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figure_7_cross_validation.R
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figure_7_cross_validation.R
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# libraries ----
setwd("~/phd/")
library(ncdf4)
library(abind)
library(lubridate)
library(ggplot2)
library(viridis)
library(circular)
library(raster)
library(snow)
library(zoo)
library(rgdal)
library(graticule)
library(MASS)
library(lubridate)
library(maps)
library(proj4)
library(cowplot)
library(gridExtra)
library(grid)
library(plotly)
library(scales)
library(reshape2)
library(rworldmap)
library(mice)
library(Kendall)
library(trend)
library(glmnet)
library(dimRed)
library(animation)
library(R.utils)
library(gridBase)
library(brew)
library(ggpubr)
## ggplot2 colors
gg_color_hue <- function(n) {
hues = seq(15, 375, length = n + 1)
hcl(h = hues, l = 65, c = 100)[1:n]
}
# function to scale the variables
scal <- function(x) {
y <- (x - mean(x, na.rm = T))/sd(x, na.rm = T)
return(y)
}
# r^2
rsquare <- function(true, predicted) {
sse <- sum((predicted - true)^2)
sst <- sum((true - mean(true))^2)
rsq <- 1 - (sse / sst)
# For this post, impose floor...
#if (rsq < 0) rsq <- 0
return(rsq)
}
# half life function
half.time <- function(init = 1, t, t_half = 30) {
lambda <- log(2)/t_half
t <- 1:t
y <- vector()
y[1] <- init
for (i in 2:length(t)) {
y[i] <- init * exp(1)^(-lambda*t[i])
#y[i] <- init * ((1/2)^((t[i] - 1)/(1/2)))
}
y
return(y)
}
# function to estimate the mean per day
roll.d <- function(x, by, FUN, na.rm = T) {
y <- matrix(x, ncol = (length(x)/by), byrow = F)
y <- apply(y, MARGIN = 2, FUN = FUN, na.rm = na.rm)
return(y)
}
## tools
source("bin/gpp/tools.R")
# base map
wmap <- readOGR(dsn = "data/tools/shapefiles/ne_110m_land")
# convert to dataframe
wmap_robin <- spTransform(wmap, CRS("+proj=robin"))
wmap_df_robin <- fortify(wmap_robin)
theme_opts <- list(theme(panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
panel.background = element_blank(),
plot.background = element_rect(fill = "white"),
panel.border = element_blank(),
axis.line = element_blank(),
axis.text.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
plot.title = element_blank()))
#### Selecting only sites with at least 7 years of duration ----
m5 <- read.csv("m5.csv")
View(m5)
## All the data indexed in a single array ----
time.serie <- array(list(list()))
## quality filter value
qc.level <- 2
#st <- 23
for (st in 1:nrow(m5)) {
if (m5$FILTER2[st] == 1) {
folder <- "FLUXNET_sites/F15/"
nc.file <- nc_open(paste(folder, m5$SITE_ID[st], ".HH.", m5$DATA_START[st],".",
m5$DATA_END[st],".nc", sep = ""))
# time original for index
hours.re <- ncvar_get(nc = nc.file, varid = "time", start = 1, count = -1)
index.days <- as.character(as.Date(hours.re, origin = "1582-10-15 00:00", format = "%Y-%m-%d %R"))
# GPP
# GPP without filter
gpp.o <- ncvar_get(nc = nc.file, varid = "GPP_NT_VUT_USTAR50", start = 1, count = -1)
# GPP with filter
quality.filter.gpp <- ncvar_get(nc = nc.file, varid = "NEE_VUT_USTAR50_QC", start = 1, count = -1)
gpp.o[which(quality.filter.gpp >= qc.level)] <- NA
time.serie[st][[1]][[1]] <- tapply(gpp.o, INDEX = index.days, FUN = quantile, probs = 0.9, na.rm = T)
# time
hours.re <- ncvar_get(nc = nc.file, varid = "time", start = 1, count = -1)
time.serie[st][[1]][[2]] <- as.Date(hours.re, origin = "1582-10-15 00:00", format = "%Y-%m-%d %R")
unique.days <- unique(as.character(time.serie[st][[1]][[2]]))
time.serie[st][[1]][[2]] <- as.Date(unique.days)
length(time.serie[st][[1]][[1]]) == length(time.serie[st][[1]][[2]])
# Tair
time.serie[st][[1]][[3]] <- tapply(X = ncvar_get(nc = nc.file, varid = "TA_F", start = 1, count = -1), INDEX = index.days, FUN = mean, na.rm = T)
# Precipitation
time.serie[st][[1]][[4]] <- tapply(X = ncvar_get(nc = nc.file, varid = "P_F", start = 1, count = -1), INDEX = index.days, FUN = sum, na.rm = T)
# Swin
time.serie[st][[1]][[5]] <- tapply(X = ncvar_get(nc = nc.file, varid = "SW_IN_F", start = 1, count = -1), INDEX = index.days, FUN = mean, na.rm = T)
# VPD
time.serie[st][[1]][[7]] <- tapply(X = ncvar_get(nc = nc.file, varid = "VPD_F", start = 1, count = -1), INDEX = index.days, FUN = mean, na.rm = T)
print(paste(st, m5$SITE_ID[st], sep = "-"))
}
}
m5$mean.temp <- NA
for (i in 1:nrow(m5)) {
if (m5$FILTER2[i] == 1) {
y <- year(time.serie[i][[1]][[2]])
m5$mean.temp[i] <- mean(tapply(X = time.serie[i][[1]][[3]], INDEX = y, FUN = mean, na.rm = T))
}
}
## detecting incogruencies -----
# depuring site by site
rm.year <- function(x, y.){
# x Vector with original days.
# y. years to remove
# return a vector with the positions correspondig to the year y.
mid <- lubridate::year(x)
if (length(y.) == 1) {
ret <- which(mid == y.)
}else{
ret <- match(mid, y.)
ret <- which(!is.na(ret))
}
return(ret)
}
# generating an index list with the necessary modifications
inde <- list()
inde[[1]] <- c(1991)
inde[[2]] <- c(1995)
inde[[3]] <- c(2003, 1996:1998)
inde[[4]] <- c(1996)
inde[[12]] <- c(1998)
inde[[13]] <- c(1998,2000,2001,2004)
inde[[15]] <- c(2007:2010)
inde[[16]] <- c(1997, 2004:2005)
inde[[18]] <- c(2011)
inde[[20]] <- c(2000, 2009,2011:2013)
inde[[22]] <- c(2000)
inde[[23]] <- c(2001)
inde[[25]] <- c(1997:1998)
inde[[29]] <- c(2008:2011)
inde[[31]] <- c(2006)
inde[[32]] <- c(2002,2014)
inde[[33]] <- c(2002)
inde[[37]] <- c(1997:1999, 2009)
inde[[40]] <- c(2013)
inde[[41]] <- c(2013)
inde[[42]] <- c(2013)
inde[[43]] <- c(2014)
inde[[45]] <- c(2014)
inde[[46]] <- c(1995, 1996,2007)
inde[[54]] <- c(2007:2009)
inde[[60]] <- c(2009)
inde[[62]] <- c(2004)
inde[[65]] <- c(2006:2008)
inde[[68]] <- c(2004)
inde[[73]] <- c(1996,1999,2000,2006)
inde[[78]] <- c(2004,2005)
inde[[80]] <- c(2000)
inde[[81]] <- c(1996,1997,2005)
inde[[82]] <- c(1995,2004)
## tier2
inde[[9]] <- 2014 #NL-Loo
inde[[31]] <- c(inde[[31]], 2011,2012,2013) #ZA-Kru
inde[[55]] <- c(2014) #FR-Gri
sites.changed <- c(1,2,3,4,9,12,13,15,16,18,20,22,23,25,29,31,32,33,37,40,41,42,43,45,46,54,55,60,62,65,68,73,78,80,81,82, 9, 31, 55)
for (st in 1:length(inde)) {
if (is.null(inde[[st]]) == F) {
if (m5$FILTER2[st] == 1) {
time.serie[st][[1]][[1]] <- time.serie[st][[1]][[1]][-rm.year(time.serie[st][[1]][[2]], y. = inde[[st]])]
# index 6 correspond to the time vector modified from vector 2
time.serie[st][[1]][[6]] <- time.serie[st][[1]][[2]][-rm.year(time.serie[st][[1]][[2]], y. = inde[[st]])]
}
}
}
## sites to remove
s.r <- c(38, 72, 74, 75, 76, 77, 83)
for (i in 1:length(s.r)) {
time.serie[s.r[i]][[1]][[1]] <- NA
}
# number of sites with information
nrow(m5[-s.r,])
row.names(m5) <- 1:nrow(m5)
length(which(m5$FILTER2 == 1))
## Sites distribution -----
world <- map_data("world")
map <- ggplot() + geom_polygon(data = world, aes(x = long, y = lat, group = group)) + theme_map() +
coord_fixed(1.3) +
geom_point(data = m5[which(m5$FILTER2 == 1),], aes(x = LON, y = LAT, color = IGBP), size = 1)
map
## Extracting GPPmax GPPmax timing ----
options(warn = 2)
# template to extract the gppmax
gpp.day.template.f <- list()
for (st in 1:nrow(m5)) {
if (m5$FILTER2[st] == 1) {
if (is.element(st, sites.changed)) {
gpp.nt.vut.ref <- time.serie[st][[1]][[1]]
gpp.nt.vut.ref.time <- year(time.serie[st][[1]][[6]])
hours.re <- time.serie[st][[1]][[6]]
}else{
gpp.nt.vut.ref <- time.serie[st][[1]][[1]]
gpp.nt.vut.ref.time <- year(time.serie[st][[1]][[2]])
hours.re <- time.serie[st][[1]][[2]]
}
l.years <- length(unique(gpp.nt.vut.ref.time))
u.years <- unique(gpp.nt.vut.ref.time)
all.years <- matrix(NA, nrow = 365, ncol = l.years)
for (y in 1:l.years) {
salida <- time.serie[st][[1]][[1]][which(gpp.nt.vut.ref.time == u.years[y])]
if (leap_year(u.years[y]) == T) {
salida <- salida[1:365]
} else{
#
}
all.years[,y] <- salida
}
# mean seasonal cycle
msc <- apply(X = all.years, MARGIN = 1, FUN = mean, na.rm = T)
if (any(is.nan(msc))) {
msc[which(is.nan(msc))] <- 0
}
if (any(is.na(msc)) == FALSE) {
# number of cycles
n.cycles <- as.integer(which.max(abs(fft(msc))[2:4]))
# Variance explained of the seassonal patterns
# Measure of the confidence that really exist a growing season. (TO INCORPORATE IN THE RESULTS)
var_in_cycle <- (2*sum(abs(fft(msc)^2)[2:4]) / sum(abs(fft(msc)[-1]^2)))
m5[st, "n.cycles"] <- n.cycles
# timing template
gpp.day.template <- vector()
fy <- fft(msc)
ny <- length(fy)
fy[c(1,5:(ny - 3))] <- 0
yfiltered <- fft(fy,inverse = T)
yfiltered <- Re(yfiltered)
for (d in 1:365) {
if (is.na(yfiltered[d])) {
#pass
}else{
if (d == 1) {
if (yfiltered[d] > yfiltered[365] & yfiltered[d] > yfiltered[(d + 1)]) {
gpp.day.template <- c(gpp.day.template, d)
}
} else if (d == 365) {
if (yfiltered[d] > yfiltered[(d - 1)] & yfiltered[d] > yfiltered[1]) {
gpp.day.template <- c(gpp.day.template, d)
}
} else {
if (yfiltered[d] > yfiltered[(d - 1)] & yfiltered[d] > yfiltered[(d + 1)]) {
gpp.day.template <- c(gpp.day.template, d)
}
}
}
}
# checking that the number of peaks is equal to the number of cycles (only selecting the highest peaks)
if (length(gpp.day.template) == n.cycles) {
gpp.day.template.f[[st]] <- gpp.day.template
} else {
tempora <- yfiltered[gpp.day.template]
gpp.day.template <- gpp.day.template[order(tempora, decreasing = T)[1:n.cycles]]
gpp.day.template <- sort(gpp.day.template)
gpp.day.template.f[[st]] <- gpp.day.template
}
}
}
}
gpp.day.template.f[[37]]
#write.csv(m5, "data/fluxnet/m5.csv", row.names = F)
# extracting gppmax and gppmax timing from original data with quality flags
# sites.info ----
sites.info <- array(list(list()))
#st <- 23
for (st in 1:nrow(m5)) {
if (m5$FILTER2[st] == 1) {
if (is.null(gpp.day.template.f[[st]]) == F) {
if (is.element(st, sites.changed)) {
gpp.nt.vut.ref <- time.serie[st][[1]][[1]]
gpp.nt.vut.ref.time <- year(time.serie[st][[1]][[6]])
hours.re <- time.serie[st][[1]][[6]]
}else{
gpp.nt.vut.ref <- time.serie[st][[1]][[1]]
gpp.nt.vut.ref.time <- year(time.serie[st][[1]][[2]])
hours.re <- time.serie[st][[1]][[2]]
}
l.years <- length(unique(gpp.nt.vut.ref.time))
u.years <- unique(gpp.nt.vut.ref.time)
all.years <- matrix(NA, nrow = 365, ncol = l.years)
for (y in 1:l.years) {
salida <- gpp.nt.vut.ref[which(gpp.nt.vut.ref.time == u.years[y])]
if (leap_year(u.years[y]) == T) {
salida <- salida[1:365]
}
all.years[,y] <- salida
}
gpp.timing <- vector()
gpp.max <- vector()
gpp.year <- vector()
n.cycles <- length(gpp.day.template.f[[st]])
gpp.day.template <- gpp.day.template.f[[st]]
for (y in 1:l.years) {
#y <- 1 # to remove
windows.size <- 90 / n.cycles
gpp.temp <- all.years[,y]
for (d in 1:n.cycles) {
if ((gpp.day.template[d] - windows.size) <= 0) {
back <- abs(gpp.day.template[d] - windows.size)
new.vector <- c((length(all.years[,y]) - back):length(all.years[,y]),
1:(gpp.day.template[d] + windows.size))
timing <- new.vector[order(gpp.temp[new.vector], decreasing = T)[1:10]] #new.vector[which.max(gpp.temp[new.vector])] # bug fixed
gpp.timing <- c(gpp.timing, timing)
gp <- gpp.temp[timing]
gpp.max <- c(gpp.max, gp)
year.t <- rep(y, times = length(timing))
gpp.year <- c(gpp.year, year.t)
} else if ((gpp.day.template[d] + windows.size) > length(all.years[,y])) {
advance <- (gpp.day.template[d] + windows.size) - length(all.years[,y])
new.vector <- c((gpp.day.template[d] - windows.size):length(all.years[,y]), 1:advance)
timing <- new.vector[order(gpp.temp[new.vector], decreasing = T)[1:10]] #new.vector[which.max(gpp.temp[new.vector])]
gpp.timing <- c(gpp.timing, timing)
gp <- gpp.temp[timing]
gpp.max <- c(gpp.max, gp)
year.t <- rep(y, times = length(timing))
gpp.year <- c(gpp.year, year.t)
} else {
start <- gpp.day.template[d] - windows.size
end <- gpp.day.template[d] + windows.size
new.vector <- start:end
timing <- new.vector[order(gpp.temp[new.vector], decreasing = T)[1:10]] #new.vector[which.max(gpp.temp[new.vector])]
gpp.timing <- c(gpp.timing, timing)
gp <- gpp.temp[timing]
gpp.max <- c(gpp.max, gp)
year.t <- rep(y, times = length(timing))
gpp.year <- c(gpp.year, year.t)
}
}
}
sites.info[st][[1]][[1]] <- gpp.max
sites.info[st][[1]][[2]] <- gpp.timing
sites.info[st][[1]][[3]] <- gpp.year
if (n.cycles == 2) {
sites.info[st][[1]][[5]] <- rep(c(1,2), each = 10, times = length(unique(gpp.year)) )
}
}
}
}
gradient <- circular(sites.info[23][[1]][[2]] * (360/365) * (pi/180),
units = "radians", modulo = "2pi",
rotation = "clock", zero = pi/2)
color <- viridis(length(unique(sites.info[23][[1]][[3]])), direction = -1)
plot(1:130, col = color[sites.info[23][[1]][[3]]])
plot.default(as.numeric(sites.info[23][[1]][[2]]), col = color[sites.info[23][[1]][[3]]])
plot.circular(gradient, stack = T, type = "n", axes = F)
axis.circular(at = circular(seq(1,365, by = 75) * (360/365) * (pi/180), units = "radians", modulo = "2pi", rotation = "clock", zero = pi/2),
units = "radians", labels = seq(1,365, by = 75), zero = pi/2, rotation = "clock",
cex = 0.7, tick = T)
points.circular(gradient, col = color[sites.info[23][[1]][[3]]], pch = 1, stack = T, cex = 0.3 )
?points.circular
## Extracting index of the year ----
for (st in 1:nrow(m5)) {
if (m5$FILTER2[st] == 1) {
if (is.null(sites.info[[st]]) == F) {
if (is.element(st, sites.changed)) {
start.ind <- unique(year(time.serie[st][[1]][[6]]))
}else{
start.ind <- unique(year(time.serie[st][[1]][[2]]))
}
temp.date <- vector()
for (i in 1:length(sites.info[st][[1]][[3]])) {
temp.date <- c(temp.date, as.Date(sites.info[st][[1]][[2]][i],
paste((start.ind[sites.info[st][[1]][[3]][i]] - 1), 12, 31, sep = "-")))
}
sites.info[st][[1]][[4]] <- temp.date
}
}
}
## CROSS Validation ----
###CROSS DATA ----
m5 <- read.csv("data/fluxnet/m5.csv")
vegetation <- "MF"
summary(m5$IGBP[which(m5$FILTER2 == 1)])
m5[-which(m5$FILTER2 == 1 & m5$IGBP == vegetation),"FILTER2"] <- 0
half.time.DBF <- round(mean(m5$half.time[which(m5$FILTER2 == 1)]), digits = 0)
# all are DBF half.time
m5$half.time <- half.time.DBF
## Extracting Climatic data ----
half_time.g2 <- read.csv("results/gppmax/sensitivity_half_time_g2.csv")
gpp.tair <- array(list(list()))
for (st in 1:nrow(m5)) {
#st <- 1
if (m5$FILTER2[st] == 1) {
if (is.null(sites.info[[st]]) == FALSE) {
gpp.tair.data <- time.serie[st][[1]][[3]]
gpp.nt.vut.ref.time <- names(gpp.tair.data)
#hours.re <- time.serie[st][[1]][[2]][seq(1, length(time.serie[st][[1]][[2]]),
#by = 48)]
n.cycles <- m5$n.cycles[st]
window.size <- 365
gpp.tair.tempora.o <- vector()
gpp.tair.tempora.w <- vector()
years.site.o <- unique(year(time.serie[st][[1]][[2]]))
for (i in 1:length(sites.info[st][[1]][[4]])) {
#i <- 108
end.t <- which(as.character(gpp.nt.vut.ref.time) ==
as.character(as.Date(sites.info[st][[1]][[4]][i])))
if (year(as.Date(sites.info[st][[1]][[4]][i])) == years.site.o[1]) {
if (end.t < window.size) {
start.t <- 1
}else{
start.t <- end.t - window.size
}
}else{
start.t <- end.t - window.size
}
tempora <- gpp.tair.data[start.t:end.t]
tair.o <- mean(gpp.tair.data[start.t:end.t], na.rm = T)
tempora <- gpp.tair.data[start.t:end.t]
gpp.tair.tempora.o <- c(gpp.tair.tempora.o, tair.o)
if (m5$n.cycles[st] == 2) {
if (sites.info[st][[1]][[5]][i] == 1) {
v.weight <- half.time(init = 1, t = length(tempora[!is.na(tempora)]), t_half = half_time.g2$half.time[which(as.character(half_time.g2$Site.name) == as.character(m5$SITE_ID[st]) & half_time.g2$n.cycle == 1)])
}else {
v.weight <- half.time(init = 1, t = length(tempora[!is.na(tempora)]), t_half = half_time.g2$half.time[which(as.character(half_time.g2$Site.name) == as.character(m5$SITE_ID[st]) & half_time.g2$n.cycle == 2)])
}
}else{
v.weight <- half.time(init = 1, t = length(tempora[!is.na(tempora)]), t_half = m5$half.time[st])
}
sum.v.weight <- sum(v.weight)
tair.w <- (sum(tempora[!is.na(tempora)] * v.weight, na.rm = T)/sum.v.weight)
gpp.tair.tempora.w <- c(gpp.tair.tempora.w, tair.w)
}
# original data
gpp.tair[st][[1]][[1]] <- gpp.tair.tempora.o
# weighted data
gpp.tair[st][[1]][[2]] <- gpp.tair.tempora.w
}
}
}
# Precipitation
gpp.precip <- array(list(list()))
for (st in 1:nrow(m5)) {
if (m5$FILTER2[st] == 1) {
if (is.null(sites.info[[st]]) == FALSE) {
gpp.precip.data <- time.serie[st][[1]][[4]]
gpp.nt.vut.ref.time <- names(gpp.precip.data)
#hours.re <- time.serie[st][[1]][[2]][seq(1, length(time.serie[st][[1]][[2]]),
#by = 48)]
n.cycles <- m5$n.cycles[st]
window.size <- 365
gpp.precip.tempora.o <- vector()
gpp.precip.tempora.w <- vector()
years.site.o <- unique(year(time.serie[st][[1]][[2]]))
for (i in 1:length(sites.info[st][[1]][[4]])) {
end.t <- which(as.character(gpp.nt.vut.ref.time) ==
as.character(as.Date(sites.info[st][[1]][[4]][i])))
if (year(as.Date(sites.info[st][[1]][[4]][i])) == years.site.o[1]) {
if (end.t < window.size) {
start.t <- 1
}else{
start.t <- end.t - window.size
}
}else{
start.t <- end.t - window.size
}
tempora <- gpp.precip.data[start.t:end.t]
precip.o <- sum(gpp.precip.data[start.t:end.t], na.rm = T)
tempora <- gpp.precip.data[start.t:end.t]
gpp.precip.tempora.o <- c(gpp.precip.tempora.o, precip.o)
if (m5$n.cycles[st] == 2) {
if (sites.info[st][[1]][[5]][i] == 1) {
v.weight <- half.time(init = 1, t = length(tempora[!is.na(tempora)]), t_half = half_time.g2$half.time[which(as.character(half_time.g2$Site.name) == as.character(m5$SITE_ID[st]) & half_time.g2$n.cycle == 1)])
}else {
v.weight <- half.time(init = 1, t = length(tempora[!is.na(tempora)]), t_half = half_time.g2$half.time[which(as.character(half_time.g2$Site.name) == as.character(m5$SITE_ID[st]) & half_time.g2$n.cycle == 2)])
}
}else{
v.weight <- half.time(init = 1, t = length(tempora[!is.na(tempora)]), t_half = m5$half.time[st])
}
sum.v.weight <- sum(v.weight)
precip.w <- (sum(tempora[!is.na(tempora)] * v.weight, na.rm = T)/sum.v.weight)
gpp.precip.tempora.w <- c(gpp.precip.tempora.w, precip.w)
}
# original data
gpp.precip[st][[1]][[1]] <- gpp.precip.tempora.o
# weighted data
gpp.precip[st][[1]][[2]] <- gpp.precip.tempora.w
}
}
}
# Shortway incomming radiation
gpp.swrad <- array(list(list()))
for (st in 1:nrow(m5)) {
if (m5$FILTER2[st] == 1) {
if (is.null(sites.info[[st]]) == FALSE) {
gpp.swrad.data <- time.serie[st][[1]][[5]]
gpp.nt.vut.ref.time <- names(gpp.swrad.data)
#hours.re <- time.serie[st][[1]][[2]][seq(1, length(time.serie[st][[1]][[2]]),
#by = 48)]
n.cycles <- m5$n.cycles[st]
window.size <- 365
gpp.swrad.tempora.o <- vector()
gpp.swrad.tempora.w <- vector()
years.site.o <- unique(year(time.serie[st][[1]][[2]]))
for (i in 1:length(sites.info[st][[1]][[4]])) {
end.t <- which(as.character(gpp.nt.vut.ref.time) ==
as.character(as.Date(sites.info[st][[1]][[4]][i])))
if (year(as.Date(sites.info[st][[1]][[4]][i])) == years.site.o[1]) {
if (end.t < window.size) {
start.t <- 1
}else{
start.t <- end.t - window.size
}
}else{
start.t <- end.t - window.size
}
tempora <- gpp.swrad.data[start.t:end.t]
swrad.o <- mean(gpp.swrad.data[start.t:end.t], na.rm = T)
tempora <- gpp.swrad.data[start.t:end.t]
gpp.swrad.tempora.o <- c(gpp.swrad.tempora.o, swrad.o)
if (m5$n.cycles[st] == 2) {
if (sites.info[st][[1]][[5]][i] == 1) {
v.weight <- half.time(init = 1, t = length(tempora[!is.na(tempora)]), t_half = half_time.g2$half.time[which(as.character(half_time.g2$Site.name) == as.character(m5$SITE_ID[st]) & half_time.g2$n.cycle == 1)])
}else {
v.weight <- half.time(init = 1, t = length(tempora[!is.na(tempora)]), t_half = half_time.g2$half.time[which(as.character(half_time.g2$Site.name) == as.character(m5$SITE_ID[st]) & half_time.g2$n.cycle == 2)])
}
}else{
v.weight <- half.time(init = 1, t = length(tempora[!is.na(tempora)]), t_half = m5$half.time[st])
}
sum.v.weight <- sum(v.weight)
swrad.w <- (sum(tempora[!is.na(tempora)] * v.weight, na.rm = T)/sum.v.weight)
gpp.swrad.tempora.w <- c(gpp.swrad.tempora.w, swrad.w)
}
# original data
gpp.swrad[st][[1]][[1]] <- gpp.swrad.tempora.o
# weighted data
gpp.swrad[st][[1]][[2]] <- gpp.swrad.tempora.w
}
}
}
# VPD
gpp.vpd <- array(list(list()))
for (st in 1:nrow(m5)) {
if (m5$FILTER2[st] == 1) {
if (is.null(sites.info[[st]]) == FALSE) {
gpp.vpd.data <- time.serie[st][[1]][[7]]
gpp.nt.vut.ref.time <- names(gpp.vpd.data)
#hours.re <- time.serie[st][[1]][[2]][seq(1, length(time.serie[st][[1]][[2]]),
#by = 48)]
n.cycles <- m5$n.cycles[st]
window.size <- 365
gpp.vpd.tempora.o <- vector()
gpp.vpd.tempora.w <- vector()
years.site.o <- unique(year(time.serie[st][[1]][[2]]))
for (i in 1:length(sites.info[st][[1]][[4]])) {
end.t <- which(as.character(gpp.nt.vut.ref.time) ==
as.character(as.Date(sites.info[st][[1]][[4]][i])))
if (year(as.Date(sites.info[st][[1]][[4]][i])) == years.site.o[1]) {
if (end.t < window.size) {
start.t <- 1
}else{
start.t <- end.t - window.size
}
}else{
start.t <- end.t - window.size
}
tempora <- gpp.vpd.data[start.t:end.t]
vpd.o <- mean(gpp.vpd.data[start.t:end.t], na.rm = T)
tempora <- gpp.vpd.data[start.t:end.t]
gpp.vpd.tempora.o <- c(gpp.vpd.tempora.o, vpd.o)
if (m5$n.cycles[st] == 2) {
if (sites.info[st][[1]][[5]][i] == 1) {
v.weight <- half.time(init = 1, t = length(tempora[!is.na(tempora)]), t_half = half_time.g2$half.time[which(as.character(half_time.g2$Site.name) == as.character(m5$SITE_ID[st]) & half_time.g2$n.cycle == 1)])
}else {
v.weight <- half.time(init = 1, t = length(tempora[!is.na(tempora)]), t_half = half_time.g2$half.time[which(as.character(half_time.g2$Site.name) == as.character(m5$SITE_ID[st]) & half_time.g2$n.cycle == 2)])
}
}else{
v.weight <- half.time(init = 1, t = length(tempora[!is.na(tempora)]), t_half = m5$half.time[st])
}
sum.v.weight <- sum(v.weight)
vpd.w <- (sum(tempora[!is.na(tempora)] * v.weight, na.rm = T)/sum.v.weight)
gpp.vpd.tempora.w <- c(gpp.vpd.tempora.w, vpd.w)
}
# original data
gpp.vpd[st][[1]][[1]] <- gpp.vpd.tempora.o
# weighted data
gpp.vpd[st][[1]][[2]] <- gpp.vpd.tempora.w
}
}
}
#
#
#
# ## Cross validation per vegetation type ----
dbf.data <- m5[which(m5$FILTER2 == 1), ]
# preparing the data
site.id <- rep(m5$SITE_ID[which(m5$FILTER2 == 1)], m5$RANGE_ANALYSIS[which(m5$FILTER2 == 1)] * 10)
year <- vector()
for (i in 1:nrow(dbf.data)) {
temp <- rep(1:dbf.data$RANGE_ANALYSIS[i], each = 10)
year <- c(year, temp)
}
year
# tair <- vector()
PC1 <- vector()
precip <- vector()
# swir <- vector()
# vpd <- vector()
DOYmax <- vector()
options(warn = 2)
for (i in 1:nrow(m5)) {
if (m5$FILTER2[i] == 1) {
to_reduce <- matrix(data = NA, nrow = length(gpp.tair[i][[1]][[2]]), ncol = 3)
to_reduce[,1] <- gpp.tair[i][[1]][[2]]
to_reduce[,2] <- gpp.swrad[i][[1]][[2]]
to_reduce[,3] <- gpp.vpd[i][[1]][[2]]
climatic_pca <- prcomp(to_reduce, center = T, scale. = T)
PC1 <- c(PC1, climatic_pca$x[,1])
precip <- c(precip, gpp.precip[i][[1]][[2]])
DOYmax <- c(DOYmax, sites.info[i][[1]][[2]])
}
}
cross.dbf <- data.frame(site.id, year, DOYmax, PC1, precip)
cross.dbf$index.id <- paste0(cross.dbf$site.id, cross.dbf$year)
cross.dbf$index.out <- 1:nrow(cross.dbf)
# saveRDS(cross.dbf, file = paste("data/cross_validation_",vegetation ,".RDS", sep = ""))
## DBF-CROSS ----
m5 <- read.csv("data/fluxnet/m5.csv")
dbf.lat <- m5$LAT[which(m5$FILTER2 == 1 & m5$IGBP == "DBF")]
dbf.lat <- rep(dbf.lat, m5$RANGE_ANALYSIS[which(m5$FILTER2 == 1 & m5$IGBP == "DBF") ] * 10)
cross.dbf <- readRDS("data/cross_validation_DBF.RDS")
cross.dbf.predict <- readRDS("data/cross_dbf_predicted.RDS")
observed <- circular(cross.dbf$DOYmax * (360/365) * (pi/180),units = "radians", modulo = "2pi", rotation = "counter", zero = 0)
predicted <- circular(cross.dbf.predict,units = "radians", modulo = "2pi", rotation = "counter", zero = 0)
cor.dbf <- cor.circular(predicted, observed, test = T)
observed <- as.numeric(observed) / (360/365) / (pi/180)
predicted <- as.numeric(predicted) / (360/365) / (pi/180)
input.gg <- data.frame(observed, predicted, dbf.lat)
#lab <- paste("italic(R) ^ 2 ==", round(cor.dbf$cor, digits = 2))
lab <- paste("JS.Cor(cv) =", round(cor.dbf$cor, digits = 2))
scatter.dbf <- ggplot(data = input.gg, aes(x = predicted, y = observed)) +
geom_abline(slope = 1, intercept = 0, col = "red", size = 1.5) +
geom_point(aes(colour = dbf.lat), alpha = 0.6) +
theme_gray() +
#annotate("text", x = (120 + 20), y = 259, label = lab, size = 5 ) +
# geom_smooth() +
scale_color_viridis(name = "Latitude") +
ylab(expression("Observed "*"(DOY"["GPPmax"]*")")) +
xlab(expression("Predicted "*"(DOY"["GPPmax"]*")")) +
xlim(120,260) +
ylim(120, 260) +
theme_bw() +
ggtitle(paste("DBF", lab, sep = " "))
scatter.dbf
VT <- m5[which(m5$IGBP == "DBF" & m5$FILTER2 == 1),]
# Robinson transformation
places_robin_df <- project(cbind(VT$LON, VT$LAT), proj="+init=esri:54030")
places_robin_df <- as.data.frame(places_robin_df)
names(places_robin_df) <- c("LONGITUDE", "LATITUDE")
map.dbf <- ggplot(data = wmap_df_robin, aes(long,lat, group = group, fill = hole)) +
geom_polygon() +
geom_point(data = places_robin_df, aes(LONGITUDE, LATITUDE, group = NULL, fill = NULL), color = "#F4511E", size = 1.5) +
coord_equal() +
theme_opts +
scale_fill_manual(values = c("#262626", "white"), guide = "none") # change colors & remove legend
ggarrange(scatter.dbf, map.dbf, widths = c(1,1.5))
# plot.circular(observed, stack = T, shrink = 1.8)
# points.circular(predicted, stack = T, shrink = 1.8, pch = 21, col = "red")
## EBF-CROSS ----
m5 <- read.csv("data/m5.csv")
VT <- m5[which(m5$IGBP == "EBF" & m5$FILTER2 == 1),]
ebf.lat <- m5$LAT[which(m5$FILTER2 == 1 & m5$IGBP == "EBF")]
ebf.lat <- rep(ebf.lat, m5$RANGE_ANALYSIS[which(m5$FILTER2 == 1 & m5$IGBP == "EBF") ] * 10)
observed.ebf <- readRDS("data/cross_validation_EBF.RDS")
observed.ebf <- circular(observed.ebf$DOYmax * (360/365) * (pi/180),units = "radians", modulo = "2pi", rotation = "counter", zero = 0)
predicted.ebf <- readRDS("data/cross_ebf_predicted.RDS")
predicted.ebf <- circular(predicted.ebf, units = "radians", modulo = "2pi", rotation = "counter", zero = 0)
cor.ebf <- cor.circular(predicted.ebf, observed.ebf, test = T)
plot.circular(observed.ebf, stack = T)
points.circular(predicted.ebf, stack = T, pch = 21, col = "red")
#predicted.ebf[which(predicted.ebf > 4)] <- predicted.ebf[which(predicted.ebf > 4)] - (2*pi)
#observed.ebf[observed.ebf > 4] <- observed.ebf[observed.ebf > 4] - (2*pi)
predicted.ebf <- predicted.ebf / (360/365) / (pi/180)
observed.ebf <- observed.ebf / (360/365) / (pi/180)
input.gg <- data.frame(predicted.ebf, observed.ebf, ebf.lat)
lab <- paste("JS.Cor(cv) =", round(cor.ebf$cor, digits = 2))
scatter.ebf <- ggplot(data = input.gg, aes(x = predicted.ebf, y = observed.ebf)) +
geom_abline(slope = 1, intercept = 0, col = "red", size = 1.5) +
geom_point(aes(colour = ebf.lat), alpha = 0.6) +
theme_gray() +
#annotate("text", x = (0 + 50), y = 364, label = lab, size = 5) +
# geom_smooth() +
scale_color_viridis(name = "Latitude") +
ylab(expression("Observed "*"(DOY"["GPPmax"]*")")) +
xlab(expression("Predicted "*"(DOY"["GPPmax"]*")")) +
xlim(0,365) +
ylim(0, 365) +
theme_bw() +
ggtitle(paste("EBF", lab, sep = " "))
scatter.ebf
# Robinson transformation
places_robin_df <- project(cbind(VT$LON, VT$LAT), proj="+init=esri:54030")
places_robin_df <- as.data.frame(places_robin_df)
names(places_robin_df) <- c("LONGITUDE", "LATITUDE")
wmap_robin <- spTransform(wmap, CRS("+proj=robin"))
wmap_df_robin <- fortify(wmap_robin)
map.ebf <- ggplot(data = wmap_df_robin, aes(long,lat, group=group, fill=hole)) +
geom_polygon() +
geom_point(data = places_robin_df, aes(LONGITUDE, LATITUDE, group = NULL, fill = NULL), color="#F4511E", size = 1) +
coord_equal() +
theme_opts +
scale_fill_manual(values = c("#262626", "#e6e8ed"), guide="none") # change colors & remove legend
ggarrange(scatter.ebf, map.ebf)
## ENF-CROSS ----
m5 <- read.csv("data/m5.csv")
VT <- m5[which(m5$IGBP == "ENF" & m5$FILTER2 == 1),]
enf.lat <- m5$LAT[which(m5$FILTER2 == 1 & m5$IGBP == "ENF")]
enf.lat <- rep(enf.lat, m5$RANGE_ANALYSIS[which(m5$FILTER2 == 1 & m5$IGBP == "ENF") ] * 10)
observed.enf <- readRDS("data/cross_validation_ENF.RDS")
observed.enf <- circular(observed.enf$DOYmax * (360/365) * (pi/180),units = "radians", modulo = "2pi", rotation = "counter", zero = 0)
predicted.enf <- readRDS("data/cross_enf_predicted.RDS")
predicted.enf <- circular(predicted.enf, units = "radians", modulo = "2pi", rotation = "counter", zero = 0)
cor.enf <- cor.circular(predicted.enf, observed.enf, test = T)
plot.circular(observed.enf, stack = T)
points.circular(predicted.enf, stack = T, pch = 21, col = "red")
predicted.enf <- predicted.enf / (360/365) / (pi/180)
observed.enf <- observed.enf / (360/365) / (pi/180)
input.gg <- data.frame(predicted.enf, observed.enf, enf.lat)
lab <- paste("JS.Cor(cv) =", round(cor.enf$cor, digits = 2))
scatter.enf <- ggplot(data = input.gg, aes(x = predicted.enf, y = observed.enf)) +
geom_abline(slope = 1, intercept = 0, col = "red", size = 1.5) +
geom_point(aes(colour = enf.lat), alpha = 0.6) +
theme_gray() +
#annotate("text", x = (80 + 30), y = 274, label = lab, size = 5) +
# geom_smooth() +
scale_color_viridis(name = "Latitude") +
ylab(expression("Observed "*"(DOY"["GPPmax"]*")")) +
xlab(expression("Predicted "*"(DOY"["GPPmax"]*")")) +
xlim(80,275) +
ylim(80, 275) +
theme_bw() +
ggtitle(paste("ENF", lab, sep = " "))
scatter.enf
# Robinson transformation
places_robin_df <- project(cbind(VT$LON, VT$LAT), proj="+init=esri:54030")
places_robin_df <- as.data.frame(places_robin_df)
names(places_robin_df) <- c("LONGITUDE", "LATITUDE")
wmap_robin <- spTransform(wmap, CRS("+proj=robin"))
wmap_df_robin <- fortify(wmap_robin)
map.enf <- ggplot(data = wmap_df_robin, aes(long,lat, group = group, fill = hole)) +
geom_polygon() +
geom_point(data = places_robin_df, aes(LONGITUDE, LATITUDE, group = NULL, fill = NULL), color = "#F4511E", size = 1) +
coord_equal() +
theme_opts +
scale_fill_manual(values = c("#262626", "#e6e8ed"), guide="none") # change colors & remove legend
ggarrange(scatter.enf, map.enf)
## GRAS-CROSS ----
m5 <- read.csv("data/m5.csv")
VT <- m5[which(m5$IGBP == "GRA" & m5$FILTER2 == 1),]
gras.lat <- m5$LAT[which(m5$FILTER2 == 1 & m5$IGBP == "GRA")]
gras.lat <- rep(gras.lat, m5$RANGE_ANALYSIS[which(m5$FILTER2 == 1 & m5$IGBP == "GRA") ] * 10)
observed.gras <- readRDS("data/cross_validation_GRA.RDS")
observed.gras <- circular(observed.gras$DOYmax * (360/365) * (pi/180),units = "radians", modulo = "2pi", rotation = "counter", zero = 0)
predicted.gras <- readRDS("data/cross_gra_predicted.RDS")
predicted.gras <- circular(predicted.gras, units = "radians", modulo = "2pi", rotation = "counter", zero = 0)
cor.gras <- cor.circular(predicted.gras, observed.gras, test = T)
plot.circular(observed.gras, stack = T, shrink = 1.2)
points.circular(predicted.gras, stack = T, pch = 21, col = "red")
predicted.gras <- predicted.gras / (360/365) / (pi/180)
observed.gras <- observed.gras / (360/365) / (pi/180)
input.gg <- data.frame(predicted.gras, observed.gras, gras.lat)
lab <- paste("JS.Cor(cv) =", round(cor.gras$cor, digits = 2))
scatter.gras <- ggplot(data = input.gg, aes(x = predicted.gras, y = observed.gras)) +
geom_abline(slope = 1, intercept = 0, col = "red", size = 1.5) +
geom_point(aes(colour = gras.lat), alpha = 0.6) +
theme_gray() +
#annotate("text", x = (20 + 30), y = (307 - 1), label = lab, size = 5) +
# geom_smooth() +
scale_color_viridis(name = "Latitude") +
ylab(expression("Observed "*"(DOY"["GPPmax"]*")")) +
xlab(expression("Predicted "*"(DOY"["GPPmax"]*")")) +
xlim(20,307) +
ylim(20,307) +
theme_bw() +
ggtitle(paste("GRAS", lab, sep = " "))
scatter.gras
# Robinson transformation
places_robin_df <- project(cbind(VT$LON, VT$LAT), proj="+init=esri:54030")
places_robin_df <- as.data.frame(places_robin_df)
names(places_robin_df) <- c("LONGITUDE", "LATITUDE")
wmap_robin <- spTransform(wmap, CRS("+proj=robin"))
wmap_df_robin <- fortify(wmap_robin)
map.gras <- ggplot(data = wmap_df_robin, aes(long,lat, group = group, fill = hole)) +
geom_polygon() +
geom_point(data = places_robin_df, aes(LONGITUDE, LATITUDE, group = NULL, fill = NULL), color="#F4511E", size = 1) +
coord_equal() +
theme_opts +
scale_fill_manual(values = c("#262626", "#e6e8ed"), guide="none") # change colors & remove legend
ggarrange(scatter.gras, map.gras)
## MF-CROSS ----
m5 <- read.csv("data/fluxnet/m5.csv")
VT <- m5[which(m5$IGBP == "MF" & m5$FILTER2 == 1),]
mf.lat <- m5$LAT[which(m5$FILTER2 == 1 & m5$IGBP == "MF")]
mf.lat <- rep(mf.lat, m5$RANGE_ANALYSIS[which(m5$FILTER2 == 1 & m5$IGBP == "MF") ] * 10)