curvefits_LocalModel {phenofit} | R Documentation |
curvefits by local model functions of TIMESAT
Description
Local model functions f_L(t)
, f_C(t)
and f_R(t)
describe the VI variation in intervals around the left minima, the central
maxima and the right minima.
Local model function are merged into global model function via merge_LocalModels()
and Per J\"onsson et al. (2004; their Eq. 12),
where cut-off function sharply drop from 1 to 0 in small intervals around
(t_L + t_C)/2
and (t_C + t_R)/2
.
F(t)= \begin{cases}
\alpha(t) f_{L}(t)+[1-\alpha(t)] f_{C}(t), t_{L}<t<t_{C} \\
\beta(t) f_{C}(t)+[1-\beta(t)] f_{R}(t), t_{C}<t<t_{R}\end{cases}
Usage
curvefits_LocalModel(INPUT, brks, options = list(), ...)
merge_LocalModels(fits)
Arguments
INPUT |
A list object with the elements of 't', 'y', 'w', 'Tn' (optional)
and 'ylu', returned by |
brks |
A list object with the elements of 'fit' and 'dt', returned by
|
options |
see section: options for fitting for details. |
... |
other parameters to |
fits |
List objects returned by |
options for fitting
-
methods
(defaultc('AG', 'Beck', 'Elmore', 'Zhang')``): Fine curve fitting methods, can be one or more of
c('AG', 'Beck', 'Elmore', 'Zhang', 'Gu', 'Klos')‘. Note that ’Gu' and 'Klos' are very slow. -
iters
(default 2): max iterations of fine fitting. -
wFUN
(defaultwTSM
): Character or function, weights updating function of fine fitting function. -
wmin
(default 0.1): min weights in the weights updating procedure. -
use.rough
(default FALSE): Whether to use rough fitting smoothed time-series as input? Iffalse
, smoothed VI by rough fitting will be used for Phenological metrics extraction; Iftrue
, original inputy
will be used (rough fitting is used to divide growing seasons and update weights. -
use.y0
(default TRUE): boolean. whether to use originaly0
as the input ofplot_input
, note that not for curve fitting.y0
is the original value before the process ofcheck_input
. -
nextend
(default 2): Extend curve fitting window, untilnextend
good or marginal points are found in the previous and subsequent growing season. -
maxExtendMonth
(default 1): Search good or marginal good values in previous and subsequentmaxExtendMonth
period. -
minExtendMonth
(default 0.5): Extend period defined bynextend
andmaxExtendMonth
, should be no shorter thanminExtendMonth
. When all points of the input time-series are good value, then the extending period will be too short. In that situation, we can't make sure the connection between different growing seasons is smoothing. -
minPercValid
: (default 0, not use). If the percentage of good- and marginal- quality points is less thanminPercValid
, curve fiting result is set toNA
. -
minT
: (not use). IfTn
not provided inINPUT
,minT
will not be used.minT
use night temperature Tn to define backgroud value (days withTn < minT
treated as ungrowing season).
References
Per J\"onsson, P., Eklundh, L., 2004. TIMESAT - A program for analyzing time-series of satellite sensor data. Comput. Geosci. 30, 833-845. doi:10.1016/j.cageo.2004.05.006.
See Also
Examples
## Not run:
library(phenofit)
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)
# Rough fitting and growing season dividing
wFUN <- "wTSM"
brks2 <- season_mov(INPUT,
options = list(
rFUN = "smooth_wWHIT", wFUN = wFUN,
r_min = 0.05, ypeak_min = 0.05,
lambda = 10,
verbose = FALSE
))
# plot_season(INPUT, brks2, d)
# Fine fitting
fits <- curvefits_LocalModel(
INPUT, brks2,
options = list(
methods = c("AG", "Beck", "Elmore", "Zhang", "Gu"), #,"klos", "Gu"
wFUN = wFUN,
nextend = 2, maxExtendMonth = 2, minExtendMonth = 1, minPercValid = 0.2
),
constrain = TRUE
)
# merge local model function into global model function
fits_merged = merge_LocalModels(fits)
## Visualization ---------------------------------------------------------------
l_fitting = map(fits %>% guess_names, get_fitting) #%>% melt_list("period")
d_merged = get_fitting(fits_merged[[2]]) %>% cbind(type = "Merged")
d_raw = l_fitting[2:4] %>% set_names(c("Left", "Central", "Right")) %>%
melt_list("type")
d_obs = d_raw[, .(t, y, QC_flag)] %>% unique()
d_fit = rbind(d_merged, d_raw)[meth == "Zhang"]
levs = c("Left", "Central", "Right", "Merged")
levs_new = glue("({letters[1:4]}) {levs}") %>% as.character()
d_fit$type %<>% factor(levs, levs_new)
p = ggplot(d_obs, aes(t, y)) +
geom_point() +
geom_line(data = d_fit, aes(t, ziter2, color = type)) +
facet_wrap(~type) +
labs(x = "Date", y = "EVI") +
scale_x_date(date_labels = "%b %Y", expand = c(1, 1)*0.08) +
theme_bw(base_size = 13) +
theme(legend.position = "none",
strip.text = element_text(size = 14))
p
## End(Not run)