In albhasan/sits.starfm: Use the StarFM Fusion Model on Landsat 8 Images

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(sits.starfm)
library(dplyr)
library(kableExtra)
library(knitr)
library(ggplot2)

#setwd("/home/alber/Documents/data/experiments/l8mod-fusion/Rpackage/sits.starfm")
#devtools::load_all()

Landsat 8

Approximately 250 Landsat-8 images.
Scenes 225063, 226964, and 233067.
Ranging from August 2013 to July 2017.
Radometrically corrected (surface reflectance).
Processed and downloaded from NASA between February and August 2017.

Statistics by scene year

BRICK_IMAGES %>%
    dplyr::select(-c(files, tile, neigh)) %>% 
    dplyr::group_by(scene, prodes_year) %>%
    dplyr::summarize(count = n(), 
                     cloud_cover_mean = mean(cloud_cov),
                     cloud_cover_sd = sd(cloud_cov),
                     cloud_cover_min = min(cloud_cov),
                     cloud_cover_max = max(cloud_cov)) %>%
    knitr::kable() %>%
    kableExtra::kable_styling(full_width = F)

BRICK_IMAGES %>%
    dplyr::select(-c(files, tile, neigh)) %>%
    dplyr::group_by(scene, prodes_year) %>% 
    dplyr::mutate(id = seq_along(sat_image)) %>%
        ggplot2::ggplot() +
        ggplot2::geom_line( aes(x = id, y = cloud_cov, color = cloud_cov, group = prodes_year)) +
    ggplot2::geom_point(aes(x = id, y = cloud_cov, color = cloud_cov)) +
    ggplot2::facet_grid(scene ~ prodes_year) +
    ggplot2::ggtitle("Cloud cover")
# ggsave(filename = "deleteme.png")