In kongdd/PhenoAsync: Extract Remote Sensing Vegetation Phenology

knitr::opts_chunk$set(echo = TRUE)

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load('../data/flux/phenoflux115.rda')
load('../data/flux/phenoflux115_ET&GPP&VI.rda')

library(data.table)
library(plyr)
library(tidyverse)

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get_scale <- function(df){
    varnames <- colnames(df)
    if ("scale" %in% varnames){
        df <- df[scale == "0m",]
    }
    df
}
lst <- llply(lst_sm, get_scale)

Check and prepare to remove sites

nday   <- 16
df_obs <- df[, .(site, date, GPP_DT)] %>% add_dn(nday) %>% 
    .[, .(GPP_obs = mean(GPP_DT, na.rm = T)), .(site, year, d16)] %>%
    .[, date := as.Date(sprintf("%d%03d", year, (d16-1)*nday+1), "%Y%j")]
df_vi <- lst[c("MOD13A1", "MOD13Q1")] %>% melt_list("model") %>% 
    melt(id.vars = c("site", "date", "t", "year", "doy", "SummaryQA", "model"), c("NDVI", "EVI"))

df_comb <- merge(df_obs, df_vi, by = c("site", "date", "year"))

## rm sites with a low correlation with GPP
info <- df_comb[, .(r = cor(GPP_obs, value, use = "complete.obs")), 
                .(site, variable, model)]
info_bad <- info[r < 0.2, ] %>% dcast(site~variable+model, value.var = "r")

ggplot(info, aes(variable, r)) + geom_boxplot() + 
    geom_text(data =info[r < 0.2, ], aes(label = site)) + 
    facet_wrap(~model)

st_97 <- fread("../data/flux/st_flux97.csv")
sites_rm <- setdiff(st$site, st_97$site)

setdiff(st$site, st_97$site)

kongdd/PhenoAsync documentation built on April 21, 2024, 2:36 a.m.