smooth_wSG: Weighted Savitzky-Golay

In phenofit: Extract Remote Sensing Vegetation Phenology

Weighted Savitzky-Golay

Description

Weighted Savitzky-Golay

Usage

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

1. 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.
   

2. 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)

phenofit documentation built on Feb. 16, 2023, 6:21 p.m.