R: Smooth the time series using Savitsky-Golay filter

sits_sgolay {sits}

R Documentation

Smooth the time series using Savitsky-Golay filter

Description

An optimal polynomial for warping a time series. The degree of smoothing depends on the filter order (usually 3.0). The order of the polynomial uses the parameter 'order' (default = 3), the size of the temporal window uses the parameter 'length' (default = 5), and the temporal expansion uses the parameter 'scaling'.

Usage

sits_sgolay(
  data = NULL,
  order = 3,
  length = 5,
  scaling = 1,
  bands_suffix = "sg"
)

Arguments

`data`	A tibble with time series data and metadata.
`order`	Filter order (integer).
`length`	Filter length (must be odd)
`scaling`	Time scaling (integer).
`bands_suffix`	Suffix to be appended to the smoothed filters.

Value

A tibble with smoothed sits time series.

References

A. Savitzky, M. Golay, "Smoothing and Differentiation of Data by Simplified Least Squares Procedures". Analytical Chemistry, 36 (8): 1627–39, 1964.

Examples

#' # Retrieve a time series with values of NDVI
point_ndvi <- sits_select(point_mt_6bands, bands = "NDVI")
# Filter the point using the Savitsky Golay smoother
point_sg <- sits_filter(point_ndvi, sits_sgolay(order = 3, length = 5))
# Plot the two points to see the smoothing effect
plot(sits_merge(point_ndvi, point_sg))

[Package sits version 0.14.0 Index]