Download this notebook from github.

XSAR example¶

[1]:

import xsar
import os
import numpy as np

[2]:

# get test file. You can replace with an path to other SAFE
filename = xsar.get_test_file('S1A_IW_GRDH_1SDV_20170907T103020_20170907T103045_018268_01EB76_Z010.SAFE')

Open a dataset with a xsar.Sentinel1Meta object¶

A xsar.Sentinel1Meta object handles all attributes and methods that can’t be embdeded in a xarray.Dataset object. It can also replace a filename in xarray.open_dataset

When using it in a notebook, it also have an html representation.

[3]:

sar_meta = xsar.Sentinel1Meta(filename)
sar_meta

[3]:

Single dataset

SENTINEL1_DS:S1A_IW_GRDH_1SDV_20170907T103020_20170907T103045_018268_01EB76_Z010.SAFE:IW

ipf	2.84
platform	SENTINEL-1A
swath	IW
product	GRDH
pols	VV VH
orbit_pass	Descending
pixel size	10.2 * 10.0 meters (atrack * xtrack)
coverage	170km * 251km (atrack * xtrack )
start_date	2017-09-07 10:30:20.936409
stop_date	2017-09-07 10:30:45.935264
dsid	IW

sar_meta object is an xsar.Sentinel1Meta object that can be given to xarray.open_dataset or xsar.Sentinel1Dataset , as if it was a filename:

[4]:

sar_ds = xsar.Sentinel1Dataset(sar_meta)
sar_ds

[4]:

full dataset coverage

SENTINEL1_DS:S1A_IW_GRDH_1SDV_20170907T103020_20170907T103045_018268_01EB76_Z010.SAFE:IW

pixel size	10.2 * 10.0 meters (atrack * xtrack)
coverage	170km * 251km (atrack * xtrack )

Open a dataset at lower resolution¶

We can use sar_meta properties to compute the pixel count we need to average to get a resolution of 200*200 meters

[5]:

resolution = {'atrack' : int(np.round(200 / sar_meta.pixel_atrack_m)), 'xtrack': int(np.round(200 / sar_meta.pixel_xtrack_m))}
resolution

[5]:

{'atrack': 20, 'xtrack': 20}

Then we can instantiate a xsar.Sentinel1Dataset, with the given resolution. Note that the above pixel size has changed.

[6]:

sar_ds = xsar.Sentinel1Dataset(sar_meta, resolution=resolution)
sar_ds

[6]:

full dataset coverage

SENTINEL1_DS:S1A_IW_GRDH_1SDV_20170907T103020_20170907T103045_018268_01EB76_Z010.SAFE:IW

pixel size	203.1 * 199.7 meters (atrack * xtrack)
coverage	170km * 251km (atrack * xtrack )

Extract a sub image of 10*10km around a lon/lat point¶

Convert (lon,lat) to (atrack, xtrack)¶

we can use sar_meta.ll2coords to convert (lon,lat) to (atrack, xtrack):

[7]:

# from a shapely object
point_lonlat =  sar_meta.footprint.centroid
point_coords = sar_meta.ll2coords(point_lonlat.x, point_lonlat.y)
point_coords

[7]:

(8421.566059451987, 12589.902956209728)

The result is floating, because it is the position inside the pixel. If real indexes from existing dataset is needed, you’ll have to use sar_ds.ll2coords Result will be the nearest (atrack, xtrack) in the dataset

[8]:

point_coords = sar_ds.ll2coords(point_lonlat.x, point_lonlat.y)
point_coords

[8]:

(8430.0, 12590.0)

Extract the sub-image¶

[9]:

box_size = 10000 # 10km
dist = {'atrack' : int(np.round(box_size / 2 / sar_meta.pixel_atrack_m)), 'xtrack': int(np.round(box_size / 2 / sar_meta.pixel_xtrack_m))}
dist

[9]:

{'atrack': 490, 'xtrack': 500}

The xarray/dask dataset is available as a property : sar_ds.dataset. This attribute can be set to a new values, so the attributes like pixel spacing and coverage are correctly recomputed:

[10]:

# select 10*10 km around point_coords
sar_ds.dataset = sar_ds.dataset.sel(atrack=slice(point_coords[0] - dist['atrack'], point_coords[0] + dist['atrack']), xtrack=slice(point_coords[1] - dist['xtrack'], point_coords[1] + dist['xtrack']))
sar_ds

[10]:

dataset slice

SENTINEL1_DS:S1A_IW_GRDH_1SDV_20170907T103020_20170907T103045_018268_01EB76_Z010.SAFE:IW

pixel size	203.6 * 199.7 meters (atrack * xtrack)
coverage	9km * 10km (atrack * xtrack )

[11]:

sar_ds.dataset

[11]:

<xarray.Dataset>
Dimensions:         (atrack: 49, pol: 2, xtrack: 51)
Coordinates:
  * pol             (pol) object 'VV' 'VH'
  * atrack          (atrack) float64 7.95e+03 7.97e+03 ... 8.89e+03 8.91e+03
  * xtrack          (xtrack) float64 1.209e+04 1.211e+04 ... 1.307e+04 1.309e+04
Data variables: (12/14)
    digital_number  (pol, atrack, xtrack) uint16 dask.array<chunksize=(1, 49, 51), meta=np.ndarray>
    time            (atrack) datetime64[ns] 2017-09-07T10:30:32.793691648 ......
    longitude       (atrack, xtrack) float32 dask.array<chunksize=(49, 51), meta=np.ndarray>
    latitude        (atrack, xtrack) float32 dask.array<chunksize=(49, 51), meta=np.ndarray>
    land_mask       (atrack, xtrack) int8 dask.array<chunksize=(49, 51), meta=np.ndarray>
    ground_heading  (atrack, xtrack) float32 dask.array<chunksize=(49, 51), meta=np.ndarray>
    ...              ...
    sigma0_raw      (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 49, 51), meta=np.ndarray>
    nesz            (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 49, 51), meta=np.ndarray>
    gamma0_raw      (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 49, 51), meta=np.ndarray>
    negz            (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 49, 51), meta=np.ndarray>
    sigma0          (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 49, 51), meta=np.ndarray>
    gamma0          (pol, atrack, xtrack) float64 dask.array<chunksize=(1, 49, 51), meta=np.ndarray>
Attributes: (12/14)
    ipf:               2.84
    platform:          SENTINEL-1A
    swath:             IW
    product:           GRDH
    pols:              VV VH
    name:              SENTINEL1_DS:/tmp/S1A_IW_GRDH_1SDV_20170907T103020_201...
    ...                ...
    footprint:         POLYGON ((-69.12627509838202 20.21794031159714, -69.22...
    coverage:          9km * 10km (atrack * xtrack )
    pixel_atrack_m:    203.5819198789976
    pixel_xtrack_m:    199.7376700425335
    orbit_pass:        Descending
    platform_heading:  -167.7668824808032

xarray.Dataset

Dimensions:
- atrack: 49
- pol: 2
- xtrack: 51

Coordinates: (3)

pol
(pol)
object
'VV' 'VH'
```
array(['VV', 'VH'], dtype=object)
```

atrack

(atrack)

float64

7.95e+03 7.97e+03 ... 8.91e+03

array([7950., 7970., 7990., 8010., 8030., 8050., 8070., 8090., 8110., 8130.,
       8150., 8170., 8190., 8210., 8230., 8250., 8270., 8290., 8310., 8330.,
       8350., 8370., 8390., 8410., 8430., 8450., 8470., 8490., 8510., 8530.,
       8550., 8570., 8590., 8610., 8630., 8650., 8670., 8690., 8710., 8730.,
       8750., 8770., 8790., 8810., 8830., 8850., 8870., 8890., 8910.])

xtrack

(xtrack)

float64

1.209e+04 1.211e+04 ... 1.309e+04

array([12090., 12110., 12130., 12150., 12170., 12190., 12210., 12230., 12250.,
       12270., 12290., 12310., 12330., 12350., 12370., 12390., 12410., 12430.,
       12450., 12470., 12490., 12510., 12530., 12550., 12570., 12590., 12610.,
       12630., 12650., 12670., 12690., 12710., 12730., 12750., 12770., 12790.,
       12810., 12830., 12850., 12870., 12890., 12910., 12930., 12950., 12970.,
       12990., 13010., 13030., 13050., 13070., 13090.])

Data variables: (14)

digital_number

(pol, atrack, xtrack)

uint16

dask.array<chunksize=(1, 49, 51), meta=np.ndarray>

	Array	Chunk
Bytes	9.76 kiB	4.88 kiB
Shape	(2, 49, 51)	(1, 49, 51)
Count	8 Tasks	2 Chunks
Type	uint16	numpy.ndarray

time

(atrack)

datetime64[ns]

2017-09-07T10:30:32.793691648 .....

array(['2017-09-07T10:30:32.793691648', '2017-09-07T10:30:32.823558656',
       '2017-09-07T10:30:32.853425920', '2017-09-07T10:30:32.883293184',
       '2017-09-07T10:30:32.913160448', '2017-09-07T10:30:32.943027456',
       '2017-09-07T10:30:32.972894720', '2017-09-07T10:30:33.002761984',
       '2017-09-07T10:30:33.032629248', '2017-09-07T10:30:33.062496512',
       '2017-09-07T10:30:33.092363520', '2017-09-07T10:30:33.122230784',
       '2017-09-07T10:30:33.152098048', '2017-09-07T10:30:33.181965312',
       '2017-09-07T10:30:33.211832576', '2017-09-07T10:30:33.241699584',
       '2017-09-07T10:30:33.271566848', '2017-09-07T10:30:33.301434112',
       '2017-09-07T10:30:33.331301376', '2017-09-07T10:30:33.361168640',
       '2017-09-07T10:30:33.391035648', '2017-09-07T10:30:33.420902912',
       '2017-09-07T10:30:33.450770176', '2017-09-07T10:30:33.480637440',
       '2017-09-07T10:30:33.510504448', '2017-09-07T10:30:33.540371712',
       '2017-09-07T10:30:33.570238976', '2017-09-07T10:30:33.600106240',
       '2017-09-07T10:30:33.629973504', '2017-09-07T10:30:33.659840512',
       '2017-09-07T10:30:33.689707776', '2017-09-07T10:30:33.719575040',
       '2017-09-07T10:30:33.749442304', '2017-09-07T10:30:33.779309568',
       '2017-09-07T10:30:33.809176576', '2017-09-07T10:30:33.839043840',
       '2017-09-07T10:30:33.868911104', '2017-09-07T10:30:33.898778368',
       '2017-09-07T10:30:33.928645632', '2017-09-07T10:30:33.958512640',
       '2017-09-07T10:30:33.988379904', '2017-09-07T10:30:34.018247168',
       '2017-09-07T10:30:34.048114432', '2017-09-07T10:30:34.077981440',
       '2017-09-07T10:30:34.107848704', '2017-09-07T10:30:34.137715968',
       '2017-09-07T10:30:34.167583232', '2017-09-07T10:30:34.197450496',
       '2017-09-07T10:30:34.227317504'], dtype='datetime64[ns]')

longitude

(atrack, xtrack)

float32

dask.array<chunksize=(49, 51), meta=np.ndarray>

	Array	Chunk
Bytes	9.76 kiB	9.76 kiB
Shape	(49, 51)	(49, 51)
Count	7 Tasks	1 Chunks
Type	float32	numpy.ndarray

latitude

(atrack, xtrack)

float32

dask.array<chunksize=(49, 51), meta=np.ndarray>

	Array	Chunk
Bytes	9.76 kiB	9.76 kiB
Shape	(49, 51)	(49, 51)
Count	7 Tasks	1 Chunks
Type	float32	numpy.ndarray

land_mask

(atrack, xtrack)

int8

dask.array<chunksize=(49, 51), meta=np.ndarray>

	Array	Chunk
Bytes	2.44 kiB	2.44 kiB
Shape	(49, 51)	(49, 51)
Count	4 Tasks	1 Chunks
Type	int8	numpy.ndarray

ground_heading

(atrack, xtrack)

float32

dask.array<chunksize=(49, 51), meta=np.ndarray>

	Array	Chunk
Bytes	9.76 kiB	9.76 kiB
Shape	(49, 51)	(49, 51)
Count	5 Tasks	1 Chunks
Type	float32	numpy.ndarray

elevation

(atrack, xtrack)

float32

dask.array<chunksize=(49, 51), meta=np.ndarray>

	Array	Chunk
Bytes	9.76 kiB	9.76 kiB
Shape	(49, 51)	(49, 51)
Count	6 Tasks	1 Chunks
Type	float32	numpy.ndarray

incidence

(atrack, xtrack)

float32

dask.array<chunksize=(49, 51), meta=np.ndarray>

	Array	Chunk
Bytes	9.76 kiB	9.76 kiB
Shape	(49, 51)	(49, 51)
Count	6 Tasks	1 Chunks
Type	float32	numpy.ndarray

sigma0_raw

(pol, atrack, xtrack)

float64

dask.array<chunksize=(1, 49, 51), meta=np.ndarray>

	Array	Chunk
Bytes	39.05 kiB	19.52 kiB
Shape	(2, 49, 51)	(1, 49, 51)
Count	32 Tasks	2 Chunks
Type	float64	numpy.ndarray

nesz

(pol, atrack, xtrack)

float64

dask.array<chunksize=(1, 49, 51), meta=np.ndarray>

	Array	Chunk
Bytes	39.05 kiB	19.52 kiB
Shape	(2, 49, 51)	(1, 49, 51)
Count	41 Tasks	2 Chunks
Type	float64	numpy.ndarray

gamma0_raw

(pol, atrack, xtrack)

float64

dask.array<chunksize=(1, 49, 51), meta=np.ndarray>

	Array	Chunk
Bytes	39.05 kiB	19.52 kiB
Shape	(2, 49, 51)	(1, 49, 51)
Count	32 Tasks	2 Chunks
Type	float64	numpy.ndarray

negz

(pol, atrack, xtrack)

float64

dask.array<chunksize=(1, 49, 51), meta=np.ndarray>

	Array	Chunk
Bytes	39.05 kiB	19.52 kiB
Shape	(2, 49, 51)	(1, 49, 51)
Count	41 Tasks	2 Chunks
Type	float64	numpy.ndarray

sigma0

(pol, atrack, xtrack)

float64

dask.array<chunksize=(1, 49, 51), meta=np.ndarray>

	Array	Chunk
Bytes	39.05 kiB	19.52 kiB
Shape	(2, 49, 51)	(1, 49, 51)
Count	61 Tasks	2 Chunks
Type	float64	numpy.ndarray

gamma0

(pol, atrack, xtrack)

float64

dask.array<chunksize=(1, 49, 51), meta=np.ndarray>

	Array	Chunk
Bytes	39.05 kiB	19.52 kiB
Shape	(2, 49, 51)	(1, 49, 51)
Count	61 Tasks	2 Chunks
Type	float64	numpy.ndarray

Attributes: (14)
ipf :
2.84
platform :
SENTINEL-1A
swath :
IW
product :
GRDH
pols :
VV VH
name :
SENTINEL1_DS:/tmp/S1A_IW_GRDH_1SDV_20170907T103020_20170907T103045_018268_01EB76_Z010.SAFE:IW
start_date :
2017-09-07 10:30:20.936409
stop_date :
2017-09-07 10:30:45.935264
footprint :
POLYGON ((-69.12627509838202 20.21794031159714, -69.22217959996208 20.23509299694786, -69.23994091576878 20.14694340052122, -69.14409175642585 20.12978289801671, -69.12627509838202 20.21794031159714))
coverage :
9km * 10km (atrack * xtrack )
pixel_atrack_m :
203.5819198789976
pixel_xtrack_m :
199.7376700425335
orbit_pass :
Descending
platform_heading :
-167.7668824808032