phenofit (version 0.3.2)

season_mov: Moving growing season division

Description

Moving growing season division

Usage

season_mov(INPUT, options = list(r_min = 0), ..., 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 details

...

others to season()

years.run

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

options

• len_min, len_max: minimum and maximum length (in the unit of days) of growing season

• .lambda_vcurve: Boolean. If the Whittaker's parameter lambda not provided, whether to optimize lambda by V-curve theory? This parameter only works when lambda not provided.

• maxExtendMonth: Previous and subsequent maxExtendMonth data were added for every year curve fitting.

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.

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

c(d_fit, info_peak) %<-% rough_fitting(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)