Raster Calculator

Perform raster algebra using one or more raster/band variables. Inputs are aligned to a reference grid before the mathematical expression is evaluated.

Shared Raster ToolsEnd-User DocumentationTheory + Formula + Parameters

1. What This Feature Does

Perform raster algebra using one or more raster/band variables. Inputs are aligned to a reference grid before the mathematical expression is evaluated.

Cloud workflow: choose data → configure the scientific method → run → review the resulting layer/report. No programming is required.

2. Recommended Workflow

Add raster variables/aliases. ↓ Map variable names to raster bands. ↓ Choose the reference raster/grid. ↓ Write the expression. ↓ Choose resampling for alignment. ↓ Run and inspect nodata/invalid math.

3. Theory, Methods & Equations

Raster Algebra

Evaluates the same mathematical expression independently for every aligned pixel.

Y(x,y)=f(A(x,y),B(x,y),…)
NDVI=(NIR−Red)/(NIR+Red)Example expression.
Best used when: a custom index, conditional transform, ratio, difference or multi-raster formula is required.

4. Input Data

InputTypeRequirementDescription
Input RastersrasterRequiredSelect one or more raster layers. Variables such as red, nir, b1, r1_red, r1_b1, r2_b1, etc. are created automatically.

5. Parameters Available in the Application

ParameterDefaultChoices / RangeHow to Use It
Code——NumExpr raster expression. Example: (nir - red) / (nir + red).

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: Division by zero, invalid square roots/logarithms, nodata, and misaligned inputs need careful handling. Choose Nearest for classes and continuous interpolation for continuous variables.

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.
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