SAR example

Let’s use EOReader with SAR data.

Warning: SAR data is processed with SNAP, so be sure to have it installed and that GPT is in your path.

Create logger

# Create logger
import logging

logger = logging.getLogger("eoreader")
logger.setLevel(logging.INFO)

# create console handler and set level to debug
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)

# create formatter
formatter = logging.Formatter('%(message)s')

# add formatter to ch
ch.setFormatter(formatter)

# add ch to logger
logger.addHandler(ch)

Open the COSMO-SkyMed product

Please be aware that:

  • EOReader will orthorectify your SAR data to get UTM tiles.

  • complex data is not handled as is, EOReader will convert them to ground range.

import os
from eoreader.reader import Reader

# First of all, we need some VHR data, let's use some COSMO-SkyMed data
path = os.path.join("/home", "data", "DATA", "PRODS", "COSMO", "1st_GEN", "1001512-735097")

# Open your product
prod = Reader().open(path, remove_tmp=True)
# Print some data
print(f"Acquisition datetime: {prod.datetime}")
print(f"Condensed name: {prod.condensed_name}")

# Open here some more interesting geographical data: extent and footprint
base = prod.extent.plot(color='cyan', edgecolor='black')
prod.footprint.plot(ax=base, color='blue', edgecolor='black', alpha=0.5)
Acquisition datetime: 2020-10-08 22:40:18.446381
Condensed name: 20201008T224018_CSK_HI_DGM
Executing processing graph
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 done.
<AxesSubplot:>
../_images/SAR_4_49.png

For SAR data, the footprint needs the orthorectified data ! For that, SNAP uses its own DEM, but you can change it when positionning the EOREADER_SNAP_DEM_NAME environment variable. Available DEMs are:

  • ACE2_5Min

  • ACE30

  • ASTER 1sec GDEM

  • Copernicus 30m Global DEM(buggy for now, do not use it)

  • Copernicus 90m Global DEM(buggy for now, do not use it)

  • GETASSE30 (by default)

  • SRTM 1Sec HGT

  • SRTM 3Sec

  • External DEM

Warning: If External DEM is set, you must specify the DEM you want by positioning the EOREADER_DEM_PATH to a DEM that can be read by SNAP.

Load bands

from eoreader.bands import *
from eoreader.env_vars import DEM_PATH

# Set the DEM
os.environ[DEM_PATH] = os.path.join("/home", "data", "DS2", "BASES_DE_DONNEES", "GLOBAL", "COPDEM_30m", "COPDEM_30m.vrt")

# Select some bands you wish to load without knowing if they exist
bands = [VV, HH, VV_DSPK, HH_DSPK, HILLSHADE, SLOPE]

# Only keep those selected
ok_bands = [band for band in bands if prod.has_band(band)]

# This product does not have VV band and HILLSHADE band cannot be computed from SAR band
print(to_str(ok_bands))
['HH', 'HH_DSPK', 'SLOPE']
# Load those bands as a dict of xarray.DataArray, with a 20m resolution
band_dict = prod.load(ok_bands, resolution=20.)
band_dict[HH]
Executing processing graph
first_line_time metadata value is null
last_line_time metadata value is null
...10%...21%...32%...43%.
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 done.
<xarray.DataArray 'HH' (band: 1, y: 2474, x: 2689)>
array([[[nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        ...,
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan],
        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)
Coordinates:
  * x            (x) float64 2.058e+05 2.059e+05 ... 2.596e+05 2.596e+05
  * y            (y) float64 1.746e+06 1.746e+06 ... 1.697e+06 1.697e+06
  * band         (band) int64 1
    spatial_ref  int64 0
Attributes:
    scale_factor:      1.0
    add_offset:        0.0
    long_name:         HH
    sensor:            COSMO-SkyMed
    sensor_id:         CSK
    product_path:      /home/data/DATA/PRODS/COSMO/1st_GEN/1001512-735097
    product_name:      CSKS4_DGM_B_HI_09_HH_RA_FF_20201008224018_20201008224025
    product_filename:  1001512-735097
    product_type:      DGM
    acquisition_date:  20201008T224018
    condensed_name:    20201008T224018_CSK_HI_DGM
Some SAR band (i.e. COSMO) may contain null pixels that are not really nodata (but very low values like water).

This can lead the Terrain Correction step to create large nodata area when projecting on a DEM.

If it happens, you can set the keyword SAR_INTERP_NA to True when loading or stacking SAR data to fill these area with interpolated data.

from eoreader.keywords import SAR_INTERP_NA
band_dict = prod.load(
    ok_bands, 
    resolution=20., 
    **{SAR_INTERP_NA: True}
)
# Plot a subsampled version
band_dict[SLOPE][:, ::10, ::10].plot()
<matplotlib.collections.QuadMesh at 0x7f2b9b7b2dc0>
../_images/SAR_9_1.png

Stack some data

# You can also stack those bands
stack = prod.stack(ok_bands)
stack
<xarray.DataArray 'HH HH_DSPK SLOPE' (z: 3, y: 9897, x: 10755)>
array([[[        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        ...,
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan]],

       [[        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
...
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan]],

       [[ 0.4255329 ,  0.4255329 ,  0.4255329 , ...,  0.        ,
          0.        ,  0.        ],
        [ 0.4255329 ,  0.4255329 ,  0.4255329 , ...,  0.        ,
          0.        ,  0.        ],
        [ 0.4255329 ,  0.4255329 ,  0.4255329 , ...,  0.        ,
          0.        ,  0.        ],
        ...,
        [16.66219   , 16.66219   , 16.66219   , ...,  0.0870695 ,
          0.0870695 ,  0.0870695 ],
        [16.66219   , 16.66219   , 16.66219   , ...,  0.0870695 ,
          0.0870695 ,  0.0870695 ],
        [17.018255  , 17.018255  , 17.018255  , ...,  0.08737368,
          0.08737368,  0.08737368]]], dtype=float32)
Coordinates:
    spatial_ref  int64 0
  * x            (x) float64 2.058e+05 2.058e+05 ... 2.596e+05 2.596e+05
  * y            (y) float64 1.746e+06 1.746e+06 ... 1.697e+06 1.697e+06
  * z            (z) MultiIndex
  - variable     (z) object 'HH' 'HH_DSPK' 'SLOPE'
  - band         (z) int64 1 1 1
Attributes:
    long_name:         HH HH_DSPK SLOPE
    sensor:            COSMO-SkyMed
    sensor_id:         CSK
    product_path:      /home/data/DATA/PRODS/COSMO/1st_GEN/1001512-735097
    product_name:      CSKS4_DGM_B_HI_09_HH_RA_FF_20201008224018_20201008224025
    product_filename:  1001512-735097
    product_type:      DGM
    acquisition_date:  20201008T224018
    condensed_name:    20201008T224018_CSK_HI_DGM
# Plot a subsampled version
import matplotlib.pyplot as plt

nrows = len(stack)
fig, axes = plt.subplots(nrows=nrows, figsize=(3 * nrows, 6 * nrows), subplot_kw={"box_aspect": 1})
for i in range(nrows):
    stack[i, ::10, ::10].plot(x="x", y="y", ax=axes[i])
../_images/SAR_12_0.png