xsar example

%load_ext autoreload
%autoreload 2

The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

import bokeh.io
bokeh.io.output_notebook()
import holoviews as hv
hv.extension('bokeh')
import datashader as ds
from holoviews.operation.datashader import datashade,rasterize
from IPython.display import display, HTML

Loading BokehJS ...

import xsar 
import xsarsea # need pip install git+https://gitlab.ifremer.fr/sarlib/xsarsea
import time
import rasterio 
import xarray as xr

filename = '/home/oarcher/SAFE/S1A_WV_SLC__1SSV_20160510T101603_20160510T102143_011195_010EA1_7038.SAFE'


# different way to open a product

# just one subdataset (need to use 'gdalinfo' on filename to list them)
sar_ds = xsar.open_dataset(filename,sub_datasets='WV_004')[0]

# all subdatasets (may take some time if many subdatasets)
# sar_ds_list = xsar.open_dataset(filename)
# sar_ds = sar_ds_list[0]

# resample at 200m. WV pixel size is 5m, so 200m is 40 pix
# sar_ds = xsar.open_dataset(filename,resolution={'atrack': 40, 'xtrack': 40},sub_datasets='WV_001')[0]


sar_ds

<xarray.Dataset>
Dimensions:         (atrack: 4776, pol: 1, xtrack: 4500)
Coordinates:
  * pol             (pol) object 'VV'
  * atrack          (atrack) int64 0 1 2 3 4 5 ... 4770 4771 4772 4773 4774 4775
  * xtrack          (xtrack) int64 0 1 2 3 4 5 ... 4494 4495 4496 4497 4498 4499
Data variables:
    digital_number  (pol, atrack, xtrack) complex128 dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>
    time            (atrack) datetime64[ns] dask.array<chunksize=(1000,), meta=np.ndarray>
    longitude       (atrack, xtrack) float32 dask.array<chunksize=(1000, 1000), meta=np.ndarray>
    latitude        (atrack, xtrack) float32 dask.array<chunksize=(1000, 1000), meta=np.ndarray>
    incidence       (atrack, xtrack) float32 dask.array<chunksize=(1000, 1000), meta=np.ndarray>
    elevation       (atrack, xtrack) float32 dask.array<chunksize=(1000, 1000), meta=np.ndarray>
    sigma0_raw      (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>
    nesz            (pol, atrack, xtrack) float32 dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>
    gamma0_raw      (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>
    negz            (pol, atrack, xtrack) float32 dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>
    sigma0          (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>
    gamma0          (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 1000, 1000), meta=np.ndarray>
Attributes:
    footprint:       POLYGON ((-62.63201467647821 39.40883908115116, -62.8657...
    coverage:        20km * 19km (xtrack * atrack )
    pixel_xtrack_m:  4.5
    pixel_atrack_m:  4.1
    ipf_version:     2.7
    swath_type:      WV
    polarizations:   VV
    product_type:    SLC
    mission:         SENTINEL-1
    satellite:       A
    start_date:      2016-05-10 10:16:03.843410
    stop_date:       2016-05-10 10:21:43.669704
    path:            /home/oarcher/SAFE/S1A_WV_SLC__1SSV_20160510T101603_2016...
    denoised:        {'VV': False}
    subdataset:      WV_004
    geometry:        {'VV': {'atrack':   atracks xtracks                     ...
    Conventions:     CF-1.7

# sar_ds.sigma0 plot (it's allready denoised)
rasterize(hv.Image(sar_ds.sigma0.sel(pol='VV')).opts(cmap='gray',colorbar=True,tools=['hover']))

# detrend sigma0 (suppress incidence angle effect)
sar_ds['sigma0_detrend'] = xsarsea.sigma0_detrend(sar_ds.sigma0,sar_ds.incidence)
rasterize(hv.Image(sar_ds.sigma0_detrend.sel(pol='VV')).opts(cmap='gray',colorbar=True,tools=['hover']))