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scottyhq/topsApp-rendered.ipynb

Created August 16, 2023 19:19
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topsApp-rendered.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"# Processing your Data with topsApp.py\n",
"\n",
"**Authors**: Bekaert David, Heresh Fattahi, Piyush Agram, 2022 updates Eric Fielding, 2023 updates Scott Henderson\n",
"\n",
"Learning Goals:\n",
"\n",
"* Understand unique features of Sentinel-1 SAR data (TOPS mode)\n",
"* ISCE2's topsApp.py generates InSAR products for Sentinel-1 SLCs\n",
"* Understand the numerous processing steps needed to generate a geocoded interferogram\n",
"* Visualize intermediate and final results\n",
"\n",
"Estimated time: 2 hours+ exercises\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**topsApp.py is a pair-by-pair interferometric processor that takes as input two Sentinel-1 SAR acquisitions acquired in TOPS mode.** \n",
"\n",
"topsApp.py will not work for other Sentinel-1 acquisition formats such as Stripmap. ISCE's stripmapApp.py supports interferometric stripmap processing of Sentinel-1 and other sensors. At this time, topsApp only supports SLC data from Sentinel-1 A and B. Processing is supported across the Sentinel-1 constellation, i.e. data acquired from A and B can be combined.\n",
"\n",
"To illustrate the usage of topsApp.py, we will use a Sentinel-1 dataset capturing the surface deformation as result of the 15 May 2020 Mw6.5 Monte Cristo Range Earthquake that occurred in Nevada [(details here)](https://en.wikipedia.org/wiki/2020_Nevada_earthquake). The exercise runs the workflow step by step to generate an interferogram!"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true
},
"source": [
"## 0. Initial setup of the notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebooks uses the following directory structure\n",
"\n",
"```\n",
".\n",
"├── topsApp.ipynb (This notebook)\n",
"├── insar (This is where we will process the interferogram)\n",
"├── support_docs (Figures used in this notebook)\n",
"└── data (This is where we will download data for this notebook)\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"The cell below performs initial setup of the notebook and must be run every time the notebook is used. It is possible to partially complete the exercise, close the notebook, and come back and continue later from that point, but this initialization must be re-run before restarting (as well as Step 1.2.1 if the slc download step 1.2 is skipped). Initialization defines the processing locations as well as a few plotting routines that will be used throughout the tutorial, and this information is lost when the notebook is closed."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ISCE2 Version: 2.6.3\n"
]
}
],
"source": [
"# Configure ISCE2 python library\n",
"# https://github.com/isce-framework/isce2/issues/258\n",
"import isce\n",
"import logging\n",
"import os \n",
"\n",
"root_logger = logging.getLogger()\n",
"root_logger.setLevel('WARNING')\n",
"\n",
"# Set Environment variables so that you can call ISCE2 Apps from the command line\n",
"os.environ['ISCE_HOME'] = os.path.dirname(isce.__file__)\n",
"os.environ['PATH']+='{ISCE_HOME}/bin:{ISCE_HOME}/applications'.format(**os.environ)\n",
"\n",
"print('ISCE2 Version:', isce.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Import other python analysis and visualization packages\n",
"import asf_search \n",
"import matplotlib.pyplot as plt \n",
"import xarray as xr\n",
"\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format='retina'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"hidden": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Notebook directory: /home/jovyan/Geo-SInC/EarthScope2023/2.2_TOPS_Data_Processing\n"
]
}
],
"source": [
"tutorial_home_dir = os.getcwd()\n",
"print(\"Notebook directory: \", tutorial_home_dir)\n",
"\n",
"# directory for data downloads\n",
"slc_dir = os.path.join(tutorial_home_dir,'data', 'slcs')\n",
"orbit_dir = os.path.join(tutorial_home_dir, 'data', 'orbits')\n",
"insar_dir = os.path.join(tutorial_home_dir, 'insar')\n",
"\n",
"# generate all the folders in case they do not exist yet\n",
"os.makedirs(slc_dir, exist_ok=True)\n",
"os.makedirs(orbit_dir, exist_ok=True)\n",
"os.makedirs(insar_dir, exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"download: s3://asf-jupyter-data/TOPS/demLat_N37_N39_Lon_W119_W117.dem.wgs84.vrt to data/demLat_N37_N39_Lon_W119_W117.dem.wgs84.vrt\n",
"download: s3://asf-jupyter-data/TOPS/S1B_OPER_AUX_POEORB_OPOD_20200606T110735_V20200516T225942_20200518T005942.EOF to data/S1B_OPER_AUX_POEORB_OPOD_20200606T110735_V20200516T225942_20200518T005942.EOF\n",
"download: s3://asf-jupyter-data/TOPS/S1A_OPER_AUX_POEORB_OPOD_20200531T120757_V20200510T225942_20200512T005942.EOF to data/S1A_OPER_AUX_POEORB_OPOD_20200531T120757_V20200510T225942_20200512T005942.EOF\n",
"download: s3://asf-jupyter-data/TOPS/Stack_Processor_v1.pdf to data/Stack_Processor_v1.pdf\n",
"download: s3://asf-jupyter-data/TOPS/demLat_N37_N39_Lon_W119_W117.dem.wgs84.xml to data/demLat_N37_N39_Lon_W119_W117.dem.wgs84.xml\n",
"download: s3://asf-jupyter-data/TOPS/InSAR_Time_Series_v1.pdf to data/InSAR_Time_Series_v1.pdf\n",
"download: s3://asf-jupyter-data/TOPS/demLat_N37_N39_Lon_W119_W117.dem.wgs84 to data/demLat_N37_N39_Lon_W119_W117.dem.wgs84\n",
"download: s3://asf-jupyter-data/TOPS/S1B_IW_SLC__1SDV_20200517T135026_20200517T135056_021622_0290CB_99E2.zip to data/S1B_IW_SLC__1SDV_20200517T135026_20200517T135056_021622_0290CB_99E2.zip\n",
"download: s3://asf-jupyter-data/TOPS/S1A_IW_SLC__1SDV_20200511T135117_20200511T135144_032518_03C421_7768.zip to data/S1A_IW_SLC__1SDV_20200511T135117_20200511T135144_032518_03C421_7768.zip\n"
]
}
],
"source": [
"%%bash\n",
"\n",
"# Sync SLCS, orbits, DEM from S3 for this tutorial\n",
"\n",
"aws --no-sign-request s3 sync s3://asf-jupyter-data/TOPS ./data\n",
"mv ./data/*zip ./data/slcs/\n",
"mv ./data/*EOF ./data/orbits"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true
},
"source": [
"## 1. Overview of the tutorial input dataset"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"Let us first take a look at the dataset. For our dataset we are focusing on Descending track 71 and acquisitions dates of 2020-05-11 and 2020-05-17. \n",
"\n",
"More information on the event can be found on USGS's Monte Cristo Range Earthquake page [here](https://earthquake.usgs.gov/earthquakes/eventpage/nn00725272/executive) \n",
"\n",
"You can see the scenes of interest on ASF's Vertex page [here](https://search.asf.alaska.edu/#/?zoom=6.9117542264702365&center=-117.963049,38.048229&polygon=POLYGON((-118.2093%2038.0347,-117.6875%2038.0347,-117.6875%2038.3268,-118.2093%2038.3268,-118.2093%2038.0347))&path=71-71&start=2020-05-11T00:00:00Z&end=2020-05-17T23:59:00Z&productTypes=SLC&resultsLoaded=true)\n",
"\n",
"The area of interest is captured by a single SLC on each pass and we shall use those in this tutorial. Even, if the event were to occur at an image boundary - all one would need to do is to download the multiple SLCs on the same track and topsApp.py will process and mosaic them for you."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**<font color=\"blue\">Note:<br>**\n",
" This earthquake was really well imaged on multiple tracks and with 6-day temporal sampling. If interested, feel free to process the other tracks - 64 and 144. We encourage students to do this to understand the impact of imaging geometry and projection of deformation in line-of-sight direction</font>"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true
},
"source": [
"### 1.1 Background on TOPS mode"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"The TOPS acquisition strategy is different than conventional stripmap mode. TOPS stands for Terrain Observation with Progressive Scans. As the name indicates, the radar sensor performs a scan of the surface by electronically steering the antenna beam from a backward-pointing along-track direction to a forward-pointing along-track direction for a fixed range swath (also called a subswath).\n",
"![title](support_docs/tops_mode.png)\n",
"After a successful scan at that range extent, the antenna beam is electronically rolled back to its initial position, the range swath is electronically directed outward to a new area to increase coverage, and the next scan is made from backward to forward along track at this new range swath. After a third scan at a third range swath, the entire process is repeated over and over to create a continuous image as the satellite flies along. The timing of the scans is such that there is a small geographic overlap between successive scans at a given range to ensure continuous coverage. Each scan at a given range swath is known as a \"burst\". When inspecting one of the downloaded SLC products you will note that data are provided in 3 individual subswaths (IW1, IW2, IW3) each with a set of bursts. All together they form a Sentinel-1 frame, typically ~250 x 250 km in size. The bursts are approximately 20 km in length and overlap by 2 km. Due to the TOPS acquisition mode, the overlap region in successive bursts is seen from two different directions (forward looking and backward looking).\n",
"![title](support_docs/tops.png)\n",
"The ESA website provides detailed background information on Sentinel-1 products and technical information of the TOPS sensors. Whenever during the tutorial you are waiting for the processing to complete, you could explore their webpage at https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true
},
"source": [
"### 1.2 SLC download"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"The ASF vertex page (https://www.asf.alaska.edu/sentinel/) offers a GUI to visually search for available Sentinel-1 data over your area of interest. Once you have found your data, you can download it from the GUI. ASF provides a bulk-download python script."
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"Below we use ASF's Python Search Client (https://github.com/asfadmin/Discovery-asf_search) to download two SLCs"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Reads your ~/.netrc file\n",
"session = asf_search.ASFSession()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true,
"hidden": true,
"jupyter": {
"outputs_hidden": true
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/asf_search/download/download.py:65: UserWarning: File already exists, skipping download: /home/jovyan/Geo-SInC/EarthScope2023/2.2_TOPS_Data_Processing/data/slcs/S1B_IW_SLC__1SDV_20200517T135026_20200517T135056_021622_0290CB_99E2.zip\n",
" warnings.warn(f'File already exists, skipping download: {os.path.join(path, filename)}')\n",
"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/asf_search/download/download.py:65: UserWarning: File already exists, skipping download: /home/jovyan/Geo-SInC/EarthScope2023/2.2_TOPS_Data_Processing/data/slcs/S1A_IW_SLC__1SDV_20200511T135117_20200511T135144_032518_03C421_7768.zip\n",
" warnings.warn(f'File already exists, skipping download: {os.path.join(path, filename)}')\n"
]
},
{
"ename": "ConnectionError",
"evalue": "None: Max retries exceeded with url: /METADATA_SLC/SB/S1B_IW_SLC__1SDV_20200517T135026_20200517T135056_021622_0290CB_99E2.iso.xml (Caused by None)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRemoteTraceback\u001b[0m Traceback (most recent call last)",
"\u001b[0;31mRemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/urllib3/connection.py\", line 174, in _new_conn\n conn = connection.create_connection(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/urllib3/util/connection.py\", line 95, in create_connection\n raise err\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/urllib3/util/connection.py\", line 85, in create_connection\n sock.connect(sa)\nOSError: [Errno 113] No route to host\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/urllib3/connectionpool.py\", line 703, in urlopen\n httplib_response = self._make_request(\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/urllib3/connectionpool.py\", line 386, in _make_request\n self._validate_conn(conn)\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/urllib3/connectionpool.py\", line 1042, in _validate_conn\n conn.connect()\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/urllib3/connection.py\", line 363, in connect\n self.sock = conn = self._new_conn()\n ^^^^^^^^^^^^^^^^\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/urllib3/connection.py\", line 186, in _new_conn\n raise NewConnectionError(\nurllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x7f78c3f00890>: Failed to establish a new connection: [Errno 113] No route to host\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/requests/adapters.py\", line 486, in send\n resp = conn.urlopen(\n ^^^^^^^^^^^^^\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/urllib3/connectionpool.py\", line 787, in urlopen\n retries = retries.increment(\n ^^^^^^^^^^^^^^^^^^\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/urllib3/util/retry.py\", line 592, in increment\n raise MaxRetryError(_pool, url, error or ResponseError(cause))\nurllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='datapool.asf.alaska.edu', port=443): Max retries exceeded with url: /METADATA_SLC/SB/S1B_IW_SLC__1SDV_20200517T135026_20200517T135056_021622_0290CB_99E2.iso.xml (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f78c3f00890>: Failed to establish a new connection: [Errno 113] No route to host'))\n\nDuring handling of the above exception, another exception occurred:\n\nTraceback (most recent call last):\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/multiprocessing/pool.py\", line 125, in worker\n result = (True, func(*args, **kwds))\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/multiprocessing/pool.py\", line 48, in mapstar\n return list(map(*args))\n ^^^^^^^^^^^^^^^^\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/asf_search/ASFSearchResults.py\", line 84, in _download_product\n product.download(path=path, session=session, fileType=fileType)\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/asf_search/ASFProduct.py\", line 85, in download\n download_url(url=url, path=path, filename=filename, session=session)\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/asf_search/download/download.py\", line 71, in download_url\n response = _try_get_response(session=session, url=url)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/tenacity/__init__.py\", line 289, in wrapped_f\n return self(f, *args, **kw)\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/tenacity/__init__.py\", line 379, in __call__\n do = self.iter(retry_state=retry_state)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/tenacity/__init__.py\", line 314, in iter\n return fut.result()\n ^^^^^^^^^^^^\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/concurrent/futures/_base.py\", line 449, in result\n return self.__get_result()\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/concurrent/futures/_base.py\", line 401, in __get_result\n raise self._exception\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/tenacity/__init__.py\", line 382, in __call__\n result = fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/asf_search/download/download.py\", line 103, in _try_get_response\n response = session.get(url, stream=True, hooks={'response': strip_auth_if_aws})\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/requests/sessions.py\", line 602, in get\n return self.request(\"GET\", url, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/requests/sessions.py\", line 589, in request\n resp = self.send(prep, **send_kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/requests/sessions.py\", line 703, in send\n r = adapter.send(request, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jovyan/.local/envs/earthscope_insar/lib/python3.11/site-packages/requests/adapters.py\", line 519, in send\n raise ConnectionError(e, request=request)\nrequests.exceptions.ConnectionError: HTTPSConnectionPool(host='datapool.asf.alaska.edu', port=443): Max retries exceeded with url: /METADATA_SLC/SB/S1B_IW_SLC__1SDV_20200517T135026_20200517T135056_021622_0290CB_99E2.iso.xml (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f78c3f00890>: Failed to establish a new connection: [Errno 113] No route to host'))\n\"\"\"",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[0;31mConnectionError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m<timed exec>:6\u001b[0m\n",
"File \u001b[0;32m~/.local/envs/earthscope_insar/lib/python3.11/site-packages/asf_search/ASFSearchResults.py:71\u001b[0m, in \u001b[0;36mASFSearchResults.download\u001b[0;34m(self, path, session, processes, fileType)\u001b[0m\n\u001b[1;32m 69\u001b[0m pool \u001b[38;5;241m=\u001b[39m Pool(processes\u001b[38;5;241m=\u001b[39mprocesses)\n\u001b[1;32m 70\u001b[0m args \u001b[38;5;241m=\u001b[39m [(product, path, session, fileType) \u001b[38;5;28;01mfor\u001b[39;00m product \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m]\n\u001b[0;32m---> 71\u001b[0m \u001b[43mpool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_download_product\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 72\u001b[0m pool\u001b[38;5;241m.\u001b[39mclose()\n\u001b[1;32m 73\u001b[0m pool\u001b[38;5;241m.\u001b[39mjoin()\n",
"File \u001b[0;32m~/.local/envs/earthscope_insar/lib/python3.11/multiprocessing/pool.py:367\u001b[0m, in \u001b[0;36mPool.map\u001b[0;34m(self, func, iterable, chunksize)\u001b[0m\n\u001b[1;32m 362\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmap\u001b[39m(\u001b[38;5;28mself\u001b[39m, func, iterable, chunksize\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 363\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m'''\u001b[39;00m\n\u001b[1;32m 364\u001b[0m \u001b[38;5;124;03m Apply `func` to each element in `iterable`, collecting the results\u001b[39;00m\n\u001b[1;32m 365\u001b[0m \u001b[38;5;124;03m in a list that is returned.\u001b[39;00m\n\u001b[1;32m 366\u001b[0m \u001b[38;5;124;03m '''\u001b[39;00m\n\u001b[0;32m--> 367\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_map_async\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miterable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmapstar\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchunksize\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/.local/envs/earthscope_insar/lib/python3.11/multiprocessing/pool.py:774\u001b[0m, in \u001b[0;36mApplyResult.get\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 772\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_value\n\u001b[1;32m 773\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 774\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_value\n",
"File \u001b[0;32m~/.local/envs/earthscope_insar/lib/python3.11/multiprocessing/pool.py:125\u001b[0m, in \u001b[0;36mworker\u001b[0;34m()\u001b[0m\n\u001b[1;32m 123\u001b[0m job, i, func, args, kwds \u001b[38;5;241m=\u001b[39m task\n\u001b[1;32m 124\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 125\u001b[0m result \u001b[38;5;241m=\u001b[39m (\u001b[38;5;28;01mTrue\u001b[39;00m, func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds))\n\u001b[1;32m 126\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 127\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m wrap_exception \u001b[38;5;129;01mand\u001b[39;00m func \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _helper_reraises_exception:\n",
"File \u001b[0;32m~/.local/envs/earthscope_insar/lib/python3.11/multiprocessing/pool.py:48\u001b[0m, in \u001b[0;36mmapstar\u001b[0;34m()\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmapstar\u001b[39m(args):\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mmap\u001b[39m(\u001b[38;5;241m*\u001b[39margs))\n",
"File \u001b[0;32m~/.local/envs/earthscope_insar/lib/python3.11/site-packages/asf_search/ASFSearchResults.py:84\u001b[0m, in \u001b[0;36m_download_product\u001b[0;34m()\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_download_product\u001b[39m(args) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 83\u001b[0m product, path, session, fileType \u001b[38;5;241m=\u001b[39m args\n\u001b[0;32m---> 84\u001b[0m product\u001b[38;5;241m.\u001b[39mdownload(path\u001b[38;5;241m=\u001b[39mpath, session\u001b[38;5;241m=\u001b[39msession, fileType\u001b[38;5;241m=\u001b[39mfileType)\n",
"File \u001b[0;32m~/.local/envs/earthscope_insar/lib/python3.11/site-packages/asf_search/ASFProduct.py:85\u001b[0m, in \u001b[0;36mdownload\u001b[0;34m()\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid FileDownloadType provided, the valid types are \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDEFAULT_FILE\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mADDITIONAL_FILES\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, and \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mALL_FILES\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 84\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m filename, url \u001b[38;5;129;01min\u001b[39;00m urls:\n\u001b[0;32m---> 85\u001b[0m download_url(url\u001b[38;5;241m=\u001b[39murl, path\u001b[38;5;241m=\u001b[39mpath, filename\u001b[38;5;241m=\u001b[39mfilename, session\u001b[38;5;241m=\u001b[39msession)\n",
"File \u001b[0;32m~/.local/envs/earthscope_insar/lib/python3.11/site-packages/asf_search/download/download.py:71\u001b[0m, in \u001b[0;36mdownload_url\u001b[0;34m()\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m session \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 69\u001b[0m session \u001b[38;5;241m=\u001b[39m ASFSession()\n\u001b[0;32m---> 71\u001b[0m response \u001b[38;5;241m=\u001b[39m _try_get_response(session\u001b[38;5;241m=\u001b[39msession, url\u001b[38;5;241m=\u001b[39murl)\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(path, filename), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwb\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m response\u001b[38;5;241m.\u001b[39miter_content(chunk_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m8192\u001b[39m):\n",
"File \u001b[0;32m~/.local/envs/earthscope_insar/lib/python3.11/site-packages/tenacity/__init__.py:289\u001b[0m, in \u001b[0;36mwrapped_f\u001b[0;34m()\u001b[0m\n\u001b[1;32m 287\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(f)\n\u001b[1;32m 288\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped_f\u001b[39m(\u001b[38;5;241m*\u001b[39margs: t\u001b[38;5;241m.\u001b[39mAny, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw: t\u001b[38;5;241m.\u001b[39mAny) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m t\u001b[38;5;241m.\u001b[39mAny:\n\u001b[0;32m--> 289\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m(f, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw)\n",
"File \u001b[0;32m~/.local/envs/earthscope_insar/lib/python3.11/site-packages/tenacity/__init__.py:379\u001b[0m, in \u001b[0;36m__call__\u001b[0;34m()\u001b[0m\n\u001b[1;32m 377\u001b[0m retry_state \u001b[38;5;241m=\u001b[39m RetryCallState(retry_object\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m, fn\u001b[38;5;241m=\u001b[39mfn, args\u001b[38;5;241m=\u001b[39margs, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[1;32m 378\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m--> 379\u001b[0m do \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miter(retry_state\u001b[38;5;241m=\u001b[39mretry_state)\n\u001b[1;32m 380\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(do, DoAttempt):\n\u001b[1;32m 381\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
"File \u001b[0;32m~/.local/envs/earthscope_insar/lib/python3.11/site-packages/tenacity/__init__.py:314\u001b[0m, in \u001b[0;36miter\u001b[0;34m()\u001b[0m\n\u001b[1;32m 312\u001b[0m is_explicit_retry \u001b[38;5;241m=\u001b[39m fut\u001b[38;5;241m.\u001b[39mfailed \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fut\u001b[38;5;241m.\u001b[39mexception(), TryAgain)\n\u001b[1;32m 313\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (is_explicit_retry \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mretry(retry_state)):\n\u001b[0;32m--> 314\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fut\u001b[38;5;241m.\u001b[39mresult()\n\u001b[1;32m 316\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mafter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 317\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mafter(retry_state)\n",
"File \u001b[0;32m~/.local/envs/earthscope_insar/lib/python3.11/concurrent/futures/_base.py:449\u001b[0m, in \u001b[0;36mresult\u001b[0;34m()\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m 448\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 449\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__get_result()\n\u001b[1;32m 451\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_condition\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[1;32m 453\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n",
"File \u001b[0;32m~/.local/envs/earthscope_insar/lib/python3.11/concurrent/futures/_base.py:401\u001b[0m, in \u001b[0;36m__get_result\u001b[0;34m()\u001b[0m\n\u001b[1;32m 399\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[1;32m 400\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 401\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m 402\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 403\u001b[0m \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[1;32m 404\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
"File \u001b[0;32m~/.local/envs/earthscope_insar/lib/python3.11/site-packages/tenacity/__init__.py:382\u001b[0m, in \u001b[0;36m__call__\u001b[0;34m()\u001b[0m\n\u001b[1;32m 380\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(do, DoAttempt):\n\u001b[1;32m 381\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 382\u001b[0m result \u001b[38;5;241m=\u001b[39m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 383\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m: \u001b[38;5;66;03m# noqa: B902\u001b[39;00m\n\u001b[1;32m 384\u001b[0m retry_state\u001b[38;5;241m.\u001b[39mset_exception(sys\u001b[38;5;241m.\u001b[39mexc_info()) \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n",
"File \u001b[0;32m~/.local/envs/earthscope_insar/lib/python3.11/site-packages/asf_search/download/download.py:103\u001b[0m, in \u001b[0;36m_try_get_response\u001b[0;34m()\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[38;5;129m@retry\u001b[39m(reraise\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 98\u001b[0m retry\u001b[38;5;241m=\u001b[39mretry_if_result(_is_burst_processing),\n\u001b[1;32m 99\u001b[0m wait\u001b[38;5;241m=\u001b[39mwait_fixed(\u001b[38;5;241m1\u001b[39m),\n\u001b[1;32m 100\u001b[0m stop\u001b[38;5;241m=\u001b[39mstop_after_delay(\u001b[38;5;241m90\u001b[39m),\n\u001b[1;32m 101\u001b[0m )\n\u001b[1;32m 102\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_try_get_response\u001b[39m(session: ASFSession, url: \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m--> 103\u001b[0m response \u001b[38;5;241m=\u001b[39m session\u001b[38;5;241m.\u001b[39mget(url, stream\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, hooks\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresponse\u001b[39m\u001b[38;5;124m'\u001b[39m: strip_auth_if_aws})\n\u001b[1;32m 105\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 106\u001b[0m response\u001b[38;5;241m.\u001b[39mraise_for_status()\n",
"File \u001b[0;32m~/.local/envs/earthscope_insar/lib/python3.11/site-packages/requests/sessions.py:602\u001b[0m, in \u001b[0;36mget\u001b[0;34m()\u001b[0m\n\u001b[1;32m 594\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Sends a GET request. Returns :class:`Response` object.\u001b[39;00m\n\u001b[1;32m 595\u001b[0m \n\u001b[1;32m 596\u001b[0m \u001b[38;5;124;03m:param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[1;32m 597\u001b[0m \u001b[38;5;124;03m:param \\*\\*kwargs: Optional arguments that ``request`` takes.\u001b[39;00m\n\u001b[1;32m 598\u001b[0m \u001b[38;5;124;03m:rtype: requests.Response\u001b[39;00m\n\u001b[1;32m 599\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 601\u001b[0m kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m--> 602\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrequest(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGET\u001b[39m\u001b[38;5;124m\"\u001b[39m, url, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
"File \u001b[0;32m~/.local/envs/earthscope_insar/lib/python3.11/site-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mrequest\u001b[0;34m()\u001b[0m\n\u001b[1;32m 584\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 585\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[1;32m 586\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[1;32m 587\u001b[0m }\n\u001b[1;32m 588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend(prep, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msend_kwargs)\n\u001b[1;32m 591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
"File \u001b[0;32m~/.local/envs/earthscope_insar/lib/python3.11/site-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36msend\u001b[0;34m()\u001b[0m\n\u001b[1;32m 700\u001b[0m start \u001b[38;5;241m=\u001b[39m preferred_clock()\n\u001b[1;32m 702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m adapter\u001b[38;5;241m.\u001b[39msend(request, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[1;32m 706\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m preferred_clock() \u001b[38;5;241m-\u001b[39m start\n",
"File \u001b[0;32m~/.local/envs/earthscope_insar/lib/python3.11/site-packages/requests/adapters.py:519\u001b[0m, in \u001b[0;36msend\u001b[0;34m()\u001b[0m\n\u001b[1;32m 515\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, _SSLError):\n\u001b[1;32m 516\u001b[0m \u001b[38;5;66;03m# This branch is for urllib3 v1.22 and later.\u001b[39;00m\n\u001b[1;32m 517\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m SSLError(e, request\u001b[38;5;241m=\u001b[39mrequest)\n\u001b[0;32m--> 519\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(e, request\u001b[38;5;241m=\u001b[39mrequest)\n\u001b[1;32m 521\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ClosedPoolError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 522\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mConnectionError\u001b[39;00m(e, request\u001b[38;5;241m=\u001b[39mrequest)\n",
"\u001b[0;31mConnectionError\u001b[0m: None: Max retries exceeded with url: /METADATA_SLC/SB/S1B_IW_SLC__1SDV_20200517T135026_20200517T135056_021622_0290CB_99E2.iso.xml (Caused by None)"
]
}
],
"source": [
"%%time \n",
"\n",
"reference = 'S1A_IW_SLC__1SDV_20200511T135117_20200511T135144_032518_03C421_7768'\n",
"secondary = 'S1B_IW_SLC__1SDV_20200517T135026_20200517T135056_021622_0290CB_99E2'\n",
"granules = [reference, secondary]\n",
"\n",
"results = asf_search.granule_search(granules)\n",
"results.download(path=slc_dir, processes=2, session=session)"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"Files size are 4+GB each."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"hidden": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"total 8.7G\n",
"-rw-r--r-- 1 jovyan users 4.2G May 10 2021 S1A_IW_SLC__1SDV_20200511T135117_20200511T135144_032518_03C421_7768.zip\n",
"-rw-r--r-- 1 jovyan users 4.6G May 10 2021 S1B_IW_SLC__1SDV_20200517T135026_20200517T135056_021622_0290CB_99E2.zip\n"
]
}
],
"source": [
"!ls -lh {slc_dir}"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true
},
"source": [
"### 1.3 SLC filenaming convention"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"TOPS SLC product files delivered from ESA are zip archives. When unpacked the zip extension will be replaced by SAFE. The products are therefore also frequently called SAFE files. topsApp.py can read the data from either a zip file or a SAFE file. To limit disk usage, it is recommended to not unzip the individual files.\n",
"\n",
"The zip or SAFE filenames provide information on the product type, the polarization, and the start and stop acquisition time. For example: S1A_IW_SLC__1SDV_20200511T135117_20200511T135144_032518_03C421_7768.zip\n",
"- Type = slc\n",
"- Polarization = Dual polarization\n",
"- Date = 20200511\n",
"- UTC time of acquisition = ~13:51\n",
"- Sensing start for the acquisition was 20200511 at 13:51:17\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true
},
"source": [
"### 1.3 Orbits download"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"In addition to the **SAFE files**, **orbit files** and the **auxiliary instrument files** are required for ISCE processing. Both the orbit and instrument files are provided by ESA and can be downloaded at: https://qc.sentinel1.eo.esa.int/."
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"Although Sentinel-1 restituted orbits (RESORB) are of good quality, it is recommended to use the precise orbits (POEORB) when available. Typically, precise orbits are available with a 15 to 20-day lag from the day of the acquisition."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reference time: 2020-05-11 13:51:44\n",
"Satellite name: S1A\n",
"Downloading URL: https://scihub.copernicus.eu/gnss/odata/v1/Products('bfcd88fa-7454-4470-aedc-416eaa57ad07')/$value\n"
]
}
],
"source": [
"#/home/jovyan/.local/envs/earthscope_insar/share/isce2/topsStack/fetchOrbit.py\n",
"!fetchOrbit.py -i {reference} -o {orbit_dir}"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reference time: 2020-05-17 13:50:56\n",
"Satellite name: S1B\n",
"Downloading URL: https://scihub.copernicus.eu/gnss/odata/v1/Products('2ac6a573-7a5e-468c-93c2-fde245e39664')/$value\n"
]
}
],
"source": [
"!fetchOrbit.py -i {secondary} -o {orbit_dir}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1.4 Instrument calibration file"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sentinel-1 SLCs are usually well calibrated products and in general, we don't have to deal with reading instrument calibration files. One exception is SLCs generated with **IPF version 002.36**. However, the number of such SLCs is well below 0.5% of the data and only impacts data from earlier in the mission in 2015. We do not need these files for our dataset. More information on how to download these files can be found in the example xml files included on the isce github page. These files are also included in \"support_docs\" folder here."
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true
},
"source": [
"## 2. topsApp.py input variables"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"Like the other apps in ISCE, the input variables to topsApp.py are controlled through an app xml file. All apps in ISCE have example xml files included in the ISCE distribution. You can find these under [**examples/input_files**](https://github.com/isce-framework/isce2/tree/main/examples/input_files) on the github repo. For convenience, we have included the *topsApp.xml* and *reference_TOPS_SENTINEL1.xml* example in the support_docs folder. "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def configure_inputs(outDir):\n",
" \"\"\"Write an XML Parameter file for topsApp.py \"\"\"\n",
" \n",
" cmd_topsApp_config ='''<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n",
"<topsApp>\n",
" <component name=\"topsinsar\">\n",
" <property name=\"Sensor name\">SENTINEL1</property>\n",
" <component name=\"reference\">\n",
" <catalog>reference.xml</catalog>\n",
" </component>\n",
" <component name=\"secondary\">\n",
" <catalog>secondary.xml</catalog>\n",
" </component>\n",
" <property name=\"swaths\">[3]</property>\n",
" <property name=\"range looks\">7</property>\n",
" <property name=\"azimuth looks\">3</property>\n",
" <property name=\"region of interest\">[37.98, 38.33, -118.21, -117.68]</property>\n",
" <property name=\"do unwrap\">True</property>\n",
" <property name=\"unwrapper name\">snaphu_mcf</property>\n",
" <property name=\"do denseoffsets\">True</property>\n",
" <!--<property name=\"demfilename\">path_to_your_dem</property>-->\n",
" <!--<property name=\"geocode demfilename\">path_to_your_dem</property>-->\n",
" <!--property name=\"geocode list\">['merged/phsig.cor', 'merged/filt_topophase.unw', 'merged/los.rdr', 'merged/topophase.flat', 'merged/filt_topophase.flat','merged/topophase.cor','merged/filt_topophase.unw.conncomp']</property>-->\n",
" </component>\n",
"</topsApp>'''\n",
" print(\"writing topsApp.xml\")\n",
" with open(os.path.join(outDir,\"topsApp.xml\"), \"w\") as fid:\n",
" fid.write(cmd_topsApp_config)\n",
" \n",
" cmd_reference_config = '''<component name=\"reference\">\n",
" <property name=\"orbit directory\">../data/orbits</property>\n",
" <property name=\"output directory\">reference</property>\n",
" <property name=\"safe\">['../data/slcs/S1A_IW_SLC__1SDV_20200511T135117_20200511T135144_032518_03C421_7768.zip']</property>\n",
"</component>'''\n",
" print(\"writing reference.xml\")\n",
" with open(os.path.join(outDir,\"reference.xml\"), \"w\") as fid:\n",
" fid.write(cmd_reference_config)\n",
" \n",
" cmd_secondary_config = '''<component name=\"secondary\">\n",
" <property name=\"orbit directory\">../data/orbits</property>\n",
" <property name=\"output directory\">secondary</property>\n",
" <property name=\"safe\">['../data/slcs/S1B_IW_SLC__1SDV_20200517T135026_20200517T135056_021622_0290CB_99E2.zip']</property>\n",
"</component>'''\n",
" print(\"writing secondary.xml\")\n",
" with open(os.path.join(outDir,\"secondary.xml\"), \"w\") as fid:\n",
" fid.write(cmd_secondary_config)"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true
},
"source": [
"### 2.1 Required versus optional topsApp.py inputs"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"The example *topsApp.xml* contains all input variables with a description. Let us first read this file. You can open the file by launching a **terminal** and using your preferred editor to open the file. For vim type:\n",
"```\n",
" vim support_docs/example/topsApp.xml\n",
" vim support_docs/example/reference_TOPS_SENTINEL1.xml\n",
"```\n",
"When it comes to the actual processing with topsApp.py, you do not need to specify all the input variables as shown in the example topsApp.xml. Defaults will be assumed when properties are not set by the user. You can get a simple table overview of the required variables by calling the help of topsApp.py."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true,
"hidden": true,
"jupyter": {
"outputs_hidden": true
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2023-08-16 18:30:07,203 - isce.insar - INFO - ISCE VERSION = 2.6.3, RELEASE_SVN_REVISION = ,RELEASE_DATE = 20230418, CURRENT_SVN_REVISION = \n",
"ISCE VERSION = 2.6.3, RELEASE_SVN_REVISION = ,RELEASE_DATE = 20230418, CURRENT_SVN_REVISION = \n",
"None\n",
"The currently supported sensors are: ['SENTINEL1']\n",
"\n",
"Usages: \n",
"topsApp.py <input-file.xml>\n",
"topsApp.py --steps\n",
"topsApp.py --help\n",
"topsApp.py --help --steps\n",
"\n",
"\n",
"See the table of configurable parameters listed \n",
"below for a list of parameters that may be specified in the\n",
"input file. See example input xml files in the isce 'examples'\n",
"directory. Read about the input file in the ISCE.pdf document.\n",
"\n",
"The user configurable inputs are given in the following table.\n",
"Those inputs that are of type 'component' are also listed in\n",
"table of facilities below with additional information.\n",
"To configure the parameters, enter the desired value in the\n",
"input file using a property tag with name = to the name\n",
"given in the table.\n",
"name type mandatory doc \n",
"=========================== ========== ========== ==============================\n",
"ampcormargin int False Ampcor margin offset. Used in \n",
" runDenseOffsets. \n",
"ampcoroversamplingfactor int False Ampcor oversampling factor. \n",
" Used in runDenseOffsets. \n",
"ampcorsearchwindowheight int False Ampcor search window size \n",
" height. Used in \n",
" runDenseOffsets. \n",
"ampcorsearchwindowwidth int False Ampcor search window size \n",
" width. Used in \n",
" runDenseOffsets. \n",
"ampcorskipheight int False Ampcor skip down height. Used \n",
" in runDenseOffsets. \n",
"ampcorskipwidth int False Ampcor skip across width. Used\n",
" in runDenseOffsets. \n",
"ampcorwindowheight int False Ampcor main window size \n",
" height. Used in \n",
" runDenseOffsets. \n",
"ampcorwindowwidth int False Ampcor main window size width.\n",
" Used in runDenseOffsets. \n",
"applyionospherecorrection bool False N/A \n",
"applypolynomialfitbeforefil bool False N/A \n",
"teringionospherephase \n",
"areasmaskedoutinionospheric int False areas masked out in \n",
"phaseestimation ionospheric phase estimation \n",
"azimuthlooks int False N/A \n",
"azimuthshift int False Ampcor gross offset down. Used\n",
" in runDenseOffsets. \n",
"considerburstpropertiesinio bool False N/A \n",
"nospherecomputation \n",
"correctphaseerrorcausedbyio int False N/A \n",
"nosphereazimuthshift \n",
"demfilename str False Filename of the Digital \n",
" Elevation Model (DEM) \n",
"demstitcher component False Object that based on the frame\n",
" bounding boxes creates a DEM \n",
"dodenseoffsets bool False Perform dense offset \n",
" estimation \n",
"doesd bool False Perform ESD estimation \n",
"dointerferogram bool False Perform interferometry. Set to\n",
" false to skip insar steps. \n",
"doionospherecorrection bool False N/A \n",
"dounwrap bool False True if unwrapping is desired.\n",
" To be unsed in combination \n",
" with UNWRAPPER_NAME. \n",
"dounwrap2stage bool False True if unwrapping is desired.\n",
" To be unsed in combination \n",
" with UNWRAPPER_NAME. \n",
"endionospherestep str False N/A \n",
"esdazimuthlooks int False Number of azimuth looks for \n",
" overlap IFGs \n",
"esdcoherencethreshold float False ESD coherence threshold \n",
"esdrangelooks int False Number of range looks for \n",
" overlap IFGs \n",
"extraesdcycles float False Extra ESD cycles to interpret \n",
" overlap phase \n",
"family str False Instance family name \n",
"filternullfactor float False NULL factor to use in \n",
" filtering offset fields to \n",
" avoid numpy type issues. \n",
"filterstrength float False N/A \n",
"filterwindowsize int False Window size for median_filter.\n",
"geocodeboundingbox float False Bounding box for geocoding - \n",
" South, North, West, East in \n",
" degrees \n",
"geocodedemfilename str False Filename of the DEM for \n",
" geocoding \n",
"geocodelist str False List of products to geocode. \n",
"heightofionospherelayerinkm float False N/A \n",
"maximumwindowsizeforfilteri int False N/A \n",
"ngionosphereazimuthshift \n",
"maximumwindowsizeforfilteri int False N/A \n",
"ngionospherephase \n",
"minimumwindowsizeforfilteri int False N/A \n",
"ngionosphereazimuthshift \n",
"minimumwindowsizeforfilteri int False N/A \n",
"ngionospherephase \n",
"name str False Instance name \n",
"numberofazimuthlooksatfirst int False N/A \n",
"stageforionospherephaseunwr \n",
"apping \n",
"numberofrangelooksatfirstst int False N/A \n",
"ageforionospherephaseunwrap \n",
"ping \n",
"offsetgeocodelist str False List of offset-specific files \n",
" to geocode. \n",
"offsetsnrthreshold float False Offset SNR threshold \n",
"pickledumpdirectory str False If steps is used, the \n",
" directory in which to store \n",
" pickle objects. \n",
"pickleloaddirectory str False If steps is used, the \n",
" directory from which to \n",
" retrieve pickle objects. \n",
"rangelooks int False N/A \n",
"rangeshift int False Ampcor gross offset across. \n",
" Used in runDenseOffsets. \n",
"reference component True Reference raw data component \n",
"regionofinterest float False User defined area to crop in \n",
" SNWE \n",
"renderer str True Format in which the data is \n",
" serialized when using steps. \n",
" Options are xml (default) or \n",
" pickle. \n",
"rununwrapper component False Unwrapping module \n",
"rununwrapper2stage component False Unwrapping module \n",
"secondary component True Secondary raw data component \n",
"sensorname str True Sensor name \n",
"snrthresholdfactor float False SNR Threshold factor used in \n",
" filtering offset field \n",
" objects. \n",
"solver_2stage str False Linear Programming Solver for \n",
" 2Stage; Options: pulp, gurobi,\n",
" glpk; Used only for Redundant \n",
" Arcs \n",
"startionospherestep str False N/A \n",
"swaths int False Swaths to process \n",
"topsproc component False TopsProc object \n",
"totalnumberofazimuthlooksin int False N/A \n",
"theionosphereprocessing \n",
"totalnumberofrangelooksinth int False N/A \n",
"eionosphereprocessing \n",
"unwrapper2stagename str False 2 Stage Unwrapping method to \n",
" use. Available: MCF, REDARC0, \n",
" REDARC1, REDARC2 \n",
"unwrappername str False Unwrapping method to use. To \n",
" be used in combination with \n",
" UNWRAP. \n",
"usegpu bool False Allow App to use GPU when \n",
" available \n",
"usehighresolutiondemonly int False If True and a dem is not \n",
" specified in input, it will \n",
" only download the SRTM highest\n",
" resolution dem if it is \n",
" available and fill the missing\n",
" portion with null values \n",
" (typically -32767). \n",
"usevirtualfiles bool False Use virtual files when \n",
" possible to save space \n",
"\n",
"\n",
"The configurable facilities are given in the following table.\n",
"Enter the component parameter values for any of these facilities in the\n",
"input file using a component tag with name = to the name given in\n",
"the table. The configurable parameters for a facility are entered with \n",
"property tags inside the component tag. Examples of the configurable\n",
"parameters are available in the examples/inputs directory.\n",
"For more help on a given facility run\n",
"iscehelp.py -t type\n",
"where type (if available) is the second entry in the table\n",
"\n",
"name type \n",
" \n",
"==================== ===============\n",
"demstitcher DataManager \n",
"reference GRDSensor \n",
"rununwrapper N/A \n",
"rununwrapper2stage N/A \n",
"secondary GRDSensor \n",
"topsproc N/A \n"
]
}
],
"source": [
"!topsApp.py --help"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"From the table, you can see that the **reference** and **secondary** components are to be specified. This can be done directly with its specific properties in the topsApp.xml, or alternatively, one can point to a dedicated xml file for the **reference** and **secondary**, each of which contain their individual properties. The required properties for the reference and secondary should at least include orbit information as \"orbit directory\" and a list of products under the \"safe\" property tag. \n",
"\n",
"<br>\n",
"<div class=\"alert alert-danger\">\n",
"<b>POTENTIAL ISSUE:</b> \n",
"If you specify a **region of interest**, make sure it covers at last two bursts. If this is not the case, ESD will fail in the processing as it is being estimated from the burst overlap region. You could always decide to only geocode a smaller region with the ** ** property.\n",
"</div>"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true
},
"source": [
"### 2.2 topsApp inputs for this tutorial"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"Let us now examine the files *topsApp.xml*, *reference.xml* and *secondary.xml* that will be used for this tutorial."
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true
},
"source": [
"#### 2.2.1 topsApp.xml"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"```xml\n",
"<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n",
"<topsApp>\n",
" <component name=\"topsinsar\">\n",
" <property name=\"Sensor name\">SENTINEL1</property>\n",
" <component name=\"reference\">\n",
" <catalog>reference.xml</catalog>\n",
" </component>\n",
" <component name=\"secondary\">\n",
" <catalog>secondary.xml</catalog>\n",
" </component>\n",
" <property name=\"swaths\">[3]</property>\n",
" <property name=\"range looks\">7</property>\n",
" <property name=\"azimuth looks\">3</property>\n",
" <property name=\"region of interest\">[37.98, 38.33, -118.21, -117.68]</property>\n",
" <property name=\"do unwrap\">True</property>\n",
" <property name=\"unwrapper name\">snaphu_mcf</property>\n",
" <property name=\"do denseoffsets\">True</property>\n",
" <!--<property name=\"demfilename\">path_to_your_dem</property>-->\n",
" <!--<property name=\"geocode demfilename\">path_to_your_dem</property>-->\n",
" <!--property name=\"geocode list\">['merged/phsig.cor', 'merged/filt_topophase.unw', 'merged/los.rdr', 'merged/topophase.flat', 'merged/filt_topophase.flat','merged/topophase.cor','merged/filt_topophase.unw.conncomp']</property>-->\n",
" </component>\n",
"</topsApp>\n",
"```\n",
"\n",
"\n",
"- The reference and secondary components refer to their own *.xml* files \n",
"- The **swaths** property controls the number of swaths to be processed. As the earthquake occurred in the subswath three, we can directly limit the list to the single entry list [3] only.\n",
"- We specify the **range looks** and **azimuth looks** to be 7 and 3. The range resolution for sentinel varies from near to far range, but is roughly 5m, while the azimuth resolution is approximately 15m, leading to a multi-looked product that will be approximately 35m by 45m.\n",
"- By specifying the **region of interest** as [S, N, W, E] to only capture the extent of the earthquake, topsApp.py will only extract those bursts from subswath 3 needed to cover the earthquake.\n",
"- By default, topsApp can download a DEM on the fly. By including **demFilename** a local DEM can be specified as input for the processing. For this notebook exercise, a sample 1-arc second DEM in WGS84 is provided in the DEM/DEM1 folder.\n",
"- By default, the geocoding in topsApp.py is performed at the same sampling as processing DEM. However, a different DEM *to be used specifically for geocoding* can be specified using the **geocode demfilename** property. This is used for the case when data has been multilooked to order of 100m or greater and when geocoding to 30m is an overkill.\n",
"- By default, no unwrapping is done. In order to turn it on, set the property **do unwrap** to *True*.\n",
"- In case unwrapping is requested, the default unwrapping strategy to be applied is the *icu* unwrapping method. For this tutorial, we will use *snaphu_mcf*.\n",
"- Lastly, we request topsApp.py to run the dense-offsets using the **do denseoffsets** property. By enabling this, topsApp.py will estimate the range and azimuth offsets on the amplitude of the reference and secondary SLC files.\n",
"\n",
"\n",
"You will see that a few of the above properties are commented out in the xml files provided. We will come back to these later in the tutorial. The commented properties have the form:\n",
"```xml\n",
"<!--<property> ... </property>--> \n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true
},
"source": [
"#### 2.2.2 reference.xml and secondary.xml"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"**reference.xml**\n",
"``` xml\n",
"<component name=\"reference\">\n",
" <property name=\"orbit directory\">../data/orbits</property>\n",
" <property name=\"output directory\">reference</property>\n",
" <property name=\"safe\">['../data/slcs/S1A_IW_SLC__1SDV_20200511T135117_20200511T135144_032518_03C421_7768.zip']</property>\n",
"</component>\n",
"```\n",
"\n",
"**secondary.xml**\n",
"``` xml\n",
"<component name=\"secondary\">\n",
" <property name=\"orbit directory\">../data/orbits</property>\n",
" <property name=\"output directory\">secondary</property>\n",
" <property name=\"safe\">['../data/slcs/S1B_IW_SLC__1SDV_20200517T135026_20200517T135056_021622_0290CB_99E2.zip']</property>\n",
"</component>\n",
"```\n",
"\n",
"- The value associated with the reference **safe** property corresponds to a list of SAFE files that are to be mosaiced when generating the interferogram. \n",
"- The **orbit directory** points to the directory where we have stored the POEORB (precise) orbits for this example.\n",
"\n",
"\n",
"<br>\n",
"<div class=\"alert alert-warning\">\n",
"<b>SIGN CONVENTION:</b> \n",
"By selecting the secondary to be the one acquired after the reference, and keeping in mind that the interferogram formation is reference* conj(secondary), then a positive phase value for the interferogram indicates the surface has moved towards the satellite between 2020-05-11 and 2020-05-17. \n",
"</div>\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"writing topsApp.xml\n",
"writing reference.xml\n",
"writing secondary.xml\n"
]
}
],
"source": [
"configure_inputs(insar_dir)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"reference.xml secondary.xml topsApp.xml\n"
]
}
],
"source": [
"# Ensure our processing directory only has input XML files at this point:\n",
"!ls {insar_dir}"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true
},
"source": [
"## 3. topsApp.py processing steps"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"The topsApp.py workflow can be called with a single command-line call to topsApp.py; by default it will run all the required processing steps with inputs pulled from the topsApp.xml file. Although this is an attractive feature, it is recommended to run topsApp.py with “steps” enabled. This will allow you to re-start the processing from a given processing step. If “steps” are not used, users must restart processing from the beginning of the workflow after fixing any downstream issues with the processing. \n",
"\n",
"The \"--help\" switch lists all the steps involved in the processing:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"hidden": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2023-08-16 18:30:08,300 - isce.insar - INFO - ISCE VERSION = 2.6.3, RELEASE_SVN_REVISION = ,RELEASE_DATE = 20230418, CURRENT_SVN_REVISION = \n",
"ISCE VERSION = 2.6.3, RELEASE_SVN_REVISION = ,RELEASE_DATE = 20230418, CURRENT_SVN_REVISION = \n",
"None\n",
"The currently supported sensors are: ['SENTINEL1']\n",
"\n",
"Usages: \n",
"topsApp.py <input-file.xml>\n",
"topsApp.py --steps\n",
"topsApp.py --help\n",
"topsApp.py --help --steps\n",
"\n",
"None\n",
"A description of the individual steps can be found in the README file\n",
"and also in the ISCE.pdf document\n",
"\n",
"Command line options for steps processing are formed by\n",
"combining the following three options as required:\n",
"\n",
"'--start=<step>', '--end=<step>', '--dostep=<step>'\n",
"\n",
"The step names are chosen from the following list:\n",
"\n",
"['startup', 'preprocess', 'computeBaselines', 'verifyDEM', 'topo']\n",
"['subsetoverlaps', 'coarseoffsets', 'coarseresamp', 'overlapifg', 'prepesd']\n",
"['esd', 'rangecoreg', 'fineoffsets', 'fineresamp', 'ion']\n",
"['burstifg', 'mergebursts', 'filter', 'unwrap', 'unwrap2stage']\n",
"['geocode', 'denseoffsets', 'filteroffsets', 'geocodeoffsets']\n",
"\n",
"If --start is missing, then processing starts at the first step.\n",
"If --end is missing, then processing ends at the final step.\n",
"If --dostep is used, then only the named step is processed.\n",
"\n",
"In order to use either --start or --dostep, it is necessary that a\n",
"previous run was done using one of the steps options to process at least\n",
"through the step immediately preceding the starting step of the current run.\n",
"\n"
]
}
],
"source": [
"!topsApp.py --help --steps"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"<br>\n",
"<div class=\"alert alert-danger\">\n",
"<b>POTENTIAL ISSUE:</b> \n",
"**Steps are to be run in the following prescribed order**:\n",
"*<center>\n",
"['startup', 'preprocess', 'computeBaselines', 'verifyDEM', 'topo']\n",
"['subsetoverlaps', 'coarseoffsets', 'coarseresamp', 'overlapifg', 'prepesd']\n",
"['esd', 'rangecoreg', 'fineoffsets', 'fineresamp', 'burstifg']\n",
"['mergebursts', 'filter', 'unwrap', 'unwrap2stage', 'geocode']\n",
"['denseoffsets', 'filteroffsets', 'geocodeoffsets']\n",
"</center>*\n",
"</div>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true
},
"source": [
"### 3.0 Directory setup"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"We will now \"cd\" into our InSAR directory where our configuration files are for topsApp.py."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"os.chdir(insar_dir)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.1 Quick overview of your interferogram"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true
},
"source": [
"#### 3.1.1 Step startup"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"We will start with running the first step **startup**. \n",
"\n",
"NOTE: `2&>1 | tee startup.log` is a linux pipe command to show output and errors both in the current terminal (jupyter notebook) and a file (startup.log). This is convenient for keeping logs from different steps organized"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"!topsApp.py --dostep=startup &> startup.log"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2023-08-16 18:30:09,064 - isce.insar - INFO - ISCE VERSION = 2.6.3, RELEASE_SVN_REVISION = ,RELEASE_DATE = 20230418, CURRENT_SVN_REVISION = \n",
"ISCE VERSION = 2.6.3, RELEASE_SVN_REVISION = ,RELEASE_DATE = 20230418, CURRENT_SVN_REVISION = \n",
"Step processing\n",
"Running step startup\n",
"None\n",
"The currently supported sensors are: ['SENTINEL1']\n",
"Dumping the application's pickle object _insar to file PICKLE/startup\n",
"The remaining steps are (in order): ['preprocess', 'computeBaselines', 'verifyDEM', 'topo', 'subsetoverlaps', 'coarseoffsets', 'coarseresamp', 'overlapifg', 'prepesd', 'esd', 'rangecoreg', 'fineoffsets', 'fineresamp', 'ion', 'burstifg', 'mergebursts', 'filter', 'unwrap', 'unwrap2stage', 'geocode', 'denseoffsets', 'filteroffsets', 'geocodeoffsets']\n"
]
}
],
"source": [
"!head startup.log"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"When topsApp.py is run in steps, PICKLE files are used to store state information between the steps. A PICKLE folder is created during the startup step. The PICKLE folder is used to store the processing parameters for each processing step. By exploring the pickle folder, you will find a binary pickle file and an *xml* file associated with the **startup** step."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"hidden": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"startup startup.xml\n"
]
}
],
"source": [
"!ls PICKLE"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"The information contained within the **startup** pickle and *xml* files allows topsApp to start or re-start processing from where **startup** was completed. "
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true
},
"source": [
"#### 3.1.2 Step preprocess"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"Keeping in mind that the order of steps matter, we move to the second step of topsApp.py processing, which is to **preprocess** the data."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"!topsApp.py --dostep=preprocess &> preprocess.log"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"secondary.sensor.mission = S1B\n",
"secondary.sensor.name = topsswathslc_name\n",
"secondary.sensor.numberofbursts = 4\n",
"secondary.sensor.processingfacility = DLR-Oberpfaffenhofen, Germany\n",
"secondary.sensor.processingsoftwareversion = 003.20\n",
"secondary.sensor.processingsystem = Sentinel-1 IPF\n",
"secondary.sensor.spacecraftname = Sentinel-1\n",
"####################################################################################################\n",
"Dumping the application's pickle object _insar to file PICKLE/preprocess\n",
"The remaining steps are (in order): ['computeBaselines', 'verifyDEM', 'topo', 'subsetoverlaps', 'coarseoffsets', 'coarseresamp', 'overlapifg', 'prepesd', 'esd', 'rangecoreg', 'fineoffsets', 'fineresamp', 'ion', 'burstifg', 'mergebursts', 'filter', 'unwrap', 'unwrap2stage', 'geocode', 'denseoffsets', 'filteroffsets', 'geocodeoffsets']\n"
]
}
],
"source": [
"# Just examine last lines of log\n",
"!tail preprocess.log"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"During preprocessing, the orbits, the IPF (Instrument Processing Facility, which is the processing version used by ESA to focus the data), the bursts, and, if needed, the antenna pattern are extracted. \n",
"\n",
"Note if you had processed the complete region without limitation, there would be three subswaths (IW1,IW2,IW3). For our example,\n",
"we limited the processing to IW3 alone. Therefore, for our tutorial, within the secondary and reference folders you will find only an **IW3** folder and an **IW3.xml** file."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"hidden": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"reference/IW3.xml\n",
"\n",
"reference/IW3:\n",
"burst_01.slc.vrt burst_02.slc.vrt burst_03.slc.vrt burst_04.slc.vrt\n",
"burst_01.slc.xml burst_02.slc.xml burst_03.slc.xml burst_04.slc.xml\n"
]
}
],
"source": [
"ls reference/*"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"The **IW3.xml** file contains metadata information specific for each subswath (e.g. orbit state vectors, doppler, sensing times, IPF version). You can open the file using your preferred editor from the command line:\n",
"```\n",
" vim reference/IW3.xml\n",
"```\n",
"The **IW3** folder contains the unpacked bursts and their meta-data.\n",
"Typically you will find *.xml* and *.vrt* files, and in certain cases *.slc* files, as described below.\n",
"-\tIf a bounding box is specified, only the reference bursts covering the box are unpacked, otherwise the complete reference SLC is used. The bursts of the reference in reference/IW* are relabeled from 1 to n.\n",
"-\tThe secondary/IW* folders typically contain a larger set of bursts to cover the reference extent completely, where the bursts are labelled from 1 to m. The burst numbering is not coordinated between reference and secondary folders: burst 1 in the reference does not necessarily correspond to burst 1 in the secondary.\n",
"-\tAll data are unpacked virtually (i.e., only *.xml* and *.vrt* files in *IW* folders) unless: (1) user requested to physically unpack the data, which can be requested in topsApp.xml by property **usevirtualfiles**, or (2) if an antenna pattern correction needs to be applied (not controlled by user) which happens mainly for the initially acquired Sentinel-1 data.\n",
"-\tThe IPF version of unpacked bursts is tracked and a combined IPF is assigned for the reference and one for the secondary. Note: bursts processed with different IPF versions cannot be stitched and attempting to do so will cause an error message. \n",
"\n",
"<br>\n",
"<div class=\"alert alert-danger\">\n",
"<b>POTENTIAL ISSUE:</b> \n",
"There is a gap in spatial coverage, e.g. you are missing the middle SAFE file.\n",
"</div>\n",
"<div class=\"alert alert-danger\">\n",
"<b>POTENTIAL ISSUE:</b> \n",
"The SAFE frames that need to be stitched do not have a consistent IPF version\n",
"</div>"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true
},
"source": [
"#### 3.1.3 Step computeBaselines"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"hidden": true,
"scrolled": true
},
"outputs": [],
"source": [
"!topsApp.py --dostep=computeBaselines &> computeBaselines.log"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2023-08-16 18:30:33,824 - isce.insar - INFO - ISCE VERSION = 2.6.3, RELEASE_SVN_REVISION = ,RELEASE_DATE = 20230418, CURRENT_SVN_REVISION = \n",
"ISCE VERSION = 2.6.3, RELEASE_SVN_REVISION = ,RELEASE_DATE = 20230418, CURRENT_SVN_REVISION = \n",
"Step processing\n",
"Running step computeBaselines\n",
"Estimated burst offset: 0\n",
"2023-08-16 18:30:34,349 - isce.topsinsar.runPreprocessor - INFO - \n",
"####################################################################################################\n",
" runComputeBaseline\n",
"----------------------------------------------------------------------------------------------------\n",
"baseline.IW-3 Bpar at midrange for first common burst = 28.203256463294537\n",
"baseline.IW-3 Bpar at midrange for last common burst = 28.02300133553921\n",
"baseline.IW-3 Bperp at midrange for first common burst = -21.99744857327688\n",
"baseline.IW-3 Bperp at midrange for last common burst = -21.64387533400502\n",
"baseline.IW-3 First common burst in reference = 0\n",
"baseline.IW-3 First common burst in secondary = 0\n",
"baseline.IW-3 Last common burst in reference = 4\n",
"baseline.IW-3 Last common burst in secondary = 4\n",
"baseline.IW-3 Number of bursts in reference = 4\n",
"baseline.IW-3 Number of bursts in secondary = 4\n",
"baseline.IW-3 Number of common bursts = 4\n",
"####################################################################################################\n",
"Dumping the application's pickle object _insar to file PICKLE/computeBaselines\n",
"The remaining steps are (in order): ['verifyDEM', 'topo', 'subsetoverlaps', 'coarseoffsets', 'coarseresamp', 'overlapifg', 'prepesd', 'esd', 'rangecoreg', 'fineoffsets', 'fineresamp', 'ion', 'burstifg', 'mergebursts', 'filter', 'unwrap', 'unwrap2stage', 'geocode', 'denseoffsets', 'filteroffsets', 'geocodeoffsets']\n",
"Polynomial Order: 0 - by - 0 \n",
"0\t\n",
"Polynomial Order: 0 - by - 0 \n",
"0\t\n"
]
}
],
"source": [
"# Look at full log for this step\n",
"!cat computeBaselines.log"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"In this step, the perpendicular and parallel baselines are computed using the orbit (state vector information) for the first and last burst of the reference image (center range). Each subswath is processed individually, with output sent to the screen. As the processing for the tutorial is limited to IW3, the baselines are only computed for this subswath."
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true
},
"source": [
"### 3.2 Step verifyDEM"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"!topsApp.py --dostep=verifyDEM &> verifyDEM.log"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step processing\n",
"Running step verifyDEM\n",
"command = curl -n -L -c $HOME/.earthdatacookie -b $HOME/.earthdatacookie -k -f -O https://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.003/2000.02.11/N38W119.SRTMGL1.hgt.zip\n",
"command = curl -n -L -c $HOME/.earthdatacookie -b $HOME/.earthdatacookie -k -f -O https://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.003/2000.02.11/N38W118.SRTMGL1.hgt.zip\n",
"command = curl -n -L -c $HOME/.earthdatacookie -b $HOME/.earthdatacookie -k -f -O https://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.003/2000.02.11/N37W119.SRTMGL1.hgt.zip\n",
"command = curl -n -L -c $HOME/.earthdatacookie -b $HOME/.earthdatacookie -k -f -O https://e4ftl01.cr.usgs.gov/MEASURES/SRTMGL1.003/2000.02.11/N37W118.SRTMGL1.hgt.zip\n",
"Writing geotrans to VRT for demLat_N37_N39_Lon_W119_W117.dem\n",
"Writing geotrans to VRT for demLat_N37_N39_Lon_W119_W117.dem.wgs84\n",
"Dumping the application's pickle object _insar to file PICKLE/verifyDEM\n",
"The remaining steps are (in order): ['topo', 'subsetoverlaps', 'coarseoffsets', 'coarseresamp', 'overlapifg', 'prepesd', 'esd', 'rangecoreg', 'fineoffsets', 'fineresamp', 'ion', 'burstifg', 'mergebursts', 'filter', 'unwrap', 'unwrap2stage', 'geocode', 'denseoffsets', 'filteroffsets', 'geocodeoffsets']\n"
]
}
],
"source": [
"!tail verifyDEM.log"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"This step will check the DEM file specified in the topsApp.xml. If no DEM file has been specified, topsApp.py will download the DEM on the fly and track the filename for subsequent processing. \n",
"\n",
"<div class=\"alert alert-danger\">\n",
"<b>POTENTIAL ISSUE:</b> \n",
"You did not set your earthdata credentials as detailed in the ISCE installation. Find instructions here: [Step 2 here](https://wiki.earthdata.nasa.gov/display/EL/How+To+Access+Data+With+cURL+And+Wget)\n",
"</div>\n",
"\n",
"<div class=\"alert alert-danger\">\n",
"<b>POTENTIAL ISSUE:</b> \n",
"You did set up your earthdata credentials as detailed in the ISCE installation but you have special characters in your password. \"Escape\" these characters by adding a backslash in front of them. \n",
"</div>\n",
"\n",
"<div class=\"alert alert-danger\">\n",
"<b>POTENTIAL ISSUE:</b> \n",
"The DEM ftp site is down and returns no data tiles.\n",
"</div>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true
},
"source": [
"#### ONLY if download fails"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"If your DEM download failed and you are unable to resolve the issue, you can use the DEM provided with this tutorial. This requires three steps to complete, before you can rerun the **verifyDEM** step:\n",
"\n",
"1) Copy over the DEM from the backup folder to your processing location"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"# files = ['demLat_N37_N39_Lon_W119_W117.dem.wgs84', #Binary raster\n",
"# 'demLat_N37_N39_Lon_W119_W117.dem.wgs84.xml', #XML needed by ISCE\n",
"# 'demLat_N37_N39_Lon_W119_W117.dem.wgs84.vrt'] #VRT needed by ISCE\n",
"\n",
"# for file in files:\n",
"# if not os.path.exists(os.path.join(insar_dir,file)):\n",
"# copy_from_bucket(os.path.join(\"TOPS\",file),\n",
"# os.path.join(insar_dir,file))\n",
"# print(file + \" done\")\n",
"# else:\n",
"# print(file + \" already exists\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"2) To use this DEM, you may need to edit the **topsApp.xml** file and uncomment the property for the **demFilename**.\n",
"``` vim\n",
" vim insar/topsApp.xml\n",
"```\n",
"Remember the *xml* guidelines:\n",
"```xml\n",
"<!--<property> ..COMMENTED.. </property>--> \n",
"<property> ..UNCOMMENTED.. </property>\n",
"```\n",
"Since the staged file name is exactly the same as what `topsApp.py` was trying to download internally, then the edit may not be necessary."
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"Now rerun the **verifyDEM** step to ensure the new DEM information is correctly loaded into the processing:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"#!topsApp.py --dostep=verifyDEM "
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true
},
"source": [
"### 3.3 Step topo"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"During topo processing, the DEM is mapped into the radar coordinates of the **reference** image. As output this generates the **reference_geom** folder containing the longitude (*lon_XX.rdr*), latitude (*lat_XX.rdr*), height (*hgt_XX.rdr*) and LOS angles (*los_XX.rdr*) on a pixel by pixel grid for each burst (*XX*). This step is the most time-consuming step. It is parallelized for performance and can also be ran with GPU support enabled. \n",
"\n",
"For our tutorial, anticipate this step will take 20+ min in CPU mode with 4 threads. \n",
"***Depending on the size of the class the instructor might recommend to decrease the number of treads, pair up in teams, or stagger this processing step***.\n",
"\n",
"This is a good opportunity to familiarize yourself a bit more with the TOPS mode and input parameters.\n",
"- Can you find which property in the topsApp.xml controls the GPU processing? Tip: see “topsApp.py --help”.\n",
"- Can you find the typical incidence angle range for IW3? Tip: You could try to load the los.rdr file for a burst or search the ESA website for TOPS documentation.\n",
"\n",
"Once the step is complete you can have a look at the files that have been generated for each burst:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"hidden": true,
"tags": []
},
"outputs": [],
"source": [
"!topsApp.py --dostep=topo &> topo.log"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"hidden": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hgt_01.rdr hgt_04.rdr.vrt lat_03.rdr.xml lon_03.rdr los_02.rdr.vrt\n",
"hgt_01.rdr.vrt hgt_04.rdr.xml lat_04.rdr lon_03.rdr.vrt los_02.rdr.xml\n",
"hgt_01.rdr.xml lat_01.rdr lat_04.rdr.vrt lon_03.rdr.xml los_03.rdr\n",
"hgt_02.rdr lat_01.rdr.vrt lat_04.rdr.xml lon_04.rdr los_03.rdr.vrt\n",
"hgt_02.rdr.vrt lat_01.rdr.xml lon_01.rdr lon_04.rdr.vrt los_03.rdr.xml\n",
"hgt_02.rdr.xml lat_02.rdr lon_01.rdr.vrt lon_04.rdr.xml los_04.rdr\n",
"hgt_03.rdr lat_02.rdr.vrt lon_01.rdr.xml los_01.rdr los_04.rdr.vrt\n",
"hgt_03.rdr.vrt lat_02.rdr.xml lon_02.rdr los_01.rdr.vrt los_04.rdr.xml\n",
"hgt_03.rdr.xml lat_03.rdr lon_02.rdr.vrt los_01.rdr.xml\n",
"hgt_04.rdr lat_03.rdr.vrt lon_02.rdr.xml los_02.rdr\n"
]
}
],
"source": [
"ls geom_reference/*"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"Were you able to figure out the incidence angle for subswath 3? It is about 41$^\\circ$-46$^\\circ$. This infomation is contained in the *los.XX.rdr* files. Its first band contains the incidence angle and the second band the azimuth angle of the satellite. There is an easy way to retrieve its average value with GDAL:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"hidden": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Driver: VRT/Virtual Raster\n",
"Files: geom_reference/IW3/los_02.rdr.vrt\n",
" geom_reference/IW3/los_02.rdr\n",
"Size is 24492, 1515\n",
"Corner Coordinates:\n",
"Upper Left ( 0.0, 0.0)\n",
"Lower Left ( 0.0, 1515.0)\n",
"Upper Right (24492.0, 0.0)\n",
"Lower Right (24492.0, 1515.0)\n",
"Center (12246.0, 757.5)\n",
"Band 1 Block=24492x1 Type=Float32, ColorInterp=Undefined\n",
" Minimum=41.670, Maximum=46.160, Mean=43.960, StdDev=1.275\n",
" Metadata:\n",
" STATISTICS_MAXIMUM=46.159740447998\n",
" STATISTICS_MEAN=43.960012280913\n",
" STATISTICS_MINIMUM=41.670024871826\n",
" STATISTICS_STDDEV=1.274622231491\n",
" STATISTICS_VALID_PERCENT=100\n",
"Band 2 Block=24492x1 Type=Float32, ColorInterp=Undefined\n",
" Minimum=-99.771, Maximum=-99.170, Mean=-99.468, StdDev=0.170\n",
" Metadata:\n",
" STATISTICS_MAXIMUM=-99.170288085938\n",
" STATISTICS_MEAN=-99.467780585371\n",
" STATISTICS_MINIMUM=-99.770805358887\n",
" STATISTICS_STDDEV=0.17049617958397\n",
" STATISTICS_VALID_PERCENT=100\n"
]
}
],
"source": [
"!gdalinfo -stats geom_reference/IW3/los_02.rdr.vrt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plot results of topo step"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {
"image/png": {
"height": 183,
"width": 580
}
},
"output_type": "display_data"
}
],
"source": [
"path = 'geom_reference/IW3/hgt_01.rdr'\n",
"da = xr.open_dataarray(path, parse_coordinates=False, engine='rasterio')\n",
"\n",
"plt.imshow(da.sel(band=1), aspect=1)\n",
"plt.title(path)\n",
"plt.xlabel('Range (pixel)')\n",
"plt.ylabel('Azimuth (pixel')\n",
"\n",
"cb = plt.colorbar(orientation='horizontal', shrink=0.5);\n",
"cb.set_label('Elevation (m)')"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {
"image/png": {
"height": 183,
"width": 580
}
},
"output_type": "display_data"
}
],
"source": [
"path = 'geom_reference/IW3/los_01.rdr'\n",
"da = xr.open_dataarray(path, parse_coordinates=False, engine='rasterio')\n",
"\n",
"plt.imshow(da.sel(band=1), aspect=1)\n",
"plt.title(path)\n",
"plt.xlabel('Range (pixel)')\n",
"plt.ylabel('Azimuth (pixel')\n",
"\n",
"cb = plt.colorbar(orientation='horizontal', shrink=0.5);\n",
"cb.set_label('Incidence (degrees)')"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {
"image/png": {
"height": 183,
"width": 580
}
},
"output_type": "display_data"
}
],
"source": [
"path = 'geom_reference/IW3/los_01.rdr'\n",
"da = xr.open_dataarray(path, parse_coordinates=False, engine='rasterio')\n",
"\n",
"plt.imshow(da.sel(band=2), aspect=1)\n",
"plt.title(path)\n",
"plt.xlabel('Range (pixel)')\n",
"plt.ylabel('Azimuth (pixel')\n",
"\n",
"cb = plt.colorbar(orientation='horizontal', shrink=0.5);\n",
"cb.set_label('Azimuth Angle (degrees)')"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"# Alternatively, this repository includes code for customized plots:\n",
"\n",
"import customPlots as cp\n",
"# cp.plotdata('geom_reference/IW3/hgt_01.rdr', band=1,\n",
"# title='IW3: Height of Burst 1 [meter]',\n",
"# colormap='terrain')\n",
"# cp.plotdata('geom_reference/IW3/los_01.rdr', band=1,\n",
"# title='IW3: Incidence angle of Burst 1 [degrees]',\n",
"# colormap='jet')\n",
"# cp.plotdata('geom_reference/IW3/los_01.rdr', band=2,\n",
"# title='IW3: Azimuth angle of Burst 1 [degrees]',\n",
"# colormap='jet')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.4 Enhanced spectral diversity"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"!topsApp.py --start=subsetoverlaps --end=esd &> subsetoverlaps.log"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Initializing Sinc interpolator\n",
" Azimuth Carrier Poly\n",
" Range Carrier Poly\n",
" Range offsets poly\n",
" Azimuth offsets poly\n",
" Doppler poly\n",
" Reading in the image\n",
" At line 1000\n",
" Interpolating image\n",
" Elapsed time: 14.8359375 \n"
]
}
],
"source": [
"!tail subsetoverlaps.log"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true,
"hidden": true
},
"source": [
"#### 3.4.1 Step subsetoverlaps"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"Due to the large doppler frequency variation inherent to the TOPS acquisition mode, images supporting interferometry must be coregistered in the azimuth direction to better than 0.001 pixels, compared to regular stripmap data (0.1 of a pixel). While conventional cross-correlation of the amplitude works for the range direction, it does not provide sufficient accuracy for azimuth. The solution is to apply an Enhanced Spectral Diversity (ESD) approach to estimate the azimuth coregistration. In ESD processing, a double difference interferogram is made between the **reference** and **secondary** in the burst overlap region. As the range displacement is cancelled out, this interferogram will only show azimuthal motion. In absence of large ground deformation this azimuthal motion can be interpreted as an azimuthal coregistration offset. \n",
"\n",
"The following processing steps up to ESD are specific to burst overlap regions alone. \n",
"\n",
"By running the **subsetoverlaps** step, the top and bottom overlap between bursts is computed for the reference geometry. The information is then stored within the *overlaps* folder of the reference directory. \n",
"\n",
"Note, the nomenclature \"top\" and \"bottom\" can be confusing. In this case, it is **not** referring to the top and bottom of the burst. It is referring to burst *n* as \"top\" and burst *n+1* as \"bottom\", visualizing it as the earlier image being laid on top of the later image, which is on the bottom. \n",
"\n",
"![title](support_docs/esd.png)\n",
"(Figure from Fattahi et. al., SCEC)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"hidden": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"reference/overlaps/bottom_IW3.xml reference/overlaps/top_IW3.xml\n",
"\n",
"reference/overlaps/IW3:\n",
"burst_bot_01_02.slc.vrt burst_bot_03_04.slc.vrt burst_top_02_03.slc.vrt\n",
"burst_bot_01_02.slc.xml burst_bot_03_04.slc.xml burst_top_02_03.slc.xml\n",
"burst_bot_02_03.slc.vrt burst_top_01_02.slc.vrt burst_top_03_04.slc.vrt\n",
"burst_bot_02_03.slc.xml burst_top_01_02.slc.xml burst_top_03_04.slc.xml\n"
]
}
],
"source": [
"!ls reference/overlaps/*"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"For each subswath being processed, you will find a *bottom_IW[].xml* and a *top_IW[].xml* file, associated with the overlap region in the the bottom and top overlapping bursts, respectively. Like before, only a IW3 folder is present for this tutorial. If you explore the file, you will find additional information such as the FM rate and the doppler information.\n",
"```\n",
" vim reference/overlaps/bottom_IW3.xml\n",
"```\n",
"Within the *overlaps* directory you will also find for each processed subswath a folder containing the cropped SLC's for each burst overlap region. The convention for top and bottom is as follows:\n",
"- Burst_bot_01_02.slc and Burst_top_01_02.slc both refer to the same burst overlap region, where:\n",
" - Burst_bot_01_02.slc is the part of burst 2 (bot burst)\n",
" - Burst_top_01_02.slc is the part of burst 1 (top burst)\n",
"- Burst_bot_02_03.slc and Burst_top_02_03.slc both refer to the same burst overlap region, where:\n",
" - Burst_bot_02_03.slc is the part of burst 3\n",
" - Burst_top_02_03.slc is the part of burst 2\n",
"- etc...\n",
"\n",
"Though the overlap is not large, we will try to visualize the overlap by comparing:\n",
"- Burst_bot_01_02.slc and Burst_top_01_02.slc\n",
"\n",
"Can you spot the overlap with respect to the full burst?"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"hidden": true
},
"outputs": [
{
"data": {
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