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This vignette outlines a basic use scenario for smapr. We will acquire and process NASA (Soil Moisture Active-Passive) SMAP data, and generate some simple visualizations.

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. 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

This vignette uses the level 4 SPL4SMAU (Surface/Rootzone Soil Moisture Analysis Update) data product.

Preparing to access SMAP data

NASA requires a username and password from their Earthdata portal to access SMAP data. You can get these credentials here: https://earthdata.nasa.gov/

Once you have your credentials, you can use the set_smap_credentials function to set them for use by the smapr package:

set_smap_credentials("myusername", "mypassword")

This function saves your credentials for later use unless you use the argument save = FALSE.

Finding data

To find out which SMAP data are available, we’ll use the find_smap function, which takes a data set ID, date(s) to search, and a dataset version.

available_data <- find_smap(id = 'SPL4SMAU', dates = '2018-06-01', version = 5)

This returns a data frame, where every row is one data file that is available on NASA’s servers.

str(available_data)
#> 'data.frame':    8 obs. of  3 variables:
#>  $ name: chr  "SMAP_L4_SM_aup_20180601T030000_Vv5030_001" "SMAP_L4_SM_aup_20180601T060000_Vv5030_001" "SMAP_L4_SM_aup_20180601T090000_Vv5030_001" "SMAP_L4_SM_aup_20180601T120000_Vv5030_001" ...
#>  $ date: Date, format: "2018-06-01" "2018-06-01" "2018-06-01" "2018-06-01" ...
#>  $ dir : chr  "SPL4SMAU.005/2018.06.01/" "SPL4SMAU.005/2018.06.01/" "SPL4SMAU.005/2018.06.01/" "SPL4SMAU.005/2018.06.01/" ...

Downloading data

To download the data, we can use download_smap. Note that this may take a while, depending on the number of files being downloaded, and the speed of your internet connection. Because we’re downloading multiple files, we will use the verbose = FALSE argument to avoid printing excessive output to the console.

local_files <- download_smap(available_data, overwrite = FALSE, verbose = FALSE)

Each file corresponds to different times as indicated by the file names:

local_files$name[1:2]
#> [1] "SMAP_L4_SM_aup_20180601T030000_Vv5030_001" "SMAP_L4_SM_aup_20180601T060000_Vv5030_001"

Exploring data

Each file that we downloaded is an HDF5 file with multiple datasets bundled together. To list all of the data in a file we can use list_smap. By default, if we give list_smap a data frame of local files, it will return a list of data frames. Because all of these data files are of the same data product, using list_smap on one file (e.g., the first) will tell us what’s available in all of the files:

list_smap(local_files[1, ])
#> $SMAP_L4_SM_aup_20180601T030000_Vv5030_001
#>                                name                              group       otype      dclass         dim
#> 1                                 y                                  . H5I_DATASET   H5T_FLOAT        1624
#> 2                     Forecast_Data                                  .   H5I_GROUP        <NA>        <NA>
#> 3               sm_surface_forecast                      Forecast_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 4                     tb_v_forecast                      Forecast_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 5             surface_temp_forecast                      Forecast_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 6              tb_v_forecast_ensstd                      Forecast_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 7         soil_temp_layer1_forecast                      Forecast_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 8               sm_profile_forecast                      Forecast_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 9                     tb_h_forecast                      Forecast_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 10             sm_rootzone_forecast                      Forecast_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 11             tb_h_forecast_ensstd                      Forecast_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 12                             time                                  . H5I_DATASET   H5T_FLOAT           1
#> 13          EASE2_global_projection                                  . H5I_DATASET  H5T_STRING           1
#> 14                    Analysis_Data                                  .   H5I_GROUP        <NA>        <NA>
#> 15       sm_surface_analysis_ensstd                      Analysis_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 16     surface_temp_analysis_ensstd                      Analysis_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 17      sm_rootzone_analysis_ensstd                      Analysis_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 18              sm_surface_analysis                      Analysis_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 19       sm_profile_analysis_ensstd                      Analysis_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 20             sm_rootzone_analysis                      Analysis_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 21              sm_profile_analysis                      Analysis_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 22        soil_temp_layer1_analysis                      Analysis_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 23 soil_temp_layer1_analysis_ensstd                      Analysis_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 24            surface_temp_analysis                      Analysis_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 25                         cell_lat                                  . H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 26                         cell_row                                  . H5I_DATASET H5T_INTEGER 3856 x 1624
#> 27                         cell_lon                                  . H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 28                         Metadata                                  .   H5I_GROUP        <NA>        <NA>
#> 29                           Source                           Metadata   H5I_GROUP        <NA>        <NA>
#> 30                           L1C_TB                    Metadata/Source   H5I_GROUP        <NA>        <NA>
#> 31           AcquisitionInformation                           Metadata   H5I_GROUP        <NA>        <NA>
#> 32                         platform    Metadata/AcquisitionInformation   H5I_GROUP        <NA>        <NA>
#> 33                 platformDocument    Metadata/AcquisitionInformation   H5I_GROUP        <NA>        <NA>
#> 34                    radarDocument    Metadata/AcquisitionInformation   H5I_GROUP        <NA>        <NA>
#> 35                            radar    Metadata/AcquisitionInformation   H5I_GROUP        <NA>        <NA>
#> 36               radiometerDocument    Metadata/AcquisitionInformation   H5I_GROUP        <NA>        <NA>
#> 37                       radiometer    Metadata/AcquisitionInformation   H5I_GROUP        <NA>        <NA>
#> 38                      DataQuality                           Metadata   H5I_GROUP        <NA>        <NA>
#> 39                              TBH               Metadata/DataQuality   H5I_GROUP        <NA>        <NA>
#> 40                DomainConsistency           Metadata/DataQuality/TBH   H5I_GROUP        <NA>        <NA>
#> 41             CompletenessOmission           Metadata/DataQuality/TBH   H5I_GROUP        <NA>        <NA>
#> 42                              TBV               Metadata/DataQuality   H5I_GROUP        <NA>        <NA>
#> 43                DomainConsistency           Metadata/DataQuality/TBV   H5I_GROUP        <NA>        <NA>
#> 44             CompletenessOmission           Metadata/DataQuality/TBV   H5I_GROUP        <NA>        <NA>
#> 45             SeriesIdentification                           Metadata   H5I_GROUP        <NA>        <NA>
#> 46            DatasetIdentification                           Metadata   H5I_GROUP        <NA>        <NA>
#> 47                           Extent                           Metadata   H5I_GROUP        <NA>        <NA>
#> 48                             CRID                           Metadata   H5I_GROUP        <NA>        <NA>
#> 49                              AUP                      Metadata/CRID   H5I_GROUP        <NA>        <NA>
#> 50                             Root                      Metadata/CRID   H5I_GROUP        <NA>        <NA>
#> 51                           Config                           Metadata   H5I_GROUP        <NA>        <NA>
#> 52        GridSpatialRepresentation                           Metadata   H5I_GROUP        <NA>        <NA>
#> 53                         Latitude Metadata/GridSpatialRepresentation   H5I_GROUP        <NA>        <NA>
#> 54                        Longitude Metadata/GridSpatialRepresentation   H5I_GROUP        <NA>        <NA>
#> 55                      ProcessStep                           Metadata   H5I_GROUP        <NA>        <NA>
#> 56                      cell_column                                  . H5I_DATASET H5T_INTEGER 3856 x 1624
#> 57                Observations_Data                                  .   H5I_GROUP        <NA>        <NA>
#> 58                         tb_v_obs                  Observations_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 59                tb_v_obs_time_sec                  Observations_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 60                         tb_h_obs                  Observations_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 61                  tb_h_orbit_flag                  Observations_Data H5I_DATASET H5T_INTEGER 3856 x 1624
#> 62             tb_h_resolution_flag                  Observations_Data H5I_DATASET H5T_INTEGER 3856 x 1624
#> 63                  tb_h_obs_errstd                  Observations_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 64                  tb_v_orbit_flag                  Observations_Data H5I_DATASET H5T_INTEGER 3856 x 1624
#> 65             tb_v_resolution_flag                  Observations_Data H5I_DATASET H5T_INTEGER 3856 x 1624
#> 66                   tb_v_obs_assim                  Observations_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 67                   tb_h_obs_assim                  Observations_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 68                tb_h_obs_time_sec                  Observations_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 69                  tb_v_obs_errstd                  Observations_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 70                                x                                  . H5I_DATASET   H5T_FLOAT        3856

To dig deeper, we can use the all argument to list_smap:

list_smap(local_files[1, ], all = TRUE)
#> $SMAP_L4_SM_aup_20180601T030000_Vv5030_001
#>                                name                              group       otype      dclass         dim
#> 1                                 y                                  . H5I_DATASET   H5T_FLOAT        1624
#> 2                     Forecast_Data                                  .   H5I_GROUP        <NA>        <NA>
#> 3               sm_surface_forecast                      Forecast_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 4                     tb_v_forecast                      Forecast_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 5             surface_temp_forecast                      Forecast_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 6              tb_v_forecast_ensstd                      Forecast_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 7         soil_temp_layer1_forecast                      Forecast_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 8               sm_profile_forecast                      Forecast_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 9                     tb_h_forecast                      Forecast_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 10             sm_rootzone_forecast                      Forecast_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 11             tb_h_forecast_ensstd                      Forecast_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 12                             time                                  . H5I_DATASET   H5T_FLOAT           1
#> 13          EASE2_global_projection                                  . H5I_DATASET  H5T_STRING           1
#> 14                    Analysis_Data                                  .   H5I_GROUP        <NA>        <NA>
#> 15       sm_surface_analysis_ensstd                      Analysis_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 16     surface_temp_analysis_ensstd                      Analysis_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 17      sm_rootzone_analysis_ensstd                      Analysis_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 18              sm_surface_analysis                      Analysis_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 19       sm_profile_analysis_ensstd                      Analysis_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 20             sm_rootzone_analysis                      Analysis_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 21              sm_profile_analysis                      Analysis_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 22        soil_temp_layer1_analysis                      Analysis_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 23 soil_temp_layer1_analysis_ensstd                      Analysis_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 24            surface_temp_analysis                      Analysis_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 25                         cell_lat                                  . H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 26                         cell_row                                  . H5I_DATASET H5T_INTEGER 3856 x 1624
#> 27                         cell_lon                                  . H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 28                         Metadata                                  .   H5I_GROUP        <NA>        <NA>
#> 29                           Source                           Metadata   H5I_GROUP        <NA>        <NA>
#> 30                           L1C_TB                    Metadata/Source   H5I_GROUP        <NA>        <NA>
#> 31           AcquisitionInformation                           Metadata   H5I_GROUP        <NA>        <NA>
#> 32                         platform    Metadata/AcquisitionInformation   H5I_GROUP        <NA>        <NA>
#> 33                 platformDocument    Metadata/AcquisitionInformation   H5I_GROUP        <NA>        <NA>
#> 34                    radarDocument    Metadata/AcquisitionInformation   H5I_GROUP        <NA>        <NA>
#> 35                            radar    Metadata/AcquisitionInformation   H5I_GROUP        <NA>        <NA>
#> 36               radiometerDocument    Metadata/AcquisitionInformation   H5I_GROUP        <NA>        <NA>
#> 37                       radiometer    Metadata/AcquisitionInformation   H5I_GROUP        <NA>        <NA>
#> 38                      DataQuality                           Metadata   H5I_GROUP        <NA>        <NA>
#> 39                              TBH               Metadata/DataQuality   H5I_GROUP        <NA>        <NA>
#> 40                DomainConsistency           Metadata/DataQuality/TBH   H5I_GROUP        <NA>        <NA>
#> 41             CompletenessOmission           Metadata/DataQuality/TBH   H5I_GROUP        <NA>        <NA>
#> 42                              TBV               Metadata/DataQuality   H5I_GROUP        <NA>        <NA>
#> 43                DomainConsistency           Metadata/DataQuality/TBV   H5I_GROUP        <NA>        <NA>
#> 44             CompletenessOmission           Metadata/DataQuality/TBV   H5I_GROUP        <NA>        <NA>
#> 45             SeriesIdentification                           Metadata   H5I_GROUP        <NA>        <NA>
#> 46            DatasetIdentification                           Metadata   H5I_GROUP        <NA>        <NA>
#> 47                           Extent                           Metadata   H5I_GROUP        <NA>        <NA>
#> 48                             CRID                           Metadata   H5I_GROUP        <NA>        <NA>
#> 49                              AUP                      Metadata/CRID   H5I_GROUP        <NA>        <NA>
#> 50                             Root                      Metadata/CRID   H5I_GROUP        <NA>        <NA>
#> 51                           Config                           Metadata   H5I_GROUP        <NA>        <NA>
#> 52        GridSpatialRepresentation                           Metadata   H5I_GROUP        <NA>        <NA>
#> 53                         Latitude Metadata/GridSpatialRepresentation   H5I_GROUP        <NA>        <NA>
#> 54                        Longitude Metadata/GridSpatialRepresentation   H5I_GROUP        <NA>        <NA>
#> 55                      ProcessStep                           Metadata   H5I_GROUP        <NA>        <NA>
#> 56                      cell_column                                  . H5I_DATASET H5T_INTEGER 3856 x 1624
#> 57                Observations_Data                                  .   H5I_GROUP        <NA>        <NA>
#> 58                         tb_v_obs                  Observations_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 59                tb_v_obs_time_sec                  Observations_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 60                         tb_h_obs                  Observations_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 61                  tb_h_orbit_flag                  Observations_Data H5I_DATASET H5T_INTEGER 3856 x 1624
#> 62             tb_h_resolution_flag                  Observations_Data H5I_DATASET H5T_INTEGER 3856 x 1624
#> 63                  tb_h_obs_errstd                  Observations_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 64                  tb_v_orbit_flag                  Observations_Data H5I_DATASET H5T_INTEGER 3856 x 1624
#> 65             tb_v_resolution_flag                  Observations_Data H5I_DATASET H5T_INTEGER 3856 x 1624
#> 66                   tb_v_obs_assim                  Observations_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 67                   tb_h_obs_assim                  Observations_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 68                tb_h_obs_time_sec                  Observations_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 69                  tb_v_obs_errstd                  Observations_Data H5I_DATASET   H5T_FLOAT 3856 x 1624
#> 70                                x                                  . H5I_DATASET   H5T_FLOAT        3856

Looking at this output, we can conclude that the file contains multiple arrays (notice the dim column). These arrays correspond to things like estimated root zone soil moisture (/Analysis_Data/sm_rootzone_analysis), estimated surface soil moisture (/Analysis_Data/sm_surface_analysis), and estimated surface temperature (/Analysis_Data/surface_temp_analysis). See https://nsidc.org/data/smap/spl4sm/data-fields#sm_surface_analysis for more detailed information on what these datasets represent and how they were generated.

Extracting data

The datasets that we are interested in are spatial grids. The smapr package can extract these data into raster objects with the extract_smap function, which takes a dataset name as an argument. These names are paths that can be generated from the output of list_smap. For example, if we want to get rootzone soil moisture, we can see a dataset with name sm_rootzone_analysis in group /Analysis_Data, so that the path to the dataset is /Analysis_Data/sm_rootzone_analysis:

sm_raster <- extract_smap(local_files, '/Analysis_Data/sm_rootzone_analysis')

This will extract all of the data in the data frame local_files, generating a terra SpatRaster object with one layer per file:

sm_raster
#> class       : SpatRaster 
#> dimensions  : 1624, 3856, 8  (nrow, ncol, nlyr)
#> resolution  : 8984.982, 8205.308  (x, y)
#> extent      : -17367530, 17278561, -6010879, 7314541  (xmin, xmax, ymin, ymax)
#> coord. ref. : +proj=cea +lat_ts=30 +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs 
#> source      : tmp.tif 
#> names       : SMAP_~0_001, SMAP_~0_001, SMAP_~0_001, SMAP_~0_001, SMAP_~0_001, SMAP_~0_001, ... 
#> min values  :   0.0065597,  0.00659073, 0.006612024, 0.006668247, 0.006737789, 0.006590234, ... 
#> max values  :   0.8000000,  0.80000001, 0.800000012, 0.800000012, 0.800000012, 0.800000012, ...

We can visualize each layer:

plot(sm_raster)
plot of chunk plot-raster
plot of chunk plot-raster

Cropping, masking, and summarization can then proceed using the terra R package.

For example, to get mean soil moisture values across layers, useterra::app():

mean_sm <- app(sm_raster, fun = mean)
plot(mean_sm, main = 'Mean soil moisture')
plot of chunk get-mean
plot of chunk get-mean

Comparing surface and soil moisture

Our SPL4SMAU data have estimated surface and rootzone soil moisture layers. If we want to compare these values, we can load the surface soil moisture data, compute the mean value over layers as we did for the rootzone soil moisture raster, and generate a scatterplot.

surface_raster <- extract_smap(local_files,
                               name = '/Analysis_Data/sm_surface_analysis')

mean_surface_sm <- app(surface_raster, fun = mean)

# compare values
plot(values(mean_sm), values(mean_surface_sm), col = 'dodgerblue', cex = .1,
     xlab = 'Rootzone soil moisture', ylab = 'Surface soil moisture', bty = 'n')
abline(0, 1, lty = 2)
plot of chunk surface-vs-rootzone
plot of chunk surface-vs-rootzone