# Create id_df, combining gia and gmia. Remove values < 0.01, most of which are
# probably caused by reprojecting the maps
ir_df <- as.data.frame(rasterToPoints(stack(grid, grid_size, gmia, gia))) %>%
filter(!is.na(gridID)) %>%
dplyr::select(-x, -y) %>%
mutate(gia = ifelse(gia < 0.01, 0, gia),
gmia = ifelse(gmia < 0.01, 0, gmia))# Create ranking by first taking the maximum of the irrigated area share,
# calculate irrigated area, and then rank. In this way we prefer the largest
# area, and hence prefer GIA over GMIA when the resolution is 30 arcsec (GIA is
# 1 or 0). At a resolution of 5 arcmin the GMIA and grid cells with a lot of GIA
# observations get a high rank, which is also desirable.
ir_df <- ir_df %>%
dplyr::mutate(ir_max = pmax(gmia, gia, na.rm = T),
ir_rank = cut(ir_max, labels = c(1:10), breaks = seq(0, 1, 0.1),
include.lowest = T),
ir_rank = dense_rank(desc(ir_rank)),
ir_max = ir_max * grid_size) %>%
filter(!is.na(ir_rank), ir_max > 0) %>%
dplyr::select(-gmia, -gia, -grid_size)############### CREATE IR MAX AND IR RANK MAPS ###############
# ir_max
ir_max_map <- ir_df %>%
left_join(grid_df,.) %>%
dplyr::select(x, y, ir_max)
ir_max_map <- rasterFromXYZ(ir_max_map)
crs(ir_max_map) <- crs(param$crs)
plot(ir_max_map)
plot(adm_map$geometry, add = T)
############### SAVE ###############
temp_path <- file.path(param$spam_path, glue("processed_data/maps/irrigated_area/{param$res}"))
dir.create(temp_path, showWarnings = FALSE, recursive = TRUE)
writeRaster(ir_max_map, file.path(temp_path,
glue::glue("ia_max_{param$res}_{param$year}_{param$iso3c}.tif")),overwrite = T)
writeRaster(ir_rank_map, file.path(temp_path,
glue::glue("ia_rank_{param$res}_{param$year}_{param$iso3c}.tif")),overwrite = T)