The function operates at MODIS/VIIRS products on a daily basis. Given that the raw hdf files are downloaded from NASA, standard file names include a data retrieval date flag starting with letter "A". Leveraging that piece of information, the function will select files of scope on the date of interest. Please note that this function does not provide a function to filter swaths or tiles, so it is strongly recommended to check and pre-filter the file names at users' discretion.
Usage
calc_modis_daily(
from = NULL,
locs = NULL,
locs_id = "site_id",
radius = 0L,
date = NULL,
name_extracted = NULL,
fun_summary = "mean",
max_cells = 3e+07,
geom = FALSE,
...
)
Arguments
- from
SpatRaster. Preprocessed objects.
- locs
SpatVector/sf/sftime object. Locations where MODIS values are summarized.
- locs_id
character(1). Field name where unique site identifiers are stored. Default is
"site_id"
- radius
numeric. Radius to generate circular buffers.
- date
Date(1). date to query.
- name_extracted
character. Names of calculated covariates.
- fun_summary
function. Summary function for multilayer rasters. Passed to
foo
. Seeexactextractr::exact_extract
for details.- max_cells
integer(1). Maximum number of cells to be read at once. Higher values will expedite processing, but will increase memory usage. Maximum possible value is
2^31 - 1
.- geom
logical(1). Should the function return a
SpatVector
? Default isFALSE
. The coordinate reference system of theSpatVector
is that offrom.
Seeexactextractr::exact_extract
for details.- ...
Placeholders.
See also
Preprocessing:
process_modis_merge()
,process_modis_swath()
,process_blackmarble()
Parallelization:
calc_modis_par()
Examples
## NOTE: Example is wrapped in `\dontrun{}` as function requires a large
## amount of data which is not included in the package.
if (FALSE) { # \dontrun{
locs <- data.frame(lon = -78.8277, lat = 35.95013, id = "001")
calc_modis_daily(
from = mod06l2_warp, # dervied from process_modis() example
locs = locs,
locs_id = "id",
radius = 0,
date = "2024-01-01",
name_extracted = "cloud_fraction_0",
fun_summary = "mean",
max_cells = 3e7
)
} # }