Step 1: Install Required Libraries Ensure gstools and numpy are installed in QGIS's Python environment: pip install gstools numpy

Step 2: Create the Python Script Save the following code as variogram_kriging.py in your QGIS Processing scripts directory (e.g., ~/.local/share/QGIS/QGIS3/profiles/default/processing/scripts/)

Step 3: Using the Tool in QGIS Open QGIS and go to the Processing Toolbox.

Navigate to Scripts > Tools and find the script named "Variogram Modeling & Ordinary Kriging".

Input Parameters:

• Input point layer: Select your point vector layer.
• Field to interpolate: Choose the numeric field for interpolation.
• Input polygon layer: Polygon Boundary (Only interpolates within polygon area)
• Variogram model type: Select the desired model (e.g., spherical).
• Lag distance: Set the distance between bins. (max_dist = lag_distance * n_lags)
• Number of lags: Define how many lag bins to use.
• Extent: Specify the output raster's extent.
• Cell size: Set the resolution of the output raster.
• Output raster: Choose the output file path.

Dependencies: Ensure gstools and numpy are installed in your QGIS Python environment.

CRS Warning: Use a projected CRS (in meters) for accurate distance measurements.

Performance: Large datasets or small cell sizes may increase processing time.

This tool automates variogram modeling and kriging within QGIS, providing a user-friendly interface for spatial interpolation.

1. Input Validation:

• Ensure your point layer has ≥4 points with meaningful variation
• Confirm the polygon covers part of the grid

2. Performance Tips:

• Start with coarse cell sizes (e.g., 100m) for large areas
• Use spherical/exponential models for better convergence

3. Troubleshooting:

• Check QGIS Log for model parameters
• Verify output raster statistics in Properties > Information

GSTools

SciKit GStat