PRO Production

Workflow-grade Pro analysis with audit-ready outputs.

workflow pro

## Workflow Narrative

Surface Reflectance Consistency Analysis

### Problem It Solves

Are directional and terrain illumination effects sufficiently normalized for reliable scene-to-scene reflectance comparison?

### Who It Is For

- Optical remote sensing teams preparing cross-scene reflectance products for trend/change workflows.

### Primary User

EO product teams, environmental mapping agencies, and analytics groups with multi-date reflectance pipelines.

### What It Does

- Harmonizes reflectance across dates and sensors after terrain correction.
- Quantifies normalization magnitude and confidence for QA-aware downstream analysis.
- Packages reflectance consistency as a reproducible workflow-stage product.

### How It Works

- Runs an OSS terrain-correction prep stage when the input scene is still topographically distorted.
- Derives normalization delta from pre/post correction residual magnitude.
- Computes consistency confidence using exponential damping of large correction residuals.
- Indicative formula: $C = \exp(-\Delta / s) \cdot Q$, where $\Delta$ is normalization delta and $Q$ is quality confidence.

### Why It Wins

- Couples normalization output with explicit delta and confidence diagnostics, enabling QA-aware downstream acceptance rules.

### Typical Buying Trigger

Teams observe inconsistent reflectance behavior across acquisition geometries and need a reproducible consistency gate.

### Typical Presets

- fast: quicker normalization for large-area throughput.
- balanced: default quality/speed tradeoff.
- conservative: stronger correction confidence thresholding.

## Inputs

Parameter | Optional | Description
--- | --- | ---
input_red, input_nir, input_dem | no | Core optical + terrain inputs used for topographic/reflectance normalization workflows.
solar_zenith_deg, solar_azimuth_deg | no | Solar geometry parameters used to model illumination and terrain incidence effects.
optional input_green | yes | Optional green band used by workflows that include green-channel diagnostics.
profile: fast | balanced | conservative | no | Processing profile controlling sensitivity, quality strictness, and runtime tradeoffs.

## Outputs

Parameter | Type | Description
--- | --- | ---
brdf_normalized_reflectance | GeoTIFF | BRDF-normalized reflectance raster with improved angular consistency.
normalization_delta | GeoTIFF | Difference layer showing magnitude of BRDF normalization adjustments.
consistency_confidence | GeoTIFF | Confidence surface indicating reflectance consistency after normalization.
summary | JSON | Machine-readable summary report containing run metadata, QA diagnostics, and key metrics.
html_report | HTML | Human-readable customer-facing report generated from the summary contract for stakeholder review and QA traceability.

## Python Example

    import whitebox_workflows as wbw

    wbe = wbw.WbEnvironment(include_pro=True, tier="pro")

    normalized, delta, confidence, summary = wbe.brdf_surface_reflectance_consistency(
        input_red="data/red.tif",
        input_nir="data/nir.tif",
        input_dem="data/dem.tif",
        input_green="data/green.tif",
        solar_zenith_deg=40.0,
        solar_azimuth_deg=165.0,
        profile="balanced",
        output_prefix="output/brdf_consistency",
    )

    print(normalized)
    print(delta)
    print(confidence)
    print(summary)

## License Notice

Use of this function requires a license for Whitebox Workflows Professional (WbW-Pro). Please visit www.whiteboxgeo.com to purchase a license.

## Project Links

WbW Homepage User Manual Support WbW