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Filter time series with Savitzky-Golay filter

Source: R/sits_filters.R
sits_sgolay.Rd

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).

Usage

sits_sgolay(data = NULL, order = 3L, length = 5L)

Arguments

data

Time series or matrix.

order

Filter order (integer).

length

Filter length (must be odd).

Value

Filtered time series

References

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

Author

Rolf Simoes, rolfsimoes@gmail.com

Gilberto Camara, gilberto.camara@inpe.br

Felipe Carvalho, felipe.carvalho@inpe.br

Examples

if (sits_run_examples()) {
    # Retrieve a time series with values of NDVI
    point_ndvi <- sits_select(point_mt_6bands, bands = "NDVI")

    # Filter the point using the Savitzky-Golay smoother
    point_sg <- sits_filter(point_ndvi,
        filter = sits_sgolay(order = 3, length = 5)
    )
    # Merge time series
    point_ndvi <- sits_merge(point_ndvi, point_sg, suffix = c("", ".SG"))

    # Plot the two points to see the smoothing effect
    plot(point_ndvi)
}