check_input

Check input data, interpolate NA values in y, remove spike values, and set weights for NA in y and w.

check_input(
  t,
  y,
  w,
  QC_flag,
  nptperyear,
  south = FALSE,
  Tn = NULL,
  wmin = 0.2,
  wsnow = 0.8,
  ymin,
  missval,
  maxgap,
  alpha = 0.02,
  alpha_high = NULL,
  date_start = NULL,
  date_end = NULL,
  mask_spike = TRUE,
  ...
)

Arguments

t	Numeric vector, `Date` variable
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.
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.
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.
Tn	Numeric vector, night temperature, default is null. If provided, Tn is used to help divide ungrowing period, and then get background value in ungrowing season (see details in `phenofit::backval()`).
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`.
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.
missval	Double, which is used to replace NA values in y. If missing, the default vlaue is `ylu[1]`.
maxgap	Integer, nptperyear/4 will be a suitable value. If continuous missing value numbers less than maxgap, then interpolate those NA values by zoo::na.approx; If false, then replace those NA values with a constant value `ylu[1]`. Replacing NA values with a constant missing value (e.g. background value ymin) is inappropriate for middle growing season points. Interpolating all values by na.approx, it is unsuitable for large number continous missing segments, e.g. in the start or end of growing season.
alpha	Double, in `[0,1]`, quantile prob of ylu_min.
alpha_high	Double, `[0,1]`, quantile prob of `ylu_max`. If not specified, `alpha_high=alpha`.
date_start, date_end	starting and ending date of the original vegetation time-sereis (before `add_HeadTail`)
mask_spike	Boolean. Whether to remove spike values?
...	Others will be ignored.

Value

A list object returned:

t : Numeric vector
y0: Numeric vector, original vegetation time-series.
y : Numeric vector, checked vegetation time-series, NA values are interpolated.
w : Numeric vector
Tn: Numeric vector
ylu: = [ymin, ymax]. w_critical is used to filter not too bad values.

If the percentage good values (w=1) is greater than 30\

The else, if the percentage of w >= 0.5 points is greater than 10\ w_critical=0.5. In boreal regions, even if the percentage of w >= 0.5 points is only 10\

We can't rely on points with the wmin weights. Then,
y_good = y[w >= w_critical],
ymin = pmax( quantile(y_good, alpha/2), 0)
ymax = max(y_good).

Examples

library(phenofit)
#> Error in library(phenofit): there is no package called ‘phenofit’
data("MOD13A1")
#> Warning: data set ‘MOD13A1’ not found

df <- tidy_MOD13(MOD13A1$dt)
#> Error in tidy_MOD13(MOD13A1$dt): object 'MOD13A1' not found
st <- MOD13A1$st
#> Error in eval(expr, envir, enclos): object 'MOD13A1' not found

date_start <- as.Date('2013-01-01')
date_end   <- as.Date('2016-12-31')

sitename <- 'CA-NS6' # df$site[1]
d     <- df[site == sitename & (date >= date_start & date <= date_end), ]
#> Error in eval(expr, envir, enclos): object 'site' not found
sp    <- st[site == sitename, ]
#> Error in eval(expr, envir, enclos): object 'st' not found
south <- sp$lat < 0
#> Error in eval(expr, envir, enclos): object 'sp' not found
nptperyear <- 23

# global parameter
IsPlot = TRUE
print  = FALSE
ypeak_min  = 0.05
wFUN = wTSM

# add one year in head and tail
# d     <- add_HeadTail(d, south = south, nptperyear = nptperyear) 
INPUT    <- check_input(d$t, d$y, d$w, QC_flag = d$QC_flag,
     nptperyear = nptperyear, south = south, 
     maxgap = nptperyear/4, alpha = 0.02, wmin = 0.2)
#> Error in check_input(d$t, d$y, d$w, QC_flag = d$QC_flag, nptperyear = nptperyear,     south = south, maxgap = nptperyear/4, alpha = 0.02, wmin = 0.2): object 'd' not found

Arguments

Value

See also

Examples