Weighted Savitzky-Golay

smooth_wSG(
  y,
  w,
  nptperyear,
  ylu,
  wFUN = wTSM,
  iters = 2,
  frame = floor(nptperyear/5) * 2 + 1,
  d = 2,
  ...
)

Arguments

y: Numeric vector, vegetation index time-series
w: (optional) Numeric vector, weights of y. If not specified, weights of all NA values will be wmin, the others will be 1.0.
nptperyear: Integer, number of images per year.
ylu: (optional) [low, high] value of time-series y (curve fitting values are constrained in the range of ylu.
wFUN: weights updating function, can be one of 'wTSM', 'wChen' and 'wBisquare'.
iters: How many times curve fitting is implemented.
frame: Savitzky-Golay windows size
d: polynomial of degree. When d = 1, it becomes moving average.
...: Additional parameters are passed to wFUN.

Value

ws: weights of every iteration
zs: curve fittings of every iteration

References

Chen, J., J\"onsson, P., Tamura, M., Gu, Z., Matsushita, B., Eklundh, L., 2004. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sens. Environ. 91, 332-344. https://doi.org/10.1016/j.rse.2004.03.014.
https://en.wikipedia.org/wiki/Savitzky%E2%80%93Golay_filter

Examples

library(phenofit)
data("MOD13A1")
dt <- tidy_MOD13(MOD13A1$dt)
d <- dt[site == "AT-Neu", ]

l <- check_input(d$t, d$y, d$w, nptperyear=23)
r_wSG <- smooth_wSG(l$y, l$w, l$ylu, nptperyear = 23, iters = 2)