JSON file catalog

SpectralBridge uses JSON for three different jobs: installed scientific parameters, run-specific correction/QA records, and validation or governance evidence. They should not be edited or interpreted in the same way.

This catalog documents active JSON files without inserting new keys into runtime schemas that existing loaders may not recognize.

Packaged scientific parameters

Installed code reads these files from src/spectralbridge/data/. This location is authoritative.

File	What it controls	Units and structure	Loaded by	How to validate a change
src/spectralbridge/data/landsat_band_parameters.json	Target sensor band centers and Gaussian full widths at half maximum used for convolution/resampling	Nanometers; every sensor entry has equal-length wavelengths and fwhms arrays	standard_resample.py, pipeline convolution, sensor-panel plots	Run resampling/convolution tests; inspect output band count, wavelengths, support, and QA
src/spectralbridge/data/hyperspectral_bands.json	Reference hyperspectral wavelength axis for sensor-panel visualization	Nanometers in one ordered bands array	sensor_panel_plots.py	Confirm ordering, numeric values, and expected panel wavelength range
src/spectralbridge/data/brightness/landsat_to_micasense.json	Percent brightness adjustment coefficients for the general Landsat-to-MicaSense comparison	system_pair, human-readable description, unit: percent, and string band indices	brightness_config.py	Run tests/test_brightness_coefficients.py; inspect the convolution stage's brightness*.png fitted-versus-configured coefficient profiles
src/spectralbridge/data/brightness/landsat_tm_etm_to_micasense.json	Percent coefficients for the TM/ETM+-specific comparison	Wavelength-aligned Landsat-like order; not native Landsat numbering	brightness_config.py	Run coefficient tests and the Python brightness_correction_metrics/render_brightness_diagnostics workflow before accepting scientific changes

The same band-definition filenames under repository-root data/ are example/notebook copies. Installed package code does not load those copies. If a scientifically reviewed value changes, update both locations deliberately and keep the packaged file authoritative.

Example run configurations

These are intentionally self-documenting and are consumed only by the wrappers in examples/. Their about blocks are explanatory metadata; the wrappers pass only the nested pipeline object to SpectralBridge.

File	Purpose	Run safely
examples/config/neon_pipeline.example.json	Complete NEON download-through-QA run	python examples/run_neon_pipeline.py --check
examples/config/drone_pipeline.example.json	Local drone HDF5 correction/extraction/QA run	python examples/run_drone_pipeline.py --check

Copy an example before adapting it for a study. Record the copy with the output or analysis repository so parameters remain reproducible.

Validation and transparency records

File	Meaning	Edit policy
validation/campaigns/neon-live-100.example.json	A proposed live campaign specification and resource checklist	Populate deliberately before a live campaign; it is not evidence that runs occurred
validation/results/offline-contract.json	Generated observations from deterministic offline function contracts	Treat as generated evidence; regenerate with scripts/run_validation_campaign.py
docs/ai-transparency.json	Generated machine-readable summary of PROMPT_LOG.md	Do not hand edit; regenerate with scripts/generate_ai_transparency.py

JSON produced by a pipeline run

These files normally do not live in the repository because they belong to one flightline or run.

Filename pattern	Meaning	Consumer / check
*_brdfandtopo_corrected_envi.json	Run-specific geometry, masks, paths, and correction parameters used to create the corrected ENVI pair	stage_apply_brdf_topo_correction; must parse and match the flightline
*_brdf_model.json	Fitted BRDF coefficient model for the scene	Correction implementation and diagnose_brdf_topo_stage.py
*_qa.json	Machine-readable diagnostic summary paired with the QA PNG	Users, tests, publication/validation review
drone_qa_summary.json	Batch-level drone provenance, status counts, paths, and failure details	Drone users and QA review

Run-specific JSON is evidence. Keep it next to the artifacts it describes and do not reuse it for a different flightline merely because the filename looks similar.

Before changing any coefficient or band definition

1. Identify the authoritative packaged JSON and the Python loader above.
2. Record where the proposed value came from and its units.
3. Confirm band indices are wavelength-aligned rather than assumed native sensor numbers.
4. Add or update a focused test.
5. Run the related convolution or coefficient plot and inspect the QA output.
6. Keep the old and new values traceable in version control.

Changing scientific JSON is a scientific-method change, not a formatting task.