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Development","sub_category":"Environmental Satellites","monthly_downloads":1788,"total_dependent_repos":3,"total_dependent_packages":2,"readme":"# xarray-sentinel\n\nEasily access and explore the SAR data products of the\n[Copernicus Sentinel-1 satellite mission](https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-1)\nin Python.\n\nThis Open Source project is sponsored by B-Open - \u003chttps://www.bopen.eu\u003e.\n\n## Features\n\n*xarray-sentinel* is a Python library and [*Xarray*](https://xarray.pydata.org) backend\nwith the following functionalities:\n\n- supports the following data products as [distributed by ESA](https://scihub.copernicus.eu/dhus/#/home):\n  - Sentinel-1 Ground Range Detected (GRD):\n    - Stripmap (SM)\n    - Interferometric Wide Swath (IW)\n    - Extra Wide Swath (EW)\n  - Sentinel-1 Single Look Complex (SLC) SM/IW/EW\n- creates ready-to-use *Xarray* `Dataset`s that map the data\n  lazily and efficiently in terms of both memory usage and disk / network access\n- reads all SAR imagery data: GRD images, SLC swaths and SLC bursts\n- reads several metadata elements:\n  satellite orbit and attitude, ground control points, radiometric calibration look up tables,\n  Doppler centroid estimation and more\n- (partially broken, see [#127](https://github.com/bopen/xarray-sentinel/issues/127)) reads uncompressed and compressed SAFE data products on the local computer or\n  on a network via [*fsspec*](https://filesystem-spec.readthedocs.io)\n- supports larger-than-memory and distributed data access via [*Dask*](https://dask.org) and\n  [*rioxarray*](https://corteva.github.io/rioxarray) /\n  [*rasterio*](https://rasterio.readthedocs.io) / [*GDAL*](https://gdal.org)\n- provides a few helpers for simple operations involving metadata like\n  cropping individual bursts out of IW SLC swaths,\n  applying radiometric calibration polynomials,\n  converting slant to ground range for GRD products and\n  computing geospatial metadata.\n\nOverall, the software is in the **beta** phase and the usual caveats apply.\n\n## Install\n\nThe easiest way to install *xarray-sentinel* is in a *conda* environment.\nThe following commands create a new environment, activate it, install the package and its dependencies:\n\n```shell\n    conda create -n XARRAY-SENTINEL\n    conda activate XARRAY-SENTINEL\n    conda install -c conda-forge dask \"rasterio=\u003e1.3.0\" xarray-sentinel\n```\n\n## Usage\n\nThe SAR data products of the Copernicus Sentinel-1 satellite mission are distributed in\nthe SAFE format, composed of a few raster data files in TIFF and several metadata files in XML.\nThe aim of *xarray-sentinel* is to provide a developer-friendly Python interface to all data and\nseveral metadata elements as Xarray `Dataset`s to enable easy processing of SAR data\ninto value-added products.\n\nDue to the inherent complexity and redundancy of the SAFE format *xarray-sentinel*\nmaps it to a tree of *groups* where every *group* may be opened as a `Dataset`,\nbut it may also contain *subgroups*, that are listed in the `subgroups` attribute.\n\nThe following sections show some example of xarray-sentinel usage.\nIn the `notebooks` folder you\ncan also find notebooks, one for each supported product, that allow you to explore the\ndata in more detail using the xarray-sentinel functions.\n\n### The root dataset\n\nFor example let's explore the Sentinel-1 SLC Stripmap product in the local folder\n`./S1A_S3_SLC__1SDV_20210401T152855_20210401T152914_037258_04638E_6001.SAFE`.\nFirst, we can open the SAR data product by passing the `engine=\"sentinel-1\"` option to `xr.open_dataset`\nand access the root group of the product, also known as `/`:\n\n```python-repl\n\u003e\u003e\u003e import xarray as xr\n\u003e\u003e\u003e slc_sm_path = \"tests/data/S1A_S3_SLC__1SDV_20210401T152855_20210401T152914_037258_04638E_6001.SAFE\"\n\u003e\u003e\u003e xr.open_dataset(slc_sm_path, engine=\"sentinel-1\")\n\u003cxarray.Dataset\u003e Size: 0B\nDimensions:  ()\nData variables:\n    *empty*\nAttributes: ...\n    family_name:                         SENTINEL-1\n    number:                              A\n    mode:                                SM\n    swaths:                              ['S3']\n    orbit_number:                        37258\n    relative_orbit_number:               86\n    ...\n    start_time:                          2021-04-01T15:28:55.111501\n    stop_time:                           2021-04-01T15:29:14.277650\n    group:                               /\n    subgroups:                           ['S3', 'S3/VH', 'S3/VH/orbit', 'S3/V...\n    Conventions:                         CF-1.8\n    history:                             created by xarray_sentinel-...\n\n```\n\nThe root `Dataset` does not contain any data variable, but only attributes that provide general information\non the product and a description of the tree structure of the data.\nThe `group` attribute contains the name of the current group and the `subgroups` attribute shows\nthe names of all available groups below this one.\n\n### Measurements datasets\n\nTo open the other groups we need to add the keyword `group` to `xr.open_dataset`.\nThe measurement can then be read by selecting the desired beam mode and polarization.\nIn this example, the data contains the S3 beam mode and the VH polarization with `group=\"S3/VH\"` is selected:\n\n```python-repl\n\u003e\u003e\u003e slc_s3_vh = xr.open_dataset(slc_sm_path, group=\"S3/VH\", engine=\"sentinel-1\", chunks=2048)\n\u003e\u003e\u003e slc_s3_vh\n\u003cxarray.Dataset\u003e Size: 6GB\nDimensions:           (slant_range_time: 18998, azimuth_time: 36895)\nCoordinates:\n    pixel             (slant_range_time) int64 ...\n    line              (azimuth_time) int64 ...\n  * azimuth_time      (azimuth_time) datetime64[ns] ...\n  * slant_range_time  (slant_range_time) float64 ...\nData variables:\n    measurement       (azimuth_time, slant_range_time) complex64 ...\nAttributes: ...\n    family_name:                         SENTINEL-1\n    number:                              A\n    mode:                                SM\n    swaths:                              ['S3']\n    orbit_number:                        37258\n    relative_orbit_number:               86\n    ...\n    geospatial_lon_min:                  42.772483374347\n    geospatial_lon_max:                  43.75770573943618\n    group:                               /S3/VH\n    subgroups:                           ['orbit', 'attitude', 'azimuth_fm_ra...\n    Conventions:                         CF-1.8\n    history:                             created by xarray_sentinel-...\n\n```\n\nThe `measurement` variable contains the Single Look Complex measurements as a `complex64`\nand has dimensions `slant_range_time` and `azimuth_time`.\nThe `azimuth_time` is an `np.datetime64` coordinate that contains the UTC zero-Doppler time\nassociated with the image line\nand `slant_range_time` is an `np.float64` coordinate that contains the two-way range time interval\nin seconds associated with the image pixel.\n\nSince Sentinel-1 IPF version 3.40, a unique identifier for bursts has been added to the SLC product metadata.\nFor these products, the list of the burst ids is stored the `burst_ids` dataset attribute.\n\n### Metadata datasets\n\nThe measurement group contains several subgroups with metadata associated with the image. Currently,\n*xarray-sentinel* supports the following metadata datasets:\n\n- product XML file\n  - `orbit` from the `\u003corbit\u003e` tags\n  - `attitude` from the `\u003cattitude\u003e` tags\n  - `azimuth_fm_rate` from the `\u003cazimuthFmRate\u003e` tags\n  - `dc_estimate` from the `\u003cdcEstimate\u003e` tags\n  - `gcp` from the `\u003cgeolocationGridPoint\u003e` tags\n  - `coordinate_conversion` from the `\u003ccoordinateConversion\u003e` tags\n- calibration XML file\n  - `calibration` from the `\u003ccalibrationVector\u003e` tags\n- noise XML file\n  - `noise_range` from the `\u003cnoiseRangeVector\u003e` tags\n  - `noise_azimuth` from the `\u003cnoiseAzimuthVector\u003e` tags\n\nFor example, the image calibration metadata associated with the `S3/VH` image can be read using\n`group=\"S3/VH/calibration\"`:\n\n```python-repl\n\u003e\u003e\u003e slc_s3_vh_calibration = xr.open_dataset(slc_sm_path, group=\"S3/VH/calibration\", engine=\"sentinel-1\")\n\u003e\u003e\u003e slc_s3_vh_calibration\n\u003cxarray.Dataset\u003e Size: 172kB\nDimensions:       (line: 22, pixel: 476)\nCoordinates:\n  * line          (line) int64 ...\n  * pixel         (pixel) int64 ...\nData variables:\n    azimuth_time  (line) datetime64[ns] ...\n    sigmaNought   (line, pixel) float32 ...\n    betaNought    (line, pixel) float32 ...\n    gamma         (line, pixel) float32 ...\n    dn            (line, pixel) float32 ...\nAttributes: ...\n    family_name:                         SENTINEL-1\n    number:                              A\n    mode:                                SM\n    swaths:                              ['S3']\n    orbit_number:                        37258\n    relative_orbit_number:               86\n    ...\n    absoluteCalibrationConstant:         1.0\n    group:                               /S3/VH/calibration\n    Conventions:                         CF-1.8\n    title:                               Calibration coefficients\n    comment:                             The dataset contains calibration inf...\n    history:                             created by xarray_sentinel-...\n\n```\n\nNote that in this case, the dimensions are `line` and `pixel` with coordinates corresponding to\nthe sub-grid of the original image where the calibration Look Up Table is defined.\n\nThe groups present in a typical Sentinel-1 Stripmap product are:\n\n```\n/\n└─ S3\n   ├─ VH\n   │  ├─ orbit\n   │  ├─ attitude\n   │  ├─ azimuth_fm_rate\n   │  ├─ dc_estimate\n   │  ├─ gcp\n   │  ├─ coordinate_conversion\n   │  ├─ calibration\n   │  ├─ noise_range\n   │  └─ noise_azimuth\n   └─ VV\n      ├─ orbit\n      ├─ attitude\n      ├─ azimuth_fm_rate\n      ├─ dc_estimate\n      ├─ gcp\n      ├─ coordinate_conversion\n      ├─ calibration\n      ├─ noise_range\n      └─ noise_azimuth\n\n```\n\n## Advanced usage\n\n### TOPS burst datasets\n\nThe IW and EW products, that use the Terrain Observation with Progressive Scan (TOPS) acquisition mode,\nare more complex because they contain several beam modes in the same SAFE package,\nbut also because the measurement array is a collage of sub-images called *bursts*.\n\n*xarray-sentinel* provides a helper function that crops a burst out of a measurement dataset for you.\n\nYou need to first open the desired measurement dataset, for example, the HH polarisation\nof the first IW swath of the `S1A_IW_SLC__1SDH_20220414T102209_20220414T102236_042768_051AA4_E677.SAFE`\nproduct, in the current folder:\n\n```python-repl\n\u003e\u003e\u003e slc_iw_v340_path = \"tests/data/S1A_IW_SLC__1SDH_20220414T102209_20220414T102236_042768_051AA4_E677.SAFE\"\n\u003e\u003e\u003e slc_iw1_v340_hh = xr.open_dataset(slc_iw_v340_path, group=\"IW1/HH\", engine=\"sentinel-1\")\n\u003e\u003e\u003e slc_iw1_v340_hh\n\u003cxarray.Dataset\u003e ...\nDimensions:           (pixel: 21169, line: 13500)\nCoordinates:\n  * pixel             (pixel) int64 ...\n  * line              (line) int64 ...\n    azimuth_time      (line) datetime64[ns] ...\n    slant_range_time  (pixel) float64 ...\nData variables:\n    measurement       (line, pixel) complex64 ...\nAttributes: ...\n    family_name:                         SENTINEL-1\n    number:                              A\n    mode:                                IW\n    swaths:                              ['IW1', 'IW2', 'IW3']\n    orbit_number:                        42768\n    relative_orbit_number:               171\n    ...\n    geospatial_lon_min:                  -61.94949110259839\n    geospatial_lon_max:                  -60.24826879672774\n    group:                               /IW1/HH\n    subgroups:                           ['orbit', 'attitude', 'azimuth_fm_ra...\n    Conventions:                         CF-1.8\n    history:                             created by xarray_sentinel-...\n\n```\n\nNote that the measurement data for IW and EW acquisition modes can not be indexed by physical\ncoordinates because of the collage nature of the image.\n\nNow the 9th burst out of 9 can be cropped from the swath data using `burst_index=8`, via:\n\n```python-repl\n\u003e\u003e\u003e import xarray_sentinel\n\u003e\u003e\u003e xarray_sentinel.crop_burst_dataset(slc_iw1_v340_hh, burst_index=8)\n\u003cxarray.Dataset\u003e ...\nDimensions:           (slant_range_time: 21169, azimuth_time: 1500)\nCoordinates:\n    pixel             (slant_range_time) int64 ...\n    line              (azimuth_time) int64 ...\n  * azimuth_time      (azimuth_time) datetime64[ns] ...\n  * slant_range_time  (slant_range_time) float64 ...\nData variables:\n    measurement       (azimuth_time, slant_range_time) complex64 ...\nAttributes: ...\n    family_name:                         SENTINEL-1\n    number:                              A\n    mode:                                IW\n    swaths:                              ['IW1', 'IW2', 'IW3']\n    orbit_number:                        42768\n    relative_orbit_number:               171\n    ...\n    group:                               /IW1/HH\n    Conventions:                         CF-1.8\n    history:                             created by xarray_sentinel-...\n    azimuth_anx_time:                    2136.774327\n    burst_index:                         8\n    burst_id:                            365923\n\n```\n\nIf IPF processor version is 3.40 or higher, it is also possible to select the burst\nto be cropped using the `burst_id` key:\n\n```python-repl\n\u003e\u003e\u003e xarray_sentinel.crop_burst_dataset(slc_iw1_v340_hh, burst_id=365923)\n\u003cxarray.Dataset\u003e ...\nDimensions:           (slant_range_time: 21169, azimuth_time: 1500)\nCoordinates:\n    pixel             (slant_range_time) int64 ...\n    line              (azimuth_time) int64 ...\n  * azimuth_time      (azimuth_time) datetime64[ns] ...\n  * slant_range_time  (slant_range_time) float64 ...\nData variables:\n    measurement       (azimuth_time, slant_range_time) complex64 ...\nAttributes: ...\n    family_name:                         SENTINEL-1\n    number:                              A\n    mode:                                IW\n    swaths:                              ['IW1', 'IW2', 'IW3']\n    orbit_number:                        42768\n    relative_orbit_number:               171\n    ...\n    group:                               /IW1/HH\n    Conventions:                         CF-1.8\n    history:                             created by xarray_sentinel-...\n    azimuth_anx_time:                    2136.774327\n    burst_index:                         8\n    burst_id:                            365923\n\n```\n\nNote that the helper function also performs additional changes, such as swapping the dimensions\nto the physical coordinates and adding burst attributes.\n\nAs a quick way to access burst data, you can add the `burst_index` to the group specification on\nopen, for example, `group=\"IW1/VH/8\"`.\nThe burst groups are not listed in the `subgroup` attribute because they are not structural.\n\n```python-repl\n\u003e\u003e\u003e slc_iw_v330_path = \"tests/data/S1B_IW_SLC__1SDV_20210401T052622_20210401T052650_026269_032297_EFA4.SAFE\"\n\u003e\u003e\u003e xr.open_dataset(slc_iw_v330_path, group=\"IW1/VH/8\", engine=\"sentinel-1\")\n\u003cxarray.Dataset\u003e ...\nDimensions:           (slant_range_time: 21632, azimuth_time: 1501)\nCoordinates:\n    pixel             (slant_range_time) int64 ...\n    line              (azimuth_time) int64 ...\n  * azimuth_time      (azimuth_time) datetime64[ns] ...\n  * slant_range_time  (slant_range_time) float64 ...\nData variables:\n    measurement       (azimuth_time, slant_range_time) complex64 ...\nAttributes: ...\n    family_name:                         SENTINEL-1\n    number:                              B\n    mode:                                IW\n    swaths:                              ['IW1', 'IW2', 'IW3']\n    orbit_number:                        26269\n    relative_orbit_number:               168\n    ...\n    geospatial_lon_max:                  12.093126130070317\n    group:                               /IW1/VH\n    azimuth_anx_time:                    2210.634453\n    burst_index:                         8\n    Conventions:                         CF-1.8\n    history:                             created by xarray_sentinel-...\n\n```\n\n### Calibration\n\n*xarray-sentinel* provides helper functions to calibrate the data using the calibration metadata.\nYou can compute the gamma intensity for part of the Stripmap image above with:\n\n```python-repl\n\u003e\u003e\u003e xarray_sentinel.calibrate_intensity(slc_s3_vh.measurement[:2048, :2048], slc_s3_vh_calibration.gamma)\n\u003cxarray.DataArray (azimuth_time: 2048, slant_range_time: 2048)\u003e ...\ndask.array\u003cpow, shape=(2048, 2048), dtype=float32, chunksize=(2048, 2048), chunktype=numpy.ndarray\u003e\nCoordinates:\n    pixel             (slant_range_time) int64 ...\n    line              (azimuth_time) int64 ...\n  * azimuth_time      (azimuth_time) datetime64[ns] ...\n  * slant_range_time  (slant_range_time) float64 ...\nAttributes: ...\n    family_name:                         SENTINEL-1\n    number:                              A\n    mode:                                SM\n    swaths:                              ['S3']\n    orbit_number:                        37258\n    relative_orbit_number:               86\n    ...\n    geospatial_lat_min:                  -12.17883496921861\n    geospatial_lat_max:                  -10.85986742252814\n    geospatial_lon_min:                  42.772483374347\n    geospatial_lon_max:                  43.75770573943618\n    units:                               m2 m-2\n    long_name:                           gamma\n\n```\n\n### Advanced data access via fsspec\n\n**You need the unreleased rasterio \u003e= 1.3.0 for fsspec to work on measurement data**\n\n*xarray-sentinel* can read data from a variety of data stores including local file systems,\nnetwork file systems, cloud object stores and compressed file formats, like Zip.\nThis is done by passing *fsspec* compatible URLs to `xr.open_dataset` and optionally\nthe `storage_options` keyword argument.\n\nFor example you can open a product directly from a zip file with:\n\n```python-repl\n\u003e\u003e\u003e slc_iw_zip_path = \"tests/data/S1B_IW_SLC__1SDV_20210401T052622_20210401T052650_026269_032297_EFA4.zip\"\n\u003e\u003e\u003e xr.open_dataset(f\"zip://*/manifest.safe::{slc_iw_zip_path}\", group=\"IW1/VH\", engine=\"sentinel-1\")  # doctest: +SKIP\n\u003cxarray.Dataset\u003e ...\nDimensions:           (pixel: 21632, line: 13509)\nCoordinates:\n  * pixel             (pixel) int64 ...\n  * line              (line) int64 ...\n    azimuth_time      (line) datetime64[ns] ...\n    slant_range_time  (pixel) float64 ...\nData variables:\n    measurement       (line, pixel) complex64 ...\nAttributes: ...\n    family_name:                         SENTINEL-1\n    number:                              B\n    mode:                                IW\n    swaths:                              ['IW1', 'IW2', 'IW3']\n    orbit_number:                        26269\n    relative_orbit_number:               168\n    ...\n    number_of_bursts:                    9\n    lines_per_burst:                     1501\n    group:                               /IW1/VH\n    subgroups:                           ['orbit', 'attitude', 'azimuth_fm_ra...\n    Conventions:                         CF-1.8\n    history:                             created by xarray_sentinel-...\n\n```\n\nAs an example of remote access, you can open a product directly from a GitHub repo with:\n\n```python-repl\n\u003e\u003e\u003e xr.open_dataset(f\"github://bopen:xarray-sentinel@/{slc_iw_path}\", group=\"IW1/VH\", engine=\"sentinel-1\")  # doctest: +SKIP\n\u003cxarray.Dataset\u003e ...\nDimensions:           (pixel: 21632, line: 13509)\nCoordinates:\n  * pixel             (pixel) int64 ...\n  * line              (line) int64 ...\n    azimuth_time      (line) datetime64[ns] ...\n    slant_range_time  (pixel) float64 ...\nData variables:\n    measurement       (line, pixel) complex64 ...\nAttributes: ...\n    family_name:                         SENTINEL-1\n    number:                              B\n    mode:                                IW\n    swaths:                              ['IW1', 'IW2', 'IW3']\n    orbit_number:                        26269\n    relative_orbit_number:               168\n    ...\n    number_of_bursts:                    9\n    lines_per_burst:                     1501\n    group:                               /IW1/VH\n    subgroups:                           ['orbit', 'attitude', 'azimuth_fm_ra...\n    Conventions:                         CF-1.8\n    history:                             created by xarray_sentinel-...\n\n```\n\n*fsspec* is very powerful and supports caching and chaining, for example you can open a\nzip file off a GitHub repo and cache the file locally with:\n\n```python-repl\n\u003e\u003e\u003e xr.open_dataset(\n...     f\"zip://*/manifest.safe::simplecache::github://bopen:xarray-sentinel@/{slc_iw_zip_path}\",\n...     engine=\"sentinel-1\",\n...     group=\"IW1/VH\",\n...     storage_options={\n...         \"simplecache\": {\"cache_storage\": \"/tmp/zipfiles/\"},\n...     },\n... )  # doctest: +SKIP\n\u003cxarray.Dataset\u003e ...\nDimensions:           (pixel: 21632, line: 13509)\nCoordinates:\n  * pixel             (pixel) int64 ...\n  * line              (line) int64 ...\n    azimuth_time      (line) datetime64[ns] ...\n    slant_range_time  (pixel) float64 ...\nData variables:\n    measurement       (line, pixel) complex64 ...\nAttributes: ...\n    family_name:                         SENTINEL-1\n    number:                              B\n    mode:                                IW\n    swaths:                              ['IW1', 'IW2', 'IW3']\n    orbit_number:                        26269\n    relative_orbit_number:               168\n    ...\n    number_of_bursts:                    9\n    lines_per_burst:                     1501\n    group:                               /IW1/VH\n    subgroups:                           ['orbit', 'attitude', 'azimuth_fm_ra...\n    Conventions:                         CF-1.8\n    history:                             created by xarray_sentinel-...\n\n```\n\n## Reference documentation\n\nThis is the list of the reference documents:\n\n- Sentinel-1 document library:\n  - [user guides](https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-1-sar)\n  - [technical guides](https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-1-sar)\n- [Sentinel-1 Product Specification v3.9 07 May 2021 S1-RS-MDA-52-7441-3-9 documenting IPF 3.40](https://sentinel.esa.int/documents/247904/1877131/S1-RS-MDA-52-7441-3-9-2_Sentinel-1ProductSpecification.pdf)\n- [Sentinel-1 Product Specification v3.7 27 February 2020 S1-RS-MDA-52-7441 documenting IPF 3.30](https://sentinel.esa.int/documents/247904/1877131/Sentinel-1-Product-Specification)\n- [Radiometric Calibration of S-1 Level-1 Products Generated by the S-1 IPF v1.0 21/05/2015 ESA-EOPG-CSCOP-TN-0002](https://sentinel.esa.int/documents/247904/685163/S1-Radiometric-Calibration-V1.0.pdf)\n\n## Design decisions\n\n- The main design choice for *xarray-sentinel* is for it to be as much as viable a pure map of\n  the content of the SAFE data package, with as little interpretation as possible.\n  - The tree-like structure follows the structure of the SAFE package even when information,\n    like orbit and attitude, is expected to be identical for different beam modes.\n    We observed at least a case where the number of orbital state vectors reported\n    was different between beam modes.\n  - Data and metadata are converted to the closest available data-type in *Python* / *numpy*.\n    The most significant conversion is from `CInt16` to `np.complex64` for the SLC measurements\n    that double the space requirements for the data.\n    Also, *xarray-sentinel* converts UTC times to `np.datetime64` and makes no attempt to support\n    *leap seconds*, acquisitions containing leap seconds may crash or silently return corrupted data.\n    See the rationale for choices of the coordinates data-types below.\n  - We try to keep all naming as close as possible to the original names.\n    In particular, for metadata we use the names of the XML tags, only converting them\n    from *camelCase* to *snake_case*.\n- Whenever possible *xarray-sentinel* indexes the data with physical coordinates\n  `azimuth_time` and `slant_range_time`, but keeps image `line` and `pixel` as auxiliary coordinates.\n- As an exception to the metadata naming rule above we add some attributes to get\n  CF-Conventions compliance.\n- We aim at opening available data and metadata even for partial SAFE packages, for example,\n  *xarray-sentinel* can open a measurement dataset for a beam mode even when the TIFF files of other\n  beam modes / polarizations are missing.\n- Accuracy considerations and rationale for coordinates data-types:\n  - `azimuth_time` can be expressed as `np.datetime64[ns]` since\n    spatial resolution at LEO speed is 10km/s * 1ns ~= 0.001cm.\n  - `slant_range_time` on the other hand cannot be expressed as `np.timedelta64[ns]` as\n    spatial resolution at the speed of light is 300_000km/s * 1ns / 2 ~= 15cm,\n    i.e. not enough for interferometric applications.\n    `slant_range_time` needs a spatial resolution of 0.001cm at a 1_000km distance,\n    i.e. around 1e-9, well within the 1e-15 resolution of IEEE-754 float64.\n\n## Project badges\n\n[![on-push](https://github.com/bopen/xarray-sentinel/actions/workflows/on-push.yml/badge.svg)](https://github.com/bopen/xarray-sentinel/actions/workflows/on-push.yml)\n[![codecov](https://codecov.io/gh/bopen/xarray-sentinel/branch/main/graph/badge.svg?token=OLw9it0i18)](https://codecov.io/gh/bopen/xarray-sentinel)\n\n## Contributing\n\nThe main repository is hosted on GitHub.\nTesting, bug reports and contributions are highly welcomed and appreciated:\n\n\u003chttps://github.com/bopen/xarray-sentinel\u003e\n\nLead developers:\n\n- [Aureliana Barghini](https://github.com/aurghs) - [B-Open](https://www.bopen.eu)\n- [Alessandro Amici](https://github.com/alexamici) - [B-Open](https://www.bopen.eu)\n\nMain contributors:\n\n- [Corrado Avolio](https://github.com/corrado9999) - [e-GEOS](https://www.e-geos.it)\n\nSee also the list of [contributors](https://github.com/bopen/xarray-sentinel/contributors) who participated in this project.\n\n## Sponsoring\n\n[B-Open](https://bopen.eu) commits to maintain the project long term and we are happy to accept sponsorships to develop new features.\n\nWe wish to express our gratitude to the project sponsors:\n\n- [Microsoft](https://microsoft.com) has sponsored the support for *GRD* products and *fsspec* data access.\n\n## License\n\n```\nCopyright 2021-2022, B-Open Solutions srl and the xarray-sentinel authors.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n    http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n```\n","funding_links":[],"readme_doi_urls":[],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["gdal","geospatial-data","rasterio","meteorology"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/6082","html_url":"https://ost.ecosyste.ms/projects/6082"}