isce-framework / isce-framework/isce3

Will Faraday Rotation correction be applied?

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Python
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Description

As of ISCE3 v0.25.14, the faradayRotation metadata is still populated with the hardcoded value 0.0 (RSLC and derived products, including quad-pol):

https://github.com/isce-framework/isce3/blob/67f08784a6768fca4ea5aeb6d4c77d2e731c8051/python/packages/nisar/products/writers/SLC.py#L1104-L1105

Products Affected: RSLC (root cause), GCOV, GSLC (propagated)

Test Granules Checked:
Granules checked were produced by mission operations with CRID 05016; these confirm that metadata is populated with 0.0:

NISAR_L1_PR_RSLC_009_148_A_024_2005_DHDH_M_20260107T105830_20260107T105839_X05016_P_P_J_001
NISAR_L2_PR_GSLC_001_149_D_080_2005_QPDH_A_20251003T130952_20251003T131016_X05016_N_P_J_001
NISAR_L2_PR_GCOV_001_149_D_080_2005_QPDH_A_20251003T130952_20251003T131016_X05016_N_P_J_001

Location:

  • RSLC: /science/LSAR/RSLC/metadata/calibrationInformation/frequency[A|B]/faradayRotation
  • GCOV: /science/LSAR/GCOV/metadata/calibrationInformation/frequency[A|B]/faradayRotation
  • GSLC: /science/LSAR/GSLC/metadata/calibrationInformation/frequency[A|B]/faradayRotation

Related tools:
It looks like some faraday rotation correction tools already exist in ISCE3, but are separate workflows:

cc: @hfattahi @rad-eng-59 @bhawkins @gshiroma

Contributor guide

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First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with the hardcoded faradayRotation value in python/packages/nisar/products/writers/SLC.py and read the related workflows in python/packages/nisar/workflows/faraday_rot_angle_from_rslc.py and python/packages/nisar/cal/faraday_rotation_angle_slc.py. Determine how the existing estimators should connect to RSLC, GCOV, and GSLC generation; done means the affected metadata contains the applicable correction rather than the hardcoded 0.0.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
data
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Quiet
Clarity
Needs clarification
Newbie friendliness
35/100

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