Moving growing season division

Source: R/season_mov.R

Moving growing season division

season_mov(INPUT, options = list(), ..., years.run = NULL)

Arguments

INPUT

A list object with the elements of t, y, w, Tn (optional) and ylu, returned by check_input().

options

see the following section options for season for details.

...

others parameter to set_options()

years.run

Numeric vector. Which years to run? If not specified, it is all years.

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.

References

  1. Kong, D., Zhang, Y., Wang, D., Chen, J., & Gu, X. (2020). Photoperiod Explains the Asynchronization Between Vegetation Carbon Phenology and Vegetation Greenness Phenology. Journal of Geophysical Research: Biogeosciences, 125(8), e2020JG005636. https://doi.org/10.1029/2020JG005636

  2. Kong, D., Zhang, Y., Gu, X., & Wang, D. (2019). A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 155, 13-24.

See also

season()

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
)

# curve fitting by year
brks_mov <- season_mov(INPUT,
    options = list(
        rFUN = "smooth_wWHIT", wFUN = "wTSM",
        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)
# Phenological Metrics from rough fitting
r <- get_pheno(rfit)