Programmatic interface to the NASA AppEEARS API services where, and I quote, “The Application for Extracting and Exploring Analysis Ready Samples (AρρEEARS) offers a simple and efficient way to access and transform geospatial data from a variety of federal data archives. AρρEEARS enables users to subset geospatial datasets using spatial, temporal, and band/layer parameters.”
How to cite this package
You can cite this package like this “we obtained data through the NASA AppEEARS API using the {appeears} R package (Hufkens 2023)”. Here is the full bibliographic reference to include in your reference list (don’t forget to update the ‘last accessed’ date):
Hufkens, K. (2023). appeears: Programmatic interface to the NASA AppEEARS API. Zenodo. https://doi.org/10.5281/zenodo.7938190.
Installation
stable release
To install the current stable release use a CRAN repository:
install.packages("appeears")
library("appeears")development release
To install the development releases of the package run the following commands:
if(!require(remotes)){install.packages("remotes")}
remotes::install_github("bluegreen-labs/appeears")
library("appeears")Vignettes are not rendered by default, if you want to include additional documentation please use:
if(!require(remotes)){install.packages("remotes")}
remotes::install_github("bluegreen-labs/appeears", build_vignettes = TRUE)
library("appeears")Use
Setup
Before starting save the provided NASA Earth Data password to your local keychain. The package does not allow you to use your password inline in scripts to limit security issues when sharing scripts on github or otherwise.
# set a key to the keychain
rs_set_key(
user = "earth_data_user",
password = "XXXXXXXXXXXXXXXXXXXXXX"
)
# you can retrieve the password using
rs_get_key(user = "earth_data_user")
# the output should be the key you provided
# "XXXXXXXXXXXXXXXXXXXXXX"Downloads are managed using a Bearer/session token. This token is valid for 48 hours, after which it will expire and you will need to request a new one. Although downloads are managed using the user (keychain) details only, you can request the current token using rs_login(), while rs_logout() will explicitly invalidate the current session token.
Point based data requests
All point based queries are made by first creating a tidy data frame with the desired products and layers to query.
In this data frame task specifies the overall name of the task to run (this prefix will be used to name the final downloaded files). The subtask denotes the various locations and or products you want to query. As such, you can query multiple locations in the same larger task, avoiding multiple queries to the API.
The latitude and longitude fields specify geographic coordinates of query locations, while start and end columns define the range of the data queried. Note that the date range will cover the maximum date range across all subtasks. If date ranges vary widely it is advised to create separate tasks.
Finally the product and layer columns denote the remote sensing product and particular layer to download. A full list of products can be queried using rs_products(), while the layers of a particular product can be listed using rs_layers(). Note that the product needs to be specified using the full product name, including the version of the product (as stored in the ProductAndVersion field).
For point and area based queries all data are saved in a subdirectory of the main path as defined by the task name. An abbreviated workflow can be found below, while a full worked example is provided in the vignettes.
# Load the library
library(appeears)
# list all products
rs_products()
# list layers of the MOD11A2.061 product
rs_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 name
task <- rs_build_task(df = df)
# request the task to be executed
rs_request(
request = task,
user = "earth_data_user",
transfer = TRUE,
path = "~/some_path",
verbose = TRUE
)Area based data requests
You can select a region-of-interest (ROI) instead of point based data, using both sf polygons or the extent (bounding box) of an existing terra SpatRaster object. Both methods follow the same workflow.
{sf} polygon ROI
When using an sf object, provide it to the roi argument of the rs_build_task() function. The sf object must be of class sf not sfc when required convert sfc data using st_as_sf().
Note however that at the time only as simple polygon is supported. Multiple polygons in the same sf 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 an sf 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 libraries
library(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 county
roi <- 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 name
df$task <- "polygon"
task <- rs_build_task(
df = df,
roi = roi,
format = "geotiff"
)
# request the task to be executed
rs_request(
request = task,
user = "earth_data_user",
transfer = TRUE,
path = "~/some_path",
verbose = TRUE
){terra} SpatRaster ROI
The terra based region-of-interest workflow is similar to that of sf polygon based queries. One only has to provide a SpatRaster as an roi argument in rs_build_task() to query a region of the same extent as the SpatRaster. The use case for this functionality is obvious, creating a quick way to sample new data for an existing data set (using the same coverage).
Note that unlike the sf method a bounding box is used and masked data is ignored (the full extent is downloaded and masking will have to be repeated afterwards).
# load the required libraries
library(terra)
# create a SpatRaster ROI from the terra demo file
f <- system.file("ex/elev.tif", package="terra")
roi <- terra::rast(f)
# build the area based request/task
# rename the task name so data will
# be saved in the "raster" folder
# as defined by the task name
df$task <- "raster"
task <- rs_build_task(
df = df,
roi = roi,
format = "geotiff"
)
# request the task to be executed
rs_request(
request = task,
user = "earth_data_user",
transfer = TRUE,
path = "~/some_path",
verbose = TRUE
)File based keychains
On linux you can opt to use a file based keyring, instead of a GUI based keyring manager. This is helpful for headless setups such as servers. For this option to work linux users must set an environmental option.
options(keyring_backend="file")You will be asked to provide a password to encrypt the keyring with. Upon the start of each session you will be asked to provide this password, unlocking all appeears 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.