check_input  R Documentation 
Check input data, interpolate NA values in y, remove spike values, and set weights for NA in y and w.
check_input( t, y, w, QC_flag, nptperyear, south = FALSE, wmin = 0.2, wsnow = 0.8, ymin, missval, maxgap, alpha = 0.02, alpha_high = NULL, date_start = NULL, date_end = NULL, mask_spike = TRUE, na.rm = FALSE, ... )
t 
Numeric vector, 
y 
Numeric vector, vegetation index timeseries 
w 
(optional) Numeric vector, weights of 
QC_flag 
Factor (optional) returned by 
nptperyear 
Integer, number of images per year. 
south 
Boolean. In south hemisphere, growing year is 1 July to the following year 31 June; In north hemisphere, growing year is 1 Jan to 31 Dec. 
wmin 
Double, minimum weight of bad points, which could be smaller the weight of snow, ice and cloud. 
wsnow 
Doulbe. Reset the weight of snow points, after get 
ymin 
If specified, 
missval 
Double, which is used to replace NA values in y. If missing,
the default vlaue is 
maxgap 
Integer, nptperyear/4 will be a suitable value. If continuous
missing value numbers less than maxgap, then interpolate those NA values by
zoo::na.approx; If false, then replace those NA values with a constant value

alpha 
Double, in 
alpha_high 
Double, 
date_start, date_end 
starting and ending date of the original vegetation
timesereis (before 
mask_spike 
Boolean. Whether to remove spike values? 
na.rm 
Boolean. If 
... 
Others will be ignored. 
A list object returned:
t
: Numeric vector
y0
: Numeric vector, original vegetation timeseries.
y
: Numeric vector, checked vegetation timeseries, NA
values are interpolated.
w
: Numeric vector
Tn
: Numeric vector
ylu
: = [ymin, ymax]
. w_critical
is used to filter not too bad values.
If the percentage good values (w=1) is greater than 30\
The else, if the percentage of w >= 0.5 points is greater than 10\
w_critical
=0.5. In boreal regions, even if the percentage of w >= 0.5
points is only 10\
We can't rely on points with the wmin weights. Then,
y_good = y[w >= w_critical]
,
ymin = pmax( quantile(y_good, alpha/2), 0)
ymax = max(y_good)
.
data("CA_NS6") d = CA_NS6 # head(d) 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)
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