Access NASA OB.DAAC ocean colour and sea surface temperature

Downloads Level-3 mapped satellite fields from NASA's Ocean Biology DAAC and returns them as an sf point object with one row per grid cell and time step — the same shape accessCopernicus(), accessFVCOM(), accessHYCOM(), accessCCMP(), accessERDDAP() and accessCEFI() return, so matchData() joins it unchanged.

Usage

accessOBDAAC(
  vars,
  years = NULL,
  months = NULL,
  bounding_box,
  dates = NULL,
  frequency = c("daily", "monthly"),
  sensor = "MODISA",
  resolution = "4km",
  overwrite = FALSE
)

Arguments

vars: variables to read, from obdaac_variables()
years: years to read. Required unless dates is given.
months: months to read. Required unless dates is given.
bounding_box: named list with xmin, xmax, ymin, ymax, or an sf/sfc object. Longitudes negative west.
dates: the exact dates to read, as YYYYMMDD strings, YYYY-MM-DD strings, or Date objects
frequency: "daily" (the default) or "monthly"
sensor: which mission to read, from obdaac_sensors()
resolution: "4km" (the default) or "9km". At 9 km a global file is about a third the size, which is the difference worth knowing for a long record.
overwrite: re-read time steps already cached

Value

one row per grid cell per time step, with YEAR, MONTH, DAY and a column per requested variable

This is the only source in this package that will not work until you have created an account, and the failure without one is misleading: NASA answers an unauthenticated request with HTTP 200 and the login page. Register once at https://urs.earthdata.nasa.gov/users/new, generate an appkey at https://oceandata.sci.gsfc.nasa.gov/appkey/, and put it in ~/.Renviron:

EARTHDATA_APPKEY=your-key-here

A ~/.netrc entry for urs.earthdata.nasa.gov works too. See obdaac_credentials() for both routes and where they are looked for. A download that comes back as the login page is refused by name rather than being written to disk as a broken file.

It downloads the whole globe

OB.DAAC serves Level-3 mapped fields as global static files with no server-side subsetting, so one variable for one day is one global file however small the bounding box, and the subset is taken locally. At 4 km a daily chlorophyll field is about 15 MB and at 9 km about 5, so resolution = "9km" is three times cheaper for a study whose grid is coarser than 4 km anyway.

The exact total is reported before anything is transferred, because the file search returns each file's size, and the extracted subsets are cached, so a long record is paid for once.

Why there is no eight-day option

OB.DAAC publishes eight-day composites and they are the obvious answer to cloud gaps, but this package cannot join them honestly. matchData() joins on an hour, a day, a month or a year, and an eight-day bin is none of those: stamped as a day it would demand an observation fall on the bin's first date, and nearly every row would go unmatched. Use "monthly", which is a step the join understands, or fill_satellite_gaps() on the daily field, which records what it filled.

Which sensor, and why it matters

sensor defaults to "MODISA", which has the longest current record with both colour and SST. It is a choice you should make deliberately rather than inherit — see the section of obdaac_sensors() on why the sensors are not interchangeable — and source_of() records which one answered.

Examples

if (FALSE) { # \dontrun{
bb <- list(xmin = -70, xmax = -66, ymin = 41, ymax = 44)

# The long record: SeaWiFS chlorophyll from the late 1990s
early <- accessOBDAAC(vars = "CHL", years = 1998, months = 1:12,
                      bounding_box = bb, frequency = "monthly",
                      sensor = "SEAWIFS")

# Light and clarity alongside chlorophyll, from Aqua
light <- accessOBDAAC(vars = c("CHL", "PAR", "KD490"),
                      dates = unique(observations$date), bounding_box = bb)

matched <- matchData(observations, light)
} # }