r.fillnulls fills NULL pixels (no data areas) in input map and stores filled map to a new output map. The fill data are interpolated from the no data area boundaries buffer using or interpolation.


Each area boundary buffer is set to three times the map resolution to get nominally three points around the edge. This way the algorithm interpolates into the hole with a trained slope and curvature at the edges, in order to avoid that such a flat plane is generated in a hole.

During the interpolation following warning may occur when using the RST method:

Warning: strip exists with insufficient data
Warning: taking too long to find points for interpolation--please change the region to area where your points are

This warning is generated if large data holes exist within the surface. As the idea of r.fillnulls is to fill such holes, the user may ignore the warning. The interpolation will be continued. However, the user may pay attention to below notes.


When using the default RST method, the algorithm is based on regularized splines with tension interpolation module which interpolates the raster cell values for NULL data areas from the boundary values of the NULL data area. An eventual raster MASK is respected during the NULL data area(s) filling. The interpolated values are patched into the NULL data area(s) of the input map and saved into a new raster map. Otherwise, either the bilinear or bicubic method can be selected (based on


Depending on the shape of the NULL data area(s) problems may occur due to an insufficient number of input cell values for the interpolation process. Most problems will occur if a NULL data area reaches a large amount of the map boundary. The user will have to carefully check the result using r.mapcalc (generating a difference map to the input map and applying the "differences" color table with r.colors) and/or d.what.rast to query individual cell values.


In this example, the SRTM elevation map in the North Carolina sample dataset location is filtered for outlier elevation values; missing pixels are then re-interpolated to obtain a complete elevation map:
g.region rast=elev_srtm_30m -p
d.mon x0
d.histogram elev_srtm_30m
# remove too low elevations (esp. lakes)
r.mapcalc "elev_srtm_30m_filt = if(elev_srtm_30m < 50.0, null(), elev_srtm_30m)"
d.histogram elev_srtm_30m_filt
d.rast elev_srtm_30m_filt
r.fillnulls input=elev_srtm_30m_filt output=elev_srtm_30m_complete tension=20
d.histogram elev_srtm_30m_complete
d.rast elev_srtm_30m_complete


r.fill.dir, r.mapcalc,,


Mitas, L., Mitasova, H., 1999, Spatial Interpolation. In: P.Longley, M.F. Goodchild, D.J. Maguire, D.W.Rhind (Eds.), Geographical Information Systems: Principles, Techniques, Management and Applications, Wiley, pp.481-492

Mitasova H., Mitas L.,  Brown W.M.,  D.P. Gerdes, I. Kosinovsky, Baker, T.1995, Modeling spatially and temporally distributed phenomena: New methods and tools for GRASS GIS. International Journal of GIS, 9 (4), special issue on Integrating GIS and Environmental modeling, 433-446.

Mitasova H. and Mitas L. 1993: Interpolation by Regularized Spline with Tension: I. Theory and Implementation, Mathematical Geology 25, 641-655.

Mitasova H. and Hofierka L. 1993: Interpolation by Regularized Spline with Tension: II. Application to Terrain Modeling and Surface Geometry Analysis, Mathematical Geology 25, 657-667.


r.fillnulls: Markus Neteler, University of Hannover and Fondazione Edmund Mach

and authors of
Improvement by Hamish Bowman, NZ

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