Use case 2: retrieving the corrections

Notebook setup

[45]:

%matplotlib inline
%load_ext autoreload
%autoreload 2

[46]:

import sys

sys.path.append("../..")

[47]:

import numpy as np
import matplotlib.pyplot as plt

[48]:

import s1etad
from s1etad import Sentinel1Etad, ECorrectionType

Open the dataset

[49]:

filename = (
    "data/"
    "S1A_IW_ETA__AXDV_20230806T211729_20230806T211757_049760_05FBCB_9DD6.SAFE"
)

[50]:

eta = Sentinel1Etad(filename)

[51]:

swath = eta["IW1"]

[52]:

burst = swath[1]

[53]:

burst

[53]:

Sentinel1EtadBurst("/IW1/Burst0001")  0x75fbd39d60b0
Swaths ID: IW1
Burst index: 1
Shape: (108, 402)
Sampling start:
  x: 0.0
  y: 0.0
  units: s
Sampling:
  x: 8.131672451354599e-07
  y: 0.02932551319648094
  units: s

Get corrections

The Sentinel1EtadBurst class allows to access the netcdf product to retrieve the corrections burst by burst.

The recommended way to retrieve a correction is:

s1etad.Sentinel1EtadBurst.get_correction(name, set_auto_mask=False,
                                         transpose=True, meter=False)

Available correction types are:

[54]:

s1etad.ECorrectionType.__members__

[54]:

mappingproxy({'TROPOSPHERIC': <ECorrectionType.TROPOSPHERIC: 'tropospheric'>,
              'TROPOSPHERIC_GRADIENT': <ECorrectionType.TROPOSPHERIC_GRADIENT: 'tropospheric_gradient'>,
              'IONOSPHERIC': <ECorrectionType.IONOSPHERIC: 'ionospheric'>,
              'GEODETIC': <ECorrectionType.GEODETIC: 'geodetic'>,
              'OTL': <ECorrectionType.OTL: 'otl'>,
              'BISTATIC': <ECorrectionType.BISTATIC: 'bistatic'>,
              'DOPPLER': <ECorrectionType.DOPPLER: 'doppler'>,
              'FMRATE': <ECorrectionType.FMRATE: 'fmrate'>,
              'SUM': <ECorrectionType.SUM: 'sum'>})

Example:

[55]:

# correction = burst.get_correction('ionospheric')
#
# or equivalently
correction = burst.get_correction(
    s1etad.ECorrectionType.IONOSPHERIC, meter=True
)
correction.keys()

[55]:

dict_keys(['x', 'unit', 'name'])

[56]:

az, rg = burst.get_burst_grid()
extent = [rg[0] * 1e6, rg[-1] * 1e6, az[0], az[-1]]

plt.figure()
plt.imshow(correction["x"], extent=extent, aspect="auto")
plt.xlabel(r"Range [$\mu s$]")
plt.ylabel("Azimuth [s]")
plt.grid()
plt.colorbar().set_label(f"[{correction['unit']}]")
plt.title(
    f"{correction['name']} correction (swath {burst.swath_id} "
    f"burst {burst.burst_index})"
)

[56]:

Text(0.5, 1.0, 'ionospheric correction (swath IW1 burst 1)')

../_images/notebooks_use-case-02_corrections_17_1.png

Retrieving merged corrections

The Sentinel1Etad and Sentinel1EtadSwath classes provides methods to retrieve a specific correction for multiple bursts merged together for easy representation purposes.

NOTE: the current implementation uses a very simple algorithm that iterates over selected bursts and stitches correction data together. In overlapping regions new data simply overwrite the old ones. This is an easy algorithm and perfectly correct for atmospheric and geodetic correction. It is, instead, sub-optimal for system corrections (bi-static, Doppler, FM Rate) which have different values in overlapping regions.

First select the bursts

[57]:

import dateutil.parser

first_time = dateutil.parser.parse("2023-08-06T21:17:34.000000")
last_time = dateutil.parser.parse("2023-08-06T21:17:53.000000")

product_name = (
    "S1A_IW_SLC__1SDV_20230806T211729_20230806T211757_049760_05FBCB_BC56.SAFE"
)

# query the catalogue for a subset of the swaths
df = eta.query_burst(
    first_time=first_time, last_time=last_time, product_name=product_name
)

# df = df[df.bIndex != 13]   # exclude burst n. 13 (IW1))
#                            # to test extended selection capabilities
# df = df[df.bIndex != 17]   # exclude burst n. 17 (IW2))
#                            # to test extended selection capabilities
# df = df[df.bIndex != 15]   # exclude burst n. 17 (IW3))
#                            # to test extended selection capabilities

[58]:

df

[58]:

	bIndex	pIndex	sIndex	productID	swathID	azimuthTimeMin	azimuthTimeMax
2	7	1	1	S1A_IW_SLC__1SDV_20230806T211729_20230806T2117...	IW1	2023-08-06 21:17:34.721407480	2023-08-06 21:17:37.888562906
12	8	1	2	S1A_IW_SLC__1SDV_20230806T211729_20230806T2117...	IW2	2023-08-06 21:17:35.659823903	2023-08-06 21:17:38.826979328
21	9	1	3	S1A_IW_SLC__1SDV_20230806T211729_20230806T2117...	IW3	2023-08-06 21:17:36.627565838	2023-08-06 21:17:39.794721263
3	10	1	1	S1A_IW_SLC__1SDV_20230806T211729_20230806T2117...	IW1	2023-08-06 21:17:37.478005721	2023-08-06 21:17:40.645161146
13	11	1	2	S1A_IW_SLC__1SDV_20230806T211729_20230806T2117...	IW2	2023-08-06 21:17:38.416422143	2023-08-06 21:17:41.612903082
22	12	1	3	S1A_IW_SLC__1SDV_20230806T211729_20230806T2117...	IW3	2023-08-06 21:17:39.384164079	2023-08-06 21:17:42.580645017
4	13	1	1	S1A_IW_SLC__1SDV_20230806T211729_20230806T2117...	IW1	2023-08-06 21:17:40.234603961	2023-08-06 21:17:43.401759387
14	14	1	2	S1A_IW_SLC__1SDV_20230806T211729_20230806T2117...	IW2	2023-08-06 21:17:41.173020384	2023-08-06 21:17:44.369501322
23	15	1	3	S1A_IW_SLC__1SDV_20230806T211729_20230806T2117...	IW3	2023-08-06 21:17:42.140762319	2023-08-06 21:17:45.337243258
5	16	1	1	S1A_IW_SLC__1SDV_20230806T211729_20230806T2117...	IW1	2023-08-06 21:17:42.991202202	2023-08-06 21:17:46.158357627
15	17	1	2	S1A_IW_SLC__1SDV_20230806T211729_20230806T2117...	IW2	2023-08-06 21:17:43.929618624	2023-08-06 21:17:47.126099563
24	18	1	3	S1A_IW_SLC__1SDV_20230806T211729_20230806T2117...	IW3	2023-08-06 21:17:44.897360560	2023-08-06 21:17:48.064515985
6	19	1	1	S1A_IW_SLC__1SDV_20230806T211729_20230806T2117...	IW1	2023-08-06 21:17:45.747800442	2023-08-06 21:17:48.914955868
16	20	1	2	S1A_IW_SLC__1SDV_20230806T211729_20230806T2117...	IW2	2023-08-06 21:17:46.686216865	2023-08-06 21:17:49.882697803
25	21	1	3	S1A_IW_SLC__1SDV_20230806T211729_20230806T2117...	IW3	2023-08-06 21:17:47.653958800	2023-08-06 21:17:50.850439739
7	22	1	1	S1A_IW_SLC__1SDV_20230806T211729_20230806T2117...	IW1	2023-08-06 21:17:48.504398683	2023-08-06 21:17:51.671554108
17	23	1	2	S1A_IW_SLC__1SDV_20230806T211729_20230806T2117...	IW2	2023-08-06 21:17:49.442815105	2023-08-06 21:17:52.639296043

Common variables

[59]:

from scipy import constants

dy = eta.grid_spacing["y"]
dx = eta.grid_sampling["x"] * constants.c / 2
nswaths = len(df.swathID.unique())
vg = eta.grid_spacing["y"] / eta.grid_sampling["y"]
vmin = 2.5
vmax = 3.5
to_km = 1.0 / 1000

Iterate on swath to get de-bursted data (selected burst merged together)

[60]:

fig, ax = plt.subplots(nrows=1, ncols=nswaths, figsize=[13, 8])
for idx, swath in enumerate(eta.iter_swaths(df)):
    merged_correction = swath.merge_correction(
        ECorrectionType.TROPOSPHERIC, selection=df, meter=True
    )
    merged_correction_data = merged_correction["x"]

    ysize, xsize = merged_correction_data.shape
    x0 = merged_correction["first_slant_range_time"] * constants.c / 2  # [m]
    y0 = merged_correction["first_azimuth_time"] * vg  # [m]
    x_axis = (x0 + np.arange(xsize) * dx) * to_km
    y_axis = (y0 + np.arange(ysize) * dy) * to_km

    extent = [x_axis[0], x_axis[-1], y_axis[0], y_axis[-1]]
    im = ax[idx].imshow(
        merged_correction_data,
        origin="lower",
        extent=extent,
        vmin=vmin,
        vmax=vmax,
        aspect="equal",
    )
    ax[idx].set_title(swath.swath_id)
    ax[idx].set_xlabel("Slant Range [km]")

ax[0].set_ylabel("Azimuth [km]")

name = merged_correction["name"]
unit = merged_correction["unit"]

fig.suptitle(f"{name.title()} correction")
fig.colorbar(im, ax=ax[:].tolist(), label=f"[{unit}]")

[60]:

<matplotlib.colorbar.Colorbar at 0x75fbd014dbd0>

../_images/notebooks_use-case-02_corrections_26_1.png

Get merged swaths

[61]:

fig, ax = plt.subplots(figsize=[13, 7])
merged_correction = eta.merge_correction(
    ECorrectionType.TROPOSPHERIC, selection=df, meter=True
)
merged_correction_data = merged_correction["x"]

ysize, xsize = merged_correction_data.shape
x_axis = np.arange(xsize) * dx * to_km
y_axis = np.arange(ysize) * dy * to_km

extent = [x_axis[0], x_axis[-1], y_axis[0], y_axis[-1]]
im = ax.imshow(
    merged_correction_data,
    origin="lower",
    extent=extent,
    vmin=vmin,
    vmax=vmax,
    aspect="equal",
)

ax.set_xlabel("Slant Range [km]")
ax.set_ylabel("Azimuth [km]")

name = merged_correction["name"]
unit = merged_correction["unit"]

ax.set_title(f"Merged swaths for {name} correction")
fig.colorbar(im, ax=ax, label=f"[{unit}]")

[61]:

<matplotlib.colorbar.Colorbar at 0x75fbd0f84690>

../_images/notebooks_use-case-02_corrections_28_1.png