Fill Missing Values in Satellite Data

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.

Get started

The package can be installed with

R> install.packages("gapfill")

To get started the help pages

R> ?Gapfill

News

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.

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("gapfill")

0.9.6 by Florian Gerber, 3 months ago


https://git.math.uzh.ch/florian.gerber/gapfill


Report a bug at https://git.math.uzh.ch/florian.gerber/gapfill/issues


Browse source code at https://github.com/cran/gapfill


Authors: Florian Gerber


Documentation:   PDF Manual  


Task views: Handling and Analyzing Spatio-Temporal Data


GPL (>= 2) license


Imports fields, foreach, Rcpp, quantreg

Depends on ggplot2

Suggests roxygen2, spam, testthat, abind

Enhances raster, doParallel, doMPI

Linking to Rcpp


See at CRAN