install.packages("appeears")library("appeears")

if(!require(remotes)){install.packages("remotes")}remotes::install_github("bluegreen-labs/appeears")library("appeears")

if(!require(remotes)){install.packages("remotes")}remotes::install_github("bluegreen-labs/appeears", build_vignettes=TRUE)library("appeears")

# set a key to the keychainrs_set_key(  user="earth_data_user",  password="XXXXXXXXXXXXXXXXXXXXXX")# you can retrieve the password usingrs_get_key(user="earth_data_user")# the output should be the key you provided# "XXXXXXXXXXXXXXXXXXXXXX"

# request the current tokentoken<-rs_login(user="earth_data_user")# invalidate the current sessionrs_logout(token)

File based keychains

On linux, or systems with key management issues, you can opt to use a file based keyring instead of a graphical user interface (GUI) based keyring manager. For example, this is helpful for headless setups such as servers without a GUI. For this option to work users must set anR environmental option at the beginning of each session or script (before executing any login routines).

On theR console (command line) set the environmental option as shown below and set your key using your username and password.

# set the environmental variable# on the command line or in any# script that runs on a file based# keychainoptions(keyring_backend="file")

# on the command line set your keyrs_set_key(  user="earth_data_user",  password="XXXXXXXXXXXXXXXXXXXXXX")

When using a file based keychain you will need to set the environmental option at the beginning of each session (on the command line) or at the beginning of each script (before login commands).

# set the environmental variable# on the command line or in any# script that runs on a file based# keychainoptions(keyring_backend="file")# submit a requestrs_request(...)

Upon the start of each session/script you will be asked to provide your password, unlocking allappeears credentials for this session. Should you ever forget the password just delete the file at:~/.config/r-keyring/appeears.keyring and re-enter all your credentials on the command line (see above).

# Load the librarylibrary(appeears)# list all productsrs_products()# list layers of the MOD11A2.061 productrs_layers("MOD11A2.061")df<-data.frame(  task="time_series",  subtask="US-Ha1",  latitude=42.5378,  longitude=-72.1715,  start="2010-01-01",  end="2010-12-31",  product="MCD43A4.061",  layer=c("Nadir_Reflectance_Band3","Nadir_Reflectance_Band4"))# build the area based request/task# rename the task name so data will# be saved in the "point" folder# as defined by the task nametask<-rs_build_task(df=df)# request the task to be executedrs_request(  request=task,  user="earth_data_user",  transfer=TRUE,  path="~/some_path",  verbose=TRUE)

{sf} polygon ROI

When using ansf object, provide it to theroi argument of thers_build_task() function. Thesf object must be of classsf notsfc when required convertsfc data usingst_as_sf().

Note however that at the time only as simple polygon is supported. Multiple polygons in the samesf object might result in failure to query the data.

Furthermore, no other means will be provided to specify a region-of-interest. As such, you will always have to query a region-of-interest using ansf object. This ensures consistency across queries and allows for rapid visualization of a region of interest (in contrast to a simple list of e.g. top-left, bottom-right coordinates).

# load the required librarieslibrary(appeears)library(sf)library(dplyr)df<-data.frame(  task="time_series",  subtask="subtask",  latitude=42.5378,  longitude=-72.1715,  start="2010-01-01",  end="2010-12-31",  product="MCD12Q2.006",  layer=c("Greenup"))# load the north carolina demo data# included in the {sf} package# and only retain Camden countyroi<-st_read(system.file("gpkg/nc.gpkg", package="sf"), quiet=TRUE)|>filter(NAME=="Camden")# build the area based request/task# rename the task name so data will# be saved in the "polygon" folder# as defined by the task namedf$task<-"polygon"task<-rs_build_task(  df=df,  roi=roi,  format="geotiff")# request the task to be executedrs_request(  request=task,  user="earth_data_user",  transfer=TRUE,  path="~/some_path",  verbose=TRUE)

{terra} SpatRaster ROI

Theterra based region-of-interest workflow is similar to that ofsf polygon based queries. One only has to provide aSpatRaster as anroi argument inrs_build_task() to query a region of the same extent as theSpatRaster. The use case for this functionality is obvious, creating a quick way to sample new data for an existing data set (using the same coverage).

Note that unlike thesf method a bounding box is used and masked data is ignored (the full extent is downloaded and masking will have to be repeated afterwards).

# load the required librarieslibrary(terra)# create a SpatRaster ROI from the terra demo filef<-system.file("ex/elev.tif", package="terra")roi<-terra::rast(f)# build the area based request/task# rename the task name so data will# be saved in the "raster" folder# as defined by the task namedf$task<-"raster"task<-rs_build_task(  df=df,  roi=roi,  format="geotiff")# request the task to be executedrs_request(  request=task,  user="earth_data_user",  transfer=TRUE,  path="~/some_path",  verbose=TRUE)