ds.rst.krige() or Raster.krige()

ds.rst.krige() or Raster.krige()#

RasterBase.krige(variogram, ref=None, *, backend='gstools', max_overlap=None, exact=True, pseudo_inverse=True, mp_config=None, error_structure=None)[source]#

Interpolate raster values by ordinary kriging on this grid or a reference grid.

The longest fitted variogram range defines how far GeoUtils searches for source cells. max_overlap can limit that distance and the extra source data read around chunk edges. Repeated raster neighborhoods reuse previously solved weights, while edges and missing-data patterns get weights for their exact source cells.

Parameters:

  • variogram (Variogram | VariogramModel) – Fitted GeoUtils variogram used to calculate covariance.

  • ref (RasterBase | DataArray | None) – Optional destination raster grid. By default, interpolate on this raster’s own grid.

  • backend (Literal['gstools', 'gpytorch']) – Library used to solve the kriging system, "gstools" or "gpytorch".

  • max_overlap (float | None) – Optional physical-distance cap on the modeled decorrelation range.

  • exact (bool) – Return a source value exactly when a target has the same coordinates.

  • pseudo_inverse (bool) – Let GSTools use a pseudo inverse when source coordinates repeat or are redundant.

  • mp_config (MultiprocConfig | None) – Optional multiprocessing output configuration.

  • error_structure (ErrorStructure | None) – Optional observation errors added to the covariance used for the kriging fit.

Return type:

RasterType

Returns:

Kriged raster. Use geoutils.uncertainty.propagate() to calculate output uncertainty.