| sits_whittaker {sits} | R Documentation |
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.
sits_whittaker(data = NULL, lambda = 0.5, bands_suffix = "wf")
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"). |
A tibble with smoothed sits time series.
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.
# 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))