download Landsat 7

data-raw/04_download_landsat7_data.R

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+# Download Landsat 7 data to validate
+# the approach if possible with independent
+# remote sensing data - with the
+# caveat that the frequency is temporally
+# less dense
+
+# change this depending on system settings
+python_path = "/usr/local/bin/python"
+
+library(dplyr)
+
+# site info
+sites <- readr::read_csv("data/flue_stocker18nphyt.csv") |>
+ select(site) |>
+ unique()
+info <- readr::read_csv("data/site_info.csv") |>
+ filter(
+ sitename %in% !!sites$site
+ ) |>
+ select(
+ sitename,
+ lat,
+ lon,
+ date_start,
+ date_end
+ )
+
+# clone the gee_subset project
+# relies on git being installed
+# and will work out of the box for most
+# on OSX or Linux.
+
+# basic gee_subset requirements apply (working GEE install)
+setwd(here::here("src"))
+system("git clone https://github.com/khufkens/google_earth_engine_subsets.git")
+path = here::here("src/google_earth_engine_subsets/gee_subset/")
+
+# set product parameters, such as
+# product name, band(s) to query, start and end date of the range
+# and the lcoation
+product = 'LANDSAT/LE07/C02/T1_L2'
+band = "SR_B1 SR_B2 SR_B3 SR_B4 SR_B5 ST_B6 SR_B7 QA_PIXEL"
+
+info |>
+ group_by(sitename) |>
+ do({
+
+ x <- .
+
+ # location
+ location = sprintf("%s %s", x$lat, x$lon)
+
+ # store output in the R temporary directory
+ directory = tempdir()
+
+ # make the gee_subset.py python call
+ # time the duration of the call for reporting
+ system(
+ sprintf(
+ "%s %s/gee_subset.py -p %s -b %s -s %s -e %s -l %s -d %s -sc 30",
+ python_path,
+ path,
+ product,
+ band,
+ x$date_start,
+ x$date_end,
+ location,
+ directory
+ ),
+ wait = TRUE
+ )
+
+ # read in the data stored in the temporary directory
+ df <- read.table(
+ paste0(
+ directory, "/site_",
+ tail( unlist( strsplit( product, "[/]" ) ), n=1 ), "_gee_subset.csv" ),
+ sep = ",",
+ header = TRUE,
+ stringsAsFactors = FALSE
+ )
+
+ df
+ })