sits (version 0.11.0)

sits_sgolay: 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<U+2013>39, 1964.

Examples

# NOT RUN {
#' # 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))

# }