season: Growing season division

Description

Divide growing seasons according to rough fitting (rFUN) result .

For season, rough fitting is applied for whole. For season_mov rough fitting is applied in every year, during which maxExtendMonth is extended.

Usage

season(
  INPUT,
  rFUN,
  wFUN,
  iters = 2,
  wmin = 0.1,
  lambda,
  nf = 3,
  frame = floor(INPUT$nptperyear/5) * 2 + 1,
  minpeakdistance = NULL,
  ypeak_min = 0.1,
  r_max = 0.2,
  r_min = 0.05,
  rtrough_max = 0.6,
  MaxPeaksPerYear = 2,
  MaxTroughsPerYear = 3,
  calendarYear = FALSE,
  adj.param = TRUE,
  rm.closed = TRUE,
  is.continuous = TRUE,
  .check_season = TRUE,
  verbose = FALSE,
  ...
)
stat_season(INPUT, d_fit)

Value

fit: A data.table of Rough fitting result, with the columns of (t, y, witer1, ..., witerN, ziter1, ..., ziterN).
dt: A data.table of growing season division information, with the columns of (beg, peak, end, y_beg, y_peak, y_end, len, year, season, flag).

Arguments

INPUT: A list object with the elements of t, y, w, Tn (optional) and ylu, returned by check_input().
rFUN: character, the name of rough curve fitting function, can be one of c("smooth_wSG", "smooth_wWHIT", "smooth_wHANTS"), which are corresponding to smooth_wSG(), smooth_wWHIT() and smooth_wHANTS().
wFUN: weights updating function, can be one of .
iters: integer, the number of rough fitting iterations
wmin: double, minimum weigth (i.e. weight of snow, ice and cloud).
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.
nf: The parameter of smooth_wHANTS(), number of frequencies to be considered above the zero frequency.
frame: The parameter of smooth_wSG(), moving window size. Suggested by TIMESAT, default frame = floor(nptperyear/7)*2 + 1.
minpeakdistance: double, in points (default as nptperyear/6). The minimum distance of two peaks. If the distance of two maximum extreme value less than minpeakdistance, only the real maximum value will be left.
ypeak_min: y_peak >= ypeak_min
r_max: Similar as r_min, The maximum threshold should be greater than r_max.
r_min: Threshold is defined as the difference of peak value with trough value. There are two threshold (left and right). The minimum threshold should be greater than r_min.
rtrough_max: ytrough <= rtrough_max*A, A is the amplitude of y.
MaxPeaksPerYear: This parameter is used to adjust lambda in iterations. If PeaksPerYear > MaxPeaksPerYear, then lambda = lambda*2.
MaxTroughsPerYear: This parameter is used to adjust lambda in iterations. If TroughsPerYear > MaxTroughsPerYear, then lambda = lambda*2.
calendarYear: If true, only one static calendar growing season will be returned.
adj.param: , .
rm.closed: boolean. Whether check the two closest peaks (or troughs).
is.continuous: boolean. Whether the input is continuous? This parameter is for fluxsite site-year data.
.check_season: not used (only for debug)
verbose: whether to print options_season into console?
...: ignored.
d_fit: A data.frame with the columns of t, y, witer... and ziter....

Details

Before growing season division, INPUT should be added a year in head and tail first by add_HeadTail.

Finally, use findpeaks() to get local maximum and local minimum values. Two local minimum define a growing season. If two local minimum(maximum) are too closed, then only the smaller(biger) is left.

Examples

Run this code

data("CA_NS6")
d <- CA_NS6

nptperyear <- 23
INPUT <- check_input(d$t, d$y, d$w,
    QC_flag = d$QC_flag,
    nptperyear = nptperyear, south = FALSE,
    maxgap = nptperyear / 4, alpha = 0.02, wmin = 0.2
)
# plot_input(INPUT)

wFUN <- "wTSM"
# all year as a whole
options = list(rFUN = "smooth_wWHIT", wFUN = wFUN, lambda = 10)
brks <- season(INPUT, lambda = 10)
plot_season(INPUT, brks, d)

brks2 = season_input(INPUT, options)
all.equal(brks2, brks)

c(d_fit, info_peak) %<-% roughFit(INPUT)
d_season = find_season.peaks(d_fit, info_peak)

c(t, ypred) %<-% d_fit[, .(t, ziter2)]
d_season = find_season.default(ypred, t)
all.equal(brks$dt, d_season)

# opt <- .options$season
# brks$fit - d_fit # function passed test

# curve fitting by year
brks_mov <- season_mov(INPUT,
    options = list(
        rFUN = "smooth_wWHIT", wFUN = wFUN,
        lambda = 10,
        r_min = 0.05, ypeak_min = 0.05,
        verbose = TRUE
    )
)
plot_season(INPUT, brks_mov)

rfit <- brks2rfit(brks_mov)
r <- get_pheno(rfit)