Overview

Description

The package provides tools to fill-in missing values in satellite data. It can be used to gap-fill, e.g., MODIS NDVI data and is helpful when developing new gap-fill algorithms. The methods are tailored to data (images) observed at equally-spaced points in time. This is typically the case for MODIS land surface products and AVHRR NDVI data, among others.
The predictions of the missing values are based on a subset-predict procedure, i.e., each missing value is predicted separately by (1) selecting subsets of the data that are in a neighborhood around the missing point and (2) predicting the missing value based on the subset.
The main function of the package is Gapfill.

Features

• Gap-filling can be executed in parallel.

• Users may define new Subset and Predict functions and run alternative prediction algorithms with little effort. See Extend for more information and examples.

• Visualization of space-time data are simplified through the ggplot2-based function Image.

Author(s)

Florian Gerber, florian.gerber@math.uzh.ch.

References

F. Gerber, R. Furrer, G. Schaepman-Strub, R. de Jong, M. E. Schaepman, 2016, Predicting missing values in spatio-temporal satellite data. http://arxiv.org/abs/1605.01038.

See Also

Gapfill, Subset-Predict, Extend, Image.

Results