gapfill-package: Overview

In gapfill: Fill Missing Values in Satellite Data

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, [e-mail].

References

F. Gerber, R. de Jong, M. E. Schaepman, G. Schaepman-Strub, and R. Furrer (2018) in IEEE Transactions on Geoscience and Remote Sensing, pp. 1-13, doi: 10.1109/TGRS.2017.2785240.

See Also

Gapfill, Subset-Predict, Extend, Image.

gapfill documentation built on Feb. 12, 2021, 5:06 p.m.