Anomalous radiance bump at 620 nm in cloudy conditions
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- Dominant language
- Python
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Description
Our research group uses a pySAS system with HyperOCR radiometers in the Laurentian Great Lakes. After the 2025 field season, we had our radiometers calibrated and characterized at the University of Tartu. When re-processing the 2025 data, we still observed the anomalous radiance bumps around 620 nm that had previously been brought up in #345. They only seem to appear in the more oligotrophic waters that we sample in and only when the L_sky sensor shows cloud contamination.
We have processed the data with HyperCP using both the class-based and sensor-specific processing regimes, and the feature is visible in both with the sensor-specific processing appearing slightly worse. Here is an example showing the derived reflectance using the class-based approach (CS pySAS), the sensor-specific approach (SS pySAS), and the reflectance from our handheld radiometer (ASD FieldSpec 4) at one discrete station from April 2025.
I have put the raw file, HyperCP config file, and .sip used as inputs to HyperCP, as well as the generated reports for both class-based and sensor-specific runs, in a Drive folder available here:
https://drive.google.com/drive/folders/1uGs-wDUiU2q2sdMpnJAMD0bxZLPdJ0HO?usp=sharing
Thanks,
Karl
Contributor guide
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the supplied raw file, HyperCP configuration, .sip input, and generated reports, then compare the class-based and sensor-specific results around 620 nm under cloud-contaminated L_sky conditions. Review issue #345 for prior investigation and use the ASD FieldSpec 4 comparison as the reference; done means identifying the processing cause and confirming the anomalous bump is resolved or consistently explained in both regimes.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 45/100