calval-RTC

Tools for validating OPERA RTC products.

OPERA RTC Product Information

OPERA Validation Plan

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Create the opera_calval_rtc Conda Environment

If you are working locally:

• Make sure you have conda/mamba installed
  • Instructions to install miniforge (containing conda and mamba)
• Create the opera_calval_rtc conda environment using one of the yaml files in the environments directory
  • In a terminal, run:
    • mamba env create -f path/to/calval-RTC/environment/jlab4_environment.yaml
      • Use calval-RTC/environment/jlab3_environment.yaml if you'd like to use Jupyter Lab~=3.0 instead of Jupyter Lab~=4.0
• Activate your environment and start Jupyter Lab.
  • In a terminal run:
    • cd path/to/calval-RTC
    • conda activate opera_calval_rtc
    • python -m ipykernel install --user --name opera_calval_rtc
    • jupyter lab

If you are working in OpenSARLab:

• create the opera_calval_rtc conda environment using environment/osl_environment.yaml
  • Use the ~/conda_environments/Create_OSL_Conda_Environments.ipynb notebook to create the environment

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Start Jupyter Lab in the opera_calval_rtc Environment

• In a terminal, run:
  • cd path/to/calval-RTC
  • conda activate opera_calval_rtc
  • python -m ipykernel install --user --name opera_calval_rtc
  • jupyter lab

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Bulk Validation (Recommended)

• Launch the Bulk_Validation.ipynb notebook.
• Depending on where you run the notebook (locally, in OpenSARLab, or on another JupyterHub), you may need to select the kernel for the conda environment after launching the notebook
  • Using the kernel selector at the top right of your notebook Jupyter Lab tab or the Change Kernel option in the Kernel menu, select opera_calval_rtc
• Run the notebook
  • The notebook will prompt you to select a validation module, validation site, and Sentinel-1 orbital path
    • It will download the needed data and perform validation on every available scene for a given module, site, and orbital path
    • All output will be stored at the path: ../OPERA_L2-RTC_CalVal

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Piecemeal Validation (Use to validate specific scenes or batches of scenes)

CalVal Data Access

Option 1: Run The Notebook

Run the OPERA_RTC_download_reproject_mosaic_sample_bursts.ipynb notebook to create RTC and static file mosaics as inputs to CalVal modules.

The notebook:

• Inputs a Sentinel-1 scene ID
• Identifies potential bursts associated with the scene
• Searches s3://opera-pst-rs-pop1/products/RTC_S1 for bursts
• Filters discovered bursts for the most recent, 2nd to most recent, etc... batch of samples
• Downloads VV, VH, incidence angle map, local incidence angle map, and layover/shadow mask
• Projects all bursts in scene to predominant UTM (if necessary)
• Merges all bursts and saves mosaics
• Deletes bursts

Option 2: Run as a Script with Papermill

Run the OPERA_RTC_download_reproject_mosaic_sample_bursts.ipynb notebook on a list of Sentinel-1 scenes using the papermill_OPERA_RTC_download_reproject_mosaic_sample_bursts.py Python script.

• Update the scenes list in the script to contain the desired scene IDs
• Update opera_dir to the directory in which you would like to store your RTC directories
• Update keep_date_index to target a given batch of samples
  • 0: oldest sample, -1: most recent sample, -2: 2nd to most recent sample, etc...
• In a terminal, run the following commands:
  1. conda activate opera_calval_rtc
  2. python path/to/papermill_OPERA_RTC_download_reproject_mosaic_sample_bursts.py

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Point Target-Based Absolute Geolocation Evaluation Module

1. Download and mosaic OPERA RTC sample burst data for a given Sentinel-1 scene using the notebook OPERA_RTC_download_reproject_mosaic_sample_bursts.ipynb or the Python script papermill_OPERA_RTC_download_reproject_mosaic_sample_bursts.py (see above instructions)
2. Run the notebook to evaluate absolute geolocation on a single scene or the Python script to evaluate multiple scenes (instructions follow)

Option 1: Run the Absolute Geolocation Validation Notebook:

Run absolute_geolocation_evaluation/ALE_OPERA-RTC.ipynb notebook

The notebook:

• Inputs a path to the directory holding an RTC mosaic
• Creates an output directory named absolute_geolocation_{S1 scene ID}
• Deletes all mosaics except VV polarization, which is moved to the output directory
• Identifies corner reflector site intersecting data
• Downloads corner reflector data
• Filters corner reflectors, removing those facing away from the sensor
• Calculates the geolocation error in Northing and Easting
• Removes corner reflectors that fall on the edge of a pixel
• Plots the error
• Appends results to a CSV file

Option 2: Run as a Script with Papermill:

Run the absolute_geolocation_evaluation/ALE_OPERA-RTC.ipynb notebook on a list of paths to OPERA RTC mosaics using the calval-RTC/absolute_geolocation_evaluation/papermill_absolute_geolocation_evaluation.py Python script

• Update the data_dirs list in the script with paths to directories containing OPERA RTC mosaics
• In a terminal, run the following commands:
  1. conda activate opera_calval_rtc
  2. python path/to/absolute_geolocation_evaluation/papermill_absolute_geolocation_evaluation.py

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Cross-Correlation-Based Relative Geolocation Evaluation Module

1. Download and mosaic a OPERA RTC sample burst data for a stack of Sentinel-1 scenes using the notebook OPERA_RTC_download_reproject_mosaic_sample_bursts.ipynb or the Python script papermill_OPERA_RTC_download_reproject_mosaic_sample_bursts.py (see above instructions)
2. Run the notebook to evaluate relative geolocation between scenes with a cross-correlation approach (instructions follow)

Option 1: Run the Cross-Correlation-Based Relative Geolocation Evaluation Notebook:

Run the coregistration/coregistration.ipynb notebook

The notebook:

• Buffers all scenes in the stack so they have the same extents
• Remove the top and bottom 1% Gamma0 values
• Divide each scene into 64 tiles
• Perform phase cross correlation on:
  • nearest neighbors
    • scene1 scene2, scene2 scene3, scene3 scene4, etc...
  • first and last scene in stack
  • nearest 4th neighbor scenes
    • scene1 scene4, scene4 scene8, scene8 scene12, etc...
• Plot results
• Write results to CSV

**Option 2: Run as a Script With Papermill

Run the coregistration/papermill_coregistration.py script on multiple RTC stacks

1. Update stack_dirs, adding paths to the directories holding RTC stacks
2. Update delete_mosaics

• True: deletes input data
• False: saves input data

1. Update cleanup_list to save or delete intermediate data

• uncomment data products to delete

1. Save changes to script
2. Run script in opera_calval_rtc conda environment
   1. Open a terminal and run the following commands
   2. conda activate opera_calval_rtc
   3. Python path/to/coregistration/papermill_coregistration.py

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3) Radiometric Terrain Flattening Performance Evaluation Module: Gamma Naught Comparisons of Foreslope, Flat, and Backslope Pixels in Forested Regions

1. Download and mosaic OPERA RTC sample burst data for a given Sentinel-1 scene using the notebook OPERA_RTC_download_reproject_mosaic_sample_bursts.ipynb or the Python script papermill_OPERA_RTC_download_reproject_mosaic_sample_bursts.py (see above instructions)

Option 1: Run the 3 Data Prep Notebooks and the Slope Comparison Notebook

1. Prepare the data for the analysis notebook by running the following 3 data prep notebooks
   1. flattening/data_prep/prep_flattening_part_1.ipynb
   • Downloads Copernicus Global Land Cover (100m) tiles, projects them to OPERA RTC's UTM, mosaics them, and subsets mosaic to OPERA RTC extents
   3. flattening/data_prep/prep_flattening_part_2.ipynb
   • Creates geotiffs for each polarization and slope. All non-forested pixels are masked and sets of tiffs are produced for each polarization containing only foreslope pixels, backslope pixels, or flat pixels.
   4. flattening/data_prep/Prep_OPERA_RTC_CalVal_Slope_Compare_Part_3.ipynb
   • Create MGRS tiles for each prepared geotiff
2. Run analysis notebook
   1. flattening/flattening_analysis/flattening_analysis.ipynb

Option 2: Run All Four Notebooks with a Script Using Papermill

Run flattening/papermill_flattening.py to run all four notebooks on multiple RTCs

• Update data_dirs, adding paths to directories holding RTCs
• Update log to set log or power scale
• Update save to save output plots as PNGs, or not

1. Save changes to script
2. Run script in opera_calval_rtc conda environment
   1. Open a terminal and run the following commands
   2. conda activate opera_calval_rtc
   3. Python path/to/flattening/papermill_flattening.py