path: character string. mandatory. The path to the local directory where the data are stored.
collection: string. mandatory. Collection of interest (see details of mf_get_url ).
output_class: character string. Output object class. Currently only "SpatRaster" implemented.
proj_epsg: numeric. EPSG of the desired projection for the output raster (default : source projection of the data).
roi_mask: SpatRaster or SpatVector or sf. Area beyond which data will be masked. Typically, the input ROI of mf_get_url (default : NULL (no mask))
vrt: boolean. Import virtual raster instead of SpatRaster. Useful for very large files. (default : FALSE)
verbose: string. Verbose mode ("quiet", "inform", or "debug"). Default "inform".
...: not used
Returns
a terra::SpatRast object
Note
Although the data downloaded through modisfast could be imported with any netcdf-compliant R package (terra, stars, ncdf4, etc.), care must be taken. In fact, depending on the collection, some “issues” were raised. These issues are independent from modisfast : they result most of time of a lack of full implementation of the OPeNDAP framework by the data providers. Namely, these issues are :
for MODIS and VIIRS collections : CRS has to be provided
for GPM collections : CRS has to be provided + data have to be flipped
The function mf_import_data includes the processing that needs to be done at the data import phase in order to safely use the data as terra objects.
Also note that reprojecting over large ROIs using the argument proj_epsg might take long. In this case, setting the argument vrt to TRUE might be a solution.
Examples
## Not run:### Login to EOSDIS Earthdata with your username and passwordlog <- mf_login(credentials = c("earthdata_un","earthdata_pw"))### Set-up parameters of interestcoll <-"MOD11A1.061"bands <- c("LST_Day_1km","LST_Night_1km")time_range <- as.Date(c("2017-01-01","2017-01-30"))roi <- sf::st_as_sf( data.frame( id ="roi_test", geom ="POLYGON ((-5.82 9.54, -5.42 9.55, -5.41 8.84, -5.81 8.84, -5.82 9.54))"), wkt ="geom", crs =4326)### Get the URLs of the data(urls_mod11a1 <- mf_get_url( collection = coll, variables = bands, roi = roi, time_range = time_range
))### Download the datares_dl <- mf_download_data(urls_mod11a1)### Import the data as terra::SpatRastmodis_ts <- mf_import_data(dirname(res_dl$destfile[1]), collection = coll)### Plot the dataterra::plot(modis_ts)## End(Not run)