Sentinel-1 data in Python

OpenGeoHub Summer School 2023

• Lorena Abad
• 2023-08-31

Querying S1-SLC data

Sentinel-1 data comes at different levels and provides different products. For applications such as measuring deformation due to tectonic or volcanic activity, quantifying ground subsidence or to generate digital elevation models (DEM), interferometric SAR (InSAR) techniques can be used.

To apply such workflows with Sentinel data, we can use Sentinel-1 Level 1 Single Look Complex products.

SM mode is designed to support ERS (European Remote Sensing) and Envisat missions; IW mode is the default mode over land; EW mode is designed for maritime, ice, and polar zone observation services where wide coverage and short revisit times are demanded; and WV mode is the default mode over the open ocean.

So far, very few cloud computing capabilities are available to compute such complex workflows, therefore, there is still a need to download data. Depending on the application, we will need to download data with certain characteristics.

Figure from: Xiong S, Muller J-P, Li G. The Application of ALOS/PALSAR InSAR to Measure Subsurface Penetration Depths in Deserts. Remote Sensing. 2017; 9(6):638. https://doi.org/10.3390/rs9060638

Note on terminology.

For DEM generation, for example, we would require a pair of Sentinel-1 scenes acquired closely in time and that have a perpendicular baseline between 150 and 300 m. Usually, computing the perpendicular baseline between two images requires the download of the image pairs.

To avoid downloading several unnecessary Sentinel-1 scenes, we can make use of the Alaska Satellite Facility (ASF) geographic and baseline tools to query the data we need via their API.

Libraries needed for this exercise are imported below:

import asf_search as asf
import geopandas as gpd
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

Define extent

We will define an aoi and a start and end date for our queries.

aoi = gpd.read_file("D:/Daten/OpenGeoHub2023/Sentinel Lorena/ogh23/data/poznan.geojson")
aoi.explore()

Make this Notebook Trusted to load map: File -> Trust Notebook

footprint = aoi.to_wkt()
date_start = "2022/05/01"
date_end = "2022/10/01"

Geographical search

Now we can use the asf_search Python module to perform our geographical search. We specify here the platform and the processing level (SLC) that we are looking for, and we limit the results for this exercise to 10 scenes.

products = asf.geo_search(platform=[asf.PLATFORM.SENTINEL1],
                          intersectsWith=footprint.geometry[0],
                          processingLevel=[asf.PRODUCT_TYPE.SLC],
                          # beamSwath='IW', #Nothing will come for EW, because that would be for the arctic
                          start=date_start,
                          end=date_end,
                          maxResults=10)

products

ASFSearchResults([<asf_search.ASFProduct.ASFProduct at 0x1b1ed935090>,
                  <asf_search.ASFProduct.ASFProduct at 0x1b19332d450>,
                  <asf_search.ASFProduct.ASFProduct at 0x1b19332e7d0>,
                  <asf_search.ASFProduct.ASFProduct at 0x1b19332ee10>,
                  <asf_search.ASFProduct.ASFProduct at 0x1b19332f410>,
                  <asf_search.ASFProduct.ASFProduct at 0x1b19332fa10>,
                  <asf_search.ASFProduct.ASFProduct at 0x1b19332ff90>,
                  <asf_search.ASFProduct.ASFProduct at 0x1b193328390>,
                  <asf_search.ASFProduct.ASFProduct at 0x1b193328950>,
                  <asf_search.ASFProduct.ASFProduct at 0x1b19332f990>])

We can then add the results of the query to a pandas dataframe for easier inspection:

products_df = pd.DataFrame([p.properties for p in products])
products_df

	beamModeType	browse	bytes	centerLat	centerLon	faradayRotation	fileID	flightDirection	groupID	granuleType	...	processingDate	processingLevel	sceneName	sensor	startTime	stopTime	url	pgeVersion	fileName	frameNumber
0	IW	None	4657751122	52.2492	17.9661	None	S1A_IW_SLC__1SDV_20220929T163603_20220929T1636...	ASCENDING	S1A_IWDV_0167_0173_045222_175	SENTINEL_1A_FRAME	...	2022-09-29T16:36:03.000Z	SLC	S1A_IW_SLC__1SDV_20220929T163603_20220929T1636...	C-SAR	2022-09-29T16:36:03.000Z	2022-09-29T16:36:30.000Z	https://datapool.asf.alaska.edu/SLC/SA/S1A_IW_...	003.52	S1A_IW_SLC__1SDV_20220929T163603_20220929T1636...	167
1	IW	None	4580763215	52.6218	18.4544	None	S1A_IW_SLC__1SDV_20220926T050057_20220926T0501...	DESCENDING	S1A_IWDV_0416_0423_045171_124	SENTINEL_1A_FRAME	...	2022-09-26T05:00:57.000Z	SLC	S1A_IW_SLC__1SDV_20220926T050057_20220926T0501...	C-SAR	2022-09-26T05:00:57.000Z	2022-09-26T05:01:24.000Z	https://datapool.asf.alaska.edu/SLC/SA/S1A_IW_...	003.52	S1A_IW_SLC__1SDV_20220926T050057_20220926T0501...	417
2	IW	None	4609579705	53.0143	15.6840	None	S1A_IW_SLC__1SDV_20220922T164428_20220922T1644...	ASCENDING	S1A_IWDV_0169_0176_045120_073	SENTINEL_1A_FRAME	...	2022-09-22T16:44:28.000Z	SLC	S1A_IW_SLC__1SDV_20220922T164428_20220922T1644...	C-SAR	2022-09-22T16:44:28.000Z	2022-09-22T16:44:56.000Z	https://datapool.asf.alaska.edu/SLC/SA/S1A_IW_...	003.52	S1A_IW_SLC__1SDV_20220922T164428_20220922T1644...	170
3	IW	None	4646654586	51.5302	16.1188	None	S1A_IW_SLC__1SDV_20220922T164404_20220922T1644...	ASCENDING	S1A_IWDV_0165_0170_045120_073	SENTINEL_1A_FRAME	...	2022-09-22T16:44:04.000Z	SLC	S1A_IW_SLC__1SDV_20220922T164404_20220922T1644...	C-SAR	2022-09-22T16:44:04.000Z	2022-09-22T16:44:31.000Z	https://datapool.asf.alaska.edu/SLC/SA/S1A_IW_...	003.52	S1A_IW_SLC__1SDV_20220922T164404_20220922T1644...	165
4	IW	None	4686861613	53.0102	16.5167	None	S1A_IW_SLC__1SDV_20220919T050904_20220919T0509...	DESCENDING	S1A_IWDV_0415_0421_045069_022	SENTINEL_1A_FRAME	...	2022-09-19T05:09:04.000Z	SLC	S1A_IW_SLC__1SDV_20220919T050904_20220919T0509...	C-SAR	2022-09-19T05:09:04.000Z	2022-09-19T05:09:31.000Z	https://datapool.asf.alaska.edu/SLC/SA/S1A_IW_...	003.52	S1A_IW_SLC__1SDV_20220919T050904_20220919T0509...	415
5	IW	None	4652270299	52.2492	17.9662	None	S1A_IW_SLC__1SDV_20220917T163602_20220917T1636...	ASCENDING	S1A_IWDV_0167_0173_045047_175	SENTINEL_1A_FRAME	...	2022-09-17T16:36:02.000Z	SLC	S1A_IW_SLC__1SDV_20220917T163602_20220917T1636...	C-SAR	2022-09-17T16:36:02.000Z	2022-09-17T16:36:30.000Z	https://datapool.asf.alaska.edu/SLC/SA/S1A_IW_...	003.52	S1A_IW_SLC__1SDV_20220917T163602_20220917T1636...	167
6	IW	None	4734573721	52.6220	18.4523	None	S1A_IW_SLC__1SDV_20220914T050057_20220914T0501...	DESCENDING	S1A_IWDV_0416_0423_044996_124	SENTINEL_1A_FRAME	...	2022-09-14T05:00:57.000Z	SLC	S1A_IW_SLC__1SDV_20220914T050057_20220914T0501...	C-SAR	2022-09-14T05:00:57.000Z	2022-09-14T05:01:24.000Z	https://datapool.asf.alaska.edu/SLC/SA/S1A_IW_...	003.52	S1A_IW_SLC__1SDV_20220914T050057_20220914T0501...	417
7	IW	None	4707081403	53.0145	15.6814	None	S1A_IW_SLC__1SDV_20220910T164429_20220910T1644...	ASCENDING	S1A_IWDV_0169_0176_044945_073	SENTINEL_1A_FRAME	...	2022-09-10T16:44:29.000Z	SLC	S1A_IW_SLC__1SDV_20220910T164429_20220910T1644...	C-SAR	2022-09-10T16:44:29.000Z	2022-09-10T16:44:56.000Z	https://datapool.asf.alaska.edu/SLC/SA/S1A_IW_...	003.52	S1A_IW_SLC__1SDV_20220910T164429_20220910T1644...	170
8	IW	None	4721237800	51.5303	16.1161	None	S1A_IW_SLC__1SDV_20220910T164404_20220910T1644...	ASCENDING	S1A_IWDV_0164_0171_044945_073	SENTINEL_1A_FRAME	...	2022-09-10T16:44:04.000Z	SLC	S1A_IW_SLC__1SDV_20220910T164404_20220910T1644...	C-SAR	2022-09-10T16:44:04.000Z	2022-09-10T16:44:32.000Z	https://datapool.asf.alaska.edu/SLC/SA/S1A_IW_...	003.52	S1A_IW_SLC__1SDV_20220910T164404_20220910T1644...	165
9	IW	None	4575317676	53.0107	16.5132	None	S1A_IW_SLC__1SDV_20220907T050904_20220907T0509...	DESCENDING	S1A_IWDV_0415_0421_044894_022	SENTINEL_1A_FRAME	...	2022-09-07T05:09:04.000Z	SLC	S1A_IW_SLC__1SDV_20220907T050904_20220907T0509...	C-SAR	2022-09-07T05:09:04.000Z	2022-09-07T05:09:31.000Z	https://datapool.asf.alaska.edu/SLC/SA/S1A_IW_...	003.52	S1A_IW_SLC__1SDV_20220907T050904_20220907T0509...	415

10 rows × 28 columns

Ascending vs. Descending: - - “/>

Baseline search

Now that we have scenes that intersect with our defined extent, we can do a baseline search that will allow us to fetch all the S1 scenes that pair with the first S1 result from our geographical query. The baseline search returns a set of products with precomputed perpendicular baselines, so that we can focus our download on the data that we need.

stack = products[0].stack()

print(f'{len(stack)} products found in stack')

446 products found in stack

We can take a look at the data again as a pandas data frame and we will see that the last two columns correspond to the temporal and perpendicular baseline.

stack_df = pd.DataFrame([p.properties for p in stack])
stack_df

# sort_values()

	beamModeType	browse	bytes	centerLat	centerLon	faradayRotation	fileID	flightDirection	groupID	granuleType	...	sceneName	sensor	startTime	stopTime	url	pgeVersion	fileName	frameNumber	temporalBaseline	perpendicularBaseline
0	IW	None	4619446112	52.5775	17.8534	None	S1A_IW_SLC__1SDV_20141029T163519_20141029T1635...	ASCENDING	S1A_IWDV_0168_0173_003047_175	SENTINEL_1A_FRAME	...	S1A_IW_SLC__1SDV_20141029T163519_20141029T1635...	C-SAR	2014-10-29T16:35:19.000Z	2014-10-29T16:35:46.000Z	https://datapool.asf.alaska.edu/SLC/SA/S1A_IW_...	002.71	S1A_IW_SLC__1SDV_20141029T163519_20141029T1635...	168	-2892	7.0
1	IW	None	4722951640	52.5774	17.8506	None	S1A_IW_SLC__1SDV_20141122T163518_20141122T1635...	ASCENDING	S1A_IWDV_0168_0173_003397_175	SENTINEL_1A_FRAME	...	S1A_IW_SLC__1SDV_20141122T163518_20141122T1635...	C-SAR	2014-11-22T16:35:18.000Z	2014-11-22T16:35:46.000Z	https://datapool.asf.alaska.edu/SLC/SA/S1A_IW_...	002.71	S1A_IW_SLC__1SDV_20141122T163518_20141122T1635...	168	-2868	69.0
2	IW	None	4522111160	52.5774	17.8509	None	S1A_IW_SLC__1SDV_20141204T163518_20141204T1635...	ASCENDING	S1A_IWDV_0168_0173_003572_175	SENTINEL_1A_FRAME	...	S1A_IW_SLC__1SDV_20141204T163518_20141204T1635...	C-SAR	2014-12-04T16:35:18.000Z	2014-12-04T16:35:45.000Z	https://datapool.asf.alaska.edu/SLC/SA/S1A_IW_...	002.71	S1A_IW_SLC__1SDV_20141204T163518_20141204T1635...	168	-2856	90.0
3	IW	None	4728062249	52.5777	17.8487	None	S1A_IW_SLC__1SDV_20141216T163518_20141216T1635...	ASCENDING	S1A_IWDV_0168_0173_003747_175	SENTINEL_1A_FRAME	...	S1A_IW_SLC__1SDV_20141216T163518_20141216T1635...	C-SAR	2014-12-16T16:35:18.000Z	2014-12-16T16:35:45.000Z	https://datapool.asf.alaska.edu/SLC/SA/S1A_IW_...	002.71	S1A_IW_SLC__1SDV_20141216T163518_20141216T1635...	168	-2844	-55.0
4	IW	None	4486483588	52.5775	17.8488	None	S1A_IW_SLC__1SDV_20141228T163517_20141228T1635...	ASCENDING	S1A_IWDV_0167_0174_003922_175	SENTINEL_1A_FRAME	...	S1A_IW_SLC__1SDV_20141228T163517_20141228T1635...	C-SAR	2014-12-28T16:35:17.000Z	2014-12-28T16:35:44.000Z	https://datapool.asf.alaska.edu/SLC/SA/S1A_IW_...	002.71	S1A_IW_SLC__1SDV_20141228T163517_20141228T1635...	168	-2832	-56.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
441	IW	None	4718179946	52.2499	17.9680	None	S1A_IW_SLC__1SDV_20230702T163603_20230702T1636...	ASCENDING	S1A_IWDV_0167_0173_049247_175	SENTINEL_1A_FRAME	...	S1A_IW_SLC__1SDV_20230702T163603_20230702T1636...	C-SAR	2023-07-02T16:36:03.964Z	2023-07-02T16:36:31.045Z	https://datapool.asf.alaska.edu/SLC/SA/S1A_IW_...	003.61	S1A_IW_SLC__1SDV_20230702T163603_20230702T1636...	167	276	NaN
442	IW	None	4626122112	52.2499	17.9659	None	S1A_IW_SLC__1SDV_20230714T163604_20230714T1636...	ASCENDING	S1A_IWDV_0167_0172_049422_175	SENTINEL_1A_FRAME	...	S1A_IW_SLC__1SDV_20230714T163604_20230714T1636...	C-SAR	2023-07-14T16:36:04.988Z	2023-07-14T16:36:32.070Z	https://datapool.asf.alaska.edu/SLC/SA/S1A_IW_...	003.61	S1A_IW_SLC__1SDV_20230714T163604_20230714T1636...	167	288	NaN
443	IW	None	4675450333	52.2497	17.9655	None	S1A_IW_SLC__1SDV_20230726T163605_20230726T1636...	ASCENDING	S1A_IWDV_0167_0173_049597_175	SENTINEL_1A_FRAME	...	S1A_IW_SLC__1SDV_20230726T163605_20230726T1636...	C-SAR	2023-07-26T16:36:05.545Z	2023-07-26T16:36:32.627Z	https://datapool.asf.alaska.edu/SLC/SA/S1A_IW_...	003.61	S1A_IW_SLC__1SDV_20230726T163605_20230726T1636...	167	300	NaN
444	IW	None	4713498249	52.2496	17.9664	None	S1A_IW_SLC__1SDV_20230807T163606_20230807T1636...	ASCENDING	S1A_IWDV_0167_0173_049772_175	SENTINEL_1A_FRAME	...	S1A_IW_SLC__1SDV_20230807T163606_20230807T1636...	C-SAR	2023-08-07T16:36:06.044Z	2023-08-07T16:36:33.128Z	https://datapool.asf.alaska.edu/SLC/SA/S1A_IW_...	003.61	S1A_IW_SLC__1SDV_20230807T163606_20230807T1636...	167	312	NaN
445	IW	None	4643445782	52.2501	17.9667	None	S1A_IW_SLC__1SDV_20230819T163607_20230819T1636...	ASCENDING	S1A_IWDV_0167_0173_049947_175	SENTINEL_1A_FRAME	...	S1A_IW_SLC__1SDV_20230819T163607_20230819T1636...	C-SAR	2023-08-19T16:36:07.038Z	2023-08-19T16:36:34.120Z	https://datapool.asf.alaska.edu/SLC/SA/S1A_IW_...	003.61	S1A_IW_SLC__1SDV_20230819T163607_20230819T1636...	167	324	NaN

446 rows × 30 columns

To have an idea of how spread our data is, we can plot the temporal and the perpendicular baselines against each other.

stack_df.plot.scatter(x="temporalBaseline", y="perpendicularBaseline")

<Axes: xlabel='temporalBaseline', ylabel='perpendicularBaseline'>

Ideally, we will filter those values where temporalBaseline <= 30 and 150 <= perpendicularBaseline <= 300 for instance to get image pairs suitable for DEM generation. So we can filter our data frame for those values. We look for absolute values since the order of the images is not relevant.

stack_df[(abs(stack_df['temporalBaseline']) <= 30) &
         (abs(stack_df['perpendicularBaseline']) >= 150) &
         (abs(stack_df['perpendicularBaseline']) <= 300)]

	beamModeType	browse	bytes	centerLat	centerLon	faradayRotation	fileID	flightDirection	groupID	granuleType	...	sceneName	sensor	startTime	stopTime	url	pgeVersion	fileName	frameNumber	temporalBaseline	perpendicularBaseline
416	IW	None	4563266039	52.2489	17.9638	None	S1A_IW_SLC__1SDV_20220905T163603_20220905T1636...	ASCENDING	S1A_IWDV_0167_0173_044872_175	SENTINEL_1A_FRAME	...	S1A_IW_SLC__1SDV_20220905T163603_20220905T1636...	C-SAR	2022-09-05T16:36:03.000Z	2022-09-05T16:36:30.000Z	https://datapool.asf.alaska.edu/SLC/SA/S1A_IW_...	003.52	S1A_IW_SLC__1SDV_20220905T163603_20220905T1636...	167	-24	-184.0

1 rows × 30 columns

We only get one image fitting the characteristics we require. Let’s look at its properties:

stack[416].properties

{'beamModeType': 'IW',
 'browse': None,
 'bytes': 4563266039,
 'centerLat': 52.2489,
 'centerLon': 17.9638,
 'faradayRotation': None,
 'fileID': 'S1A_IW_SLC__1SDV_20220905T163603_20220905T163630_044872_055BFF_5F88-SLC',
 'flightDirection': 'ASCENDING',
 'groupID': 'S1A_IWDV_0167_0173_044872_175',
 'granuleType': 'SENTINEL_1A_FRAME',
 'insarStackId': None,
 'md5sum': '72e8baa2456d336cb9b71e2c5f7e93be',
 'offNadirAngle': None,
 'orbit': 44872,
 'pathNumber': 175,
 'platform': 'Sentinel-1A',
 'pointingAngle': None,
 'polarization': 'VV+VH',
 'processingDate': '2022-09-05T16:36:03.000Z',
 'processingLevel': 'SLC',
 'sceneName': 'S1A_IW_SLC__1SDV_20220905T163603_20220905T163630_044872_055BFF_5F88',
 'sensor': 'C-SAR',
 'startTime': '2022-09-05T16:36:03.000Z',
 'stopTime': '2022-09-05T16:36:30.000Z',
 'url': 'https://datapool.asf.alaska.edu/SLC/SA/S1A_IW_SLC__1SDV_20220905T163603_20220905T163630_044872_055BFF_5F88.zip',
 'pgeVersion': '003.52',
 'fileName': 'S1A_IW_SLC__1SDV_20220905T163603_20220905T163630_044872_055BFF_5F88.zip',
 'frameNumber': 167,
 'temporalBaseline': -24,
 'perpendicularBaseline': -184}

Let’s also remember this is paired with the original product we calcualted the baselines for.

products[0].properties

{'beamModeType': 'IW',
 'browse': None,
 'bytes': 4657751122,
 'centerLat': 52.2492,
 'centerLon': 17.9661,
 'faradayRotation': None,
 'fileID': 'S1A_IW_SLC__1SDV_20220929T163603_20220929T163630_045222_0567B7_46CF-SLC',
 'flightDirection': 'ASCENDING',
 'groupID': 'S1A_IWDV_0167_0173_045222_175',
 'granuleType': 'SENTINEL_1A_FRAME',
 'insarStackId': None,
 'md5sum': 'a37a41c9155ed7a93b4055066710e224',
 'offNadirAngle': None,
 'orbit': 45222,
 'pathNumber': 175,
 'platform': 'Sentinel-1A',
 'pointingAngle': None,
 'polarization': 'VV+VH',
 'processingDate': '2022-09-29T16:36:03.000Z',
 'processingLevel': 'SLC',
 'sceneName': 'S1A_IW_SLC__1SDV_20220929T163603_20220929T163630_045222_0567B7_46CF',
 'sensor': 'C-SAR',
 'startTime': '2022-09-29T16:36:03.000Z',
 'stopTime': '2022-09-29T16:36:30.000Z',
 'url': 'https://datapool.asf.alaska.edu/SLC/SA/S1A_IW_SLC__1SDV_20220929T163603_20220929T163630_045222_0567B7_46CF.zip',
 'pgeVersion': '003.52',
 'fileName': 'S1A_IW_SLC__1SDV_20220929T163603_20220929T163630_045222_0567B7_46CF.zip',
 'frameNumber': 167}

Downloading the data

Finally, with the ASF API we can download our data to further analyse it with, e.g. SNAP. To do so we can make use of the url property.

urls = [
    products[0].properties['url'],
    stack[416].properties['url']
]

Once that is set we can use the download_urls() function as speccified below to get our data in a desired directory. To download the data we will need EarthData credentials. This notebook from the ASF describes the authentication process.

asf.download_urls(urls=urls, path='data/s1', session=user_pass_session, processes=5)

Exploring S1-RTC data

import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import rioxarray as rio
import xarray as xr

Now let’s take a look at a bit more processed data that we can directly work with. Still in Level-1 you will see the Ground Range Detected (GRD) product in the figure above. This is S1 data that has been further processed (it has been detected, multi-looked and projected to ground range). The SLC products we queried before preserve phase information and are processed at the natural pixel spacing whereas GRD products contain the detected amplitude and are multi-looked to reduce the impact of speckle.

An extra processing step is to perform Radiometric Terrain Correction, and some data providers like Microsoft Planetary Computer make this dataset available worldwide. Feel free to explore the Planetary Computer access options to work on larger datasets if you are interested.

In the spirit to avoid the need for you to get credentials for this particular workshop, we will use a Sentinel-1 RTC dataset for the Contiguous United States (CONUS) which is freely accessible.

We will access this data using the Amazon Web Services (AWS) CLI directly (with the awscli package). Let’s explore the available data:

!aws s3 ls s3://sentinel-s1-rtc-indigo/tiles/RTC/1/ --no-sign-request

                           PRE IW/

Dateityp "Python.File" nicht gefunden, oder diesem Dateityp wurde kein ™ffnen-Befehl
zugeordnet.

!aws s3 ls s3://sentinel-s1-rtc-indigo/tiles/RTC/1/IW/ --no-sign-request

                           PRE 10/
                           PRE 11/
                           PRE 12/
                           PRE 13/
                           PRE 14/
                           PRE 15/
                           PRE 16/
                           PRE 17/
                           PRE 18/
                           PRE 19/

Dateityp "Python.File" nicht gefunden, oder diesem Dateityp wurde kein ™ffnen-Befehl
zugeordnet.

!aws s3 ls s3://sentinel-s1-rtc-indigo/tiles/RTC/1/IW/10/S/DH/2016/S1A_20160729_10SDH_ASC/Gamma0_VV.tif/ --no-sign-request

Dateityp "Python.File" nicht gefunden, oder diesem Dateityp wurde kein ™ffnen-Befehl
zugeordnet.
Dateityp "Python.File" nicht gefunden, oder diesem Dateityp wurde kein ™ffnen-Befehl
zugeordnet.

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download: s3://sentinel-s1-rtc-indigo/tiles/RTC/1/IW/12/R/UV/2021/S1B_20210121_12RUV_DSC/Gamma0_VV.tif to S1B_20210121_12RUV_DSC\Gamma0_VV.tif

To download a scene we can directly request the data as:

# Run only if you want to have 110MB of random data on your laptop!
!aws s3 cp s3://sentinel-s1-rtc-indigo/tiles/RTC/1/IW/12/R/UV/2021/S1B_20210121_12RUV_DSC/Gamma0_VV.tif S1B_20210121_12RUV_DSC/Gamma0_VV.tif --no-sign-request

^C

The available bands have the prefix Gamma0. This is the result of the RTC algorithm. Read more about the backscatter types here.

We will also see that the data has a suffix, either VV or VH, this is the polarization. That refers to the way data is collected.

Read more about it here, here and here.

Let’s start exploring the data. For this we will use rioxarray. We will set an environment key to establish no sign request for AWS. And we will also be leveraging the tight integration between xarray and dask to lazily read in data via the chunks parameter.

We will get both the vv and vh data.

os.environ['AWS_NO_SIGN_REQUEST'] = 'YES'

url_vv = 's3://sentinel-s1-rtc-indigo/tiles/RTC/1/IW/10/U/CU/2019/S1B_20190109_10UCU_ASC/Gamma0_VV.tif'
url_vh = 's3://sentinel-s1-rtc-indigo/tiles/RTC/1/IW/10/U/CU/2019/S1B_20190109_10UCU_ASC/Gamma0_VH.tif'
s1_vv = rio.open_rasterio(url_vv, chunks=True)
s1_vh = rio.open_rasterio(url_vh, chunks=True)

s1_vv

<xarray.DataArray (band: 1, y: 5490, x: 5490)>
dask.array<open_rasterio-19cbe1dffda706ec0d650050fad8aee1<this-array>, shape=(1, 5490, 5490), dtype=float32, chunksize=(1, 5490, 5490), chunktype=numpy.ndarray>
Coordinates:
  * band         (band) int32 1
  * x            (x) float64 3e+05 3e+05 3e+05 ... 4.098e+05 4.098e+05 4.098e+05
  * y            (y) float64 5.4e+06 5.4e+06 5.4e+06 ... 5.29e+06 5.29e+06
    spatial_ref  int32 0
Attributes: (12/18)
    ABSOLUTE_ORBIT_NUMBER:  14411
    AREA_OR_POINT:          Area
    DATE:                   2019-01-09
    MISSION_ID:             S1B
    NUMBER_SCENES:          2
    ORBIT_DIRECTION:        ascending
    ...                     ...
    TILE_ID:                10UCU
    VALID_PIXEL_PERCENT:    87.946
    _FillValue:             0.0
    scale_factor:           1.0
    add_offset:             0.0
    long_name:              Gamma0_VV

xarray.DataArray

• band: 1
• y: 5490
• x: 5490

• dask.array<chunksize=(1, 5490, 5490), meta=np.ndarray>

  Array Chunk Bytes 114.98 MiB 114.98 MiB Shape (1, 5490, 5490) (1, 5490, 5490) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Coordinates: (4)
  • band
    (band)
    int32
    1

    array([1])

  • x
    (x)
    float64
    3e+05 3e+05 ... 4.098e+05 4.098e+05

    array([300010., 300030., 300050., ..., 409750., 409770., 409790.])

  • y
    (y)
    float64
    5.4e+06 5.4e+06 ... 5.29e+06

    array([5399990., 5399970., 5399950., ..., 5290250., 5290230., 5290210.])

  • spatial_ref
    ()
    int32
    0

    crs_wkt :

    PROJCS["WGS 84 / UTM zone 10N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-123],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32610"]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    inverse_flattening :

    298.257223563

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    horizontal_datum_name :

    World Geodetic System 1984

    projected_crs_name :

    WGS 84 / UTM zone 10N

    grid_mapping_name :

    transverse_mercator

    latitude_of_projection_origin :

    0.0

    longitude_of_central_meridian :

    -123.0

    false_easting :

    500000.0

    false_northing :

    0.0

    scale_factor_at_central_meridian :

    0.9996

    spatial_ref :

    PROJCS["WGS 84 / UTM zone 10N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-123],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32610"]]

    GeoTransform :

    300000.0 20.0 0.0 5400000.0 0.0 -20.0

    array(0)

• Indexes: (3)
  • band
    PandasIndex

    PandasIndex(Index([1], dtype='int32', name='band'))

  • x
    PandasIndex

    PandasIndex(Index([300010.0, 300030.0, 300050.0, 300070.0, 300090.0, 300110.0, 300130.0,
           300150.0, 300170.0, 300190.0,
           ...
           409610.0, 409630.0, 409650.0, 409670.0, 409690.0, 409710.0, 409730.0,
           409750.0, 409770.0, 409790.0],
          dtype='float64', name='x', length=5490))

  • y
    PandasIndex

    PandasIndex(Index([5399990.0, 5399970.0, 5399950.0, 5399930.0, 5399910.0, 5399890.0,
           5399870.0, 5399850.0, 5399830.0, 5399810.0,
           ...
           5290390.0, 5290370.0, 5290350.0, 5290330.0, 5290310.0, 5290290.0,
           5290270.0, 5290250.0, 5290230.0, 5290210.0],
          dtype='float64', name='y', length=5490))

• Attributes: (18)

  ABSOLUTE_ORBIT_NUMBER :

  14411

  AREA_OR_POINT :

  Area

  DATE :

  2019-01-09

  MISSION_ID :

  S1B

  NUMBER_SCENES :

  2

  ORBIT_DIRECTION :

  ascending

  OVR_RESAMPLING_ALG :

  AVERAGE

  SCENES :

  S1B_IW_GRDH_1SDV_20190109T020931_20190109T020956_014411_01AD36_7CA8,S1B_IW_GRDH_1SDV_20190109T020956_20190109T021021_014411_01AD36_9989

  SCENE_1_METADATA :

  {"title": "S1B_IW_GRDH_1SDV_20190109T020931_20190109T020956_014411_01AD36_7CA8", "mission_id": "S1B", "sensor_operational_mode": "IW", "product_type": "RTC", "resolution_class": "H", "processing_level": "1", "polarization_mode": "DV", "start_time": "2019-01-09T02:09:31", "end_time": "2019-01-09T02:09:56", "absolute_orbit_number": "14411", "mission_data_take_id": "01AD36", "product_unique_identifier": "7CA8", "footprint": "{\"type\": \"Polygon\", \"coordinates\": [[[-126.535973, 47.750252], [-123.125053, 48.149876], [-122.782219, 46.652382], [-126.098656, 46.253361], [-126.535973, 47.750252]]]}", "orbit_direction": "ASC", "ingested_at": "2019-01-09T07:16:25.694000", "size": "914219674"}

  SCENE_1_PRODUCT_INFO :

  {"id": "S1B_IW_GRDH_1SDV_20190109T020931_20190109T020956_014411_01AD36_7CA8", "path": "scenes/RTC/1/2019/1/9/IW/DV/S1B_IW_GRDH_1SDV_20190109T020931_20190109T020956_014411_01AD36_7CA8/", "missionId": "S1B", "productType": "RTC", "mode": "IW", "polarization": "DV", "startTime": "2019-01-09T02:09:31", "stopTime": "2019-01-09T02:09:56", "absoluteOrbitNumber": 14411, "missionDataTakeId": "01AD36", "productUniqueIdentifier": "7CA8", "sciHubIngestion": "2019-01-09T07:16:25.694000Z", "s3Ingestion": "2020-09-23T18:01:22.339964Z", "sciHubId": "073d0c7d-47c7-44dd-be5f-bea7479c3cfa", "footprint": {"type": "Polygon", "coordinates": [[[-126.535973, 47.750252], [-123.125053, 48.149876], [-122.782219, 46.652382], [-126.098656, 46.253361], [-126.535973, 47.750252]]]}}

  SCENE_2_METADATA :

  {"title": "S1B_IW_GRDH_1SDV_20190109T020956_20190109T021021_014411_01AD36_9989", "mission_id": "S1B", "sensor_operational_mode": "IW", "product_type": "RTC", "resolution_class": "H", "processing_level": "1", "polarization_mode": "DV", "start_time": "2019-01-09T02:09:56", "end_time": "2019-01-09T02:10:21", "absolute_orbit_number": "14411", "mission_data_take_id": "01AD36", "product_unique_identifier": "9989", "footprint": "{\"type\": \"Polygon\", \"coordinates\": [[[-126.984451, 49.24667], [-123.469872, 49.647362], [-123.123749, 48.150101], [-126.535995, 47.75034], [-126.984451, 49.24667]]]}", "orbit_direction": "ASC", "ingested_at": "2019-01-09T07:40:51.331000", "size": "981747202"}

  SCENE_2_PRODUCT_INFO :

  {"id": "S1B_IW_GRDH_1SDV_20190109T020956_20190109T021021_014411_01AD36_9989", "path": "scenes/RTC/1/2019/1/9/IW/DV/S1B_IW_GRDH_1SDV_20190109T020956_20190109T021021_014411_01AD36_9989/", "missionId": "S1B", "productType": "RTC", "mode": "IW", "polarization": "DV", "startTime": "2019-01-09T02:09:56", "stopTime": "2019-01-09T02:10:21", "absoluteOrbitNumber": 14411, "missionDataTakeId": "01AD36", "productUniqueIdentifier": "9989", "sciHubIngestion": "2019-01-09T07:40:51.331000Z", "s3Ingestion": "2020-09-23T18:01:22.340412Z", "sciHubId": "709311d1-d033-487d-875a-786ff0570ebc", "footprint": {"type": "Polygon", "coordinates": [[[-126.984451, 49.24667], [-123.469872, 49.647362], [-123.123749, 48.150101], [-126.535995, 47.75034], [-126.984451, 49.24667]]]}}

  TILE_ID :

  10UCU

  VALID_PIXEL_PERCENT :

  87.946

  _FillValue :

  0.0

  scale_factor :

  1.0

  add_offset :

  0.0

  long_name :

  Gamma0_VV

Let’s visualize a slice of the data:

s1_vv_ss = s1_vv.isel(x=slice(5000, 5500), y=slice(1000, 1500)).compute()

s1_vv_ss.plot(cmap=plt.cm.Greys_r)

<matplotlib.collections.QuadMesh at 0x1b192be6750>

To better visualize the data, we can apply a power to dB scale. This transformation applies a logarithmic scale to the data for easier visualisation, but it is not recommended to use this for any computations, since the data gets distorted.

def power_to_db(input_arr):
    return (10*np.log10(np.abs(input_arr)))

power_to_db(s1_vv_ss).plot(cmap=plt.cm.Greys_r)

<matplotlib.collections.QuadMesh at 0x1b192588810>

Exercise:

• Try to combine the s1_vv and s1_vh objects together, compute a new band with the result of VH/VV and use these three layers to generate a false color RGB composite.

s1_vv

<xarray.DataArray (band: 1, y: 5490, x: 5490)>
dask.array<open_rasterio-19cbe1dffda706ec0d650050fad8aee1<this-array>, shape=(1, 5490, 5490), dtype=float32, chunksize=(1, 5490, 5490), chunktype=numpy.ndarray>
Coordinates:
  * band         (band) int32 1
  * x            (x) float64 3e+05 3e+05 3e+05 ... 4.098e+05 4.098e+05 4.098e+05
  * y            (y) float64 5.4e+06 5.4e+06 5.4e+06 ... 5.29e+06 5.29e+06
    spatial_ref  int32 0
Attributes: (12/18)
    ABSOLUTE_ORBIT_NUMBER:  14411
    AREA_OR_POINT:          Area
    DATE:                   2019-01-09
    MISSION_ID:             S1B
    NUMBER_SCENES:          2
    ORBIT_DIRECTION:        ascending
    ...                     ...
    TILE_ID:                10UCU
    VALID_PIXEL_PERCENT:    87.946
    _FillValue:             0.0
    scale_factor:           1.0
    add_offset:             0.0
    long_name:              Gamma0_VV

xarray.DataArray

• band: 1
• y: 5490
• x: 5490

• dask.array<chunksize=(1, 5490, 5490), meta=np.ndarray>

  Array Chunk Bytes 114.98 MiB 114.98 MiB Shape (1, 5490, 5490) (1, 5490, 5490) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Coordinates: (4)
  • band
    (band)
    int32
    1

    array([1])

  • x
    (x)
    float64
    3e+05 3e+05 ... 4.098e+05 4.098e+05

    array([300010., 300030., 300050., ..., 409750., 409770., 409790.])

  • y
    (y)
    float64
    5.4e+06 5.4e+06 ... 5.29e+06

    array([5399990., 5399970., 5399950., ..., 5290250., 5290230., 5290210.])

  • spatial_ref
    ()
    int32
    0

    crs_wkt :

    PROJCS["WGS 84 / UTM zone 10N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-123],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32610"]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    inverse_flattening :

    298.257223563

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    horizontal_datum_name :

    World Geodetic System 1984

    projected_crs_name :

    WGS 84 / UTM zone 10N

    grid_mapping_name :

    transverse_mercator

    latitude_of_projection_origin :

    0.0

    longitude_of_central_meridian :

    -123.0

    false_easting :

    500000.0

    false_northing :

    0.0

    scale_factor_at_central_meridian :

    0.9996

    spatial_ref :

    PROJCS["WGS 84 / UTM zone 10N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-123],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32610"]]

    GeoTransform :

    300000.0 20.0 0.0 5400000.0 0.0 -20.0

    array(0)

• Indexes: (3)
  • band
    PandasIndex

    PandasIndex(Index([1], dtype='int32', name='band'))

  • x
    PandasIndex

    PandasIndex(Index([300010.0, 300030.0, 300050.0, 300070.0, 300090.0, 300110.0, 300130.0,
           300150.0, 300170.0, 300190.0,
           ...
           409610.0, 409630.0, 409650.0, 409670.0, 409690.0, 409710.0, 409730.0,
           409750.0, 409770.0, 409790.0],
          dtype='float64', name='x', length=5490))

  • y
    PandasIndex

    PandasIndex(Index([5399990.0, 5399970.0, 5399950.0, 5399930.0, 5399910.0, 5399890.0,
           5399870.0, 5399850.0, 5399830.0, 5399810.0,
           ...
           5290390.0, 5290370.0, 5290350.0, 5290330.0, 5290310.0, 5290290.0,
           5290270.0, 5290250.0, 5290230.0, 5290210.0],
          dtype='float64', name='y', length=5490))

• Attributes: (18)

  ABSOLUTE_ORBIT_NUMBER :

  14411

  AREA_OR_POINT :

  Area

  DATE :

  2019-01-09

  MISSION_ID :

  S1B

  NUMBER_SCENES :

  2

  ORBIT_DIRECTION :

  ascending

  OVR_RESAMPLING_ALG :

  AVERAGE

  SCENES :

  S1B_IW_GRDH_1SDV_20190109T020931_20190109T020956_014411_01AD36_7CA8,S1B_IW_GRDH_1SDV_20190109T020956_20190109T021021_014411_01AD36_9989

  SCENE_1_METADATA :

  {"title": "S1B_IW_GRDH_1SDV_20190109T020931_20190109T020956_014411_01AD36_7CA8", "mission_id": "S1B", "sensor_operational_mode": "IW", "product_type": "RTC", "resolution_class": "H", "processing_level": "1", "polarization_mode": "DV", "start_time": "2019-01-09T02:09:31", "end_time": "2019-01-09T02:09:56", "absolute_orbit_number": "14411", "mission_data_take_id": "01AD36", "product_unique_identifier": "7CA8", "footprint": "{\"type\": \"Polygon\", \"coordinates\": [[[-126.535973, 47.750252], [-123.125053, 48.149876], [-122.782219, 46.652382], [-126.098656, 46.253361], [-126.535973, 47.750252]]]}", "orbit_direction": "ASC", "ingested_at": "2019-01-09T07:16:25.694000", "size": "914219674"}

  SCENE_1_PRODUCT_INFO :

  {"id": "S1B_IW_GRDH_1SDV_20190109T020931_20190109T020956_014411_01AD36_7CA8", "path": "scenes/RTC/1/2019/1/9/IW/DV/S1B_IW_GRDH_1SDV_20190109T020931_20190109T020956_014411_01AD36_7CA8/", "missionId": "S1B", "productType": "RTC", "mode": "IW", "polarization": "DV", "startTime": "2019-01-09T02:09:31", "stopTime": "2019-01-09T02:09:56", "absoluteOrbitNumber": 14411, "missionDataTakeId": "01AD36", "productUniqueIdentifier": "7CA8", "sciHubIngestion": "2019-01-09T07:16:25.694000Z", "s3Ingestion": "2020-09-23T18:01:22.339964Z", "sciHubId": "073d0c7d-47c7-44dd-be5f-bea7479c3cfa", "footprint": {"type": "Polygon", "coordinates": [[[-126.535973, 47.750252], [-123.125053, 48.149876], [-122.782219, 46.652382], [-126.098656, 46.253361], [-126.535973, 47.750252]]]}}

  SCENE_2_METADATA :

  {"title": "S1B_IW_GRDH_1SDV_20190109T020956_20190109T021021_014411_01AD36_9989", "mission_id": "S1B", "sensor_operational_mode": "IW", "product_type": "RTC", "resolution_class": "H", "processing_level": "1", "polarization_mode": "DV", "start_time": "2019-01-09T02:09:56", "end_time": "2019-01-09T02:10:21", "absolute_orbit_number": "14411", "mission_data_take_id": "01AD36", "product_unique_identifier": "9989", "footprint": "{\"type\": \"Polygon\", \"coordinates\": [[[-126.984451, 49.24667], [-123.469872, 49.647362], [-123.123749, 48.150101], [-126.535995, 47.75034], [-126.984451, 49.24667]]]}", "orbit_direction": "ASC", "ingested_at": "2019-01-09T07:40:51.331000", "size": "981747202"}

  SCENE_2_PRODUCT_INFO :

  {"id": "S1B_IW_GRDH_1SDV_20190109T020956_20190109T021021_014411_01AD36_9989", "path": "scenes/RTC/1/2019/1/9/IW/DV/S1B_IW_GRDH_1SDV_20190109T020956_20190109T021021_014411_01AD36_9989/", "missionId": "S1B", "productType": "RTC", "mode": "IW", "polarization": "DV", "startTime": "2019-01-09T02:09:56", "stopTime": "2019-01-09T02:10:21", "absoluteOrbitNumber": 14411, "missionDataTakeId": "01AD36", "productUniqueIdentifier": "9989", "sciHubIngestion": "2019-01-09T07:40:51.331000Z", "s3Ingestion": "2020-09-23T18:01:22.340412Z", "sciHubId": "709311d1-d033-487d-875a-786ff0570ebc", "footprint": {"type": "Polygon", "coordinates": [[[-126.984451, 49.24667], [-123.469872, 49.647362], [-123.123749, 48.150101], [-126.535995, 47.75034], [-126.984451, 49.24667]]]}}

  TILE_ID :

  10UCU

  VALID_PIXEL_PERCENT :

  87.946

  _FillValue :

  0.0

  scale_factor :

  1.0

  add_offset :

  0.0

  long_name :

  Gamma0_VV

s1_vh

<xarray.DataArray (band: 1, y: 5490, x: 5490)>
dask.array<open_rasterio-d85bc69bab7c5795dc7b6c42fe93892e<this-array>, shape=(1, 5490, 5490), dtype=float32, chunksize=(1, 5490, 5490), chunktype=numpy.ndarray>
Coordinates:
  * band         (band) int32 1
  * x            (x) float64 3e+05 3e+05 3e+05 ... 4.098e+05 4.098e+05 4.098e+05
  * y            (y) float64 5.4e+06 5.4e+06 5.4e+06 ... 5.29e+06 5.29e+06
    spatial_ref  int32 0
Attributes: (12/18)
    ABSOLUTE_ORBIT_NUMBER:  14411
    AREA_OR_POINT:          Area
    DATE:                   2019-01-09
    MISSION_ID:             S1B
    NUMBER_SCENES:          2
    ORBIT_DIRECTION:        ascending
    ...                     ...
    TILE_ID:                10UCU
    VALID_PIXEL_PERCENT:    87.946
    _FillValue:             0.0
    scale_factor:           1.0
    add_offset:             0.0
    long_name:              Gamma0_VH

xarray.DataArray

• band: 1
• y: 5490
• x: 5490

• dask.array<chunksize=(1, 5490, 5490), meta=np.ndarray>

  Array Chunk Bytes 114.98 MiB 114.98 MiB Shape (1, 5490, 5490) (1, 5490, 5490) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Coordinates: (4)
  • band
    (band)
    int32
    1

    array([1])

  • x
    (x)
    float64
    3e+05 3e+05 ... 4.098e+05 4.098e+05

    array([300010., 300030., 300050., ..., 409750., 409770., 409790.])

  • y
    (y)
    float64
    5.4e+06 5.4e+06 ... 5.29e+06

    array([5399990., 5399970., 5399950., ..., 5290250., 5290230., 5290210.])

  • spatial_ref
    ()
    int32
    0

    crs_wkt :

    PROJCS["WGS 84 / UTM zone 10N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-123],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32610"]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314245179

    inverse_flattening :

    298.257223563

    reference_ellipsoid_name :

    WGS 84

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    WGS 84

    horizontal_datum_name :

    World Geodetic System 1984

    projected_crs_name :

    WGS 84 / UTM zone 10N

    grid_mapping_name :

    transverse_mercator

    latitude_of_projection_origin :

    0.0

    longitude_of_central_meridian :

    -123.0

    false_easting :

    500000.0

    false_northing :

    0.0

    scale_factor_at_central_meridian :

    0.9996

    spatial_ref :

    PROJCS["WGS 84 / UTM zone 10N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-123],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32610"]]

    GeoTransform :

    300000.0 20.0 0.0 5400000.0 0.0 -20.0

    array(0)

• Indexes: (3)
  • band
    PandasIndex

    PandasIndex(Index([1], dtype='int32', name='band'))

  • x
    PandasIndex

    PandasIndex(Index([300010.0, 300030.0, 300050.0, 300070.0, 300090.0, 300110.0, 300130.0,
           300150.0, 300170.0, 300190.0,
           ...
           409610.0, 409630.0, 409650.0, 409670.0, 409690.0, 409710.0, 409730.0,
           409750.0, 409770.0, 409790.0],
          dtype='float64', name='x', length=5490))

  • y
    PandasIndex

    PandasIndex(Index([5399990.0, 5399970.0, 5399950.0, 5399930.0, 5399910.0, 5399890.0,
           5399870.0, 5399850.0, 5399830.0, 5399810.0,
           ...
           5290390.0, 5290370.0, 5290350.0, 5290330.0, 5290310.0, 5290290.0,
           5290270.0, 5290250.0, 5290230.0, 5290210.0],
          dtype='float64', name='y', length=5490))

• Attributes: (18)

  ABSOLUTE_ORBIT_NUMBER :

  14411

  AREA_OR_POINT :

  Area

  DATE :

  2019-01-09

  MISSION_ID :

  S1B

  NUMBER_SCENES :

  2

  ORBIT_DIRECTION :

  ascending

  OVR_RESAMPLING_ALG :

  AVERAGE

  SCENES :

  S1B_IW_GRDH_1SDV_20190109T020931_20190109T020956_014411_01AD36_7CA8,S1B_IW_GRDH_1SDV_20190109T020956_20190109T021021_014411_01AD36_9989

  SCENE_1_METADATA :

  {"title": "S1B_IW_GRDH_1SDV_20190109T020931_20190109T020956_014411_01AD36_7CA8", "mission_id": "S1B", "sensor_operational_mode": "IW", "product_type": "RTC", "resolution_class": "H", "processing_level": "1", "polarization_mode": "DV", "start_time": "2019-01-09T02:09:31", "end_time": "2019-01-09T02:09:56", "absolute_orbit_number": "14411", "mission_data_take_id": "01AD36", "product_unique_identifier": "7CA8", "footprint": "{\"type\": \"Polygon\", \"coordinates\": [[[-126.535973, 47.750252], [-123.125053, 48.149876], [-122.782219, 46.652382], [-126.098656, 46.253361], [-126.535973, 47.750252]]]}", "orbit_direction": "ASC", "ingested_at": "2019-01-09T07:16:25.694000", "size": "914219674"}

  SCENE_1_PRODUCT_INFO :

  {"id": "S1B_IW_GRDH_1SDV_20190109T020931_20190109T020956_014411_01AD36_7CA8", "path": "scenes/RTC/1/2019/1/9/IW/DV/S1B_IW_GRDH_1SDV_20190109T020931_20190109T020956_014411_01AD36_7CA8/", "missionId": "S1B", "productType": "RTC", "mode": "IW", "polarization": "DV", "startTime": "2019-01-09T02:09:31", "stopTime": "2019-01-09T02:09:56", "absoluteOrbitNumber": 14411, "missionDataTakeId": "01AD36", "productUniqueIdentifier": "7CA8", "sciHubIngestion": "2019-01-09T07:16:25.694000Z", "s3Ingestion": "2020-09-23T18:00:51.876503Z", "sciHubId": "073d0c7d-47c7-44dd-be5f-bea7479c3cfa", "footprint": {"type": "Polygon", "coordinates": [[[-126.535973, 47.750252], [-123.125053, 48.149876], [-122.782219, 46.652382], [-126.098656, 46.253361], [-126.535973, 47.750252]]]}}

  SCENE_2_METADATA :

  {"title": "S1B_IW_GRDH_1SDV_20190109T020956_20190109T021021_014411_01AD36_9989", "mission_id": "S1B", "sensor_operational_mode": "IW", "product_type": "RTC", "resolution_class": "H", "processing_level": "1", "polarization_mode": "DV", "start_time": "2019-01-09T02:09:56", "end_time": "2019-01-09T02:10:21", "absolute_orbit_number": "14411", "mission_data_take_id": "01AD36", "product_unique_identifier": "9989", "footprint": "{\"type\": \"Polygon\", \"coordinates\": [[[-126.984451, 49.24667], [-123.469872, 49.647362], [-123.123749, 48.150101], [-126.535995, 47.75034], [-126.984451, 49.24667]]]}", "orbit_direction": "ASC", "ingested_at": "2019-01-09T07:40:51.331000", "size": "981747202"}

  SCENE_2_PRODUCT_INFO :

  {"id": "S1B_IW_GRDH_1SDV_20190109T020956_20190109T021021_014411_01AD36_9989", "path": "scenes/RTC/1/2019/1/9/IW/DV/S1B_IW_GRDH_1SDV_20190109T020956_20190109T021021_014411_01AD36_9989/", "missionId": "S1B", "productType": "RTC", "mode": "IW", "polarization": "DV", "startTime": "2019-01-09T02:09:56", "stopTime": "2019-01-09T02:10:21", "absoluteOrbitNumber": 14411, "missionDataTakeId": "01AD36", "productUniqueIdentifier": "9989", "sciHubIngestion": "2019-01-09T07:40:51.331000Z", "s3Ingestion": "2020-09-23T18:00:51.876961Z", "sciHubId": "709311d1-d033-487d-875a-786ff0570ebc", "footprint": {"type": "Polygon", "coordinates": [[[-126.984451, 49.24667], [-123.469872, 49.647362], [-123.123749, 48.150101], [-126.535995, 47.75034], [-126.984451, 49.24667]]]}}

  TILE_ID :

  10UCU

  VALID_PIXEL_PERCENT :

  87.946

  _FillValue :

  0.0

  scale_factor :

  1.0

  add_offset :

  0.0

  long_name :

  Gamma0_VH

s1_comined = xr.merge([s1_vv, s1_vh])

ValueError: unable to convert unnamed DataArray to a Dataset without providing an explicit name

More resources:

Working with Sentinel-1 SLC data can also be done with Python. There are a couple of packages available for this (snappy and snapista), but ESA is currently working on a follow up of the snappy package called esa-snappy which will be compatible with the upcoming SNAP-10. Since the developers claim its worth the wait, I would at this point direct you to the webpage where they seem to be documenting basic usage of the tool. So for that feel free to check this site more or less at the end of August.

For more complex InSAR analysis on the cloud, there are some other alternatives. Take a look at this paper by Piter et al. (2021) for an overview and comparison. There are some other attempts still under development, e.g., dinoSAR.

A lot of the examples for this notebook, mainly for the RTC processing were adapted from Emma Marshall’s excellent tutorial on Sentinel-1 RTC data workflows with xarray.