sits (version 0.10.0)

sits_whittaker: 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 = 1, bands_suffix = "wf")

Arguments

data

A tibble with time series data and metadata.

lambda

Smoothing factor to be applied (default 1.0).

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

# NOT RUN {
# Retrieve a time series with values of NDVI
data(point_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))
# }