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Temporal gapfilling for image time series

Introduction

Gapfilling for time series replaces invalid pixels (as designated by a mask) by an interpolation using the valid dates of the series.

Linear gapfilling

  1. Linear interpolation of invalid pixels using the 2 (before and after) closest valid pixels.
  2. At the beginning (resp. the end) just replicate the first (resp. the last) valid pixel.

Spline gapfilling

Depending on the number of valid dates in the temporal profile, the interpolation will be performed differently. All algorithms use the GNU/GSL library.

  • Less than 3 valid dates will apply linear interpolation
  • With 3 or 4 valid dates, cubic splines with natural boundary conditions will be used. The resulting curve is piecewise cubic on each interval, with matching first and second derivatives at the supplied data-points. The second derivative is chosen to be zero at the first point and last point.
  • With more than 4 valid dates, a non-rounded Akima spline with natural boundary conditions is used.