Rough fitting

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.

roughFit(
  INPUT,
  options = list(),
  frame = floor(INPUT$nptperyear/5) * 2 + 1,
  ...
)

Arguments

INPUT: A list object with the elements of t, y, w, Tn (optional) and ylu, returned by check_input().
options: see details
...: ignored.

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).

Details

Before division growing season, 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.

options for season

(a) Parameters for rough fitting

rFUN : character (default smooth_wWHIT), 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 : character (default wTSM), the name of weights updating functions, can be one of c("wTSM", "wChen", "wBisquare", "wSELF"). See wTSM(), wChen(), wBisquare() and wSELF() for details.
iters : integer (default 2), the number of rough fitting iterations.
wmin : double, the minimum weight of bad points (i.e. snow, ice and cloud).
verbose : logical (default FALSE). If TRUE, options$season will be printed on the console.
lambda : double (default NULL), the smoothing parameter of smooth_wWHIT().
- If lambda = NULL, V-curve theory will be employed to find the optimal lambda. See lambda_vcurve() for details.
frame : integer (default NULL), the parameter of smooth_wSG(), moving window size.
- If frame = NULL, frame will be reset as floor(nptperyear/5)*2 + 1 (refered by TIMESAT).
nf : integer (default 4), the number of frequencies in smooth_wHANTS().
maxExtendMonth: integer (default 12), previous and subsequent maxExtendMonth (in month) data were added to the current year for rough fitting.
nextend : integer (default NULL), same as maxExtendMonth, but in points.
- If nextend provided, maxExtendMonth will be ignored.
- If nextend = NULL, nextend will be reset as ceiling(maxExtendMonth/12*nptperyear)

(b) Parameters for growing season division

minpeakdistance : double (default NULL), the minimum distance of two peaks (in points). If the distance of two maximum extreme value less than minpeakdistance, only the maximum one will be kept.
- If minpeakdistance = NULL, it will be reset as nptperyear/6.
r_max : double (default 0.2; in (0, 1)). r_max and r_min are used to eliminate fake peaks and troughs.
- The real peaks should satisfy:
  1. $max(h_{peak, L}, h_{peak, R}) > r_{max} A$
  2. $min(h_{peak, L}, h_{peak, R}) > r_{min} A,$ where $h_{peak, L}, h_{peak, R}$ are height difference from the peak to the left- and right-hand troughs.
- The troughs should satisfy:
  1. $max(h_{trough, L}, h_{trough, R}) > r_{max} A,$ where $h_{trough, L}, h_{trough, R}$ are height difference from the trough to the left- and right-hand peaks.
r_min : double (default 0.05; in (0, 1)), see above r_max for details. r_min < r_max.
rtrough_max : double (default 0.6, in (0, 1)), $y_{peak} <= rtrough_max * A + ylu[1]$.
ypeak_min : double 0.1 (in VI unit), $y_{peak} >= ypeak_min$.
.check_season : logical (default TRUE). check the growing season length according to len_min and len_max. If FALSE, len_min and len_max will lose their effect.
len_min : integer (default 45), the minimum length (in days) of growing season
len_max : integer (default 650), the minimum length (in days) of growing season
adj.param : logical. If TRUE (default), if there are too many or too less peaks and troughs, phenofit will automatically adjust rough curve fitting function parameters. See MaxPeaksPerYear and MaxTroughsPerYear for details.
MaxPeaksPerYear (optional) : integer (default 2), the max number of peaks per year. If PeaksPerYear > MaxPeaksPerYear, then lambda = lambda*2.
MaxTroughsPerYear (optional) : integer (default 3), the max number of troughs per year. If TroughsPerYear > MaxTroughsPerYear, then lambda = lambda*2.
calendarYear : logical (default FALSE). If TRUE, the start and end of a calendar year will be regarded as growing season division (North Hemisphere is from 01 Jan to 31 Dec; South Hemisphere is from 01 Jul to 30 Jun).
rm.closed : logical (default TRUE). If TRUE, closed peaks (or troughs) will be further tidied. Only the maximum
is.continuous (not used): logical (default TRUE). This parameter is for fluxnet2015 fluxsite data, where the input might be not continuous.

Examples

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)
#> [1] TRUE

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)
#> [1] TRUE

# 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
    )
)
#>   [season_mov] running 1 ... 
#> iloop = 1: lambda = 10.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 2: lambda = 5.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 3: lambda = 2.5, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#>   [season_mov] running 2 ... 
#> iloop = 1: lambda = 10.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 2: lambda = 5.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 3: lambda = 2.5, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#>   [season_mov] running 3 ... 
#> iloop = 1: lambda = 10.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 2: lambda = 5.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 3: lambda = 2.5, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#>   [season_mov] running 4 ... 
#> iloop = 1: lambda = 10.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 2: lambda = 5.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 3: lambda = 2.5, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#>   [season_mov] running 5 ... 
#> iloop = 1: lambda = 10.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 2: lambda = 5.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 3: lambda = 2.5, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#>   [season_mov] running 6 ... 
#> iloop = 1: lambda = 10.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 2: lambda = 5.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 3: lambda = 2.5, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#>   [season_mov] running 7 ... 
#> iloop = 1: lambda = 10.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 2: lambda = 5.0, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
#> iloop = 3: lambda = 2.5, ntrough_PerYear = 0.00, npeak_PerYear = 0.00
plot_season(INPUT, brks_mov)


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