Neighborhood Gap Interpolation
Estimates missing cells from nearby valid values found within a search radius.
ẑ(p) ≈ weighted combination of nearby valid z(q)
Best used when: small holes or acquisition gaps need interpolation.
Fill Nodata / Gap Fill
Interpolate small nodata gaps from surrounding valid raster values.
Raster AnalysisEnd-User DocumentationTheory + Formula + Parameters1. What This Feature Does
Interpolate small nodata gaps from surrounding valid raster values.
Cloud workflow: choose data → configure the scientific method → run → review the resulting layer/report. No programming is required.
2. Recommended Workflow
Choose bands.
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Set maximum search distance.
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Choose optional smoothing iterations.
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Fill gaps.
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Inspect whether interpolated areas are small enough to be scientifically defensible.
3. Theory, Methods & Equations
Post-Fill Smoothing
Optionally smooths the newly interpolated surface to reduce local artifacts.
Best used when: filled continuous data appear locally rough.
4. Input Data
| Input | Type | Requirement | Description |
|---|---|---|---|
| Input Raster | raster | Required |
5. Parameters Available in the Application
| Parameter | Default | Choices / Range | How to Use It |
|---|---|---|---|
| Raster Bands | — | — | Leave empty for All bands |
| Maximum Search Distance (pixels) | 100 | 1 to — | |
| Smoothing Iterations | 0 | 0 to 20 |
6. Output & Interpretation
The result should be interpreted according to the selected method and the physical meaning of the input data. Preserve source units, coordinate reference information, NoData meaning, acquisition date, and preprocessing level when comparing results.
Scientific interpretation: an algorithm can produce a numerically valid result even when the input data are unsuitable. Always check masks, units, sensor characteristics, spatial resolution and reference data.
Important: Do not use gap filling to invent large missing regions. Interpolated values are estimates, not observations.
7. Best Practices
- Use analysis-ready inputs and remove invalid/cloud/noise artifacts that are not part of the target phenomenon.
- Choose parameters from the physical scale of the data, not only from visual appearance.
- Keep categorical and continuous rasters conceptually separate when selecting interpolation/resampling methods.
- Compare the result with the source image and independent reference information.
- Document the settings used when results will be compared across dates, sensors or study areas.