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smapr

codecov CRAN_Status_Badge lifecycle Project Status: Active – The project has reached a stable, usable state and is being actively developed.

An R package for acquisition and processing of NASA (Soil Moisture Active-Passive) SMAP data

Installation

To install smapr from CRAN:

install.packages("smapr")

To install the development version from GitHub:

# install.packages("devtools")
devtools::install_github("ropensci/smapr")

Docker instructions (alternative to a local installation)

If a local installation is not possible for some reason, we have made a Docker image available with smapr and all its dependencies.

docker run -d -p 8787:8787 earthlab/smapr

In a web browser, navigate to localhost:8787 and log in with username: rstudio, password: rstudio.

Authentication

Access to the NASA SMAP data requires authentication through NASA’s Earthdata portal. If you do not already have a username and password through Earthdata, you can register for an account here: https://urs.earthdata.nasa.gov/ You cannot use this package without an Earthdata account.

Once you have an account, you need to pass your Earthdata username (ed_un) and password (ed_pw) as environmental variables that can be read from within your R session. There are a couple of ways to do this:

Recommended approach

Use set_smap_credentials('yourusername', 'yourpasswd'). This will save your credentials by default, overwriting existing credentials if overwrite = TRUE.

Alternative approaches

  • Use Sys.setenv() interactively in your R session to set your username and password (not including the < and >):
Sys.setenv(ed_un = "<your username>", ed_pw = "<your password>")
  • Create a text file .Renviron in your home directory, which contains your username and password. If you don’t know what your home directory is, execute normalizePath("~/") in the R console and it will be printed. Be sure to include a new line at the end of the file or R will fail silently when loading it.

Example .Renviron file (note the new line at the end!):

ed_un=slkdjfsldkjfs
ed_pw=dlfkjDD124^

Once this file is created, restart your R session and you should now be able to access these environment variables (e.g., via Sys.getenv("ed_un")).

SMAP data products

Multiple SMAP data products are provided by the NSIDC, and these products vary in the amount of processing. Currently, smapr primarily supports level 3 and level 4 data products, which represent global daily composite and global three hourly modeled data products, respectively. There are a wide variety of data layers available in SMAP products, including surface soil moisture, root zone soil moisture, freeze/thaw status, surface temperature, vegetation water content, vegetation opacity, net ecosystem carbon exchange, soil temperature, and evapotranspiration. NSIDC provides documentation for all SMAP data products on their website, and we provide a summary of data products supported by smapr below.

Dataset id Description Resolution
SPL2SMAP_S SMAP/Sentinel-1 Radiometer/Radar Soil Moisture 3 km
SPL3FTA Radar Northern Hemisphere Daily Freeze/Thaw State 3 km
SPL3SMA Radar Global Daily Soil Moisture 3 km
SPL3SMP Radiometer Global Soil Moisture 36 km
SPL3SMAP Radar/Radiometer Global Soil Moisture 9 km
SPL4SMAU Surface/Rootzone Soil Moisture Analysis Update 9 km
SPL4SMGP Surface/Rootzone Soil Moisture Geophysical Data 9 km
SPL4SMLM Surface/Rootzone Soil Moisture Land Model Constants 9 km
SPL4CMDL Carbon Net Ecosystem Exchange 9 km

Typical workflow

At a high level, most workflows follow these steps:

  1. Find SMAP data with find_smap()
  2. Download data with download_smap()
  3. List data contents with list_smap()
  4. Extract data with extract_smap()

Each of these steps are outlined below:

Finding SMAP data

Data are hosted on a server by the National Snow and Ice Data Center. The find_smap() function searches for specific data products and returns a data frame of available data. As data mature and pass checks, versions advance. At any specific time, not all versions of all datasets for all dates may exist. For the most up to date overview of dataset versions, see the NSIDC SMAP data version webpage.

library(smapr)
library(terra)
#> terra 1.7.18
available_data <- find_smap(id = "SPL3SMAP", date = "2015-05-25", version = 3)
str(available_data)
#> 'data.frame':    1 obs. of  3 variables:
#>  $ name: chr "SMAP_L3_SM_AP_20150525_R13080_001"
#>  $ date: Date, format: "2015-05-25"
#>  $ dir : chr "SPL3SMAP.003/2015.05.25/"

Downloading and inspecting SMAP data

Given a data frame produced by find_smap, download_smap downloads the data onto the local file system. Unless a directory is specified as an argument, the data are stored in the user’s cache.

downloads <- download_smap(available_data)
#> Downloading https://n5eil01u.ecs.nsidc.org/SMAP/SPL3SMAP.003/2015.05.25/SMAP_L3_SM_AP_20150525_R13080_001.h5
#> Downloading https://n5eil01u.ecs.nsidc.org/SMAP/SPL3SMAP.003/2015.05.25/SMAP_L3_SM_AP_20150525_R13080_001.qa
#> Downloading https://n5eil01u.ecs.nsidc.org/SMAP/SPL3SMAP.003/2015.05.25/SMAP_L3_SM_AP_20150525_R13080_001.h5.iso.xml
str(downloads)
#> 'data.frame':    1 obs. of  4 variables:
#>  $ name     : chr "SMAP_L3_SM_AP_20150525_R13080_001"
#>  $ date     : Date, format: "2015-05-25"
#>  $ dir      : chr "SPL3SMAP.003/2015.05.25/"
#>  $ local_dir: chr "~/.cache/smap"

The SMAP data are provided in HDF5 format, and in any one file there are actually multiple data sets, including metadata. The list_smap function allows users to inspect the contents of downloaded data at a high level (all = FALSE) or in depth (all = TRUE).

list_smap(downloads, all = FALSE)
#> $SMAP_L3_SM_AP_20150525_R13080_001
#>                           name group     otype dclass  dim
#> 1                     Metadata     . H5I_GROUP   <NA> <NA>
#> 2 Soil_Moisture_Retrieval_Data     . H5I_GROUP   <NA> <NA>

To see all of the data fields, set all = TRUE.

Extracting gridded data products

The extract_smap function extracts gridded data products (e.g., global soil moisture). If more than one file has been downloaded and passed into the first argument, extract_smap extracts data for each file

sm_raster <- extract_smap(downloads, "Soil_Moisture_Retrieval_Data/soil_moisture")
plot(sm_raster, main = "Level 3 soil moisture: May 25, 2015")

The path “Soil_Moisture_Retrieval_Data/soil_moisture” was determined from the output of list_smap(downloads, all = TRUE), which lists all of the data contained in SMAP data files.

Saving GeoTIFF output

The data can be saved as a GeoTIFF using the writeRaster function from the terra pacakge.

writeRaster(sm_raster, "sm_raster.tif")

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