xsar: efficient level 1 sar reader for xarray/dask

xsar is a distributed level 1 SAR file reader designed to write efficient distributed processing algorhitm with xarray and dask.

It currently handles Level-1 Sentinel-1 data in SAFE format, as found on scihub or PEPS.

xsar is as simple to use as the well known xarray.open_dataset : simply give the dataset path, and xsar.open_dataset() will return an xarray.Dataset:

import xsar
import os
filename = xsar.get_test_file('S1A_IW_GRDH_1SDV_20170907T103020_20170907T103045_018268_01EB76_Z010.SAFE')
sar_ds = xsar.open_dataset(filename)
sar_ds

<xarray.Dataset>
Dimensions:         (atrack: 16778, pol: 2, xtrack: 25187)
Coordinates:
  * pol             (pol) object 'VV' 'VH'
  * atrack          (atrack) float64 0.5 1.5 2.5 ... 1.678e+04 1.678e+04
  * xtrack          (xtrack) float64 0.5 1.5 2.5 ... 2.519e+04 2.519e+04
Data variables:
    digital_number  (pol, atrack, xtrack) uint16 dask.array<chunksize=(1, 5000, 5000), meta=np.ndarray>
    time            (atrack) datetime64[ns] 2017-09-07T10:30:20.936409088 ......
    longitude       (atrack, xtrack) float32 dask.array<chunksize=(5000, 5000), meta=np.ndarray>
    latitude        (atrack, xtrack) float32 dask.array<chunksize=(5000, 5000), meta=np.ndarray>
    elevation       (atrack, xtrack) float32 dask.array<chunksize=(5000, 5000), meta=np.ndarray>
    incidence       (atrack, xtrack) float32 dask.array<chunksize=(5000, 5000), meta=np.ndarray>
    sigma0_raw      (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 5000, 5000), meta=np.ndarray>
    nesz            (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 5000, 5000), meta=np.ndarray>
    gamma0_raw      (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 5000, 5000), meta=np.ndarray>
    negz            (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 5000, 5000), meta=np.ndarray>
    sigma0          (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 5000, 5000), meta=np.ndarray>
    gamma0          (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 5000, 5000), meta=np.ndarray>
Attributes: (12/15)
    ipf:               2.84
    platform:          SENTINEL-1A
    swath:             IW
    product:           GRDH
    pols:              VV VH
    name:              SENTINEL1_DS:/tmp/S1A_IW_GRDH_1SDV_20170907T103020_201...
    ...                ...
    coverage:          170km * 251km (atrack * xtrack )
    pixel_atrack_m:    10.152619433217325
    pixel_xtrack_m:    9.986179379582328
    orbit_pass:        Descending
    platform_heading:  -167.7668824808032
    Conventions:       CF-1.7

Documentation

Getting Started

Installation

Quick install:

conda create -n xsar
conda activate xsar
# FIXME 'docutils>=0.16' with nbsphinx give  "WARNING: Line 710 exceeds the line-length-limit." when converting ipynb to html
conda install -c conda-forge rasterio gdal dask[array] dask[distributed] jupyterlab geoviews cartopy holoviews datashader nbsphinx pandoc jq 'docutils<0.16' pip fiona git lxml
pip install git+https://github.com/oarcher/xsar.git

Overview

xsar rely on xarray.open_rasterio, rasterio and GDAL to read digital_number from SAFE product to return an xarray.Dataset object with dask chunks.

Luts are decoded from xml files and applied to digital_number, following official ESA thermal denoising document and ESA Sentinel-1 Product Specification.

So end user can directly use for example sigma0 variable, thas is the denoised sigma0 computed from digital_number and by applying relevants luts.

Because xsar rely on dask, it have a small memory footprint: variables are read from file and computed only if needed.

xsar.open_dataset() is very close to xarray.open_dataset, but in the followings examples, you will find some aditionnal keywords and classes that allow to:

Examples

Note

With recommended installation you will be able to download and execute those examples in jupyter notebook.

Those examples will automatically download test data from https://cyclobs.ifremer.fr/static/sarwing_datarmor/xsardata/

Those file are not official ones: they are resampled to a lower resoltion and compressed to avoid big network transfert and disk usage.

Don’t use them for real science !

Reference

Get in touch

  • Report bugs, suggest features or view the source code on github.

Last documentation build: May 11 2021 at 14:06