Tools to fill missing values in satellite data and to develop new gap-fill algorithms. The methods are tailored to data (images) observed at equally-spaced points in time. The package is illustrated with MODIS NDVI data.

The package provides tools to fill missing values in satellite data. It can be used to gap-fill, e.g., MODIS NDVI data, and is helpful for the development of new gap-fill algorithms. The predictions are based on a subset-predict procedure, i.e., each missing value is predicted separately by (1) subsetting the data to a neighborhood around it and (2) predict the values based on that subset. * Gap-filling can be executed in parallel. * Users may define Subset and Predict functions and run alternative prediction algorithms with little effort. See ?Extend for more information and examples. * The visualization of space-time data is simplified through the ggplot2 based function Image.

The package can be installed with

R> install.packages("gapfill")

To get started see the example in

R> ?Gapfill

version 0.9.6

- updated citation information.
- bug fix: in former versions of Gapfill the error 'Error in rq.fit.br(x, y, tau = tau, ...) : Singular design matrix' occurred if a subset used for the prediction contained very few observed values. This error does no longer stop the gap-filling. See the new argument 'qrErrorToNA' in ?Predict.
- it is now also possible to predict observed values. See the description of the 'subset' argument in ?Gapfill.

version 0.9.5-3

- new case-sensitifity of message test with testthat is now respected.

version 0.9.5-2

- dependency on ggplot2 version 2.2.1 is now correctly set.

version 0.9.5

- improved version of the Image() plotting function.

version 0.9.3

- first CRAN release.