash or silently return corrupted data. See the rationale for choices of the coordinates data-types below.
    • We try to keep all naming as close as possible to the original names, in particular, for metadata we use the names of the XML tags, only converting them from camelCase to snake_case. Except for the high-level attributes, see below.
  • Whenever possible xarray-sentinel indexes the data with physical coordinates azimuth_time and slant_range_time, but keeps image line and pixel as auxiliary coordinates.
  • As an exception to the metadata naming rule above for high-level attributes, we aim at STAC Index and CF-Conventions compliance (in this order).
  • We aim at opening available data and metadata even for partial SAFE packages, for example, xarray-sentinel can open a measurement dataset for a beam mode even when the TIFF files of other beam modes / polarization are missing.
  • Accuracy considerations and rationale for the data-types of the coordinates
    • azimuth_time can be expressed as np.datetime64[ns] because spatial resolution at LEO speed is 10km/s * 1ns ~= 0.001cm.
    • slant_range_time on the other hand cannot be expressed as np.timedelta64[ns] because spatial resolution at the speed of light is 300_000km/s * 1ns / 2 ~= 15cm, that it is not enough for interferometric applications. slant_range_time needs a spatial resolution of 0.001cm at a 1_000km distance so around 1e-9 that is well within 1e-15 resolution of IEEE-754 float64.
  • This is a list of reference documents:

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Contributing

The main repository is hosted on GitHub, testing, bug reports and contributions are highly welcomed and appreciated:

https://github.com/bopen/xarray-sentinel

Lead developers:

Main contributors:

See also the list of contributors who participated in this project.

Sponsoring

B-Open commits to maintain the project long term and we are happy to accept sponsorships to develop new features.

We wish to express our gratitude to the project sponsors:

  • Microsoft has contributed to adding GRD products and fsspec access

License

Copyright 2021-2022, B-Open Solutions srl and the xarray-sentinel authors.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

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Xarray backend to Copernicus Sentinel-1 satellite data products

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