Extract Vegetation Phenology at site scale

process_phenofit(
  y,
  t,
  w,
  QC_flag = NULL,
  nptperyear = 36,
  brks = NULL,
  rFUN = "smooth_wWHIT",
  wFUN = wTSM,
  lambda = NULL,
  TRS = c(0.1, 0.2, 0.5, 0.6, 0.8, 0.9),
  iters = 2,
  maxExtendMonth = 12,
  minExtendMonth = 0.5,
  minPercValid = 0,
  south = FALSE,
  verbose = TRUE,
  methods = c("AG", "Zhang", "Beck", "Elmore", "Gu"),
  outfile = NULL,
  title = NULL,
  ylab = "EVI",
  yticks = NULL,
  IsPlot.brks = FALSE,
  write.fig = TRUE,
  show = FALSE,
  ymin = 0.1,
  wmin = 0.1,
  wsnow = 0.8,
  use.y0 = FALSE,
  alpha = 0.02,
  cex = 1.5,
  overwrite = FALSE,
  .v_curve = FALSE,
  run.curvefit = TRUE,
  ...
)

Arguments

y	Numeric vector, vegetation index time-series
t	Numeric vector, `Date` variable
w	(optional) Numeric vector, weights of `y`. If not specified, weights of all `NA` values will be `wmin`, the others will be 1.0.
QC_flag	Factor (optional) returned by `qcFUN`, levels should be in the range of `c("snow", "cloud", "shadow", "aerosol", "marginal", "good")`, others will be categoried into `others`. `QC_flag` is used for visualization in `get_pheno()` and `plot_curvefits()`.
nptperyear	Integer, number of images per year.
brks	A list object with the elements of 'fit' and 'dt', returned by `season` or `season_mov`, which contains the growing season dividing information.
rFUN	Rough curve fitting function, can be one of `smooth_wSG()`, `smooth_wWHIT()` and `smooth_wHANTS()`.
wFUN	Character or function, weights updating function of fine fitting function. If not specified, it will be determined by phenofit options `wFUN_fine`.
lambda	The smoothing parameter of `smooth_wWHIT()`. For `season_mov()`, if lambda is `NULL`, `init_lambda()` will be used. Generally, it was set as 10000, 15, and 5 for daily, 8-day and 16-day inputs respectively.
iters	How many times curve fitting is implemented.
maxExtendMonth	Search good or marginal good values in previous and subsequent `maxExtendMonth` period.
minExtendMonth	Extending perid defined by `nextend` and `maxExtendMonth` should be no shorter than `minExtendMonth`. When all points of the input time-series are good value, then the extending period will be too short. In that situation, we can't make sure the connection between different growing seasons is smoothing.
minPercValid	(optional, default not use). If the percentage of good and marginal quality points is less than `minPercValid`, curve fiting result is set to `NA`.
south	Boolean. In south hemisphere, growing year is 1 July to the following year 31 June; In north hemisphere, growing year is 1 Jan to 31 Dec.
methods	Fine curve fitting methods, can be one or more of `c('AG', 'Beck', 'Elmore', 'Zhang', 'Gu', 'Klos')`. Note that 'Gu' and 'Klos' are very slow. f not specified, it will be determined by phenofit options `methods_fine`.
ymin	If specified, `ylu[1]` is constrained greater than ymin. This value is critical for bare, snow/ice land, where vegetation amplitude is quite small. Generally, you can set ymin=0.08 for NDVI, ymin=0.05 for EVI, ymin=0.5 gC m-2 s-1 for GPP.
wmin	Double, minimum weight of bad points, which could be smaller the weight of snow, ice and cloud.
wsnow	Doulbe. Reset the weight of snow points, after get `ylu`. Snow flag is an important flag of ending of growing season. Snow points is more valuable than marginal points. Hence, the weight of snow should be great than that of `marginal`.
use.y0	boolean. whether to use original `y0`, which is before the process of `check_input`.
alpha	Double, in `[0,1]`, quantile prob of ylu_min.
...	other parameters to `curvefits()`