In mdsumner/roc: Tools for Ocean Colour

knitr::opts_chunk$set(echo = TRUE)

Get a map of chlorophyll-a, for January 2017.

The chla_compute function reads the L3 bins and computes a running sum and count per bin for every input date.

(This is currently in development so is not yet generally available. )

library(roc)

library(raadtools)

dates <- seq(as.Date("2017-01-01"), as.Date("2017-01-31"), by = "1 day")
bins <- chla_compute(dates)

#' In-dev function ...
bin_chl <- function(bins, value, gridmap, platform = "MODISA") {
  bins <- tibble(bin_num = bins, value = value)
  if (!platform == "MODISA") stop("only MODISA  platform currently supported")
  if (missing(gridmap)) { 

    gridmap <- raster(extent(-180, 180, -90, 0), ncol = 8640, nrow = 2160, crs = "+init=epsg:4326")

    ll <- coordinates(gridmap)
    bins <- tibble(bin_num = lonlat2bin(ll[,1], ll[, 2], NUMROWS = 4320), gridcell = seq(ncell(gridmap))) %>% inner_join(bins, "bin_num")
    gridmap[bins$gridcell] <- bins$value
  } else {
    #roc:::regridder
  }
  gridmap
}

## we can choose nasa rather than "rj"
rjchl <- bin_chl(bins$bin_num, bins$cumul_rj/bins$count_rj)

nasachl <- bin_chl(bins$bin_num, bins$cumul_nasa/bins$count_nasa)

Plot the result!

e <- extent(30, 120, -75, -30)
pal <- palr::chlPal(palette = TRUE)
plot(crop(rjchl, e), col = pal$cols, breaks = pal$breaks, legend = FALSE, main = "Johnson algo")

plot(crop(nasachl, e), col = pal$cols, breaks = pal$breaks, legend = FALSE, main = "NASA algo")

Optionally regrid to lower resolution.

rj <- aggregate(crop(rjchl, e), fact = 8, fun = mean)
nasa <- aggregate(crop(nasachl, e), fact = 8, fun = mean)
plot(rj, col = pal$cols, breaks = pal$breaks, legend = FALSE, main = "Johnson algo")

plot(nasa, col = pal$cols, breaks = pal$breaks, legend = FALSE, main = "NASA algo")