process_phenofit: Extract Vegetation Phenology at site scale
In eco-hydro/phenofit2: Extract Remote Sensing Vegetation Phenology

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,
  ...
)

`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()`