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 Learn More