Download MODIS/MCD19A2 AOD product with R

This can be easily achieved using the MODIS package, version 1.1.3 or higher (previous versions didn't provide access to this product).

library(MODIS)

# set MODIS options (these can also be passed to '...' in getHdf())
MODISoptions(localArcPath = "OutputDestinationFolder", quiet = FALSE)

# download data
hdf = getHdf("MCD19A2", collection = "006"
             , tileH = 9, tileV = 4
             , begin = "2018.08.28", end = "2018.08.31")
hdf
# $`MCD19A2.006`
# [1] "OutputDestinationFolder/MODIS/MCD19A2.006/2018.08.28/MCD19A2.A2018240.h09v04.006.2018242043359.hdf"
# [2] "OutputDestinationFolder/MODIS/MCD19A2.006/2018.08.29/MCD19A2.A2018241.h09v04.006.2018250043019.hdf"
# [3] "OutputDestinationFolder/MODIS/MCD19A2.006/2018.08.30/MCD19A2.A2018242.h09v04.006.2018250043020.hdf"
# [4] "OutputDestinationFolder/MODIS/MCD19A2.006/2018.08.31/MCD19A2.A2018243.h09v04.006.2018250043021.hdf"

This downloads all .hdf files found for the specified tile indices and time frame and puts them in the desired target folder. You might also use runGdal() instead to download and extract the data in one go, provided that GDAL is available. This renders unnecessary an explicit call to getHdf().

tfs = runGdal("MCD19A2", collection = "006"
              , tileH = 9, tileV = 4
              , begin = "2018.08.28", end = "2018.08.31"
              , job = "MCD19A2", SDSstring = "1010000000000")
tfs
# $`MCD19A2.006`
# $`MCD19A2.006`$`2018-08-28`
# [1] "OutputDestinationFolder/PROCESSED/MCD19A2/MCD19A2.A2018240.Optical_Depth_047.tif"
# [2] "OutputDestinationFolder/PROCESSED/MCD19A2/MCD19A2.A2018240.AOD_Uncertainty.tif"  
# 
# $`MCD19A2.006`$`2018-08-29`
# [1] "OutputDestinationFolder/PROCESSED/MCD19A2/MCD19A2.A2018241.Optical_Depth_047.tif"
# [2] "OutputDestinationFolder/PROCESSED/MCD19A2/MCD19A2.A2018241.AOD_Uncertainty.tif"  
# ...

For further information, see also

• ?getProduct() for a list of available products;
• getProduct("MCD19A2") for details on a paricular product;
• argument 'outDirPath' in ?MODISoptions() to specify a target folder for extracted layers;
• and arguments 'dlmethod' and 'dataFormat' in ?MODISoptions() for supported download methods and write formats, respectively.

Okay after a bit of digging, I've figured out how to download MCD19A2 AOD data using wget and R!

I've created a basic R function that downloads all .hdf files for a given date based on the specified MODIS tile. If you are a linux user and have a NASA EarthData account, then the following code should also work for you!

getAOD <- function(user = 'EarthDataUsername'
               ,pw = 'EarthDataPassword'
               ,dir = 'OutputDestinationFolder'
               ,product = 'MCD19A2' # default product for MAIAC AOD
               ,date = '2018.08.28' # day of interest
               ,tile = 'h09v04' # your tile id (depends on your location)
               ,collection = "006") {

  # parse date and julian date for url building
  Date <- lubridate::ymd(date)
  julianDate <- paste0(lubridate::year(Date)
                   ,lubridate::yday(Date))

  # extracts hdf files for a given tile across all observation times
  pattern <- paste0(product, ".A", julianDate, ".", tile, ".", collection, "*.hdf")

  # base url for wget
  url <- paste0("https://e4ftl01.cr.usgs.gov/MOTA/", product, ".", collection, "/", date, "/")

  # call wget from command line
  system(paste0("wget -r -A", pattern, " -L --user=", user
            ," --password=", pw
            ," --directory-prefix=", dir
            ," --load-cookies ~/.cookies --save-cookies ~/.cookies ", url))

}

I keep my EarthDataPassword in a file called batteries.dppss. I then scan this while calling the getAOD() function.

getAOD(user = 'myUserName'
    ,pw = scan("./batteries.dppss", what = "")
    ,dir = "./test")

If you don't have wget installed, or prefer not to rely on system calls, see https://github.com/AustralianAntarcticDivision/blueant/issues/11. It's very similar to your code, but the recursive downloading all happens in R, not wget.