Download this notebook from github.

A notebook to illustrate how to compute and correct Impulse Response in IW SLC product

save IR file to netCDF

[2]:

out_file = "/tmp/S1_IR_IW1_VV_AUX_FILE.nc"
if 'footprint' in IRs_IW1_VV.attrs:
    IRs_IW1_VV.attrs.update({'footprint': str(IRs_IW1_VV.footprint)})
if 'multidataset' in IRs_IW1_VV.attrs:
    IRs_IW1_VV.attrs.update({'multidataset': str(IRs_IW1_VV.multidataset)})
IRs_IW1_VV.to_netcdf(out_file)
print(out_file)

/tmp/S1_IR_IW1_VV_AUX_FILE.nc

display IW1 range impulse response for VV and VH

[3]:

from matplotlib import pyplot as plt
IRs_IW1_VV['range_IR'].plot(label='VV')
#IRs_IW1_VH['range_IR'].plot(label='VH',alpha=0.7)
plt.legend()
plt.grid(True)
plt.title('burst = %s subswath = %s'%(IRs_IW1_VV.burst.values,IRs_IW1_VV.attrs['subswath']))

[3]:

Text(0.5, 1.0, 'burst = 1 subswath = IW1')

../_images/examples_example_IW_compute_and_correct_from_impulse_response_6_1.png

display IW1 azimuth impulse response for VV and VH

[4]:

from matplotlib import pyplot as plt
IRs_IW1_VV['azimuth_IR'].plot(label='VV')
#IRs_IW1_VH['azimuth_IR'].plot(label='VH',alpha=0.7)
plt.legend()
plt.grid(True)
plt.title('burst = %s subswath = %s'%(IRs_IW1_VV.burst.values,IRs_IW1_VV.attrs['subswath']))

[4]:

Text(0.5, 1.0, 'burst = 1 subswath = IW1')

../_images/examples_example_IW_compute_and_correct_from_impulse_response_8_1.png

apply IR correction when computing a cross spectra

[5]:

import xsarslc
import xsar
import os
one_tiff = '/home/datawork-cersat-public/cache/project/mpc-sentinel1/data/esa/sentinel-1a/L1/IW/S1A_IW_SLC__1S/2022/127/S1A_IW_SLC__1SDV_20220507T162411_20220507T162439_043107_0525DE_734D.SAFE/measurement/s1a-iw1-slc-vv-20220507t162411-20220507t162439-043107-0525de-004.tiff'
fullpathsafeSLC = os.path.dirname(os.path.dirname(one_tiff))
imagette_number = os.path.basename(one_tiff).split('-')[1].replace('iw','')
str_gdal = 'SENTINEL1_DS:%s:IW%s'%(fullpathsafeSLC,imagette_number)
print('str_gdal',str_gdal)
xsarobj = xsar.Sentinel1Dataset(str_gdal) #,resolution='30m'
xsarobj

str_gdal SENTINEL1_DS:/home/datawork-cersat-public/cache/project/mpc-sentinel1/data/esa/sentinel-1a/L1/IW/S1A_IW_SLC__1S/2022/127/S1A_IW_SLC__1SDV_20220507T162411_20220507T162439_043107_0525DE_734D.SAFE:IW1

[5]:

<Sentinel1Dataset full coverage object>

[6]:

dt = xsarobj.datatree
dt.load()
dt

[7]:

import xsarslc.processing.xspectra
intra_xs_without_IR = xsarslc.processing.xspectra.compute_IW_subswath_intraburst_xspectra(dt,
                                         tile_width={'sample': 5.e3, 'line': 5.e3},
                                         tile_overlap={'sample': -10.e3, 'line': -10.e3},
                                         periodo_width = {'sample': 4000, 'line': 4000},
                                        periodo_overlap = {'sample': 2000, 'line': 2000},
                                        polarization='VV',dev=True)
intra_xs_without_IR

intrabursts: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:08<00:00,  8.78s/it]

[8]:

IR_path = out_file
intra_xs_with_IR = xsarslc.processing.xspectra.compute_IW_subswath_intraburst_xspectra(dt,
                                         tile_width={'sample': 5.e3, 'line': 5.e3},
                                         tile_overlap={'sample': -10.e3, 'line': -10.e3},
                                         periodo_width = {'sample': 4000, 'line': 4000},
                                        periodo_overlap = {'sample': 2000, 'line': 2000},
                                        polarization='VV',dev=True,IR_path=IR_path)
intra_xs_with_IR

intrabursts: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.44s/it]

[9]:

tile_line_i = 0
tile_sample_i = 0
onespec_without_IR = intra_xs_without_IR['xspectra_2tau'].isel({'tile_sample':tile_sample_i,'tile_line':tile_line_i}).squeeze()
onespec_without_IR = onespec_without_IR.swap_dims({'freq_line': 'k_az', 'freq_sample': 'k_rg'})
abs(onespec_without_IR.real.mean(dim='k_rg')).plot(label='real without IR')

onespec_with_IR = intra_xs_with_IR['xspectra_2tau'].isel({'tile_sample':tile_sample_i,'tile_line':tile_line_i}).squeeze()
onespec_with_IR = onespec_with_IR.swap_dims({'freq_line': 'k_az', 'freq_sample': 'k_rg'})
abs(onespec_with_IR.real.mean(dim='k_rg')).plot(label='real with IR')
plt.legend()
plt.grid(True)

../_images/examples_example_IW_compute_and_correct_from_impulse_response_14_0.png

[10]:

onespec_without_IR.imag.mean(dim='k_rg').plot(label='imag. without IR')
onespec_with_IR.imag.mean(dim='k_rg').plot(label='imag. with IR')
plt.legend()
plt.grid(True)

../_images/examples_example_IW_compute_and_correct_from_impulse_response_15_0.png