R: Filter the time series using Whittaker smoother

sits_whittaker {sits} | R Documentation

## Filter the time series using Whittaker smoother

### Description

The algorithm searches for an optimal warping polynomial. The degree of smoothing depends on smoothing factor lambda (usually from 0.5 to 10.0). Use lambda = 0.5 for very slight smoothing and lambda = 5.0 for strong smoothing.

### Usage

    sits_whittaker(data = NULL, lambda = 0.5, bands_suffix = "wf")

### Arguments

`data` | A tibble with time series data and metadata.
`lambda` | Smoothing factor to be applied (default 0.5).
`bands_suffix` | Suffix to be appended (default "wf").

### Value

A tibble with smoothed sits time series.

### References

Francesco Vuolo, Wai-Tim Ng, Clement Atzberger, "Smoothing and gap-filling of high resolution multi-spectral timeseries: Example of Landsat data", Int Journal of Applied Earth Observation and Geoinformation, vol. 57, pg. 202-213, 2107.

### Examples

    # Retrieve a time series with values of NDVI
    point_ndvi <- sits_select(point_mt_6bands, bands = "NDVI")
    # Filter the point using the whittaker smoother
    point_whit <- sits_filter(point_ndvi, sits_whittaker(lambda = 3.0))
    # Plot the two points to see the smoothing effect
    plot(sits_merge(point_ndvi, point_whit))

[Package sits version 0.12.0 Index]